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Earth system models (ESMs) have a limited capacity to represent plant functional diversity and shifts in trait distributions. Approaches to improving the representation of this complexity in ESMs include (i) optimality‐based approaches that predict trait–environment responses and (ii) explicitly modelling coexistence and community assembly. These approaches are expected to converge only when optimality‐based approaches identify competitively dominant strategies, which often differ from strategies that maximize ecosystem functioning or fitness components in monoculture.We used two models, LM3‐PPA (a vegetation demographic model designed as an ESM component) and BiomeE (a computationally efficient analog for LM3‐PPA), to explore how water limitation affects carbon allocation strategies of canopy trees. We compared competitive allocation strategies and those that maximize biomass or productivity in monoculture. We did not explicitly model coexistence or community assembly. Rather, we used model experiments to identify competitive and maximizing strategies in a two‐dimensional trait space under different precipitation and mortality scenarios.At 10 eastern US locations, we simulated historical, wet and dry climate scenarios, novel drought and three different mortality scenarios (low, medium or high sensitivity to water deficit). For each site and scenario, we identified the competitive strategy and three maximizing strategies (maximum biomass, productivity or drought‐tolerance).Root: leaf ratios tended to increase and leaf area tended to decrease with increasing water stress (increasing water limitation and its effects on mortality). However, relative to maximizing strategies, competitive strategies shifted towards greater allocation to roots and leaves with increasing water stress.Competitive overinvestments (greater allocation to roots and leaves by competitive strategies compared with maximizing strategies) were robust across different modelling contexts, including vegetation parameter sets (Acer vs. Populus), models (LM3‐PPA vs. BiomeE) and uncalibrated vsersus calibrated BiomeE versions.Synthesis: The theoretical prediction that competitive and maximizing allocation strategies differ under water limitation is confirmed for a demographic model designed as an ESM component. Optimality‐based trait predictions can simplify representing trait diversity in ESMs but do not always correspond to competitive outcomes. Explicitly modelling coexistence and community assembly in ESMs is challenging but is likely the most general approach to representing trait diversity. [ABSTRACT FROM AUTHOR]

The global water and carbon cycles are greatly influenced by terrestrial vegetation, making trustworthy representations of dynamic biosphere--hydrosphere interactions a crucial component of both ecosystem and climate models. This paper comprehensively evaluates the hydrological performance of a leading dynamic global vegetation model Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS), using a broad range of the latest available global observation-based gridded datasets that cover the main components of the hydrological cycle. Overall, we find that the hydrological components modelled by LPJ-GUESS agree well with global gridded datasets of runoff, evapotranspiration and surface soil moisture, though there are discrepancies in some regions and periods. Furthermore, LPJ-GUESS accurately captures both interand intra-annual variations of runoff in most regions and catchment areas, including the Danube, Murray, Yangtze, Yenisei and Nile basins. Total evapotranspiration modelled by LPJ-GUESS agrees closely with the evapotranspiration estimates of the Global Land Evaporation Amsterdam Model and PML-V2 datasets, but with some disagreement in the individual components, especially for evaporation. The surface soil moisture simulated by LPJ-GUESS aligns with ESA-CCI (v5.3) surface soil moisture datasets in most regions, with greatest discrepancies in subarctic areas. We attribute these discrepancies to two main sources: (1) absent or poor representation of processes such as river routing, storage and supply of water bodies, and cropland irrigation; and (2) uncertainties in both reference datasets and input to the model, including precipitation, soil texture, and land use. [ABSTRACT FROM AUTHOR]

This article discusses a study that examines the relationship between functional traits of plants and mean annual precipitation (MAP). The researchers analyzed data from the AusTraits database, which includes information on various traits of plant species in Australia. They found that certain traits, such as leaf area and leaf nitrogen per area, were strongly correlated with MAP. The study also investigated the influence of plant growth form on these relationships and found that woody taxa exhibited stronger responses to climate variables compared to nonwoody taxa. Overall, the findings suggest that MAP is an important factor in shaping plant functional traits. [Extracted from the article]

Despite their low contribution to forest carbon stocks, lianas (woody vines) play an important role in the carbon dynamics of tropical forests. As structural parasites, they hinder tree survival, growth and fecundity; hence, they negatively impact net ecosystem productivity and long-term carbon sequestration.Competition (for water and light) drives various forest processes and depends on the local abundance of resources over time. However, evaluating the relative role of resource availability on the interactions between lianas and trees from empirical observations is particularly challenging. Previous approaches have used labour-intensive and ecosystem-scale manipulation experiments, which are infeasible in most situations.We propose to circumvent this challenge by evaluating the uncertainty of water and light capture processes of a process-based vegetation model (ED2) including the liana growth form. We further developed the liana plant functional type in ED2 to mechanistically simulate water uptake and transport from roots to leaves, and start the model from prescribed initial conditions. We then used the PEcAn bioinformatics platform to constrain liana parameters and run uncertainty analyses.Baseline runs successfully reproduced ecosystem gas exchange fluxes (gross primary productivity and latent heat) and forest structural features (leaf area index, aboveground biomass) in two sites (Barro Colorado Island, Panama and Paracou, French Guiana) characterized by different rainfall regimes and levels of liana abundance.Model uncertainty analyses revealed that water limitation was the factor driving the competition between trees and lianas at the drier site (BCI), and during the relatively short dry season of the wetter site (Paracou). In young patches, light competition dominated in Paracou but alternated with water competition between the wet and the dry season on BCI according to the model simulations.The modelling workflow also identified key liana traits (photosynthetic quantum efficiency, stomatal regulation parameters, allometric relationships) and processes (water use, respiration, climbing) driving the model uncertainty. They should be considered as priorities for future data acquisition and model development to improve predictions of the carbon dynamics of liana-infested forests. Synthesis. Competition for water plays a larger role in the interaction between lianas and trees than previously hypothesized, as demonstrated by simulations from a process-based vegetation model.

The leaf area index (LAI) shows a significant increasing trend from global to regional scales, which is known as greening. Greening will further enhance photosynthesis, but it is unclear whether the contribution of greening has exceeded the CO2 fertilization effect and become the dominant factor in the gross primary productivity (GPP) variation. We took the Yangtze River Delta (YRD) of China, where cropland and natural vegetation are significantly greening, as an example. Based on the boreal ecosystem productivity simulator (BEPS) and Revised-EC-LUE models, the GPP in the YRD from 2001 to 2020 was simulated, and attribution analysis of the interannual variation in GPP was performed. In addition, the reliability of the GPP simulated by the dynamic global vegetation model (DGVM) in the area was further investigated. The research results showed that GPP in the YRD had three significant characteristics consistent with LAI: (1) GPP showed a significant increasing trend; (2) the multiyear mean and trend of natural vegetation GPP were higher than those of cropland GPP; and (3) cropland GPP showed double-high peak characteristics. The BEPS and Revised-EC-LUE models agreed that the effect of LAI variation (4.29 Tg C yr−1 for BEPS and 2.73 Tg C yr−1 for the Revised-EC-LUE model) determined the interannual variation in GPP, which was much higher than the CO2 fertilization effect (2.29 Tg C yr−1 for BEPS and 0.67 Tg C yr−1 for the Revised-EC-LUE model). The GPP simulated by the 7 DGVMs showed a huge inconsistency with the GPP estimated by remote sensing models. The deviation of LAI simulated by DGVM might be a potential cause for this phenomenon. Our study highlights that in significant greening areas, LAI has dominated GPP variation, both spatially and temporally, and DGVM can correctly simulate GPP only if it accurately simulates LAI variation. [ABSTRACT FROM AUTHOR]

Long‐distance movements are hypothesized to positively influence population size and stability of mobile species. We tested this hypothesis with a novel modeling approach in which moving herbivores interact with the environment created by a dynamic global vegetation model using highly mobile Mongolian gazelles in the eastern Mongolian grasslands as a case study. Gazelle population dynamics were modeled from 1901 to 2018 under two scenarios, one allowing free movement and one restricting movement. Gazelles were 2.2 times more abundant when they could move freely and were extirpated in 71% of the study area when mobility was restricted. Mobility resulted in greater population increases during times of abundant forage and smaller population decreases during drought. Reduced thermoregulatory costs associated with climate change, combined with an increase in vegetation biomass, increased gazelle abundance. Since high abundances often resulted in overgrazing and, thus, extirpation when movement was restricted, mobility had an important role in maintaining higher densities. The novel modeling approach shows how accounting for not just herbivore but also plant ecophysiology can improve our understanding of the population dynamics of highly mobile herbivores, in particular when examining the effects of habitat and climate change. Since the model simulates herbivores based on general physiological mechanisms that apply across large herbivores and the vegetation model can be applied globally, it is possible to adapt the model to other large‐herbivore systems. [ABSTRACT FROM AUTHOR]

There is mounting empirical evidence that lianas affect the carbon cycle of tropical forests. However, no single vegetation model takes into account this growth form, although such efforts could greatly improve the predictions of carbon dynamics in tropical forests. In this study, we incorporated a novel mechanistic representation of lianas in a dynamic global vegetation model (the Ecosystem Demography Model). We developed a liana-specific plant functional type and mechanisms representing liana-tree interactions (such as light competition, liana-specific allometries, and attachment to host trees) and parameterized them according to a comprehensive literature meta-analysis. We tested the model for an old-growth forest (Paracou, French Guiana) and a secondary forest (Gigante Peninsula, Panama). The resulting model simulations captured many features of the two forests characterized by different levels of liana infestation as revealed by a systematic comparison of the model outputs with empirical data, including local census data from forest inventories, eddy flux tower data, and terrestrial laser scanner-derived forest vertical structure. The inclusion of lianas in the simulations reduced the secondary forest net productivity by up to 0.46 tC  ha-1  year-1 , which corresponds to a limited relative reduction of 2.6% in comparison with a reference simulation without lianas. However, this resulted in significantly reduced accumulated above-ground biomass after 70 years of regrowth by up to 20 tC /ha (19% of the reference simulation). Ultimately, the simulated negative impact of lianas on the total biomass was almost completely cancelled out when the forest reached an old-growth successional stage. Our findings suggest that lianas negatively influence the forest potential carbon sink strength, especially for young, disturbed, liana-rich sites. In light of the critical role that lianas play in the profound changes currently experienced by tropical forests, this new model provides a robust numerical tool to forecast the impact of lianas on tropical forest carbon sinks.

Spectroscopic reflectance data provide novel information on the properties of the Earth's terrestrial and aquatic surfaces. Until recently, imaging spectroscopy missions were dependent mainly on airborne instruments, such as the Next Generation Airborne Visible InfraRed Imaging Spectrometer (AVIRIS‐NG), providing limited spatial and temporal observations. Currently, there is an emergence of spaceborne imaging spectroscopy missions, which require advances in end‐to‐end model support for traceability studies. To provide this support, the LPJ‐wsl dynamic global vegetation model is coupled with the canopy radiative transfer model, PROSAIL, to generate global, gridded, daily visible to shortwave infrared (VSWIR) spectra (400–2,500 nm). LPJ‐wsl variables are cross‐walked to meet required PROSAIL parameters, which include leaf structure, chlorophyll a + b, brown pigment, equivalent water thickness, and dry matter content. Simulated spectra are compared to a boreal forest site, a temperate forest, managed grassland, a dryland and a tropical forest site using reflectance data from tower‐mounted, aircraft, and spaceborne imagers. We find that canopy nitrogen and leaf‐area index are the most uncertain variables in translating LPJ‐wsl to PROSAIL parameters but at first order, LPJ‐PROSAIL successfully simulates surface reflectance dynamics. Future work will optimize functional relationships required for improving PROSAIL parameters and include the development of the LPJ‐model to represent improvements in leaf water content and canopy nitrogen. The LPJ‐PROSAIL model is intended to support missions such as NASA's Surface Biology and Geology and subsequent modeled products related to the carbon cycle and hydrology. Plain Language Summary: The reflectance of the land surface provides information on vegetation composition, health, and productivity. New satellite missions are designed to better capture finely resolved reflectance information using imaging spectroscopy or hyperspectral techniques. These missions require modeling support to evaluate uncertainties. Here we present a new integrated land surface model that simulates reflectance spectra from 400 to 2,500 nm at 10 nm resolution for the entire global land surface at daily resolution. The model is evaluated using tower and pathfinder hyperspectral missions. We find that the modeling approach reproduces surface reflectance and identifies areas of model and observational improvements. Key Points: The Earth's surface reflectance yields important information on vegetation composition, health, and productivityA new modeling approach is developed to simulate surface reflectance to support imaging spectroscopy missionsThe modeling approach reproduces tower‐based measurements and pathfinder satellite missions and identifies areas for model improvement [ABSTRACT FROM AUTHOR]

1. To assess the impacts of climate change on vegetation from stand to global scales, models of forest dynamics that include tree demography are needed. Such models are now available for 50 years, but the currently existing diversity of model formulations and its evolution over time are poorly documented. This hampers systematic assessments of structural uncertainties in model-based studies. 2. We conducted a meta-analysis of 28 models, focusing on models that were used in the past five years for climate change studies. We defined 52 model attributes in five groups (basic assumptions, growth, regeneration, mortality and soil moisture) and characterized each model according to these attributes. Analyses of model complexity and diversity included hierarchical cluster analysis and redundancy analysis. 3. Model complexity evolved considerably over the past 50 years. Increases in complexity were largest for growth processes, while complexity of modelled establishment processes increased only moderately. Model diversity was lowest at the global scale, and highest at the landscape scale. We identified five distinct clusters of models, ranging from very simple models to models where specific attribute groups are rendered in a complex manner and models that feature high complexity across all attributes. 4. Most models in use today are not balanced in the level of complexity with which they represent different processes. This is the result of different model purposes, but also reflects legacies in model code, modelers' preferences, and the ‘prevailing spirit of the epoch’. The lack of firm theories, laws and ‘first principles’ in ecology provides high degrees of freedom in model development, but also results in high responsibilities for model developers and the need for rigorous model evaluation. 5. Synthesis. The currently available model diversity is beneficial: convergence in simulations of structurally different models indicates robust projections, while convergence of similar models may convey a false sense of certainty. The existing model diversity—with the exception of global models—can be exploited for improved projections based on multiple models. We strongly recommend balanced further developments of forest models that should particularly focus on establishment and mortality processes, in order to provide robust information for decisions in ecosystem management and policymaking. [ABSTRACT FROM AUTHOR]

The maximum photosynthetic carboxylation rate (Vcmax ) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross primary production (GPP). Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global Vcmax distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM). Global GPP varied from 108.1 to 128.2 PgC yr-1 , 65% of the range of a recent model intercomparison of global GPP. The variation in GPP propagated through to a 27% coefficient of variation in net biome productivity (NBP). All hypotheses produced global GPP that was highly correlated (r = 0.85-0.91) with three proxies of global GPP. Plant functional type-based nutrient limitation, underpinned by a core SDGVM hypothesis that plant nitrogen (N) status is inversely related to increasing costs of N acquisition with increasing soil carbon, adequately reproduced global GPP distributions. Further improvement could be achieved with accurate representation of water sensitivity and agriculture in SDGVM. Mismatch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater effort is needed understand Vcmax variation in the field, particularly in northern latitudes.

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomes of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world's forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO2 fertilization effects may considerably reduce the range of projections.

Simple Summary: After the collapse of the Soviet Union, changes in the agricultural structure led to widespread abandonment of cropland and natural vegetation restoration in Russia, Ukraine, and Belarus. In consequence, corresponding changes in the terrestrial carbon cycle need to be quantified. We simulated this process using a dynamic vegetation model, and found that the conversion of cropland to natural vegetation generally formed a significant carbon sink at 0.99 GtC; the growth of the vegetation carbon pool, especially, was significantly higher than that of soil carbon pool. Land use and cover changes (LUCC) have a fundamental impact on the terrestrial carbon cycle. The abandonment of cropland as a result of the collapse of the Soviet Union offers a typical case of the conversion from cropland to natural vegetation, which could have a significant effect on the terrestrial carbon cycle. Due to the inaccuracy of LUCC records, the corresponding impact on the terrestrial carbon cycle has not been well quantified. In this study, we estimated the carbon flux using the Vegetation-Global-Atmosphere-Soil (VEGAS) model over the region of Russia, Belarus and Ukraine during 1990–2017. We first optimized the LUCC input data by adjusting the Food and Agriculture Organization (FAO) data by Russian statistical data and redistributing the spatiotemporal input data from the Historical Database of the Global Environment (HYDE) to the original model. Between 1990 and 2017, the area of cropland abandonment was estimated to be 36.82 Mha, compared to 11.67 Mha estimated by FAO. At the same time, the carbon uptake from the atmosphere to the biosphere was 9.23 GtC (vs fixed cropland 8.24 and HYDE 8.25 GtC) during 1990–2017, which means by optimizing the cropland distribution data, the total carbon absorption during the abandonment process increased by 0.99 GtC. Meanwhile, the growth of the vegetation carbon pool was significantly higher than that of the soil carbon pool. Therefore, we further highlight the importance of accurate cropland distribution data in terrestrial carbon cycle simulation. [ABSTRACT FROM AUTHOR]

Abstract Background Global warming has brought many negative impacts on terrestrial ecosystems, which makes the vulnerability of ecosystems one of the hot issues in current ecological research. Here, we proposed an assessment method based on the IPCC definition of vulnerability. The exposure to future climate was characterized using a moisture index (MI) that integrates the effects of temperature and precipitation. Vegetation stability, defined as the proportion of intact natural vegetation that remains unchanged under changing climate, was used together with vegetation productivity trend to represent the sensitivity and adaptability of ecosystems. Using this method, we evaluated the vulnerability of ecosystems in Southwestern China under two future representative concentration pathways (RCP 4.5 and RCP 8.5) with MC2 dynamic global vegetation model. Results (1) Future (2017–2100) climate change will leave 7.4% (under RCP 4.5) and 57.4% of (under RCP 8.5) of areas under high or very high vulnerable climate exposure; (2) in terms of vegetation stability, nearly 45% of the study area will show high or very high vulnerability under both RCPs. Beside the impacts of human disturbance on natural vegetation coverage (vegetation intactness), climate change will cause obvious latitudinal movements in vegetation distribution, but the direction of movements under two RCPs were opposite due to the difference in water availability; (3) vegetation productivity in most areas will generally increase and remain a low vulnerability in the future; (4) an assessment based on the above three aspects together indicated that future climate change will generally have an adverse impact on all ecosystems in Southwestern China, with non-vulnerable areas account for only about 3% of the study area under both RCPs. However, compared with RCP 4.5, the areas with mid- and high-vulnerability under RCP 8.5 scenario increased by 13% and 16%, respectively. Conclusion Analyses of future climate exposure and projected vegetation distribution indicate widespread vulnerability of ecosystems in Southwestern China, while vegetation productivity in most areas will show an increasing trend to the end of twenty-first century. Based on new climate indicators and improved vulnerability assessment rules, our method provides an extra option for a more comprehensive evaluation of ecosystem vulnerability, and should be further tested at larger spatial scales in order to provide references for regional, or even global, ecosystem conservation works.

Agriculture production is primarily influenced by human-induced management interventions whereas climate plays an important role. Climate change with increasing global carbon dioxide concentration and changes in temperature, precipitation and other climatic parameters play a significant role in influencing agriculture productivity. The assessment of the vulnerability of agriculture production under the influence of climate change can be done using process-based vegetation models. We assessed the vulnerability of agriculture production for the South Asian Association for Regional Cooperation (SAARC) nations comprising of Afghanistan, Bangladesh, Bhutan, India, Nepal, Maldives, Pakistan and Sri Lanka using the Joint UK Land Environment Simulator (JULES) model. Net Primary Productivity (NPP) was simulated under the forcing of climatic variables of the climate change scenarios of representative concentration pathways (RCP2.6, 4.5 & 8.5) for the year 2050 using JULES. JULES is a process-based model that simulates fluxes of carbon, energy, and momentum between the atmosphere, land surface, and water to estimate the productivity of a system. The sensitivity was calculated as the inter-annual dispersion from the average NPP of assessment period 2016 to 2050, whereas adaptive capacity was calculated as the slope of productivity regressed over the assessment period. The vulnerability was calculated by subtracting the values of adaptability from sensitivity. The JULES simulated NPP was also compared with Moderate Resolution Imaging Spectrometer (MODIS 17A) derived NPP for the period 2000–2014. The simulated NPP had a decreasing trend under projected climate change scenarios of 2050 for all the SAARC nations compared to recent years average of 2000–2014. The analysis reveals that one-third of the SAARC region is highly sensitive under low to moderate emission scenarios, while it was observed that under high emission scenarios the sensitivity would reduce with increased vulnerability. We observed that the vulnerability of the system is not alone influenced by sensitivity and any change in adaptive capacity or resilience would also impact the overall vulnerability of the system.

The rising atmospheric CO2 concentration leads to a CO2 fertilization effect on plants—that is, increased photosynthetic uptake of CO2 by leaves and enhanced water‐use efficiency (WUE). Yet, the resulting net impact of CO2 fertilization on plant growth and soil moisture (SM) savings at large scale is poorly understood. Drylands provide a natural experimental setting to detect the CO2 fertilization effect on plant growth since foliage amount, plant water‐use and photosynthesis are all tightly coupled in water‐limited ecosystems. A long‐term change in the response of leaf area index (LAI, a measure of foliage amount) to changes in SM is likely to stem from changing water demand of primary productivity in water‐limited ecosystems and is a proxy for changes in WUE. Using 34‐year satellite observations of LAI and SM over tropical and subtropical drylands, we identify that a 1% increment in SM leads to 0.15% (±0.008, 95% confidence interval) and 0.51% (±0.01, 95% confidence interval) increments in LAI during 1982‒1998 and 1999‒2015, respectively. The increasing response of LAI to SM has contributed 7.2% (±3.0%, 95% confidence interval) to total dryland greening during 1999‒2015 compared to 1982‒1998. The increasing response of LAI to SM is consistent with the CO2 fertilization effect on WUE in water‐limited ecosystems, indicating that a given amount of SM has sustained greater amounts of photosynthetic foliage over time. The LAI responses to changes in SM from seven dynamic global vegetation models are not always consistent with observations, highlighting the need for improved process knowledge of terrestrial ecosystem responses to rising atmospheric CO2 concentration. [ABSTRACT FROM AUTHOR]

Land use and cover changes (LUCC) have a fundamental impact on the terrestrial carbon cycle. The abandonment of cropland as a result of the collapse of the Soviet Union offers a typical case of the conversion from cropland to natural vegetation, which could have a significant effect on the terrestrial carbon cycle. Due to the inaccuracy of LUCC records, the corresponding impact on the terrestrial carbon cycle has not been well quantified. In this study, we estimated the carbon flux using the Vegetation-Global-Atmosphere-Soil (VEGAS) model over the region of Russia, Belarus and Ukraine during 1990–2017. We first optimized the LUCC input data by adjusting the Food and Agriculture Organization (FAO) data by Russian statistical data and redistributing the spatiotemporal input data from the Historical Database of the Global Environment (HYDE) to the original model. Between 1990 and 2017, the area of cropland abandonment was estimated to be 36.82 Mha, compared to 11.67 Mha estimated by FAO. At the same time, the carbon uptake from the atmosphere to the biosphere was 9.23 GtC (vs fixed cropland 8.24 and HYDE 8.25 GtC) during 1990–2017, which means by optimizing the cropland distribution data, the total carbon absorption during the abandonment process increased by 0.99 GtC. Meanwhile, the growth of the vegetation carbon pool was significantly higher than that of the soil carbon pool. Therefore, we further highlight the importance of accurate cropland distribution data in terrestrial carbon cycle simulation.

Aim Climate change threatens to shift vegetation, disrupting ecosystems and damaging human well-being. Field observations in boreal, temperate and tropical ecosystems have detected biome changes in the 20th century, yet a lack of spatial data on vulnerability hinders organizations that manage natural resources from identifying priority areas for adaptation measures. We explore potential methods to identify areas vulnerable to vegetation shifts and potential refugia.Location Global vegetation biomes.Methods We examined nine combinations of three sets of potential indicators of the vulnerability of ecosystems to biome change: (1) observed changes of 20th-century climate, (2) projected 21st-century vegetation changes using the MC1 dynamic global vegetation model under three Intergovernmental Panel on Climate Change (IPCC) emissions scenarios, and (3) overlap of results from (1) and (2). Estimating probability density functions for climate observations and confidence levels for vegetation projections, we classified areas into vulnerability classes based on IPCC treatment of uncertainty.Results One-tenth to one-half of global land may be highly (confidence 0.80-0.95) to very highly (confidence ≥ 0.95) vulnerable. Temperate mixed forest, boreal conifer and tundra and alpine biomes show the highest vulnerability, often due to potential changes in wildfire. Tropical evergreen broadleaf forest and desert biomes show the lowest vulnerability.Main conclusions Spatial analyses of observed climate and projected vegetation indicate widespread vulnerability of ecosystems to biome change. A mismatch between vulnerability patterns and the geographic priorities of natural resource organizations suggests the need to adapt management plans. Approximately a billion people live in the areas classified as vulnerable. © 2010 Blackwell Publishing Ltd.

Aim: We analyse how functional diversity (FD) varies across European natural forests to understand the effects of environmental and competitive filtering on plant trait distribution. Location: Forest ecosystems in Europe from 11°W to 36°E and 29.5°N to 62°N. Taxon: Pinaceae, Fagaceae and Betulaceae, Oleaceae, Tiliaceae, Aceraceae, Leguminosae (unspecific). Methods: We adopted the existing Dynamic Global Vegetation Model Lund‐Potsdam‐Jena managed Land of flexible individual traits (LPJmL‐FIT) for Europe by eliminating both bioclimatic limits of plant functional types (PFTs) and replacing prescribed values of functional traits for PFTs with emergent values under influence of environmental filtering and competition. We quantified functional richness (FR), functional divergence (FDv) and functional evenness (FE) in representative selected sites and at Pan‐European scale resulting from simulated functional and structural trait combinations of individual trees. While FR quantifies the amount of occupied trait space, FDv and FE describe the distribution and abundance of trait combinations, respectively, in a multidimensional trait space. Results: Lund‐Potsdam‐Jena managed Land of flexible individual traits reproduces spatial PFTs and local trait distributions and agrees well with observed productivity, biomass and tree height of European natural forests. The observed site‐specific trait distributions and spatial gradients of traits of the leaf‐ and stem‐resource economics spectra coincide with environmental filtering and the competition for light and water in environments with strong abiotic stress. Where deciduous and needle‐leaved trees co‐occur, for example, in boreal and mountainous forests, the potential niche space is wide (high FR), and extreme ends in the niche space are occupied (high FDv). We find high FDv in Mediterranean forests where drought increasingly limits tree growth, thus niche differentiation becomes more important. FDv decreases in temperate forests where a cold climate increasingly limits growth efficiency of broad‐leaved summer green trees, thus reducing the importance of competitive exclusion. Highest FE was simulated in wet Atlantic and southern Europe which indicated relatively even niche occupation and thus high resource‐use efficiency. Main Conclusions: We find FD resulting from both environmental and competitive filtering. Pan‐European FR, FDv and FE demonstrate the influence of climate gradients and intra‐ and inter‐PFT competition. The indices underline a generally high FD of natural forests in Europe. Co‐existence of functionally diverse trees across PFTs emerges from alternative (life‐history) strategies, disturbance and tree demography. [ABSTRACT FROM AUTHOR]

Background: Global warming has brought many negative impacts on terrestrial ecosystems, which makes the vulnerability of ecosystems one of the hot issues in current ecological research. Here, we proposed an assessment method based on the IPCC definition of vulnerability. The exposure to future climate was characterized using a moisture index (MI) that integrates the effects of temperature and precipitation. Vegetation stability, defined as the proportion of intact natural vegetation that remains unchanged under changing climate, was used together with vegetation productivity trend to represent the sensitivity and adaptability of ecosystems. Using this method, we evaluated the vulnerability of ecosystems in Southwestern China under two future representative concentration pathways (RCP 4.5 and RCP 8.5) with MC2 dynamic global vegetation model. Results: (1) Future (2017–2100) climate change will leave 7.4% (under RCP 4.5) and 57.4% of (under RCP 8.5) of areas under high or very high vulnerable climate exposure; (2) in terms of vegetation stability, nearly 45% of the study area will show high or very high vulnerability under both RCPs. Beside the impacts of human disturbance on natural vegetation coverage (vegetation intactness), climate change will cause obvious latitudinal movements in vegetation distribution, but the direction of movements under two RCPs were opposite due to the difference in water availability; (3) vegetation productivity in most areas will generally increase and remain a low vulnerability in the future; (4) an assessment based on the above three aspects together indicated that future climate change will generally have an adverse impact on all ecosystems in Southwestern China, with non-vulnerable areas account for only about 3% of the study area under both RCPs. However, compared with RCP 4.5, the areas with mid- and high-vulnerability under RCP 8.5 scenario increased by 13% and 16%, respectively. Conclusion: Analyses of future climate exposure and projected vegetation distribution indicate widespread vulnerability of ecosystems in Southwestern China, while vegetation productivity in most areas will show an increasing trend to the end of twenty-first century. Based on new climate indicators and improved vulnerability assessment rules, our method provides an extra option for a more comprehensive evaluation of ecosystem vulnerability, and should be further tested at larger spatial scales in order to provide references for regional, or even global, ecosystem conservation works. [ABSTRACT FROM AUTHOR]

Forests play a critical role in mitigating climate change, and, at the same time, are predicted to experience large-scale impacts of climate change that will affect the efficiency of forests in mitigation efforts. Projections of future carbon sequestration potential typically do not account for the changing economic costs of timber and agricultural production and land use change. We integrated a dynamic forward-looking economic optimization model of global land use with results from a dynamic global vegetation model and meta-analysis of climate impacts on crop yields to project future carbon sequestration in forests. We find that the direct impacts of climate change on forests, represented by changes in dieback and forest growth, and indirect effects due to lost crop productivity, together result in a net gain of 17 Gt C in aboveground forest carbon storage from 2000 to 2100. Increases in climate-driven forest growth rates will result in an 81%–99% reduction in costs of reaching a range of global forest carbon stock targets in 2100, while the increases in dieback rates are projected to raise the costs by 57%–132%. When combined, these two direct impacts are expected to reduce the global costs of climate change mitigation in forests by more than 70%. Inclusion of the third, indirect impact of climate change on forests through reduction in crop yields, and the resulting expansion of cropland, raises the costs by 11%–38% and widens the uncertainty range. While we cannot rule out the possibility of climate change increasing mitigation costs, the central outcomes of the simultaneous impacts of climate change on forests and agriculture are 64%–86% reductions in the mitigation costs. Overall, the results suggest that concerns about climate driven dieback in forests should not inhibit the ambitions of policy makers in expanding forest-based climate solutions.

There is mounting empirical evidence that lianas affect the carbon cycle of tropical forests. However, no single vegetation model takes into account this growth form, although such efforts could greatly improve the predictions of carbon dynamics in tropical forests. In this study, we incorporated a novel mechanistic representation of lianas in a dynamic global vegetation model (the Ecosystem Demography Model). We developed a liana‐specific plant functional type and mechanisms representing liana–tree interactions (such as light competition, liana‐specific allometries, and attachment to host trees) and parameterized them according to a comprehensive literature meta‐analysis. We tested the model for an old‐growth forest (Paracou, French Guiana) and a secondary forest (Gigante Peninsula, Panama). The resulting model simulations captured many features of the two forests characterized by different levels of liana infestation as revealed by a systematic comparison of the model outputs with empirical data, including local census data from forest inventories, eddy flux tower data, and terrestrial laser scanner‐derived forest vertical structure. The inclusion of lianas in the simulations reduced the secondary forest net productivity by up to 0.46 tC ha−1 year−1, which corresponds to a limited relative reduction of 2.6% in comparison with a reference simulation without lianas. However, this resulted in significantly reduced accumulated above‐ground biomass after 70 years of regrowth by up to 20 tC/ha (19% of the reference simulation). Ultimately, the simulated negative impact of lianas on the total biomass was almost completely cancelled out when the forest reached an old‐growth successional stage. Our findings suggest that lianas negatively influence the forest potential carbon sink strength, especially for young, disturbed, liana‐rich sites. In light of the critical role that lianas play in the profound changes currently experienced by tropical forests, this new model provides a robust numerical tool to forecast the impact of lianas on tropical forest carbon sinks. [ABSTRACT FROM AUTHOR]

The distribution of biomes in South Asia is expected to be affected severely by climate change. Understanding plant-climate interactions and the impact of climate change, rising CO 2 , land use change, deforestation and fire on vegetation has become a major challenge for ecologists. Therefore, developing the capacity to project vegetation change is of critical importance if we are to mitigate and efficiently adapt to climate change impacts. The lack of an accurate representation of different vegetation types and ecosystem processes at regional scale is a main source of uncertainty in Dynamic Global Vegetation Models (DGVMs). This manifests in a lack of key growth forms such as bamboo, lianas and mangroves and biome types such as savanna which are essential components of ecosystems in South Asia. Plant communities like mangroves and bamboos, despite covering just small areas, account for high carbon sequestration whereas lianas can decrease carbon sequestration capacity of host trees. Here, we review the current state of vegetation modeling for South Asia and we propose a research agenda for an improved representation of biome diversity in DGVMs. We account for both the traditional plant functional type (PFT) approach and for the functional trait (FT) approach that considers growing knowledge on plant-trait variability and eco-evolutionary principles of different plant communities. We argue that an adequate representation of different vegetation types and growth forms characteristic of South Asian biomes is necessary in DGVMs for robust assessments of climate change impacts on their distribution, diversity and carbon budget. Unlabelled Image • South Asia's rich biodiversity is threatened by climate change and land use. • Understanding climate-human-biodiversity interactions requires predictive models. • We propose a research agenda to better represent South Asia's vegetation in models. • Models need to represent trait variability, diversity and evolutionary principles. • Improvements will facilitate development of adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

Understanding the effects of global change in terrestrial communities requires an understanding of how limiting resources interact with plant traits to affect productivity. Here, we focus on nitrogen and ask whether plant community nitrogen uptake rate is determined (a) by nitrogen availability alone or (b) by the product of nitrogen availability and fine‐root mass. Surprisingly, this is not empirically resolved. We performed controlled microcosm experiments and reanalyzed published pot experiments and field data to determine the relationship between community‐level nitrogen uptake rate, nitrogen availability, and fine‐root mass for 46 unique combinations of species, nitrogen levels, and growing conditions. We found that plant community nitrogen uptake rate was unaffected by fine‐root mass in 63% of cases and saturated with fine‐root mass in 29% of cases (92% in total). In contrast, plant community nitrogen uptake rate was clearly affected by nitrogen availability. The results support the idea that although plants may over‐proliferate fine roots for individual‐level competition, it comes without an increase in community‐level nitrogen uptake. The results have implications for the mechanisms included in coupled carbon‐nitrogen terrestrial biosphere models (CN‐TBMs) and are consistent with CN‐TBMs that operate above the individual scale and omit fine‐root mass in equations of nitrogen uptake rate but inconsistent with the majority of CN‐TBMs, which operate above the individual scale and include fine‐root mass in equations of nitrogen uptake rate. For the much smaller number of CN‐TBMs that explicitly model individual‐based belowground competition for nitrogen, the results suggest that the relative (not absolute) fine‐root mass of competing individuals should be included in the equations that determine individual‐level nitrogen uptake rates. By providing empirical data to support the assumptions used in CN‐TBMs, we put their global climate change predictions on firmer ground. We performed controlled microcosm experiments and reanalyzed published pot experiments and field data to determine the relationship between community‐level nitrogen uptake rate, nitrogen availability, and fine‐root mass for 46 unique combinations of species, nitrogen levels, and growing conditions. Plant community nitrogen uptake rate was unaffected by fine‐root mass in 63% of cases and saturated with fine‐root mass in 29% of cases. In contrast, plant community nitrogen uptake rate was clearly affected by nitrogen availability. The results support the idea that although plants may over‐proliferate fine roots for individual‐level competition, it comes without an increase in community‐level nitrogen uptake. [ABSTRACT FROM AUTHOR]

Ecosystem service (ES) models can only inform policy design adequately if they incorporate ecological processes. We used the Lund-Potsdam-Jena managed Land (LPJmL) model, to address following questions for Mexico, Bolivia and Brazilian Amazon: (i) How different are C stocks and C sequestration quantifications under standard (when soil and litter C and heterotrophic respiration are not considered) and comprehensive (including all C stock and heterotrophic respiration) approach? and (ii)Howdoes the valuation of C stock and C sequestration differ in national payments for ES and global C funds or marketswhen comparing both approach?Wefound that up to 65%of C stocks have not been taken into account by neglecting to include C stored in soil and litter, resulting in gross underpayments (up to 500 times lower). Since emissions from heterotrophic respiration of organic material offset a large proportion of C gained through growth of living matter, we found thatmarkets and decision-makers are inadvertently overestimating up to 100 times C sequestrated. Newapproaches formodelling C services relevant ecological process-based can help accounting for C in soil, litter and heterotrophic respiration and become important for the operationalization of agreements on climate change mitigation following the COP21 in 2015. [ABSTRACT FROM AUTHOR]

In the past several decades, dynamic global vegetation models (DGVMs) have been the most widely used and appropriate tool at the global scale to investigate vegetation-climate interactions. At the Institute of Atmospheric Physics, a new version of DGVM (IAP-DGVM) has been developed and coupled to the Common Land Model (CoLM) within the framework of the Chinese Academy of Sciences’ Earth System Model (CAS-ESM). This work reports the performance of IAP-DGVM through comparisons with that of the default DGVM of CoLM (CoLM-DGVM) and observations. With respect to CoLMDGVM, IAP-DGVM simulated fewer tropical trees, more “needleleaf evergreen boreal tree” and “broadleaf deciduous boreal shrub”, and a better representation of grasses. These contributed to a more realistic vegetation distribution in IAP-DGVM, including spatial patterns, total areas, and compositions. Moreover, IAP-DGVM also produced more accurate carbon fluxes than CoLM-DGVM when compared with observational estimates. Gross primary productivity and net primary production in IAP-DGVM were in better agreement with observations than those of CoLM-DGVM, and the tropical pattern of fire carbon emissions in IAP-DGVM was much more consistent with the observation than that in CoLM-DGVM. The leaf area index simulated by IAP-DGVM was closer to the observation than that of CoLM-DGVM; however, both simulated values about twice as large as in the observation. This evaluation provides valuable information for the application of CAS-ESM, as well as for other model communities in terms of a comparative benchmark. [ABSTRACT FROM AUTHOR]