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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.

The unique biodiversity and vast carbon stocks of the Amazon rainforests are essential to the Earth System but are threatened by future water balance changes. Empirical evidence suggests that species and trait diversity may mediate forest drought responses, yet little evidence exists for tropical forest responses. In this simulation study, we identify key axes of trait variation and quantify the extent to which functional trait diversity increases tropical forests’ drought resistance. Using a vegetation model capable of simulating observed tropical forest drought responses and trait diversity, we identify emergent trade-offs between water-related traits (hereafter hydraulic traits) as a key axis of variation. Our simulations reveal that higher functional trait diversity reduces site-scale biomass loss during sudden catastrophic drought, i.e., a 50% precipitation reduction for four and seven years, by 17% and 32%, respectively, and continental-scale biomass loss due to severe chronic climate change-associated precipitation reductions, i.e., RCP8.5, constant CO 2 at 380 ppm, and a 50% precipitation reduction over 100 years, by 34%. Additionally, we find that functional trait diversity-mediated biomass resistance is stronger under more severe drought conditions. These findings quantify the essential role of hydraulic-trait diversity in enhancing tropical forest drought resistance and highlight the critical linkages between biodiversity conservation and climate change mitigation. The hyper-diverse tropical forests of the Amazon store vast carbon stocks, but whether this diversity increases their resistance to future drought remains uncertain. This simulation study suggests that higher diversity in plant functional traits increases biomass resistance to both short- and long-term drought.

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.

Dynamic vegetation models provide process-based explanations of the dynamics and the distribution of plant ecosystems. They offer significant advantages over static, correlative modelling approaches, particularly for ecosystems that are outside their equilibrium due to global change or climate change. A persistent problem, however, is their parameterization. Parameters and processes of dynamic vegetation models (DVMs) are traditionally determined independently of the model, while model outputs are compared to empirical data for validation and informal model comparison only. But field data for such independent estimates of parameters and processes are often difficult to obtain, and the desire to include better descriptions of processes such as biotic interactions, dispersal, phenotypic plasticity and evolution in future vegetation models aggravates limitations related to the current parameterization paradigm. In this paper, we discuss the use of Bayesian methods to bridge this gap. We explain how Bayesian methods allow direct estimates of parameters and processes, encoded in prior distributions, to be combined with inverse estimates, encoded in likelihood functions. The combination of direct and inverse estimation of parameters and processes allows a much wider range of vegetation data to be used simultaneously, including vegetation inventories, species traits, species distributions, remote sensing, eddy flux measurements and palaeorecords. The possible reduction of uncertainty regarding structure, parameters and predictions of DVMs may not only foster scientific progress, but will also increase the relevance of these models for policy advice.

Smallholder farming systems in southern Africa are characterized by low-input management and integrated livestock and crop production. Low yields and dry-season feed shortages are common. To meet growing food demands, sustainable intensification (SI) of these systems is an important policy goal. While mixed crop–livestock farming may offer greater productivity, it implies trade-offs between feed supply, soil nutrient replenishment, soil carbon accumulation, and other ecosystem functions (ESFs) and ecosystem services (ESSs). Such settings require a detailed system understanding to assess the performance of prevalent management practices and identify potential SI strategies. Models can evaluate different management scenarios on extensive spatiotemporal scales and help identify suitable management strategies. Here, we linked the process-based models APSIM (Agricultural Production Systems sIMulator) for cropland and aDGVM2 (Adaptive Dynamic Global Vegetation Model) for rangeland to investigate the effects of (i) current management practices (minimum input crop–livestock agriculture), (ii) an SI scenario for crop production (with dry-season cropland grazing), and (iii) a scenario with separated rangeland and cropland management (livestock exclusion from cropland) in two representative villages of the Limpopo Province, South Africa, for the period from 2000 to 2010. We focused on the following ESFs and ESSs provided by cropland and rangeland: yield and feed provision, soil carbon storage, cropland leaf area index (LAI), and soil water. Village surveys informed the models of farming practices, livelihood conditions, and environmental circumstances. We found that modest SI measures (small fertilizer quantities, weeding, and crop rotation) led to moderate yield increases of between a factor of 1.2 and 1.6 and reduced soil carbon loss, but they sometimes caused increased growing-season water limitation effects. Thus, SI effects strongly varied between years. Dry-season crop residue grazing reduced feed deficits by approximately a factor of 2 compared with the rangeland-only scenario, but it could not fully compensate for the deficits during the dry-to-wet season transition. We expect that targeted deficit irrigation or measures to improve water retention and the soil water holding capacity may enhance SI efforts. Off-field residue feeding during the dry-to-wet season transition could further reduce feed deficits and decrease rangeland grazing pressure during the early growing season. We argue that integrative modeling frameworks are needed to evaluate landscape-level interactions between ecosystem components, evaluate the climate resilience of landscape-level ecosystem services, and identify effective mitigation and adaptation strategies.

The dominant vegetation over much of the global land surface is not predetermined by contemporary climate, but also influenced by past environmental conditions. This confounds attempts to predict current and future biome distributions, because even a perfect model would project multiple possible biomes without knowledge of the historical vegetation state. Here we compare the distribution of tree- and grass-dominated biomes across Africa simulated using a dynamic global vegetation model (DGVM). We explicitly evaluate where and under what conditions multiple stable biome states are possible for current and projected future climates. Our simulation results show that multiple stable biomes states are possible for vast areas of tropical and subtropical Africa under current conditions. Widespread loss of the potential for multiple stable biomes states is projected in the 21st Century, driven by increasing atmospheric CO2. Many sites where currently both tree-dominated and grass-dominated biomes are possible become deterministically tree-dominated. Regions with multiple stable biome states are widespread and require consideration when attempting to predict future vegetation changes. Testing for behaviour characteristic of systems with multiple stable equilibria, such as hysteresis and dependence on historical conditions, and the resulting uncertainty in simulated vegetation, will lead to improved projections of global change impacts.

Tropical savannas cover a large proportion of the Earth's land surface and many people are dependent on the ecosystem services that savannas supply. Their sustainable management is crucial. Owing to the complexity of savanna vegetation dynamics, climate change and land use impacts on savannas are highly uncertain. We used a dynamic vegetation model, the adaptive dynamic global vegetation model (aDGVM), to project how climate change and fire management might influence future vegetation in northern Australian savannas. Under future climate conditions, vegetation can store more carbon than under ambient conditions. Changes in rainfall seasonality influence future carbon storage but do not turn vegetation into a carbon source, suggesting that CO₂fertilization is the main driver of vegetation change. The application of prescribed fires with varying return intervals and burning season influences vegetation and fire impacts. Carbon sequestration is maximized with early dry season fires and long fire return intervals, while grass productivity is maximized with late dry season fires and intermediate fire return intervals. The study has implications for management policy across Australian savannas because it identifies how fire management strategies may influence grazing yield, carbon sequestration and greenhouse gas emissions. This knowledge is crucial to maintaining important ecosystem services of Australian savannas.

Large proportions of the Earth's land surface are covered by biomes dominated by C4 grasses. These C4-dominated biomes originated during the late Miocene, 3–8 million years ago (Ma), but there is evidence that C4 grasses evolved some 20 Ma earlier during the early Miocene / Oligocene. Explanations for this lag between evolution and expansion invoke changes in atmospheric CO2, seasonality of climate and fire. However, there is still no consensus about which of these factors triggered C4 grassland expansion. We use a vegetation model, the adaptive dynamic global vegetation model (aDGVM), to test how CO2, temperature, precipitation, fire and the tolerance of vegetation to fire influence C4 grassland expansion. Simulations are forced with late Miocene climates generated with the Hadley Centre coupled ocean–atmosphere–vegetation general circulation model. We show that physiological differences between the C3 and C4 photosynthetic pathways cannot explain C4 grass invasion into forests, but that fire is a crucial driver. Fire-promoting plant traits serve to expand the climate space in which C4-dominated biomes can persist. We propose that three mechanisms were involved in C4 expansion: the physiological advantage of C4 grasses under low atmospheric CO2 allowed them to invade C3 grasslands; fire allowed grasses to invade forests; and the evolution of fire-resistant savanna trees expanded the climate space that savannas can invade.

Savannas are characterized by a competitive tension between grasses and trees, and theoretical models illustrate how this competitive tension is influenced by resource availability, competition for these resources, and disturbances. How this universe of theoretical possibilities translates into the real world is, however, poorly understood. In this paper we indirectly parameterize a theoretical model of savanna dynamics with the aim of gaining insights as to how the grass-tree balance changes across a broad biogeographical gradient. We use data on the abundance of trees in African savannas and Markov chain Monte Carlo methods to estimate the model parameters. The analysis shows that grasses and trees can coexist over a broad range of rainfall regimes. Further, our results indicate that savannas may be regulated by either asymptotically stable dynamics (in the absence of fire) or by stable limit cycles (in the presence of fire). Rainfall does not influence which of these two classes of dynamics occurs. We conclude that, even though fire might not be necessary for grass-tree coexistence, it nonetheless is an important modifier of grass : tree ratios.

Tree regeneration is a key process in forest dynamics, particularly in the context of forest resilience and climate change. Models are pivotal for assessing long‐term forest dynamics, and they have been in use for more than 50 years. However, there is a need to evaluate their capacity to accurately represent tree regeneration. We assess how well current models capture the overall abundance, species composition, and mortality of tree regeneration. Using 15 models built to capture long‐term forest dynamics at the stand, landscape, and global levels, we simulate tree regeneration at 200 sites representing large environmental gradients across Central Europe. The results are evaluated against extensive data from unmanaged forests. Most of the models overestimate recruitment levels, which is compensated only in some models by high simulated mortality rates in the early stages of individual‐tree dynamics. Simulated species diversity of recruitment generally matches observed ranges. Models simulating higher stand‐level species diversity do not feature higher species diversity in the recruitment layer. The effect of light availability on recruitment levels is captured better than the effects of temperature and soil moisture, but patterns are not consistent across models. Increasing complexity in the tree regeneration modules is not related to higher accuracy of simulated tree recruitment. Furthermore, individual model design is more important than scale (stand, landscape, and global) and approach (empirical and process‐based) for accurately capturing tree regeneration. Despite the mismatches between simulation results and data, it is remarkable that most models capture the essential features of the highly complex process of tree regeneration, while not having been parameterized with such data. We conclude that much can be gained by evaluating and refining the modeling of tree regeneration processes. This has the potential to render long‐term projections of forest dynamics under changing environmental conditions much more robust.