Growing biodiverse carbon‐rich forests

Regrowing forests on cleared land is a key strategy to achieve both biodiversity conservation and climate change mitigation globally. Maximizing these co‐benefits, however, remains theoretically and technically challenging because of the complex relationship between carbon sequestration and biodiversity in forests, the strong influence of climate variability and landscape position on forest development, the large number of restoration strategies possible, and long time‐frames needed to declare success. Through the synthesis of three decades of knowledge on forest dynamics and plant functional traits combined with decision science, we demonstrate that we cannot always maximize carbon sequestration by simply increasing the functional trait diversity of trees planted. The relationships between plant functional diversity, carbon sequestration rates above ground and in the soil are dependent on climate and landscape positions. We show how to manage ‘identities’ and ‘complementarities’ between plant functional traits to achieve systematically maximal cobenefits in various climate and landscape contexts. We provide examples of optimal planting and thinning rules that satisfy this ecological strategy and guide the restoration of forests that are rich in both carbon and plant functional diversity. Our framework provides the first mechanistic approach for generating decision‐makingrules that can be used to manage forests for multiple objectives, and supports joined carbon credit and biodiversity conservation initiatives, such as Reducing Emissions from Deforestation and forest Degradation REDD+. The decision framework can also be linked to species distribution models and socio‐economic models to find restoration solutions that maximize simultaneously biodiversity, carbon stocks, and other ecosystem services across landscapes. Our study provides the foundation for developing and testing cost‐effective and adaptable forest management rules to achieve biodiversity, carbon sequestration, and other socio‐economic co‐benefits under global change.     

Traditional shifting agriculture: tracking forest carbon stock and biodiversity through time in western Panama

Reducing emissions from deforestation and forest degradation (REDD+) requires developing countries to quantify greenhouse gas emissions and removals from forests in a manner that is robust, transparent, and as accurate as possible. Although shifting cultivation is a dominant practice in several developing countries, there is still very limited information available on how to monitor this land‐use practice for REDD+ as little is known about the areas of shifting cultivation or the net carbon balance. In this study, we propose and test a methodology to monitor the effect of the shifting cultivation on above‐ground carbon stocks. We combine multiyear remote sensing information, taken from a 12‐year period, with an in‐depth community forest carbon stock inventory in Palo Seco Forest Reserve, western Panama. Using remote sensing, we were able to separate four forest classes expressing different forest‐use intensity and time‐since‐intervention, which demonstrate expected trends in above‐ground carbon stocks. The addition of different interventions observed over time is shown to be a good predictor, with remote sensing variables explaining 64.2% of the variation in forest carbon stocks in cultivated landscapes. Multitemporal and multispectral medium‐resolution satellite imagery is shown to be adequate for tracking land‐use dynamics of the agriculture‐fallow cycle. The results also indicate that, over time, shifting cultivation has a transitory effect on forest carbon stocks in the study area. This is due to the rapid recovery of forest carbon stocks, which results in limited net emissions. Finally, community participation yielded important additional benefits to measuring carbon stocks, including transparency and the valorization of local knowledge for biodiversity monitoring. Our study provides important inputs regarding shifting cultivation, which should be taken into consideration when national forest monitoring systems are created, given the context of REDD+ safeguards.     

Forest disturbance and recovery in Peruvian Amazonia

Amazonian forests function as biomass and biodiversity reservoirs, contributing to climate change mitigation. While they continuously experience disturbance, the effect that disturbances have on biomass and biodiversity over time has not yet been assessed at a large scale. Here, we evaluate the degree of recent forest disturbance in Peruvian Amazonia and the effects that disturbance, environmental conditions and human use have on biomass and biodiversity in disturbed forests. We integrate tree-level data on aboveground biomass (AGB) and species richness from 1840 forest plots from Peru's National Forest Inventory with remotely sensed monitoring of forest change dynamics, based on disturbances detected from Landsat-derived Normalized Difference Moisture Index time series. Our results show a clear negative effect of disturbance intensity tree species richness. This effect was also observed on AGB and species richness recovery values towards undisturbed levels, as well as on the recovery of species composition towards undisturbed levels. Time since disturbance had a larger effect on AGB than on species richness. While time since disturbance has a positive effect on AGB, unexpectedly we found a small negative effect of time since disturbance on species richness. We estimate that roughly 15% of Peruvian Amazonian forests have experienced disturbance at least once since 1984, and that, following disturbance, have been increasing in AGB at a rate of 4.7 Mg ha⁻¹ year⁻¹ during the first 20 years. Furthermore, the positive effect of surrounding forest cover was evident for both AGB and its recovery towards undisturbed levels, as well as for species richness. There was a negative effect of forest accessibility on the recovery of species composition towards undisturbed levels. Moving forward, we recommend that forest-based climate change mitigation endeavours consider forest disturbance through the integration of forest inventory data with remote sensing methods.     

Estimating carbon carrying capacity in natural forest ecosystems across heterogeneous landscapes: addressing sources of error

Evaluating contributions of forest ecosystems to climate change mitigation requires well‐calibrated carbon cycle models with quantified baseline carbon stocks. An appropriate baseline for carbon accounting of natural forests at landscape scales is carbon carrying capacity (CCC); defined as the mass of carbon stored in an ecosystem under prevailing environmental conditions and natural disturbance regimes but excluding anthropogenic disturbance. Carbon models require empirical measurements for input and calibration, such as net primary production (NPP) and total ecosystem carbon stock (equivalent to CCC at equilibrium). We sought to improve model calibration by addressing three sources of errors that cause uncertainty in carbon accounting across heterogeneous landscapes: (1) data‐model representation, (2) data‐object representation, (3) up‐scaling. We derived spatially explicit empirical models based on environmental variables across landscape scales to estimate NPP (based on a synthesis of global site data of NPP and gross primary productivity, n=27), and CCC (based on site data of carbon stocks in natural eucalypt forests of southeast Australia, n=284). The models significantly improved predictions, each accounting for 51% of the variance. Our methods to reduce uncertainty in baseline carbon stocks, such as using appropriate calibration data from sites with minimal human disturbance, measurements of large trees and incorporating environmental variability across the landscape, have generic application to other regions and ecosystem types. These analyses resulted in forest CCC in southeast Australia (mean total biomass of 360 t C ha^?1, with cool moist temperate forests up to 1000 t C ha^?1) that are larger than estimates from other national and international (average biome 202 t C ha^?1) carbon accounting systems. Reducing uncertainty in estimates of carbon stocks in natural forests is important to allow accurate accounting for losses of carbon due to human activities and sequestration of carbon by forest growth.     

Predicting alpha diversity of African rain forests: models based on climate and satellite‐derived data do not perform better than a purely spatial model

Aim Our aim was to evaluate the extent to which we can predict and map tree alpha diversity across broad spatial scales either by using climate and remote sensing data or by exploiting spatial autocorrelation patterns. Location Tropical rain forest, West Africa and Atlantic Central Africa. Methods Alpha diversity estimates were compiled for trees with diameter at breast height ≥ 10 cm in 573 inventory plots. Linear regression (ordinary least squares, OLS) and random forest (RF) statistical techniques were used to project alpha diversity estimates at unsampled locations using climate data and remote sensing data [Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), Quick Scatterometer (QSCAT), tree cover, elevation]. The prediction reliabilities of OLS and RF models were evaluated using a novel approach and compared to that of a kriging model based on geographic location alone. Results The predictive power of the kriging model was comparable to that of OLS and RF models based on climatic and remote sensing data. The three models provided congruent predictions of alpha diversity in well‐sampled areas but not in poorly inventoried locations. The reliability of the predictions of all three models declined markedly with distance from points with inventory data, becoming very low at distances > 50 km. According to inventory data, Atlantic Central African forests display a higher mean alpha diversity than do West African forests. Main conclusions The lower tree alpha diversity in West Africa than in Atlantic Central Africa may reflect a richer regional species pool in the latter. Our results emphasize and illustrate the need to test model predictions in a spatially explicit manner. Good OLS or RF model predictions from inventory data at short distance largely result from the strong spatial autocorrelation displayed by both the alpha diversity and the predictive variables rather than necessarily from causal relationships. Our results suggest that alpha diversity is driven by history rather than by the contemporary environment. Given the low predictive power of models, we call for a major effort to broaden the geographical extent and intensity of forest assessments to expand our knowledge of African rain forest diversity.     

Altered dynamics of forest recovery under a changing climate

Forest regeneration following disturbance is a key ecological process, influencing forest structure and function, species assemblages, and ecosystem–climate interactions. Climate change may alter forest recovery dynamics or even prevent recovery, triggering feedbacks to the climate system, altering regional biodiversity, and affecting the ecosystem services provided by forests. Multiple lines of evidence – including global‐scale patterns in forest recovery dynamics; forest responses to experimental manipulation of CO₂, temperature, and precipitation; forest responses to the climate change that has already occurred; ecological theory; and ecosystem and earth system models – all indicate that the dynamics of forest recovery are sensitive to climate. However, synthetic understanding of how atmospheric CO₂ and climate shape trajectories of forest recovery is lacking. Here, we review these separate lines of evidence, which together demonstrate that the dynamics of forest recovery are being impacted by increasing atmospheric CO₂ and changing climate. Rates of forest recovery generally increase with CO₂, temperature, and water availability. Drought reduces growth and live biomass in forests of all ages, having a particularly strong effect on seedling recruitment and survival. Responses of individual trees and whole‐forest ecosystems to CO₂ and climate manipulations often vary by age, implying that forests of different ages will respond differently to climate change. Furthermore, species within a community typically exhibit differential responses to CO₂ and climate, and altered community dynamics can have important consequences for ecosystem function. Age‐ and species‐dependent responses provide a mechanism by which climate change may push some forests past critical thresholds such that they fail to recover to their previous state following disturbance. Altered dynamics of forest recovery will result in positive and negative feedbacks to climate change. Future research on this topic and corresponding improvements to earth system models will be a key to understanding the future of forests and their feedbacks to the climate system.     

Climate change,woodpeckers, and forests: Current trends and future modeling needs

The structure and composition of forest ecosystems are expected to shift with climate‐induced changes in precipitation, temperature, fire, carbon mitigation strategies, and biological disturbance. These factors are likely to have biodiversity implications. However, climate‐driven forest ecosystem models used to predict changes to forest structure and composition are not coupled to models used to predict changes to biodiversity. We proposed integrating woodpecker response (biodiversity indicator) with forest ecosystem models. Woodpeckers are a good indicator species of forest ecosystem dynamics, because they are ecologically constrained by landscape‐scale forest components, such as composition, structure, disturbance regimes, and management activities. In addition, they are correlated with forest avifauna community diversity. In this study, we explore integrating woodpecker and forest ecosystem climate models. We review climate–woodpecker models and compare the predicted responses to observed climate‐induced changes. We identify inconsistencies between observed and predicted responses, explore the modeling causes, and identify the models pertinent to integration that address the inconsistencies. We found that predictions in the short term are not in agreement with observed trends for 7 of 15 evaluated species. Because niche constraints associated with woodpeckers are a result of complex interactions between climate, vegetation, and disturbance, we hypothesize that the lack of adequate representation of these processes in the current broad‐scale climate–woodpecker models results in model–data mismatch. As a first step toward improvement, we suggest a conceptual model of climate–woodpecker–forest modeling for integration. The integration model provides climate‐driven forest ecosystem modeling with a measure of biodiversity while retaining the feedback between climate and vegetation in woodpecker climate change modeling.     

Fire effects and ecological recovery pathways of tropical montane cloud forests along a time chronosequence

Tropical montane cloud forests (TMCFs) harbour high levels of biodiversity and large carbon stocks. Their location at high elevations make them especially sensitive to climate change, because a warming climate is enhancing upslope species migration, but human disturbance (especially fire) may in many cases be pushing the treeline downslope. TMCFs are increasingly being affected by fire, and the long‐term effects of fire are still unknown. Here, we present a 28‐year chronosequence to assess the effects of fire and recovery pathways of burned TMCFs, with a detailed analysis of carbon stocks, forest structure and diversity. We assessed rates of change of carbon (C) stock pools, forest structure and tree‐size distribution pathways and tested several hypotheses regarding metabolic scaling theory (MST), C recovery and biodiversity. We found four different C stock recovery pathways depending on the selected C pool and time since last fire, with a recovery of total C stocks but not of aboveground C stocks. In terms of forest structure, there was an increase in the number of small stems in the burned forests up to 5–9 years after fire because of regeneration patterns, but no differences on larger trees between burned and unburned plots in the long term. In support of MST, after fire, forest structure appears to approximate steady‐state size distribution in less than 30 years. However, our results also provide new evidence that the species recovery of TMCF after fire is idiosyncratic and follows multiple pathways. While fire increased species richness, it also enhanced species dissimilarity with geographical distance. This is the first study to report a long‐term chronosequence of recovery pathways to fire suggesting faster recovery rates than previously reported, but at the expense of biodiversity and aboveground C stocks.     

Linking Land‐Use Scenarios,Remote Sensing and Monitoring to Project Impact of Management Decisions

Large‐scale modifications of natural ecosystems lead to mosaics of natural, semi‐natural and intensively used habitats. To improve communication in conservation planning, managers and other stakeholders need spatially explicit projections at the landscape scale of future biodiversity under different land‐use scenarios. For that purpose, we visualized the potential effect of five forest management scenarios on the avifauna of Kakamega Forest, western Kenya using different measures of bird diversity and GIS data. Future projections of bird diversity combined: (1) remotely sensed data on the spatial distribution of different forest management types; (2) field‐based data on the biodiversity of birds in the different management types; and (3) forest management scenarios that took into account possible views of various stakeholder groups. Management scenarios based on the species richness of forest specialists were very informative, because they reflected differences in the proportions of near‐natural forest types among the five scenarios. Projections based on community composition were even more meaningful, as they mirrored not only the proportions of near‐natural forest types, but also their perimeter to area ratios. This highlights that it is important to differentiate effects of the total area of available habitat and the degree of habitat fragmentation, both for species richness and community composition. Furthermore, our study shows that an approach that combines land‐use scenarios, remote sensing and field data on biodiversity can be used to visualize future biodiversity. As such, visualizations of alternative scenarios are valuable for successful communication about conservation planning considering different groups of stakeholders in species‐rich tropical forests.     

Are we restoring enough? Simulating impacts of restoration efforts on the suitability of forest landscapes for a locally critically endangered umbrella species

Habitat restoration is often implemented to mitigate the negative effects of intensive forestry on biodiversity. It may be increasingly adopted in future to alleviate additional negative effects of climate change. Ascertaining the restoration effort needed to fulfill project goals is difficult. Insights may be gained through simulating the effects of restoration efforts on landscape dynamics through time. Here we used a spatially explicit landscape simulation model to simulate the effects of different restoration efforts on forest landscapes in Sweden to assess the level of mitigation that is needed to allow viable populations of the locally critically endangered White‐backed Woodpecker (Dendrocopos leucotos); an umbrella species whose protection may serve the protection of a range of other species. Based on the goals of the protection plan for the species, which reflect its habitat requirements, we evaluated which of several restoration scenarios could fulfill goals with respect to (1) the amount of deciduous forest; (2) the amount of dead wood; and (3) the age of the forest. We found that whereas it may be relatively easy and quick to acquire high levels of dead wood, increasing the proportions of deciduous forest and of old forests require considerably more time and effort. Also, current management actions would not be sufficient to create the required amount of habitat to conserve the White‐backed Woodpecker in our study region. Simulations like ours can provide valuable information about the levels of restoration needed through time to fulfill project goals and may prevent wasting valuable resources, time, effort, and money.     

Long‐term recovery of the functional community assembly and carbon pools in an African tropical forest succession

On the African continent, the population is expected to expand fourfold in the next century, which will increasingly impact the global carbon cycle and biodiversity conservation. Therefore, it is of vital importance to understand how carbon stocks and community assembly recover after slash‐and‐burn events in tropical second growth forests. We inventoried a chronosequence of 15 1‐ha plots in lowland tropical forest of the central Congo Basin and evaluated changes in aboveground and soil organic carbon stocks and in tree species diversity, functional composition, and community‐weighted functional traits with succession. We aimed to track long‐term recovery trajectories of species and carbon stocks in secondary forests, comparing 5 to 200 + year old secondary forest with reference primary forest. Along the successional gradient, the functional composition followed a trajectory from resource acquisition to resource conservation, except for nitrogen‐related leaf traits. Despite a fast, initial recovery of species diversity and functional composition, there were still important structural and carbon stock differences between old growth secondary and pristine forest, which suggests that a full recovery of secondary forests might take much longer than currently shown. As such, the aboveground carbon stocks of 200 + year old forest were only 57% of those in the pristine reference forest, which suggests a slow recovery of aboveground carbon stocks, although more research is needed to confirm this observation. The results of this study highlight the need for more in‐depth studies on forest recovery in Central Africa, to gain insight into the processes that control biodiversity and carbon stock recovery.     

Landscape patterns and environmental controls of deciduousness in forests of central Panama

Aim Dry season deciduousness affects intra‐ and inter‐annual patterns of carbon, water and energy balance in seasonal tropical forests. Because it is affected by rainfall, temperature and solar radiation, deciduousness may be an indicator of the response of vegetation to climate change. Better understanding of how spatial patterns of deciduousness are affected by climate and other environmental gradients will improve the ability to predict responses to climate change. This study develops remote sensing methods for quantifying tropical forest deciduousness and examines the relationship between deciduousness and environmental factors in semi‐deciduous tropical forest. Location Central Panama. Methods I applied spectral mixture analysis (SMA) and the normalized difference vegetation index (NDVI) to Landsat images to predict deciduousness which was ground‐truthed with field observations of the percentage of overstorey deciduous trees. Using predicted deciduousness from SMA, patterns of deciduousness at three spatial scales were analysed. I determined how deciduousness varied spatially with rainfall and geological substrate. Results Both SMA and NDVI had strong correlations (r > 0.9) with field observations of deciduousness. On a landscape scale, deciduousness increased as rainfall decreased, but geological substrate altered this relationship. On some geological substrates, deciduousness was much greater than expected for a given rainfall total or showed a slight but significant increase with rainfall. At an intermediate spatial scale, there were highly deciduous patches from 3 to 250 ha in size embedded in non‐deciduous forest, which may have resulted from topography, soil variation or past land use. Main conclusions Dry season deciduousness can be accurately quantified using satellite images indicating that remote sensing can be a valuable tool for detecting change and understanding ecosystem processes in tropical forests from landscape to regional scales.     

How are biodiversity and carbon stock recovered during tropical forest restoration? Supporting the ecological paradigms and political context involved

To meet agendas for biodiversity conservation and mitigation of climate change, large-scale restoration initiatives propose ecological restoration as an alternative that can reconcile these two objectives. In ongoing ecosystem restoration, increased diversity is always associated with increased productivity (and consequent carbon stock), which is among the most important ecosystem functions. The ecological paradigm of this association is that ecosystem biodiversity (B) is positively related to both ecosystem functions and services (EF and ES). However, BEF and BES relationships vary spatially and temporally, which makes understanding these relationships relevant and important for practical restoration actions. In this study, we asked how biodiversity and carbon stock recovery occurs during tropical forest restoration. We reviewed literature of the relationships between BEF and BES in the context of ecological restoration and asked whether ecological restoration can recover both. In addition, we conducted a metadata analysis of studies on the recovery of biodiversity and biomass in regenerating tropical forests (n = 83) to find the best model that describes this relationship. In general, studies showed that ecosystem biodiversity and productivity are positively related, and that restoration can recover both. We found an asymptotic and positive correlation between biodiversity and biomass in tropical forests, suggesting limitation of the mutual gains of these two ecosystem properties during restoration. We discuss these results in the context of ecological theory and the practice of ecological restoration.     

A large‐scale field assessment of carbon stocks in human‐modified tropical forests

Tropical rainforests store enormous amounts of carbon, the protection of which represents a vital component of efforts to mitigate global climate change. Currently, tropical forest conservation, science, policies, and climate mitigation actions focus predominantly on reducing carbon emissions from deforestation alone. However, every year vast areas of the humid tropics are disturbed by selective logging, understory fires, and habitat fragmentation. There is an urgent need to understand the effect of such disturbances on carbon stocks, and how stocks in disturbed forests compare to those found in undisturbed primary forests as well as in regenerating secondary forests. Here, we present the results of the largest field study to date on the impacts of human disturbances on above and belowground carbon stocks in tropical forests. Live vegetation, the largest carbon pool, was extremely sensitive to disturbance: forests that experienced both selective logging and understory fires stored, on average, 40% less aboveground carbon than undisturbed forests and were structurally similar to secondary forests. Edge effects also played an important role in explaining variability in aboveground carbon stocks of disturbed forests. Results indicate a potential rapid recovery of the dead wood and litter carbon pools, while soil stocks (0–30 cm) appeared to be resistant to the effects of logging and fire. Carbon loss and subsequent emissions due to human disturbances remain largely unaccounted for in greenhouse gas inventories, but by comparing our estimates of depleted carbon stocks in disturbed forests with Brazilian government assessments of the total forest area annually disturbed in the Amazon, we show that these emissions could represent up to 40% of the carbon loss from deforestation in the region. We conclude that conservation programs aiming to ensure the long‐term permanence of forest carbon stocks, such as REDD+, will remain limited in their success unless they effectively avoid degradation as well as deforestation.     

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Phenological events, such as bud burst, are strongly linked to ecosystem processes in temperate deciduous forests. However, the exact nature and magnitude of how seasonal and interannual variation in air temperatures influence phenology is poorly understood, and model‐based phenology representations fail to capture local‐ to regional‐scale variability arising from differences in species composition. In this paper, we use a combination of surface meteorological data, species composition maps, remote sensing, and ground‐based observations to estimate models that better represent how community‐level species composition affects the phenological response of deciduous broadleaf forests to climate forcing at spatial scales that are typically used in ecosystem models. Using time series of canopy greenness from repeat digital photography, citizen science data from the USA National Phenology Network, and satellite remote sensing‐based observations of phenology, we estimated and tested models that predict the timing of spring leaf emergence across five different deciduous broadleaf forest types in the eastern United States. Specifically, we evaluated two different approaches: (i) using species‐specific models in combination with species composition information to ‘upscale’ model predictions and (ii) using repeat digital photography of forest canopies that observe and integrate the phenological behavior of multiple representative species at each camera site to calibrate a single model for all deciduous broadleaf forests. Our results demonstrate variability in cumulative forcing requirements and photoperiod cues across species and forest types, and show how community composition influences phenological dynamics over large areas. At the same time, the response of different species to spatial and interannual variation in weather is, under the current climate regime, sufficiently similar that the generic deciduous forest model based on repeat digital photography performed comparably to the upscaled species‐specific models. More generally, results from this analysis demonstrate how in situ observation networks and remote sensing data can be used to synergistically calibrate and assess regional parameterizations of phenology in models.     

Modeling landscape patterns of understory tree regeneration in the Pacific Northwest,USA

Abstract. Vegetation maps serve as the basis for spatial analysis of forest ecosystems and provide initial information for simulations of forest landscape change. Because of the limitations of current remote sensing technology, it is not possible to directly measure forest understory attributes across large spatial extents. Instead we used a predictive vegetation mapping approach to model Tsuga heterophylla and Picea sitchensis seedling patterns in a 3900‐ha landscape in the Oregon Coast Range, USA, as a function of Landsat TM imagery, aerial photographs, digital elevation models, and stream maps. Because the models explained only moderate amounts of variability (R² values of 0.24–0.56), we interpreted the predicted patterns as qualitative spatial trends rather than precise maps. P. sitchensis seedling patterns were tightly linked to the riparian network, with highest densities in coastal riparian areas. T. heterophylla seedlings exhibited complex patterns related to topography and overstory forest cover, and were also spatially clustered around patches of old‐growth forest. We hypothesize that the old growth served as refugia for this fire‐sensitive species following wildfires in the late 19th and early 20th centuries. Low levels of T. heterophylla regeneration in hardwood‐dominated forests suggest that these patches may succeed to shrublands rather than to conifer forest. Predictive models of seedling patterns could be developed for other landscapes where georeferenced inventory plots, remote sensing data, digital elevation models, and climate maps are available.     

The high value of logged tropical forests: lessons from northern Borneo

The carbon storage and conservation value of old-growth tropical forests is clear, but the value of logged forest is less certain. Here we analyse >100,000 observations of individuals from 11 taxonomic groups and >2,500 species, covering up to 19?years of post-logging regeneration, and quantify the impacts of logging on carbon storage and biodiversity within lowland dipterocarp forests of Sabah, Borneo. We estimate that forests lost ca. 53% of above-ground biomass as a result of logging but despite this high level of degradation, logged forest retained considerable conservation value: floral species richness was higher in logged forest than in primary forest and whilst faunal species richness was typically lower in logged forest, in most cases the difference between habitats was no greater than ca. 10%. Moreover, in most studies >90% of species recorded in primary forest were also present in logged forest, including species of conservation concern. During recovery, logged forest accumulated carbon at five times the rate of natural forest (1.4 and 0.28?Mg?C?ha^?1?year^?1, respectively). We conclude that allowing the continued regeneration of extensive areas of Borneo??s forest that have already been logged, and are at risk of conversion to other land uses, would provide a significant carbon store that is likely to increase over time. Protecting intact forest is critical for biodiversity conservation and climate change mitigation, but the contribution of logged forest to these twin goals should not be overlooked.     

Trade‐offs between carbon stocks and timber recovery in tropical forests are mediated by logging intensity

Forest degradation accounts for ~70% of total carbon losses from tropical forests. Substantial emissions are from selective logging, a land‐use activity that decreases forest carbon density. To maintain carbon values in selectively logged forests, climate change mitigation policies and government agencies promote the adoption of reduced‐impact logging (RIL) practices. However, whether RIL will maintain both carbon and timber values in managed tropical forests over time remains uncertain. In this study, we quantify the recovery of timber stocks and aboveground carbon at an experimental site where forests were subjected to different intensities of RIL (4, 8, and 16 trees/ha). Our census data span 20 years postlogging and 17 years after the liberation of future crop trees from competition in a tropical forest on the Guiana Shield, a globally important forest carbon reservoir. We model recovery of timber and carbon with a breakpoint regression that allowed us to capture elevated tree mortality immediately after logging. Recovery rates of timber and carbon were governed by the presence of residual trees (i.e., trees that persisted through the first harvest). The liberation treatment stimulated faster recovery of timber albeit at a carbon cost. Model results suggest a threshold logging intensity beyond which forests managed for timber and carbon derive few benefits from RIL, with recruitment and residual growth not sufficient to offset losses. Inclusion of the breakpoint at which carbon and timber gains outpaced postlogging mortality led to high predictive accuracy, including out‐of‐sample R² values >90%, and enabled inference on demographic changes postlogging. Our modeling framework is broadly applicable to studies that aim to quantify impacts of logging on forest recovery. Overall, we demonstrate that initial mortality drives variation in recovery rates, that the second harvest depends on old growth wood, and that timber intensification lowers carbon stocks.     

Biodiversity as a solution to mitigate climate change impacts on the functioning of forest ecosystems

Forest ecosystems are critical to mitigating greenhouse gas emissions through carbon sequestration. However, climate change has affected forest ecosystem functioning in both negative and positive ways, and has led to shifts in species/functional diversity and losses in plant species diversity which may impair the positive effects of diversity on ecosystem functioning. Biodiversity may mitigate climate change impacts on (I) biodiversity itself, as more‐diverse systems could be more resilient to climate change impacts, and (II) ecosystem functioning through the positive relationship between diversity and ecosystem functioning. By surveying the literature, we examined how climate change has affected forest ecosystem functioning and plant diversity. Based on the biodiversity effects on ecosystem functioning (B→EF), we specifically address the potential for biodiversity to mitigate climate change impacts on forest ecosystem functioning. For this purpose, we formulate a concept whereby biodiversity may reduce the negative impacts or enhance the positive impacts of climate change on ecosystem functioning. Further B→EF studies on climate change in natural forests are encouraged to elucidate how biodiversity might influence ecosystem functioning. This may be achieved through the detailed scrutiny of large spatial/long temporal scale data sets, such as long‐term forest inventories. Forest management strategies based on B→EF have strong potential for augmenting the effectiveness of the roles of forests in the mitigation of climate change impacts on ecosystem functioning.     

Trade‐offs between carbon stocks and biodiversity in European temperate forests

Policies to mitigate climate change and biodiversity loss often assume that protecting carbon‐rich forests provides co‐benefits in terms of biodiversity, due to the spatial congruence of carbon stocks and biodiversity at biogeographic scales. However, it remains unclear whether this holds at the scales relevant for management, and particularly large knowledge gaps exist for temperate forests and for taxa other than trees. We built a comprehensive dataset of Central European temperate forest structure and multi‐taxonomic diversity (beetles, birds, bryophytes, fungi, lichens, and plants) across 352 plots. We used Boosted Regression Trees (BRTs) to assess the relationship between above‐ground live carbon stocks and (a) taxon‐specific richness, (b) a unified multidiversity index. We used Threshold Indicator Taxa ANalysis to explore individual species’ responses to changing above‐ground carbon stocks and to detect change‐points in species composition along the carbon‐stock gradient. Our results reveal an overall weak and highly variable relationship between richness and carbon stock at the stand scale, both for individual taxonomic groups and for multidiversity. Similarly, the proportion of win‐win and trade‐off species (i.e., species favored or disadvantaged by increasing carbon stock, respectively) varied substantially across taxa. Win‐win species gradually replaced trade‐off species with increasing carbon, without clear thresholds along the above‐ground carbon gradient, suggesting that community‐level surrogates (e.g., richness) might fail to detect critical changes in biodiversity. Collectively, our analyses highlight that leveraging co‐benefits between carbon and biodiversity in temperate forest may require stand‐scale management that prioritizes either biodiversity or carbon in order to maximize co‐benefits at broader scales. Importantly, this contrasts with tropical forests, where climate and biodiversity objectives can be integrated at the stand scale, thus highlighting the need for context‐specificity when managing for multiple objectives. Accounting for critical change‐points of target taxa can help to deal with this specificity, by defining a safe operating space to manipulate carbon while avoiding biodiversity losses.