Dynamic global vegetation models (DGVMs) are powerful tools to project past, current and future vegetation patterns and associated biogeochemical cycles. However, most models are limited by how they ...define vegetation and by their simplistic representation of competition.
We discuss how concepts from community assembly theory and coexistence theory can help to improve vegetation models. We further present a trait- and individual-based vegetation model (aDGVM2) that allows individual plants to adopt a unique combination of trait values. These traits define how individual plants grow and compete. A genetic optimization algorithm is used to simulate trait inheritance and reproductive isolation between individuals. These model properties allow the assembly of plant communities that are adapted to a site's biotic and abiotic conditions.
The aDGVM2 simulates how environmental conditions influence the trait spectra of plant communities; that fire selects for traits that enhance fire protection and reduces trait diversity; and the emergence of life-history strategies that are suggestive of colonization–competition trade-offs.
The aDGVM2 deals with functional diversity and competition fundamentally differently from current DGVMs. This approach may yield novel insights as to how vegetation may respond to climate change and we believe it could foster collaborations between functional plant biologists and vegetation modellers.
Aim It remains poorly understood why the position of the forest–savanna biome boundary, in a domain defined by precipitation and temperature, differs in South America, Africa and Australia. Process ...based Dynamic Global Vegetation Models (DGVMs) are a valuable tool to investigate the determinants of vegetation distributions; however, many DGVMs fail to predict the spatial distribution or indeed presence of the South American savanna biome. Evidence suggests that fire plays a significant role in mediating forest–savanna biome boundaries; however, fire alone appears to be insufficient to predict these boundaries in South America. We hypothesize that interactions between precipitation, constraints on tree rooting depth and fire affect the probability of savanna occurrence and the position of the savanna–forest boundary. Location Tropical forest and savanna sites in Brazil and Venezuela north of 23°S. Methods We tested our hypotheses using a novel DGVM, aDGVM2, which allows plant trait spectra, constrained by trade-offs between traits, to evolve in response to abiotic and biotic conditions. Plant hydraulics is represented by the cohesion–tension theory, this allowed us to explore how soil and plant hydraulics control biome distributions and plant traits. The resulting community trait distributions are emergent properties of model dynamics. Results We showed that across much of South America the biome state is not determined by climate alone. Interactions between plant rooting depth, fire and precipitation affected the probability of observing a given biome state and the emergent traits of plant communities. Simulations where plant rooting depth varied in space provided the best match to satellite derived biomass estimates and generated biome distributions that reproduced contemporary biome maps well. Main conclusions Our findings support the contention that areas where multiple vegetation states are possible are widespread and highlight the importance of considering the influence of fire and constraints on plant rooting depth for predicting biome boundaries.
Vegetation in tropical Asia is highly diverse due to large environmental gradients and heterogeneity of landscapes. This biodiversity is threatened by intense land use and climate change. However, ...despite the rich biodiversity and the dense human population, tropical Asia is often underrepresented in global biodiversity assessments. Understanding how climate change influences the remaining areas of natural vegetation is therefore highly important for conservation planning. Here, we used the adaptive Dynamic Global Vegetation Model version 2 (aDGVM2) to simulate impacts of climate change and elevated CO2 on vegetation formations in tropical Asia for an ensemble of climate change scenarios. We used climate forcing from five different climate models for representative concentration pathways RCP4.5 and RCP8.5. We found that vegetation in tropical Asia will remain a carbon sink until 2099, and that vegetation biomass increases of up to 28% by 2099 are associated with transitions from small to tall woody vegetation and from deciduous to evergreen vegetation. Patterns of phenology were less responsive to climate change and elevated CO2 than biomes and biomass, indicating that the selection of variables and methods used to detect vegetation changes is crucial. Model simulations revealed substantial variation within the ensemble, both in biomass increases and in distributions of different biome types. Our results have important implications for management policy, because they suggest that large ensembles of climate models and scenarios are required to assess a wide range of potential future trajectories of vegetation change and to develop robust management plans. Furthermore, our results highlight open ecosystems with low tree cover as most threatened by climate change, indicating potential conflicts of interest between biodiversity conservation in open ecosystems and active afforestation to enhance carbon sequestration.
Tropical Asia includes a rich biodiversity, threatened by climate change and intense land‐use. We used a dynamic vegetation model to investigate how an ensemble of climate change scenarios may influence future biomass, vegetation height, and phenology in tropical Asia. We found a robust trend of increasing vegetation biomass, and transitions to taller, evergreen vegetation. Open ecosystems with deciduous vegetation such as grasslands and savannas were most susceptible to climate change. Understanding climate change impacts on remaining areas of natural vegetation is essential for conservation planning and management in tropical Asia.
•Drought-induced tree mortality threatens vegetation under future climate change.•Developing models simulating tree mortality at individual plant level is essential.•We used the aDGVM2 to simulate ...drought-induced mortality in tropical Asia.•Productivity and biomass decreased under repeated drought but can recover.•Community trait composition also responds to drought by community re-assembly.
The projected increase of drought occurrence in many tropical and sub-tropical regions globally under future climates will affect terrestrial ecosystems, particularly by increasing drought-induced plant mortality. The capacity to simulate drought mortality in vegetation models is therefore essential to understand future ecosystem dynamics. Using the trait-based vegetation model aDGVM2, we assessed drought mortality and resilience in tropical Asia under climate change. We conducted model simulations for ten sites in tropical Asia, representing a biogeographic gradient. Responses of vegetation attributes and mortality rates were simulated until 2099 for hypothetical drought scenarios and recovery times were calculated. Model simulations showed biomass dieback during drought due to increased plant mortality, primarily among tall and old trees. Drought responses were related to hydraulic traits and associated ecological strategies. Despite severe drought impacts, recovery was possible, but recovery times differed between ecosystem attributes. We conclude that the aDGVM2 enhances our ability to understand drought impacts in tropical ecosystems. The model can simulate increased mortality during drought in a trait- and individual-based modeling framework. It indicated drought resilience of forests and adaptation to drought by changes in community trait composition and the demographic structure. Yet, further model improvements are required to better represent drought impact and recovery.
Aim
Biome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence biome patterns, and vegetation ...models can be used to assess the susceptibility of biomes to experience transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences current and projected future biome patterns.
Location
Africa, Australia, Tropical Asia.
Time period
2000–2099.
Major taxa studied
Tropical vegetation.
Methods
We used the adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of functional types, (2) a cluster analysis based on the cover of functional types and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation‐based biome maps and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes.
Results
As expected, biome patterns were strongly related to the scheme used for biome classification. The highest data‐model agreement was derived for a cluster analysis using 21 simulated traits. Traits related to size were most important for classification. Considering all classification schemes, the area projected to undergo biome transitions under climate change varied between 16.5% and 32.1%. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes.
Main conclusions
Studies projecting biome patterns and transitions under current and future climate should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.
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•Asian savannas have been misinterpreted as degraded forest since the colonial period.•Savannas are threatened due to fire suppression policy and afforestation ...initiatives.•Understorey grass layer should be considered for separating forests from savannas.•Our model predicts woody encroachment and associated biome shifts towards forest.•Appropriate interpretation of Asian savannas for conservation is urgently needed.
Savannas cover large areas of tropical Asia. Yet, these ecosystems are threatened by intense land-use and governmental afforestation initiatives. They are vulnerable to woody encroachment due to fire suppression and climate change. Despite their ancient origins, Asian savannas have been misinterpreted as degraded forest since the colonial period. The consequences of this misinterpretation and climate change on ecosystem functions and diversity of savannas are highly uncertain. We used a dynamic vegetation model, the aDGVM2 to simulate vegetation state under different climate change scenarios to assess how different interpretations of simulated vegetation influence biome patterns in South Asia. Our results show that large areas in South Asia can be interpreted as woodland or degraded forest if we ignore the grassy component and as savanna if the grassy component is considered. The model projects woody encroachment in open savannas due to CO2-fertilization of woody plants and associated biome transitions towards forest by 2099. Our analysis shows that 23.7%–76.6% of the protected areas in the study region are at risk of change. Misclassifying grassy savannas as areas that are suitable for afforestation would lead to a 35–40% loss of these unique ecosystems. A grass-centric biome classification accounting for the grass component in addition to the woody component is necessary to correctly identify Asian savannas. We conclude that there is an urgent need for a correct interpretation of Asian savannas to allow sustainable management and conservation of biodiversity, which is already strongly threatened due to woody encroachment caused by climate change.
South Asian vegetation provides essential ecosystem services to the 1.7 billion inhabitants living in the region. However, biodiversity and ecosystem services are threatened by climate and land-use ...change. Understanding and assessing how ecosystems respond to simultaneous increases in atmospheric CO2 and future climate change is of vital importance to avoid undesired ecosystem change. Failed reaction to increasing CO2 and climate change will likely have severe consequences for biodiversity and humankind. Here, we used the adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation dynamics in South Asia under RCP4.5 and RCP8.5, and we explored how the presence or absence of CO2 fertilization influences vegetation responses to climate change. Simulated vegetation under both representative concentration pathways (RCPs) without CO2 fertilization effects showed a decrease in tree dominance and biomass, whereas simulations with CO2 fertilization showed an increase in biomass, canopy cover, and tree height and a decrease in biome-specific evapotranspiration by the end of the 21st century. The predicted changes in aboveground biomass and canopy cover triggered transition towards tree-dominated biomes. We found that savanna regions are at high risk of woody encroachment and transitioning into forest. We also found transitions of deciduous forest to evergreen forest in the mountain regions. Vegetation types using C3 photosynthetic pathway were not saturated at current CO2 concentrations, and the model simulated a strong CO2 fertilization effect with the rising CO2. Hence, vegetation in the region has the potential to remain a carbon sink. Projections showed that the bioclimatic envelopes of biomes need adjustments to account for shifts caused by climate change and elevated CO2. The results of our study help to understand the regional climate–vegetation interactions and can support the development of regional strategies to preserve ecosystem services and biodiversity under elevated CO2 and climate change.
South Asian vegetation provides essential ecosystem services to the 1.7 billion inhabitants living in the region. However, biodiversity and ecosystem services are threatened by climate and land-use ...change. Understanding and assessing how ecosystems respond to simultaneous increases in atmospheric CO.sub.2 and future climate change is of vital importance to avoid undesired ecosystem change. Failed reaction to increasing CO.sub.2 and climate change will likely have severe consequences for biodiversity and humankind. Here, we used the adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation dynamics in South Asia under RCP4.5 and RCP8.5, and we explored how the presence or absence of CO.sub.2 fertilization influences vegetation responses to climate change. Simulated vegetation under both representative concentration pathways (RCPs) without CO.sub.2 fertilization effects showed a decrease in tree dominance and biomass, whereas simulations with CO.sub.2 fertilization showed an increase in biomass, canopy cover, and tree height and a decrease in biome-specific evapotranspiration by the end of the 21st century. The predicted changes in aboveground biomass and canopy cover triggered transition towards tree-dominated biomes. We found that savanna regions are at high risk of woody encroachment and transitioning into forest. We also found transitions of deciduous forest to evergreen forest in the mountain regions. Vegetation types using C.sub.3 photosynthetic pathway were not saturated at current CO.sub.2 concentrations, and the model simulated a strong CO.sub.2 fertilization effect with the rising CO.sub.2 . Hence, vegetation in the region has the potential to remain a carbon sink. Projections showed that the bioclimatic envelopes of biomes need adjustments to account for shifts caused by climate change and elevated CO.sub.2 . The results of our study help to understand the regional climate-vegetation interactions and can support the development of regional strategies to preserve ecosystem services and biodiversity under elevated CO.sub.2 and climate change.
South Asian vegetation provides essential ecosystem services to the 1.7 billion inhabitants living in the region. However, biodiversity and ecosystem services are threatened by climate and land-use ...change. Understanding and assessing how ecosystems respond to simultaneous increases in atmospheric CO2 and future climate change is of vital importance to avoid undesired ecosystem change. Failed reaction to increasing CO2 and climate change will likely have severe consequences for biodiversity and humankind. Here, we used the adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation dynamics in South Asia under RCP4.5 and RCP8.5, and we explored how the presence or absence of CO2 fertilization influences vegetation responses to climate change.
Simulated vegetation under both representative concentration pathways (RCPs) without CO2 fertilization effects showed a decrease in tree dominance and biomass, whereas simulations with CO2 fertilization showed an increase in biomass, canopy cover, and tree height and a decrease in biome-specific evapotranspiration by the end of the 21st century. The predicted changes in aboveground biomass and canopy cover triggered transition towards tree-dominated biomes. We found that savanna regions are at high risk of woody encroachment and transitioning into forest. We also found transitions of deciduous forest to evergreen forest in the mountain regions. Vegetation types using C3 photosynthetic pathway were not saturated at current CO2 concentrations, and the model simulated a strong CO2 fertilization effect with the rising CO2. Hence, vegetation in the region has the potential to remain a carbon sink. Projections showed that the bioclimatic envelopes of biomes need adjustments to account for shifts caused by climate change and elevated CO2. The results of our study help to understand the regional climate–vegetation interactions and can support the development of regional strategies to preserve ecosystem services and biodiversity under elevated CO2 and climate change.
Summary
Community trait assembly in highly diverse tropical rainforests is still poorly understood. Based on more than a decade of field measurements in a biodiversity hotspot of southern Ecuador, we ...implemented plant trait variation and improved soil organic matter dynamics in a widely used dynamic vegetation model (the Lund‐Potsdam‐Jena General Ecosystem Simulator, LPJ‐GUESS) to explore the main drivers of community assembly along an elevational gradient.
In the model used here (LPJ‐GUESS‐NTD, where NTD stands for nutrient‐trait dynamics), each plant individual can possess different trait combinations, and the community trait composition emerges via ecological sorting. Further model developments include plant growth limitation by phosphorous (P) and mycorrhizal nutrient uptake.
The new model version reproduced the main observed community trait shift and related vegetation processes along the elevational gradient, but only if nutrient limitations to plant growth were activated. In turn, when traits were fixed, low productivity communities emerged due to reduced nutrient‐use efficiency. Mycorrhizal nutrient uptake, when deactivated, reduced net primary production (NPP) by 61–72% along the gradient.
Our results strongly suggest that the elevational temperature gradient drives community assembly and ecosystem functioning indirectly through its effect on soil nutrient dynamics and vegetation traits. This illustrates the importance of considering these processes to yield realistic model predictions.
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