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Bayesian inference of biomass growth characteristics for sugi ( C. japonica ) and hinoki ( C. obtusa ) forests in self-thinned and managed stands

Forests are an important sink for atmospheric carbon and could release that carbon upon deforestation and degradation. Knowing stand biomass dynamic of evergreen forests has become necessary to improve current...

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Diversity of plants and mammals as indicators of the effects of land management types in woodlands

The ecological indicators are useful tools to determine the effects of human disturbances on woodland biodiversity. Nevertheless, ecological indicators not always responded in the same way to disturbances, and...

Forest disturbances and the attribution derived from yearly Landsat time series over 1990–2020 in the Hengduan Mountains Region of Southwest China

Natural forests in the Hengduan Mountains Region (HDMR) have pivotal ecological functions and provide diverse ecosystem services. Capturing long-term forest disturbance and drivers at a regional scale is cruci...

Partitioning tree water usage into storage and transpiration in a mixed forest

Water migration and use are important processes in trees. However, it is possible to overestimate transpiration by equating the water absorbed through the plant roots to that diffused back to the atmosphere th...

Determinants of species assemblages of insect pests in alpine forest ecosystems of western China

Insect pests are a significant threat to natural resources and social development. Modeling species assemblages of insect pests can predict spatiotemporal pest dynamics. However, research gaps remain regarding...

Nutrient resorption strategies of three oak tree species in response to interannual climate variability

Nutrient resorption is critical for plants toward balancing their nutritional requirements and adapting to environmental variabilities, which further impacts litter quality and nutrient cycling. However, the i...

Spatial patterns of insect herbivory within a forest landscape: the role of soil type and forest stratum

Insect herbivory has profound impacts on ecosystem processes and services. Although many efforts have been made to recognize the main drivers of insect herbivory at different scales, the results are inconsiste...

Topography modulates effects of nitrogen deposition on microbial resource limitation in a nitrogen-saturated subtropical forest

Nitrogen (N) saturation theory proposes that an ecosystem might switch from N limitation to carbon (C), phosphorus (P), or other nutrient limitations if it receives continuous N input. Yet, after N limitation ...

Individual tree extraction from terrestrial laser scanning data via graph pathing

Individual tree extraction from terrestrial laser scanning (TLS) data is a prerequisite for tree-scale estimations of forest biophysical properties. This task currently is undertaken through laborious and time...

A 40-year evaluation of drivers of African rainforest change

Tropical forests are repositories of much of the world’s biodiversity and are critical for mitigation of climate change. Yet, the drivers of forest dynamics are poorly understood. This is in large part due to ...

Impacts of forestation on the annual and seasonal water balance of a tropical catchment under climate change

This study aims to assess the effects of a forestation program and climate change on the annual and seasonal water balance of the Bogowonto catchment (597 km 2 ) in Java, Indonesia. The catchment study is rare exam...

Solar radiation effects on leaf nitrogen and phosphorus stoichiometry of Chinese fir across subtropical China

Solar radiation (SR) plays critical roles in plant physiological processes and ecosystems functions. However, the exploration of SR influences on the biogeochemical cycles of forest ecosystems is still in a sl...

Large scale mapping of forest attributes using heterogeneous sets of airborne laser scanning and National Forest Inventory data

The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over...

Effect of thinning intensity on the stem CO 2 efflux of Larix principis-rupprechtii Mayr

Stem CO 2 efflux ( E S ) plays a critical role in the carbon budget of forest ecosystems. Thinning is a core practice for sustainable management of plantations. It is therefore necessary and urgent to study the effec...

Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests

Black alder ( Alnus glutinosa ) forests are in severe decline across their area of distribution due to a disease caused by the soil-borne pathogenic Phytophthora alni species complex (class Oomycetes), “alder Phyto...

Distribution of Panama’s narrow-range trees: are there hot-spots?

Tree species with narrow ranges are a conservation concern because heightened extinction risk accompanies their small populations. Assessing risks for these species is challenging, however, especially in tropi...

Introduction of Dalbergia odorifera enhances nitrogen absorption on Eucalyptus through stimulating microbially mediated soil nitrogen-cycling

There is substantial evidence that Eucalyptus for nitrogen (N) absorption and increasing the growth benefit from the introduction of N-fixing species, but the underlying mechanisms for microbially mediated soil N...

Reduced turnover rate of topsoil organic carbon in old-growth forests: a case study in subtropical China

Old-growth forests are irreplaceable with respect to climate change mitigation and have considerable carbon (C) sink potential in soils. However, the relationship between the soil organic carbon (SOC) turnover...

Linkage of microbial living communities and residues to soil organic carbon accumulation along a forest restoration gradient in southern China

Forest restoration has been considered an effective method to increase soil organic carbon (SOC), whereas it remains unclear whether long-term forest restoration will continuously increase SOC. Such large unce...

Effects of harvest intensity on the marketable organ yield, growth and reproduction of non-timber forest products (NTFPs): implication for conservation and sustainable utilization of NTFPs

Non-timber forest products (NTFPs) are an important part of forest biodiversity, and the subsistence and trade of local people, especially in less developed countries. Because of the high ecological and econom...

Geoecological parameters indicate discrepancies between potential and actual forest area in the forest-steppe of Central Mongolia

Forest distribution in the forest-steppe of Mongolia depends on relief, permafrost, and climate, and is highly sensitive to climate change and anthropogenic disturbance. Forest fires and logging decreased the ...

Forest management required for consistent carbon sink in China’s forest plantations

Forest is the largest biomass carbon (C) pool in China, taking up a substantial amount of atmospheric carbon dioxide. Although it is well understood that planted forests (PFs) act as a large C sink, the contri...

Effect of thinning intensity on understory herbaceous diversity and biomass in mixed coniferous and broad-leaved forests of Changbai Mountain

Herbs are an important part of the forest ecosystem, and their diversity and biomass can reflect the restoration of vegetation after forest thinning disturbances. Based on the near-mature secondary coniferous ...

Characteristics and chemical reactivity of biogenic volatile organic compounds from dominant forest species in the Jing-Jin-Ji area, China

Biogenic volatile organic compounds (BVOCs) play an essential role in tropospheric atmospheric chemical reactions. There are few studies conducted on BVOCs emission of dominant forest species in the Jing-Jin-J...

Mature stand developmental stage has ceased to constitute the most suitable habitat for the capercaillie in the Augustów Forest, Poland

Forest management affects the habitat conditions for many forest-dwelling species. Among them, the capercaillie ( Tetrao urogallus ) is a rare forest grouse inhabiting old, mature forests. We compared the structure...

Tree mortality and regeneration of Euphrates poplar riparian forests along the Tarim River, Northwest China

Tree mortality and regeneration (seedling and sapling recruitment) are essential components of forest dynamics in arid regions, especially where subjected to serious eco-hydrological problems. In recent decade...

Forest management for optimizing soil protection: a landscape-level approach

Soil erosion is still identified as the main cause of land degradation worldwide, threatening soil functions and driving several research and policy efforts to reverse it. Trees are commonly associated to some...

Soil carbon and nutrient stocks under Scots pine plantations in comparison to European beech forests: a paired-plot study across forests with different management history and precipitation regimes

Organic carbon stored in forest soils (SOC) represents an important element of the global C cycle. It is thought that the C storage capacity of the stable pool can be enhanced by increasing forest productivity...

Simultaneous optimization of even flow and land and timber value in forest planning: a continuous approach

Forest management planning involves deciding which silvicultural treatment should be applied to each stand and at what time to best meet the objectives established for the forest. For this, many mathematical f...

Deforestation and fragmentation trends of seasonal dry tropical forest in Ecuador: impact on conservation

Fragmentation and deforestation are one of the greatest threats to forests, and these processes are of even more concern in the tropics, where the seasonal dry forest is possibly one of the most threatened eco...

Changes in plant debris and carbon stocks across a subalpine forest successional series

As a structurally and functionally important component in forest ecosystems, plant debris plays a crucial role in the global carbon cycle. Although it is well known that plant debris stocks vary greatly with t...

Zonal aspects of the influence of forest cover change on runoff in northern river basins of Central Siberia

Assessment of the reasons for the ambiguous influence of forests on the structure of the water balance is the subject of heated debate among forest hydrologists. Influencing the components of total evaporation...

Different mechanisms underlying divergent responses of autotrophic and heterotrophic respiration to long-term throughfall reduction in a warm-temperate oak forest

There are many studies on disentangling the responses of autotrophic (AR) and heterotrophic (HR) respiration components of soil respiration (SR) to long-term drought, but few studies have focused on the mechan...

Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery

Pine wilt disease (PWD) is a major ecological concern in China that has caused severe damage to millions of Chinese pines ( Pinus tabulaeformis ). To control the spread of PWD, it is necessary to develop an effecti...

C:N:P stoichiometry as an indicator of Histosol drainage in lowland and mountain forest ecosystems

Peatlands form one of the largest carbon pools in the terrestrial ecosystems, representing approximately one-third of the world’s soil carbon. The aim of this study was to evaluate C:N:P stoichiometry as an in...

Effects of stand features and soil enzyme activity on spontaneous pedunculate oak regeneration in Scots pine dominated stands – implication for forest management

A challenge in current forestry is adaptation of managed forests to climate change, which is likely to alter the main processes of forest dynamics, i.e. natural regeneration. Scots pine will probably lose some...

Wood substitution potential in greenhouse gas emission reduction– review on current state and application of displacement factors

Replacing non-renewable materials and energy with wood offers a potential strategy to mitigate climate change if the net emissions of ecosystem and technosystem are reduced in a considered time period. Displac...

Effects of root dominate over aboveground litter on soil microbial biomass in global forest ecosystems

Inputs of above- and belowground litter into forest soils are changing at an unprecedented rate due to continuing human disturbances and climate change. Microorganisms drive the soil carbon (C) cycle, but the ...

Different responses of soil respiration and its components to nitrogen and phosphorus addition in a subtropical secondary forest

Nitrogen (N) and phosphorus (P) deposition have largely affected soil respiration ( R s ) in forest ecosystems. However, few studies have explored how N and P individually or in combination to influence R s and its c...

A century of national forest inventories – informing past, present and future decisions

In 2019, 100 years had elapsed since the first National Forest Inventory (NFI) was established in Norway. Motivated by a fear of over-exploitation of timber resources, NFIs today enable informed policy making ...

Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle ( Anoplophora glabripennis ) at the tree level

Anoplophora glabripennis (Motschulsky), commonly known as Asian longhorned beetle (ALB), is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees. In Gansu Province, northwest China,...

Natural forests in New Zealand – a large terrestrial carbon pool in a national state of equilibrium

Natural forests cover approximately 29% of New Zealand’s landmass and represent a large terrestrial carbon pool. In 2002 New Zealand implemented its first representative plot-based natural forest inventory to ...

An approximate point-based alternative for the estimation of variance under big BAF sampling

A new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes Bitterlich sampling (point sampling) with two BAF sizes, a small BAF for...

Impacts of oak deforestation and rainfed cultivation on soil redistribution processes across hillslopes using 137 Cs techniques

As one of the main components of land-use change, deforestation is considered the greatest threat to global environmental diversity with possible irreversible environmental consequences. Specifically, one exam...

Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China

Vegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions.

Plant–rodent interactions after a heavy snowfall decrease plant regeneration and soil carbon emission in an old-growth forest

Climate extremes are likely to become more common in the future and are expected to change ecosystem processes and functions. As important consumers of seeds in forests, rodents are likely to affect forest reg...

Stand-level biomass models for predicting C stock for the main Spanish pine species

National and international institutions periodically demand information on forest indicators that are used for global reporting. Among other aspects, the carbon accumulated in the biomass of forest species mus...

Paludification reduces black spruce growth rate but does not alter tree water use efficiency in Canadian boreal forested peatlands

Black spruce ( Picea mariana (Mill.) BSP)-forested peatlands are widespread ecosystems in boreal North America in which peat accumulation, known as the paludification process, has been shown to induce forest growt...

Influence of individual tree characteristics, spatial structure and logging history on tree-related microhabitat occurrence in North American hardwood forests

Tree-related microhabitats (hereafter, “TreMs”) are key components of forest biodiversity but they are still poorly known in North American hardwood forests. The spatial patterns of living trees bearing TreMs ...

Effects of local neighbourhood diversity on crown structure and productivity of individual trees in mature mixed-species forests

Species-specific genotypic features, local neighbourhood interactions and resource supply strongly influence the tree stature and growth rate. In mixed-species forests, diversity-mediated biomass allocation ha...

• 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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Assessment of forest ecosystem service research trends and methodological approaches at global level: a meta-analysis

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Ecosystem service (ES) is a growing field of research characterized by an increase in publication number. The review was conducted to provide an overview of trends of forest ecosystem services (FES) research and methodological approach to studied FES. Currently, the number of publications on ES was more than 18,000, but small publications were linked with FES. Based on the selection criteria, 41 peer-reviewed publications were screened to analyze the type of FES studied and the method used to quantify ES. The result showed that most of the research articles, to date, had focused largely on provisioning, regulating and cultural ecosystem services, which were timber production, water supply, carbon sequestration, and recreation. FES like a pollination, genetic resources, pest regulation, and aesthetic values have not been studied in any literature reviews. The review result showed that different studies used diverse methodological approaches and had inconsistent and scattered conclusions. From the selected studies, the majority of them were conducted in Europe and Asia. Particularly, the publication number from Ethiopia was very low and needs to conduct studies before the forest resources are further degraded.

Ecosystem services are “the benefits that people obtain from ecosystems” (MEA 2005 ). It is defined as “The direct and indirect contributions of ecosystems to human wellbeing” (TEEB 2010 ). Ecosystem services provide various materials and non-material benefits to human beings (Costanza et al. 1997 ; Nelson et al. 2009 ; Vizzarri et al. 2015 ; Englund et al. 2017 ). These services are grouped into four broad categories of provisioning, regulating, cultural and supporting ES. Provisioning services which are familiar part of the economy and includes goods obtained from ecosystems like food, fiber, fresh water, and genetic resources. Regulating services include benefits obtained from the regulation of an ecosystem processes, including air quality regulation, climate regulation, water regulation, an erosion regulation, a pollination, and natural hazard regulation. Cultural services are the nonmaterial benefits that human beings are acquired from the ecosystem through aesthetic experience, reflection, recreation, the spiritual enrichment, and knowledge system and education. Supporting services are fundamental to maintain the conditions for life on Earth and include services like soil formation, photosynthesis, and nutrient cycling, and habitats for species (De Groot 2002 ; MEA 2005 ; Englund et al. 2017 ). Therefore, the flow of ES is determine the level of human-well beings, which is linked to ecosystem composition and function (Cruz-Garcia et al. 2016 ).

Ecosystem services as one field of studies began after the studies by Daily in 1997 and Costanza et al. in 1997 (Aznar-Sánchez et al. 2018 ). Later on, the concept is introduced to the political agenda by the MEA project in 2005 ; the TEEB in 2010 and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) in 2012. They are benchmarks that turned the concept into a political instrument to achieve the sustainable use of natural resources. They can benefit policy-makers to make an informed decision that is based on scientific evidence (Aznar-Sánchez et al. 2018 ). Since the publication of the MEA document in 2005, ecosystems have become widely recognized as natural capital assets that support and supply services which are highly valuable to the human well beings. As a result, there is a growing appreciation worldwide on the importance of an ecosystem to human welfare by providing goods and services, and on the impact of human actions on ecosystems (Ojea et al. 2012 ). The ecosystem that provides services is sometimes referred to as ‘natural capital’. Thus, “ecosystem services refer to the relative contribution of natural capital to the production of various human benefits, in interaction with the other forms of capital”. Ecosystem services can flow to the human wellbeing through an interaction process (Harmácková and Vackár 2015; Costanza et al. 2017 ) (See Additional file 1 : Table S2). To make more specific about the natural capital and ecosystem services, various ecosystem classification was done for scientific analysis, economic valuation and policy issues. For instance, following Daily ( 1997 ) and Costanza et al. ( 1997 ), various classification schemes were developed such as MEA in 2005 classified into 22 under four groups: provisioning, regulating, cultural and supporting. TEEB ( 2010 ) uses a classification that includes 22 ES grouped into four main categories: provisioning, regulating, habitat, and cultural and amenity. The important difference between MEA and TEEB is that the TEEB classification omitted supporting services—is considered it as a subset of ecosystem processes and the inclusion of habitat services category under its classification schemes. The Common International Classification of Ecosystem Services (CICES) was developed to provide a hierarchically consistent and science-based classification to be used for natural capital accounting purposes (Costanza et al. 2017 ).

The continued and the rapid degradation and unsustainable use of ecosystem services all over the world put the health and livelihoods of millions of people at risk (Egoh et al. 2007 ; Aerts and Honnay 2011 ). These urges a sustainable way of management to balance the potential of forest ecosystem services with human needs. The balance between forest resource exploitation for human wellbeing and ecosystem conservation is a key to bring sustainable development (Rukundo et al. 2018 ). As a result, scientists and policy maker started to work together. Researcher, policy-maker, and practitioners develop an interest in ES that has come from several sources (Balvanera et al. 2014 ). The widely acknowledged source is perhaps the report of MEA by the United Nations in 2005, which was the first comprehensive global assessment of the implications of ecosystem change for people (Cuni-Sanchez et al. 2016 ). Following the MEA 2005 ) report, the ecosystem services concept got broader attention worldwide. The existence of strong link between biodiversity and ecosystem services lead to the creation of TEEB which was initiated by UNEP (United Nations Environment Programme) in 2010, EC (European Commission), and the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) in 2012. In addition, an international conference was held on ecosystem services. The growing on the importance of ecosystem services fields at the global level has also resulted in the launch of new journal type with a title of Ecosystem Services in 2012 (Braat and de Groot 2012 ; Ninan and Inoue 2013b ), and special issue of the journal was opened which is Ecological Economics on ecosystem services valuation. The aim is strengthening a science-policy interface that can contribute to conserve and sustainably use the biodiversity resources, prolonging human well-being and to bring sustainable development (García-nieto et al. 2013 ; Balvanera et al. 2014 ). During the past decade thus, ecosystem service research is a rapidly evolving field and the number of publication is rising (Fisher et al. 2008 ).

Ecosystem services concept was initially introduced to raise public awareness on the importance of biodiversity for human existence and to conserve biodiversity. Forest is thus, the main sources of ecosystem services and are fundamental for life support systems (Vizzarri et al. 2015 ). Forest ecosystem services have direct and indirect services. Direct provisioning services are timber, fiber, bioenergy, grazing, clean water, traditional medicines, and the socio-cultural benefits that include ritual services, aesthetic, and ecotourism. Forests have also regulatory services such as erosion regulation, landslide control, and regulation of water, air, drought, disease, and climate through carbon sequestration. Supporting services from forests can include pollination, nutrient cycling, and sources of propagates for shade and agroforestry trees, bio-control of agricultural pests, and biodiversity conservation (Power 2010 ; Tadesse et al. 2014 ).

Forest plays a major role in global climate regulation through sequestration of carbon and serves as a carbon sinks during its most stage of development. It can also serve as a habitat for various plants and animal species, for mitigating pollution, flood control and other ecosystem services (Deal et al. 2012 ). Tropical forests are one of the most diverse in biodiversity and ecosystems on earth. Biodiversity is widely acknowledged that it has a significant role in the provision of various ecosystem services to people (Beenhouwer et al. 2013 ; Tekalign et al. 2018 ). It maintains the indigenous culture, provides means of livelihoods for millions of people, and sequestering about 40% of the global terrestrial carbon (Tekalign et al. 2018 ). However, the high rate of deforestation and degradation are eroding the biodiversity composition, function, and structures of forests. The degradation process causes a decline in resistance of forests to natural or anthropogenic disturbances (Brockerhoff et al. 2017 ). These, in turn, caused a decline both in quality and quantity of services that people have from forests because biodiversity and ecosystem services are inseparably linked, and both are declining at the global level (Egoh et al. 2007 ; Aerts and Honnay 2011 ; Balvanera et al. 2014 ; Tekalign et al. 2018 ). These are caused by human-induced effects such as forest removal, degradation, and encroachments which caused a decline in biodiversity and ecosystem services (Tadesse et al. 2014 ; Tolessa et al. 2017 ). According to Aznar-Sánchez et al. ( 2018 ), the expansion of farming land, urbanization, and effects of climate change also threaten the forest resources and its service flow. Thus, the forest resource needs management that sustainably prolongs the ecosystem services. To introduce sustainable forest resource to maintain an ecosystem services, understanding of their importance at the spatial and societal level is critical. Understanding the types of forest-based ecosystem service which has importance to human wellbeing is the main part of ecosystem service assessments.

The reviewed work is a systematic study dealing with ecosystem services of forests. Particularly, the study focuses on answering the following two questions: (1) what are the trends of forest ecosystem services in comparison to ecosystem services; (2) what are the methodological approaches employed to value FES. In the discussion section, the research gaps of the selected studies and future research needs are explained. Although the monetary value for forest ES has limitation and technical challenges (Spangenberg and Settele 2010 ; Luck et al. 2012 ), representing FES values in monetary terms has more role to expand the sustainable utilization of resources. Therefore, the main aim of the present study was to assess and gather the available knowledge and information on forest ecosystem services, its monetary value, and areas demanding further works through a meta-analysis of individual case studies from the peer-reviewed English journal articles. The specific aim is to (1) provide and develop timely and relevant scientific knowledge regarding ecosystem services and forest ecosystem services; (2) quantify the monetary value and the methodology used for forest ecosystem services; and (3) identifying and discussing future research areas of forest ecosystem services.

Methodology

Data collection.

Data were collected from the literature found in Scopus and Science Direct databases. The search focus on peer-reviewed journal articles written in English, excluding books and book series, conference proceedings, editorials, letters, patents, reference works, research notes and trade publications. The search was conducted in December 2018 and peer-reviewed journal articles cover those published between 2005 (coinciding with the publication of the Millennium Ecosystem Assessment synthesis reports) up to 2018 that focus on ecosystem services and forest ecosystem services in a title, keywords or abstracts. It also has been carried out a general search of articles on forest ecosystem services indexed by Google scholar during the same period.

The term used and frameworks of data collection

A list of research articles was generated using “ecosystem service” both on title and title-abstract-keyword. The next search was using the filtering process of “forest ecosystem services” both on title and title-abstract-keyword. It must be taken into consideration is that different search parameters would give different results. Figure  2 below presents a flow diagram of the selection process of publications for this review paperwork. It is based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) rules and templates (Moher et al. 2010 ). The selection was based on the following criteria. In the first step, a research article that constitutes forest ecosystem services or forest services in the article title was selected by excluding conference/proceedings and books. The literature resulting from the keyword search was narrowed down by reading the abstracts and screening the texts. It limits studies that explicitly stated forest-related ecosystem services and it excluded any paper dealing with ecosystems other than forests. Using the filtering process, the total number of articles found was 17,911 from Scopus and 6321 from science direct databases, without excluding duplicated articles. The second step was to include only case studies that took place on forest ecosystem services. This resulted in research articles of about 1911 from Scopus and 429 from science direct databases. The third step was to exclude publications that was reviewed articles and research papers that lack the quantification of forest ecosystem services either using InVEST or other economic valuation techniques. Document abstracts were read to evaluate the occurrence of the different forest ecosystem services and the monetary value of forest services. This help to ensure that the articles were included that focused on valuation, quantification, and mapping of forest ecosystem services, but were excluded when they did not meet the selection criteria. Duplicated articles were manually deleted. Therefore, 41 articles were selected for further analysis. For full transparency, a list of all publications retained for further analysis is provided in the Additional file 1 : Table S1.

From the final list of the papers, there were various types of forest ecosystem services have been analyzed, as most of the reviewed papers address on several forest ecosystem services. It is conceded that these publications do not comprise every single paper that stated forest ecosystem services, but they do allow one to gain a broad overview of the most significant literature and to draw reliable conclusions on recent approaches to forest ecosystem services research and its methodology used to assess the various ecosystem services. Science direct includes a lower number of indexed journals than Scopus however, Scopus is easily accessible, has tools to view and analysis data, and downloading data in various ways (Aznar-Sánchez et al. 2018 ) (Fig.  1 ).

Source: Modified from Moher et al. ( 2010 ).

The flow diagram for database search of publications for systematic reviews. This figure demonstrates the process of selecting the final articles related on the topic for further analysis. The number of articles was large in number, when the search was any ecosystem service research work. However, when the criteria is going to be more specific to FES and its methods used to quantify/qualify, mapping and value the ecosystem services, thus the size of the articles was reduced

Data analysis and presentation

The data gathered in this review were analyzed using descriptive statistics. For each of the identified studies, therefore, the following information has been coded for further analysis: types of ecosystem services investigated, the methodology used to assess the ecosystem services and monetary estimates, scale, year of study, institution, country, and journal. Later each forest ecosystem services was classified to provisioning, regulating, cultural and supportive services based on the millennium ecosystem assessment categories. After the FES type was identified, statistics of the four ES categories were calculated and displayed using different tables and figures. The results were drawn up in order to view and analyze the data using an Excel spreadsheet.

Results and discussions

Trends of scientific research production on es.

The two databases had no equal publication number using the same search terms. Table  1 summarizes the main outcomes of the literature review both on ecosystem and forest ecosystem services from 2005 until December 31, 2018. From the first search of research articles using a combinations of the term “ecosystem service” and title-abstract or keyword “ecosystem services”, about 6321 articles (5656 research and 665 reviewed articles) were found in science direct data basis and from Scopus data basis, a total of 17,911 articles of which 15,927 research and 1984 reviewed articles were found. Comparatively, the Scopus database had more published articles in its archive than the science direct database. In all search engines except the “supporting services” OR “supporting ecosystem services”, the Scopus had larger number of published articles than science direct.

Using the second search engine, “forest service” OR “forest ecosystem services”, the publication amount from Scopus database was larger than the science direct database. About 1911 publication (1762 research articles and 149 reviewed articles) and 429 articles (421 research and 8 reviewed articles) were found from Scopus and science direct databases, respectively. Based on the four main categories of ES, most of the articles were cover cultural, provisioning and regulating ES. The number of studies addressing habitat/supporting ES were small, which might be linked with the absence of well-developed methodology, unlike the others.

From the third searching engine category, “ecosystem services” AND “Ethiopia”, “forest ecosystem services” AND “Ethiopia”, and “ecosystem services” AND “forests” AND “Ethiopia”, relatively Scopus database had larger publication number. However, when the search term was becoming more specific to the types of ES, the amount of publication was too small. For example, based on “forest ecosystem services” AND “Ethiopia” searching terms, only two and one research articles were found from Scopus and science direct databases (see Table  1 ).

Figure  2 shows a comparison between the numbers of published research on forest ecosystem services in particular and on ecosystem services in general from the science direct database. In order to compare the growth in the number of articles within each line of research, the annual accumulation of publication was calculated. Based on publication number, in 2005 and 2018, ES increased from 23 to 1291, and FES from 21 to 50 publication using science direct database. The result showed that there is an increment of research publication mainly on ecosystem services. Though the term ecosystem services existed before the 1970s, it has been mainstreamed in the scientific literature in 1990s (Costanza et al. 1997 ; Daily 1997 ). After the publication of the millennium assessment report in 2005, ecosystem research work became popular and exponentially increases (Fisher et al. 2008 ; Luck et al. 2012 ; Philipp et al. 2013 ). For instance, the number of publications on ecosystem services has grown exponentially from one in 1996 to more than ten per year to 2008 (Crossman et al. 2013 ).

Comparative trends on journal articles published on ecosystem and forest ecosystem services from 2005 to 2018 using science direct database. According to the data source of science direct since 2005 to 2018, more number of articles were published on ecosystem services mainly after the publication of the Millennium Ecosystem Assessment. The publication linked to forest ecosystem services are not that increasing like the ecosystem services research trends. A very small number of articles were produced to map, value and quantify forest based ecosystem services

Figure  3 shows the trends of forest ecosystem service publications. However, the amount of publication on forest ecosystem services was rising after 2009. From the last 3 years, the year 2017 had the highest number of articles (217 articles) than the year 2016 (184 articles) and 2018 (162 articles). The last year had the smallest article number than the previous 2 years in terms of its publication number.

Trends of forest ecosystem services study from 2005 to 2018 using Scopus database. This figure exclusively explains the trends of forest ecosystem services since 2005 on wards using Scopus as the main data source. It showed that the number of publication is increasing in positive ways up to 2017. However, the last year (2018) had the lower number of publication and it might be linked with the time demands to register the published articles from various journal types in Scopus data base

Bojovic et al. ( 2013 ) reviewed journals during the 5 year period from 2006 to 2010 to identify research trends of forestry journals. They found out about 16,258 documents from 42 journals which were classified in 22 sub-disciplines. However, none of these categories were linked to ecosystem services. This was due to the existence of various journal types that published forest-based ecosystem service research works. Nowadays, there are over 18,000 research articles on ecosystem services. It can be concluded that ecosystem topic has got more attention in the scientific community and also the relevance of forest in its ecosystem services are increasing. Therefore, the study proved the existence of growth on ecosystem service publication using the sample sources. However, this analysis was not considering articles that are stating ecosystem services but not used the term ecosystem/forest ecosystem services in their title topic, keywords or abstract.

Distribution and scientific journal production on FES

Subject categories.

Note that one article may be simultaneously included in more than one category. Figure  4 shows the subject categories of authors based on Scopus classification from 2005 to 2018. Throughout the whole period analyzed, 35.8% of the published articles were classified under Agricultural and Biological Sciences. The next which was 34.3% in Environmental Science, 10.8% Social Sciences, 4.4% Earth and Planetary Sciences, and 3.1% under Economics, Econometrics and Finance. The remaining categories account for less than 3% of published articles.

The percentage of ecosystem service publication based on subject areas. This figure stated authors’ background who published articles in relation to ecosystem services. Most of the articles were published from authors with agricultural and biological, and environmental science background

Based on principal subject categories under which Scopus classified articles on forest ecosystem services, two subject areas shared about 70%. These were Agricultural and Biological Sciences, and Environmental Science. Similarly, in the reviewed work of Aznar-Sánchez et al. ( 2018 ), these two subject areas were dominant and shared more than 60% of the published articles from 1998 to 2017. This slight variation between these two reviews might be due to variation in data sources and time period of the review covered.

Dobbertin and Nobis ( 2010 ) reviewed the publications of 6 journal articles on forests during the 1979–2008 periods. As a result, they mentioned that the titles of forest or forestry publications are increasingly include topics from Natural Sciences but Economic and Social Sciences topics are still underrepresented. Their result showed that social and economic topics are understudied. However, in the work of Aznar-Sánchez et al. ( 2018 ), they could observe that there is a relevant presence of Social Sciences in forest ecosystem services but the economic presence is still limited. On this review work, however, the Social Science and Economics, Econometrics and Finance had a relatively better share in forest ecosystem services research. However, other social science like Business, Management and Accounting had weak involvement in the FES studies.

Publication by countries

Table  2 shows the evolution in the number of articles for FES in the top 10 countries from 2005 to 2018. The United States placed first, followed by, Canada, Germany, China, and the United Kingdom. Compared with Aznar-Sánchez et al. ( 2018 , reviewed work, two countries, South Korea and Finland- which were replaced by Brazil and France were absent in the top ten productive country lists. This variation might be related to the search engine term we used and the sources of the database.

Table  2 also shows the number of articles published on ES and if the number of ES articles were considered, the USA comes first and South Korea tenth. These countries were also considered to act as the main research drivers in the ecosystem services research, except for Finland and South Korea, which their position was replaced by Spain and Netherlands, respectively. The United Kingdom is the country with the second highest article number next to the USA if we consider its publications on ES and followed by Germany.

Table  2 also shows the percentage of ecosystem services articles which deal with forest ecosystem services per journal {(FES/ES: number of articles on forest ecosystem services/number of published articles on ecosystem services) × 100}. The analysis period of each journal begins in 2005 with its publication on FES. The country with the highest ratio was in South Korea of which from the total ecosystem publication, about 36.17% was on FES. However, this did not imply that South Korea leading others in its publication number rather the small variation between the number of ES and FES publication made the ration highest. The next highest was in the USA, which was 21.65%, but the least was in the United Kingdom (1.94%).

Publication by journal types

Figure  5 shows the five journals with the most publications on FES from 2005 to 2018. The most productive journal in this field was Journal of Forestry , with a total of 106 (7.1%) articles. Forest Ecology and Management , with a total of 87 (5.8%) article was the second largest journal. This journal publishes a lower number of articles than the Journal of Forestry , but its SCImago Journal Rank (SJR) index (1.625) is the highest from all journals. According to Aznar-Sánchez et al. ( 2018 ), Forest Ecology and Management journal had the first article on FES in 2001 and had the first position after 2005. The third and fourth journals with the largest number of published articles were USDA Forest Service General Technical Report PNW Gtr and USDA Forest Service General Technical Report RMRS Gtr , which equally had 65 (4.4%) articles. USDA Forest Service General Technical Report PNW Gtr was the most recent publication to join this field of research, and publishing its first article on FES in 2008. Despite this, it takes the lead in the number of articles published in the most recent period, 2011 and 2016, but has the lowest SJR index (0.128). Forest Policy and Economics were in fifth place with 51(3.4%) articles. These five journals comprise only 25.12% of the total number of articles published because scientific articles on FES are published in a very wide range of journals.

The first five journal types that published research works on FES. The issues on ecosystem services were published in diverse journal types. Among them the first five journal lists that had a large number of research publication on ecosystem services were listed. The concept of ecosystem services motivated to launch a new journal of ecosystem services by known publishers to publish research on ecosystem services

The Journal of Ecosystem Services has low rank based on the number of publication, which was 30 articles (2.0%) and had the eighth place. It is the most recent publication that joins this field of research and published its first article on FES in 2013 (Aznar-Sánchez et al. 2018 ). Despite this, it takes the lead in the number of articles published and has the highest SJR index (1.743). It should be noted that the journals with the highest number of articles on FES are of the highest quality.

The reviewed publications on FES

Overview of fes types and their distribution.

As marked in Fig.  6 , the 41 studies included were conducted in six continents; Asia (18 studies), Europe (9 studies), Africa (6 studies), Australia (1 study), North America (2 studies) and South America (5 studies). The study represented 20 countries and a single study in Europe cover 26 countries, which was a study at a regional scale. The selected studies had cover eight countries from Asia, five countries from America, three countries from Europe and Africa each, and one from Australia. The study covered small number of world countries and were not enough to cover the forest resources of the continents. For instance, at country level, China and Spain had the largest number of publication, which each has seven and five published articles, respectively. Thus, quantitative inference based on the existed results were less sound.

Distribution map of the FES reviewed publication. The figure was used to show the distribution of the final list of published articles on the world. Most of the articles were conducted in Asia (China and India), Europe (Spain and Italy), and in Africa

Figure  7 shows the list of eighteen journal types where the reviewed articles were published. The journal with the most publication number was Ecosystem Services that published 13 papers out of the 41 reviewed articles. The next was Forest Policy and Economics that had a publication of four papers. Two journals i.e. Ecological Indicators and Land Use Policy each had three publications. From the remaining 14 journal types, four journals each had two publications and the rest 10 journals had one publication each. The number of ecosystem services investigated is different across the reviewed articles, ranging from a minimum of one to a maximum of 13 in a single paper.

List of journals that published selected publication used for further analysis. It includes journal types that published the articles selected for final analysis using the selection criteria. The total number was 18 journal types and 10 of them got published only one article and one journal (ecosystem services) alone published 13 articles out of 41

From the ecosystem services included in the sample, 85.4% and 82.9% were regulatory and provisioning ecosystem services i.e. were 35 and 34 publications, respectively. Twenty reviewed (48.8%) papers dealt with cultural services, and 18 (37.5%) papers deal with supporting services. The studies differ in their spatial scope, time of the study, the ecosystem services assessed and the methodology used.

The 41 selected articles deal with mapping, valuation, and quantification/qualification of FES and in general cover about 243 FES indicators. Figure  8 listed the most common ES addressed in the reviewed literature. The most common services investigated from provisioning ES were timber production and water provision, which had equally 46.3%; carbon sequestration (65.9%) and erosion control (34.1%) from regulating ES; recreation (46.3%) and, tourism, education and research (14.6%) from cultural ecosystem services and soil conservation (22%) from supporting ecosystem services. However, cultural and supporting ecosystem services were most under-researched ecosystem service category (Howe et al. 2014 ; Cruz-garcia et al. 2016 ). This has been common in the literature (Defries et al. 2004 ; Rodriguez et al. 2006 ) and the reason could be one or more of the following. These services are not well defined and understood like provisioning or regulating services (Crossman et al. 2013 ), and the methodology might not be easy to study and/or measure the given ecosystem services.

Most common FES investigated from the selected reviewed literature. Using the final list of articles, the most common forest ecosystem services well studied were described. Majority of the work concentrated on regulating (carbon sequestration, erosion control), provisioning services (timber production and water supply) and recreation from cultural ecosystem category

Based on the mode of FES assessment, 22 publications (53.7%) of the reviewed paper used economic valuation, 8 publications (19.5%) of FES mapping, and 10 publications (24.4%) of FES quantification/qualification. Publications on economic valuation was higher than mapping and quantification/qualification of FES. Two publication (Joshi and Negi 2011 ; Duc et al. 2018 ) combined two modes of FES assessments, that is ‘quantification/qualification’ and valuation, and ‘mapping’ and ‘valuation’ and one publication (Escobedo et al. 2015 ) was undefined. However, they ended up with more focus and detail investigation on one of the assessment mode. For each reviewed paper, the following basic information was summarized: authors and location; methods employed to assess ecological functions; methods employed for estimating monetary values; estimates for the total FES; and estimates per hectare (Tables  3 , 4 , 5 , 6 , 7 and 8 ).

Methodological approaches used by the selected papers

Provisioning fes.

Provisioning ecosystem service category mainly includes Food, Fiber, Genetic resources, Freshwater, Ornamental resources, Bio-chemicals, Natural Medicines, etc. (MEA 2005 ). From these ecosystem services, the most covered in the reviewed articles were water yields and timber production. According to Table  3 , each author used a distinct method of water yield valuation. This implies that the valuation techniques for water yield estimation was diverse. In contrast, the economic valuation techniques for timber productions were related to market prices. However, the total values and the per hectare and per year estimate were local dependent, which was higher in most developing countries and lower in less developing countries. For instance, in Kenya by Huxham et al. ( 2015 ), it was 206 US$ ha −1 , and in Italy by Häyhä et al. ( 2015 ), estimated 218£ ha −1 year −1 (248.084 US$). Therefore, the economic value of timber was local dependent.

Regulatory FES

This category includes air quality regulation, climate regulation, water regulation, erosion regulation, disease regulation, pest regulation and pollination (MEA 2005 ). Among them, from the 24 reviewed literature, most of them addressed climate regulation and erosion regulation. Through photosynthesis, carbon is stored in forests and is a function of forest productivity (Brockerhoff et al. 2017 ). Based on Table  5 , the economic valuation of carbon sequestration was diverse in its types. The volume of carbon sequestered by forests was commonly estimated based on market/carbon market price (Ninan and Inoue 2013a ; Morri et al. 2014 ; Huxham et al. 2015 ; Ninan and Kontoleon 2016 ; Kibria et al. 2017 ; Wang et al. 2018 ). Morri et al. ( 2014 ) used 20£/tCo 2 in Italy and Huxham et al. ( 2015 ) in Kenya used 10US$/tCo 2 . Few studies used carbon tax method of economic valuation for carbon sequestration. These were Kibria et al. ( 2017 ) used carbon tax using InVEST model with a value of 141US$ hectare −1  year −1 in Cambodia, Li et al. ( 2017 ) used carbon tax law method with estimation value of 3.41 × 10 7  ton −1  year −1 in China and Delphin et al. ( 2016 ) in the USA. There were two other case studies (Gaodi et al. 2010 ; Duc et al. 2018 ) which used social and afforestation costs for climate regulation ecosystem services. In addition, the estimated monetary value per hectare basis for the same ecosystem services was vary. For instance, Morri et al. study in Italy at two watersheds (Marecchia and Foglia) estimates the total ES value of 7.32 × 10 6 £ year −1 and 6.60 × 10 6 £ year −1 respectively (Morri et al. 2014 ). According to D’Amato et al. ( 2016 ), for hydrological estimates in China, the monetary value was range from a few dollars to thousands. The next regulatory ecosystem services that had economic and biophysical estimation was erosion regulation of forests. Table  6 shows each author used different techniques of estimating soil erosion in their study. The valuation and monetary estimation techniques were also varied across different studies. However, air quality regulation of forest was studied in South Korea by Song et al. ( 2016 ), and the finding showed that forests can sequester about 8.6 kg SO 2 and 16.8 kg NO 2 ha −1 . In 2011, the whole forest was sequestered of 52,150 and 93,254 tons of SO 2 and NO 2 , respectively.

FES benefits that lack direct market valuation could be valued in monetary terms using non-market valuation methods like cost-based methods (i.e. avoided cost or damage) which was particularly suitable for approaching regulating services (de Groot et al. 2012 ). In addition, the benefit transfer, opportunity cost, willingness to pay and replacement price methods were commonly used. These methods help to estimate the monetary value of local ecological functions performed by using replacement price method. It helps to measure the cost of replacing the ecosystem services through other means. For example, the value of forest for soil conservation and soil formation could be estimated using the replacement price method of fertilizers and organic manure. For FES where the use values were not clear or non-use values were dominant, other methods like hedonic pricing, willingness to pay and travel cost could be employed.

Supporting FES

Supporting ecosystem services includes soil formation, photosynthesis, primary production, nutrient cycling and water cycling (MEA 2005 ). From the 24 reviewed literature result, the most studied ES were soil formation and nutrient cycling ecosystem service. According to Table  7 , soil formation was included in six studies and was typically based on replacement price method of the price of fertilizer, where the values estimated was 351.45 × 10 6 ¥ and organic manure from the decomposition of litter had a value estimate of 728.67 × 10 6 ¥ per year in China in 2004. The other estimation method was hedonic pricing, opportunity cost and benefit transfer of Costanza et al. ( 1997 ). The value of FES was therefore high in some areas. For instance, the studies of Kibria et al. ( 2017 ) in India and Ninan and Kontoleon ( 2016 ) in Cambodia proved the high cost of FES, which might be related with the land price.

Cultural FES

Cultural services include recreation, education, aesthetic, and sense of place, cultural heritage, spiritual and religious, and inspirational services. These services were the nonmaterial benefits which people can obtain from ecosystems through a spiritual enrichment, cognitive development, reflection, recreation, and aesthetic experiences (MEA 2005 ). The case study covers section of the cultural ecosystem services, mainly recreation, and tourism, education and research. According to Chan et al. ( 2012 ), the possible reason for the absence of most cultural ecosystem services is lack of attention on the non-monetary valuation of ecosystem services. Similarly, the result of this meta-analysis and systematic work showed that cultural ecosystem services were less valued and mapped ecosystem services except for recreation ecosystem services. Another study by Lara-Pulido et al. ( 2018 ) finding, cultural ecosystem services was less addressed than regulatory and provisioning services as it has a lower value than other ecosystem services. Moreover, (Milcu et al. 2013 ) noted that cultural ecosystem services are understudied because of two factors. First, cultural ecosystem services are not emphasized on the whole range of cultural ecosystem services rather on specific parts. Secondly, it is considered as a complementary rather than being leading incentive for decision making.

Table  8 shows the recreation services from the 24 reviewed articles. The methodology used includes benefit transfer, willingness to pay, cost method (travel cost and cost of hunting) and the direct valuation method. As a result, there was a large variation of the monetary values reported. However, recreational ecosystem service was generally valued highly in high income countries (such as Italy and Japan) than low-income countries (e.g. India and Cambodia).

In sum, Tables  3 , 4 , 5 , 6 , 7 and 8 infers that there was a great variation in types of ES indicators, economic valuation methods, and reference prices between studies. The reviewed literature employed the market price method (direct market price, stumpage price, and market valuation) and cost methods (avoided cost, opportunity cost, travel cost, replacement cost, cost of removal, social cost) to produce a general approximation of the economic value of water yield and timber production from provisioning, and carbon sequestration and soil erosion control/sediment yields from regulating FES. Benefit transfer methods were more used in cultural FES for economic valuation of recreation service. Further, only a limited number of studies provided a monetary value of the specific ES per unit of area and most of the studies did not provide the size of the forest area used in the study. Researchers lack consistency in their use of terminology. For instance, some discuss ‘carbon sequestration’, ‘maintenance of favorable climate’ or ‘climate mitigation’. Other researchers were used the term ‘CO 2 sequestration’. Some of them provided the total economic value of the ES, however, drawing comparisons and inferences using total ES values were less worthy since the size of the total area used for the investigation was varied across studies.

The studies used different currency types and conducted at varied years. Thus, a simple conversion of the monetary values to international currency might not consider the rate of inflation. In addition, most of the difficulty was related to the different methodological approaches used by the studies. Portman ( 2013 ) conducted a critical review on challenges to implement ES in the real world and finally the author concluded two challenges. These were the challenge to mainstreaming the ES concept into policy-making institution and challenge of the assessment methods.

Furthermore, there were papers for example (Zhang et al. 2010 ; Uddin et al. 2013 ; Häyhä et al. 2015 ; Huxham et al. 2015 ) which did not define well the ecosystem services under investigation, its category, and in some of the reviewed articles, two or more services with different outputs were jointly valued. These challenges had obstruct from making a comparative analysis between different case studies. Therefore, Zhang et al. ( 2010 ) recommend first to adopt a unified definition of ecosystem services and to use only standardized methodologies for valuing ecosystem services.

Trends of FES studies in Ethiopia

In Ethiopia, the amount of publication included both in Scopus and science direct databases were small compared to the amount and diversity of resources we have had. Using the search engine of “ecosystem services” AND “Ethiopia”, 107 and 48 publication was found from Scopus and science direct databases, respectively. From these number, five and two papers were reviewed papers from Scopus and science direct databases, respectively. By using the more specific search engine, which was “forest ecosystem services” AND “Ethiopia”, the amount of publication was too small, i.e. two and one from the two data sources. This showed that the amount of publication on FES case studies was too small, almost nil, and the existed studies cover ecosystem services at watershed/landscape level. Therefore, more efforts are needed from scholars of the area to produce a scientific publication on both FES and ES in Ethiopia (see Table  1 ).

The number of publication on ES has increased, however, the FES has shown no remarkable progress in its publication size. Mainly after 2010, there is an increment in the amount of publication on ES. This indicates that both ES has become an increasingly important research area at the global level. They have provided much insight into how to ensure that ecosystem service research is scientifically vigorous and reliable and also conveys a clear message to decision makers.

Based on the review work, the amount and coverage of FES studies available in English in peer-reviewed journals were too small, had limited coverage of FES, and only limited countries have available forest value estimates. From the various FES, water yield and timber production from provisioning; climate change regulation and erosion control from regulating ecosystem services were the most common services addressed by most case studies. However, other basic ecosystem services from forest resources lack attention from the scientific community. Thus, most of the existed research work focus on provisioning, regulatory and cultural services that had a relatively well-developed methodology. Therefore, unlike the forest ecosystem services discussed above, other services, such as pollination, genetic resources and gen poll protection, regulation of pests and human disease, the forest’s aesthetic values, waste treatment, environmental purification, and disease regulation, have received less attention in the scientific community due to lack of data, challenges in estimating their value, and lack of well-designed methods, among other things. There is a need for more information on these neglected forest ecosystem services in order to know the dynamic nature of FES and how local situation impacting the given service types.

Most of the studies used either biophysical and or/economic valuation methods to estimate the given ecosystem services using per hectare per year basis. They have investigated ecosystem services per unit area per year. However, based on this review work, it was not possible to draw a conclusion on the effect of the methodology used on the monetary estimate of the given ecosystem services. This was due to multiple factors like ecosystem services and related monetary values were context-dependent, i.e. it was linked to geographical, ecological and socio-economic nature of the study area. The other factor was the issue of conversion of monetary values to international currency. The studies used different currency types and conducted at varied years. This work, therefore, can be extended with a quantitative analysis of the articles by including other types of database. In sum, the methodologies for the mapping, quantifying and valuation of FES are developing rapidly both from economic and biophysical valuation techniques. Even if they are developing, most of them were general evaluations which are less likely linked with decision-making processes (Additional file 1 : Appendix).

Availability of data and materials

Not applicable.

Abbreviations

The Common International Classification of Ecosystem Services

European Commission

Ecosystem Services

forest ecosystem services

Integrated Valuation of Ecosystem Services and Tradeoffs

Kwacha Rebased (the currency of Zambia)

Millennium Ecosystem Assessment

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Rupees (currency of India)

SCImago Journal Rank

the Economics of Ecosystems and Biodiversity

United States Dollar

Yen (currency of Japan)

Yuan (currency of China)

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We are grateful to Addis Ababa and Debre-Berhan University for their academic support to conduct this review paper. We would also like to extend our gratitude to the two anonymous reviewers who have provided us with their invaluable scientific comments and advices that have greatly improved the quality of the paper.

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Mengist, W., Soromessa, T. Assessment of forest ecosystem service research trends and methodological approaches at global level: a meta-analysis. Environ Syst Res 8 , 22 (2019). https://doi.org/10.1186/s40068-019-0150-4

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Original research article, assessment of tradeoffs between ecosystem services in large spatially constrained forest management planning problems.

• 1 Forest Research Center and Associated Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, Lisbon, Portugal
• 2 College of Agriculture and Environmental Sciences, University of Gondar, Gondar, Ethiopia
• 3 Research Centre for Mathematics and Applications, University of Évora, Colégio Luis Verney, Évora, Portugal
• 4 Department of Industrial Engineering, University of Chile and Institute for Complex Engineering Systems (ISCI), Santiago, Chile
• 5 Center for Mathematics, Fundamental Applications and Operational Research, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
• 6 School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, United States

Forests provide multiple ecosystem services, some of which are competitive, while others are complementary. Pareto frontier approaches are often used to assess the trade-offs among these ecosystem services. However, when dealing with spatial optimization problems, one is faced with problems that are computationally complex. In this paper, we study the sources of this complexity and propose an approach to address adjacency conflicts while analyzing trade-offs among wood production, cork, carbon stock, erosion, fire resistance and biodiversity. This approach starts by sub-dividing a large landscape-level problem into four smaller sub-problems that do not share border stands. Then, it uses a Pareto frontier method to get a solution to each. A fifth sub-problem included all remaining stands. The solution of the latter by the Pareto frontier method is constrained by the solutions of the four sub-problems. This approach is applied to a large forested landscape in Northwestern Portugal. The results obtained show the effectiveness of using Pareto frontier approaches to analyze the trade-offs between ecosystem services in large spatial optimization problems. They highlight the existence of important trade-offs, notably between carbon stock and wood production, alongside erosion, biodiversity and wildfire resistance. These trade-offs were particularly clear at higher levels of these optimized services, while spatial constraints primarily affected the magnitude of the services rather than the underlying trade-off patterns. Moreover, in this paper, we study the impact of the size and complexity of the spatial optimization problem on the accuracy of the Pareto frontiers. Results suggest that the number of stands, and the number of adjacency conflicts do not affect accuracy. They show that accuracy decreases in the case of spatial optimization problems but it is within an acceptable range of discrepancy, thus showing that our approach can effectively support the analysis of trade-offs between ecosystem services.

1 Introduction

Worldwide demographic, socio-economic and environmental changes over the last three decades have led to a shift in forestry from a purely timber production focus toward the consideration of a broader range of ecosystem services. Simultaneous provision of multiple services in forest management is a complex problem where many trade-offs need to be considered. Some objectives are competitive, while others are complementary ( Tóth et al., 2006 ). As a result, harmonizing ecological, economic, and sociocultural values of forest ecosystems and simultaneously managing multiple services poses a considerable challenge to forest managers ( Baskent et al., 2020 ).

The complexity of this problem prompted the development of decision support tools to examine trade-offs among objectives. In the literature, one can find examples of the application of several multiple-criteria decision analysis (MCDA) approaches to help solve multiple objective forest management planning problems (e.g., Mendoza and Martins, 2006 ; Ananda and Herath, 2009 ; Borges et al., 2014 , 2017 ). Moreover, several MCDA-based decision support tools are available that can help users and scientific researchers both to learn and understand the impacts of management plans on the provision of forest ecosystem services ( Baskent and Jordan, 2002 ; Baskent et al., 2014 ).

A Pareto frontier approach does not require the definition of ecosystem service targets a priori , i.e., does not information about potential supply values or trade-offs among those services. The approach provides the decision-maker with information about (i) the production possibilities (i.e., the potential of the landscape to provide ecosystem services) and (ii) the extent to which increasing the supply of an ecosystem service requires accepting reduction in the provision of others ( Borges et al., 2017 ). This method thus provides decision-makers with the information needed to assess the trade-offs between ecosystem services and to set supply targets aligned with their preferences. The approach integrates the functionality of both mathematical programming and interactive decision-maps techniques to compute and display the Pareto frontier when considering two or more ecosystem services ( Borges et al., 2014 ). In particular, the Pareto frontier approach discussed in Tóth and McDill, (2009) , Borges et al. (2014) is a linear programming-based technique that can consider both continuous and integer variables. Nevertheless, the generation of Pareto frontiers of integer or mixed integer optimization problems requires a substantial computation effort ( Marques S, et al., 2021 ). Most applications of Pareto frontier techniques in forest management consider continuous variables (e.g., Tóth and McDill, 2009 ; Borges et al., 2014 , 2017 ; Marques et al., 2017 , 2020 ; Abate et al., 2022 ). There are very few applications to forest management problems with integer variables (e.g., Tóth et al., 2006 ; Marques S, et al., 2021 ). Furthermore, addressing forest management questions related to a wider range of ecosystem services requires spatial optimization models ( Borges and Hoganson, 2000 ). The latter considers both integer variables as well as spatial constraints (e.g., adjacency constraints) but the end result is a computationally complex optimization problem (e.g., McDill et al., 2002 ; Murray and Weintraub, 2002 ; Constantino et al., 2008 ; Könnyu and Tóth, 2013 ; Constantino and Martins, 2018 ).

Computational complexity of spatial optimization problems is the limiting factor for the use of Pareto frontier methods that are based on solving of integer or mixed integer programs ( Tóth et al., 2006 ; Marques S, et al., 2021 ). When addressing large and complex problems, decomposition methods are commonly employed to circumvent this problem. Existing techniques such as the Branch and Price decomposition ( Barnhart et al., 1998 ) and the more recent method by Meselhi et al. (2022) known as the Decomposition of Overlapping Functions (DOV) method have been exploited to solve such problems. However, these have only been applied to single-objective optimization rather than to the generation of the Pareto frontier in scenarios involving multiple objectives. Recent work by Marques S, et al. (2021) showcased an approach for constructing the Pareto frontiers of large integer problems derived from the Pareto frontier of smaller sub-problems. Riffo (2020) emphasized the challenge in creating Pareto frontiers for integer problems that incorporate adjacency constraints. While he suggested decomposing large problems into smaller, more manageable components, his approach produces infeasible optimization problems when applied to the construction of the Pareto frontier for large landscape problems bound by adjacency constraints. Hence, the challenge remains, namely, how to address the adjacencies between stands bordering the sub-problems. Our study aims at addressing this challenge. It proposes an approach that builds from information about trade-offs of ecosystem services in smaller spatial optimization sub-problems that may be solved independently. The emphasis is thus on building the frontier from smaller sub-problems that are designed so that there are no violations of adjacency constraints. A case study in the Northwest of Portugal is used for testing and demonstration purposes.

2.1 Case study area

A forested landscape located in Paiva County in northwest Portugal ( Figure 1 ) was used for testing our approach. Its area extends over 7,487 ha and was partitioned into 686 homogeneous units, each of which is a forest stand with the same cover type (e.g., forest species), age and productivity. The stands are mainly pure eucalypt ( Eucalyptus globulus L.) covering 6,428 ha, with some stands being a mixture of eucalypt and maritime pine ( Pinus pinaster Ait.). The latter extends over 611 ha. The landscape includes too pure chestnut ( Castanea sativa Mill.) and cork oak ( Quercus suber L.) stands extending over 23 ha. Moreover, some of the land (347 ha) is currently bare and available for new plantations. A recent wildfire has burned about 46% of the area. The landscape has the potential to provide several ecosystem services. In the work described in this paper, we leverage the information on the preferences of stakeholders reported in Marques M, et al. (2021) and consider wood as well as cork production, biodiversity conservation, erosion protection, carbon storage, and wildfire prevention as objectives to be taken into consideration. We also rely on the work developed by Marques M, et al. (2021) to simulate species conversions and changes in the area occupied by each species that might reflect the preferences of stakeholders outlined by these authors.

Figure 1 . Location of the case study area.

2.2 Forest management prescriptions, simulations, and ecosystem services estimation

The 686 homogeneous land units were assigned to different management prescriptions based on a forest inventory coordinated by the authors and conducted by the Forest Owner Association [Associação Florestal de Vale do Sousa (AFVS)], an association responsible for the development of a joint management plan for the forested landscape. The definition of the silvicultural options to be considered when simulating prescriptions that can be used to manage each homogeneous unit was made in cooperation with AFVS. This involved the definition of rotation ages, thinning regimes, shrub cleaning periodicity, and cork oak extraction schedules (only for cork oak). Specifically, according to AFVS, eucalypt stands are to be managed through coppicing, encompassing three cutting cycles, each cycle spanning over 10 to 12 years. Rotations of pine and chestnut stands extend from 40 to 55 years and from 35 to 50 years, respectively, and include different levels of thinning intensity. Pedunculate oak ( Quercus robur L.) management includes clear-cut ages from 40 to 60 years, along with different thinning ages and intensities. Cork oak silviculture involves thinning at different ages, without harvesting options. In the case of homogeneous units with eucalypt and pine, the prescriptions also include species conversion options, e.g., conversion to maritime pine (from eucalypt), chestnut, cork oak, or pedunculate oak. Bare land units may be converted into pure maritime pine stands, pure pedunculate oak stands, pure chestnut stands and pure cork oak stands. The landscape includes riparian buffers along water streams. All possible management prescriptions, including harvesting ages and species conversion options, have been identified through collaborative discussions sponsored by AFVS with relevant stakeholders. The combination of management alternatives and land units resulted in a total of 47,448 prescriptions.

The study focuses on six ecosystem services or indicators thereof: wood, cork, carbon, biodiversity, fire resistance and soil erosion. Wood and cork are examples of provisioning services. Carbon stock is considered as an indicator for the climate regulation service, while fire resistance and soil erosion are among the indicators of regulatory services. Furthermore, biodiversity stands as the foundational element underpinning all these services.

Several approaches were employed for estimating these ecosystem services. Specifically, growth models and simulation tools were used to estimate wood and cork production and carbon stocks ( Marques S, et al., 2021 ). Other ecosystem services were estimated using the approaches by Ferreira et al. (2015 ; fire resistance), Rodrigues et al. (2021 ; soil erosion), and Botequim et al. (2015 ; biodiversity). In summary, a wildfire resistance indicator, crafted by Ferreira et al. (2015) , was used to gauge the vulnerability of forest stands to wildfires. This indicator integrates wildfire occurrence and damage models developed for the most important forest species in Portugal ( Ferreira et al., 2015 ). It considers further spatial information such as the configuration of the stand as well as stand adjacency relations ( Ferreira et al., 2015 ). The values of this indicator range from 1 (low resistance) to 5 (highest resistance). For soil erosion assessment, the methodology ( Rodrigues et al., 2021 ) considers the yearly fluctuations in the cover-management factor ( C ) within the Revised Universal Soil Loss Equation (RUSLE) to estimate the annual soil loss. The C factor is a function of average crown diameter and other biometric characteristics of the stand. The biodiversity indicator considers the tree species composition (e.g., maritime pine, eucalypt, chestnut, pedunculate oak, cork oak, and riparian trees), stand age, and understory coverage ( Botequim et al., 2015 ). The biodiversity score ranges from 0 (indicating minimal biodiversity or barren land) to 8 (representing the highest level of biodiversity). The reader is referred to Ferreira et al. (2015) , Rodrigues et al. (2021) , and Botequim et al. (2015) for further detail about these indicators.

The stand and landscape level values of the aforementioned ecosystem services indicators were estimated for a 50-year planning horizon, subdivided into five planning periods of 10 years each.

2.3 The optimization model

This research considered a Model I ( Johnson and Scheurman, 1977 ) integer problem formulation, with an integer decision variable X jkp (which takes the value 1 if prescription p ( 1, 2, …, P ) is assigned to species k ( 1, 2, …, K ) in stand j (1,2, …, J) and 0 otherwise). The equations that characterize the optimization problem:

J : Set of stands ( j  = 1,2,3, …, 686 stands).

K : Set of species ( k  = 1,2, …, 6 tree species).

T : Set of periods in the planning horizon ( t  = 1, 2, …, 5).

P : Set of management schedules (prescriptions).

X jkp : binary decision variable which takes the value 1 if prescription p ( 1, 2, …, P ) is assigned to species k ( 1, 2, …, K ) in stand j (1,2, …, J) and 0 otherwise.

TWOOD : the total wood production over the planning horizon.

VTHIN kt : Volume of wood from thinning of species k in period t.

VHARV kt : Volume of wood from harvesting (clearcutting) of species k in period t.

Thin jkpt : Volume of thinning of species k in stand j in period t where prescription p is applied.

H jkpt : Volume of wood from harvesting species k in stand j in period t where prescription p is applied.

CARB : Average carbon stock over the planning horizon.

C kt : Carbon stock of each species k in period t.

C jk p t : Carbon stock in period t that results from applying prescription p on species k in stand j.

CORK : Total amount of cork produced over the planning horizon.

Cork t : Cork produced in period t.

Z jk p t : Cork produced in period t that results from applying prescription p on species k (in this case Cork species) in stand j.

EROSION : Total soil erosion (in ton) over the planning horizon.

erosion kt : total soil loss in period t from stands covered by species k.

F jk p t : soil loss in period t that results from applying prescription p on species k in stand j.

FIRE : The average fire resistance indicator in the planning horizon.

Rait kt : The total fire resistance indicator from species k in period t.

R jkpt : Fire resistance indicator in period t that results from assigning prescription p to species k in stand j . Values range from 1 (less resistance) to 5 (highest resistance).

BIOD : The average biodiversity score in the planning horizon.

Bio kt : The total biodiversity score from species k in period t.

B jkpt : Biodiversity indicator in period t that results from assigning prescription p to species k in stand j . Values range from 0 (bare land or no biodiversity) to 8 (highest level of biodiversity).

a jk : area (in ha) covered by each species k in stand j.

A : Total area of the landscape (ha).

Equation 1 represents the total wood production from all species over the planning horizon. This is computed by adding Equation 2 (volume from thinning) and Equation 3 (volume from clear-cuts). The last equation ( Equation 14 ) is used to ensure that a stand (and any species in a stand) is assigned only to one prescription over the planning horizon. The remaining equations represent the provision of other ecosystem services: carbon ( Equations 4 , 5 ), cork ( Equations 6 , 7 ), erosion ( Equations 8 , 9 ), fire resistance ( Equations 10 , 11 ), and biodiversity ( Equations 12 , 13 ).

The equations above define the integer program resource capability model used to generate the Pareto frontier of the forested landscape. The latter depicts trade-offs among ecosystem services such as wood ( TWOOD ), cork ( CORK ), biodiversity ( BIOD ), carbon stock ( CARB ), fire resistance ( FIRE ), and erosion (EROSION). The provision of the first five is to be maximized while erosion is to be minimized.

2.3.1 Wood flow constraints

The total wood production across consecutive periods was regulated by constraining the model

where TWOOD t is the total wood production in period t , and σ is the allowable fluctuation in percentage (20–25% was considered for the current case study).

2.3.2 Formulation of the adjacency constraints

The adjacency constraint limits the size of clear-cut areas resulting from the harvesting of contiguous (adjacent) stands. We limit the maximum clear-cut area to not exceed 50 ha (as declared by the Portuguese law for the Integrated Management System for Rural Lands). The path algorithm proposed by McDill et al. (2002) was used to generate the corresponding Area Restriction Model ( Murray, 1999 ). The algorithm starts by defining a binary variable Y jt , for each stand which takes a value 1 if it is to be harvested in period t and a value 0 if not. In order to apply the algorithm to our problem, this binary variable was created as a function of the original decision variable X jkp using Equation ( Equation 17 ) for pure stands and Equations ( Equations 18 , 19 ) for mixed stands:

where h jpt is a parameter that takes the value one if the assignment of prescription p to stand j involves a clear-cut in period t . Equations 18 , 19 stipulate that clear-cutting is considered only to stand j if all its species are subject to clear-cutting in the same period.

Afterwards, the path algorithm proceeds with the enumeration of minimal infeasible clusters C ∈ A + ( A + is a set of all minimal infeasible clusters, i.e., infeasible clusters that would become feasible if one stand is excluded from it) and prohibits cutting of contiguous stands exceeding the harvest limit, i.e., 50 ha:

Where|C| is the number of stands in the cluster.

Python code (Python version 3.11) was developed to enumerate minimally infeasible clusters ( Appendix I ). Respective equations (constraints) were generated for the whole case study area.

2.4 Decomposition approach and the generation of Pareto frontier

The large number of adjacency constraints (176,955) complicates the process of generating the Pareto frontier. Initial attempts aimed at solving a single objective problem (e.g., wood) did not produce an optimal solution even after 24 h. To address computational complexity issues, a strategy was implemented that involved subdividing the problem. A first try involving just two sub-problems had negligible impact on the computational complexity. Hence, the problem was partitioned into four sub-areas ( Figure 2 ), named East Paiva_1, East Paiva_2, West Paiva_1 and West Paiva_2.

Figure 2 . The four sub-areas and the border stands (stands bordering the sub-areas).

The sub-areas were created using ArcGIS, ensuring that the number of stands in each sub-area was roughly equal. Besides, efforts were made to minimize the number of stands bordering the sub-areas. This was done by selecting a boundary line that touched as few stands as possible. Nevertheless, to prevent the violation of the adjacency constraint along the border between the sub-areas, stands adjacent to this border were first removed from the sub-area problem and included in the set of border stands ( Figure 2 ).

Two different models were used for generating the Pareto frontier for each sub-problem. The first only considered Equations 1 – 16 while the second added the adjacency constraints, Equations 17 – 20 . The frontier was generated using the approach outlined in Borges et al. (2017) and Marques S, et al. (2021) . Each Pareto frontier provided information about the trade-offs between the criteria, which corresponds to the levels of provision of ecosystem services, e.g., wood, cork, carbon stock, biodiversity, fire resistance, and soil erosion, in the corresponding sub-problem. Afterward, we proceeded with the selection of points in each frontier, i.e., the selection of ecosystem service values to be provided by each sub-problem that included adjacency constraints. Points representing the Pareto frontier for each sub-problem were selected purposively to maintain consistency among sub-problems. These points aimed to approximate the average between the maximum and minimum values of the ecosystem services indicators. Achieving this consistency involved leveraging the tool used to generate the Pareto frontier, which provided minimum and maximum achievement levels for the optimized criteria. For each sub-problem, the tool retrieved these minimum and maximum values of the optimized criteria. From this range, a point positioned at (Max – Min) /2 was selected. The corresponding management plans were then used to constrain the models representing the border sub-problem. Specifically, we eliminated from the latter the stand-level prescriptions that had an adjacency conflict with the prescriptions in those management plans. We then developed a Pareto frontier for the border sub-problem ( Equations 1 – 20 ). Within all four sub-problems, the wood flow constraint allowed a 20% variation between consecutive periods (refer to Equations 15 , 16 ). However, to maintain the feasibility specifically for the border sub-problem, adjustments were made to the wood flow constraint.

2.5 The Edgeworth–Pareto hull (EPH) and its approximation

The approach we followed to generate the Pareto frontier involved approximating the actual Edgeworth–Pareto hull (EPH). The EPH is the envelope or boundary formed by the set of solutions that are not outperformed by other solutions. In integer optimization problems, it is important to note that the non-dominated points forming the EPH may not necessarily form a convex hull ( Marques S, et al., 2021 ). As a result, there is a need to approximate the EPH with a surrogate Edgeworth–Pareto hull (cEPH; see the visual illustration in Figure 3 ). The cEPH serves as an approximation of the non-convex EPH, allowing for a more tractable representation. In our research, we conducted an assessment of the accuracy of this approximation technique, evaluating how well the cEPH represents the original EPH. For this purpose, the retrieval and comparison of the values was done for six randomly selected points (just for the sake of illustration) representing different levels of the optimized criteria. Moreover, we also analyzed the impact of problem size (e.g., number of stands and decision variables) and complexity (e.g., with and without adjacency constraints) on the accuracy of the generation of the Pareto frontier.

Figure 3 . Visual illustration of the actual Pareto frontier (EPH), indicated in solid line, and the approximated surrogate frontier (cEPH), indicated in dashed line when two objectives are being optimized. The points (Z1–Z5) represent Pareto optimal integer solutions (Modified from Marques S, et al., 2021 ).

The computations were performed on a personal computer with an Intel ® Core ™ i7-4790 processor with a 3.6 GHz frequency and 20 GB memory, using the CPLEX(R) Interactive Optimizer 12.6.3.0.

3.1 Pareto frontier for sub-problems

By dividing the larger problem into sub-problems, it was possible to generate the Pareto frontier of each of the four sub-problems even for the case where they included adjacency constraints (i.e., the maximum harvest patch size). The frontiers depicted trade-offs among the optimized objectives: Total wood ( TWOOD ), Cork ( CORK ) carbon stock ( CARB ), erosion ( EROSION ), fire resistance ( FIRE ) and biodiversity ( BIOD ). The mean generation time for sub problems with adjacency constraints extended over 1,271 s, while the mean generation time was about 340 s if no adjacency constraints were considered ( Table 1 ). This is a significant improvement given that it was not even possible to generate Pareto frontier for the whole landscape without subdivision. The effect of constraints on generation time was more pronounced for problems with more decision variables ( Table 1 ).

Table 1 . Problem size and Pareto frontier generation time for the four sub-areas and border problem.

The tool we have used to build the Pareto frontier is capable of generating a variety of decision maps, depending on the number of criteria (ecosystem services indicators) under optimization. The challenge arises when trying to effectively convey these maps to readers, especially when dealing with more than three criteria. In our current study, we created a six-dimensional set of decision maps for a specific sub-problem, illustrating trade-offs among all six criteria (i.e., levels of provision of the ecosystem services; Figure 4 ). Within the six-dimensional map, each segment or section reflects varying values of the fire resistance indicator (displayed horizontally) and biodiversity score (displayed vertically). A specific segment or set of decision maps, based on a combination of fire and biodiversity values, highlights the trade-offs between carbon and timber at different levels of soil erosion (represented by different colors, each associated with a decision map). These decision maps show that an increase in timber production is associated with a reduction in carbon stock, particularly at higher levels, illustrating the trade-offs involved.

Figure 4 . Six dimensional set of decision maps, Pareto frontier, showing trade-off among ecosystem services, for the West Paiva_2 sub-area. TWOOD: total amount of wood harvested and thinned (10 6  m 3 ); CARB: carbon stock (10 4 ton); EROSION: the total soil erosion (10 6 ton); FIRE: the average fires resistance indicator (value range from 1 to 5); BIOD: the average biodiversity score; CORK: the total cork production (10 6 arrobas. Arroba = 14.7 kg).

To enhance readability and comprehension, subsequent interpretation and discussion of results for each sub-problem were conducted using three-dimensional decision maps, focusing on three ecosystem services ( Figures 5A – D ). The Pareto frontier, along with the associated decision maps for the four sub-problems, highlight the trade-offs between ecosystem services in each sub-problem ( Figures 5A – D ). The optimized criteria present a range of minimum and maximum values that vary across these sub-problems, likely attributable to differences in the total land area. Despite an overall similarity in trade-off patterns among the sub-problems, some disparities are noticeable between the East and West Paiva sub-problems ( Figures 5A – D ). Notably, the Eastern sub-problems (5A and 5B) exhibit a relatively steeper slope in the trade-off compared to their Western counterparts (5C and 5D), particularly at a higher level of the optimized criteria.

Figure 5 . Pareto frontier showing trade-offs between three ecosystem services in the case of East Paiva_1 (A) , East Paiva_2 (B) , West Paiva_1 (C) and West Paiva_2 (D) . TWOOD: total amount of wood harvested and thinned (106 m 3 ); CARB: carbon stock (10 4 ton); EROSION: the total soil erosion (10 6 ton). NOTE: the levels of other ecosystem services (e.g., BIOD, FIRE) are fixed.

3.2 Pareto frontier for the border problem: addressing the adjacency conflicts between sub-problems

Selection of a single point from the Pareto front of each sub-problem was undertaken, with these points indicated by a ‘+’ sign in Figures 5A – D . These points represent approximate average (of maximum and minimum) values of each optimized criterion ( Table 2 ). From these points, solutions, i.e., management plans were derived and used to generate the Pareto frontier for the border problem. The latter encompasses all stands in the landscape and yet considers only decisions to be made in the stands along the border ( Figure 6 ). All other stands are to be managed according to the solutions to the four earlier sub problems.

Table 2 . Values of the optimized criteria in each of the solutions selected from the four sub-problems.

Figure 6 . Pareto frontier showing trade-offs between ecosystem services in the case of the East Paiva_2 sub-area, without (left) and with (right) adjacency constraint. TWOOD-total amount of wood harvested and thinned (10 6  m 3 ); CARB-carbon stock (10 4 ton); EROSION-the total soil erosion (10 6 ton). The values of fire resistance and biodiversity score were fixed at 3.6 and 2.6 in both decision maps.

Using this process, the decisions to be made in the stands along the border are constrained by the management plans selected for each sub-problem. As a result, out of the 4,665 alternatives available for these stands ( Table 1 ), 883 were eliminated. The border Pareto frontier highlights thus the trade-offs among ecosystem services that result from the integration of the four management plans with the decisions to be made in the stands over the border ( Figure 6 ).

3.3 Approximation of the EPH and its accuracy

Results show that a relatively larger discrepancy (larger difference-in the values of the optimized criteria-between the actual EPH and the surrogate cEPH) was observed when constraints were added to the larger sub-problem (East Paiva_2) compared to the no-constraint counterpart ( Table 3 ). In the latter, the minimum and maximum percentages of discrepancy were 0 and 0.03 in the case of the EROSION criteria in point 1 and the case of the CARBON criteria in point four, respectively. On the other hand, when adjacency constraints were added to the problem, the minimum and maximum discrepancy percentages were 0.001 and 0.19, respectively, in the case of the value of the EROSION criteria in point one and the value of the CARBON criteria in point six. In both constrained and unconstrained problems, a higher discrepancy was observed in the case of the CARBON criteria, indicating the higher sensitive of carbon stock to changes in the number of decision variables or in the number of constraints as compared to other criteria such as wood production.

Table 3 . Solution of EPH and cEPH retrieved from six random points for East Paiva_2.

To examine how sensitive the approximation is with respect to problem size, the discrepancy was also evaluated for the smallest sub-problem, East Paiva_1 ( Table 4 ). It can be hypothesized that as the number of decision variables increases, the optimization problem becomes more complex, and it may be more difficult for the optimization solver to find the optimal solutions. With a higher number of decision variables, the number of possible combinations of the decision variables increases exponentially, making it more difficult to accurately generate the Pareto frontier curve. However, in this study, we did not find a significant difference in the discrepancies among solutions to the small and large problems.

Table 4 . Solution of EPH and cEPH retrieved from six random points for East Paiva_1.

This was also observed when we evaluated the discrepancy between the EPH and the cEPH solution for the border sub-problem, whose Pareto frontier was generated by retrieving solutions from the sub-problems. The result shows that the discrepancy between the actual feasible solution (EPH) and the one approximated by the Pareto frontier tool was very low ( Table 5 ).

Table 5 . Solution of EPH and cEPH retrieved from six randomly chosen points for the border sub-problem.

3.4 Impact of spatial constraint on ecosystem service trade-offs

In order to assess the potential influence of spatial constraints on trade-off curves and patterns, an in-depth analysis was conducted to scrutinize variations. This was done by taking the East Paiva_2 sub-area as an example and fixing the values of the fire resistance and biodiversity score at 3.6 and 2.6, respectively (basically the median values shown in the decision map) for both scenarios, i.e., with and without the adjacency constraints. Upon examining the resulting trade-off maps (depicted in Figure 7 ), a noteworthy observation emerged: the spatial constraint showed limited influence on trade-offs. Across both representations, the trade-off between carbon and wood quantity remained prominent, especially evident at higher levels of annual soil loss. This persistent trade-off signifies that the impact of the spatial constraint appeared to be minimal in altering this relationship.

Figure 7 . Pareto frontier showing trade-offs between ecosystem services in the case of the border sub-problem. TWOOD-total amount of wood harvested and thinned (10 6  m 3 ); CARB-carbon stock (10 4 ton); EROSION-the total soil erosion (10 6 ton).

However, a clear consequence of the spatial constraint was identified: it impacted the ecosystem services provision possibilities. As expected, the constraint visibly restricted the range of attainable wood quantities. While the spatial constraint impacted negatively the wood production, it did not influence the broader trade-off pattern between carbon and wood quantity at any level of soil loss. Consequently, this analysis suggests that decision-makers retain the flexibility to set specific targets for ecosystem service achievements while remaining coherent with harvesting area constraints imposed by the spatial considerations.

4 Discussion

Decomposing large problems into some sub-problems has been a common approach for dealing with the computational difficulties of solving large optimization problems (e.g., Hoganson and Rose, 1984 ; Borges et al., 1999 ). The application of this approach to generate Pareto frontiers is, however, limited to a few studies; e.g., see Riffo (2020) and Marques S, et al. (2021) . By following the approach proposed in Lotov (2015) , the study of Marques S, et al. (2021) has shown the potential application of the decomposition approach for generating Pareto frontiers of multiple ecosystem services of a forest landscape. Our study extends these findings. It proposes an approach to address the fact that it is not computationally feasible to build the Pareto frontier of large landscape-level problems with adjacency constraints and that the decomposition approach proposed by Marques S, et al. (2021) is not capable of addressing adjacency across sub problems. The main success of this research is the demonstration that it is feasible to generate Pareto frontiers displaying the trade-offs between ecosystem services in the case of very large forest management problems that include adjacency constraints.

We opt for a comprehensive subdivision of the landscape-level problem, followed by a systematic workflow to generate the Pareto frontier for the entire landscape. This is achieved by incorporating the plans selected for each sub-problem and considering a restricted decision space for border stands. Specifically, this restriction entails the removal of management alternatives for border stands that are in conflict with the plans selected for the sub-areas. Though this approach may result in sub-optimal solutions, it effectively integrates spatial constraints into a Pareto frontier method. Despite sub-optimality (“sub-optimality” in our context refers to the compromise made in achieving the global optimal solution; given that our approach hinges on solving sub-problems individually, the final solution represents a compromise based on these sub-problem solutions), the study has illustrated how one can incorporate spatial constraints into a Pareto frontier method. Our approach may thus provide useful information on the trade-offs among different management objectives when adjacency conflicts are a concern. This in turn can be used to inform forest management decisions. By understanding the different trade-off patterns that emerge based on different management decisions, forest managers can make more informed choices about managing forest resources while complying with harvest regulations.

Previously, to the best of our knowledge, there are no results on the generation of Pareto frontiers when spatial constraints are included. Adjacency constraints pose a computational challenge to the analysis of trade-offs between ecosystem services. The approach implemented in the current study suggests that while ecosystem service supply possibilities may decrease as a consequence of the adjacency constraints, the trade-offs pattern themselves remain largely unaffected. This insight by our innovative approach underscores the importance of understanding how spatial considerations may impact those supply possibilities, while affirming the underlying trade-off patterns in multi-objective optimization scenarios. This insight may be useful to forest managers. It suggests that they might combine information provided by Pareto frontier approaches that ignore spatial constraints with information about the impact of the latter on supply possibilities provided by standard spatial optimization approaches (e.g., exact and heuristic (genetic algorithms, simulated annealing approaches) Baskent et al., 2020 ).

An important aspect that merits discussion is what are the advantages and disadvantages associated with our approach. On the one hand, our approach offers distinct advantages in cases where solving the entire landscape problem as a whole is computationally infeasible. For instance, even in the context of single-objective optimization, obtaining a solution for the entire landscape was often beyond the computational limits, requiring extensive computation times that exceeded 24 h. A vast body of literature has highlighted the large computational burden required to solve the resulting combinatorial optimization problems (e.g., Weintraub and Murray, 2006 ). Our study reaffirms this, shedding light on the computational complexity of the resulting mixed-integer problems, especially when one has multiple objectives.

To address this computational bottleneck, we proposed to break down the master problem into more manageable sub-problems. However, grappling with spatial constraints complicates this straightforward decomposition. Merely segmenting the problem and solving them independently poses challenges, primarily because these sub-problems share units or variables, potentially intertwining their solutions. There is no guarantee that solving one sub-problem adheres to the global spatial constraint, complicating the aggregation of solutions. Recently, Meselhi et al. (2022) delved into optimization strategies, proposing a decomposition approach involving overlapping sub-problems sharing certain variables. Their methodology tackled these sub-problems independently, employing three strategies-information sharing, mean value adaptation, and random selection-for handling overlapping variables. However, their approach does not seamlessly align with multiple objective optimizations, particularly in scenarios encompassing spatial constraints where units are interconnected across adjacent stands and clusters. While this avenue could merit exploration in future research, our approach of removing stands that share borders between sub-problems has shown to be a practical method of adhering to spatial constraints. This approach aims to navigate the spatial intricacies by eliminating stands that straddle the borders between sub-problems, mitigating the challenges posed by spatial constraints in multi-objective optimizations.

However, there are inherent disadvantages and limitations that we need to acknowledge. First, the removal of management alternatives from border stands due to adjacency conflicts may inadvertently introduce bias and impact the optimization of the master problem objective function. Second, quantifying the exact impact of these removed alternatives can be a complex undertaking, warranting further research. Lastly, the approach’s sensitivity to different sub-divisions may lead to divergent solutions, potentially undermining its robustness in different scenarios. It is crucial to recognize the potential biases introduced by the removal of management alternatives due to adjacency conflicts and to further investigate the exact implications of these removals on the optimization process.

Regardless of the problem size and whether or not there is a need to decompose a large problem, generating the Pareto frontier for the integer-type optimization problem is further complicated by the fact that the feasible domains (integer solutions) are disconnected, making it nonconvex. As a result, Pareto frontier generations are based on the approximation of the EPH ( Lotov et al., 2004 ; Lotov, 2015 ). A study by Burachik et al. (2021) applied for a small problem found insignificant differences between the approximated and real optimal solution, and by Marques S, et al. (2021) applied for a larger forest management problem also found acceptable discrepancies. In the current study, even though the discrepancies were found to be relatively higher for some ecosystem services than the previous studies (might be related to the nature of the problem or of sensitivity of the criteria being optimized), the approach was still able to provide a reasonable approximation.

An interesting finding in this regard is the fact that the accuracy of the approximation varies depending on the characteristics of the problem, such as the presence of constraints. For larger problems with many decision variables, adding constraints led to a larger discrepancy between the surrogate solution and the Pareto frontier solution. We assume that adding constraints reduces the feasible region and could make the optimization problem more complex and may make it more difficult for the solver to find the optimal solution. Furthermore, the study also found that the level of discrepancy did not vary significantly between small and large problems, which suggests that our approach is able to handle problems of different sizes reasonably well ( Marques S, et al., 2021 ). The fact that the accuracy of the approximation is influenced by problem characteristics, particularly the presence of constraints, underscores the need for robust optimization techniques capable of handling diverse problem sizes and complexities.

Our study highlights further that the impact of spatial constraint in forest management planning optimization problems is more pronounced when the latter has a larger number of decision variables. Nevertheless, the complexity of the solution process may vary with the algorithm used to formulate the spatial constraints (path algorithm was used in our study). Future research should explore the potential for using different algorithms such as the bucket algorithm, the clique approach, branch and cut algorithm etc. ( Constantino et al., 2008 ; Könnyu and Tóth, 2013 ; Constantino and Martins, 2018 ) to enhance the efficiency and effectiveness of landscape-level optimization in forest management planning.

5 Conclusion and future research directions

In conclusion, our study sheds light on the critical issue of spatial constraints in forest management and optimization. Spatial constraints pose a significant challenge in effectively balancing multiple objectives in forest management planning, and our research tackles this challenge head-on. The contribution of our study lies in our approach to decomposing large forest management optimization problems into smaller, more manageable sub-problems, and depict trade-offs among multiple ecosystem services. The information about these trade-offs is important to stakeholders (e.g., Tóth et al., 2006 ; Borges et al., 2017 ; Marques S, et al., 2021 ), namely in the case of forested landscapes that involve several decision-makers. As highlighted by these authors, it supports the development of participatory negotiation processes to come up with consensual ecosystem services target levels.

This partitioning not only ensures the adherence to harvest patch size constraint but also effectively addresses the complexities of spatial constraints. Moreover, our research successfully approximates the EPH with remarkable accuracy, although it is worth noting that spatial constraints can slightly increase the discrepancy between approximated and actual Pareto optimal solutions. While we acknowledge the limitations and challenges associated with our approach, from a management perspective, our study provides practical solutions and valuable insights for forest planners to design effective strategies tailored to meet both ecological and economic objectives while addressing spatial harvest regulations. Moreover, the practicability of the use of the Pareto frontier tool by AFVS and relevant stakeholders has been demonstrated by several authors (e.g., Borges et al., 2017 ; Marques S, et al., 2021 ). This research highlights thus the potential for practical use of this tool to address emerging forest ecosystem management problems that include spatial constraints. Its effectiveness will rely on outreach strategies as outlined by Borges et al. (2017) and Marques S, et al. (2021) . Future work will focus on its use to address similar problems in other contexts.

An important point for future improvement of the proposed approach is that the partitioning of the problem into sub-problems may not be straightforward, as it can be difficult to determine how to partition the landscape in a way that is both meaningful and effective for the optimization process. Moreover, as the optimal solution for the border stands depends on the specific solutions generated for the sub-problems, the final solution is not robust. Therefore, new approaches need to be developed that do not need partitioning or where the partitioning is optimal. Neural network-based technique and reinforcement learning approaches that have recently been applied to the solution of large integer problems (e.g., Tang et al., 2020 ; Huang et al., 2022 ) could be possible avenues for future research. Finally, our study, while providing valuable insights, does not consider climate change scenarios. When process-based models are available to project forest growth under climate change in the study area’s forested landscape, research may explore these scenarios for a more comprehensive understanding of their potential influence on trade-off patterns and the respective computational complexities. Nevertheless, our approach to generate Pareto frontiers can be as useful and applicable in this context as it is independent of the models used to make projections.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

DA: Conceptualization, Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing. SM: Methodology, Software, Supervision, Writing – review & editing. VB: Methodology, Software, Visualization, Writing – review & editing. JR: Conceptualization, Methodology, Writing – review & editing. AW: Supervision, Writing – review & editing. MC: Methodology, Software, Supervision, Writing – review & editing. CL: Methodology, Writing – review & editing. JB: Conceptualization, Formal analysis, Funding acquisition, Methodology, Software, Supervision, Writing – review & editing, Writing – original draft.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was partially funded by the Forest Research Centre, a research unit funded by Fundação para a Ciência e a Tecnologia I.P. (FCT) by project reference UIDB/00239/2020 (DOI identifier 10.54499/UIDB/00239/2020) and the Ph.D. grant of Dagm Abate (UI/BD/151525/2021); in the scope of Norma Transitória—DL57/2016/CP1382/CT15 (UIDB/00239/2020); as well as by the projects ref. H2020-MSCA-RISE-2020/101007950, with the title “DecisionES - Decision Support for the Supply of Ecosystem Services under Global Change,” funded by the Marie Curie International Staff Exchange Scheme and ref. H2020-LC-GD-2020-3/101037419, with the title “FIRE-RES—Innovative technologies and socio-ecological-economic solutions for fire resilient territories in Europe,” funded by the EU Horizon 2020—Research and Innovation Framework Program,” and the project MODFIRE—A multiple criteria approach to integrate wildfire behavior in forest management planning with the reference PCIF/MOS/0217/2017.

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.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/ffgc.2024.1368608/full#supplementary-material

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Keywords: spatial-optimization, integer-programming, forest management, Pareto frontier, ecosystem services

Citation: Abate D, Marques S, Bushenkov V, Riffo J, Weintraub A, Constantino M, Lagoa C and Borges JG (2024) Assessment of tradeoffs between ecosystem services in large spatially constrained forest management planning problems. Front. For. Glob. Change . 7:1368608. doi: 10.3389/ffgc.2024.1368608

Received: 10 January 2024; Accepted: 25 March 2024; Published: 11 April 2024.

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Copyright © 2024 Abate, Marques, Bushenkov, Riffo, Weintraub, Constantino, Lagoa and Borges. 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: Dagm Abate, [email protected]

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ICEPG 2022: Environmental Pollution Governance and Ecological Remediation Technology pp 553–564 Cite as

Research on Forest Carbon Sequestration and Management Strategies

• Bo Wang 6 ,
• Xintong Yao 6 ,
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Living trees in forests and forest products make an important contribution to slowing climate warming. Meanwhile, deforestation can also bring economic benefits. Our purpose is to develop a predictive model of forest carbon sequestration. We hope to make recommendations for forest managers to make appropriate decisions under the constraints of different benefit measures. Two models were developed in this paper: Model 1: Carbon sequestration model; Model 2: Forest management decision model. Besides, we derived the relationship between the average economic value and ecological value with the age of the trees to determine the optimal period for cutting trees. However, the vertical structure of the forest, the competitive relationship between populations and the influence of insect pests were not considered. Therefore, improvements on these aspects need to be made.

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Wang, B., Yao, X., Liu, Z., Wang, H. (2023). Research on Forest Carbon Sequestration and Management Strategies. In: Zhang, J., Ruan, R., Bashir, M.J.K. (eds) Environmental Pollution Governance and Ecological Remediation Technology. ICEPG 2022. Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-25284-6_60

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• Int J Environ Res Public Health

Estimating Carbon Sequestration Potential of Forest and Its Influencing Factors at Fine Spatial-Scales: A Case Study of Lushan City in Southern China

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Accurate prediction of forest carbon sequestration potential requires a comprehensive understanding of tree growth relationships. However, the studies for estimating carbon sequestration potential concerning tree growth relationships at fine spatial-scales have been limited. In this paper, we assessed the current carbon stock and predicted sequestration potential of Lushan City, where a region has rich vegetation types in southern China, by introducing parameters of diameter at breast height (DBH) and tree height in the method of coupling biomass expansion factor (BEF) and tree growth equation. The partial least squares regression (PLSR) was used to explore the role of combined condition factors (e.g., site, stand, climate) on carbon sequestration potential. The results showed that (1) in 2019, the total carbon stock of trees in Lushan City was 9.22 × 10 5 t, and the overall spatial distribution exhibited a decreasing tendency from northwest to south-central, and the carbon density increased with elevation; (2) By 2070, the carbon density of forest in Lushan City will reach a relatively stable state, and the carbon stock will continue to rise to 2.15 × 10 6 t, which is 2.33 times of the current level, indicating that Lushan forest will continue to serve as a carbon sink for the next fifty years; (3) Excluding the effect of tree growth, regional forest carbon sequestration potential was significantly influenced on site characteristics, which achieved the highest Variable Importance in Projection (VIP) value (2.19) for slope direction. Our study provided a better understanding of the relationships between forest growth and carbon sequestration potential at fine spatial-scales. The results regarding the condition factors and how their combination characteristics affect the potential for carbon sequestration could provide crucial insights for Chinese carbon policy and global carbon neutrality goals.

1. Introduction

As an integral component of terrestrial ecosystems, forest ecosystems are a massive global carbon reservoir [ 1 ]. Forests sequester 2/3 of the total terrestrial carbon sequestration annually [ 2 ]. They perform a critical and irreplaceable function in lowering the rate of accumulation of greenhouse gases in the atmosphere, which helps to mitigate global warming [ 3 ]. Since the 1980s, due to large-scale afforestation programs, forests in southern China have accounted for more than 65% of the national carbon sink [ 4 , 5 ], much higher than in northern regions. At the present stage, China’s strategic goal of “reaching a carbon peak by 2030 and achieving carbon neutrality by 2060” requires a focus on emission reduction and sink enhancement, so it is necessary to quantify the current carbon stock and sequestration potential, i.e., the maximum carbon capacity that can be stored in forest ecosystems without human interference [ 6 ]. For a thorough understanding of the role of forest ecosystems in the carbon cycle, an accurate calculation of the carbon sequestration potential of forest ecosystems is required. Not only does it aid in quantifying the impacts of forests on global warming, it also aids forest management decision-making processes [ 7 , 8 ].

Existing methods for estimating carbon sequestration potential are shaped by the extension of carbon stock estimates. The estimation of carbon stocks using the biomass expansion factor (BEF) is considered relatively reliable [ 9 ], which determines the forest biomass and forest volume as a fixed ratio and estimates the carbon stock of the region by the mean ratio method (MRM) [ 10 ]. The continuous BEF method was proposed by Fang et al. and was used to estimate the carbon stock of forests in China [ 11 ]. The forest carbon sequestration potential is the difference between the maximum forest carbon capacity and the current (or a given year) forest carbon stock. Since the carbon density of mature forests can represent the maximum carbon density of forests in similar regions, the carbon stock at this time is frequently assumed to be the maximum forest carbon capacity [ 12 ]. In the natural state, the carbon stock of forest vegetation usually increases rapidly with the increase of forest age (successional stage), then slows down and reaches a steady state [ 13 ]. This increasing trend, described as S-shaped, was also reported by Taylor et al. [ 14 ] and Rothstein et al. [ 15 ]. The carbon stock of existing forests in China increases with the age of the forest, and all types of forests at different age stages can sustain carbon sequestration [ 12 ].

Current studies have introduced age into the estimation, using the relationship between biomass density and tree age to estimate carbon sequestration potential. Mostly used for large-scale study areas, such as the whole of China [ 16 ] and Finland [ 17 ], the estimation method has been thoroughly developed. Due to the large geographical span, diverse climate types, and complex tree growth in the large-scale areas, it is feasible to use this simplified connection to estimate carbon sequestration potential as a reference value. However, for the fine-scale regions, such as the county-level study areas in Hebei Province [ 18 ] and Tibet Autonomous Region [ 19 ], the carbon density and carbon sequestration potential of forest vegetation in 2050 were estimated by directly fitting the biomass-forest age relationship using the biomass converted from storage volume, ignoring the fact that the change of forest stock volume is disturbed by various conditions and is an artificial estimate during the survey [ 20 ]. This does not accurately reflect the growth of trees, and the estimation for this scale is still questionable, affecting the actual forestry carbon sink project design. As a result, we take the more accurate depiction of tree growth as an entry point. According to the widely established model for estimating storage volume, i.e., the binary standing volume model, DBH and tree height can visually represent the growth of volume [ 21 ], which can be combined with the tree growth equation [ 22 ] to reduce the uncertainty in estimation. The stochastic simulation is used to more accurately represent the change in accumulation volume during the growth of trees and to improve the accuracy of estimating forest carbon sequestration potential.

The forest carbon sequestration potential is not only influenced by forest growth but also by climatic factors [ 23 ], topographic factors [ 24 ], land use change [ 25 ], management measures [ 26 ], etc. Since the carbon stock of forest ecosystems is the fundamental parameter for studying the carbon exchange between forest ecosystems and the atmosphere [ 27 ], these existing studies mostly choose the current state of carbon stock as the variable and analyze its influencing factors, ignoring the growth status of the forest and focusing solely on the impact of environmental conditions under the current state of carbon stock [ 24 , 28 ]. The current carbon stock is influenced by age group composition and dominant species type, so the carbon stock is often not stable [ 12 ]. The carbon sequestration potential is the maximum possible growth of forest carbon stock under the current scenario, which is the predicted result after the dynamic growth of the forest. At this time, the average age of all tree species has reached the mature forest stage, and the carbon stock is relatively stable, which is convenient to reveal the relationship between the carbon sequestration potential and the current condition factors in the study area. Furthermore, most previous research has concentrated on single components such as elevation factor, canopy density, rainfall factor, and so on [ 23 , 24 , 28 ], and less attention has been paid to the combination characteristics among factors. The forest growth condition of the carbon sequestration potential is used in our study as an entry point to analyze the effect of single factors of the condition factor on forest carbon sequestration. Based on this, we also try to combine single factors of the same type and examine the magnitude to which influence on carbon sequestration potential among various combined features, this will assist in eliminating the interference of uninterpretable information such as multiple correlations and better analyze the influence of multiple condition factors such as site, stand, and climate in a comprehensive manner.

Given the above, Lushan City in southern China, a “natural laboratory” for studying forest ecology [ 29 ], was selected as a case region of the study. The specific objectives of the study were to (1) better understand the relationships between forest growth and carbon sequestration potential at the fine-scale study area; (2) estimate the region’s carbon sequestration potential by analyzing the current characteristics of carbon stock; and (3) reveal the influence of condition factors and their combination characteristics (site, stand, and climate) on carbon sequestration potential without forest growth disturbances.

2. Materials and Methods

2.1. study area.

Lushan City (115°49′42″–116°8′18″ E, 29°9′6″–29°38′32″ N) is located in the north of Jiangxi Province, with a total area of 764.54 km 2 ( Figure 1 ). The city, in the East Asian monsoon region, has a humid subtropical climate influenced by both Lushan Mountain and Poyang Lake. The annual average temperature is 15.3~17.3 °C, the precipitation is uneven in all seasons, and the dry and wet seasons are obvious. Lushan City owns Lushan Nature Reserve, which has been operating for 41 years since 1981 and has a subtropical forest ecosystem as its main conservation object. The forest coverage rate has increased from 42.00% before the establishment of the nature reserve to 80.70% at present, and the forest resources are abundant and well-maintained, with a wide diversity of tree species [ 29 , 30 ]. The total area of arboreal woodland in Lushan is 274.47 km², consisting of (i) natural forests (formed by natural underplanting, artificially promoted renewal or sprouting after a disturbance such as natural forest harvesting) and (ii) planted forests (formed entirely by machine seeding or artificial sowing, such as seedling planting, seeding and fly sowing). Among them, the area of natural forest is 201.30 km 2 (73.34%), and the area of planted forest is 73.17 km 2 (26.66%). Based on the main dominant species of the forest fine patches in the forest management inventory, the forest patches were classified into 7 types: Pinus massoniana , Pinus taiwanensis , and Pinus elliottii constitute pine forest (PF); Cunninghamia lanceolata and Cryptomeria japonica constitute Chinese fir forest (CFF); Cinnamomum camphora , Quercus L. and other hard broad species constitute broadleaf hardwood (BLH); Populus L., Paulownia fortunei and other soft broad species constitute broadleaf softwood (BLS); and mixed coniferous forest (MCF), mixed broadleaf forests (MBF) and mixed conifer-broadleaf forests (MCBF). Among them, PF (41.86%), MCF (18.30%), and CFF (17.95%) accounted for a higher percentage. According to the age of trees, the patches of forests in Lushan are mainly young and middle-aged, with the majority of trees between 20–40 years old (59.33%) and very few patches with an average age of more than 60 years old (0.72%). The complex and varied mountainous landscape of Lushan presents an elevation difference of about 1465 m. The vegetation shows more obvious vertical distribution characteristics, and 81% of the forest patches have an elevation greater than 100 m. Furthermore, initiatives including closing hills for afforestation, rehabilitating degraded forests, and tending to forests have been taken seriously in Lushan City to increase their capacity as carbon sinks (For example, the above projects involved 400 ha of forest in 2019). In conclusion, Lushan City has a diverse range of forest types and a considerable mountain microclimate, with the typical characteristics of subtropical mountain forests in southern China.

Location, topography, basic forest information, and forest patch distribution in the study area. ( a ) shows the location and topography of the area; ( b ) shows the distribution of different forest types. Based on the main dominant species of the forest fine patches in the forest management inventory, the forest patches were classified into 7 types: PF refers to pine forest composed of Pinus massoniana , Pinus taiwanensis , and Pinus elliottii ; CFF refers to Chinese fir forest composed of Cunninghamia lanceolata and Cryptomeria japonica ; BLH refers to hard broad forest composed of Cinnamomum camphora , Quercus L. and other hard broad species; BLS refers to soft broad forest composed of Populus L., Paulownia fortunei and other soft broad species; and three types of mixed forests: mixed coniferous forest (MCF), mixed broadleaf forests (MBF) and mixed conifer-broadleaf forests (MCBF); ( c ) shows the area share of different forest patches; ( d ) shows average age composition of forest patches; ( e ) shows the origin of the forest patches (natural forest/planted forest).

2.2. Data Sources

Forest management inventory is an important basic task for understanding the current state of forest resources and the ecological environment, providing a foundation for a scientific formulation of forestry development planning. The base data for this study was obtained from the forest management inventory data (FMID) in Lushan City, Jiangxi Province. Excluding economic forest, shrub forest, bamboo forest, and other forest patch types (The above types are incomplete in FMID), there were 5162 forest patches. After removing invalid data, 5077 valid forest survey patches were obtained. The survey recorded 76 forest class factors, including (i) site conditions (e.g., average elevation, slope direction, slope gradient, soil thickness, etc.); (ii) stand characteristics (average tree age, average DBH, average tree height, canopy density, etc.); and (iii) evaluation factors (forest naturalness, stand protection class, etc.), which provide sufficient variables and considerations for modeling. The survey accuracy of the sampled volume and forest patch factors is ensured through systematic sampling, and the overall regional volume accuracy reaches 80–85%; the error of the average tree height does not exceed 10%, and the error of the average DBH does not exceed 1 cm; the allowable error of the average age of natural forests is less than one age class period (10 years for PF, BLH, 5 years for CFF, BLS, mixed forests depending on the actual species composition), and the average age of planted forests is basically error-free. Furthermore, the FMID was completed in 2019, and we traveled to the field in September 2021 to conduct research and select some typical sample sites to verify the data’s legitimacy.

Climate data were obtained from nine meteorological stations in and around Lushan city from 2014 to 2018 daily rainfall and average temperature data from the China Meteorological Science Data Sharing Service ( http://data.cma.cn/ , accessed on 15 June 2022). Radiation data were high-temporal (3 h) surface solar radiation data from 2014 to 2017 sunshine hours in the Lushan area from the National Qinghai-Tibet Plateau Scientific Data Center [ 31 ] ( http://www.tpdc.ac.cn , accessed on 15 June 2022). Data points were extracted using the fishnet extraction tool and then spatially interpolated using the inverse distance weighting (IDW) approach in ArcGIS Pro2.5 [ 32 ] to create the grid data of multi-year average temperature, precipitation, and radiation. We compared the effect of IDW interpolation with other spatial interpolation of climate data in the Poyang Lake basin study [ 33 ] and finally chose to use the result of the IDW for influence factor analysis. All raster spatial resolutions were unified at 30 m, and the projection coordinate system was unified at CGCS2000_3_Degree_GK_Zone_39.

2.3. Methods for Estimating Carbon Sequestration Potential

2.3.1. dbh-tree height growth model.

There is an obvious positive correlation between tree standing volume and its DBH and tree height [ 11 ]. We used the binary standing volume model, which has sufficient accuracy and is the most widely used [ 21 ], to describe the functional relationship, as shown in Equation (1).

where V is the stumpage volume (m 3 ), D is the average DBH (cm), H is the tree height (m); a 0 , a 1 , a 2 are the parameters to be fitted. The FMID were counted by forest patches, and 3–5 standard trees of the dominant species were selected in each forest patch for measurement, and the average DBH ( D ) of the cross-sectional area was used as the DBH data, and the average tree height ( H ) was used as the tree height data. The classifications were fitted to a 0 , a 1 , and a 2 to obtain model parameters that better fit this study area.

Changes in tree DBH and tree height are distinctive features of the performance with increasing tree age, and we selected samples of forest patches with similar natural conditions, divided into age groups, and proposed a simplified model of DBH and tree height growth. After a sufficient number of data samples passed the Shapiro-Wilk normality test [ 34 ], it can be assumed that the mean DBH and mean tree height of forest patches of the same mean age follow a normal distribution, and the trend of the model normal distribution parameters with mean age can be studied further. The relationship between DBH-tree height and age is difficult to construct with a uniform expression, so an attempt was made to employ the existing growth models Gompertz, Logistic, Korf, Mitscherlich, and Richards growth functions [ 22 , 35 ]. The above models were used to fit nonlinear curves for the expectation of DBH-tree height and their age, respectively. Using the highest R² and lowest RMSE as the test criteria, the best growth model for the forest type in this region was determined to be the logistic [ 36 ], which was selected for subsequent analysis, as shown in Equation (2).

where Y is the DBH or tree height, T is the age of the tree, e is the natural exponential, c 0 , c 1 , and c 2 are the parameters to be fitted. This equation describes a three-parameter S-shaped growth curve, with c 0 showing the exact upper boundary of growth and c 1 , c 2 jointly determining the growth rate of the curve, which is an ideal population growth model with important ecological significance and is widely used.

2.3.2. Stochastic Simulation of Volume Growth

Under the premise that the mean DBH and tree height of the same mean age forest patch obey normal distribution, respectively, the mean volume of the forest patches should satisfy some joint probability distribution function of DBH and tree height according to the binary standing volume model (Equation (1)). Since the form of the distribution obtained from the solution of this function is complicated, it is not conducive to practical application. Therefore, we use MATLAB R2020b (9.9) and Origin 2019b to conduct a stochastic simulation. Based on the age series and the DBH-tree height growth function, the samples of DBH and tree height were drawn reflecting normal distribution. Further, we got a sample matrix of volume. The sample means were used as point estimates, leading to the expectation of volume under different forest types and ages. By stochastic simulation of volume growth, we gained a more accurate fit to the logistic growth function. More details about stochastic simulation can be found in Appendix A .

2.3.3. Estimation of Carbon Sequestration Potential

Tree biomass density was significantly and linearly positively correlated with volume density [ 11 ] (Equation (3)), and forest carbon stock estimates were derived by multiplying forest biomass by the amount of elemental carbon in the biomass (i.e., the carbon content factor). Carbon density is the amount of carbon stored per unit area of forest biomass.

where W is the biomass density (kg/ha), V is the volume density (m³/ha), β 1 , β 0 are model parameters, mainly based on the forest type conversion model proposed by Fang et al. [ 11 ] and Zeng et al. [ 37 ]. Due to the different tree species composition, age, and population structure of different vegetation types [ 38 ], the carbon content conversion coefficients may vary greatly. In this study, forest carbon stocks were measured based on the carbon content coefficients of each tree species (group) in the “Guidelines for carbon sink measurement and monitoring in afforestation projects” issued by the State Forestry Administration and previous research results [ 39 , 40 ] ( Table 1 ).

BEF parameters and carbon content coefficients of forest types.

PF refers to pine forest; CFF refers to Chinese fir forest; BLH refers to hard broad forest; BLS refers to soft broad forest; and three types of mixed forests: MCF refers to mixed coniferous forest, MBF refers to mixed broadleaf forests, and MCBF refers to mixed conifer-broadleaf forests.

Carbon sink capacity indicates the ability of vegetation to fix carbon per unit time, expressed as the increment of carbon stock in a certain time (Equation (4)). When the carbon density of the forest is relatively stable, the carbon sequestration potential is the difference between the carbon stock tending to the maximum and the carbon stock in the current year.

where the annual carbon sink C S is the difference between the corresponding carbon stocks of C t and C t − 1 in adjacent years and is equivalent to the product of the carbon content factor and the biomass. Equations (3) and (4) were implemented on Origin 2019b software.

2.4. Influencing Factors Analysis Method

In this study, the PLSR was used to explore the conditional factors of carbon sequestration potential [ 41 , 42 ] to effectively remove the interference of non-interpretative information. The results of carbon sequestration potential values were used as dependent variables to analyze the influence of single trait factors. A combination of single traits of the same type was attempted to construct the combined traits separately ( Table 2 ) to reflect the degree of influence of a certain factor type comprehensively.

Selection of single-factor and combined characteristics of the conditional factors.

X ˜ is the normalization of single factor X in the table.

Multiple correlation diagnostics were first performed to calculate the variance inflation factor ( V I F ). It is generally considered that when V I F > 10, multiple correlations among the factors will seriously affect the estimates of partial least squares. After testing, all single and combined factors satisfy V I F < 10, indicating no significant linear correlation between the factors and can be used for PLSR. The factors that passed the diagnostic were selected for PLSR, and Variable Importance in Projection ( V I P ) was calculated to indicate the degree of explanation of the standard deviation by the factors, as shown in Equation (5).

where q denotes the number of variables involved in the analysis, m denotes the number of iterations, and in the i th iteration, r ( Y , x i ) is calculated as the correlation coefficient between the dependent variable and all variables. w i j is the weight of variable j , which reflects the degree of explanation of the variables in the model. The sum of squares of V I P values of all variables is equal to 1. Factors with V I P < 1 are considered to have a low degree of explanation of the model, and factors with V I P ≥ 1 have a high degree of explanation. All the above tests were performed with Python 3.9.

3.1. Modeling Results of Tree Forest Volume Growth

The fitted parameters of the binary standing volume model and growth simulation for each type of forest in Lushan City are shown in Table 3 . The fitted parameters of the binary standing volume model and the DBH-Tree Height growth model generally had R² values above 0.90, which were well fitted, as shown in Figure 2 . For each age group of different forest types, the growth function of volume expectation with tree age was fitted. It was found that the fitted logistic curves using continuous derivable logistic curves yielded good fitting results for the volume expectancy as a function of mean age. For the curves of relative tree height, relative DBH, and forest volume with age for specific forest types, please refer to Figure A1 in Appendix B . The fitted models for the major forest types showed statistical significance at the 0.01 level, and the R² values close to 1 confirmed the good applicability of the model for estimating tree forest volume in Lushan. This volume growth model illustrated the relationship between the volume of a dominant species forest with age in a simplified form, which provides a good basis for the estimation and prediction of carbon sequestration potential.

Fitting the growth model of accumulation volume of the forest types in Lushan City. ( a ) shows the tree height-standardized age fitting relationship; ( b ) shows the DBH-standardized age fitting relationship; ( c ) shows the accumulation volume expectation-mean age fitting relationship after random simulation. ( a – c ) compare the relative tree height, relative DBH, and accumulation volume with age curves of different forest types, respectively, which illustrates that the Logistic function has a good fitting effect and also describes the differences between the curves of different forest types, fully reflecting the growth characteristics of forest types in Lushan City. (The points in the graph were forest patches sampling in FMID data).

Fitting results of the volume growth models in different forest types.

3.2. Characteristics of the Current Carbon Sequestration Capacity of Tree Forests

The average carbon density of tree forests in Lushan City in 2019 was 33.59 t/ha. The current state of carbon density showed an overall distribution pattern of high in the northwest and low in the south, decreasing from north to south, as shown in Figure 3 . The carbon density contribution of different age groups of forest types at various altitudes was analyzed. Forest patches were more distributed at 0–100 m and 100–300 m altitudes, and the carbon density was 26.41–28.97 t/ha here, which was lower than the average carbon density of the study area (33.59 t/ha). The carbon density increased with elevation in the four gradient intervals higher than 300 m, closely related to the forest types at various elevations. The main contributing forest types for carbon density were CFF and MCF in the 300–600 m and 600–900 m gradient intervals. PF was the most significant contributory species in the other four gradient intervals, particularly in the highest elevation interval (1200–1465 m), where PF accounted for a considerable proportion under all conditions.

Characteristics of the current carbon sequestration capacity of forests in Lushan City. The carbon sequestration capacity of forests in Lushan City was calculated from four aspects: spatial distribution, elevation, age group, and forest types. The geographic distribution map in the middle shows the spatial distribution of carbon density of forests in 2019; the carbon density stacking figures on both sides show the carbon density share of each age group of forest types in Lushan City at (0, 100], (100, 300], (300, 600], (600, 900], (900, 1200], and (1200, 1465] altitude gradients.

In 2019, the volume of forest storage in Lushan City was 2.34 × 10 6 m³, the biomass was about 1.73 × 10 6 t, and the total carbon stock was 9.22 × 10 5 t ( Table 4 ). The carbon stock indicates the overall state of a forest type, and the percentage of carbon stock contributed by each type of forest varies. Among them, the four forest types of PF, CFF, MCF, and MCBF provided 86.12% of carbon stock, with PF mainly providing 33.39% of carbon stock. The annual carbon sink of the Lushan forest was 3.02 × 10 4 t from 2019 to 2020, and its main contributing sources were PF (40.66%), CFF (15.39%), and MCF (19.50%). The average carbon density of tree forests in Lushan had grown about 1.10 t/ha/a with a growth rate of 3.28% from 2019 to 2020. The lowest growth rate of BLS was 0.74 t/ha/a with a growth rate of 1.84%, and the highest growth rate of MBF was 1.35 t/ha/a with a growth rate of 3.41%.

Status of carbon density/carbon stock in forest patches of different types.

3.3. Predicted Carbon Sequestration Potential of Tree Forests

The relationship between the carbon density of forests and tree age was examined based on the distribution of current forest age groups. A significant increase in carbon density will experience in the next 20 to 50 years, and it will achieve a stable state after 50 years. This relationship indicates that the upper limit of carbon density will be between 55 and 75 years (The year here refers to the average age of the forest stands). The carbon density of Lushan City will reach a relatively stable state in 2070, achieving the maximum carbon sequestration potential in the study area ( Figure 4 ). The change of overall carbon stock in tree forests from 2019 to 2070 shows an upward trend of decreasing growth rate: The carbon stock continuously will increase from 9.22 × 10 5 t to 2.15 × 10 6 t, and the overall carbon density will raise from 33.59 t/ha in 2019 to 78.33 t/ha in 2070, increasing to 2.33 times of the original one. The potential carbon sequestration is about 1.23 × 10 6 t, with a higher contribution from PF and MCF ( Figure 5 ). PF has the highest carbon sequestration potential because of its absolute dominance of the land area. The annual carbon sink of tree forests shows a trend of increasing and then decreasing: the highest annual carbon sink will occur in 2030 with 3.39 × 10 4 t, and will decrease to 8.06 × 10 3 t in 2070 ( Figure 5 ). The peak yearly carbon sink of diverse dominant species forest patches occurs in different years due to the varied tree species structure and age composition. Among them, the peak annual carbon sink of CFF is the earliest, reaching the maximum in 2019; the peak annual carbon sink of MCF is the latest, reaching the maximum in 2039; the remaining dominant tree types will reach the peak annual carbon sink in 2022–2032.

Box-and-whisker plot for forests carbon density by age in Lushan City. The figure shows the median, 25th and 75th percentile, mean (triangles), range, and extreme values outside the range (the proportion of the interquartile range past the low and high quartiles is 1.5, points outside this range will be identified as outliers).

Carbon sequestration potential of different forest types in Lushan City from 2019–2070: ( a ) depicts the change of carbon stock of forest types in Lushan, ( b ) records the contribution of different forest types to the total carbon stock more visually in percentage; ( c ) depicts the change of annual carbon sink of forests types in Lushan; and the contribution of different forest types to the total annual carbon sink is visually represented in ( d ); ( e ) compares the change of carbon stock of different forest types, and ( f ) compares the annual carbon sink changes of different forest types and records the peak and arrival years.

The carbon sequestration potential of natural forests is significantly higher than that of planted forests ( Figure 6 ). And the carbon stock of natural forests is about 2.31 times higher than that of planted forests in 2019, increasing to 3.15 by 2070. The annual carbon sinks in planted forests will peak between 2025 and 2026, while that of natural forests will peak between 2031 and 2032. The growth rate of carbon density in natural forests is also consistently higher than that in planted forests, with both reaching the same level between 2035 and 2036. By 2070, the carbon density of natural forests will reach 80.03 t/ha, higher than that of planted forests at 69.99 t/ha, indicating that natural forests can provide a more effective carbon sequestration function for Lushan City.

Carbon sequestration potential of natural and planted forests in Lushan City from 2019–2070: ( a ) shows the change of carbon stock, the two curves show an upward trend of decreasing growth rate, and the natural forest curve is always above the planted forest; ( b ) shows the change of annual carbon sink, the two curves show an increasing and then decreasing trend; ( c ) shows the change of carbon density, the two curves show an upward trend, and the natural forest carbon density exceeds the planted forest in 2040. ( d ) is the change of carbon density growth rate, and the change trend is similar to ( b ).

3.4. Exploration of Factors Influencing Carbon Sequestration Potential

As shown in Figure 7 , we analyzed the single factors of all samples, in which the VIP values of slope direction (2.19), slope gradient (1.24), and soil thickness (1.02) were greater than 1. Slope direction (SD) had the highest importance, indicating that the carbon sequestration potential was significantly influenced by site characteristics. Adding the combination factors for analysis, the VIP value of stand characteristics was 1.29 based on the original key factors. All were higher than their three single factors (forest density (1.28), vegetation cover (0.44), and canopy density (0.42)), indicating that the combination of stand characteristics had stronger explanations than the single factors. The effect of combined factors of site conditions and climatic factors was average and less important than some single factors. When the effect sizes of the combined factors were compared, the explanatory effects of both stand characteristics (1.27) and climatic factors (1.16) were larger than 1, with the explanatory effects of stand characteristics being stronger than those of site characteristics.

Variable Importance in Projection (VIP) for condition factors. ( a ) shows the comparison of VIP values between single factors for overall, natural forest, and planted forest; ( b ) shows the comparison of VIP values between combined features for overall, natural forest, and planted forest; ( c ) shows the comparison of VIP values between single factors and combined features. Mean elevation (ELE), Slope direction (SD), Slope gradient (SG), Soil thickness (ST), Humus thickness (HT), Forest density (FD), Vegetation cover (VC), Canopy density (CD), Precipitation (PRE), Radiation (RAD), Temperature (TEM).

Furthermore, the parameters impacting carbon sequestration capability varied depending on the forest type. The VIP values of single factors of natural forests and the overall regional forests were not significantly different, and their key factors were all slope direction factors in site characteristics. It is worth noting that the influencing factors of carbon sequestration potential of planted forests are different from the overall regional forests, and the VIP values of two factors, soil thickness (1.67) and vegetation cover (1.42), are greater than 1. Regarding the combination of characteristics, the climatic characteristics of both natural and planted forests had stronger explanatory effects than the single factors. Comparing the effect sizes among the combination characteristics, climate characteristics had higher explanatory effects on natural and planted forests, respectively. The explanatory role of site characteristics on the carbon sequestration potential of natural forests was high (1.18).

4. Discussion

4.1. estimation methodology and estimation results.

The biomass-forest age relationship has become a frequently utilized method for predicting future forest carbon pools and estimating forest biomass carbon stocks [ 16 , 43 , 44 ]. Existing research has mostly employed biomass-forest age connections to estimate carbon sequestration potential in larger-scale study areas, such as national [ 16 ] and provincial [ 45 ] scales, as well as incorporating stand age in the present stand growth model framework to reduce estimation bias [ 46 ]. However, unlike the predictive growth equation with DBH and tree height factors, Liu et al. [ 20 ] and Zhou et al. [ 47 ] showed that, while many studies have reported successful applications of fitting biomass-forest age relationships directly using biomass converted from forest volume [ 48 , 49 ], there are still questions about the accuracy and precision of volume estimates, particularly concerning reducing the uncertainty of model parameters [ 50 ]. As a result, explicitly fitting the biomass-age relationship using biomass transformed from volume fails to appropriately depict tree growth [ 51 ]. Since the actual forestry carbon sink projects are frequently carried out for fine scales, more accurate forecast results of carbon sequestration potential are required. In this study, considering the complexity of the forest survey samples in the fine-scale study area, the tree growth equation was re-fitted based on the characteristics of the binary standing volume model in which the DBH and tree height can visually represent the growth of storage volume, using the relationship between DBH, tree height, standing volume storage, and tree age. Compared with the original model, the model constructed in this study further refines the relationship between volume and age of a forest type using a stochastic simulation process, which can be applied even with limited forest biomass data and forest age observation, and provides a reference for the prediction of forest carbon sequestration potential at fine-scale regions.

In addition, our estimating results are consistent with previous research literature [ 52 , 53 , 54 ]. Excluding differences in the age structure of the study area and study methods, they are generally consistent with the results of previous research on carbon density estimation in Jiangxi Province ( Table 5 ). Compared with carbon density estimates at the same study scale in Jiangxi Province, the average carbon density in Lushan City estimated in this study was higher than the carbon density in Taihe County estimated by Wu et al. [ 55 ], and the carbon density in Xingguo County estimated by Li et al. [ 56 ]. A possible reason is that our study was investigated 16 years later than those two, during which the tree forest maintained stable growth and carbon density continued to increase. The average carbon density in Lushan City is close to 36.0 t/ha, which was estimated by Zhang et al. [ 24 ] in the whole of Jiangxi Province. Compared with the carbon density in Jiangxi Province estimated by Li et al. [ 52 ] and Wu et al. [ 53 ] (23.87–27.2 t/ha), the average carbon density in Lushan City is slightly higher, and its contribution to the forest carbon sequestration function in Jiangxi Province is greater. Compared with the predicted carbon sequestration potential of arboreal forests based on biomass-age relationships in previous literature, the results were similar to those predicted by Wu et al. [ 55 ] and Qiu et al. [ 54 ], indicating the reasonableness of the model. Additionally, compared with the national data, the carbon density in Lushan City in 2020 is lower than the 50.51 t/ha predicted by Zhang et al. [ 46 ] and the 59.8 t/ha predicted by Xu et al. [ 16 ], which may be mainly because the tree forests in Lushan City are dominated by middle-aged and young forests, and the forest management in Jiangxi Province is primarily rough management with slow growth [ 57 ]. The predicted carbon density in Lushan City in 2050 is close to the predicted values for the national forest carbon density in 2050, which indicates that the forest vegetation in Lushan City has significant potential for carbon sequestration.

Comparison with the estimated and predicted values of forest carbon density in Jiangxi Province from previous studies.

4.2. Factors Influencing Carbon Sequestration Potential

Based on the growth of forest age of different forest types, we quantified the future carbon sequestration potential of Lushan City forests. After incorporating information on stand developmental stages into predicting future forest carbon sequestration potential, this study found that forest carbon stocks accumulated rapidly at young ages and gradually saturated at later stages, which is consistent with He et al. [ 43 , 59 ]. After changes in forest carbon density have stabilized, mature and over-mature forests can also continue to accumulate carbon as stand age increases [ 60 ], and still hold a crucial role in the carbon cycle despite decreasing growth efficiency. Therefore, the carbon sequestration benefits given by forests as they grow and expand are ongoing. In addition to forest growth and development, forest carbon sequestration capability is intimately tied to large-scale afforestation and regional extension of ecological restoration efforts. In the next five decades, ecological restoration programs and sustainable forest management in China will increase forest area and biomass carbon intensity, making forests of various ages a carbon sink [ 46 ]. And according to the China Forestry Sustainable Development Strategy Research Group, the quantity and quality of China’s forests are expected to enter a phase of steady development, which implies that the capacity of increasing forest carbon sequestration potential may be limited. As a result, to acquire better forest carbon sequestration potential assuming normal forest growth and development, it is required to investigate the influence of condition factors on carbon sequestration potential.

The predicted carbon sequestration potential value was used as the dependent variable in this study. The site characteristics had a significant impact on carbon sequestration potential, with slope direction having the most impact, which was significantly and positively correlated with the value of carbon sequestration potential. This result is consistent with the previous regional research findings in Jiangxi Province. Wu et al. [ 61 ] examined the vegetation carbon density of major forests in the Poyang Lake basin. They discovered that slope direction and gradient had a substantial impact on vegetation carbon density. Since the slope direction, slope gradient, elevation, and other site features have redistribution effects on surface light, heat, and water resources, which affect the forest growth and, consequently, the carbon pool. The findings imply that the research area’s carbon sequestration capacity is greatly influenced by the azimuth of solar irradiation, and the sunny slope (i.e., south slope) may yield stronger carbon sequestration [ 24 ].

The key factors influencing the carbon sequestration capability of various origins’ forests are diverse, resulting in various management strategies. Natural forests and the overall forests in the region have comparable crucial features, and they are all tied to site characteristics. The protection of natural forests should be encouraged, and the slope direction and slope gradient should be emphasized in the implementation of natural forest protection projects, which will avoid the reduction of forest carbon sink capacity caused by problems such as soil erosion. On the other hand, the key factors of planted forests are soil thickness and vegetation cover. Relatively thicker soil and relatively higher vegetation cover can provide a higher carbon sink. Therefore, when predicting the carbon sequestration potential of planted forests in the future, the above factors can be considered as the main control factors for modeling to improve the prediction accuracy. To provide favorable conditions for the expansion of carbon sink in a planted forest, more consideration should also be given to the aforementioned components when developing planted forest initiatives. Furthermore, when the findings of the multifactor combination were compared, the climatic combination had a greater impact than the site and stand characteristics. The growing season was effectively extended by the rises in temperature and precipitation, which also increased microbial activity, photosynthetic capacity, and plant growth and respiration [ 62 ]. This improved the capacity of forests to store carbon [ 5 ]. Therefore, the climatic combination characteristics can be considered to incorporate into the prediction model, allowing multiple climate condition scenarios to be established to more correctly estimate the future carbon sequestration potential of forests.

4.3. Uncertainties and Potential Constraints

Carbon stocks in forest ecosystems are primarily influenced by two aspects. On the one hand, changes in forest biomass and the accompanying changes in the carbon cycle, and on the other hand, changes in the forest soil carbon pool, namely the balance between imports and losses of organic carbon into the soil [ 9 ]. Solar radiation also plays an important role in plant carbon sequestration. For example, sunny slopes can lead to strong soil mineralization and evapotranspiration, which may limit plant carbon sequestration. The estimation of carbon sequestration potential is somewhat biased because actual measurements of soil nutrient mineralization and evapotranspiration have not been carried out. Due to the lack of data on understory vegetation, herbaceous layer, deadwood layer, dead wood, and soil layer in the FMID, this study did not cover the carbon stocks of the categories mentioned above and only considered the carbon stocks of live trees, so the estimation of forest ecosystem carbon stocks in Lushan City was quite underestimated.

The predictions in this study are also based on certain assumptions, which lead to some uncertainties in the results: first, the maximum carbon sequestration potential is an estimate based on spatial and temporal intergeneration, assuming no forest disease or mortality, and that existing forests grow naturally according to the growth equation, which only represents the maximum potential that a forest type or age can achieve under ideal conditions. In actuality, forests are affected by disease and mortality during the growth process, which may result in exaggerated estimations of carbon sequestration potential [ 38 ]. Second, if China’s forestry development and forest cover expand, the fraction of newly generated forests may fluctuate in the forecast process [ 6 ]. On the other hand, there are high uncertainties in the tree species composition and age groups of newly created forests, which may lead to inaccurate prediction results [ 58 ]. Hence, the newly created forests are not included in the estimation, and the prediction of carbon sequestration potential is slightly underestimated.

Finally, the impacts of anthropogenic and natural disturbances on forest carbon sequestration were not considered. With the increasing emphasis on forest protection through regulations such as “peak carbon dioxide emissions and carbon neutrality”, it is reasonable to expect that human activities such as logging will cause minimal direct disruption of natural forests in the future [ 6 ]. However, for the disturbance of planted forests under the influence of various anthropogenic activities (e.g., afforestation, logging, irrigation), the future carbon sequestration potential of forests still varies greatly [ 16 , 23 ]. Factors such as climate change, elevated atmospheric CO 2 concentration, and nitrogen deposition may also affect the accumulation process of forest biomass density, and estimating forest carbon sequestration capability based on current climate circumstances may also introduce some uncertainty [ 23 , 54 ]. A more comprehensive study, including climate changes such as warming and drought, as well as the effects of other anthropogenic disturbances on future forest carbon sequestration, should be conducted.

5. Conclusions

Our study provided a better understanding of the relationships between forest growth and carbon sequestration potential at fine spatial-scales by introducing BEF and tree growth equations. Moreover, we further explored the effect of the combination of factor characteristics on the carbon sequestration potential, excluding forest growth effects, which provides crucial insights for Chinese carbon policy and global carbon neutrality goals.

By 2070, the carbon density of forests in Lushan City will reach a relatively stable state, and its carbon stock will be close to the maximum, indicating that Lushan forests will serve as a long-term carbon sink in the next fifty years. Among them, pine forests and mixed coniferous forests have a higher carbon sequestration contribution. In addition, the carbon sequestration potential of natural forests was much higher than that of planted forests, with the gap widening as the woods aged. Thus, conserving natural forests should be encouraged to sustain carbon sequestration capacity in future afforestation projects, and replantation site characteristics should be carefully considered in the afforestation projects to increase carbon sequestration capacity. Slope direction, slope gradient, soil thickness, and vegetation cover factors are important factors of forestry carbon sink, which should be paid attention to in implementing forestry carbon sink projects.

More importantly, incorporating DBH and tree height data from the binary standing volume model can better represent forest growth changes. A stochastic simulation process could be used to further refine the relationship between the standing volume of forest types and the age of the trees, which improved the accuracy of the prediction of carbon sequestration potential at the fine-scale areas. It can also be applied in the case of limited forest biomass data and stand age observation, enriching the ways of predicting forest carbon sequestration potential. Future work should also consider climate changes on future forest carbon sequestration for better achieving global carbon neutrality goals.

Appendix A contains the specific process of stochastic simulation of volume growth. The details are as follows:

Under the premise that the mean DBH and tree height of a stand at the same mean age are normally distributed, the mean volume of a stand should satisfy some joint probability distribution model of DBH and tree height according to the binary standing volume model (Equation (1)). In practical applications, we are more interested in the expectation of volume as a function of tree age. The expectation of volume at a certain average age can be calculated based on probability density, which can be integrated as shown in Equation (A1).

where, E ( V ) ( t ) is the expected volume at the age of t . f ( v ) ( t ) is the probability density distribution function of the volume at the age of t . Therefore, the probability distribution model of volume, expectation, and mean age function models are theoretically uniquely determined and solvable; however, their solution process is complex and tedious. To solve this problem, we construct the stochastic simulation algorithm in the following steps:

(1) Construct the time series vector: t → = ( t 1 , t 2 , ⋯ , t M ) ( M is the number of age groups). In turn, the two normal distribution models obeyed by DBH and tree height are randomly sampled (each group has a large enough sample size, N = 500) to obtain two M × N sample matrices: ( d i j ) M × N and ( h i j ) M × N , respectively. The sample matrix of volume ( v i j ) M × N is obtained by matrix operation of Equation (A2).

where, d i j , h i j and v i j are the i th DBH, tree height, and volume sampling data of the j th age group. a 0 , a 1 , a 2 are the parameters obtained by fitting the binary standing volume model (Equation (1)). From Equation (A2), we got a sample matrix of volume ( v i j ) M × N . M is the length of time series, and N is the number of samples simulated.

(2) μ ^ is the mean vector calculated as the point estimate of μ , indicating the accumulation expectation under the year series as shown in Equation (A3).

The j th element e v j in vector is the average of the accumulation volume in j th column, indicating the accumulation expectation under a single year.

(3) A nonlinear fit to t → and e v → using a continuously derivable logistic curve was performed in Origin 2019b to obtain the accumulation expectation v i j as a function of mean age: the volume-tree age growth model.

Variation curves of tree height, DBH, and volume expectation with age for different forest types. ( a ) shows the tree height-standardized age fitting relationship; ( b ) shows the DBH-standardized age fitting relationship; ( c ) shows the accumulation volume expectation-mean age fitting relationship after random simulation. PF refers to pine forest; CFF refers to Chinese fir forest; BLH refers to hard broad forest; BLS refers to soft broad forest; and three types of mixed forests: MCF refers to mixed coniferous forest, MBF refers to mixed broadleaf forests, and MCBF refers to mixed conifer-broadleaf forests. (The points in the graph represent forest patches sampling data).

Funding Statement

This research was funded by Big Data-Driven Ecological Security and Natural Resources Early Warning Plan, Key Projects of Philosophy and Social Science Research, Chinese Ministry of Education (Grant No. 19JZD023), and Analysis of Ecological Characteristics and Ecological Value Transformation of the Whole Area of Lushan City.

Author Contributions

Conceptualization, G.H., Z.Z. and Y.C.; methodology, G.H. and Z.Z.; formal analysis, G.H. and Z.Z.; investigation, G.H. and Y.C.; writing—original draft preparation, G.H.; writing—review and editing, Q.Z., W.W. and W.P.; visualization, Z.Z.; supervision, Y.C.; funding acquisition, Y.C. and W.P.; G.H. and Z.Z. contributed equally to this paper. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

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

The authors declare no conflict of interest.

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Study uncovers the role of soil microbes in forest ecosystems

by Higher Education Press

Assessing the function of forest ecosystems requires a deep understanding of the mechanisms of soil nitrogen mineralization. A study conducted by a team of researchers has shed light on how soil N-cycling genes drive soil nitrogen mineralization during afforestation.

The findings , published in Soil Ecology Letters , provide valuable insights into the relationship between soil microbial communities , functional genes, and the rate of soil nitrogen mineralization.

The researchers collected soil samples from a chronosequence of Robinia pseudoacacia L (RP14, RP20, RP30, and RP45) at different stages of afforestation , along with a sloped farmland (FL) as a control. Metagenomic sequencing analysis revealed significant changes in the diversity and composition of soil microbial communities involved in N-cycling as the forestation progressed. Afforestation was found to effectively increase the diversity of soil microbial communities.

To further investigate the relationship between soil microbial communities and nitrogen mineralization, the researchers conducted indoor culture experiments and analyzed correlations.

The results showed a significant increase in both soil nitrification rate (Rn) and soil nitrogen mineralization rate (Rm) with increasing stand age. The study also found a strong correlation between soil Rm and soil microbial diversity as well as the abundance of soil N-cycling genes.

Using partial least squares path modeling (PLS-PM) analysis, the researchers discovered that nitrification and denitrification genes had a greater direct effect on soil Rm than soil microbial communities. This suggests that functional genes related to soil nitrogen cycling play a crucial role in driving soil nitrogen mineralization during afforestation.

The study was conducted on the Loess Plateau, an important region for afforestation efforts. The findings provide a better understanding of the effects of microorganisms on soil nitrogen mineralization rate during afforestation and offer a new theoretical basis for evaluating soil nitrogen mineralization mechanisms during forest succession.

"These findings have important implications for forest management and ecosystem restoration ," said Professor Ren, the lead author of the study. "By understanding the role of soil microbes and functional genes in soil nitrogen mineralization, we can optimize afforestation practices and enhance the ecological functions of forest ecosystems."

The research team hopes that these findings will contribute to the development of more sustainable and effective forest management strategies, especially in regions undergoing afforestation efforts.

By considering the role of soil microbes and functional genes, forest restoration projects can be designed to maximize the benefits to both the environment and society.

Provided by Higher Education Press

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