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.
It is possible that anthropogenic climate change will drive the Earth system into a qualitatively different state. Although different types of uncertainty limit our capacity to assess this risk, ...Earth system scientists are particularly concerned about tipping elements, large-scale components of the Earth system that can be switched into qualitatively different states by small perturbations. Despite growing evidence that tipping elements exist in the climate system, whether large-scale vegetation systems can tip into alternative states is poorly understood. Here we show that tropical grassland, savanna and forest ecosystems, areas large enough to have powerful impacts on the Earth system, are likely to shift to alternative states. Specifically, we show that increasing atmospheric CO2 concentration will force transitions to vegetation states characterized by higher biomass and/or woody-plant dominance. The timing of these critical transitions varies as a result of between-site variance in the rate of temperature increase, as well as a dependence on stochastic variation in fire severity and rainfall. We further show that the locations of bistable vegetation zones (zones where alternative vegetation states can exist) will shift as climate changes. We conclude that even though large-scale directional regime shifts in terrestrial ecosystems are likely, asynchrony in the timing of these shifts may serve to dampen, but not nullify, the shock that these changes may represent to the Earth system.
Recent IPCC projections suggest that Africa will be subject to particularly severe changes in atmospheric conditions. How the vegetation of Africa and particularly the grassland-savanna-forest ...complex will respond to these changes has rarely been investigated. Most studies on global carbon cycles use vegetation models that do not adequately account for the complexity of the interactions that shape the distribution of tropical grasslands, savannas and forests. This casts doubt on their ability to reliably simulate the future vegetation of Africa. We present a new vegetation model, the adaptive dynamic global vegetation model (aDGVM) that was specifically developed for tropical vegetation. The aDGVM combines established components from existing DGVMs with novel process-based and adaptive modules for phenology, carbon allocation and fire within an individual-based framework. Thus, the model allows vegetation to adapt phenology, allocation and physiology to changing environmental conditions and disturbances in a way not possible in models based on fixed functional types. We used the model to simulate the current vegetation patterns of Africa and found good agreement between model projections and vegetation maps. We simulated vegetation in absence of fire and found that fire suppression strongly influences tree dominance at the regional scale while at a continental scale fire suppression increases biomass in vegetation by a more modest 13%. Simulations under elevated temperature and atmospheric CO₂ concentrations predicted longer growing periods, higher allocation to roots, higher fecundity, more biomass and a dramatic shift toward tree dominated biomes. Our analyses suggest that the CO₂ fertilization effect is not saturated at ambient CO₂ levels and will strongly increase in response to further increases in CO₂ levels. The model provides a general and flexible framework for describing vegetation response to the interactive effects of climate and disturbances.
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.
Anthropogenic climate change is expected to impact ecosystem structure, biodiversity and ecosystem services in Africa profoundly. We used the adaptive Dynamic Global Vegetation Model (aDGVM), which ...was originally developed and tested for Africa, to quantify sources of uncertainties in simulated African potential natural vegetation towards the end of the 21st century. We forced the aDGVM with regionally downscaled high‐resolution climate scenarios based on an ensemble of six general circulation models (GCMs) under two representative concentration pathways (RCPs 4.5 and 8.5). Our study assessed the direct effects of climate change and elevated CO2 on vegetation change and its plant‐physiological drivers. Total increase in carbon in aboveground biomass in Africa until the end of the century was between 18% to 43% (RCP4.5) and 37% to 61% (RCP8.5) and was associated with woody encroachment into grasslands and increased woody cover in savannas. When direct effects of CO2 on plants were omitted, woody encroachment was muted and carbon in aboveground vegetation changed between –8 to 11% (RCP 4.5) and –22 to –6% (RCP8.5). Simulated biome changes lacked consistent large‐scale geographical patterns of change across scenarios. In Ethiopia and the Sahara/Sahel transition zone, the biome changes forecast by the aDGVM were consistent across GCMs and RCPs. Direct effects from elevated CO2 were associated with substantial increases in water use efficiency, primarily driven by photosynthesis enhancement, which may relieve soil moisture limitations to plant productivity. At the ecosystem level, interactions between fire and woody plant demography further promoted woody encroachment. We conclude that substantial future biome changes due to climate and CO2 changes are likely across Africa. Because of the large uncertainties in future projections, adaptation strategies must be highly flexible. Focused research on CO2 effects, and improved model representations of these effects will be necessary to reduce these uncertainties.
Climate change and elevated CO2 are expected to drive vegetation changes in Africa. We used an ensemble of dynamic vegetation model simulations to assess the impacts of these drivers on carbon stocks and biomes until 2099. Climate change and elevated CO2 led to an 18% to 61% increase in carbon stocks, which was primarily driven by CO2 fertilization. Associated biome changes are likely across Africa, especially changes from savanna to forest. Disabling CO2 fertilization resulted in a −22% to +11% change in carbons stocks. These large uncertainties in future projections imply that adaptation strategies need to be flexible.
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.
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.
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.
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.
Estimating the latent dynamics underlying biological processes is a central problem in computational biology. State-space models with Gaussian statistics are widely used for estimation of such latent ...dynamics and have been successfully utilized in the analysis of biological data. Gaussian statistics, however, fail to capture several key features of the dynamics of biological processes (e.g., brain dynamics) such as abrupt state changes and exogenous processes that affect the states in a structured fashion. Although Gaussian mixture process noise models have been considered as an alternative to capture such effects, data-driven inference of their parameters is not well-established in the literature. The objective of this paper is to develop efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and to utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting. We develop an algorithm based on Expectation-Maximization to estimate the process noise parameters from state-space observations. We apply our algorithm to simulated and experimentally-recorded MEG data from auditory experiments in the cocktail party paradigm to estimate the underlying dynamic Temporal Response Functions (TRFs). Our simulation results show that the richer representation of the process noise as a Gaussian mixture significantly improves state estimation and capturing the heterogeneity of the TRF dynamics. Application to MEG data reveals improvements over existing TRF estimation techniques, and provides a reliable alternative to current approaches for probing neural dynamics in a cocktail party scenario, as well as attention decoding in emerging applications such as smart hearing aids. Our proposed methodology provides a framework for efficient inference of Gaussian mixture process noise models, with application to a wide range of biological data with underlying heterogeneous and latent dynamics.
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