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• Review Paper
• Open access
• Published: 14 January 2015

Climate change impacts and adaptation in forest management: a review

• Rodney J. Keenan 1  

Annals of Forest Science volume  72 ,  pages 145–167 ( 2015 ) Cite this article

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Key message

Adaptation of forest management to climate change requires an understanding of the effects of climate on forests, industries and communities; prediction of how these effects might change over time; and incorporation of this knowledge into management decisions. This requires multiple forms of knowledge and new approaches to forest management decisions. Partnerships that integrate researchers from multiple disciplines with forest managers and local actors can build a shared understanding of future challenges and facilitate improved decision making in the face of climate change.

Climate change presents significant potential risks to forests and challenges for forest managers. Adaptation to climate change involves monitoring and anticipating change and undertaking actions to avoid the negative consequences and to take advantage of potential benefits of those changes.

This paper aimed to review recent research on climate change impacts and management options for adaptation to climate change and to identify key themes for researchers and for forest managers.

The study is based on a review of literature on climate change impacts on forests and adaptation options for forest management identified in the Web of Science database, focusing on papers and reports published between 1945 and 2013.

One thousand one hundred seventy-two papers were identified in the search, with the vast majority of papers published from 1986 to 2013. Seventy-six percent of papers involved assessment of climate change impacts or the sensitivity or vulnerability of forests to climate change and 11 % (130) considered adaptation. Important themes from the analysis included (i) predicting species and ecosystem responses to future climate, (ii) adaptation actions in forest management, (iii) new approaches and tools for decision making under uncertainty and stronger partnerships between researchers and practitioners and (iv) policy arrangements for adaptation in forest management.

Conclusions

Research to support adaptation to climate change is still heavily focused on assessing impacts and vulnerability. However, more refined impact assessments are not necessarily leading to better management decisions. Multi-disciplinary research approaches are emerging that integrate traditional forest ecosystem sciences with social, economic and behavioural sciences to improve decision making. Implementing adaptation options is best achieved by building a shared understanding of future challenges among different institutions, agencies, forest owners and stakeholders. Research-policy-practice partnerships that recognise local management needs and indigenous knowledge and integrate these with climate and ecosystem science can facilitate improved decision making.

1 Introduction

Anthropogenic climate change presents potential risks to forests and future challenges for forest managers. Responding to climate change, through both mitigation and adaptation, may represent a paradigm shift for forest managers and researchers (Schoene and Bernier 2012 ). Climate change is resulting in increasing air temperature and changing precipitation regimes, including changes to snowfall and to the timing, amount and inter-annual variability of rainfall (IPCC 2013 ). Forests are widespread, long-lived ecosystems that are both intensively and extensively managed. They are potentially sensitive to these longer term climatic changes, as are the societies and economies that depend on them (Bernier and Schöne 2009 ). Climate change increases the potential consequences of many existing challenges associated with environmental, social or economic change.

Whilst forest ecosystems are resilient and many species and ecosystems have adapted historically to changing conditions, future changes are potentially of such magnitudes or will occur at rates that are beyond the natural adaptive capacity of forest species or ecosystems, leading to local extinctions and the loss of important functions and services, including reduced forest carbon stocks and sequestration capacity (Seppälä et al. 2009 ).

Recent global warming has already caused many changes in forests (Lucier et al. 2009 ). Aspects of climate change may be positive for some tree species in some locations. Tree growth is observed to be increasing in some locations under longer growing seasons, warmer temperatures and increased levels of CO 2 . However, many projected future changes in climate and their indirect effects are likely to have negative consequences for forests. Observed shifts in vegetation distribution (Kelly and Goulden 2008 ; Lenoir et al. 2010 ) or increased tree mortality due to drought and heat in forests worldwide (Allen et al. 2010 ) may not be due to human-induced climate change but demonstrate the potential impacts of rapid climate change. These impacts may be aggravated by other human-induced environmental changes such as increases in low elevation ozone concentrations, nitrogenous pollutant deposition, the introduction of exotic insect pests and pathogens, habitat fragmentation and increased disturbances such as fire (Bernier and Schöne 2009 ). Other effects of climate change may also be important for forests. Sea level rise is already impacting on tidal freshwater forests (Doyle et al. 2010 ) and tidal saltwater forests (mangroves) are expanding landward in sub-tropical coastal reaches taking over freshwater marsh and forest zones (Di Nitto et al. 2014 ).

With projected future change, species ranges will expand or contract, the geographic location of ecological zones will shift, forest ecosystem productivity will change and ecosystems could reorganise following disturbances into ecological systems with no current analogue (Campbell et al. 2009 ; Fischlin et al. 2009 ). Forests types differ in their sensitivity to climatic change. Bernier and Schöne ( 2009 ) considered boreal, mountain, Mediterranean, mangrove and tropical moist forests most vulnerable to climate change. However, there has been recent debate about the vulnerability of tropical moist forests (Corlett 2011 ; Huntingford et al. 2013 ; Feeley et al. 2012 ), and temperate forests in areas subject to drier climates may be more at risk (Choat et al. 2012 ).

Adapting to these changing and uncertain future conditions can be considered from a number of perspectives (McEvoy et al. 2013 ). Policy and management might be directed at avoiding or reducing the impact of climate-related events, reducing vulnerability to future climatic conditions, managing a broader suite of climate ‘risks’ or increasing resilience and capacity in forest ecological and production systems to recover from climate ‘shocks’.

Adapting forest management to climate change involves monitoring and anticipating change and undertaking actions to avoid the negative consequences or take advantage of potential benefits of those changes (Levina and Tirpak 2006 ). Adopting the principles and practices of sustainable forest management (SFM) can provide a sound basis for addressing the challenges of climate change. However, Innes et al. ( 2009 ) pointed out that our failure to implement the multi-faceted components of sustainable forest management in many forests around the world is likely to limit capacity to adapt to climate change. Forest managers will need to plan at multiple spatial and temporal scales and adopt more adaptive and collaborative management approaches to meet future challenges.

Whilst forest managers are accustomed to thinking in long time scales—considering the long-term implications of their decisions and factoring in uncertainty and unknowns into management—many are now responding to much shorter term social or economic imperatives. Local forestry practices are often based on an implicit assumption that local climate conditions will remain constant (Guariguata et al. 2008 ). Other social and economic changes will also continue to drive changes in forest management (Ince et al. 2011 ). For example, a growing global population, rapid economic development and increased wealth are driving demand for food and fibre crops and forest conversion to agriculture in many developing countries (Gibbs et al. 2010 ). Climate change mitigation objectives are increasing demands for wood-based bioenergy and the use of wood in construction and industrial systems. Increasing urbanisation is changing the nature of social demands on forests, and decreasing rural populations is limiting the availability of labour and capacity for intensive forest management interventions.

Ecosystem-based adaptation is being promoted as having the potential to incorporate sustainable management, conservation and restoration of ecosystems into adaptation to climate change (IUCN 2008 ). This can be achieved more effectively by integrating ecosystem management and adaptation into national development policies through education and outreach to raise societal awareness about the value of ecosystem services (Vignola et al. 2009 ).

Kimmins ( 2002 ) invoked the term ‘future shock’, first coined by Toffler ( 1970 ) to describe the situation where societal expectations from forests were changing faster than the institutional capacity for change in forest management organisations. The pace of climate change is likely to intensify this phenomenon. Empirically based management based on traditional ‘evidence-based’ approaches therefore will potentially not develop quickly enough for development of effective future management options. How can managers consider rapid change and incorporate the prospect of very different, but uncertain, future climatic conditions into their management decisions? What types of tools are needed to improve decision making capacity?

This study aimed to review the literature on studies to support forest management in a changing climate. It builds on the major review of Seppala ( 2009 ), in particular Chapter 6 of that report by Innes et al. ( 2009 ).

The study involved a systematic assessment of the literature based on the database Web of Science (Thomson-Reuters 2014 ), an online scientific citation indexing service that provides the capacity to search multiple databases, allowing in-depth exploration of the literature within an academic or scientific discipline.

The following search terms were used in the titles of publications:

(forest* or tree* or (terrestrial and ecosystem)) and climat* and (adapt* or impact* or effect* or respons*) and

(forest* or tree*) and climat* and vulnerabilit* or sensitivit*)

The search was restricted to publications between 1945 and 2013. References related solely to climate change mitigation were excluded, as were references where the word ‘climate’ simply referred to a study in a particular climatic zone. This left a database of 1172 publications for analyses (a spreadsheet of the papers revealed in the search can be obtained from the author). References were classified into various types of studies and different regions, again based on the titles. Not all papers identified in the search are referenced. The selection of themes for discussion and papers for citation was a subjective one, based on scanning abstracts and results from relevant individual papers. The focus was important themes from key papers and literature from the last 5 years. The review includes additional papers not revealed in the search relating to these themes including selected papers from the literature in the year 2014.

Of the published papers relating to climate impacts or adaptation selected for analysis, the vast majority of papers were published from 1986 onwards. The earliest paper dated from 1949 (Gentilli 1949 ) analysing the effects of trees on climate, water and soil. Most studies prior to 1986 (and even some published later) focused on the effects of trees on local or wider regional climate (Lal and Cummings 1979 ; Otterman et al. 1984 ; Bonan et al. 1992 ), the implications of climate variability (Hansenbristow et al. 1988 ; Ettl and Peterson 1995 ; Chen et al. 1999 ), studies of tree and forest responses across climatic gradients (Grubb and Whitmore 1966 ; Bongers et al. 1999 ; Davidar et al. 2007 ) or responses to historical climate (Macdonald et al. 1993 ; Huntley 1990 ; Graumlich 1993 ).

One thousand twenty-six papers specifically addressed future climate change (rather than historical climate or gradient analysis). Of these, 88 % studied impacts, effects, vulnerability or responses to climate change in tree species, forests, forest ecosystems or the forest sector (Fig.  1 ). The first study analysing the potential impacts of future climate change on terrestrial ecosystems was published in 1985 (Emanuel et al. 1985 ) with other highly cited papers soon after (Pastor and Post 1988 ; Cannell et al. 1989 ).

Publication numbers by publication year for publications relating to climate change and forests from a search of the Web of Science database to the end of 2013 (1025 in total, 896 publications studied climate change impacts, responses or vulnerability, 129 studied adaptation)

Twelve percent of papers (129) considered adaptation options, including 10 papers on adaptation in the forest sector. The first papers to focus on adaptation in the context of climate change were in 1996 with a number of papers published in that year (Kienast et al. 1996 ; Kobak et al. 1996 ; Dixon et al. 1996 ). Publications were then relatively few each year until the late 2000s with numbers increasing to 11 in 2009, 22 in 2010 and 27 in 2011. Publications on adaptation dropped to 14 papers in 2013. The ratio of adaptation-related papers has increased more recently, with 19 % of total publications on adaptation in the last 5 years. Most papers considering adaptation since the early 2000s have related to the integration of adaptation and forest management (e.g. Lindner 2000 ; Spittlehouse 2005 ; Kellomaki et al. 2008 ; Guariguata 2009 ; Bolte et al. 2009 ; Keskitalo 2011 ; Keenan 2012 ; Temperli et al. 2012 ).

Analyses of the implications of climate change for the forest sector have focused heavily on North America: Canada (Ohlson et al. 2005 ; Van Damme 2008 ; Rayner et al. 2013 ; Johnston et al. 2012 ) and the USA (Joyce et al. 1995 ; Blate et al. 2009 ; Kerhoulas et al. 2013 ); and Europe (Karjalainen et al. 2003 ; von Detten and Faber 2013 ). There has been a stronger consideration in recent years of social, institutional and policy issues (Ogden and Innes 2007b ; Kalame et al. 2011 ; Nkem et al. 2010 ; Spies et al. 2010 ; Somorin et al. 2012 ) and the assessment of adaptive capacity in forest management organisations and in society more generally (Keskitalo 2008 ; Lindner et al. 2010 ; Bele et al. 2013a ).

Regionally, there have been relatively few published journal articles on impacts or adaptation in forests in the Southern Hemisphere (Hughes et al. 1996 ; Williams 2000 ; Pinkard et al. 2010 ; Gonzalez et al. 2011 ; Mok et al. 2012 ; Breed et al. 2013 ), although there have been more studies in the grey literature for Australian forests (Battaglia et al. 2009 ; Cockfield et al. 2011 ; Medlyn et al. 2011 ; Stephens et al. 2012 ). There have been some valuable analyses for the tropics (Guariguata et al. 2008 , 2012 ; Somorin et al. 2012 ; Feeley et al. 2012 ).

Analysis of the publications identified the following key themes: (i) predicting species and ecosystem responses to future climate, (ii) adaptation actions in forest management, (iii) new approaches and tools for decision making under uncertainty and stronger partnerships between researchers and practitioners and (iv) policy arrangements for adaptation in forest management. These are discussed in more detail below.

3.1 Predicting species and ecosystem responses to future climate

Forest managers have long used climatic information in a range of ways in planning and decision making. Climate information has been used extensively to define and map vegetation types and ecological zones and for modelling habitat distributions of vertebrates and invertebrates (Daubenmire 1978 ; Pojar et al. 1987 ; Thackway and Cresswell 1992 ), for species and provenance selection (Booth et al. 1988 ; Booth 1990 ) and seed zone identification (Johnson et al. 2004 ), for forest fire weather risk assessment and fire behaviour modelling (Carvalho et al. 2008 ), for modelling forest productivity (Battaglia et al. 2004 ) and analysing the dynamics of a range of ecological processes (Anderson 1991 ; Breymeyer and Melillo 1991 ). Predicting species responses to future climate change presents a different set of challenges, involving consideration of predictions of future climate that are often outside the historical range of variability of many species. These challenges are discussed in the next section.

3.1.1 Species responses to climate

Aitken et al. ( 2008 ) argued that there were three possible fates for forest tree populations in rapidly changing climatic conditions: persistence through spatial migration to track their ecological niches, persistence through adaptation to new conditions in current locations or the extirpation of the species. Predicting the potential fate of populations in these conditions requires the integration of knowledge across biological scales from individual genes to ecosystems, across spatial scales (for example, seed and pollen dispersal distances or breadth of species ranges) and across temporal scales from the phenology of annual developmental cycle traits to glacial and interglacial cycles.

Whilst there has been widespread use of climatic information to predict future distributions in species distribution models (SDMs, Pearson and Dawson 2003 ; Attorre et al. 2008 ; Wang et al. 2012 ; Ruiz-Labourdette et al. 2013 ), understanding of the range of climatic and non-climatic factors that will determine the future range of a particular species remains limited. Many now feel that SDMs are of limited value in adaptation decision making or species conservation strategies. Some of these limitations are summarised in Table  1 .

For example, models indicate significant shifts in patterns of tree species distribution over the next century but usually without any intrinsic consideration of the biological capacity of populations to move, internal population dynamics, the extent and role of local adaptation or the effects of climate and land use (Aitken et al. 2008 ; Thuiller et al. 2008 ). In a recent study, Dobrowski et al. ( 2013 ) found that the predicted speed of movement of species to match the predicted rate of climate change appears to be well beyond the historical rates of migration. Whilst modelled outputs suggest that migration rates of 1000 m per year or higher will be necessary to track changing habitat conditions (Malcolm et al. 2002 ), actual migration rates in response to past change are generally considered to have been less than 100 m per year. This was reinforced by model predictions that incorporate species dispersal characteristics for five tree species in the eastern USA indicated very low probabilities of dispersal beyond 10–20 km from current species boundaries by 2100 (Iverson et al. 2004 ). Corlett and Westcott ( 2013 ) also argued that plant movements are not realistically represented in models used to predict future vegetation or carbon-cycle feedbacks and that fragmentation of natural systems is likely to slow migration rates.

However, these estimates do not account for the role of humans in influencing tree species distributions, which they have done for thousands of years (Clark 2007 ), and managed translocation may be an option for conserving many tree species, but there are significant unresolved technical and social questions about implementing translocation at a larger scale (Corlett and Westcott 2013 ).

Most early SDMs relied primarily on temperature envelopes to model future distribution, but factors such as precipitation and soil moisture are potentially more limiting and more important in determining distribution patterns (Dobrowski et al. 2013 ). Aitken et al. ( 2008 ) found that the degree to which variation in precipitation explains phenotypic variation among populations is greater in general for populations from continental than from maritime climates and greater for lower latitude than higher latitude populations. However, precipitation alone is often not a good predictor of variation and there is often a strong interaction with temperature (Andalo et al. 2005 ). Heat to moisture index or aridity is probably more important in determining future distribution or productivity than precipitation alone (Aitken et al. 2008 ; Harper et al. 2009 ; Wang et al. 2012 ). Soil properties (depth, texture and organic matter content) have a major influence on plant-available water, but few SDMs incorporate these.

Future precipitation is proving more difficult to model than temperature, due to the complex effects of topography, and there are more widely varying estimates between global circulation models (GCMs) of future change in precipitation (IPCC 2013 ). As such, there is more uncertainty around the extent to which moisture stress will change with warming and the extent to which natural selection pressures will change as a result. Even without changes in precipitation, increased temperatures will increase the length of growing season and potential evapotranspiration (PET) resulting in more water use over the year and greater risk plant water shortage and drought death.

Changes in the intervals of extreme events (extreme heat, cold, precipitation, humidity, wind) may also matter more than changes in the mean. Current forecasting approaches that produce future climate averages may make it difficult to detect non-linear ecosystem dynamics, or threshold effects, that could trigger abrupt ecosystem change (Campbell et al. 2009 ). Zimmermann et al. ( 2009 ) found that predictions of spatial patterns of tree species in Switzerland were improved by incorporating measures of extremes in addition to means in SDMs.

The risks of future climate will also depend on the management goal. If the aim is simply to conserve genetic diversity, risks of extinction or reduction in genetic diversity may be overstated by SDMs because much of the genetic variation within tree species is found within rather than among their populations, and the extinction of a relatively large proportion of a population is generally likely to result in relatively little overall loss of genetic diversity (Hamrick 2004 ). Local habitat heterogeneity (elevation, slope aspect, moisture, etc.) can preserve adaptive genetic variation that, when recombined and exposed to selection in newly colonised habitats, can provide for local adaptation. The longevity of individual trees can also retard population extinction and allow individuals and populations to survive until habitat recovery or because animal and wind pollination can provide levels of pollen flow that are sufficient to counteract the effects of genetic drift in fragmented populations. Consequently, widespread species with large populations, high fecundity and higher levels of phenotypic plasticity are likely to persist and adapt and have an overall greater tolerance to changing climates than predicted by SDMs (Alberto et al. 2013 ).

Tree species distributions have always been dynamic, responding to changing environmental conditions, and populations are likely to be sub-optimal for their current environments (Namkoong 2001 ; Wu and Ying 2004 ). These lag effects are important in predicting species responses to climate change. In a modelling study of Scots pine and silver birch, Kuparinen et al. ( 2010 ) predicted that after 100 years of climate change, the genotypic growth period length of both species will lag more than 50 % behind the climatically determined optimum. This lag is reduced by increased mortality of established trees, whereas earlier maturation and higher dispersal ability had comparatively minor effects. Thuiller et al. ( 2008 ) suggest that mechanisms for incorporating these ‘trailing edge’ effects into SDMs are a major area of research potential.

Trees are also capable of long-distance gene flow, which can have both adaptive evolution benefits and disadvantages. Kremer et al. ( 2012 ) found that there may be greater positive effects of gene flow for adaptation but that the balance of positive to negative consequences of gene flow differs for leading edge, core and rear sections of forest distributions.

Epigenetics—heritable changes that are not caused by changes in genetic sequences but by differences in the way DNA methylation controls the degree of gene expression—is another complicating factor in determining evolutionary response to climate change (Brautigam et al. 2013 ). For example, a recent study in Norway spruce ( Picea abies ) showed that the temperature during embryo development can dramatically affect cold hardiness and bud phenology in the offspring. In some cases, the offspring’s phenotype varied by the equivalent of 6° of latitude from what was expected given the geographic origin of the parents. It remains uncertain whether these traits are persistent, both within an individual’s lifetime and in its offspring and subsequent generations (Aitken et al. 2008 ). It is suggested that analysis of the epigenetic processes in an ecological context, or ‘ecological epigenetics’, is set to transform our understanding of the way in which organisms function in the landscape. Increased understanding of these processes can inform efforts to manage and breed tree species to help them cope with environmental stresses (Brautigam et al. 2013 ). Others argue that whilst investigating this evolutionary capacity to adapt is important, understanding responses of species to their changing biotic community is imperative (Anderson et al. 2012 ) and ‘landscape genomics’ may offer a better approach for informing management of tree populations under climate change (Sork et al. 2013 ).

These recent results indicate the importance of accounting for evolutionary processes in forecasts of the future dynamics and productivity of forests. Species experiencing high mortality rates or populations that are subject to regular disturbances such as storms or fires might actually be the quickest to adapt to a warming climate.

Species life history characteristics are also not usually well represented in most climate-based distribution models. Important factors include age to sexual maturity, fecundity, seed dispersal, competition or chilling or dormancy requirements (Nitschke and Innes 2008b ).

Competitive relationships within and between species are likely to be altered by climate change. Most models also assume open site growth conditions, rather than those within a forest, where the growth environment will be quite different. However, increased disturbance associated with climate change may create stand reinitiation conditions more often than has occurred in the past, altering competitive interactions.

Process-based models of species range shifts and ecosystem change may capture more of the life history variables and competition effects that will be important in determining responses to climate change (Kimmins 2008 ; Nitschke and Innes 2008a , b ). These can provide the basis for a more robust assessment framework that integrates biological characteristics (e.g. shade tolerance and seedling establishment) and disturbance characteristics (e.g. insect pests, drought and fire topkill). Matthews et al. ( 2011 ) integrated these factors into a decision support system that communicates uncertainty inherent in GCM outputs, emissions scenarios and species responses. This demonstrated a greater diversity among species to adapt to climate change and provides a more practical assessment of future species projections.

In summary, whilst SDMs and other climate-based modelling approaches can provide a guide to potential species responses, the extent to which future climate conditions will result in major range shifts or extinction of species is unclear and the value of this approach in adaptation and decision making is limited. The evidence from genetic studies seems to suggest that many species are reasonably robust to potential future climate change. Those with a wide geographic range, large populations and high fecundity may suffer local population extinction but are likely to persist and adapt whilst suffering adaptational lag for a few generations. For example, Booth ( 2013 ) considered that many eucalyptus species, some of which are widely planted around the world, had a high adaptive capacity even though their natural ranges are quite small.

However, large contractions or shifts in distribution could have significant consequences for different forest values and species with small populations, fragmented ranges, low fecundity or suffering declines due to introduced insects or diseases may have a higher sensitivity and are at greater risk in a changing climate (Aitken et al. 2008 ).

3.1.2 Ecosystem responses to climate

Projecting the fate of forest ecosystems under a changing climate is more challenging than for species. It has been well understood for some time that species will respond individualistically to climate change, rather than moving in concert, and that this is likely to result in ‘novel’ ecosystems, or groups of species, that are not represented in current classifications (Davis 1986 ). Forecasts need to consider the importance of these new species interactions and the confounding effects of future human activities.

Climate change affects a wide range of ecosystem functions and processes (Table  2 ). These include direct effects of temperature and precipitation on physiological and reproductive processes such as photosynthesis, water use, flowering, fruiting and regeneration, growth and mortality and litter decomposition. Changes in these processes will have effects on species attributes such as wood density or foliar nutrient status. Indirect effects will be exhibited through changing fire and other climate-driven disturbances. These will ultimately have impacts on stand composition, habitat structure, timber supply capacity, soil erosion and water yield.

Most early studies of forest ecosystem responses to climate change were built around ecosystem process models at various scales (Graham et al. 1990 ; Running and Nemani 1991 ; Rastetter et al. 1991 ). A number of recent studies have investigated the effects of past and current climate change on forest processes, often with surprising effects (Groffman et al. 2012 ).

Observed forest growth has increased recently in a number of regions, for example over the last 100 years in Europe (Pretzsch et al. 2014 ; Kint et al. 2012 ), and for more recent observations in Amazon forests (Phillips et al. 2008 ). In a major review, Boisvenue and Running ( 2006 ) found that at finer spatial scales, a trend is difficult to decipher, but globally, based on both satellite and ground-based data, climatic changes seemed to have a generally positive impact on forest productivity when water was not limiting. However, there can be a strong difference between species, complicating ecosystem level assessments (Michelot et al. 2012 ), and there are areas with little observed change (Schwartz et al. 2013 ). Generally, there are significant challenges in detecting the response of forests to climate change. For example, in the tropics, the lack of historical context, long-term growth records and access to data are real barriers (Clark 2007 ) and temperate regions also have challenges, even with well-designed, long-term experiments (Leites et al. 2012 ).

Projections of net primary productivity (NPP) under climate change indicate that there is likely to be a high level of regional variation (Zhao et al. 2013 ). Using a process model and climate scenario projections, Peters et al. ( 2013 ) predicted that average regional productivity in forests in the Great Lakes region of North America could increase from 67 to 142 %, runoff could potentially increase from 2 to 22 % and net N mineralization from 10 to 12 %. Increased productivity was almost entirely driven by potential CO 2 fertilization effects, rather than by increased temperature or changing precipitation. Productivity in these forests could shift from temperature limited to water limited by the end of the century. Reyer et al. ( 2014 ) also found strong regional differences in future NPP in European forests, with potential growth increases in the north but reduced growth in southern Europe, where forests are likely to be more water limited in the future. Again, assumptions about the impact of increasing CO 2 were a significant factor in this study.

In a different type of study using analysis of over 2400 long-term measurement plots, Bowman et al. ( 2014 ) found that there was a peaked response to temperature in temperate and sub-tropical eucalypt forests, with maximum growth occurring at a mean annual temperature of 11 °C and maximum temperature of the warmest month of 25–27 °C. Lower temperatures directly constrain growth, whilst high temperatures primarily reduced growth by reducing water availability but they also appeared to exert a direct negative effect. Overall, the productivity of Australia’s temperate eucalypt forests could decline substantially as the climate warms, given that 87 % of these forests currently experience a mean annual temperature above the ‘optimal’ temperature.

Incorporating the effects of rising CO 2 in models of future tree growth continues to be a major challenge. The sensitivity of projected productivity to assumptions regarding increased CO 2 was high in modelling studies of climate change impacts in commercial timber plantations in the Southern Hemisphere (Kirschbaum et al. 2012 ; Battaglia et al. 2009 ), and a recent analysis indicated a general convergence of different model predictions for future tree species distribution in Europe, with most of the difference between models due to the way in which this effect is incorporated (Cheaib et al. 2012 ). Increased CO 2 has been shown to increase the water-use efficiency of trees, but this is unlikely to entirely offset the effects of increased water stress on tree growth in drying climates (Leuzinger et al. 2011 ; Booth 2013 ). In general, despite studies extending over decades and improved understanding of biochemical processes (Franks et al. 2013 ), the impacts of increased CO 2 on tree and stand growth are still unresolved (Kallarackal and Roby 2012 ).

Integrating process model outputs with spatially explicit landscape models can improve understanding and projection of responses and landscape planning and this could provide for simulations of changes in ecological processes (e.g. tree growth, succession, disturbance cycles, dispersal) with other human-induced changes to landscapes (Campbell et al. 2009 ).

Investigation of current species responses to changing climate conditions may also guide improved prediction of patterns of future change in ecosystem distribution. For example, Allen et al. ( 2010 ) suggest that spatially explicit documentation of environmental conditions in areas of forest die-off is necessary to link mortality to causal climate drivers, including precipitation, temperature and vapour pressure deficit. Better prediction of climate responses will also require improved knowledge of belowground processes and soil moisture conditions. Assessments of future productivity will depend on accurate measurements of rates (net ecosystem exchange and NPP), changes in ecosystem level storage (net ecosystem production) and quantification of disturbances effects to determine net biome production (Boisvenue and Running 2006 ).

Hydrological conditions, runoff and stream flow are of critical importance for humans and aquatic organisms, and many studies have focused on the implications of climate change for these ecosystem processes. However, most of these have been undertaken at small catchment scale (Mahat and Anderson 2013 ; Neukum and Azzam 2012 ; Zhou et al. 2011 ) with few basin-scale assessments (van Dijk and Keenan 2007 ). However, the effects of climate and forest cover change on hydrology are complicated. Loss of tree cover may increase stream flow but can also increase evaporation and water loss (Guardiola-Claramonte et al. 2011 ). The extent of increasing wildfire will also be a major factor determining hydrological responses to climate change (Versini et al. 2013 ; Feikema et al. 2013 ).

Changing forest composition will also affect the habitat of vertebrate and invertebrate species. The implications of climate change for biodiversity conservation have been subject to extensive analysis (Garcia et al. 2014 ; Vihervaara et al. 2013 ; Schaich and Milad 2013 ; Clark et al. 2011 ; Heller and Zavaleta 2009 ; Miles et al. 2004 ). An integrated analytical approach, considering both impacts on species and habitat is important. For example, in a study of climate change impacts on bird habitat in the north-eastern USA, the combination of changes in tree distribution and habitat for birds resulted in significant impacts for 60 % of the species. However, the strong association of birds with certain vegetation tempers their response to climate change because localised areas of suitable habitat may persist even after the redistribution of tree species (Matthews et al. 2011 ).

Understanding thresholds in changing climate conditions that are likely to result in a switch to a different ecosystem state, and the mechanisms that underlie ecosystem responses, will be critical for forest managers (Campbell et al. 2009 ). Identifying these thresholds of change is challenging. Detailed process-based ecosystem research that identifies and studies critical species interactions and feedback loops, coupled with scenario modelling of future conditions, could provide valuable insights (Kimmins et al. 1999 , 2008 ; Walker and Meyers 2004 ). Also, rather than pushing systems across thresholds into alternative states, climate change may create a stepwise progression to unknown transitional states that track changing climate conditions, requiring a more graduated approach in management decisions (Lin and Petersen 2013 ).

Ultimately, management decisions may not be driven by whether we can determine future thresholds of change, but by observing the stressors that determine physiological limits of species distributions. These thresholds will depend on species physiology and local site conditions, with recent research demonstrating already observed ecosystem responses to climate change, including die-back of some species (Allen et al. 2010 ; Rigling et al. 2013 ).

3.1.3 Fire, pests, invasive species and disturbance risks

Many of the impacts of a changing future climate are likely to be felt through changing disturbance regimes, in particular fire. Forest fire weather risk and fire behaviour prediction have been two areas where there has been strong historical interaction between climate science and forest management and where we may see major tipping points driving change in ecosystem composition (Adams 2013 ). Fire weather is fundamentally under the control of large-scale climate conditions with antecedent moisture anomalies and large-scale atmospheric circulation patterns, further exacerbated by configuration of local winds, driving fire weather (Brotak and Reifsnyder 1977 ; Westerling et al. 2002 , 2006 ). It is therefore important to improve understanding of both short- and long-term atmospheric conditions in determining meteorological fire risk (Amraoui et al. 2013 ).

Increased fuel loads and changes to forest structure due to long periods of fire exclusion and suppression are increasing fire intensity and limiting capacity to control fires under severe conditions (Williams 2004 , 2013 ). Increasing urbanisation is increasing the interface between urban populations and forests in high fire risk regions, resulting in greater impacts of wildfire on human populations, infrastructure and assets (Williams 2004 ). Deforestation and burning of debris and other types of human activities are also introducing fire in areas where it was historically relatively rare (Tacconi et al. 2007 ).

In a recent study, Chuvieco et al. ( 2014 ) assessed ecosystem vulnerability to fire using an index based on ecological richness and fragility, provision of ecosystem services and value of houses in the wildland–urban interface. The most vulnerable areas were found to be the rainforests of the Amazon Basin, Central Africa and Southeast Asia; the temperate forest of Europe, South America and north-east America; and the ecological corridors of Central America and Southeast Asia.

In general, fire management policies in many parts of the world will need to cope with longer and more severe fire seasons, increasing fire frequency, and larger areas exposed to fire risk. This will especially be the case in the Mediterranean region of Europe (Kolström et al. 2011 ) and other fire-prone parts of the world such as South Eastern Australia (Hennessy et al. 2005 ). This will require improved approaches to fire weather modelling and behaviour prediction that integrate a more sophisticated understanding of the climate system with local knowledge of topography, vegetation and wind patterns. It will also require the development of fire management capacity where it had previously not been necessary. Increased fire weather severity could push current suppression capacity beyond a tipping point, resulting in a substantial increase in large fires (de Groot et al. 2013 ; Liu et al. 2010 ) and increased investment in resources and management efforts for disaster prevention and recovery.

Biotic factors may be more important than direct climate effects on tree populations in a changing climate. For example, insects and diseases have much shorter generation length and are able to adapt to new climatic conditions more rapidly than trees. However, if insects move more rapidly to a new environment whilst tree species lag, some parts of the tree population may be impacted less in the future (Regniere 2009 ).

The interaction of pests, diseases and fire will also be important. For example, this interaction will potentially determine the vulnerability of western white pine ( Pinus monticola ) ecosystems in Montana in the USA. Loehman et al. ( 2011 ) found that warmer temperatures will favour western white pine over existing climax and shade tolerant species, mainly because warmer conditions will lead to increased frequency and extent of wildfires that facilitates regeneration of this species.

3.2 Adaptation actions in forest management

The large majority of published studies relating to forests and climate change have been on vulnerability and impacts. These have increased understanding of the various relationships between forest ecosystems and climate and improved capacity to predict and assess ecosystem responses. However, managers need greater guidance in anticipating and responding to potential impacts of climate change and methods to determine the efficiency and efficacy of different management responses because they are generally not responding sufficiently to potential climate risks.

3.2.1 Adaptation actions at different management levels

A number of recent reviews have described adaptation actions and their potential application in different forest ecosystems being managed for different types of goods or services (Bernier and Schöne 2009 ; Innes et al. 2009 ; Lindner et al. 2010 ; Kolström et al. 2011 ), and adaptation guides and manuals have been developed (Peterson et al. 2011 ; Stephens et al. 2012 ) for different types of forest and jurisdictions. Adaptation actions can be primarily aimed at reducing vulnerability to increasing threats or shocks from natural disasters or extreme events, or increasing resilience and capacity to respond to progressive change or climate extremes. Adaptation actions can be reactive to changing conditions or planned interventions that anticipate future change. They may involve incremental changes to existing management systems or longer term transformational changes (Stafford Smith et al. 2011 ). Adaptation actions can also be applied at the stand level or at ownership, estate or national scales (Keskitalo 2011 ).

Recent research at the stand level in forests in the SE USA showed that forest thinning, often recommended in systems that are likely to experience increased temperature and decreased precipitation as a result of climate change, will need to be more aggressive than traditionally practised to stimulate growth of large residual trees, improve drought resistance and provide greater resilience to future climate-related stress (Kerhoulas et al. 2013 ).

An analysis of three multi-aged stand-level options in Nova Scotia, Canada, Steenberg et al. ( 2011 ) found that leaving sexually immature trees to build stand complexity had the most benefit for timber supply but was least effective in promoting resistance to climate change at the prescribed harvest intensity. Varying the species composition of harvested trees proved the most effective treatment for maximising forest age and old-growth area and for promoting stands composed of climatically suited target species. The combination of all three treatments resulted in an adequate representation of target species and old forest without overly diminishing the timber supply and was considered most effective in minimising the trade-offs between management values and objectives.

An estate level analysis of Austrian Federal Forests indicated that management to promote mixed stands of species that are likely to be well adapted to emerging environmental conditions, silvicultural techniques fostering complexity and increased management intensity might successfully reduce vulnerability, with the timing of adaptation measures important to sustain supply of forest goods and services (Seidl et al. 2011 ).

Whilst researchers are analysing different management options, the extent to which they are being implemented in practice is generally limited. For example, in four regions in Germany, strategies for adapting forest management to climate change are in the early stages of development or simply supplement existing strategies relating to general risk reduction or to introduce more ‘nature-orientated’ forest management (Milad et al. 2013 ). Guariguata et al. ( 2012 ) found that forest managers across the tropics perceived that natural and planted forests are at risk from climate change but were ambivalent about the value of investing in adaptation measures, with climate-related threats to forests ranked below others such as clearing for commercial agriculture and unplanned logging.

Community-based management approaches are often argued to be the most successful approach for adaptation. An analysis of 38 community forestry organisations in British Columbia found that 45 % were researching adaptation and 32 % were integrating adaptation techniques into their work (Furness and Nelson 2012 ). Whilst these community forest managers appreciated support and advice from government for adaptation, balancing this advice with autonomy for communities to make their own decisions was considered challenging.

In a study of communities impacted by drought in the forest zone of Cameroon, Bele et al. ( 2013b ) identified adaptive strategies such as community-created firebreaks to protect their forests and farms from forest fires, the culture of maize and other vegetables in dried swamps, diversifying income activities or changing food regimes. However, these coping strategies were considered to be incommensurate with the rate and magnitude of change being experienced and therefore no longer seen as useful. Some adaptive actions, whilst effective, were resource inefficient and potentially translate pressure from one sector to another or generated other secondary effects that made them undesirable.

3.2.2 Integrating adaptation and mitigation

In considering responses to climate change, forest managers will generally be looking for solutions that address both mitigation objectives and adaptation. To maintain or increase forest carbon stocks over the long term, the two are obviously inextricably linked (Innes et al. 2009 ). Whilst there are potentially strong synergies, Locatelli et al. ( 2011 ) identified potential trade-offs between actions to address mitigation and the provision of local ecosystem services and those for adaptation. They argued that mitigation projects can facilitate or hinder the adaptation of local people to climate change, whereas adaptation projects can affect ecosystems and their potential to sequester carbon.

Broadly, there has been little integration to date of mitigation and adaptation objectives in climate policy. For example, there is little connection between policies supporting the reducing emissions from deforestation and forest degradation plus (REDD+) initiatives and adaptation. Integrating adaptation into REDD+ can advance climate change mitigation goals and objectives for sustainable forest management (Long 2013 ). Kant and Wu ( 2012 ) considered that adaptation actions in tropical forests (protection against fire and disease, ensuring adequate regeneration and protecting against coastal impacts and desertification) will improve future forest resilience and have significant climate change mitigation value.

3.2.3 Sector-level adaptation

Analyses of climate change impacts and vulnerability at the sector level have been undertaken for some time (Lindner et al. 2002 ; Johnston and Williamson 2007 ; Joyce 2007 ). However, it has recently been argued (Wellstead et al. 2014 ) that these assessments, which focus on macro system-level variables and relationships, fail to account for the multi-level or polycentric nature of governance and the possibility that policy processes may result in the non-performance of critical tasks required for adaptation.

Joyce et al. ( 2009 ) considered that a toolbox of management options for the US National Forests would include the following: practices focused on reducing future climate change effects by building resistance and resilience into current ecosystems and on managing for change by enabling plants, animals and ecosystems to adapt to climate change. Sample et al. ( 2014 ) demonstrated the utility of this approach in a coniferous forest management unit in northwestern USA. It provided an effective means for guiding management decisions and an empirical basis for setting budgetary and management priorities. In general, more widespread implementation of already known practices that reduce the impact of existing stressors represents an important ‘no regrets’ strategy.

Johnston and Hesseln ( 2012 ) found that barriers to implementing adaptation across forest sector managers in Canada included inflexible tenure arrangements and regulatory environments which do not support innovation. Echoing calls for wider implementation of SFM as a key adaptation strategy (Innes et al. 2009 ), they argued that forest certification systems, participating in the Canadian model forest programme, and adopting criteria and indicators of SFM can support sectoral level adaptation.

Decentralised management approaches are considered to be a more appropriate governance arrangement for forest management, but Rayner et al. ( 2013 ) argued that a decentralised forest policy sector in Canada has resulted in limitations where policy, such as adaptation, requires a coherent national response. Climate change adaptation has led to an expansion of departmental mandates that is not being addressed by better coordination of the available policy capacity. Relevant federal agencies are not well represented in information networks, and forest policy workers report lower levels of internal and external networking than workers in related policy subsectors.

Economic diversification can be a valuable strategy to improve resilience to climate-related shocks. This can take a range of forms: developing new industries or different types of forest-based industries based on different goods or services. For the timber sector, the value of diversification as a risk management strategy for communities is open to question. Ince et al. ( 2011 ) pointed out that the forest sector operates in an international market and is susceptible to changes in the structure of this market. In the US forest sector, globalization has accelerated structural change, favouring larger and more capital-intensive enterprises and altering historical patterns of resource use. They suggest that future markets for timber will be driven by developments in these larger scale enterprises and may not lead to expansion of opportunities for smaller scale forest enterprises because development of niche markets or customised products is likely to be pursued aggressively by larger globally oriented enterprises to develop branding, product identity and product value. How to best diversify for adaptation therefore remains an open question.

Consequently, whilst policies that support economic diversification will be important, this may involve diversification well beyond traditional sectors. For example, in areas where there is a high probability that forests will be lost in favour of other ecosystems, such as grasslands, managers should recognise early on that their efforts and resources may best be focused outside forests (Innes et al. 2009 ). These adjustments will involve taking into account the perceptions of climate risk by various stakeholders, including individuals, communities, governments, private institutions and organisations (Adger et al. 2007 ). Vulnerability assessments and adaptation measures also need to be developed in a framework that takes into account the vulnerabilities and actions in other sectors that are linked to the forest sector, such as food, energy, health and water (Sonwa et al. 2012 ).

3.3 New approaches to decision making

Climate change presents new challenges for forest managers. Change is likely to happen faster than traditional, empirical approaches can provide evidence to support changes in management. Uncertainties in a range of aspects of future climate may also not be reduced through investment in research. Given that management for activities such as timber production can no longer be based solely on empirically derived growth and yield trajectories and management plans must incorporate uncertainty and the increased probability of extreme events, what types of tools are available to support these approaches? This section presents key points from the literature on decision making under uncertainty, adaptive management and resilience as a guide to future decision making in forest management.

3.3.1 Decision making under uncertainty

The future conditions for forest managers are subject to a high degree of uncertainty, and the future prospects for reducing these large uncertainties are limited. There is uncertainty regarding the trajectory of future increases in atmospheric greenhouse gases, what kind of effects these might have on the climate system and the effects of climatic changes on ecological and social systems and their capacity to adapt (see Fig.  2 ) (Wilby and Dessai 2010 ).

The cascade of uncertainty (Wilby and Dessai 2010 )

Consequently, many forest managers consider that the future situation is too uncertain to support long-term and potentially costly decisions that may be difficult to reverse. Dessai and Hulme ( 2004 ) argued that uncertainty per se should not be a reason for inaction. However, the critical issue for managers is deciding the types of actions to take and the timing and conditions under which they should be taken (Ogden and Innes 2007a ). A more reactive ‘wait and see’ approach (or ‘purposeful procrastination’) might be justified if uncertainty or costs are high relative to the expected impacts and risks, or if it is cheaper to implement interventions by waiting until after a significant disturbance (e.g. replanting an area with more fire- or drought-resistant tree species after a wildfire or drought-induced insect outbreak).

Effective adaptation requires setting clear objectives. Managers and policy makers need to decide whether they are trying to facilitate ecosystem adaptation through changing species composition or forest structure or trying to engineer resistance to change through proactive management strategies (Joyce et al. 2008 ). Establishing objectives often depends on the integration of the preferences of different stakeholders (Prato 2008 ), but changing social preferences presents another source of potential uncertainty.

Risk assessment and management provide a foundation for decision making in considering climate change in natural resource management. This approach provides both a qualitative and quantitative framework for evaluating climate change effects and adaptation options. Incorporating risk management approaches into forest management plans can provide a basis for managers to continue to provide forest conditions that meet a range of important values (Day and Perez 2013 ).

However, risk approaches generally requiring assigning probabilities to future events. In a comprehensive review, Yousefpour et al. ( 2011 ) identified a growing body of research literature on decision making under uncertainty, much of which has focused on price uncertainty and variation in timber production but is extending to multiple forest management objectives and other types of risk. They argue that we are actually in a stochastic transition from one known stable (but variable) climate state to a new but largely unknown and likely more rapidly changing set of future conditions.

Decision makers themselves may also not be the rational actors assumed by these models, with their decisions taken according to quite different assumptions, preferences and beliefs (Ananda and Herath 2009 ; Couture and Reynaud 2008 ). Therefore, the communication approach will be important in determining whether the information is acted on. In a recent study, Yousefpour et al. ( 2014 ) considered that the speed with which decision makers will form firm beliefs about future climate depends on the divergence among climate trajectories, the speed of change and short-term climate variability. Using a Bayesian modelling approach, they found that if a large change in climate occurs, the value of investing in knowledge and taking an adaptive approach would be positive and higher than a non-adaptive approach. In communicating about uncertainty, it may be better to focus discussion on the varying time in the future when things will happen, rather than on whether they will happen at all (Lindner et al. 2014 ).

Increased investment in climate science and projections or species distribution modelling may not necessarily decrease uncertainty in climate projections or impacts. Climate models are best viewed as heuristic tools rather than as accurate forecasts of the future (Innes et al. 2009 ). Trajectories of change in many other drivers of forest management (social, political or economic) are also highly uncertain (Keskitalo 2008 ) and the effects of these on the projected performance of management can be the same order of magnitude, requiring an integrated social-ecological perspective to adaptation (Seidl and Lexer 2013 ).

In a more ‘decision-centred’ approach, plausible scenarios of the potential range of future conditions are required. These can be derived from climate models but do not need to be accurate and precise ‘predictions’ of future climate states (Wilby and Dessai 2010 ). To support this type of approach, research needs to focus on improved understanding of tree and ecosystem responses and identifying those aspects of climate to which different forest types are most sensitive.

Devising strategies that are able to meet management objectives under a range of future scenarios is likely to be the most robust approach, recognising that these strategies are unlikely to be optimal under all future conditions. In some cases, the effect of different scenarios on forest growth may not be that great and differences in the present value of different management options are relatively small. For example, Eriksson et al. ( 2011 ) found that there was limited benefit in attempting to optimise management plans in accordance with future temperature scenarios.

Integration of climate change science and adaptation in forest management planning is considered important for building resilience in forest social and ecological systems (Keskitalo 2011 ; D’Amato et al. 2011 ; Chmura et al. 2011 ; Parks and Bernier 2010 ; Lindner et al. 2014 ). Forest restoration is becoming a more prominent aspect of forest management in many parts of the world and restoration approaches will also need to integrate understanding of future climate change to be successful (Stanturf et al. 2014 ).

3.3.2 Adaptive management, resilience and decisions

Adaptive management provides a mechanism to move forward when faced with future uncertainty (Innes et al. 2009 ). It can be viewed as a systematic process for continually improving management policies and practices by monitoring and then learning from the outcomes of operational programmes as a basis for incorporating adaptation actions into forest management. Whilst many management initiatives purport to implement these principles, they often lack essential characteristics of the approach (Innes et al. 2009 ).

However, effective adaptation to changing climate cannot simply involve adaptive management as it is currently understood. The pace of climate change is not likely to allow for the use of management as a tool to learn about the system by implementing methodologies to test hypotheses concerning known uncertainties (Holling 1978 ). Future climatic conditions may result in system states and dynamics that have never previously existed (Stainforth et al. 2007 ), so observation of past experience may be a poor guide for future action. Management will need to be more ‘forward-looking’, considering the range of possible future conditions and planning actions that consider that full range.

How does this translate into the practical guidance forest managers are seeking on how to adapt their current practices and, if necessary, their goals (Blate et al. 2009 )? Managers will need to consider trade-offs between different objectives under different conditions. For example, Seidl et al. ( 2011 ) showed that, to keep climate vulnerability in an Austrian forest low, Norway spruce will have to be replaced almost entirely by better adapted species. However, indicator weights that favoured timber production over C storage or biodiversity exerted a strong influence on the results. Wider social implications of imposing such drastic changes in forest landscapes will also deserve stronger consideration in decision making.

Ecosystem management will need to be reframed to accommodate the risks of a changing climate. Adaptive strategies, even without specific information on the future climate conditions of a target ecosystem, would enhance social and ecological resilience to address the uncertainties due to changing climate (Mori et al. 2013 ). These are likely to be more subject to change over the short to medium term, in response to more rapidly changing conditions.

Analysis of ecosystem resilience can provide a framework for these assessments. Resilience can be defined as ‘the capacity of ecosystems to absorb disturbance and reorganise so as to retain essentially the same function, structure and feedbacks – to have the same identity’ (Walker and Salt 2012 ). It is a function of the capacity of an ecosystem to resist change, the extent and pace of change and the ability of an ecosystem to reorganise following disturbance. The concept of resilience holds promise for informing future forest management, but Rist and Moen ( 2013 ) argue that its contributions are, so far, largely conceptual and offer more in terms of being a problem-framing approach than analytical or practical tools. There may also be trade-offs involved with focusing on resilience through retention of current species composition or using a more adaptation-oriented management approach after disturbances (Buma and Wessman 2013 ). Complexity theory and concepts can provide an appropriate framework for managing resilience (Messier et al. 2013 ).

Management decisions will ultimately depend on the costs and benefits of different options, but there are few examples of decision making frameworks that compare the costs of future impacts with the costs of different actions and the efficacy of those actions in reducing impacts. Ogden and Innes ( 2009 ) used a structured decision making process to identify and assess 24 adaptation options that managers considered important to achieve their regional goals and objectives of sustainable forest management in light of climate change. In the analysis of options for biodiversity conservation, Wintle et al. ( 2011 ) found that the amount of funding available for adaptation was a critical factor in deciding options aimed at minimising species extinctions in the mega-diverse fynbos biome of South Africa. When the available budget is small, fire management was the best strategy. If the budget is increased to an intermediate level, the marginal returns from more fire management were limited and the best strategy was added habitat protection. Above another budget threshold, increased investment should go into more fire management. By integrating ecological predictions in an economic decision framework, they found that making the choice of how much to invest is as important as determining what actions to take.

3.3.3 Adaptation as a social learning process

Whilst adaptation has been defined as ‘adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects’ (Levina and Tirpak 2006 ), adaptation is essentially about meeting future human needs (Spittlehouse and Stewart 2003 ; Hahn and Knoke 2010 ). Consequently, it is inherently a social process. Forest landscapes are social-ecological systems that involve both nature and society (Innes et al. 2009 ), and resolving trade-offs between different management objectives to meet the different needs in society is an important element of sustainable forest management. As Kolström et al. ( 2011 ) pointed out, some proposed adaptation measures may change the balance between current objectives and stakeholder interests, and it will be important to consider the relative balance of different measures at the stand, management unit and landscape scales.

Those investigating adaptive management also recognise that it goes beyond the focus on scientific methods, statistical designs or analytical rigour favoured by its early proponents and that there is now an expectation of much greater stakeholder involvement, with the concept being renamed by some as adaptive, collaborative management (Innes et al. 2009 ). SFM and adaptation are as much about those who inhabit, work in or utilise forests as it is about managing the forest ecosystems themselves (White et al. 2010 ; Pramova et al. 2012 ; Fischer et al. 2013 ).

The choice of adaptation options will thus likely be relatively complex, even in cases where information and policy have been developed, and communication measures for forest management have been well formulated. Making such choices may require considerable knowledge, competence and commitment for implementation at the local level (Keskitalo 2011 ). Effective adaptation will require much greater cooperation between stakeholders, more flexibility for management actions and commitment of time to develop the social license for action in the absence of conclusive evidence or understanding. This will require venues for sharing perspectives on the nature of the problem (Fig.  3 ).

Adaptation as a social learning process. There is a need to provide situations to share different viewpoints on the nature of the problem as a basis for developing shared solutions (image source: John Rowley, http://ch301.cm.utexas.edu/learn/ )

3.3.4 Local and indigenous knowledge

The promotion of community-based forest management may increase local adaptive capacity by putting decisions in the hands of those people who first feel the effects of climate change (Gyampoh et al. 2009 ). In this context, local knowledge systems based on long-term observation and experience are likely to be of increasing importance in decision making. Adaptation strategies can benefit from combining scientific and indigenous knowledge, especially in developing countries (Gyampoh et al. 2009 ), with the translation of local forest knowledge into the language of formal forest science being considered an important step towards adaptation (Roberts et al. 2009 ). However, conservation and natural resource managers in government agencies have often discounted traditional local management systems (Scott 2005 ), although Spathelf et al. ( 2014 ) provided a useful approach for capturing local expert knowledge. Linking this type of knowledge with broader scientific understanding of ecosystem functioning and the global climate system will be a major challenge, requiring consideration of both technical and cultural issues (Caverley 2013 ), including intellectual property concerns of indigenous people (Lynch et al. 2010 ).

3.4 Policy arrangements for adaptation

Increasingly, many are arguing that effectively responding to climate change will require polycentric and multi-level governance arrangements (Peel et al. 2012 ). However, Nilsson et al. ( 2012 ) found that institutionalising of knowledge and knowledge exchange regarding climate change adaptation in Sweden was weak and that improved mechanisms are required for feedback from the local to the national level. Recent studies have described stronger relationships between scientific research and forest management to assess trade-offs and synergies, for participatory decision making and for shared learning (Blate et al. 2009 ; Littell et al. 2012 ; Klenk et al. 2011 ).

Many papers emphasised the need for greater flexibility in the policies, cultures and structures of forest management organisations (Brown 2009 ; von Detten and Faber 2013 ; Rayner et al. 2013 ). Because no single community or agency can prepare on their own for future impacts, inter-sectoral policy coordination will be required to ensure that policy developments in related policy sectors are not contradictory or counterproductive. Greater integration of information, knowledge and experience and collaborative projects involving scientists, practitioners and policy makers from multiple policy communities could increase focus on resilience, identify regions of large-scale vulnerability and provide a more rigorous framework for the analysis of vulnerability and adaptation actions (Thomalla et al. 2006 ).

There is also likely to be a greater need for cross-border implementation of different forest management options, requiring greater coordination between nation states and sub-national governments (Keenan 2012 ). Policy is the product of both ‘top-down’ and ‘bottom-up’ processes and these might sometimes be in conflict. Simply having ‘good policy’ in place is unlikely to be sufficient, as a great deal of what takes place at ‘street level’ is not determined by formal aims of central policy (Urwin and Jordan 2008 ). Having the right policies can send a strong political signal that adaptation needs to be considered seriously but flexibility in policy systems will be required to facilitate adaptive planning.

4 Discussion and conclusions

This broad survey of the literature indicated that, whilst there has been considerable development in research and thinking about adaptation in forest management over the last 10 years, research is still strongly focused on assessment of future impacts, responses and vulnerability of species and ecosystems (and in some cases communities and forest industries) to climate change. There has been some movement from a static view of climate based on long-term averages to a more detailed understanding of the drivers of different climate systems and how these affect the factors of greatest influence on different forest ecosystems processes, such as variability and extremes in temperature or precipitation or fire disturbance. For example, Guan et al. ( 2012 ) demonstrated that quasi-periodic climate variation on an inter-annual (ENSO) to inter-decadal (PDO) time scale can significantly influence tree growth and should be taken into account when assessing the impact of climate changes on forest productivity.

Adaptation is, in essence, about making good decisions for the future, taking into account the implications of climate change. It involves recognising and understanding potential future climate impacts and planning and managing for their consequences, whilst also considering the broader social, economic or other environmental changes that may impact on us, individually or collectively. To effectively provide a role in mitigation, delivering associated ecosystem services and benefits in poverty reduction (Eliasch 2008 ) forest management will have to adapt to a changing and highly variable climate. In achieving this, the roles and responsibilities of different levels of government, the private sector and different parts of the community are still being defined.

The broader literature emphasises that adaptation is a continuous process, involving a process of ‘adapting well’ to continuously changing conditions (Tompkins et al. 2010 ). This requires organisational learning based on past experience, new knowledge and a comprehensive analysis of future options. This can take place through ‘learning by doing’ or through a process of search and planned modification of routines (Berkhout et al. 2006 ). However, interpreting climate signals is not easy for organisations, the evidence of change is ambiguous and the stimuli are not often experienced directly within the organisation. For example, many forest managers in Australia currently feel little need to change practices to adapt to climate change, given both weak policy signals and limited perceived immediate evidence of increasing climate impacts (Cockfield et al. 2011 ). To explain and predict adaptation to climate change, the combination of personal experience and beliefs must be considered (Blennow et al. 2012 ). ‘Climate smart’ forest management frameworks can provide an improved basis for managing forested landscapes and maintaining ecosystem health and vitality based on an understanding of landscape vulnerability to future climatic change (Fig. 4 ) (Nitschke and Innes 2008a ).

Components of climate smart forest management (after Nitschke and Innes 2008a , b )

Many are now asking, do we really need more research to start adapting forest management to climate change? Whilst adaptation is often considered ‘knowledge deficit’ problem—where scientists provide more information and forest managers will automatically make better decisions—the reality is that the way in which this information is presented and how it is interpreted and received, will play major roles in determining potential responses. Successful adaptation will require dissemination of knowledge of potential climate impacts and suitable adaptation measures to decision makers at both practice and policy levels (Kolström et al. 2011 ) but it needs to go well beyond that.

Adaptation is, above all, a social learning process. It requires an understanding of sense of place, a capacity for individuals and society to consider potential future changes and what they mean for their circumstances. Leaders in forest management organisations will need to support a greater diversity of inputs into decision making, avoid creating rigid organisational hierarchies that deter innovation, and be inclusive, open and questioning (Konkin and Hopkins 2009 ). They will need to create more opportunities for interaction between researchers, managers and the community and space for reflection on the implications and the outcomes of management actions and unplanned events. Researchers will need to develop new modes of communication, providing knowledge in forms that are appropriate to the management decision and suitable for digestion by a range of different audiences.

From this analysis, key gaps in knowledge for adaptation may not be improved climate scenarios or better understanding of the biophysical responses of individual tree species or forest ecosystems to future climate. Knowledge gaps lie more in understanding the social and community attitudes and values that drive forest management and the decision making processes of forest managers, in order to work out how ‘climate intelligence’ can be built in to these processes.

The impacts of changing climate will vary locally. Consequently, managers must be given the flexibility to respond in ways that meet their particular needs and capacity to choose management options that are applicable to the local situation (Innes et al. 2009 ). This may not be consistent with rigid indicator-driven management assessment processes like forest certification. Whilst policy to support climate change mitigation is primarily a task for national governments and international agreements and processes, responsibility for supporting adaptation will fall more to sub-national and local governments, communities and the private sector. More active management will be required if specific values are to be maintained, particularly for forests in conservation reserves. This will require additional investment, but there has been little analysis to support the business case for investment in adaptation or to determine who should pay, particularly in developing countries.

We need to strengthen the relationship between climate science, forest research, forest managers and the community. Key challenges will include the setting of objectives for desired future conditions and accepting that we may not be able to maintain everything that forests have traditionally provided. It is important to discuss and agree on common goals in order to cope with, or benefit from, the challenges of future climates. Actively managing our forest ecosystems effectively and intelligently, using the best available knowledge and foresight capacity, can make those goals a reality.

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Acknowledgments

Thanks to Linda Joyce for her comments on an earlier draft of this paper, to a number of anonymous reviewers for their thoughtful suggestions and to many colleagues that I have discussed these ideas with over the past five years.

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This research was partly undertaken during the period the author was Director of the Victorian Centre for Climate Change Adaptation Research and part-funded by the Victorian Government. It was completed with support from the University of Melbourne under a Special Studies Program grant.

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Keenan, R.J. Climate change impacts and adaptation in forest management: a review. Annals of Forest Science 72 , 145–167 (2015). https://doi.org/10.1007/s13595-014-0446-5

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Original research article, the unseen effects of deforestation: biophysical effects on climate.

• 1 Department of Environmental Sciences, University of Virginia, Charlottesville, VA, United States
• 2 The Woodwell Climate Research Center, Falmouth, MA, United States
• 3 The Alliance of Bioversity International and the International Center for Tropical Agriculture, Cali, Colombia

Climate policy has thus far focused solely on carbon stocks and sequestration to evaluate the potential of forests to mitigate global warming. These factors are used to assess the impacts of different drivers of deforestation and forest degradation as well as alternative forest management. However, when forest cover, structure and composition change, shifts in biophysical processes (the water and energy balances) may enhance or diminish the climate effects of carbon released from forest aboveground biomass. The net climate impact of carbon effects and biophysical effects determines outcomes for forest and agricultural species as well as the humans who depend on them. Evaluating the net impact is complicated by the disparate spatio-temporal scales at which they operate. Here we review the biophysical mechanisms by which forests influence climate and synthesize recent work on the biophysical climate forcing of forests across latitudes. We then combine published data on the biophysical effects of deforestation on climate by latitude with a new analysis of the climate impact of the CO 2 in forest aboveground biomass by latitude to quantitatively assess how these processes combine to shape local and global climate. We find that tropical deforestation leads to strong net global warming as a result of both CO 2 and biophysical effects. From the tropics to a point between 30°N and 40°N, biophysical cooling by standing forests is both local and global, adding to the global cooling effect of CO 2 sequestered by forests. In the mid-latitudes up to 50°N, deforestation leads to modest net global warming as warming from released forest carbon outweighs a small opposing biophysical cooling. Beyond 50°N large scale deforestation leads to a net global cooling due to the dominance of biophysical processes (particularly increased albedo) over warming from CO 2 released. Locally at all latitudes, forest biophysical impacts far outweigh CO 2 effects, promoting local climate stability by reducing extreme temperatures in all seasons and times of day. The importance of forests for both global climate change mitigation and local adaptation by human and non-human species is not adequately captured by current carbon-centric metrics, particularly in the context of future climate warming.

Introduction

Failure to stabilize climate is in itself a large threat to biodiversity already at risk from deforestation. Protection, expansion, and improved management of the world’s forests represent some of the most promising natural solutions to the problem of keeping global warming below 1.5–2 degrees ( Griscom et al., 2017 ; Roe et al., 2019 ). Forests sequester large quantities of carbon; of the 450–650 Pg of carbon stored in vegetation ( IPCC, 2013 ), over 360 Pg is in forest vegetation ( Pan et al., 2013 ). Adding the carbon in soils, forests contain over 800 PgC, almost as much as is currently stored in the atmosphere ( Pan et al., 2013 ). In addition, forests are responsible for much of the carbon removal by terrestrial ecosystems which together remove 29% of annual CO 2 emissions (∼11.5 PgC; Friedlingstein et al., 2019 ). Globally, forest loss not only releases a large amount of carbon to the atmosphere, but it also significantly diminishes a major pathway for carbon removal long into the future ( Houghton and Nassikas, 2018 ). Tropical forests, which hold the greatest amount of aboveground biomass and have one of the fastest carbon sequestration rates per unit land area ( Harris et al., 2021 ), face the greatest deforestation pressure ( FAO, 2020 ). Given the long half-life and homogenous nature of atmospheric CO 2 , current forest management decisions will have an enduring impact on global climate through effects on CO 2 alone. However, forests also impact climate directly through controls on three main biophysical mechanisms: albedo, evapotranspiration (ET) and canopy roughness.

The direct biophysical effects of forests moderate local climate conditions. As a result of relatively low albedo, forests absorb a larger fraction of incoming sunlight than brighter surfaces such as bare soil, agricultural fields, or snow. Changes in albedo can impact the radiation balance at the top of the atmosphere and thus global temperature. The local climate, however, is not only impacted by albedo changes but also by how forests partition incoming solar radiation between latent and sensible heat. Deep roots and high leaf area make forests very efficient at moving water from the land surface to the atmosphere via ET, producing latent heat. Thus, beneath the forest canopy, the sensible heat flux and associated surface temperature are relatively low, especially during the growing season when ET is high ( Davin and de Noblet-Ducoudré, 2010 ; Mildrexler et al., 2011 ; Alkama and Cescatti, 2016 ). This cooling is enhanced by the relatively high roughness of the canopy, which strengthens vertical mixing and draws heat and water vapor away from the surface. Higher in the atmosphere, as water vapor condenses, the latent heat is converted to sensible heat. As a result, warming that began with sunlight striking the canopy is felt higher in the atmosphere rather than in the air near the land surface. These non-radiative processes stabilize local climate by reducing both the diurnal temperature range and seasonal temperature extremes ( Lee et al., 2011 ; Zhang et al., 2014 ; Alkama and Cescatti, 2016 ; Findell et al., 2017 ; Forzieri et al., 2017 ; Hirsch et al., 2018 ; Lejeune et al., 2018 ). Their impact on global climate, however, is less clear.

Despite high spatial variability, forest biophysical impacts do follow predictable latitudinal patterns. In the tropics, higher incoming solar radiation and moisture availability provide more energy to drive ET and convection, which in combination with roughness overcome the warming effect of low albedo, and result in year round cooling by forests. At higher latitudes, where incoming solar radiation is highly seasonal, the impacts of ET and surface roughness are diminished ( Anderson et al., 2011 ; Li et al., 2015 ) and albedo is the dominant biophysical determinant of the climate response. In boreal forests, relatively low albedo and low ET cause strong winter and spring warming. In the summer, higher incoming radiation and somewhat higher ET result in mild cooling by boreal forests ( Alkama and Cescatti, 2016 ). In the mid-latitudes, forest cover results in mild biophysical evaporative cooling in the summer months and mild albedo warming in the winter months ( Davin and de Noblet-Ducoudré, 2010 ; Li et al., 2015 ; Schultz et al., 2017 ). The latitude of zero net biophysical effect, the point at which the annual effect of the forest shifts from local cooling to local warming, ranges from 30 to 56°N in the literature ( Figure 1 ). These generalized latitudinal trends can be modified by aridity, elevation, species composition, and other characteristics, which vary across a range of spatial scales ( Anderson-Teixeira et al., 2012 ; Williams et al., 2021 ).

Figure 1. Latitude of net zero biophysical effect of forests on local temperature varies from 30 to 56°N. Above the line, forest cover causes local warming; below the line, forest cover causes local cooling. The thickness of the line indicates the number of studies that show forest cooling up to that threshold. Data sources as indicated.

Various mechanisms can amplify or dampen a forest’s direct effects on the energy and water balance, with climate impacts in the immediate vicinity, in remote locations, or both ( Bonan, 2008 ). Indirect biophysical effects are particularly important in the boreal region where snow-forest albedo interactions are prevalent. Low albedo forests typically mask high albedo snow, resulting in local radiative warming ( Jiao et al., 2017 ). At the larger scale this forest-induced warming is transferred to the oceans and further amplified by interactions with sea ice ( Brovkin et al., 2004 ; Bala et al., 2007 ; Davin and de Noblet-Ducoudré, 2010 ; Laguë and Swann, 2016 ). In fact, indirect biophysical feedbacks appear to dominate the global temperature response to deforestation in the boreal region ( Devaraju et al., 2018 ). Future climate warming may alter the strength of such feedbacks, depending on the rate at which forests expand northward and the extent and persistence of spring snow cover in a warmer world.

In the tropics, where ET and roughness are the dominant biophysical drivers, forests cool the lower atmosphere, but also provide the water vapor to support cloud formation ( Teuling et al., 2017 ). Clouds whiten the atmosphere over forests and thus increase albedo, at least partially offsetting the inherently low albedo of the forest below ( Heald and Spracklen, 2015 ; Fisher et al., 2017 ). However, the water vapor in clouds also absorbs and re-radiates heat, counteracting some of the cloud albedo-induced cooling ( Swann et al., 2012 ). In the Amazon basin, evidence suggests that deep clouds may occur more frequently over forested areas as a result of greater humidity and consequently greater convective available potential energy ( Wang et al., 2009 ). The impact of tropical deforestation on cloud formation is modified by biomass burning aerosols ( Liu et al., 2020 ) and the net impact on global climate is unclear. Quantifying these indirect biophysical feedback effects is an ongoing challenge for the modeling community particularly in the context of constraining future climate scenarios.

Forest production of biogenic volatile organic compounds (BVOC), which affect both biogeochemical and biophysical processes, further complicate quantification of the net climate impact of forests. BVOC and their oxidation products regulate secondary organic aerosols (SOA), which are highly reflective and result in biophysical cooling. SOA also act as cloud condensation nuclei, enhancing droplet concentrations and thereby increasing cloud albedo, which leads to additional biophysical cooling ( Topping et al., 2013 ). On the other hand, SOA can also cause latent heat release in deep convective cloud systems resulting in strong radiative warming of the atmosphere ( Fan et al., 2012 , 2013 ). Furthermore, through impacts on the oxidative capacity of the atmosphere, BVOC increase the lifetime of methane and lead to the formation of tropospheric ozone in the presence of nitrogen oxides ( Arneth et al., 2011 ; McFiggans et al., 2019 ). The persistence of ozone and methane (both greenhouse gases) results in a biogeochemical warming effect. The net effect of forest BVOC at both local and global scales remains uncertain. Current evidence, from modeling forest loss since 1850, suggests that BVOC result in a small net cooling, if indirect cloud effects are included ( Scott et al., 2018 ). The strongest effect is in the tropics, where BVOC production is highest ( Messina et al., 2016 ).

An improved understanding of the combined effects of forest carbon and biophysical controls on both local and global climate is necessary to guide policy decisions that support global climate mitigation, local adaptation and biodiversity conservation. The relative importance of forest carbon storage and biophysical effects on climate depend in large part on the spatial and temporal scale of interest. Local surface or air temperature may not be sensitive to the incremental impact of atmospheric CO 2 removed by forests growing in a particular landscape or watershed. In contrast, local temperature is sensitive to biophysical changes in albedo, ET and roughness. At regional and global scales, where the cumulative effects of forests on atmospheric CO 2 become apparent in the temperature response, we can usefully compare these impacts. Estimates of the relative impact of biophysical and biogeochemical (e.g., carbon cycle) processes on global or zonal climate have been provided primarily by model simulations of large-scale deforestation or afforestation ( Table 1 ). These studies generally show that CO 2 effects on global temperature are many times greater than the biophysical effects of forest cover or forest loss. In models depicting global or zonal deforestation outside the tropics, however, global warming from CO 2 release offsets only 10–90% of the global biophysical cooling. The global CO 2 effects of total deforestation in the tropics greatly outweigh the global biophysical effects ( Table 1 ). With the exception of Davin and de Noblet-Ducoudré (2010) , these studies have estimated the net contribution of biophysical processes, without isolating the individual biophysical components. Here, we provide a new analysis of CO 2 -induced warming from deforestation by 10° latitudinal increments ( Supplementary Information 1 ). We then compare the CO 2 effect with the only published determination of biophysical effects by latitude ( Davin and de Noblet-Ducoudré, 2010) to clarify the potential net impact of forest loss in a particular region on local and global climate.

Table 1. Forest effects on global temperature in modeling experiments from biogeochemical (CO 2 ) versus biophysical impacts (albedo, evapotranspiration and roughness as well as changes in atmospheric and ocean circulation, snow and ice, and clouds).

Materials and Methods

In the scientific literature, biophysical impacts have been quantified using a number of different methods. In situ observational data, including weather station and eddy flux measurements, have shaped our understanding of the direct biophysical impacts of forests on the surface energy balance. With the advantage of high temporal resolution, they allow for process level investigation of forest biophysical impacts and attribution of temperature changes to particular biophysical forcings, both radiative (albedo) and non-radiative (ET and roughness) ( Lee et al., 2011 ; Luyssaert et al., 2014 ; Vanden Broucke et al., 2015 ; Bright et al., 2017 ; Liao et al., 2018 ). Remote sensing techniques have recently been used to extrapolate to larger scales, providing a global map of forest cover effects on local climate ( Li et al., 2015 ; Alkama and Cescatti, 2016 ; Bright et al., 2017 ; Duveiller et al., 2018 ; Prevedello et al., 2019 ). However, in contrast to in situ approaches which generally measure near surface air temperature (generally but not always at 2 m), remote sensing studies have investigated the response of land surface temperature (i.e., skin temperature) which is 0.5–3 times more sensitive to forest cover change ( Alkama and Cescatti, 2016 ; Novick and Katul, 2020 ).

Generally, both in situ and remote sensing analyses have adopted a space-for-time approach where differences in surface climate of neighboring forest and non-forest sites are used as proxies for the climate signal from deforestation/afforestation over time. This approach assumes that neighboring sites share a common background climate and that any temperature differences between them can be attributed solely to differences in forest cover. Consequently, large-scale biophysical feedback effects are ignored. New observation-based methodologies have been devised to investigate impacts from ongoing land use change rather than estimating climate sensitivities to idealized forest change ( Alkama and Cescatti, 2016 ; Bright et al., 2017 ; Prevedello et al., 2019 ), however, they too measure only local biophysical impacts.

Numerical modeling of paired climate simulations with contrasting forest cover is necessary to investigate the net climate response to forest cover change, including both local and non-local impacts. Model simulations have focused on idealized scenarios of large-scale deforestation/afforestation which are more likely to trigger large-scale climate feedbacks than more realistic incremental forest cover change. Discrepancies between observed and modeled results may be due in part to the influence of indirect climate feedbacks that are not captured by observations ( Winckler et al., 2017a , 2019a ; Chen and Dirmeyer, 2020 ). Unfortunately, model resolution is currently too coarse to guide local policy decisions. Modeling results are also plagued by a number of uncertainties associated with the partitioning of energy between latent or sensible heat ( de Noblet-Ducoudré et al., 2012 ). The predicted impacts of similar land cover changes are model specific and can vary in sign, magnitude, and geographical distribution ( Devaraju et al., 2015 ; Lawrence and Vandecar, 2015 ; Garcia et al., 2016 ; Laguë and Swann, 2016 ; Stark et al., 2016 ; Quesada et al., 2017 ; Boysen et al., 2020 ) and therefore must be viewed with caution. In this paper, we synthesize all types of observational data from the literature to illustrate the biophysical impacts of forests on local climate. However, given that local impacts have been extensively explored and summarized in the past ( Anderson et al., 2011 ; Perugini et al., 2017 ), and because we wish to include indirect effects and feedbacks, we rely predominantly on modeling studies and our own calculations to elucidate the role of forests at different latitudes in shaping climate.

Effects on Global Temperature From Deforestation by 10° Latitude Band

We combined published data on biophysical effects of deforestation by latitude with our own analysis of CO 2 effects from deforestation by latitude to compare the relative strength of biophysical factors and CO 2 (the dominant biogeochemical factor) affecting global climate. Most modeling experiments available in the literature involve total deforestation at all latitudes, and the ocean feedbacks prove very strong ( Davin and de Noblet-Ducoudré, 2010) . Here, we consider land-only effects within a given 10° latitudinal band as this scale of impact is more indicative of the effects of regional or more incremental change on global temperature than the combined land/ocean effects. Finer scale, more realistic forest loss scenarios would not trigger massive cooling through albedo effects on the oceans. Area-scaled, land-only biophysical effects from deforestation provide the most realistic comparison with the effects of carbon stored by forests, and released through deforestation, at a given latitude. The biophysical response was derived from the results of Davin and de Noblet-Ducoudré (2010) who simulated total deforestation and decomposed the temperature response, by 10° latitude bands, into the fraction due to albedo, evapotranspiration, roughness and a non-linear response (see Supplementary Table 1 ).

The biogeochemical response was estimated by accounting for the CO 2 effect of deforestation, using existing biomass data and known equilibrium temperature sensitivity to doubled CO 2 . The principal input to our analysis is a 2016 global extension of the 500-m resolution aboveground carbon density (ACD) change (2003–2016) product applied by Walker et al. (2020) to the Amazon basin. It is based on an approach to pantropical ACD change estimation developed by Baccini et al. (2017) . The pantropical product combined field measurements with colocated NASA ICESat GLAS spaceborne light detection and ranging (LiDAR) data to calibrate a machine-learning algorithm that produced estimates of ACD using MODIS satellite imagery. This approach was modified for application to the extratropics, principally the temperate and boreal zones but also extratropical South America, Africa and Australia, using 47 allometric equations compiled from 27 unique literature sources for relating field-based measurements of aboveground biomass to airborne LiDAR metrics ( Chapman et al., 2020 ). These equations were used to predict ACD within the footprints of GLAS LiDAR acquisitions in each region with the result being a pseudo-inventory of LiDAR-based estimates of ACD spanning the extratropics. This dataset was then combined with the pantropical dataset first generated by Baccini et al. (2012) to produce a global database of millions of spatially explicit ACD predictions. This database was used to calibrate six ecoregional MODIS-based models for the purposes of generating a global 500-m resolution map of ACD for the year 2016. Additional details on these methods can be found in Chapman et al. (2020) .

The total aboveground carbon (GtC) was summed for each 10° latitude band and converted to CO 2 (GtC*44/12 = GtCO 2 , Supplementary Information 1 ). The mass of CO 2 was converted to ppm CO 2 in the atmosphere (2.12 Gt/ppm). The derived CO 2 concentration was reduced by 23% to account for ocean uptake ( Global Carbon Project, 2019 ). We assumed that no uptake occurred on land, as the carbon stock in vegetation was completely removed in our experiment to match what occurred in Davin and de Noblet-Ducoudré (2010) . Next, we calculated the global temperature response to the increase in atmospheric CO 2 due to the CO 2 released by completely deforesting each 10° latitudinal band using the equilibrium temperature sensitivity derived from general circulation models. Given the accepted value of 3°C (±1.5°C) for a doubling of atmospheric CO 2 (an increase of 280 ppm) (IPCC, 2013), we determined that temperature sensitivity is equivalent to 0.107°C (±0.054°C) for every 10 ppm increase in atmospheric CO 2 content.

To determine the global temperature response to deforestation of a given band, we calculated the area-weighted values for each biophysical response within each latitude band. The area encompassed by 10° of latitude increases toward the equator. Thus, to determine the contribution of a given band to a global temperature response, scaling by the surface area within the band was essential. We used average temperature responses over the land only to avoid the strong bias associated with ocean feedbacks from global scale implementation of deforestation.

For the global analysis, we also determined the contribution of BVOC to global temperature change for deforestation of each 10° of latitude. Scott et al. (2018) described the warming from deforestation due to BVOC in relation to the amount of cooling due to changes in albedo. For the tropics, the BVOC effect on global temperature was 17% of the albedo effect. For the temperate zone, it was 18% and for the boreal, it was 2% of the albedo effect. We applied these scalars (with an opposite sign) to the albedo figures for each 10° latitude band.

Effects on Regional (Local) Temperature From Deforestation by 10° Latitude Band

To analyze the effect of deforesting 10° of latitude on the temperature within that latitude zone (‘local’ effect), we did not scale by area within the band. Rather we assessed the average temperature change across the band, locally felt, as reported in the original study. The CO 2 effect was calculated as above and then scaled to reflect the sensitivity of a given latitudinal band to a global forcing. Only the CO 2 emitted by the latitudinal band itself was considered when determining the locally felt effects of CO 2 in a given band. Our experimental design involved global deforestation and all emitted CO 2 would have had an effect in a given band, but the point of the analysis was to isolate the temperature change caused by forests in a given latitude. We determined the latitudinal sensitivity to warming in response to added CO 2 from a re-analysis of global 2 m temperature data (CERA-20C) obtained from the European Centre for Medium-Range Weather https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/cera-20c . We compared average temperatures from 1901 to 1910 and 2001–2010, by latitude on land only (inadequate land only data for 50–60S and 80–90N; for those, we do not report a locally felt CO 2 effect). Then we divided the temperature change for each latitude band by the change in global temperature over the same period. We scaled the effect of CO 2 emitted by a given 10° latitude band by this sensitivity to represent the influence of non-linear responses such as polar amplification (see Supplementary Information 1 and Supplementary Table 2 ).

Biophysical Effects of Deforestation on Local Climate: A Broader Context

Our analysis is the first to compare regional scale biophysical and CO 2 impacts from regional scale deforestation but the literature is replete with data on local biophysical impacts. The results for local biophysical effects (100s of m to 100s of km) agree with our results at the regional scale (below). Figures 2 , 3 synthesize local biophysically-driven temperature responses to deforestation, as indicated by forest/no-forest comparisons or forest change over time, from the scientific literature. Satellite and flux tower data indicate that surface temperatures in tropical forests are significantly lower than in cleared areas nearby. On an annual basis, local surface cooling of 0.2–2.4°C has been observed (mean 0.96°C, Figure 2 and Supplementary Information 2 ). In the temperate zone, satellite studies of land surface temperature (which is more sensitive than the temperature of the air at 2 m) have shown biophysical cooling from forest cover, or biophysical warming from deforestation (0.02–1.0°C, mean of 0.4°C; see Figure 2 and Supplementary Information 2 ). Both in situ and satellite data generally indicate an average annual cooling of under 1°C from boreal deforestation ( Figure 2 ). Across latitudinal zones, warming from deforestation is generally greater during the day, and during the dry (hot) season ( Figure 3 ).

Figure 2. Local average annual temperature change in response to deforestation (black symbols) or afforestation (green symbols) as determined by comparing neighboring forested and open land (space for time approach) or measuring forest change over time in the tropics, temperate and boreal zones, by (A) in situ or (B) satellite based land surface temperature measurements (0 m, triangles) or air temperature measurements (2 m, circles). See Supplementary Information 2 for data sources.

Figure 3. Local temperature change in response to deforestation by season and time of day in the various climate zones as determined by comparing neighboring forested and open land (space for time approach) or measuring forest change over time. Warm/dry season response, averaged over the entire diurnal cycle, in red shading and cold/wet season response in blue shading. Daytime response, averaged over the entire annual cycle, in yellow shading and nighttime response in gray shading. See Supplementary Information 3 for data sources.

CO 2 -Induced Warming Versus Biophysical Effects on Regional (Local) Temperature From Deforestation by 10° Latitude Band

As expected, the regionally felt effect of regionally (10° band) produced CO 2 is very small compared to any individual biophysical effect or the sum of all non-CO 2 effects ( Figure 4 ). These results indicate that the net impact of all non-CO 2 effects is negligible between 20 and 30N. Beyond 30N the local biophysical response to deforestation is cooling. In the broader literature, this latitude of net zero biophysical effect on local temperature is generally between 30 and 40N ( Figure 1 ).

Figure 4. Effect of complete deforestation on local annual temperature by climate factor, averaged across the land surface within a 10° latitudinal band. Complete deforestation was implemented globally and analyzed by 10° latitudinal bands ( Davin and de Noblet-Ducoudré, 2010) . The CO 2 effect was determined from total aboveground biomass in each 10° band after Walker et al. (2020) and scaled by CERA-derived sensitivity by latitude. Inset distinguishes the sum of all local biophysical effects from local CO 2 effects.

Biophysical Effects on Global Temperature From Deforestation by 10° Latitude Band

For most latitudinal bands, the strongest biophysical effect of deforestation is cooling from albedo changes. In the tropics, however, the warming effect of lost roughness is comparable to or greater than the albedo effect ( Figure 5A ). Adding the warming from lost evapotranspiration, the net biophysical effect from tropical deforestation is global warming, as much as 0.1°C contributed each by latitudes 0°–10°S and 0°–10°N. The net biophysical effect of intact tropical forest, therefore, is global cooling; slightly more cooling if BVOCs are also considered (see Figure 5B ). Roughness effects are generally greater than evapotranspiration effects across latitudes providing a strong counterbalance to albedo effects ( Davin and de Noblet-Ducoudré, 2010 ; Burakowski et al., 2018 ; Winckler et al., 2019b ; Figure 5A ). Albedo almost balances the combined effect of roughness, evapotranspiration, BVOC and non-linear effects between 20 and 30°N resulting in close to zero net biophysical effect on global temperature ( Figure 5B ). From 30–40°N and northward, albedo dominates, and the net biophysical effect of deforestation is cooling.

Figure 5. Effect of complete deforestation on global temperature by 10° band of latitude. (A) Contribution to global temperature change by climate forcing factor. Biophysical factors are from Davin and de Noblet-Ducoudré, 2010 , area-weighted. BVOC effects are estimated relative to albedo effects based on Scott et al., 2018 . CO 2 effect is based on aboveground live biomass for each 10° latitudinal band following Baccini et al., 2017 and Walker et al., 2020 . (B) Net biophysical and BVOC effect versus CO 2 effect. (C) Cooling or warming effects of deforestation by 10° latitudinal band (BVOC included). “Forests as mountains” map of aboveground biomass carbon in woody vegetation ca. 2016 courtesy of Woodwell Climate Research Center and shaded to indicate where deforestation results in net global warming. See Supplementary Information 1 for details.

CO 2 -Induced Warming Versus Biophysical Effects on Global Temperature From Deforestation by 10° Latitude Band

From 30°S to 30°N, the biophysical effect of deforesting a given 10° latitudinal band is about half as great and in the same direction as the CO 2 effect: global warming. Biophysical warming is around 60% as great as warming from released CO 2 in the outer tropics (20°S–10°S and 10°N–20°N) and about 35% as great in the heart of the tropics (10°S–10°N). Biophysical cooling due to deforestation from 30°N to 40°N offsets about 40% of the warming associated with carbon loss from deforestation; from 40°N to 50°N biophysical effects offset 85% of CO 2 effects ( Figure 5B ). Above 50°N, biophysical global cooling is 3–6 times as great as CO 2 induced global warming. The net impact of deforestation (effects of CO 2 , biophysical processes and BVOC combined) is warming at all latitudes up to 50N ( Figure 5C ). Thus, from 50S to 50N, an area that encompasses approximately 65% of global forests ( FAO, 2020 ), deforestation results in global warming ( Figure 5C ).

All Forests Provide Local Climate Benefits Through Biophysical Effects

Ignoring biophysical effects on local climate means casting aside a powerful inducement to promote global climate goals and advance forest conservation: local self-interest. The biogeochemical effect of forests tends to dominate the biophysical effect at the global scale because physical effects in one region can cancel out effects in another. Nevertheless, biophysical effects are very important, and can be very large, at the local scale (e.g., Anderson-Teixeira et al., 2012 ; Bright et al., 2015 ; Jiao et al., 2017 ; Figures 2 – 4 ). The role of forests in maintaining critical habitat for biodiversity is well known, but new research on extinction confirms the role of forests in maintaining critical climates to support biodiversity. Changes in maximum temperature are driving extinction, not changes in average temperature ( Román-Palacios and Wiens, 2020 ). Deforestation is associated with an increase in the maximum daily temperature throughout the year in the tropics and during the summer in higher latitudes ( Lee et al., 2011 ; Zhang et al., 2014 ). Of course deforestation also increases average daytime temperatures in boreal, mid-latitude and tropical forests ( Figure 3 ). The biophysical effects of forests also moderate local and regional temperature extremes such that extremely hot days are significantly more common following deforestation even in the mid- and high latitudes ( Vogel et al., 2017 ; Stoy, 2018 ). Historical deforestation explains ∼1/3 of the present day increase in the intensity of the hottest days of the year at a given location ( Lejeune et al., 2018 ). It has also increased the frequency and intensity of hot dry summers two to four fold ( Findell et al., 2017 ). Local increases in extreme temperatures due to forest loss are of comparable magnitude to changes caused by 0.5°C of global warming ( Seneviratne et al., 2018 ). Forests provide local cooling during the hottest times of the year anywhere on the planet, improving the resilience of cities, agriculture, and conservation areas. Forests are critical for adapting to a warmer world.

Forests also minimize risks due to drought associated with heat extremes. Deep roots, high water use efficiency, and high surface roughness allow trees to continue transpiring during drought conditions and thus to dissipate heat and convey moisture to the atmosphere. In addition to this direct cooling, forest ET can influence cloud formation ( Stoy, 2018 ), enhancing albedo and potentially promoting rainfall. The production of BVOCs and organic aerosols by forests accelerates with increasing temperatures, enhancing direct or indirect (cloud formation) albedo effects. This negative feedback on temperature has been observed to counter anomalous heat events in the mid-latitudes ( Paasonen et al., 2013 ).

Some Forests Provide Global Climate Benefits Through Biophysical Effects

Disregarding the biophysical effects of specific forests on global climate means under-selling some forest actions and over-selling others. The response to local forest change is not equivalent for similar sized areas in different latitudes. According to Arora and Montenegro (2011) warming reductions per unit reforested area are three times greater in the tropics than in the boreal and northern temperate zone due to a faster carbon sequestration rate magnified by year-round biophysical cooling. Thus, considering biophysical effects significantly enhances both the local and global climate benefits of land-based mitigation projects in the tropics (see Figures 4 , 5 ).

Constraints on Forest Climate Benefits in the Future

Climate change is likely to alter the biophysical effect of forests in a variety of ways. Deforestation in a future (warmer) climate could warm the tropical surface 25% more than deforestation in a present-day climate due to stronger decreases in turbulent heat fluxes ( Winckler et al., 2017b ). In a warmer climate, reduced snow cover in the temperate and boreal regions will lead to a smaller albedo effect and thus less biophysical cooling with high latitude deforestation. In addition to snow cover change, future rainfall regimes will affect the response of climate to changes in forest cover ( Pitman et al., 2011 ) as rainfall limits the supply of moisture available for evaporative cooling. Increases in water use efficiency due to increasing atmospheric CO 2 may reduce evapotranspiration ( Keenan et al., 2013 ), potentially reducing the local cooling effect of forests and altering atmospheric moisture content and dynamics at local to global scales. Future BVOC production may increase due to warming and simultaneously decline due to CO 2 suppression ( Lathière et al., 2010 ; Unger, 2014 ; Hantson et al., 2017 ). The physiological and ecological responses of forests to warming, rising atmospheric CO 2 and changing precipitation contribute to uncertainty in the biophysical effect of future forests on climate.

Forest persistence is essential for maintaining the global benefits of carbon removals from the atmosphere and the local and global benefits of the physical processes described above. Changing disturbance regimes may limit forest growth and regrowth in many parts of the world. Dynamic global vegetation models currently show an increasing terrestrial carbon sink in the future. This sink is thought to be due to the effects of fertilization by rising atmospheric CO 2 and N deposition on plant growth as well as the effects of climate change lengthening the growing season in northern temperate and boreal areas ( Le Quéré et al., 2018 ). Free-air carbon dioxide enrichment (FACE) experiments often show increases in biomass accumulation under high CO 2 but results are highly variable due to nutrient limitations and climatic factors ( Feng et al., 2015 ; Paschalis et al., 2017 ; Terrer et al., 2018 ). Climate change effects on the frequency and intensity of pest outbreaks are poorly studied, but are likely to be significant, particularly at the margins of host ranges. Warmer springs and winters are already increasing insect-related tree mortality in boreal forests through increased stress on the tree hosts and direct effects on insect populations ( Volney and Fleming, 2000 ; Price et al., 2013 ).

Climate also affects fire regimes. In the tropics, fire regimes often follow El Niño cycles ( van der Werf et al., 2017 ). As temperatures increase, however, fire and rainfall are decoupled as the flammability of forests increases even in normal rainfall years ( Fernandes et al., 2017 ; Brando et al., 2019 ). Fire frequency is also increasing in some temperate and boreal forests, with a discernable climate change signal ( Abatzoglou and Williams, 2016 ). Modeling exercises indicate that this trend is expected to continue with increasing damage to forests as temperatures rise and fire intensity increases ( De Groot et al., 2013 ).

In addition to changes induced by warming, continued deforestation could severely stress remaining forests by warming and drying local and regional climates ( Lawrence and Vandecar, 2015 ; Costa et al., 2019 ; Gatti et al., 2021 ). In the tropics, a tipping point may occur, potentially resulting in a shift to shorter, more savannah-like vegetation and altering the impact of vast, previously forested areas on global climate ( Nobre et al., 2016 ; Brando et al., 2019 ). Some of these processes are included in climate models and some are not. The gaps leave considerable uncertainty. Nevertheless, a combination of observations, models, and theory gives us a solid understanding of the biophysical effects of forests on climate at local, regional and global scales. We can use that knowledge to plan forest-based climate mitigation and adaptation.

Mitigation Potential of Forests: Byond the Carbon/Biophysical Divide

If instead of focusing on the contrast between biophysical and biochemical impacts of forests and forest loss, we focus on the potential of forests to cool the planet through both pathways, another picture emerges. By our conservative estimate, through the combined effects on CO 2 , BVOC, roughness and evapotranspiration, forests up to 50°N provide a net global cooling that is enough to offset warming associated with their low albedo. Given the most realistic pathways of forest change in the future (not complete deforestation of a 10° latitudinal band, or an entire biome), global climate stabilization benefits likely extend beyond 50°N. For the 29% of the global land surface that lies beyond 50°N, forests may warm the planet, but only as inferred from assessing the effects of complete zonal deforestation with all the associated, and powerful, land-ocean feedbacks spawned by largescale forest change in the boreal zone. Forests above 50°N, like forests everywhere, provide essential local climate stabilization benefits by reducing surface temperatures during the warm season as well as periods of extreme heat or drought. Indeed, they also reduce extreme cold.

Creating a fair and effective global arena for market-based solutions to climate change requires attention to all the ways that forests affect climate, including the biophysical effects. Future metrics of forest climate impacts should consider the effects of deforestation beyond CO 2 . Only recently have modelers begun to include BVOC. Doing so means that the albedo of intact forests (or the atmosphere above them) is higher due to the creation of SOA and subsequent cloud formation. Modeled deforestation thus results in less of a change in albedo, reducing the biophysical cooling effect. Similarly, accounting for the ozone and methane effects of BVOC reduces the biogeochemical warming from deforestation ( Scott et al., 2018 ). In addition, especially in the tropics, deforestation reduces the strength of the soil CH 4 sink ( Dutaur and Verchot, 2007 ). While a small change relative to the atmospheric pool of CH 4 (the second most important greenhouse gas), the loss of this sink is equivalent to approximately 13% of the current rate of increase in atmospheric CH 4 ( Saunois et al., 2016 ). We already have the data ( Figure 5 ) to begin conceptualizing measures to coarsely scale CO 2 impacts of forest change by latitude. Finer resolution of latitude, background climate (current and future) and forest type would improve any such new, qualifying metric for the climate mitigation value of forests.

The role of forests in addressing climate change extends beyond the traditional concept of CO 2 mitigation which neglects the local climate regulation services they provide. The biophysical effects of forest cover can contribute significantly to solving local adaptation challenges, such as extreme heat and flooding, at any latitude. The carbon benefits of forests at any latitude contribute meaningfully to global climate mitigation. In the tropics, however, where forest carbon stocks and sequestration rates are highest, the biophysical effects of forests amplify the carbon benefits, thus underscoring the critical importance of protecting, expanding, and improving the management of tropical forests. Perhaps it is time to think more broadly about what constitutes global climate mitigation. If climate mitigation means limiting global warming, then clearly the biophysical effects of deforestation must be considered in addition to its effects on atmospheric CO 2 . We may further consider whether mitigation is too narrow a scope for considering the climate benefits provided by forests. Climate policy often separates mitigation from adaptation, but the benefits of forests clearly extend into both realms.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author.

Author Contributions

DL conceived the presented idea. All authors helped perform the computations, discussed the results, and contributed to the final manuscript.

Financial support from the University of Virginia and the Climate and Land Use Alliance grant #G-1810-55876.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

Thanks to Frances Seymour, Michael Wolosin, Billie L. Turner, Ruth DeFries, and the reviewers for feedback on this manuscript and to the University of Virginia and the Climate and Land Use Alliance grant #G-1810-55876 for financial support.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/ffgc.2022.756115/full#supplementary-material

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Keywords : forest, biophysical effects, temperature, climate policy, deforestation/afforestation

Citation: Lawrence D, Coe M, Walker W, Verchot L and Vandecar K (2022) The Unseen Effects of Deforestation: Biophysical Effects on Climate. Front. For. Glob. Change 5:756115. doi: 10.3389/ffgc.2022.756115

Received: 10 August 2021; Accepted: 02 March 2022; Published: 24 March 2022.

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Copyright © 2022 Lawrence, Coe, Walker, Verchot and Vandecar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Deborah Lawrence, [email protected]

This article is part of the Research Topic

Global Patterns and Drivers of Forest Loss and Degradation Within Protected Areas

Advances in the study of global forest wildfires

• Frontiers in Soils and Sediments • Research Article
• Open access
• Published: 02 June 2023
• Volume 23 , pages 2654–2668, ( 2023 )

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• Tong Li 1 , 2 ,
• Lizhen Cui 3 ,
• Lilan Liu 3 , 4 ,
• Yang Chen 3 ,
• Hongdou Liu   ORCID: orcid.org/0000-0001-8961-4188 1 , 5 ,
• Xiufang Song 6 , 7 &
• Zhihong Xu 1  

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Wildfire is one of the most important natural disturbances in forest and multi-vegetation ecosystems, directly or indirectly affecting the structural processes and functions of forest ecosystems with varying degrees. Wildfire releases vast amounts of carbon dioxide and other substances by destroying vegetation, making itself an important topic for the study of global change and environmental impacts. Therefore, a deeper understanding of this topic is particularly crucial for managing forest ecosystems.

This paper was based on a literature search of the Web of Science database for international forest wildfire research, utilizing bibliometric and quantity statistical analysis methods.

The results show that forest wildfire research has been rapidly growing over the last 20 years, with the number of relevant articles generally increasing yearly at an average annual growth rate of about 22.45%. The US tops the list in terms of total and independent publications, with a total of 3111 articles (49.88%). The key journals publishing on this topic include 12 journals, Stephens S.L., Bergeron Y., and Lindenmayer D.B. are the key contributing authors to the field, and research institutions are primarily concentrated in the US Forest Service. Keyword co-occurrence analysis shows that current forest wildfire research is focused on seven main areas. This paper systematically reviewed the progress and hotspots of international forest wildfire research in recent decades, mainly focusing on occurrences, severity, management, and warning techniques for wildfires, as well as the impact of climate change and human activities on wildfires.

Conclusions

The study concludes that research trends in this field have undergone a significant evolution in recent decades. The future forest wildfire research moves towards a combination of typical mechanisms and large-scale effects across spatial and temporal scales, deep integration of aerospace and earth observations and precise simulations, discipline fusion, and couplings research. We believe that this study provides a comprehensive and systematic overview for future forest wildfire observation, prediction, management, and investigation of ecological effects.

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1 Introduction

Forest wildfires are a common natural disaster, occurring almost hourly in different forests around the globe, and are the major disturbances to forest ecosystems (Phillips et al. 2022 ). In the USA, from 2012 to 2021, an average of 7.4 million acres of land per year affected by about 61,289 wildfires. In 2021, 58,968 wildfires burnt 7.1 million acres (Hoover and Hanson 2021 ). The sources of the fires are mainly divided into natural fires (such as lightning, high temperatures, volcanic eruptions, etc.) and man-made fire sources (field cooking, burning, smoking, etc.). The wildfire will begin to burn under the oxygen concentration of more than 16% (Belcher et al. 2010 , 2013 ). Over the past decades, due to global warming and land use changes (Pachauri and Meyer 2014 ), the dynamics of fires are changing across the globe, while an increasingly hot planet exacerbates heat and drought, changed precipitation rhythms, and significantly increased the risk and frequency of forest wildfires (Jolly et al. 2015 ; Abatzoglou and Williams 2016 ). The severity and length of fire seasons are expected to increase globally especially in boreal high-latitude forests by the end of the century as greenhouse gas emissions increase and global climate change intensifies (Flannigan et al. 2013 ). According to studies, over 80% of wildfires are caused by human activities (e.g., expansion of agricultural land into forests, etc.). The forest fragmentation and degradation due to human activities have severely reduced the fire resistance of forests (Hansen et al. 2020 ; Xu et al. 2020 ). This complex interplay of socio-ecological factors is causing regional changes in forest wildfire severity and frequency trends, which may further alter global climate through biophysical feedbacks, although the potential magnitude and direction of these long-term changes remain uncertain (Tyukavina et al. 2022 ). Therefore, the occurrence, distribution, and ecological environmental effects of wildfires have become one of the important issues of concern for global change research. In recent years, the extreme wildfire events in Brazil (Requia et al. 2021 ), Australia (Boer et al. 2020 ), and California (Dillis et al. 2022 ) have also drawn public attention to the issue. These extensive and severe recurrent wildfires may be a precursor to changing fire conditions elsewhere in the world, which can provide valuable experiences in guiding responses in other environments subject to widespread fires (Lindenmayer and Taylor 2020 ).

Despite the growing damage of wildfires has caused massive destruction of forest resources and atmospheric pollution, wildfires are a fundamental part of the global ecosystem that society has used to manage the landscape for thousands of years (Shuman et al. 2022 ), which play an important role in the renewal of forest ecosystems and the reshaping of landscape patterns (Moura et al. 2019 ). In other words, wildfires are an important part of the process of material exchange between the land and the atmosphere and also an important driver of ecosystem succession. In the long term and overall, moderate, naturally occurring wildfires enhance forest patchiness (i.e., small communities), create more diverse microhabitats (Fordyce et al. 2016 ), and improve ecosystem service functions essentially. Forest wildfires could, on the one hand, promote tree species turnover, optimize above-ground species patterns, alter the composition of surface vegetation, help to reduce canopy transpiration and intercept water, and significantly regulate hydrological processes on a landscape and regional scale (Guinto et al. 1999a , 2000 ; Bastias et al. 2006a ; Reilly et al. 2006 ), and on the other hand, help to reduce the spread of pests while improving soil nutrients (Guinto et al. 1999b , 2001 , 2002 ). Specifically, wildfires could warm the soil by burning off dead leaves, help accelerate the decomposition of organic matter, and kill pathogenic bacteria, inducing the release of nutrients and minerals, the improvement of soil nutrient availability, and the growth of new plant roots (Caon et al. 2014 ). At the same time, the reconstruction of the overfired area lead new animals to move in and seeds (like spruce and fir, which need to burst the seed shell with the help of high temperatures) to sprout (Shepherd et al. 2021 ), which alter the original plant, microbial, fungal and other animal composition, increase the biodiversity of forest ecosystems, and further affect the biotic patterns of adjacent ecosystems (Guinto et al.  1999b ; Bastias et al. 2006a , b ). At the global scale, wildfires contribute to the carbon–water cycle (Poulter et al. 2014 ), surface albedo (Rother and De Sales 2021 ), and effects of atmospheric aerosol and cloud properties (Chan et al. 2006 ); it is possible to eventually alter the surface energy balance. In addition, wildfires themselves are still the best way to reduce the risk of large-scale wildfires (Reverchon et al. 2012 , 2020 ; Hamilton et al. 2018 ). Allowing a portion of wildfires to burn naturally or to burn out first is a means to prevent and control forest wildfires.

Current international forest wildfire field has done a lot of research on fire occurrence, distribution, and ecological effects in the past three decades as shown in Fig.  1 . However, there is a lack of systematic combing and quantitative analysis of past literature. Bibliometrics is a good way to do this and has been widely used in forest ecology (Juárez-Orozco et al. 2017 ), grassland ecology (Li et al. 2022a , b ), remote sensing (Li et al. 2021 ), and other fields. It is an innovative way of research in important disciplines that have been proven to fully exploit a large amount of information in the literature and can generate new academic content (Chen 2017 ; Li et al. 2022a ). Therefore, the purpose of this paper was to conduct a bibliometric analysis of forest wildfire research from the perspectives of general trends, major authors, research areas, journal distribution, research countries and institutions, and keyword clustering, in order to extract current research trends and identify cutting-edge issues and development directions, providing important theoretical basis and useful reference for international forest wildfire research and forest management. Since most forest wildfire studies do not distinguish between “man-made” and “natural” causes, the literature covered in this paper includes and does not distinguish between the above two categories and is collectively referred to as “forest wildfire” studies.

Global distribution of forest wildfire studies and the extent of forest area destruction in major countries

2 Research methods

The methodology used in this study provides a bibliometric analysis of literature data on global forest wildfires. The use of this type of analysis was motivated by the need to assess scientific outcomes (Ellegaard and Wallin 2015 ). Bibliometric research is referred to as the “science of science” (Li et al. 2021 ), a way to quantitatively analyze large amounts of large data from the literature, which in turn can help researchers determine the current state of research, sort through and identify new research trends, and provide potential for future collaborative research by researchers (Viana-Lora and Nel-lo-Andreu 2022 ). The data for this study were obtained from the core collection of the Web of Science (WOS), a database with a high-quality index. The time period of the literature search was 1991–2022, the language was chosen as English, and “forest wildfire” was used as the keyword, the keyword search, and sorting tool applied after repeated testing finally produced 6975 publications, as shown in Fig.  2 ; after screening and eliminating irrelevant, a total of 6236 publications were obtained. Finally, all information such as title, author, institution, country, abstract, and keywords of these publications was exported in WOS as target files for further software analysis and visualization.

Schematic view of approach and methodology followed in searching, screening, and finalizing the literature for the research study

In this study, VOSviewer and R (bibiliometrix package) were used for data analysis and visualization. VOSviewr is an important analytical tool in the field of bibliometric research and has been widely used as a key technique that can help to achieve the construction of relationships in bibliometric data and can visualize current in its software to show the relationships between bibliometric data as well as grouping (van Eck and Waltman 2010 , 2017 ; Li et al. 2021 ). The larger the circle formed, the higher the number of papers published is represented. The connections of different colors are other clustering modules. R (bibiliometrix package) provides a set of tools for bibliometric studies. It is based on the R language, an open-source statistical programming environment with many efficient, high-quality statistical algorithms and integrated data visualization tools that allow a one-step decomposition and parsing of raw literature data (Li et al. 2022b ). The specific findings of the study are presented in detail below.

3.1 Publications general development trend, main journals, authors, and research categories in WOS

The general growth trend of a publication reflects the regularity presented by research findings in a time series of development. The time series facilitates the understanding of trends in forest wildfire research and the exploration of specific phases of research. Figure  3 a shows the dynamics in time of the number and the average number of citations of annual publications over the past 30 years. From 1991 to 2021, the paper growth curve shows an exponential growth trend with an average annual growth rate of 22.45%. However, during 1991–2003, the number of papers published per year was less than 20, but the number of citations of individual papers was high. After 2004, forest wildfire research entered a phase of high growth, increasing from the number of annual articles of 20 to 680, which was not considered in view of the incomplete data for 2022. From a journal perspective, forest wildfire research appeared in 267 journals, and the study revealed that the top 12 (4.48%) journals published 2041 (32.72%) of the literature. In contrast, 152 journals (56.92%) published only 1 paper and 76 journals (28.46%) published 2 to 5 papers on forest wildfire research. As shown in Fig.  1 b, the top 3 journals with the highest number of published papers were Forest Ecology and Management (654), International Journal of Wildland Fire (299), and Forests (212). According to Bradford’s law, the results show that the forest wildfire research literature also exhibits a high degree of dispersion, with a large proportion published in Top 12 journals, as shown in Fig.  3 b. These journals are the core sources of research in this area that play a crucial role in forest wildfire research and are a common source of interest for scholars.

Temporal evolution of documents on global forest wildfires research in the past three decades ( a ). Top 12 most published journals and its core source distribution ( b ). Bibliographic coupling of authors’ network map ( c ). Top 15 main research categories in Web of Science on forest wildfires research studies ( d )

A network analysis of the primary author groups can help understand the scholarly community in the field of forest wildfires. The author information of the sample papers was visually analyzed, and a network graph of key author collaborations was extracted. Clustering based on research content can further highlight academic and research communities (Fig.  3 c). Figure 5  shows the authors who published more than 5 papers, where no separate distinction was made between the first and corresponding authors, and the authors who published more than 50 papers were Stephens S.L. (70), Bergeron Y. (63), Lindenmayer D.B. (62), Fule P.Z. (59), and Collins B.M. (50), with author collaboration. The relationship is strong, and the collaboration index is at 4.62. According to the WOS subject area distribution results, the fields of forest wildfire research have increased from 9 fields in 1991 to 97 fields in 2021. They are mainly concentrated in Forestry, Ecology, Environmental Science, Geosciences Multidisciplinary, Meteorology Atmospheric Sciences, Remote Sensing, Biodiversity Conservation and Water Resources. Top 3 research areas accounted for 2261 out of 6326 documents, about 35.7% of the total, as shown in Fig.  3 d.

A co-authorship network analysis has identified five major clusters of researchers, each representing distinct research interests (RI). RI 1 focuses on the interactions between wildland fires and ecosystems, as well as the effects of fire and fuel treatments on forests. RI 2 centers on the effects of forest growth dynamics and disturbances, such as fire, on the forest ecosystem function. RI 3 represents research interests in the sustainable management of boreal forests in the face of climate change. RI 4 is concerned with forestry wildlife management and the environment. Finally, cluster 5 focuses on forest ecology and vegetation dynamics in relation to natural and anthropogenic disturbances, particularly those related to climate variability.

3.2 Main countries and the main affiliations of the authors

The analysis of research countries and institutions allows to clarify the distribution of research forces in specific research areas and to pinpoint the contribution of each research institution. The country clustering collaboration network is shown in Fig.  4 a, and the organizational collaboration map is shown in Fig. 4 b. The results of the study indicate that forest wildfire research is characterized by a large scale and wide scope, involving more than 100 countries and more than 2342 research institutions. At the national scale, the countries with the highest number of publications are concentrated in North America (US, 3111; Canada, 888), Europe (Spain, 685; UK, 287; Portugal, 280, etc.), and Australia (612), in which the USA being the most central to this area of research in terms of collaborative networks. Figure 4 b shows the top 10 institutions with the highest number of publications and high total citation time in WOS, where US Forest Service (832), Oregon State University (291), Northern Arizona University (217), Colorado State University (210), US Geological Survey (190), University of California Berkeley (190), and Natural Resources Canada (190) ranked in the top five. Forest Service published a significantly higher number of papers and citations than other research institutions.

Cluster characteristics distribution of major research countries ( a ), bibliometric coupling analysis of important research institutions ( b )

3.3 Keywords co-occurrence

In this study, a total of 12,738 keywords were detected in the literature on forest wildfire research from 1991 to 2021. The visualization of the 30 keywords with the highest co-occurrence and frequency of the main keywords is summarized in Table 1 and further high-frequency keywords are displayed in Fig. 6 . In addition to the keywords “wildfire” and “fire”, the most frequent keywords were “climate change” (397), forest (236), management (236), boreal forest (231), fire severity (229), disturbance (228), remote sensing (167), and prescribed fires (146). This indicates that the ecological effects and feedback processes on forest wildfires in the context of global climate change, as well as the management of forest ecosystems and wildfire management, and the application of remote sensing technology for wildfire monitoring are the hot topics and priorities of current research.

Figure 5  shows the keyword network selected from the total literature based on the keyword co-occurrence method, and these keywords formed seven clusters, which are shown in Fig.  2 based on the relationship between the link attribute weights of different keywords and the total link strength. Specifically, the seven clusters of keywords and their links in Table 1 are grouped together, with each group identified by a different color. The size of each cluster represents their relative contribution to the clusters with keywords, and the thickness of the link line between two clusters refers to the number of interactions established between two different clusters. Table 1 shows the seven clusters that were examined. These are labeled with the most frequent keywords and are ordered by the percentage of keywords they contain, as follows: cluster 1, red, forest fire; cluster 2, green, fire severity; cluster 3, dark blue, wildfires; cluster 4, yellow, disturbance; cluster 5, purple, fire; cluster 6, light blue, climate change; cluster 7, orange, California. It is also including the link weight and total link strength contributed by each representative keyword and provides the 10 most important keywords. These clusters can better help to understand the research hotspots. Based on the results of the above analysis, the fourth section will discuss the research hotspots in the field of forest wildfire in depth (Table 2 ).

Network of keywords based on the co-occurrence method on global forest wildfire research studies in the past three decades

4 Discussion

Forest wildfire research has gained the attention of many scholars globally, who come from different research regions and research institutions and have produced a large amount of literature. Based on the analysis of key co-occurrences, we obtained seven important clusters, which epitomize the most important current research content, so it is necessary to review the relevant literature behind them and then further explain and sort out the research hotspots formed by the keywords of the clusters.

4.1 Forest wildfire occurrence and severity

The 2021 wildfire season broke global records, scorching vast swaths of land from California to Siberia. A United Nations report released in February warned that the number of wildfires will increase by 50% by 2050. Over the past 50 years, the Arctic has warmed three times faster than the rest of the planet (Rantanen et al. 2022 ), and recent studies suggest that wildfires in the Northern Hemisphere appear to be contributing to the discrepancy. Assessments through the Australian Academy of Science suggest that some aspects of the current 2019–2020 wildfires are unprecedented (Lindenmayer and Taylor 2020 ). These wildfires are estimated to have burned 12 million hectares of forest and agricultural areas in southeastern Australia (Lindenmayer and Taylor 2020 ). It is estimated that over 1 billion Australian animals have been killed and it is estimated that 700 species may be driven to extinction (Boer et al. 2020 ). Several decades have seen a dramatic increase in the length of fire seasons as far away as the Arctic, as well as intense fires from the tropical wetlands of the Pantanal in South America to the peatlands of tropical Asia. In the western United States, warmer and drier conditions have spurred fires that have burned almost twice as much area in the twenty-first century compared to the late twentieth century (Shuman et al. 2022 ).

The most important hazard is reflected in the risk posed by forest loss (Anderegg et al. 2022 ). Researchers have attempted to quantify the risk to forests, carbon storage, biodiversity and forest loss using vegetation models, the relationship between climate and forest properties, and the impact of climate on forest loss (Anderegg et al. 2022 ). Wildfire fire events can wipe out large areas of forests, directly contributing to the potential risks described above. When fire itself determines when, where, and how it burns, it can be very dangerous to encounter uncontrollable fires (Dupuy et al. 2020 ). This is an underestimated risk. In addition, fires are becoming more extreme, with longer fire seasons, which are increasingly overlapping in southern and northern Europe.

4.2 Wildfire and management

Prescribed fire is a proactive approach to wildfire prevention and control by setting fires to remove combustible material from the forest by artificially controlling the intensity and extent of the fire (Mayer et al. 2020 ; Jones et al. 2022 ). In the controlled areas, low-intensity, controlled fires with little risk of spread are used to remove ground combustible material, allow old-growth stands to fall and breed new forests, and bring back the natural ecology to health. There are uses of low-intensity, controlled wildfires with little risk of spread to remove combustible material from the forest floor, dead wood that have not fallen, prescribed fires to prevent and control the major wildfires and allow forests to regenerate (Wang et al. 2015 ; Zhang et al. 2018a ; Tahmasbian et al. 2019 ; Reverchon et al. 2020 ; Jones et al. 2022 ). Allowing a portion of the wildfire to burn naturally or to burn out first is about removing flammable material from the forest for planned burning (Wang et al. 2014 , 2015 , 2020a , b ). But the central question is how much to burn, where to burn, and how to burn it.

Some scientists want to use mathematical equations to simulate fires (Quintero et al. 2021 ), to study where fire risk is greatest and how “planned burns” can minimize the risk of forest wildfires under different models of specific weather conditions, vegetation types, topography, fuel loads, etc. (Guinto et al. 1999a , b ; Long et al. 2014 ; Ma et al. 2015 ; Wang et al. 2015 , 2020c ). Under the simulation model, planned burning is theoretically effective. Burning all the flammable material in the forest that needs to be removed, regardless of any restrictions, is effective in reducing the occurrence of wildfires (Wang et al. 2014 ; Taresh et al. 2021 ). However, there are also problems in the simulation such as uncertainty of future severe fire weather. For example, the simulation model showed that on average, prescribed burning reduces wildfire extent in dry forested grasslands by only 1 ha per 3 ha. This suggests that it is almost impossible to reduce the occurrence of forest wildfires by prescribed burning alone, but such planned burning has been proven by many studies to be one of the important and useful disturbance methods to effectively reduce forest wildfires and manage forest ecosystems (Davis et al. 2022 ). However, if planned burns are used blindly to remove, it may also bring negative effects such as damage to young forests on forest land. Therefore, future research should adhere to this planned burn management, but further efforts in simulation accuracy and scale are needed. Species composition across a large bioclimatic gradient had the greatest effect on fire intensity in a long-term unburned area of northeastern Washington State, USA, with results suggesting that prior fire, harvesting, clearcutting, and especially planned burning can reduce fire intensity in subsequent forest fires (Cansler et al. 2022 ).

4.3 Wildfires and forest ecosystem changes

From an ecological point of view, wildfire is inherently part of nature and a key driver of ecosystem structure, condition, composition, and change processes. It can alter vegetation population structure, soil physicochemical properties, microbial and insect population structure, nutrient cycling pathways, etc., with a variety of positive and negative effects (Gomes et al. 2018 ; McLauchlan et al. 2020 ). Forest wildfires dramatically alter the export processes and pathways of stored and unstable soil organic matter (SOM) as well as dissolved organic matter (DOM). Ecosystem recovery after forest fires depends on soil microbial communities and revegetation (Taresh et al. 2021 ), and these processes are easily limited by the amount of nutrients in the soil, such as nitrogen and unstable water-soluble compounds, which both strongly influence ecosystem recovery after forest fires (Wang et al. 2020a , b , c ). The results of related studies show that the enrichment of SOM and DOM at different soil burning intensities is important for ecosystem recovery and water quality in the soil (Wang et al. 2020a , b , c ; Bahureksa et al. 2022 ).

Previous studies have shown that wildfires can reduce microbial biomass in soils and alter the composition of the soil microbiome (Zhou et al. 2019 ; Mayer et al. 2020 ; Singh et al. 2021 ; Babur et al. 2022 ), both of which can have implications for forest regeneration and overall ecosystem health. Recent studies have shown that 1 year after fire, many of the key ecosystem functions performed by microbes in unburned soils were absent in burned samples. In particular, the absence of ectomycorrhizal fungi following high-intensity fires may affect the ability of pine seedlings to re-establish and grow in fire-affected soils (Rodriguez-Ramos et al. 2021 ). Such studies will provide new information for the overall recovery of forest ecosystems after wildfire disturbance (Nelson et al. 2022 ).

Savannas are beginning to take hold in tropical and subtropical regions with the effects of wildfires. It is a natural landscape that is highly dependent on wildfire, with most of the trees being snuffed out at a young age. What is more, in places where water and heat conditions are adequate for forest growth, trees continue to struggle to become forested and continue to sustain the grassland landscape (Hoffmann et al. 2012 ; Belcher et al. 2013 ; Maurin et al. 2014 ; Zhang et al. 2018b ; Benton et al. 2022 ; da Rocha et al. 2022 ). Smaller and smaller overfire areas reduce aerosol concentrations, alter vegetation structure, and then increase the total terrestrial carbon sinks. Reducing fire is beneficial to combat warming but may go against natural ecosystems. Frequent fires are a necessary part of ancient grassland ecology and play an important role in species conservation. For fire-resistant plants and plants that depend on fire for reproduction, wildfire is a positive force. In Australia, plants including mountain lobelia (Banksias) have evolved reproductive strategies that use fire (Rokich and Dixon 2007 ) and its fruit spikes need to be scorched at high temperatures to burst open smoothly, releasing seeds that have been dormant for years and quickly occupying the open spaces and nutrient-rich land after wildfire burns.

4.4 Remote sensing and wildfires

Evaluating the impact of wildfires on forest landscapes is a research component that is increasingly supported by remote sensing technology (Santos et al. 2021 ). Remote sensing technology has been proven by many studies as a very feasible and effective tool to comprehensively characterize the pattern and extent of fire occurrence in forest ecosystems. The interpretation of satellite imagery is an important process by which the extent of fire expansion can be delineated, and the intensity or severity of fire reached can be described over the past decades (Chuvieco 2012 ; Allison et al. 2016 ). Through field measurements of wildfire burning, satellite remote sensing, and model simulations, a large amount of sample point data on the amount of combustible material, burn rate, and burn volume have been accumulated. Meanwhile, online satellite imagery resources have significantly improved the efficiency and coverage of wildfire mapping, such as in the Babeldaob watershed, with the expansion of wildfire mapping efforts to provide a more accurate picture of fire area and patterns (Dendy et al. 2022 ) and provide important practical value to simulation efforts. Elucidating the impact of wildfires on climate by altering radiative forcing at the global scale, either by coupling remote sensing models, vegetation models with climate models, or by inputting information on greenhouse gas emissions and aerosols generated by wildfires into climate models, will provide an important reference for accurate management of wildfires. The main methods for quantitatively assessing carbon emissions from wildfires are calculations using the amount of combustible material, the percentage burned, and the area burned by the fire. One study specifically was conducted with a bibliometric analysis from the perspective of forest wildfires and remote sensing and obtained four main research aspects: (1) analysis of forest changes caused by wildland fires over the years and the use of spectral indices in the analysis of climate change; (2) development of systems for monitoring, mapping, and detecting wildland fires based on satellite imagery; (3) studies on the relationship between emissions impacts, especially in various biomes; and (4) model-based analysis of the relationship between studies of burned areas, using remotely sensed data, and anthropogenic effects (Santos et al. 2021 ). Currently, research on extreme wildfires is just beginning and is expected in the future (Mathew et al. 2018 ); thanks to advanced remote sensing technology and artificial intelligence data analysis technology support, parties will be able to predict forecasts and environments under extreme fires.

4.5 Wildfire and black carbon

Wildfires are the largest source of carbonaceous aerosols globally (Andreae and Rosenfeld 2008 ), and organic carbon and black carbon in aerosols scatter and absorb radiation, while black carbon adds heat to the clouds so that they evaporate and change the radiation balance (Lohmann and Feichter 2005 ). Meanwhile, aerosols and greenhouse gases emitted from forest fires are transported over long distances with the atmospheric circulation, affecting regional air quality and radiation. Aerosols from forest fires in Russia have led to a 57% reduction in solar radiation in Korea and an increase in PM 10 concentration to 258 μg m −3 (Lee et al. 2005 ) and increased ozone and aerosols in the lower troposphere of the Tibetan Plateau due to forest fires in South Asia (Chan et al. 2006 ).

On the one hand, greenhouse gas emissions may accelerate the decomposition of organic carbon in permafrost at high latitudes. Observational analysis and numerical simulations show that the warming effect of water-soluble brown carbon over the Arctic is about 30% of that of black carbon, and biomass burning at mid and high latitudes in the Northern Hemisphere contributes about 60% of the warming effect of brown carbon in the Arctic. As future warming intensifies, the frequency, intensity, and extent of wildfire burning at mid and high latitudes in the Northern Hemisphere are likely to increase, thus releasing more brown and black carbon aerosols and further accelerating Arctic warming, forming a positive feedback loop. The contribution of brown carbon aerosols to Arctic warming is expected to be more important in the future. The results of the study suggest that enhancing effective management of wildfire burning in the northern hemisphere at mid and high latitudes will play an important role in mitigating Arctic and global climate change (Yue et al. 2022 ).

Disturbance of vegetation by wildfire significantly increases surface albedo and produces significant cooling effects (Tsuyuzaki et al. 2009 ). Finally, atmospheric emissions and climate impacts from wildfires also have feedbacks on vegetation growth, thus forming a feedback loop among various factors (Gatebe et al. 2014 ). The impact of wildfire on terrestrial vegetation carbon cycle is an important aspect to evaluate the ecological role of wildfire. The contribution of wildfire to the terrestrial vegetation carbon cycle also includes the increase in plant photosynthesis due to CO 2 emissions and the effect of wildfire on vegetation succession dynamics (Lehmann et al. 2014 ). Meanwhile, many simulation studies of the effects of fire on global and regional ecosystem carbon cycles using dynamic vegetation simulation models have been conducted, and the results agree that wildfire reduces terrestrial carbon sink capacity, but with great uncertainty in magnitude (Dendy et al. 2022 ).

4.6 Climate change and wildfires

Forests absorb about a quarter of the carbon dioxide emitted into the atmosphere, so they play an extremely important role in buffering the Earth’s contribution to the rise of carbon dioxide in the atmosphere (Anderegg et al. 2022 ). Since 2000, research on wildfires and climate change has been increasing (Succarie et al. 2022 ), and the effects of climate change on fire occurrence include increased temperatures and increased atmospheric CO 2 concentrations. First, warming and increased CO 2 concentrations lead to longer growing seasons, increasing forest and grassland biomass and thus surface combustible loads. Second, global warming increases the number of lightning bolts in the temperate atmosphere. The number of lightning bolts will increase by 50% in the continental, US (Romps et al. 2014 ), increasing wildfire risk significantly (Veraverbeke et al. 2017 ; Succarie et al. 2022 ). Study predicts 19.1% increase in lightning fires in California, USA, from 2020 to 2049 (Lutz et al. 2009 ). Stocks et al. ( 2002 ) find that lightning causes much larger single fires than man-made fires in Canadian boreal conifer wildfires. Third, warmer temperatures increase atmospheric evaporation, thereby exacerbating drought and increasing wildfire risk, especially in forested areas with abundant combustible material. In addition, the seasonal distribution of wildfire frequency is also changing (Succarie et al. 2022 ). Recent North American fire seasons have seen a significant increase in wildfire frequency in the late season, with greater fire intensity resulting in greater CO 2 emissions. Climate change is already significantly increasing wildfire risk, with drought-affected areas significantly (Turetsky et al. 2011 ; Jolly et al. 2014 ). Climate change is likely to be a major driver of increased fire activity. As the planet continues to warm, extreme weather such as high temperatures and heat waves are expected to become more frequent (Leigh et al. 2015 ). Higher temperatures dry out the landscape and help create the perfect environment for larger, more frequent forest fires. This, in turn, leads to increased emissions from forest fires, further exacerbating climate change and contributing to more fires as part of a fire-climate feedback loop that also exacerbates the occurrence of extreme fires (Liu et al. 2014 ), the whole process as shown in Fig.  6 .

The loop between forest wildfires and global climate change

If forests are tapped to play a more important role in climate mitigation, then a huge scientific effort will be needed to better articulate when and where forests can withstand climate change in the twenty-first century (Anderegg et al. 2022 ). The increasing intensity, extent, and frequency of boreal forest wildfires may exacerbate carbon emissions and transform the region from a globally important carbon sink to a carbon source. Some findings highlight the climate risks posed by boreal wildfires and point to fire management as a cost-effective way to limit emissions (Phillips et al. 2022 ). Boreal forests in the more productive southern part of central Canada already suffer from relatively high fire frequency (of disturbance) and thus could be used for future simulations of carbon dynamics in more boreal forests (Parisien et al. 2020 ). Fire-related carbon dynamics in southern boreal forest systems have been relatively poorly studied, with limited research on pre-fire carbon stocks and the drivers of subsequent burning. The latitude-based approach underscores previous research that northern boreal forests have the risk of sequestering less carbon under changing disturbance conditions (Dieleman et al. 2020 ). Therefore, assessing the effects of wildfire on the carbon cycle of forest ecosystems in high latitudes in the context of climate change which requires a holistic approach to ground-air interactions. The understanding of a range of fire-related processes, including the effects of fire on soil hydrothermal transport, on high-latitude forest dynamics, and on other vegetation composition, is refined through multiple pathways.

4.7 Forest wildfires and the environment and human health

Wildfires can affect the institutions, processes, and functions of forest ecosystems and also have far-reaching effects on the atmosphere and human well-being to a certain extent. The months-long forest wildfires that occurred in Australia in 2019–2020 caused very serious impacts on local ecosystems as well as on social life (Sullivan et al. 2022 ). Wildfires are a growing threat to people’s lives, property, and livelihoods (Dillis et al. 2022 ). Wildfires can also threaten crops and human health in the immediate vicinity of forests (Dillis et al. 2022 ). In addition to the direct damage caused by forest wildfires to local ecosystems, the air pollution caused by the fires also affects more distant areas with the atmospheric circulation. Forest wildfires directly contribute to elevated PM2.5 concentrations, for example, more than 20% of PM2.5 in the USA in 2014 was due to wildfires. Stanford University researchers have found that wildfire smoke is undermining decades of air quality gains and that fire smoke is exposing millions of Americans to dangerous levels of fine particulate matter each year, seriously jeopardizing human health levels (Childs et al. 2022 ). Moreover, wildfires are an important cause of tropospheric O 3 pollution (Xu et al. 2021 ). One study suggests that wildfires in Brazil may have led to a 23% increase in respiratory admissions and a 21% increase in blood circulation admissions. The impact of air pollution from forest fires on human health also varies spatially, in the northern region of Brazil (Ye et al. 2021 ), which is mostly a tropical rainforest area, and was estimated to have increased respiratory admissions by 38% and circulatory admissions by 27%. Epidemiological evidence between air pollution from forest wildfires and human health, i.e., air pollution from wildfires, is significantly associated with higher risk of cardiopulmonary disease admissions (Requia et al. 2021 ).

5 Conclusions and perspectives

This study is the first to use bibliometrics and terminology analysis to provide a centralized, unified analysis and in-depth interpretation of the literature related to the topic of forest wildfires published in the WOS database over the past 30 years (1991–2021). Mainly from the journals, authors, countries, and institutions involved in research on the abovementioned topics, and the most relevant keyword networks used during this period were mapped, and seven main research hotspots were formed based on clustering features. The results show that despite the large amount of literature reporting on the occurrence of forest wildfires, technical methods of monitoring, spatial and temporal patterns of wildfires and their ecological, environmental and evolutionary effects, and the impact of global change on wildfire activity, the combing and description of the traditional approach in our study shows clear research advantages, especially for the combing of the current research in general and the description of the research hotspots is an important guide. The results of the study show an exponential increase in the number of publications on these topics over the past 30 years, and the trend of increase, especially in the last 15 years, indicates that there is a growing academic interest in this topic, which is widely appreciated. Among all publications, 54% are related to forestry, ecology, and environmental science, showing the interest and connection between the analyzed topics and environmental dynamics. Forest Ecology and Management, as the core journal of publications, has a significantly higher number of publications than the other journals, being the subject of the seven themes formed by the keyword clustering. The seven thematic directions formed by keyword clustering are the hot content of current research, focusing on anthropogenic management of wildfire, monitoring and model simulation by remote sensing technology, ecosystem response and feedback, reciprocal feedbacks mechanisms of forest wildfire in the context of global climate change, and environmental and human health effects of aerosols.

There are still many challenges that need to be addressed. Current research further provides significant room for improvement. Firstly, wildfire research is isolated within disciplines such as forestry and atmospheric chemistry, but wildfire is a biophysical and social phenomenon that cannot be understood through the lens of any single discipline currently. It is necessary to integrate disciplines by promoting coordination among physical, biological, and social sciences. Future research needs to promote greater proactivity like societies and ecosystems becoming more resilient to reply increasing fire risks by increasing funding and getting better coordination.Furthermore, the accuracy in forest wildfire model simulations needs to be further improved, as numerical simulations are limited by a variety of factors such as climate, hydrology, atmosphere, soil, and topography. How to optimize model simulations and the integration with the interpretation of remote sensing imagery, while aiding ground verification and wildfire mapping needs to be further studied. Therefore, developing coupled models that include the human dimension to better predict future fire activity and its effects is essential. Scientists need to develop more advanced computer modeling systems that incorporate both human and non-human aspects of fire. Moreover, the impact of global climate change on wildfire landscape patterns is mainly reflected in the warming and aridity of some regions, as well as the control of wildfire frequency and overfire area by anthropogenic activities. Therefore, the dynamic assessment and early warning of wildfire risk, pre-fire prevention, and post-fire vegetation restoration will be important elements of wildfire research in the future, especially for the study of different vegetation zones and the disturbance and response of different wildfires (type, fire extent, area, etc.) to the ecosystem. Finally, future research should further enhance the extent to which wildfires pose risks to human well-being and health, such as the effects of forest fires on neighboring crops and haze on the human respiratory tract and on children and pregnant women.

In conclusion, although there are still many challenges in forest wildfire research, it has developed considerably and received a lot of attention in the past 30 years. We believe that forest wildfire, as an important driver and key disturbance factor of global climate change and forest ecosystem succession, will receive more attention and in-depth study from the scientific community in the future.

Data Availability

Data available from the corresponding authors upon request.

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Thanks to all authors for contributing to this manuscript. The authors acknowledge Griffith University and Centre of Planetary Health and Food Security. Comments and suggestions from anonymous reviewers, the academic editor, and the managing editor are greatly appreciated.

Open Access funding enabled and organized by CAUL and its Member Institutions. This research was funded by the CAS Strategic Priority Research Program (XDA20050103) and TL received the Griffith University Postgraduate Research Scholarship for his Ph.D. project. Open Access funding enabled and organized by CAUL and its Member Institutions.

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Li, T., Cui, L., Liu, L. et al. Advances in the study of global forest wildfires. J Soils Sediments 23 , 2654–2668 (2023). https://doi.org/10.1007/s11368-023-03533-8

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Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA

• Jessica E. Halofsky   ORCID: orcid.org/0000-0002-1502-4895 1 ,
• David L. Peterson 2 &
• Brian J. Harvey 2  

Fire Ecology volume  16 , Article number:  4 ( 2020 ) Cite this article

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Wildfires in the Pacific Northwest (Washington, Oregon, Idaho, and western Montana, USA) have been immense in recent years, capturing the attention of resource managers, fire scientists, and the general public. This paper synthesizes understanding of the potential effects of changing climate and fire regimes on Pacific Northwest forests, including effects on disturbance and stress interactions, forest structure and composition, and post-fire ecological processes. We frame this information in a risk assessment context, and conclude with management implications and future research needs.

Large and severe fires in the Pacific Northwest are associated with warm and dry conditions, and such conditions will likely occur with increasing frequency in a warming climate. According to projections based on historical records, current trends, and simulation modeling, protracted warmer and drier conditions will drive lower fuel moisture and longer fire seasons in the future, likely increasing the frequency and extent of fires compared to the twentieth century. Interactions between fire and other disturbances, such as drought and insect outbreaks, are likely to be the primary drivers of ecosystem change in a warming climate. Reburns are also likely to occur more frequently with warming and drought, with potential effects on tree regeneration and species composition. Hotter, drier sites may be particularly at risk for regeneration failures.

Resource managers will likely be unable to affect the total area burned by fire, as this trend is driven strongly by climate. However, fuel treatments, when implemented in a spatially strategic manner, can help to decrease fire intensity and severity and improve forest resilience to fire, insects, and drought. Where fuel treatments are less effective (wetter, high-elevation, and coastal forests), managers may consider implementing fuel breaks around high-value resources. When and where post-fire planting is an option, planting different genetic stock than has been used in the past may increase seedling survival. Planting seedlings on cooler, wetter microsites may also help to increase survival. In the driest topographic locations, managers may need to consider where they will try to forestall change and where they will allow conversions to vegetation other than what is currently dominant.

Antecedentes

Los incendios de vegetación en el Noroeste del pacífico (Washington, Oregon, Idaho, y el oeste de Montana, EEUU), han sido inmensos en años recientes, capturando la atención de los gestores de recursos, de científicos dedicados a los incendios, y del público en general. Este trabajo sintetiza el conocimiento de los efectos potenciales del cambio climático y de los regímenes de fuego en bosques del noroeste del Pacífico, incluyendo los efectos sobre las interacciones entre disturbios y distintos estreses, la estructura y composición de los bosques, y los procesos ecológicos posteriores. Encuadramos esta información en el contexto de la determinación del riesgo, y concluimos con implicancias en el manejo y la necesidad de futuras investigaciones.

Los incendios grandes y severos en el Noroeste del Pacífico están asociados con condiciones calurosas y secas, y tales condiciones muy probablemente ocurran con el incremento en la frecuencia del calentamiento global. De acuerdo a proyecciones basadas en registros históricos, tendencias actuales y modelos de simulación, condiciones prolongadas de aumento de temperaturas y sequías conducirán a menores niveles de humedad, incrementando probablemente la frecuencia y extensión de fuegos en el futuro, en comparación con lo ocurrido durante el siglo XX. Las interacciones entre el fuego y otros disturbios, son probablemente los principales conductores de cambios en los ecosistemas en el marco del calentamiento global. Los incendios recurrentes podrían ocurrir más frecuentemente con aumentos de temperatura y sequías, con efectos potenciales en la regeneración de especies forestales y en la composición de especies. Los sitios más cálidos y secos, pueden estar particularmente en riesgo por fallas en la regeneración.

Conclusiones

Los gestores de recursos no podrían tener ningún efecto sobre el área quemada, ya que esta tendencia está fuertemente influenciada por el clima. Sin embargo, el tratamiento de combustibles, cuando está implementado de una manera espacialmente estratégica, puede ayudar a reducir la intensidad y severidad de los incendios, y mejorar la resiliencia de los bosques al fuego, insectos, y sequías. En lugares en los que el tratamiento de combustibles es menos efectivo (áreas más húmedas, elevadas, y bosques costeros) los gestores deberían considerar implementar barreras de combustible alrededor de valores a proteger. Cuando y donde la plantación post fuego sea una opción, plántulas provenientes de diferentes stocks genéticos de aquellos que han sido usados en el pasado pueden incrementar su supervivencia. La plantación de plántulas en micrositios más húmedos y fríos podría ayudar también a incrementar la supervivencia de plántulas. En ubicaciones topográficas más secas, los gestores deberían considerar evitar cambios y donde estos sean posibles, permitir conversiones a tipos de vegetación diferentes a las actualmente dominantes.

Abbreviations

ENSO: El Niño-Southern Oscillation

MPB: Mountain Pine Beetle

PDO: Pacific Decadal Oscillation

Introduction

Large fires are becoming a near-annual occurrence in many regions globally as fire regimes are changing with warming temperatures and shifting precipitation patterns. The US Pacific Northwest (states of Washington, Oregon, Idaho, and western Montana, USA; hereafter the Northwest) is no exception. In 2014, the largest wildfire in recorded history for Washington State occurred, the 103 640 ha Carlton Complex Fire (Fig. 1 ). In 2015, an extreme drought year with very low snowpack across the Northwest (Marlier et al. 2017 ), 688 000 ha burned in Oregon and Washington (Fig. 2 ), with over 3.6 million ha burned in the western United States. Several fires in 2015 occurred in conifer forests on the west ( i.e. , wet) side of the Cascade Range, including a rare fire event in coastal temperate rainforest on the Olympic Peninsula. In some locations, short-interval reburns have occurred. For example, one location on Mount Adams in southwestern Washington burned three times between 2008 and 2015 (Fig. 3 ). Similarly, during the summer of 2017 in southwestern Oregon, the 77 000 ha Chetco Bar Fire burned over 40 000 ha of the 2002 Biscuit Fire, including a portion of the Biscuit Fire that had burned over part of the 1987 Silver Fire. At over 200 000 ha, the Biscuit Fire was the largest fire in the recorded history of Oregon.

Large wildfires, such as the 2014 Carlton Complex Fire in Washington, USA (103 640 ha), have occurred throughout western North America during the past several decades. These disturbances have a significant effect on landscape pattern and forest structure and will likely become more common in a warmer climate, especially in forests with heavy fuel loadings. Photo credit: Morris Johnson

Fires burning across the Pacific Northwest, USA, on 25 August 2015. This natural-color satellite image was collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. Actively burning areas, detected by MODIS’s thermal bands, are outlined in red. National Aeronautics and Space Administration image courtesy of Jeff Schmaltz, MODIS Rapid Response Team

( a ) Large fires around Mount Adams in Gifford Pinchot National Forest in southwestern Washington, USA, between 2001 and 2015 (area in orange burned twice, and area in red burned three times); and ( b ) area in Gifford Pinchot National Forest that has burned three times since 2008 (2008 Cold Springs fire, 2012 Cascade Creek fire, and 2015 Cougar Creek fire). Map credit: Robert Norheim; photo credit: Darryl Lloyd

Over the twentieth century in the Northwest, years with relatively warm and dry conditions have generally corresponded with larger fires and greater area burned (Trouet et al. 2006 ; Westerling et al. 2006 ; Littell et al. 2009 ; Littell et al. 2010 ; Abatzoglou and Kolden 2013 ; Cansler and McKenzie 2014 ; Dennison et al. 2014 ; Stavros et al. 2014 ; Westerling 2016 ; Kitzberger et al. 2017 ; Reilly et al. 2017 ; Holden et al. 2018 ). Decreasing fuel moisture and increasing duration of warm, dry weather creates large areas of dry fuels that are more likely to ignite and carry fire over a longer period of time (Littell et al. 2009 ).

A warming climate will have profound effects on fire frequency, extent, and possibly severity in the Northwest. Increased temperatures are projected to lengthen fire and growing seasons, increase evaporative demand, decrease soil and fuel moisture, increase likelihood of large fires, and increase area burned by wildfire (McKenzie et al. 2004 ; Littell et al. 2010 ; Stavros et al. 2014 ; Westerling 2016 ). Decreased summer precipitation is also projected to increase area burned (Holden et al. 2018 ).

Interactions between fire and other disturbance agents ( e.g. , drought, insect outbreaks) will likely catalyze ecosystem changes in a warming climate. Increased tree stress and interacting effects of drought may also contribute to increasing wildfire severity (damage to vegetation and soils) and area burned (McKenzie et al. 2009 ; Stavros et al. 2014 ; Littell et al. 2016 ; Reilly et al. 2017 ).

Climatic changes and associated stressors can interact with altered vegetation conditions ( e.g. , those resulting from historical management practices) to affect fire frequency, extent, and severity, as well as forest conditions in the future (Keeley and Syphard 2016 ). Human influence through domestic livestock grazing, road construction, conversion of land to agriculture, and urbanization has resulted in (direct or indirect) exclusion of fires in dry forests (Hessburg et al. 2005 ). Many larger, fire-resistant trees have been removed by selective logging. These activities, along with active fire suppression, have resulted in increased forest density and fuel buildup in forests historically characterized by frequent, low-severity and mixed-severity fires (Hessburg et al. 2005 ). Although landscape pattern and fuel limitations were key factors that limited fire size and severity historically, these limitations have been largely removed from many contemporary landscapes, thus increasing the potential for large high-severity fires, particularly in a warming climate.

Facing such changes, land managers need information on the magnitude and likelihood of altered fire regimes and forest conditions in a warming climate to help guide long-term sustainable resource management. Many published studies have explored the potential effects of climate change on forest fire in the Northwest, including paleoecological, modeling, and local- to regional-scale empirical studies. However, to our knowledge, there is no single resource that synthesizes these varied studies for the Northwest region. A synthesis of this information can help managers better understand the potential effects of climate change on ecosystem processes, assess risks, and implement actions to reduce the negative effects of climate change and transition systems to new conditions.

In this synthesis, we draw from relevant published literature to discuss potential effects of changing climate on fire frequency, extent, and severity in Northwest forests. Sources of information include: (1) long-term (centuries to millennia) paleoecological studies of climate, fire, and species distribution; (2) medium-term (decades to centuries) fire history studies; (3) near-term (years to decades) studies on trends in vegetation and fire associated with recent climatic variability and change; (4) forward-looking studies using simulation models to project future fire and vegetation change; and (5) recent syntheses focused on potential climate change effects.

We used regionally specific information where possible, including information from adjacent regions with forests of similar structure and function when relevant. Following an overview of climate projections, we (1) identified risks related to wildfire as affected by climate change in three broad ecosystem types; (2) explored the magnitude and likelihood of those risks; and (3) concluded with a discussion of uncertainties about future climate and fire, potential future research, and implications for resource management.

Overview of climate projections

Warming temperatures and changing precipitation patterns will affect amount, timing, and type of precipitation; snowmelt timing and rate (Luce et al. 2012 ; Luce et al. 2013 ; Safeeq et al. 2013 ); streamflow magnitude (Hidalgo et al. 2009 ; Mantua et al. 2010 ); and soil moisture content (McKenzie and Littell 2017 ). Compared to the historical period from 1976 to 2005, 32 global climate models project increases in mean annual temperature for the middle and end of the twenty-first century in the Northwest. These projected increases range from 2.0 to 2.6 °C for mid-century (2036 to 2065) and 2.8 to 4.7 °C for the end of the century (2071 to 2100), depending on future greenhouse gas emissions (specifically representative concentration pathway 4.5 or 8.5; Vose et al. 2017 ). Warming is expected to occur during all seasons, although most models project the largest temperature increases in summer (Mote et al. 2014 ). All models suggest a future increase in heat extremes (Vose et al. 2017 ).

Changes in precipitation are less certain than those for temperature. Global climate model projections for annual average precipitation range from −4.7 to +13.5%, averaging about +3% among models (Mote et al. 2014 ). A majority of models project decreases in summer precipitation, but projections for precipitation vary for other seasons. However, models agree that extreme precipitation events ( i.e. , number of days with precipitation >2.5 cm) will likely increase, and that the length of time between precipitation events will increase (Mote et al. 2014 ; Easterling et al. 2017 ).

Risk assessment

A risk-based approach to climate change vulnerability assessments provides a common framework to evaluate potential climate change effects and identify a structured way to choose among adaptation actions or actions to mitigate climate change risks (EPA 2014 ). Risk assessment is linked with risk management by (1) identifying risks—that is, how climate change may prevent an agency or other entity from reaching its goals; (2) analyzing the potential magnitude of consequences and likelihood for each risk; (3) selecting a set of risk-reducing actions to implement; and (4) prioritizing those actions that address risks with the highest likelihood and magnitude of consequences (EPA 2014 ).

Here, we summarized potential risks that are relevant for natural resource management associated with climate–fire interactions, including: wildfire frequency, extent, and severity; reburns; stress interactions; and regeneration for (1) moist coniferous forest (low to mid elevation), (2) dry coniferous forest and woodland (low to mid elevation), and (3) subalpine coniferous forest and woodland (high elevation). The likelihood and magnitude of consequences, and confidence in inferences are described for each risk. Although the information provided here does not constitute risk management, as described in the previous paragraph, this information can be used to inform more site- and resource-specific risk assessments and risk management.

The risks identified here were inferred from the authors’ review of the published literature described below, as well as experience with developing climate change vulnerability assessments in the study region over the past decade (Halofsky et al. 2011a , b ; Raymond et al. 2014 ; Halofsky and Peterson 2017a , b ; Halofsky et al. 2019 ; Hudec et al. 2019 ). These assessments encompassed all ecosystems and species addressed in this synthesis, and included extensive discussion of the effects of wildfire and other disturbances. Climate change effects and adaptation options in the assessments were greatly informed by input from resource managers as well as by scientific information. Thus, many fire-related vulnerabilities identified in the assessments are relevant to the risk assessment discussed here.

Risk in moist coniferous forests

Most climate–fire risks in moist coniferous forests are relatively low (Table 1 ). These forests occur west of the Cascade Range in Oregon and Washington and are frequently dominated by Douglas-fir ( Pseudotstuga menziesii [Mirb.] Franco) and western hemlock ( Tsuga heterophylla [Raf.] Sarg.). Moist coniferous forests are characterized by an infrequent, stand-replacing ( i.e. , high-severity) fire regime (Agee 1993 ). Although fire frequency and severity may increase with climate change, the frequency of fire in these moist ecosystems will likely remain relatively low.

Risk in dry coniferous forests

Climate–fire risks in dry coniferous forests and woodlands are high for increased fire frequency, extent, and severity (Table 2 ). Dry coniferous forests and woodlands occur at lower elevations in southwestern Oregon, east of the Cascade Range in Oregon and Washington, and at lower elevations in the Rocky Mountains in Idaho and Montana. Fire regimes in these forests and woodlands range from moderate frequency and mixed severity to frequent and low severity. Ponderosa pine ( Pinus ponderosa Douglas ex P. Lawson & C. Lawson) is a characteristic species, along with Douglas-fir, grand fir ( Abies grandis [Douglas ex D. Don] Lindl.), and white fir ( Abies concolor [Gordon & Glend.] Lindl. ex Hildebr.). These forests and woodlands are also at risk from interacting disturbances and hydrologic change (moderate to high likelihood and magnitude of consequences), and post-fire regeneration failures are likely to occur on some sites.

Risk in high-elevation forests

Climate–fire risks in high-elevation forests are moderate, with a primary factor being increased fire frequency and extent in lower-elevation forests spreading to higher-elevation systems (Table 3 ). Regeneration could be challenging in locations where seed availability is low due to very large fires. High-elevation forests occur in mountainous areas across the Northwest. They are characterized by species such as subalpine fir ( Abies lasiocarpa [Hook.] Nutt.), mountain hemlock ( Tsuga mertensiana [Bong.] Carrière), and lodgepole pine ( Pinus contorta var. contorta Engelm. ex S. Watson). High-elevation forests are characterized by infrequent, stand-replacement fire regimes (Agee 1993 ). Risks of stress interactions are also moderate, because drought and insect outbreaks will likely affect high-elevation forests with increasing frequency.

Historical and contemporary fire–climate relationships

Paleoclimate and fire data.

Wildfire-derived charcoal deposited in lake sediments can be used to identify individual fire events and to estimate fire frequency over hundreds to thousands of years (Itter et al. 2017 ). In combination with sediment pollen records, charcoal records help to determine how vegetation and fire frequency and severity shifted with climatic variability in the past (Gavin et al. 2007 ). Existing paleoecological reconstructions of the Northwest are based mostly on pollen and charcoal records from lakes in forested areas west of the Cascade Range, with few studies in the dry interior of the region (Kerns et al. 2017 ).

The early Holocene ( circa 10 500 to 5000 years BP) was the warmest post-glacial period in the Northwest (Whitlock 1992 ). During the early Holocene, summers were warmer and drier relative to recent historical conditions, with more intense droughts (Whitlock 1992 ; Briles et al. 2005 ). In many parts of the Northwest, these warmer and drier summer conditions were associated with higher fire frequency (Whitlock 1992 ; Walsh et al. 2008 ; Walsh et al. 2015 ).

Sediment charcoal analysis documented relatively frequent (across the paleoecological record) fire activity during the early Holocene in eight locations: North Cascade Range (Prichard et al. 2009 ), Olympic Peninsula (Gavin et al. 2013 ), Puget Lowlands (Crausbay et al. 2017 ), southwestern Washington (Walsh et al. 2008 ), Oregon Coast Range (Long et al. 1998 ), Willamette Valley (Walsh et al. 2010 ), Siskiyou Mountains (Briles et al. 2005 ), and Northern Rocky Mountains in Idaho (Brunelle and Whitlock 2003 ) (Table 4 ). Higher fire frequency in these locations was generally associated with higher abundance of tree species adapted to survive fire or regenerate soon after fire, including Douglas-fir, lodgepole pine, and Oregon white oak ( Quercus garryana Douglas ex Hook.) (Table 4 ). Other pollen analyses (without parallel charcoal analysis) support the expansion of these species during the early Holocene ( e.g. , Sea and Whitlock 1995 ; Worona and Whitlock 1995 ), in addition to the expansion of ponderosa pine and oak in drier interior forests (Hansen 1943 ; Whitlock and Bartlein 1997 ). Relatively frequent fire (across the paleoecological record) during the early Holocene likely resulted in a mosaic of forest successional stages, with species such as red alder ( Alnus rubra Bong.) dominating early-successional stages in mesic forest types (Cwynar 1987 ).

Paleoecological studies (covering the early Holocene and other time periods) indicate that climate has been a major control on fire in the Northwest over millennia, with interactions between fire and vegetation. During times of high climatic variability and fire frequency ( e.g. , the early Holocene), fires were catalysts for large-scale shifts in forest composition and structure (Prichard et al. 2009 ; Crausbay et al. 2017 ). Species that persisted during these times of rapid change have life history traits that facilitate survival in frequently disturbed environments (Brubaker 1988 ; Whitlock 1992 ), including red alder, Douglas-fir, lodgepole pine, ponderosa pine, and Oregon white oak, which suggests that these species may be successful in a warmer future climate (Whitlock 1992 ; Prichard et al. 2009 ).

Fire-scar and tree-ring records

Fire-scar studies indicate that climate was historically a primary determinant of fire frequency and extent in the Northwest. Years with increased fire frequency and area burned were generally associated with warmer and drier spring and summer conditions in the Northwest (Hessl et al. 2004 ; Wright and Agee 2004 ; Heyerdahl et al. 2008 ; Taylor et al. 2008 ). Climate of previous years does not have a demonstrated effect on fire, unlike other regions such as the Southwest, most likely because fuels are not as limiting for fire across the Northwest (Heyerdahl et al. 2002 ; Hessl et al. 2004 ).

Warmer and drier conditions in winter and spring are more common during the El Niño phase of the El Niño-Southern Oscillation (ENSO) in the Northwest (Mote et al. 2014 ). The Pacific Decadal Oscillation (PDO) is an ENSO-like pattern in the North Pacific, resulting in sea surface temperature patterns that appeared to occur in 20- to 30-year phases during the twentieth century (Mantua et al. 1997 ). Positive phases of the PDO are associated with warmer and drier winter conditions in the Northwest.

Associations between large fire years and El Niño have been found in the interior Northwest ( e.g. , Heyerdahl et al. 2002 ), as have associations between large fire years and the (warm, dry) positive phase of the PDO (Hessl et al. 2004 ). Other studies have found ambiguous or non-significant relationships between fire and these climate cycles in the Northwest ( e.g. , Hessl et al. 2004 ; Taylor et al. 2008 ). However, interactions between ENSO and PDO (El Niño plus positive phase PDO) were associated with increased area burned (Westerling and Swetnam 2003 ) and synchronized fire in some years in dry forests across the inland Northwest (Heyerdahl et al. 2008 ).

The PDO and ENSO likely affect fire extent by influencing the length of the fire season (Heyerdahl et al. 2002 ). Warmer and drier winter and spring conditions increase the length of time that fuels are flammable (Wright and Agee 2004 ). Although climate change effects on the PDO and ENSO are uncertain, both modes of climatic variation influence winter and spring conditions in the Northwest, whereas summer drought during the year of a fire has the strongest association with major fire years at the site and regional scales (Hessl et al. 2004 ). Summer drought conditions are likely more important than in other regions where spring conditions are more strongly related to fire, because the Northwest has a winter-dominant precipitation regime; fire season occurs primarily in late summer (August through September), and summer drought reduces fuel moisture (Hessl et al. 2004 ; Littell et al. 2016 ).

Contemporary climate and fire records

In the twentieth century, wildfire area burned in the Northwest was positively related to low precipitation, drought, and temperature (Littell et al. 2009 ; Abatzoglou and Kolden 2013 ; Holden et al. 2018 ). Warmer spring and summer temperatures across the western United States cause early snowmelt, increased evapotranspiration, lower summer soil and fuel moisture, and thus longer fire seasons (Westerling 2016 ). Precipitation during the fire season also exerts a strong control on area burned through wetting effects and feedbacks to vapor pressure deficit (a measure of humidity; Holden et al. 2018 ). Between 2000 and 2015, warmer temperatures and vapor pressure deficit decreased fuel moisture during the fire season in 75% of the forested area in the western US and added about nine days per year of high fire potential (defined using several measures of fuel aridity; Abatzoglou and Williams 2016 ).

Periods of high annual area burned in the Northwest are also associated with high (upper atmosphere) blocking ridges over western North America and the North Pacific Ocean. Blocking ridges occur when centers of high pressure occur over a region in such a way that they prevent other weather systems from moving through. These blocking ridges, typical in the positive phase of the PDO (Trouet et al. 2006 ), divert moisture away from the region, increasing temperature and reducing relative humidity (Gedalof et al. 2005 ). Prolonged blocking and more severe drought (Brewer et al. 2012 ) are needed to dry out fuels in mesic to wet forest types ( e.g. , Sitka spruce [ Picea sitchensis {Bong.} Carrière], western hemlock) along coastal Oregon and Washington. With increased concentrations of carbon dioxide in the atmosphere, the persistence of high blocking ridges that divert moisture from the region may increase (Lupo et al. 1997 , as cited in Flannigan et al. 2009 ), further enhancing drought conditions and the potential for fire.

Lightning ignitions also affect wildfire frequency. However, research on lightning with recent and future climate change is equivocal. Some studies suggest that lightning will increase up to 40% globally in a warmer climate (Price and Rind 1994 ; Reeve and Toumi 1999 ; Romps et al. 2014 ), although a recent study suggests that lightning may decrease by as much as 15% globally (Finney et al. 2018 ).

Increases in annual area burned are generally associated with increases in area burned at high severity. Fire size, fire severity, and high-severity burn patch size were positively correlated in 125 fires in the North Cascades of Washington over a recent 25-year period (Cansler and McKenzie 2014 ). Other analyses have similarly shown a positive correlation between annual area burned and area burned severely (in large patches) in the Northwest (Dillon et al. 2011 ; Abatzoglou et al. 2017 ; Reilly et al. 2017 ). The annual extent of fire has increased slightly in the Northwest, although the proportion of area burning at high severity did not increase over the 1985 to 2010 period, either for the region as a whole or for any subregion (Reilly et al. 2017 ). Similarly, an analysis of recent fires (1984 to 2014) in the Northwest found no decrease in the proportion of unburned area within fire perimeters (Meddens et al. 2018 ).

Many studies have found that bottom-up controls such as vegetation, fuels, and topography are more important drivers of fire severity than climate in Western forests ( e.g. , Dillon et al. 2011 ; Parks et al. 2014 ). The direct influence of climate on fire severity is intrinsically much stronger in moister and higher-elevation forests, because drying of fuels in these systems requires extended warm and dry periods. Fire severity in many dry forest types is influenced primarily by fuel quantity and structure (Parks et al. 2014 ). However, fuel accumulations associated with fire exclusion in dry forests may be strengthening the influence of climate on fire severity, likely resulting in increased fire severity in drier forest types (Parks et al. 2016a ).

Wildfire projections under changing climate

Historical patterns suggest that higher temperatures, stable or decreasing summer precipitation, and increased drought severity in the Northwest will likely increase the frequency and extent of fire. Models can help to explore potential future fire frequency and severity in a changing climate, with several types of models being used to project future fire (McKenzie et al. 2004 ). We focused here on models for which output is available in the Northwest—empirical (statistical) models and mechanistic (process-based) models. Both types of models have limitations as well as strengths, but they are conceptually useful to assess potential changes in fire with climate change.

Fire projections by empirical models

Empirical models use the statistical relationship between observed climate and area burned during the historical record (the past 100 years or so) to project future area burned. Future area burned is based on projections of future temperature and precipitation, usually from global climate models. These models do not account for the potential decreases in burn probability in areas that have recently burned, or for long-term changes in vegetation (and thus flammability) with climate change (Parks et al. 2015 ; McKenzie and Littell 2017 ; Littell et al. 2018 ). They also do not account for human influence on fire ignitions (Syphard et al. 2017 ).

Numerous studies have developed empirical models to project future area burned or fire potential at both global (Krawchuk et al. 2009 ; Moritz et al. 2012 ) and regional scales ( e.g. , western US; McKenzie et al. 2004 ; Littell et al. 2010 ; Yue et al. 2013 ; Kitzberger et al. 2017 ). All studies suggest that fire potential, area burned, or both will increase in the western US in the future with warming climate. Below we highlight a few examples that explicitly address the Northwest. These examples provide future fire projections at relatively coarse spatial scales, with changes in area burned being variable across landscapes.

McKenzie et al. ( 2004 ) projected that, with a mean temperature increase of 2 °C, area burned by wildfire will increase by a factor of 1.4 to 5 for most Western states, including Idaho, Montana, Oregon, and Washington. Kitzberger et al. ( 2017 ) projected increases in annual area burned of 5 times the median in 2010 to 2039 compared to 1961 to 2004 for the 11 conterminous Western states. Models developed by Littell et al. ( 2010 ) for Idaho, Montana, Oregon, and Washington suggested that area burned will double or triple by the 2080s, based on future climate projections for two global climate models (Fig. 4 ). Median area burned was projected to increase from about 0.2 million ha historically to 0.3 million ha in the 2020s, 0.5 million ha in the 2040s, and 0.8 million ha in the 2080s. The projections cited here are coarse scale, and area burned can be expected to vary from place to place within the area of the projections.

Conceptual model showing that indirect effects of climate change via disturbance cause faster shifts in vegetation than do direct effects of climate change. Adapted from McKenzie et al. ( 2004 )

Littell et al. ( 2010 ) also developed empirical models at a finer (ecosection) scale for the state of Washington. The relatively low frequency of fire in coastal forests makes development of empirical models difficult, so the output from these models for coastal forests is uncertain. For drier forest types, potential evapotranspiration and water balance deficit were the most important variables explaining area burned. In forested ecosystems (Western and Eastern Cascades, Okanogan Highlands, and Blue Mountains ecosections), the mean area burned was projected to increase by a factor of 3.8 in the 2040s compared to 1980 to 2006. An updated version of these models, expanded to the western US (Littell et al. 2018 ), also suggests that area burned will increase in the future for most forested ecosections of the Northwest, but increases in area burned may be tempered, or area burned may decrease, in areas that are more fuel limited ( e.g. , in non-forest vegetation types).

Another application of empirical models is to project the future incidence of very large fires, often defined as the largest 5 to 10% of fires or fires >5000 ha. Barbero et al. ( 2015 ) projected that the annual probability of very large fires will increase by a factor of 4 in 2041 to 2070 compared to 1971 to 2000. Projections by Davis et al. ( 2017 ) suggested that the proportion of forests highly suitable for fires >40 ha will increase by >20% in the next century for most of Oregon and Washington, but less so for the Coast Range and Puget Lowlands. The largest projected increases were in the Blue Mountains, Klamath Mountains, and East Cascades. The number of fires that escape initial attack will also likely increase (Fried et al. 2008 ).

Few empirical model projections are available for future fire severity. Using empirical models, Parks et al. ( 2016a ) suggested that fire severity in a warming climate may not change significantly in the Northwest, because fuels limit fire severity. However, altered fire severity will depend partly on vegetation composition and structure (as they affect fuels), and climate change is expected to alter vegetation composition and structure both directly and indirectly (through disturbance). Empirical models do not account for these potential changes in vegetation and fuels (among other limitations; see McKenzie and Littell 2017 ). In the near term, high stem density as a result of fire exclusion and past management may increase fire severity in dry, historically frequent-fire forests (Haugo et al. 2019 ).

Fire projections by mechanistic models

Mechanistic models allow for exploration of potential interactions between vegetation and fire under changing and potentially novel climate. Mechanistic models can also account for elevated carbon dioxide concentration on vegetation, which could result in increased vegetation productivity (and fuel loading). Examples of mechanistic models that simulate fire include dynamic global vegetation models, such as MC1 (Bachelet et al. 2001 ), LANDIS-II (Scheller and Mladenoff 2008 ), and Fire-BioGeoChemical (Fire-BGC; Keane et al. 1996 ).

Using the MC1 dynamic global vegetation model for the western three quarters of Oregon and Washington, Rogers et al. ( 2011 ) projected a 76 to 310% increase in annual area burned and a 29 to 41% increase in burn severity (measured as aboveground carbon consumed by fire) by the end of the twenty-first century, with the degree of increase depending on climate scenario. These projected changes were largely driven by increased summer drought. Under a hot and dry climate scenario (with more frequent droughts), large fires were projected to occur throughout the twenty-first century (including the early part), primarily in mesic forests west of the Cascade crest.

Using the MC2 model (an updated version of MC1), Sheehan et al. ( 2015 ) also projected increasing fire activity in Idaho, Oregon, Washington, and western Montana. Mean fire return interval was projected to decrease across all forest-dominated subregions, with or without fire suppression. Projected decreases in mean fire interval were as high as 82% in the interior subregions without fire suppression; projected decreases in mean fire interval for the westernmost subregion were as high as 48% without fire suppression.

The MC1 and MC2 models have also been calibrated and run for smaller subregions in the Northwest. For the Willamette Valley, Turner et al. ( 2015 ) projected (under a high temperature increase scenario) increased fire frequency, with average area burned per year increasing by a factor of nine relative to the recent historical period (1986 to 2010); area burned over the recent historical period was very low (0.2% of the area per year). For a western Washington study region, MC2 projected a 400% increase in annual area burned in the twenty-first century compared to 1980 to 2010 (Halofsky et al. 2018a ). Although the projected average annual area burned was still only 1.2% of the landscape, some fire years were very large, burning 10 to 25% of the study region.

The MC1 model projected increased fire frequency and extent in forested lands east of the Cascade crest (Halofsky et al. 2013 ; Halofsky et al. 2014 ). Fire was projected to burn more than 75% of forested lands several times between 2070 and 2100. On average, projected future fires burned the most forest under a hot, dry scenario. Applying the MC2 model to a larger south-central Oregon region, Case et al. ( 2019 ) suggested that future fire will become more frequent in most vegetation types, increasing most in dry and mesic forest types. For forested vegetation types, fire severity was projected to remain similar or increase slightly compared to historical fire severity.

The LANDIS-II model has been applied to the Oregon Coast Range in the Northwest. Creutzburg et al. ( 2017 ) found that area burned over the twenty-first century did not increase significantly with climate change compared to historical levels, but fire severity and extreme fire weather did increase.

Fire-BGC models have mostly been applied in the northern US Rocky Mountains, which overlaps with the Northwest. For northwestern Montana (Glacier National Park), Keane et al. ( 1999 ) used Fire-BGC in a warmer, wetter climate scenario to project higher vegetation productivity and fuel accumulations that contribute to more intense crown fires and larger fire sizes. Fire frequency also increased over a 250-year simulation period: fire rotation decreased from 276 to 213 years, and reburns occurred in 37% of the study area (compared to 17% under historical conditions). In drier locations (low-elevation south-facing sites), low-severity surface fires were more common, with fire return intervals of 50 years.

Mechanistic modeling suggests that fire frequency and area burned will increase in the Northwest. Fire severity may also increase, depending partly on forest composition, structure, and productivity over time. Warmer temperatures in winter and spring, and increased precipitation during the growing season (even early in the growing season), could increase forest productivity. This increase in productivity would maintain or increase fuel loadings and promote high-severity fires when drought and ignitions occur. In mechanistic model projections for the region, some of the largest increases in fire severity (Keane et al. 1999 ; Case et al. 2019 ) and the largest single fire years (Halofsky et al. 2013 ; Halofsky et al. 2018a ) occurred in wetter scenarios with increased forest productivity. Future increased fire frequency without increased vegetation productivity is likely to result in decreased fire severity because of reduction in fuels as well as the potential for type conversion to vegetation characterized by less woody biomass. However, in highly productive systems such as forests west of the Cascade crest, future fires will probably be high severity (as they were historically) and more frequent (Rogers et al. 2011 ; Halofsky et al. 2018a ).

Short-interval reburns

A reburn occurs when the perimeter of a recent past fire is breached by a subsequent fire, something that all fire-prone forests have experienced. In the Northwest, reburns in the early twentieth century were documented in some of the earliest forestry publications ( e.g. , Isaac and Meagher 1936 ). However, under a warming climate, increased frequency and extent of fire will increase the likelihood of reburns, increasing the need to understand how earlier fires affect subsequent overlapping fires and how forests respond to multiple fires. Recent concern about reburns centers on projections that short-interval, high-severity ( i.e. , stand-replacing) reburns may become more common (Westerling et al. 2011 ; Prichard et al. 2017 ). Multiple fires can interact as linked disturbances (Simard et al. 2011 ), whereby the first fire affects the likelihood of occurrence, size, or magnitude (intensity, severity) of a reburn. Multiple fires can also interact to produce compound disturbance effects (Paine et al. 1998 ), in which ecological response after a reburn is qualitatively different than after the first fire.

Effects of past fire on future fire occurrence

Interactions between past forest fires and the occurrence of subsequent fires are generally characterized by negative feedbacks: fires are less likely to start within or spread into recently burned areas ( i.e. , within the last 5 to 25 years) compared to similar areas that have not experienced recent fire. For example, lightning-strike fires within the boundary of recently burned areas in the US Rocky Mountains (Idaho, Montana) were less likely to grow to fires larger than 20 ha than were lightning-strike fires in comparable areas outside recent fire boundaries (Parks et al. 2016b ). This negative relation between past fires and likelihood of future fires is generally attributed to limits on ignition potential and initial spread of fires through fine woody fuels, which are sparse following fire. Fine fuels are consumed by the first fire and do not recover to sufficient levels until at least a decade later in many interior forest systems in the Northwest (Isaac 1940 ; Donato et al. 2013 ) and US Rocky Mountains (Nelson et al. 2016 , 2017 ). However, negative feedbacks can be short-lived (or non-existent) in productive west-side forests in the Northwest, where fuels are abundant in early-successional forests (Isaac 1940 ; Agee and Huff 1987 ; Gray and Franklin 1997 ).

Past fires in the northern US Rocky Mountains have also been effective at preventing the spread of subsequent fires into their perimeters (Teske et al. 2012 ; Parks et al. 2015 ). Similar results have been found in mixed-conifer forests of the interior Northwest, where past wildfire perimeters inhibited the spread of the 2007 Tripod Complex Fire in eastern Washington (Prichard and Kennedy 2014 ). This limitation of fire spread decreases with time. The probability that reburns will be inhibited by earlier fires is near 100% in the first year post fire, but is only 30% by 15 to 20 years post fire (Parks et al. 2015 ). However, extreme fire weather can dampen buffering effects of reburns at any interval between fires, such that past fire perimeters become less effective at inhibiting reburns during warm, dry, and windy conditions (Parks et al. 2015 ).

Effects of past fire on future fire severity

Fire severity (fire-caused vegetation mortality) in a reburn is affected by interactions among severity of the first fire, climate setting and forest type, interval between fires, and weather at the time of the reburn. Reburns are typically less severe when the interval between fires is shorter than 10 to 15 years (Parks et al. 2014 ; Harvey et al. 2016b ; Stevens-Rumann et al. 2016 ). After 10 to 15 years, the effects of past fires on reburn severity diverge in different ecological contexts.

In areas where tree and shrub regeneration is prolific following one severe fire ( e.g. , moist Douglas-fir forests, subalpine forests dominated by lodgepole pine, some mixed-conifer forests [ e.g. , southwest Oregon mixed conifer forests with a hardwood component]), fire severity can be greater in reburns than in comparable single burns once the interval between fires exceeds 10 to 12 years (Thompson et al. 2007 ; Harvey et al. 2016b ). In lower-elevation, drier, and more fuel-limited forests ( e.g. , ponderosa pine forests and woodlands, areas with slower woody plant establishment following fire), past fire limits future fire severity, often for 20 to 30 years (Parks et al. 2015 ; Harvey et al. 2016b ; Stevens-Rumann et al. 2016 ). In these lower-productivity forests, the severity of past fire has been found to be the best predictor of reburn severity (Parks et al. 2014 ; Harvey et al. 2016b ), but this is not necessarily the case in higher-productivity forests (Thompson et al. 2007 ; Stevens-Rumann et al. 2016 ). Surface fuel treatment followed by tree planting can greatly reduce the intensity of a reburn and allow most newly established trees to survive (Lyons-Tinsley and Peterson 2012 ).

Of particular concern for forest resilience is how and why forests may experience two severe fires in short succession. In the northern US Rocky Mountains, the likelihood of experiencing two successive stand-replacing fires ( i.e. , a severe fire followed by a severe reburn) is greatest (1) in areas with high post-fire regeneration capacity ( e.g. , higher-elevation subalpine forests on moist sites), and (2) when the reburn occurs during warm, dry conditions (Harvey et al. 2016b ). In high-productivity west-side forests of Oregon and Washington, the potential for two successive high-severity burns may always exist ( e.g. , Isaac 1940 ), but occurrence depends on ignition and low fuel moisture.

Effects of reburns on forest species composition and structure

Short-interval reburns can produce compound effects on tree regeneration, altering species composition in some cases and shifting to non-forest vegetation in others. For example, thin-barked species, which do not survive fire but instead regenerate from seed following fire-induced mortality ( e.g. , lodgepole pine), can face “immaturity risk” if the interval between one fire and a reburn is too short to produce a sufficient canopy seedbank (Keeley et al. 1999 ; Turner et al. 2019 ). In northern US Rocky Mountain systems, low- and moderate-severity reburns have shifted dominance from lodgepole pine toward thick-barked species that can resist fire, such as ponderosa pine (Larson et al. 2013 ; Stevens-Rumann and Morgan 2016 ).

In the western Cascades of southern Washington, areas that burned in the 1902 Yacolt Burn and subsequently reburned within 30 years were characterized by much lower conifer regeneration than areas that burned only once (Gray and Franklin 1997 ). However, in the Klamath and Siskiyou mountains of southwestern Oregon, a short-interval (15 years between fires), high-severity reburn had no compound effect on regeneration (two years post fire) of Douglas-fir, the dominant tree species (Donato et al. 2009b ), with no difference from areas that burned once at a longer interval (>100 years between fires). Plant species diversity and avian diversity were higher in reburns compared to once-burned areas, with hardwoods contributing to habitat diversity in the reburn areas (Donato et al. 2009b ; Fontaine et al. 2009 ).

The effects of reburns on post-fire conifer regeneration seem to depend on legacy trees that survive both fires, providing seed across fire events (Donato et al. 2009b ). In systems where legacy trees are rare ( i.e. , thin-barked species easily killed by fire) or where shrubs and hardwoods can outcompete trees for long durations, reburns are more likely to produce lasting compound effects on forest structure and composition, possibly resulting in a shift to non-forest vegetation.

Disturbance and stress interactions

Combinations of biotic and abiotic stressors, or stress complexes, will likely be major drivers of shifts in forest ecosystems with changing climate (Manion 1991 ). A warmer climate will affect forests directly through soil moisture stress and indirectly through increased extent and severity of disturbances, particularly fire and insect outbreaks (McKenzie et al. 2009 ).

Water deficit and disturbance interactions

Although water deficit (the condition in which potential summer atmospheric and plant demands exceed available soil moisture) is rarely fatal by itself, it is a predisposing factor that can exacerbate the forest stress complex (Manion 1991 ; McKenzie et al. 2009 ). Water deficit directly contributes to potentially lethal stresses in forest ecosystems by intensifying negative water balances (Stephenson 1998 ; Milne et al. 2002 ; Littell et al. 2008 ; Restaino et al. 2016 ). Water deficit also indirectly increases the frequency, extent, and severity of disturbances, especially wildfire and insect outbreaks (McKenzie et al. 2004 ; Logan and Powell 2009 ). These indirect disturbances alter forest ecosystem structure and function, at least temporarily, much faster than do chronic effects of water deficit ( e.g. , Loehman et al. 2017 ; Fig. 4 ).

Interactions among drought, insect outbreaks, and fire

During the past few decades, wildfires and insect outbreaks have affected a large area across the Northwest (Fig. 5 ). Increased area burned has been at least partly caused by extreme drought–wildfire dynamics, which will likely become more prominent as drought severity and area burned increase in the future (Parks et al. 2014 ; McKenzie and Littell 2017 ). Insect disturbance has likewise expanded across the Northwest since 1990, catalyzed by higher temperature and the prevalence of dense, low-vigor forests. Cambium feeders, such as bark beetles, are associated with prolonged droughts, in which tree defenses are compromised (Logan and Bentz 1999 ; Carroll et al. 2004 ; Hicke et al. 2006 ). Patches of fire–insect disturbance mosaic are starting to run into each other (Fig. 5 ), and similar to reburns, are an inevitable consequence of increasing disturbance activity, even in the absence of mechanistic links among disturbances.

Recent disturbances in the Northwest, USA, showing wildfire extent for 1984 to 2017 (orange), and insect and disease extent for 1997 to 2017 (brown). Data sources: Monitoring Trends in Burn Severity ( https://www.mtbs.gov ) and US Forest Service Insect and Disease Detection Survey ( https://www.fs.fed.us/foresthealth/applied-sciences/mapping-reporting/gis-spatial-analysis/detection-surveys.shtml ). Map credit: Robert Norheim

In a review of the fire–bark beetle literature, Hicke et al. ( 2012 ) noted that, despite varying research approaches and questions, much agreement existed on fire hazard (defined as changes to fuels and potential fire behavior) after bark beetle outbreaks. There was strong agreement that surface fire and torching potential increased during the gray phase ( e.g. , 5 to 10 years following outbreaks, when snags remain standing; but see Woolley et al. 2019 ), but that crown fire potential was reduced in this phase. Similarly, there was agreement that fire hazard was lower in the old phase ( i.e. , silver phase), which occurs one to several decades after outbreak, when beetle-killed snags have fallen, understory vegetation increases, and seedlings establish. However, there was disagreement regarding fire potential during the red phase (0 to 4 years after outbreak initiation), when trees retain their drying needles and changes in foliar chemistry can increase flammability. Many studies have concluded that during this approximately 1- to 4-year period, fire hazard increases (Klutsch et al. 2011 [but see Simard et al. 2011 ], Hoffman et al. 2012 , Jolly et al. 2012 ; Jenkins et al. 2014 ). Fire hazard has been found to increase as the proportion of the stand killed by bark beetles increases, regardless of forest type (Page and Jenkins 2007 ; DeRose and Long 2009 ; Hoffman et al. 2012 ).

Concern has also risen as to whether fire occurrence and severity will increase following outbreaks of bark beetles ( e.g. , Hoffman et al. 2013 ), although empirical support for such interactions has been lacking (Parker et al. 2006 ; Hicke et al. 2012 ). Insect outbreaks have not been shown to increase the likelihood of fire or area burned (Kulakowski and Jarvis 2011 ; Flower et al. 2014 ; Hart et al. 2015 ; Meigs et al. 2015 ). Further, when fire occurs in post-outbreak forests, most measures of fire severity related to fire-caused vegetation mortality are generally similar between beetle-affected forests and areas that were unaffected by pre-fire outbreaks. Field studies in Oregon showed that burn severity (fire-caused vegetation mortality) was actually lower in lodgepole pine forests affected by mountain pine beetle (MPB; Dendroctonus ponderosae Hopkins) than analogous unaffected forests that burned (Agne et al. 2016 ). In an analysis of recent (1987 to 2011) fires across the Northwest, Meigs et al. ( 2016 ) also found that burn severity (from satellite-derived burn severity indices) was lower in forests with higher pre-fire insect outbreak severity.

Field studies in California, the Rocky Mountains, and interior British Columbia, Canada, conducted in a range of forest types have also explored the relationship between beetle outbreak severity (pre-fire basal area killed by beetles) and burn severity (fire-caused vegetation mortality), and suggest relatively minor effects of beetle outbreaks on burn severity. When fire burned through red stages (1 to 4 years post outbreak, when trees retain red needles) in dry conifer forests of California, small increases ( e.g. , 8 to 10% increase in fire-caused tree mortality) in burn severity were observed in areas of high outbreak severity (Stephens et al. 2018 ). In dry Douglas-fir forests in Wyoming, fire severity in the gray phase (4 to 10 years post outbreak) of Douglas-fir beetle ( Dendroctonus pseudotsugae Hopkins) outbreak was unaffected by beetle outbreak severity (Harvey et al. 2013 ). Similar results of minimal beetle effect on fire severity were reported in gray-stage spruce–fir forests in Colorado, USA (Andrus et al. 2016 ). In lodgepole pine-dominated forests affected by MPB, outbreak effects on burn severity differed by weather and stage of outbreak. For example, in both green and red phases (when most beetle-killed trees retained crowns fading from green to red), fire severity increased with pre-fire beetle outbreak severity under moderate but not extreme ( e.g. , hot, dry, windy) weather (Harvey et al. 2014a ). Conversely, in the red and gray stages, fire severity increased with pre-fire outbreak severity under extreme but not moderate weather (Harvey et al. 2014b ).

In British Columbia, gray-stage post-outbreak stands did not burn more severely than unaffected stands for most measures of burn severity (Talucci et al. 2019 ). The effects of beetle outbreaks on fire severity in forest types typified by stand-replacing fire regimes seem to be overall variable and minor, especially given that such forest types are inherently characterized by severe fire. The key exception to the otherwise modest effects of pre-fire beetle outbreaks on burn severity is the effect of deep wood charring and combustion on beetle-killed snags that burn. This effect has been reported across stages and forest type when measured, and consistently increases with pre-fire beetle outbreak severity (Harvey et al. 2014b ; Talucci et al. 2019 ). Because fire intensity and thus severity are driven by topography, weather, and fuels, beetle-outbreak-induced changes to fuel structures may play a minor role in affecting fire severity. In all cases in studies above where topography and weather were quantified, fire severity responded strongly and consistently to these factors irrespective of pre-fire beetle outbreaks.

In the Northwest, lodgepole pine forests have been affected by MPB outbreaks, with high mortality in some locations ( e.g. , Okanogan-Wenatchee Forest; Fig. 6 ). Widely distributed at mid to higher elevations in the Rocky Mountains, lodgepole pine is the dominant species over much of its range there, forming nearly monospecific stands. In the Northwest, lodgepole pine occurs at mid to higher elevations in the Cascade Range and eastward, and monospecific stands are limited to early seral stages and specific soil conditions ( e.g. , Pumice Plateau in central Oregon). In some populations in the Northwest, lodgepole pine forests have also adapted to stand-replacing fires via cone serotiny.

Number of trees killed by beetles in Okanogan-Wenatchee National Forest, Washington, USA, from 1980 to 2016. Data source: C. Mehmel, Okanogan-Wenatchee National Forest, Washington, USA

Bark beetle outbreaks and subsequent fire may interact to affect post-fire forest recovery, but results differ depending on the dominant regeneration mechanism of the tree attacked by beetles. Species with a persistent canopy seedbank, such as lodgepole pine, are minimally affected by compound disturbances between beetle outbreaks and fire. For example, in the Cascade Range and Rocky Mountains, areas that experienced beetle outbreaks prior to fire had similar levels of post-fire lodgepole pine seedling establishment compared to areas that had fire only (Harvey et al. 2014a ; Harvey et al. 2014b ; Edwards et al. 2015 ; Agne et al. 2016 ). Species such as Douglas-fir, which do not have a persistent canopy seedbank, have been shown to have lower post-fire seedling establishment in areas affected by Douglas-fir beetle outbreaks and fire (Harvey et al. 2013 ), although effects may be transient and disappear with time since fire (Stevens-Rumann et al. 2015 ).

Interactions among fungal pathogens and other stressors

The effects of weather and climate on fungal pathogens vary by species, with the spread of some pathogens facilitated by drought and others by wet periods (Klopfenstein et al. 2009 ; Sturrock et al. 2011 ; Ayres et al. 2014 ). Forests with low vigor and physiologically stressed trees ( e.g. , dense stands) are generally more susceptible to fungal pathogens. In the Northwest, a wide range of root rots and other native fungal pathogens exists in all forest types. For example, on the west side of the Cascade Range, laminated root rot ( Phellinus weirii [Murrill] Gilb.) is widespread, causing small pockets of mortality in Douglas-fir (Agne et al. 2018 ). However, no evidence exists that this pathogen has been or will be accelerated by a warmer climate. Other pathogens, such as Swiss needle cast ( Phaeocryptopus gaeumannii [T. Rohde] Petrak), may be favored by warmer and wetter winters (Agne et al. 2018 ). Fungal pathogens stress trees and may increase susceptibility to insect infestations. For example, Douglas-fir beetle is closely associated with laminated root rot centers in forests on the west side of the Cascades in Oregon and Washington (Goheen and Hansen 1993 ). Overall, interactions between fungal pathogens and fire with climate change are uncertain.

Stress complexes and forest mortality

Recent large-scale tree mortality events in the Southwest (Breshears et al. 2005 ), Texas (Schwantes et al. 2016 ), and California, USA (Young et al. 2017 ), have been caused by multi-year droughts weakening trees, followed by various beetle species acting as the mortality agents. It is likely that more intense and longer droughts will increase in the future under changing climate (Trenberth et al. 2014 ), and interactions between drought and other disturbance agents are likely to cause tree mortality. As noted above, fungal pathogens may contribute to increasing insect outbreaks (Goheen and Hansen 1993 ), along with increasing temperatures, shorter winters, and tree stress. Fire-caused tree mortality will also likely be affected by interacting disturbances. In some cases, fire severity has been marginally higher in areas affected by beetle mortality (Harvey et al. 2014a ; Harvey et al. 2014b ; Stephens et al. 2018 ). However, empirical studies examining the effects of large-scale tree mortality events on fire behavior are limited (Stephens et al. 2018 ). Modeling studies suggest that fire rate of spread may increase after mortality events ( e.g. , Perrakis et al. 2014 ).

Effects of changing disturbance regimes on forest structure and composition

In Northwest forest ecosystems, warming climate and changing disturbance regimes are likely to lead to changes in species composition and structure, probably over many decades. In general, increased fire frequency will favor plant species with life history traits that allow for survival with more frequent fire (Chmura et al. 2011 ). These include (1) species that can resist fires ( e.g., thick-barked species such as Douglas-fir, western larch [ Larix laricina Nutt.], and ponderosa pine); (2) species with high dispersal ability that can establish after fires ( e.g., Douglas-fir); and (3) species with serotinous cones that allow seed dispersal from the canopy after fire ( e.g., lodgepole pine) (Rowe 1983 ; Agee 1993 ).

In the forest understory, increased fire frequency and extent will likely create more opportunities for establishment by invasive species (Hellmann et al. 2008 ). Species that can endure fires (sprouters) and seedbank species (evaders) are also likely to increase with more frequent fire. For example, sprouting shrubs and hardwoods are prolific after fire in southwest Oregon (Halofsky et al. 2011). However, high-intensity fire can consume or kill seeds stored in the upper soil layers and kill shallow belowground plant parts, and repeated fires at short intervals can deplete seed stores and belowground plant resources (Zedler et al. 1983 ).

More frequent fire will likely decrease abundance of avoider species, including shade-tolerant species, species with thin bark, and slow invaders after fire (Chmura et al. 2011 ). Forest stands composed primarily of fire-susceptible evader species, such as western hemlock, subalpine fir, and Engelmann spruce ( Picea engelmannii Parry ex Engelm.), will likely have higher mortality for a given fire intensity than stands composed of more fire-resistant species, such as mature Douglas-fir and western larch. If fire-sensitive species are not able to re-seed into burned areas and re-establish themselves (because of short fire intervals, competition, or harsh conditions for seedling establishment), these species can be lost from a site (Stevens-Rumann and Morgan 2016 ). Direct mortality or lack of regeneration of fire-sensitive species with more frequent fire will favor more fire-adapted species that can survive fire or regenerate after fire. For example, in southwest Oregon, shrubs and hardwoods are likely to increase in abundance with increased fire frequency and reduced conifer regeneration in some locations (Tepley et al. 2017 ).

Changes in disturbance regimes can influence the structure of forests at multiple spatial scales (Reilly et al. 2018 ). Within forest stands, more frequent fire will likely decrease tree density in dry forests, and open savannas may increase in area. Forest understories may shift from being duff- or forb-dominated to shrub- or grass-dominated. Tree canopy base heights will likely increase as frequent fires remove lower branches. Across forested landscapes ( i.e., among stands), fire directly influences the spatial mosaic of forest patches (Agee 1993 ). More extreme fire conditions with climate change may initially lead to larger and more frequent fires, resulting in larger burn patch sizes and greater landscape homogeneity (Harvey et al. 2016a ). More frequent severe fire will likely decrease forest age, the fraction of old-growth forest patches, and the landscape connectivity of old-growth forest patches (Baker 1995 ; McKenzie et al. 2004 ). However, more frequent low- and mixed-severity fires may eventually reduce fuels in drier forest ecosystems ( e.g., dry mixed conifer), leading to lower-intensity fires and a finer-scale patch mosaic (Chmura et al. 2011 ).

Effects of climate change on post-fire processes

Forest regeneration.

Changing climate and fire frequency, extent, and severity are likely to influence forest regeneration processes, thus affecting the structural and compositional trajectories of forest ecosystems. First, climate change is expected to affect regeneration through increased fire frequency. As fire-free intervals shorten, the time available for plants to mature and produce seed before the next fire will be limited. Such changes in fire-free intervals can have significant effects on post-fire regeneration, because different plants have varied adaptations to fire. Species that resprout following fire may decline in density, but species that are fire-killed and thus require reproduction from seed may be locally eliminated.

Second, climate change may result in increased fire severity. If the size of high-severity fire patches increases, seed sources to regenerate these patches will be limited. Regeneration of non-serotinous species will require long-distance seed dispersal and may be slower in large, high-severity patches (Little et al. 1994 ; Donato et al. 2009a ; Downing et al. 2019 ).

Third, climate change will likely result in increased forest drought stress. Warmer temperatures, lower snowpack, and increased evapotranspiration will increase summer drought stress. Warmer and drier conditions after fire events may cause recruitment failures, particularly at the seedling stage (Dodson and Root 2013 ). In this way, fire can accelerate species turnover when climatic conditions are unfavorable for establishment of dominant species (Crausbay et al. 2017 ) and seed sources are available for alternative species.

Regeneration in dry forests in the Northwest ( e.g., ponderosa pine) may be particularly sensitive to changing climate. Hotter and drier sites ( e.g., on southwestern aspects) may be particularly at risk for regeneration failures (Nitschke et al. 2012 ; Dodson and Root 2013 ; Donato et al. 2016 ; Rother and Veblen 2017 ; Tepley et al. 2017 ). High soil surface temperatures can also cause mortality (Minore and Laacke 1992 ). Forest structure (mainly shade from an existing canopy) can ameliorate harsh conditions and allow for regeneration (Dobrowski et al. 2015 ). However, after high-severity disturbance, dry forests at the warm and dry edges of their distribution (ecotones) may convert to grasslands or shrublands in a warming climate (Johnstone et al. 2010 ; Jiang et al. 2013 ; Savage et al. 2013 ; Donato et al. 2016 ; Stevens-Rumann et al. 2017 ).

In the Klamath-Siskiyou ecoregion of southwestern Oregon and northern California, Tepley et al. ( 2017 ) found that conifer regeneration was reduced by low soil moisture after fires. With lower soil moisture, greater propagule pressure (smaller high-severity patches with more live seed trees) was needed to achieve a given level of regeneration. This suggests that, at high levels of climatic water deficit, even small high-severity patches are at risk for low post-fire conifer regeneration. Successive fires could further limit conifer seed sources, thus favoring shrubs and hardwoods.

Germination of ponderosa pine is favored by moderate temperatures and low moisture stress, and survival increases when maximum temperatures are warm (but not hot) and when growing season rainfall is above average (Petrie et al. 2016 ; Rother and Veblen 2017 ). Empirical modeling by Petrie et al. ( 2017 ) projected that, with warming temperature in the middle of the twenty-first century, regeneration potential of ponderosa pine may increase slightly on many sites. However, by the end of the century, with decreased moisture availability, regeneration potential in the Northwest decreased by 67% in 2060 to 2099 compared to 1910 to 2014. In the eastern Cascade Range of Oregon, Dodson and Root ( 2013 ) found decreasing ponderosa pine regeneration with decreasing elevation and moisture availability, suggesting that moisture stress would limit regeneration.

Several studies in the Rocky Mountains have also found decreased post-fire regeneration with increased water deficits on drier, lower-elevation sites (Rother et al. 2015 ; Donato et al. 2016 ; Stevens-Rumann et al. 2017 ; Davis et al. 2019 ). Donato et al. ( 2016 ) found decreased regeneration of Douglas-fir 24 years after fire on drier, lower-elevation sites compared to more mesic sites at higher elevations. Regeneration declined with higher burn severity and was minimal beyond 100 to 200 m from a seed source. Similarly, Harvey et al. ( 2016c ) found that post-fire tree seedling establishment decreased with greater post-fire drought severity in subalpine forests of the northern US Rocky Mountains; post-fire subalpine fir and Engelmann spruce regeneration were both negatively affected by drought. Davis et al. ( 2019 ) modeled post-fire recruitment probability for ponderosa pine and Douglas-fir on sites in the Rocky Mountains, and found that recruitment probability decreased between 1988 and 2015 for both species, suggesting a decline in climatic suitability for post-fire tree regeneration.

In a study of annual regeneration and growth for 10 years following wildfire in the eastern Cascade Range of Washington, Littlefield ( 2019 ) found that establishment rates of lodgepole pine (and other species) were highest when growing seasons were cool and moist. A lagged climate signal was apparent in annual growth rates, but standardized climate–growth relationships did not vary across topographic settings, suggesting that topographic setting did not decouple site conditions from broader climatic trends to a degree that affected growth patterns. These results underscore the importance of favorable post-fire climatic conditions in promoting robust establishment and growth while highlighting the importance of topography and stand-scale processes ( e.g., seed availability and delivery). Although concerns about post-fire regeneration failure may be warranted under some conditions, failure is not a general phenomenon in all places and at all times (Littlefield 2019 ).

If warming climate trends continue as projected, without (or even with) tree planting, loss of forests may occur on the driest sites in the Northwest (Donato et al. 2016 ; Harvey et al. 2016c ; Stevens-Rumann et al. 2017 ), particularly east of the Cascade crest and in southwestern Oregon. Individual drought years are not likely to alter post-fire successional pathways, especially if wet years occur between dry years (Tepley et al. 2017 ; Littlefield 2019 ). Recruitment of conifers following a disturbance can require years to decades in the Northwest (Little et al. 1994 ; Shatford et al. 2007 ; Tepley et al. 2014 ). Thus, shrubs or grasses may dominate during drought periods, but conifers could establish and overtop shrubs and grasses during wetter and cooler periods (Dugan and Baker 2015 ; Donato et al. 2016 ).

Management actions

More frequent and larger wildfires in Northwest forests will likely be a major challenge facing resource managers of public and private lands in future decades (Peterson et al. 2011a ). Adapting forest management to climate change will help forest ecosystems transition to new conditions, while continuing to provide timber, water, recreation, habitat, and other benefits to society. Starting the process of adaptation now, before the marked increase in wildfire expected by the mid twenty-first century, will likely improve options for successful outcomes. Fortunately, some current forest management practices, including stand density management and surface fuel reduction in dry forests, and control of invasive species, are “climate smart” because they increase resilience to changing climate and disturbances (Peterson et al. 2011a ; Peterson et al. 2011b ).

Resource managers will likely be unable to prevent increasing broad-scale trends in area burned with climate change, but fuel treatments can decrease fire intensity and severity locally (Agee and Skinner 2005 ; Peterson et al. 2005 ). In drought- and fire-prone forests of the Northwest ( e.g., ponderosa pine and dry mixed-conifer forests east of the Cascades and in southwestern Oregon), reducing forest density can decrease crown fire potential (Agee and Skinner 2005 ; Safford et al. 2012 ; Martinson and Omi 2013 ; Shive et al. 2013 ), and negative effects of drought on tree growth (Clark et al. 2016 ; Sohn et al. 2016 ). Even in wetter forest types, reducing stand density can increase water availability, tree growth, and tree vigor by reducing competition (Roberts and Harrington 2008 ). Decreases in forest stand density, coupled with hazardous fuel treatments, can also increase forest resilience to wildfire in dry forest types (Agee and Skinner 2005 ; Stephens et al. 2013 ; Hessburg et al. 2015 ).

In dry forests, forest thinning prescriptions may need to reduce forest density to increase forest resistance and resilience to fire, insects, and drought (Peterson et al. 2011a ; Sohn et al. 2016 ). For example, in anticipation of a warmer climate and increased fire frequency, managers in Okanogan-Wenatchee National Forest in eastern Washington are currently basing stocking levels for thinning and fuel treatments on the next driest forest type. Thinning and fuel treatments could also be prioritized in (1) locations where climate change effects, particularly increased summer drought, are expected to be most pronounced ( e.g., on south-facing slopes); (2) high-value habitats; and (3) high-risk locations such as the wildland–urban interface. Fuel treatments must be maintained over time to remain effective (Agee and Skinner 2005 ; Peterson et al. 2005 ). Insufficient financial resources, agency capacity constraints, and air quality constraints on prescribed burning are harsh realities that will in most cases limit the extent of fuel treatments (Melvin 2018 ), necessitating strategic implementation of treatments in locations where fuel reduction will maximize ecological, economic, and political benefits.

Fewer options exist for reducing fire severity in wetter, high-elevation and coastal forests of the Northwest, historically characterized by infrequent, stand-replacement fire regimes (Halofsky et al. 2018b ). In these ecosystems, thinning and hazardous fuel treatments are unlikely to significantly affect fire behavior, because fires typically occur under extreme weather conditions ( i.e., during severe drought). However, managers may consider installing fuel breaks around high-value resources, such as municipal watersheds, key wildlife habitats, and valuable infrastructure, to reduce fire intensity and facilitate fire suppression efforts (Syphard et al. 2011 ). In addition, ecosystem resilience to a warmer climate is likely to improve by promoting landscape heterogeneity with diverse species and stand structures, and by reducing the effects of existing non-climatic stressors on ecosystems, such as landscape fragmentation and invasive species (Halofsky et al. 2018b ).

The future increase in fire will put late-successional forest at risk, potentially reducing habitat structures (large trees, snags, downed wood) that are important for many plant and animal species. In dry forests, some structures can be protected from fire by thinning around them and reducing organic material at their base (Halofsky et al. 2016 ). To increase habitat quality and connectivity, increasing the density of these structures may be particularly effective in younger forests, especially where young forests are in close proximity to late-successional forest.

Regeneration failures after fire are a risk with changing climate, particularly for drier forests. A primary method to help increase natural post-fire regeneration is to increase seed sources by both reducing fire severity (through fuel treatments and prescribed fire) and increasing the number of live residual trees (Dodson and Root 2013 ). In areas adjacent to green trees, natural regeneration may be adequate. In locations farther than 200 m from living trees, managers may want to supplement natural regeneration with planting where costs are not prohibitive because of remoteness or topography (North et al. 2019 ). Where post-fire planting is desirable, managers may consider changes from current practices. For example, they may want to consider lowering stocking density and increasing the spatial heterogeneity of plantings to increase resilience to fire and drought (North et al. 2019 ). Planting seedlings on cooler, wetter microsites will also likely help to increase survival (Rother et al. 2015 ). Managers may also consider different genetic stock than has been used in the past to increase seedling survival (Chmura et al. 2011 ). Tools such as the Seedlot Selection Tool ( https://seedlotselectiontool.org/sst ) can help identify seedling stock that will be best adapted to a given site in the future.

In general, regeneration in the driest topographic locations may be slower in a warming climate than it has been in the past. Some areas are likely to convert from conifer forest to hardwoods or non-forest (shrubland or grassland) vegetation, particularly at lower treeline. Managers may need to consider where they will try to forestall change and where they may need to allow conversions to occur (Rother et al. 2015 ).

Finally, collaboration among many groups—land management agencies, rural communities, private forest landowners, tribes, and conservation groups—is needed for successful adaptation to the effects of a warmer climate on wildfire (Joyce et al. 2009 ; Spies et al. 2010 ; Stein et al. 2013 ). Working together will ensure a common vision for stewardship of forest resources, and help produce a consistent, effective strategy for fuel treatments and other forest practices across large forest landscapes.

Uncertainties and future research needs

Changing disturbance regimes will accompany climate change in the Northwest (Tables 1 , 2 and 3 ). However, uncertainties remain, many related to future human behavior relative to greenhouse gas emissions, the rate and magnitude of climate change, and effects on vegetation and fire regimes. Human activities will also affect fire through land use and management, fire ignitions, and fire suppression, all of which are difficult to predict. For example, societal priorities may change, affecting forest management and vegetation conditions. Fire suppression is likely to continue in the future, but may become less effective under more extreme fire weather conditions (Fried et al. 2008 ), affecting area burned.

Historical relationships between climate and fire in the Northwest indicate that the ENSO and PDO can influence area burned. However, it is unclear how climate change will affect these modes of climatic variability or how they may interact with the effects of climate change on natural resources; global climate models differ in how these cycles are represented and in how they are projected to change. The frequency and persistence of high blocking ridges in summer (which divert moisture from the region) will also affect fire frequency and severity in the region, and climate change may affect the frequency of these blocking ridges (Lupo et al. 1997 ).

The lack of fire over the last few centuries in forests with low-frequency and high-severity fire regimes creates uncertainty in fire projections for the future. Although the likelihood of a large fire event in these forests is low, if large fire events start occurring as frequently as some models project ( e.g., Rogers et al. 2011 ), then major ecological changes are likely. Updating models as events occur over time may help to adjust projections in the future.

Shifts in forest productivity and composition are highly likely to occur with climate change in the region, which could affect fuel levels. However, it is uncertain how carbon dioxide fertilization will interact with moisture stress and disturbance regimes to affect forest productivity (Chmura et al. 2011 ) and thus fuel levels. Increased forest productivity, combined with hot and dry conditions in late summer, would likely produce large and severe fires (Rogers et al. 2011 ). Continued research on the potential effects of carbon dioxide fertilization on forest productivity will help to improve fire severity projections.

Other high-priority research needs include determining forest ecosystem response to multiple disturbances and stressors ( e.g., effects of repeated fire and drought on forest regeneration), and determining post-fire regeneration controls across a range of forest types and conditions. Identifying locations where vegetation type shifts ( e.g., forest to woodland or shrubland) are likely because of changing climate and disturbance regimes will help managers determine where to prioritize efforts. Managers will also benefit from evaluation of pre- and post-fire forest treatments to increase resilience or facilitate transition to new conditions in different forest types.

Although this synthesis is focused on the effects of climate change on fire and vegetation, many secondary effects are expected for natural resources and ecosystem services, some of which are already occurring. Climate change is reducing snowpack (Mote et al. 2018 ) and affecting hydrologic function in the Northwest, including more flooding in winter and lower streamflow in summer (Luce and Holden 2009 ). Higher stream temperatures are degrading cold-water fish habitat (Isaak et al. 2010 ). Altered vegetation and snowpack are expected to have long-term implications for animal habitat (Singleton et al. 2019 ). Recreational opportunities (Hand et al. 2019 ), infrastructure on public lands (Furniss et al. 2018 ), and cultural values (Davis 2018 ) will likely also be affected by changing climate, fire, and other disturbances.

Uncertainties associated with climate change require an experimental approach to resource management; using an adaptive management framework can help address uncertainties and adjust management over time. In the context of climate change adaptation, adaptive management involves: (1) defining management goals, objectives, and timeframes; (2) analyzing vulnerabilities and determining priorities; (3) developing adaptation options; (4) implementing plans and projects; and (5) monitoring, reviewing, and adjusting (Millar et al. 2014 ). Scientists and managers can work together to implement an adaptive management framework and ensure that the best available science is used to inform management actions on the ground.

Availability of data and materials

Please contact the corresponding author for data requests.

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Acknowledgements

We thank D. Donato, L. Evers, B. Glenn, M. Johnson, V. Kane, M. Reilly, and three anonymous reviewers for providing helpful suggestions that improved the manuscript. P. Loesche provided valuable editorial assistance, J. Ho assisted with literature compilation and figures, and R. Norheim developed several maps.

Funding was provided by the US Department of the Interior, Northwest Climate Adaptation Science Center, and the US Forest Service Pacific Northwest Research Station and Office of Sustainability and Climate. None of the funding bodies played any role in the design of the study, interpretation of data, or writing the manuscript.

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Jessica E. Halofsky

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Contributions

JH led the study and contributed to information collection, analysis, and interpretation, and co-wrote the paper. DP contributed to information collection, analysis, and interpretation, and co-wrote the paper. BH contributed to information collection, analysis, and interpretation, and co-wrote the paper. All authors read and approved the final manuscript.

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J. Halofsky is a research ecologist; D. Peterson is a professor of forest biology; B. Harvey is an assistant professor of forest ecology.

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Halofsky, J.E., Peterson, D.L. & Harvey, B.J. Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA. fire ecol 16 , 4 (2020). https://doi.org/10.1186/s42408-019-0062-8

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Effect of Forest Therapy on Depression and Anxiety: A Systematic Review and Meta-Analysis

Poung-sik yeon.

1 Department of Forest Sciences, Chungbuk National University, Cheongju 28644, Korea; rk.ca.kubgnuhc@llew

Jin-Young Jeon

2 Graduated Department of Forest Therapy, Chungbuk National University, Cheongju 28644, Korea; moc.revan@bb-tserof (J.-Y.J.); rk.ca.kubgnuhc@oesgnoeym (M.-S.J.); rk.ca.kubgnuhc@9105sjwka (G.-M.M.); moc.revan@0256sudkr (G.-Y.K.); moc.liamg@12nahmg (K.-M.H.); ten.liamnah@evolomaey (M.-J.S.)

Myeong-Seo Jung

Gyeong-min min, ga-yeon kim, kyung-mi han, min-ja shin, seong-hee jo.

3 National Center for Forest Therapy, Yeongju 36043, Korea; rk.ca.kubgnuhc@tneroj

Jin-Gun Kim

4 Korea Forest Therapy Forum Incorporated Association, Cheongju 28644, Korea

Won-Sop Shin

Associated data.

Not applicable.

This systematic review and meta-analysis aimed to summarize the effects of forest therapy on depression and anxiety using data obtained from randomized controlled trials (RCTs) and quasi-experimental studies. We searched SCOPUS, PubMed, MEDLINE(EBSCO), Web of science, Embase, Korean Studies Information Service System, Research Information Sharing Service, and DBpia to identify relevant studies published from January 1990 to December 2020 and identified 20 relevant studies for the synthesis. The methodological quality of eligible primary studies was assessed by ROB 2.0 and ROBINS-I. Most primary studies were conducted in the Republic of Korea except for one study in Poland. Overall, forest therapy significantly improved depression (Hedges’s g = 1.133; 95% confidence interval (CI): −1.491 to −0.775) and anxiety (Hedges’s g = 1.715; 95% CI: −2.519 to −0.912). The quality assessment resulted in five RCTs that raised potential concerns in three and high risk in two. Fifteen quasi-experimental studies raised high for nine quasi-experimental studies and moderate for six studies. In conclusion, forest therapy is preventive management and non-pharmacologic treatment to improve depression and anxiety. However, the included studies lacked methodological rigor and required more comprehensive geographic application. Future research needs to determine optimal forest characteristics and systematic activities that can maximize the improvement of depression and anxiety.

1. Introduction

Depression and anxiety are considered prevalent mental health problems worldwide. According to the World Health Organization [ 1 ], it is estimated that 4.4% of the world’s population suffers from depression, and 3.6% suffer from anxiety disorders. Namely, depression affects about 300 million people, and anxiety affects about 264 million people worldwide.

Recently, widespread psychological consequences of the coronavirus pandemic have been observed at individual, community, national, and international levels [ 2 ]. At a personal level, people are more likely to feel sick, dead, or helpless from the coronavirus infection and experience fear from lockdown-induced quarantine. As a result, mental health problems such as depression and anxiety are becoming more serious. For example, Salari et al. [ 3 ] performed a meta-analysis on the effects of COVID-19 on the spread of stress, anxiety, and depression. As a result, in five studies with a total sample size of 9074, the stress prevalence was 29.6% (95% CI: 24.3–35.4), in 17 studies with a sample size of 63,439, the anxiety prevalence was 31.9% (95% CI: 27.5–36.7), and in 14 studies with a sample size of 44,531, depression was 33.7% (95% CI: 27.5–4%.6%). In particular, common symptoms of depression can adversely affect health conditions due to sad moods, anxiety, insomnia, loss of vitality, and lack of interest in life [ 4 ]. In the worst case can lead to suicide.

Moreover, depression and anxiety are very closely related, and the coexistence rate diagnosed simultaneously is high. It has been reported that approximately 85% of patients with depression experience significant anxiety symptoms, while comorbid depression occurs in up to 90% of patients with anxiety disorder [ 5 ]. The coexistence of the two diseases increases the risk of suicide, so treatment of depression and anxiety and preventive management is crucial for public health.

Common treatments for depression and anxiety are pharmacotherapies, such as antidepressants and anti-anxiety drugs. They have advantages such as treatment accessibility and have been proven to improve depression and anxiety symptoms, but there are several disadvantages [ 6 ]. For example, the use of antidepressants can have secondary effects such as hypotension and constipation [ 7 ], decreased sexual function [ 8 ], gastrointestinal symptoms, weight gain, and metabolic abnormalities [ 9 ]. Additionally, the anti-anxiety drug can come with side effects such as insomnia, diarrhea, headaches, nausea, jitteriness, or restlessness [ 10 , 11 ]. In addition, pharmacological treatment has potential adverse effects such as the risk of dependence [ 12 , 13 ] and withdrawal symptoms [ 14 , 15 ]. Considering these disadvantages, non-pharmacological treatment can be performed. There is an abundance of evidence on the effectiveness of non-pharmacological intervention for depression and anxiety. The intervention included components of mindfulness-based therapy [ 16 , 17 ], cognitive behavioral therapy (CBT) [ 18 , 19 ], exercise [ 20 , 21 ], and yoga [ 22 , 23 ].

One of these non-pharmacological interventions is direct contact with nature. Human health benefits from exposure to forests vary and include recovery ability such as stress reduction and mental health improvement [ 24 , 25 ]. Forest therapy, also known as “forest bathing,” is a collection of activities to improve human health or welfare in a forest environment. There is quite a variety of methods applied to forest therapy. The critical element of forest therapy is recognition in the forest environment, including the five senses, which can be combined with meditation, forest walking, various recreational activities, and cognitive behavioral therapy [ 26 ].

In recent years, forest therapy and its estimated preventive effect are attracting more and more attention. Many previous studies have reported the positive effects of forest therapy on physiological and psychological health. For example, in terms of the physiological effects of forest therapy, previous studies have shown that forest therapy improves immune function by enhancing the activity of NK cells [ 27 ], lowering the concentration of cortisol which is a stress hormone [ 28 , 29 ], and balance the autonomic nervous system [ 30 , 31 , 32 ].

In terms of the psychological effect of forest therapy, it has been reported that forest therapy reduces psychological stress or mental fatigue and induces positive emotions. For example, Morita et al. [ 33 ] reported that staying and walking in the forest reduces hostility and depression, and further studies have shown that participants’ anxiety decreased. Furthermore, the amount of sleep improved after walking in the forest [ 34 ]. A study by Dolling et al. [ 35 ] of middle-aged people reported that both the group that conducted activities in the forest environment and the group that conducted indoor handicraft activities reduced fatigue and stress, and the self-health check-up score increased. Bielinis et al. [ 36 ] investigated the psychological effects of the forest environment by dividing the forest environment exposure group and the urban environment exposure group in winter for 62 college students. The results of the study showed that the interaction with the forest in winter had a significantly positive effect on the participants’ emotional and psychological recovery and vitality.

In addition, many studies have shown that natural environments such as forests positively affect mood states [ 37 , 38 , 39 ]. For example, Pretty et al. [ 37 ] reported that the participants’ mood and self-esteem improved considerably after the forest exercise. Joung et al. [ 38 ] investigated physiological and psychological reactions using near-infrared spectroscopy. The study results showed a more stable brain condition when viewing the forest landscape than the urban landscape, and negative sub-factors such as anger and fatigue were low, while vitality was high. A study by Song et al. [ 39 ] investigated female college students’ physiological and psychological effects while looking at the forest landscape while comparing exposure to the urban context and its impact. The results reported that looking at the forest landscape significantly reduces participants’ negative emotions and anxiety and increases positive emotions compared to exposure to the urban environment. Triguero-Mas et al. [ 40 ] also reported that when compared with responses to the urban environment, they found lower mood disturbance, salivary cortisol in the green exposure environment, and favorable changes in heart rate variability indicators in the blue exposure environment. As such, many previous studies have revealed the potential of forest therapy to improve depression and anxiety.

However, although many previous studies have reported that forest-based activities are practical for physiological and physiological health, studies exploring the direct link between forest therapy and depression and anxiety are insufficient. In the previous three systematic literature reviews, it was reported that forest therapy was an effective intervention in improving depression and anxiety. However, because meta-analysis was not conducted, the effect size of forest therapy on depression and anxiety could not be analyzed [ 41 , 42 , 43 ]. In addition, Kotera et al. [ 44 ] systematically reviewed and meta-analyzed 20 studies. As a result, only six studies related to depression and five studies related to anxiety were RCT study designs. Since meta-analysis was performed on a small sample size, the effect size was likely overestimated or underestimated. Therefore, this review aims to systematically prepare evidence-based data by integrating forest therapy’s contents and effects based on previous studies related to depression and anxiety.

2. Materials and Methods

2.1. literature search.

This systematic review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines [ 45 ] ( Appendix B ). We searched Scopus, PubMed, MEDLINE(EBSCO), Web of science, Embase, Korean Studies Information Service System, Research Information Sharing Service, and DBpia to identify relevant studies published from January 1990 to December 2020. The time frame was chosen because when we reviewed previous review papers [ 24 , 46 , 47 , 48 ] on the effects of forests on health, no literature was derived before 1990. So, we narrowed it from 1990 to 2020 in the search period. All search terms are listed in Appendix A , Table A1 . The language of the published article was limited to English and Korean. Our review’s flow chart is shown in Figure 1 .

Systematic Review and Meta-analysis process diagram (Adapted from ref. [ 49 ]).

2.2. Inclusion and Exclusion Criteria

This study is a systematic review and meta-analysis study to confirm the effect of forest therapy on anxiety and depression. The PICO-SD (Population, Intervention, Comparisons, Outcomes, Setting, Study design) framework was used to clarify the objectives of the review and facilitate the search strategy ( Table 1 ). The main research questions of this review were the following: (1) how effective is forest therapy in improving depressive symptoms and anxiety? And (2) what quantity and quality of evidence is reported?

Inclusion criteria based on PICO-SD (Population, Intervention, Comparison, Outcomes, Setting, and Study design).

To be eligible for further analysis, primary studies need to (1) report an empirical intervention study, using pre-and post-intervention measures, (2) use mental health measures for depression or anxiety, (3) studies including at least one control group, and (4) been published either in English or Korean. Exclusion criteria were (1) review articles, (2) studies not including humans, (3) no interventions, (4) were case studies or qualitative studies, (5) no direct exposure in the forest environments, and (6) no results presented.

2.3. Data Extraction

Five authors (J.G.; M.S.; K.M.H.; G.M.M.; G.Y.) independently first screened the titles and abstracts of articles identified by the search strategy and then retrieved and screened the full-texts of these articles. After the full-text screening, the eight authors (J.G.; J.Y.; M.S.; K.M.H.; G.M.M.; G.Y.; S.H.; M.J.) extracted data from all studies that met our eligibility criteria. Data were extracted on author name and publication year, study design, participants characteristics, sample size, age of participants, intervention description and characteristics (i.e., type, duration, time, and frequency), control description, and effects on outcomes ( Table 2 ). Any disagreements during screening or data extraction were resolved by another author (P.S.).

Characteristics of the 20 included studies.

Notes: RCT, Randomized controlled trial; BDI, Beck Depression Inventory; POMS, Profile of Mood States; T–A; tension-anxiety; D, depression; HRDS, Hamilton Rating Scale for Depression; CDI, Children’s Depression Inventory; GDSSF, Geriatric Depression Scale Short Form; MADRS, Montgomery-Asberg Depression Rating Scale; HADS, Hospital Anxiety and Depression Scale; CES-D, Center for Epidemiologic Studies Depression Scale; KDS, Korean Depression Scale; ZSDS, Zung Self-Rating Depression Scale; SRI, Stress Response Inventory; STAI, State-Trait Anxiety Inventory; HADS, Hospital Anxiety and Depression Scale; KPDSS, Korean Preschool Daily Stress Scale; y, years old; * Significant inra-group differences; # Significant inter-group differences; “↓”, indicators decline; N/A, no report. Underlined studies were written in Korean.

2.4. Risk-of-Bias (ROB) Assessment

Two authors (J.G. and J.Y.) independently assessed the ROB of RCTs, quasi-experimental studies using the revised Cochrane ROB tool for RCTs (ROB 2.0) [ 50 ], ROB in non-randomized studies of interventions (ROBINS-I) [ 51 ], respectively. Disagreements were discussed until a consensus was reached. The three ROB assessment tools used were composed of several categories: ROB 2.0 consists of five potential bias categories that are assessed as low ROB, some concerns, or high ROB utilizing a series of signaling questions. The categories are the randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported results. ROBINS-I comprises seven bias categories: baseline confounding, selection of participants, classification of interventions, deviation from intended interventions, missing data, measurement of outcomes, and selection of reported results; each is evaluated as low, moderate, serious, or critical ROB or no information. Assessment of each category mentioned above provided the basis for an overall ROB judgment for the included studies.

2.5. Meta-Analyses

All statistical analyses were performed using the Comprehensive Meta-Analysis 3.3 (Biostat, Englewood, NJ, USA). As for the effect size, standardized mean difference (SMD) was selected as the analysis method to compare outcome variables with different measurement tools or measurement units, and Cohen’s d tends to overestimate the effect size when the sample is small, so Hedges’s g to correct it was calculated as the effect size. The magnitude of the effect size was defined as small (0.2 to under 0.5), medium (0.5 to 0.8), or large (above 0.8) [ 52 ]. Cochrane’s chi-square test was performed to verify the heterogeneity of the integrated effect size, and the I-square value was calculated with a significance level of less than 5%. The magnitude of heterogeneity was interpreted as follows: low (I 2 = 0 to 24%), moderate (I 2 = 25 to 49%), large (I 2 = 50 to 74%), or extreme (I 2 = 75 to 100%) heterogeneity [ 53 ].

This study confirmed heterogeneity between analysis studies, and a random-effects model was applied. The statistical meaning of effect size (d) was determined by the total effect test and 95% confidence interval (CI) and was based on the significance level of 5%. Subject characteristics, activity types, intervention time, and intervention frequency, which are parameters that can cause heterogeneity between individual studies, were set as modulating variables. In the meta-analysis, heterogeneity was confirmed and adjusted using the CMA 3.3 program, meta-ANOVA was performed in categorical cases according to the attributes of the adjustment variable, and meta-regression was applied to analyze the moderating effect.

Publication bias was assessed using funnel plots and Egger’s regression asymmetry test, used only when at least ten studies were included in a meta-analysis [ 53 ]. We considered p < 0.05 in asymmetrical funnel plots to indicate potential publication bias.

3.1. Search Results

Figure 2 summarizes the selection process for the 20 studies in this review. After initially identifying 164,587 records (Scopus, n = 20,711; PubMed, n = 5777; MEDLINE, n = 352; Web of science, n = 14,138; Embase, n = 853; RISS, n = 5277; DBpia, n = 117,479). The full text of 13,698 potential studies was then screened and extracted for further details. We removed 4379 duplicates and then reviewed titles and abstracts of 9319 studies. We excluded 9299 publications for the following reasons: (1) 9211 records not based on forest therapy-related intervention evaluating depression or anxiety outcomes, (2) 62 records not RCTs study design or quasi-experiments, such as cross-over design, one group pretest-posttest design, and qualitative studies, (3) 14 records were indirectly exposed in the forest, such as Virtual reality (VR) and 2D-image, and (4) 12 records did not provide results data.

PRISMA flow chart of study selection.

We finally included five RCTs [ 54 , 55 , 56 , 57 , 58 ] and 15 quasi-experiments [ 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 ] in the qualitative analysis and meta-analysis.

3.2. Characteristics of Selected Studies

Table 2 summarizes the characteristics of the 20 selected studies conducted in the Republic of Korea except one conducted in Poland [ 54 ]. In the publication year, three studies (15.0%) were published from 2011, eight studies (40.0%) from 2012 to 2015, and nine studies (45.0%) from 2016 to 2019. Regarding the study design, five studies used a randomized controlled trial (RCT) design, and 15 studies used a quasi-experiment design. Participants were characterized by nine studies (45.0%) of healthy people, including college students, office workers, middle-aged women, and the elderly, followed by four studies (20.0%) of chronic diseases such as cancer, cerebral infarction, and mild cognitive impairment, as well as infants and adolescents. Moreover, there are three studies (15.0%) on patients with mental illness. The sample size ranged from 20 [ 61 ] to 240 [ 71 ], and in almost all the studies, the sample size was from 51 to 100.

3.2.1. Format and Content of Forest Therapy

Table 2 summarized the 20 selected studies, shown in the participant’s characteristics, intervention description and characteristics (i.e., type, duration, time, and frequency), and effects on outcomes. The 20 studies varied in terms of the format and content of the forest therapy. The length of duration that the intervention varied from one day to 7 months. Among them, six studies provided one-time intervention, one was one-day intervention [ 54 ], three-day intervention [ 65 ], one-day two-night intervention [ 62 ], two-day three-night intervention [ 71 ], three-day four-night intervention [ 57 ], and nine-day intervention [ 58 ], respectively. In the other 14 studies, the duration of intervention ranged from 2 weeks to 7 months. As for the frequency of intervention, eight studies were the most common once a week, followed by two times a week and three times a week, and one study did not report the frequency of the intervention [ 69 ]. The intervention time was 40 min to 120 min, which is the nine studies provided within 2 h and one study conducted to 3 h and 5 h, respectively. Three studies did not report duration details of the intervention [ 64 , 69 , 72 ].

Regarding the content of forest therapy, walking in the forest was the key component of forest therapy. Other therapeutic activities included in forest therapy were forest viewing, meditation, mindfulness-based cognitive behavior therapy, bodily stimulation exercise, and recreation (e.g., bingo game in the forest, treasure hunt in the forest, touching a natural object). On the other hand, the most common type of control intervention was “normal daily routines (12 out of 20 studies)”. Otherwise, a general program was performed, or the same activities were performed in other places such as cities and indoors rather than forests.

3.2.2. Depression or Anxiety Measures

As for the measurement used as self-reported for depressive symptoms, Beck Depression Inventory (BDI), which is widely used in adults, was the most used with eight studies [ 56 , 57 , 58 , 60 , 62 , 64 , 70 , 73 ]. Next, three studies [ 54 , 64 , 65 ] used Profile of Mood States, and two studies used Hamilton Rating Scale for Depression (HRDS) [ 57 , 70 ], Children’s Depression Inventory (CDI) [ 59 , 71 ], and Geriatric Depression Scale Short Form (GDSSF) [ 55 , 68 ], respectively. Other scales used to measure depression were the Montgomery-Asberg Depression Rating Scale (MADRS) [ 70 ], Hospital Anxiety and Depression Scale (HADS) [ 65 ], Center for Epidemiologic Studies Depression Scale (CES-D) [ 72 ], Korean Depression Scale (KDS) [ 63 ], Zung Self-Rating Depression Scale (ZSDS) [ 66 ], and Stress Response Inventory (SRI) [ 61 ].

The most used self-reported measurements for anxiety were the State-Trait Anxiety Inventory (STAI) [ 57 , 67 , 73 ] and Profile of Mood States (POMS) with three studies, respectively. In addition, the Hospital Anxiety and Depression Scale (HADS) and Korean Preschool Daily Stress Scale (KPDSS) [ 69 ] were used. Detailed information on the measurement tools included in these studies is summarized in Table 2 .

3.3. Quality Assessment

Appendix A , Table A2 shows the ROB assessments for the 20 included studies. Of the 20 studies included, five studies were RCTs, so ROB 2.0 tools were used, and 15 studies were conducted for risk of bias using ROBINS-I tools, a non-randomized controlled experimental evaluation tool. The overall quality of five RCTs raised potential concerns in three [ 54 , 55 , 57 ] and high in two [ 56 , 58 ]. The risk of bias in the randomization process was high risk in two [ 56 , 58 ], some concerns in two [ 55 , 57 ], and low risk in one [ 54 ]. Although all RCTs were rated as low ROB for Deviation from intended interventions, missing outcome data, and selection of the reported results, all RCTs raised some concerns about the measure of the outcome data.

We assessed overall ROBs as high for nine quasi-experimental studies [ 63 , 65 , 66 , 67 , 68 , 69 , 71 , 72 , 73 ] and moderate for six studies [ 61 , 62 , 63 , 64 , 70 ] due to the blinding of outcome assessors and potential sources of knowledge of the intervention received in bias arising from the measurement of outcomes. However, by item, it was judged that the risk of bias was all low ROB for baseline confounding, selection of participants, classification of intervention, deviation from intended interventions, missing data, and selection of reported results.

3.4. Effects of Forest Therapy on Depression

Forest plots in Figure 3 display results of the meta-analyses for the effects of forest therapy on depression. Of the 20 studies analyzed in this review, 18 studies confirmed depression as a dependent variable. In 18 studies, the effect size for 23 result values was calculated, and the studies were heterogeneous (I 2 = 89.35%). Therefore, the effect size was calculated with a random effect model. The overall effect size was 1.133 (95% CI: −1.491 to −0.775, p < 0.0001), indicating a high effect size.

Forest plot the effects of forest therapy on depression. Heterogeneity: Tau 2 = 0.656; Q = 206.490; I 2 = 89.35%; Test overall effect Z = −6.204 ( p < 0.001).

3.5. Effects of Forest Therapy on Anxiety

Forest plots in Figure 4 display results of the meta-analyses for the effects of forest therapy on anxiety. Of the 20 studies analyzed in this review, eight studies confirmed anxiety as a dependent variable. The effect size for nine result values was calculated in eight studies, and the studies were heterogeneous (I 2 = 93.35%). Therefore, the effect size was calculated with a random effect model. The overall effect size was 1.715 (95% CI: −2.519 to −0.912, p < 0.0001), indicating a high effect size.

Forest plot the effects of forest therapy on anxiety. Heterogeneity: Tau 2 = 1.374; Q = 120.259; I 2 = 93.35%; Test overall effect Z = −4.183 ( p < 0.001).

3.6. Verification of Heterogeneity of Effect Size: Analysis of Modulating Effect

3.6.1. depression.

To analyze the cause of heterogeneity in effect size, meta-ANOVA was conducted using the sub-groups of this review. Table 3 shows subgroup analyses by participant characteristics, activity type (day and session types), activity contents, intervention time, and duration as modulating variables.

Modulator effect analyses for the effects of forest therapy on depression.

In subgroup analyses, the intervention time was significant modulating variables of effect size. As a result of comparing the effect sizes by intervention time, 121 min or more (Hedges’s g = 2.035; 95% CI: −2.790 to −1.279) and 61 to 120 min (Hedges’s g = 0.862; 95% CI: −1.396 to −0.327) had a large effect size in the depression. However, 60 min or less (Hedges’s g = 0.356; 95% CI: −1.089 to 0.376) had a small effect size, which is no significant difference.

Compared with participant’s characteristics, mental disorder (Hedges’s g = 1.522; 95% CI: −2.190 to −0.854) was the highest, followed by healthy adults (Hedges’s g = 1.242; 95% CI: −1.783 to −0.701), patients with chronic diseases (Hedges’s g = 1.009; 95% CI: −1.974 to −0.043) had a large effect size in the depression. However, children and adolescents had a small effect size. However, there was no statistically significant difference.

By activity content, forest therapy programs (Hedges’s g = 1.291; 95% CI: −1.690 to −0.892, p < 0.001) had a larger effect size of improving depression than forest walking (Hedges’s g = 0.252; 95% CI: −1.249 to 0.745, p = 0.620) but showed no significant difference (Q = 3.595, df = 1, p = 0.058). In addition, there was no significant difference between subgroups in activity type and duration for effect size.

3.6.2. Anxiety

To analyze the cause of heterogeneity in effect size, meta-ANOVA was conducted using the sub-groups of this review. Table 4 shows subgroup analyses by participant characteristics, activity type (day and session types), intervention time, and duration as modulating variables.

Modulator effect analyses for the effects of forest therapy on anxiety.

In subgroup analyses, the activity type was the significant modulating variable of effect size. Day type (Hedges’s g = 2.711; 95% CI: −2.573 to −0.883) had a significantly larger effect size of improving anxiety than session type (Hedges’s g = 0.990; 95% CI: (−2.108 to 0.129).

By participant’s characteristics, the people with chronic disease (Hedges’s g = 3.236; 95% CI: −4.892 to −1.580) had a larger effect size of improving anxiety than healthy people (Hedges’s g = 1.442; 95% CI: −2.832 to −0.051) but showed no significant difference. In addition, there was no significant difference between subgroups in the intervention time and duration for effect size.

3.7. Publication Bias

The meta-analyses for the effect of forest therapy on the depression and anxiety domains showed visual evidence in asymmetric funnel plots ( Appendix A , Figure A1 ) and significance on Egger’s regression asymmetry tests (t = 3.25, p = 0.004 for depression and t = 2.40, p = 0.047 for anxiety) ( Appendix A , Table A3 ).

On the other hand, the impact of automatic missing data was analyzed when the asymmetric funnel plot was symmetrically changed using Trim-and-Fill proposed by Duval and Tweedie [ 74 ]. There was a publication bias in the overall effect size of this review. As a result, it was found that the effect size automatically corrected in consideration of the possibility of publication bias was significant as 1.342 (95% CI: −1.73 to −0.95) for depression and 2.476 (95% CI: −3.50 to −1.46) for anxiety. Therefore, it was confirmed that the overall effect size on depression and anxiety was not affected by publication bias.

4. Discussion

4.1. summary of findings.

This review attempted to confirm the effect of forest therapy by systematically examining and meta-analyzing the effects of forest therapy on depression and anxiety over the past 30 years. Our findings have shown that forest therapy has large effect sizes, not just significant effect evidence on depression and anxiety. These results are consistent with the previous results on psychological effects, including depression and anxiety [ 44 , 75 , 76 ]. For example, a meta-analysis of the effect of improving depression of forest therapy in 13 studies by Rosa et al. [ 75 ] showed that forest therapy is a more effective short-term intervention for adult depression prevention and treatment with an average effect size of 1.18 (95% CI: 0.86 to 1.50), p < 0.0001). Compared with no intervention/treatment, this study reported that participants in the forest healing group were 17 times more likely to achieve depression relief (Risk Ratio = 17.02, 95 % CI [3.40, 85.21], p = 0.0006), and 3 times more likely to decrease by 50% for depressive symptoms (Risk Ratio = 3.18, 95 % CI [1.94, 5.21], p < 0.00001). In addition, Kotera et al. [ 44 ] showed an effect size of −2.54 MD (95% CI: 3.56–1.52) for depressive symptoms in six RCT studies and a large effect size of −8.81 MD (95% CI: −21.91–3.57) for anxiety symptoms in five RCT studies.

The results of this review were analyzed in subgroups based on participant characteristics, activity type, intervention time, duration, and intervention content due to extreme heterogeneity between included studies. As a result, we found significant differences between participant characteristics and activity type in the sub-group.

In terms of participant characteristics, the effect size of people with mental health conditions (Hedges’s g = 1.522) was the largest, followed by healthy adults (Hedges’s g = 1.242), and chronic disease patients (Hedges’s g = 1.009), showing large effect sizes for depression. However, children and adolescents (Hedges’s g = 0.136) had a small effect size. These findings suggest that forest environments can significantly lower depression in adults, especially people with a mental health condition, more than in children or adolescents. The findings are partly consistent with the results of previous studies dealing with the possibility of forest therapy to treat specific mental and physical conditions, such as depression [ 77 ], high-risk stress groups [ 35 ], hypertension treatment [ 78 ], and patients with severe exhaustion disorder [ 79 ]. In particular, Furuyashiki et al. [ 77 ] investigated forest therapy’s physiological and psychological effects on workers. They proved that forest therapy has a significantly positive effect on mental health compared to those who do not take part. Therefore, it will be necessary to actively use forest therapy to improve the psychological health of chronically ill or mentally ill patients.

In the case of activity type, we found that the effect of forest therapy on anxiety was statistically significantly higher in the day-type forest therapy than in the session-type forest therapy. In a meta-analysis study on the effectiveness of previous forest therapy programs, the effect size was more significant in the session-type activity type than in the day-type [ 80 ]. In addition, Christup [ 81 ] argued that the participants are more likely to change when intervening in the long term than in the short term. The results of this review were different from those of previous studies [ 80 , 81 ]. However, it is thought that the previous study [ 80 ] may differ from the results of this study as the effect size of meta-analysis that integrates depression and anxiety as well as other psychological functions, cognitive functions, social functions, and physiological functions. In addition, studies that directly compare the effects of short-term forest therapy, such as one night and two days and long-term forest therapy conducted more than once a week with 12 sessions a week, are insufficient. Therefore, it is thought that future research is needed on the difference in the effectiveness of each activity type on improving anxiety.

In addition, there was no significant difference when the subgroup analysis was conducted on the intervention contents. However, the forest therapy program showed a more significant effect than simply forest walking activities. The results of this study are partly consistent with the results of previous studies that show that forest therapy programs were more effective in psychological recovery than forest walking activities [ 82 , 83 ]. Thus, it may be more effective in improving depression and anxiety than walking in the forest.

In the case of intervention time, we found that the longer the intervention time, the greater the effect of forest therapy on depression and anxiety. In particular, the effect size for depression was largest when the intervention time was more than 120 min, followed by more than 61 min and less than 120 min and less than 60 min. The results of this study are partly consistent with the results of previous studies that when the intervention time is three hours, the effect size is larger than within an hour, 1 h to two hours, and 2 h [ 84 ]. According to the previous study, it was reported that organizing the intervention time within an hour has limitations in operating in-depth programs [ 85 ] and helps improve the effectiveness of activities by securing sufficient intervention time [ 86 ]. Therefore, this result shows that spending enough time in the forest is more effective in improving depression and anxiety.

The results of this study are supported by theories used as the basis for explaining natural recovery in environmental psychology. The most influential frameworks in explaining the effects include Kaplan’s [ 87 , 88 ] attention restoration theory (ART) and Ulrich’s [ 89 ] stress reduction theory (SRT). According to the attention restoration theory (ART) [ 87 , 88 ], exposure to nature such as forests can reduce mental fatigue or psychological stress and restore attention to a more positive emotional and psychological response. The brain’s capacity to focus on a specific stimulus or task is limited, resulting in ‘directed attention fatigue.’ ART proposes that exposure to natural environments encourages more effortless brain function, thereby recovering and replenishing its directed attention capacity. Several studies support this theory, showing that staying in natural environments positively affects recovery from directed attention fatigue [ 90 , 91 ]. The stress reduction theory (SRT) [ 89 ], another commonly used theory in environmental restorativeness, focuses on psychophysiological stress. The SRT suggests that the natural environment affects the emotional state by promoting stress recovery, evoking positive emotions, and blocking negative emotions through psychophysiological pathways [ 92 , 93 ]. It has been reported that the evidence found to date is that viewing or visiting the natural environment can reduce blood pressure and stress hormone levels and positively affect mood states [ 94 , 95 ]. In addition to theories, Hartig’s in press [ 96 ] relational restoration theory (RRT) and Collective restoration theory (CRT) are more recent approaches to restoration [ 97 ]. The theories are based on the argument that restoration does not occur in a social vacuum. RRT suggests that small groups of social resources can be reduced. These resources can be restored, for example, by spending time together in nature. Likewise, CRT suggests that depletion and restoration of social resources can be collective. For example, the summer vacation period for a specific population may be correlated with an increase in group welfare during leisure. Conversely, cold summer weather can constrain group recovery activities outdoors and negatively affect downstream groups such as by increasing stress. In addition to these two theories, vitality, the feeling of activation in a recovery environment such as a forest, is defined as “having physical and mental energy” and is related to many inactive positive emotions such as satisfaction and happiness [ 98 , 99 ]. Restoration refers to the process of renewing, restoring, or rebuilding reduced physical, psychological, and social resources or functions in continuous efforts to meet adaptation needs [ 100 ]. Therefore, the natural restoration environment itself as a forest may be essential in improving anxiety and depression.

In addition, five senses stimulation due to forest characteristics such as forest scenery, forest sound, scent, and various tactile elements may be a mechanism factor related to improving anxiety and depression. It has been proven in many studies that forest landscapes promote mental and physical relaxation and improve stress resilience [ 24 , 99 ]. For example, Song et al. [ 39 ] reported that viewing at the forest landscape significantly reduced depression, tension-anxiety, and anxiety rather than the city. As a follow-up study, Song et al. [ 101 ] showed that changes in depression after viewing the forest landscape showed a significant correlation with state anxiety. This correlation showed a greater difference in individuals with high anxiety.

Auditory stimulation by the forest, such as the sound of leaves shaking in the wind, singing birds, and the sound of flowing streams, can contribute to psychophysical relaxation and stress recovery [ 102 , 103 , 104 ]. For example, Zhang et al. [ 103 ] reported that the acoustic and visual comfort given in the green environment had a strong positive correlation with low depression and anxiety. In particular, acoustic comfort showed a more significant influence than visual comfort. In addition, Ochiai et al. [ 104 ] reported that as a result of a survey of the effects of forest sound on gambling addicts, forest sound significantly reduced depression and tension-anxiety rather than urban sound. Thus, it has been proven that visual and auditory stimuli in the forest can significantly improve emotional depression and anxiety.

As another critical factor, trees in the forest release biogenic volatile organic compounds (BVOCs) such as limonene, alpha-pinene, and beta-pinene, which affects not only human health in terms of anti-inflammatory, antioxidant, or neuroprotection activities [ 105 , 106 , 107 ], but also benefits psychological and cognitive processes [ 108 ]. For example, Lee et al. [ 109 ] investigated the effect of cypress-oriented inhalation on stress and depression in college students. As a result, participants who inhaled cypress orientation had significantly decreased depression than in the pre-test, while those who did not inhale orientation had increased depression compared to the pre-test. In addition, although it is a preclinical study of animal behavior models, BVOCs showed anti-anxiety [ 110 , 111 ] and antidepressant properties [ 112 , 113 ]. It has been proven that tactile stimulation caused by the contact of bark and plant leaves of trees can also relax and stimulate parasympathetic nerve activity more than touching other materials [ 114 , 115 ]. Overall, tactile stimulation caused by touching living forest plants can play a role in calming effects. However, the primary studies on forest-based intervention did not elaborate on the forest structure where the intervention was performed. In addition, it is unclear what kind of forests are most effective in improving depression and anxiety. Future research will require research on what forest structure (e.g., the intensity of thinning, biodiversity, tree density, tree species, etc.) can maximize mental health effects.

These findings also support the evidence that forest therapy, which is part of nature therapy, can be used as a non-pharmacological intervention to improve depression and anxiety. Mental health problems are becoming more and more severe as many people are excessively exposed to stress due to urbanized society [ 116 , 117 ]. For example, according to Sundquist et al. [ 118 ], both women and men reported that urban residents had a 20% higher risk of depression than rural residents. The higher risk of anxiety disorders was also about 21% [ 119 ]. In addition, McKenzie et al. [ 117 ] reported that urban environments are associated with higher prescription rates of antipsychotic drugs for anxiety, depression, and mental illness. However, some studies have reported no difference in the proportion of residents’ depression and anxiety symptoms between suburban and rural areas [ 120 ], but instead causes more mental problems in the countryside [ 121 ]. This shows that not only green space in the area where residents live but also variables related to demographic factors such as age, gender, ethnicity, and cultural factors affect mental health. Therefore, it is necessary to provide more insight at the international level through research that considers aspects of other cultural realities.

According to the World Health Organization [ 122 ], more than half of the world’s population lived in urban environments in 2014, increasing to 65% by 2030. Accordingly, medical costs are also incurred, so reducing medical costs at the preventive level is a socially important issue. According to Buckley et al. [ 123 ], the economic value of nature reserves is evaluated based on the mental health of visitors, accounting for about 8% (about 6 trillion dollars) of the world’s gross domestic product. Becker et al. [ 124 ] also estimated the association between the five vegetation ratios of forests, shrubs, grasslands, agriculture, and urban vegetation and medical insurance premiums for 3086 counties in the United States. As a result, the ratio of forests and shrubs was significant and negatively correlated with medical insurance expenditure. In other words, it was found that 1% of the land in a country covered with forests was associated with low health insurance spending of $4.32 per person per year. Thus, a wide range of applications of forest therapy can improve the country’s overall health and lead to significant savings and productivity benefits in terms of health care and welfare systems.

The psychological benefits of forests are significant. As a preventive dimension, the forest environment and the urban green space can play an essential role in promoting mental health [ 101 ]. This is because urban green spaces such as urban forests provide cost-effective, simple, and accessible methods to improve individual quality of life and health in urban areas. Therefore, it is vital to prepare a way for urban residents to improve and access their daily lives by utilizing the restorative environment of the forest. To do this, urban planners need to pay more attention to the maintenance and increase in accessible green spaces in urban areas.

4.2. Limitations

The limitations of this study are as follows. First, the magnitude of the overall heterogeneity among studies was considerable. Accordingly, although we used a random effect model to determine the effect size, the primary studies’ interpretation may be limited due to high heterogeneity. Furthermore, we included not only five RCTs but also 15 quasi-RCTs. NRCTs tend to have a higher risk of bias than RCTs due to confounding because participants’ allocation to intervention may be related to baseline variables affecting outcomes. So, it leads to methodological heterogeneity.

Second, most primary studies showed a moderate or high risk of bias. It was almost impossible to apply blind forest therapy interventions that did not provide interventions to participants, therapists, and assessors. In addition, questionnaires measuring depression and anxiety have been self-reported. Random errors caused by incorrect memories could reduce the size of the link between forest therapy and depression and anxiety. To reduce the random error, many studies will need an objective measure for assessing the level of depression or anxiety. This is because significant correlations between physiological findings such as EEG asymmetry [ 125 , 126 ], heart rate variability [ 127 , 128 ], and perceived levels of depression and anxiety have been reported. Therefore, future studies should require better-designed, controlled intervention studies and reliable physiological measures in addition to self-reported questionnaires to find the effects of forest therapy.

Third, the effects achieved by forest therapy were usually compared to control groups without any specific intervention, and controls merely followed their “daily routine” [ 55 , 56 , 58 , 63 , 64 , 65 , 66 , 67 , 68 , 71 , 73 ]. These studies investigated the effects of depression and anxiety of forest therapy intervention without explicitly explaining the contribution of the forest environment to the achieved effects. It is still unclear whether the same results were achieved compared to the same intervention in an environment outside the forest. This point shows that special attention is paid to the appropriate control group when selecting a study design to prove the effectiveness of forest therapy.

Fourth, primary studies were carried out in Korea except for one study [ 49 ]. Therefore, we should be careful when interpreting the results, and the need for broader geographic application is emphasized in forest therapy effects on depression and anxiety.

Fifth, in the process of systematic review, it is possible that unpublished studies or studies published in other languages were excluded because only studies published in Korean and English over the past 30 years were searched. Although we could not conduct literature searches in Japanese or Chinese databases conducting much research on forest therapy, we used four major international and two major Korean databases. It seems that our approach sufficiently identified many for this systematic review and meta-analysis.

Sixth, care should be taken in interpretation, as the effect size may be overestimated or underestimated by a meta-analysis of 20 primary studies. Therefore, based on this review, we believe that repeated studies on meta-analysis are necessary.

Seventh, the selected studies did not conduct follow-up evaluation. The absence of follow-up evaluation may impair the effectiveness of clinical studies [ 129 ], and this is because it is unclear whether the effect of forest therapy can continue. Therefore, future studies need follow-up evaluations to evaluate the long-term effects of improving depression and anxiety in forest therapy.

Eighth, we could not conduct a pre-register this study in the protocol registration. Future studies should need to register protocol before starting the meta-analysis, specifically before starting the data extraction. This registration will also ensure a clear documented research plan prior to commencing the systematic review. It will allow the researchers to understand the questions already registered or reported by other scientific investigators.

Despite these limitations, this study provided an understanding of the therapeutic benefits of forest therapy. Therefore, we believe that forest therapy should be actively used as preventive management and non-pharmacologic treatment for improving depression and anxiety. This is in line with the growing support for therapeutic activities in contact with nature as a non-pharmacologic intervention method for preventing and treating mental health problems.

4.3. Future Research

The directions for further development of this field are as follows. First, our findings emphasize the need for methodologically stricter RCTs to investigate the effects of forest therapy on depression and anxiety. Future research needs to improve the methodological quality of the studies while reducing the risk of bias in the study to increase evidence-based reliability. The most common concern in previous studies is the lack of blinding of participants, therapists, and evaluators who contribute to measurement results and interventions. In other words, many studies have not performed randomization and blinding. This means that the expectations of participants, therapists, and evaluators can affect the research results. If it is revealed that it is a group allocation, participants interested in forest therapy may become more vulnerable to the placebo effect. In addition, if blinding of researchers and therapists is not performed, this can also lead to placebo effects of participants. Therefore, future studies should thoroughly conduct randomization and blinding to conduct RCT studies.

Second, it is necessary to observe the continuous effects of forest therapy for a long time. After the intervention, the results of several follow-up measures (e.g., three months, six months, 12 months) should be evaluated. In addition, this includes various exposure times (e.g., 30 versus 60 versus 90 min), frequency of exposure (e.g., weekly versus every four weeks), that is, the optimal duration for forest therapy to perform the best effect.

Third, it is necessary to investigate whether a specific forest structure can have the best effect on improving depression and anxiety. For example, it will be essential to study the recovery effect according to the stand structure (e.g., forest type, tree density, distribution of canopy layer) and whether it is managed (natural forests vs. managed forests). In addition, seasonal changes in forests can have a significant impact on the recovery effect.

Fourth, it is necessary to research which activities and combinations of forest therapy programs effectively improve depression and anxiety. The activities applied to forest therapy programs are very diverse. Key elements are exposure to forest environments with all five senses (visual, smell, hearing, tactile, taste), which can be combined with various recreational activities and cognitive behavioral therapy as well as meditation, walking, and exercise. If it consists of customized activities to improve depression and anxiety, it will be possible to maximize the health benefits of forest therapy programs.

Fifth, researchers need to describe research methods and results in detail to derive more reliable meta-analysis results in future multidisciplinary studies, including environment and public health. Some studies cannot be included in the meta-analysis due to the unavailable results (e.g., missing outcomes, unrecorded exposure levels, research design methods).

Sixth, it is necessary to evaluate studies using tools suitable and validated for individual studies’ research design. Most individual studies’ quality evaluation is conducted in the case of meta-analysis studies published on the environment and public health. However, careful consideration of quality evaluation tools is required since the intervention effect can be inflated or reduced if the quality of individual studies is not correctly evaluated [ 130 ].

5. Conclusions

This systematic review and meta-analyses summarized the currently available evidence on the association between forest therapy and depression and anxiety. As a result, it demonstrated that forest therapy has a large effect on alleviating depression and anxiety. Therefore, it is necessary to actively apply forest therapy to improve depression and anxiety in the future.

Acknowledgments

We thank the forest healing and forest welfare lab members of Chungbuk University for their help.

Search terms used in each database.

RoB and ROBINS-I assessment of the included studies.

Funnel plot of the effect of depression ( A ) and anxiety ( B ) for publication bias.

Egger’s regression and Trim and fill results.

PRISMA (Preferred Reporting Items for Systmatic review and Meta-Analysis Protocols) 2020 checklist.

Author Contributions

Conceptualization, P.-S.Y., J.-G.K. and W.-S.S.; methodology, P.-S.Y. and J.-G.K.; study selection, J.-G.K., M.-S.J., K.-M.H., G.-M.M. and G.-Y.K.; quality assessment, J.-G.K. and J.-Y.J.; investigation, J.-G.K., J.-Y.J., M.-S.J., K.-M.H., G.-M.M., G.-Y.K., M.-J.S. and S.-H.J.; writing—original draft preparation, P.-S.Y. and J.-G.K.; writing—review and editing, P.-S.Y., J.-G.K. and W.-S.S. All authors have read and agreed to the published version of the manuscript.

This research was supported by the R&D Program for Forest Science Technology (Project No. 2021403A00-2123-0102) funded by the Korea Forest Service (Korea Forestry Promotion Institute).

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Global Forest Loss Remains High, Despite Recent Progress

Wildfires and agricultural expansion offset big gains in protecting tropical forests last year.

By Manuela Andreoni

Despite major progress in protecting vast tracts of rainforest, the world failed again last year to significantly slow the pace of global forest destruction, according to a report issued on Thursday. Record wildfires in Canada and expanding agriculture elsewhere offset big gains in forest protection in Brazil and Colombia, the report found.

The annual survey by the World Resources Institute, a research organization, found that the world lost 9.1 million acres of primary tropical forest in 2023, equivalent to an area almost the size of Switzerland, about 9 percent less than the year before . But the improvement failed to put the world on course to halt all forest loss by 2030, a commitment made by 145 nations at global climate talks in Glasgow in 2021 and reaffirmed by all countries last year .

Stalled Progress

Gains in Brazil and Colombia were offset by rising deforestation in other areas.

6 million hectares

3.7 million

TROPICAL PRIMARY

FOREST LOSS

Source: World Resources Institute

Note: Tropical primary tree loss represents removal of mature forests.

Mira Rojanasakul/The New York Times

“Global leaders sent an undeniable message that forests are critical to meeting global climate goals,” said Rod Taylor, the global director for forests at the World Resources Institute. But, he added, “we are far off track and trending in the wrong direction.”

The immense wildfires in Canada last year destroyed such a huge tract of boreal forests, almost three times as much as in any other year, that they turned what would have been a 4 percent decrease in global forest loss into a 24 percent increase over the 2022 total.

The report focuses on the tropics because deforestation and fires there are mostly caused by human activity and can create longer-lasting consequences. The humid forests of tropical countries hold a quarter of all carbon stored on land and are home to a large share of animal and plant species, making their protection essential both to curb climate change and to avert biodiversity loss.

Researchers at the World Resources Institute, working in collaboration with the University of Maryland, documented tree loss across the world from deforestation, fires and other causes. Last year’s destruction resulted in 2.4 gigatons of carbon dioxide emissions, which is roughly equivalent to half of what the burning of fossil fuels in the United States produces each year.

Still, last year’s results showed that progress is possible when forest protection becomes a priority for world leaders. A recent change in leadership in Brazil and Colombia, which together hold almost a third of the world’s tropical forests, produced a steep decrease in deforestation rates in the two countries .

Brazil lost 2.8 million acres of forest last year, 36 percent less than in 2022. Ahead of taking office in 2023, President Luiz Inácio Lula da Silva said the country was “ready to resume its leading role in the fight against the climate crisis.” Brazil, which is home to more than half of the Amazon rainforest, accounted for 30 percent of the tropical forest loss globally last year.

Colombia, where President Gustavo Petro took office in 2022 vowing to protect the rainforest, recorded an even steeper improvement, slashing deforestation rates by 49 percent. Both Brazil and Colombia increased funding for environmental protection, created new programs to develop sustainable economic alternatives for rainforest regions and made efforts to protect local communities who defend forests.

But there are concerns about how permanent those gains will be. In Indonesia, one of the countries that has made the most progress in fighting deforestation over the past decade, tree loss has started ticking up again in the last two years.

“Ephemeral victories or ephemeral progress in slowing deforestation may not be progress at all,” said Matthew Hansen, the co-director of a laboratory at the University of Maryland that investigates changes in land use around the world.

But even the gains that researchers documented last year were largely offset by the expansion of agribusiness into tropical ecosystems around the world. The World Resources Institute researchers linked rising deforestation in Bolivia and Laos to the expansion of farms with the goal of increasing exports.

Forest loss in the Congo River Basin, the second-largest tropical forest area in the world, remained persistent last year, as economic hardship continued to drive communities to convert trees into firewood and charcoal for cooking .

Last year, man-made climate change fueled the record-breaking wildfires in Canada and seemed to leave its fingerprints around the world.

The Growing Threat of Fire

Fires are an increasingly dominant driver of tree loss worldwide.

20 million hectares

FIRE-RELATED

Note: Total tree loss includes areas outside of primary forests.

In Bolivia, blazes are also growing larger and burning the same areas repeatedly. It’s too early to say whether they were fueled by climate change. But the phenomenon has raised questions about whether some heavily degraded areas of the Amazon are transforming into different ecosystems, which some researchers worry could lead to a gradual forest-wide collapse .

Still, there is a lot governments, companies and communities can do to combat forest loss, beyond curbing the carbon emissions that cause climate change, said Mr. Taylor, the World Resources Institute director. New regulations and subsidies for forest protection could help, he said.

Unfortunately, these initiatives aren’t happening globally at a significant scale, Mr. Taylor said, “and that’s why we still see deforestation rates persisting.”

Manuela Andreoni is a Times climate and environmental reporter and a writer for the Climate Forward newsletter. More about Manuela Andreoni

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