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.
Biomes are important constructs for organizing understanding of how the worlds’ major terrestrial ecosystems differ from one another and for monitoring change in these ecosystems. Yet existing biome ...classification schemes have been criticized for being overly subjective and for explicitly or implicitly invoking climate. We propose a new biome map and classification scheme that uses information on (i) an index of vegetation productivity, (ii) whether the minimum of vegetation activity is in the driest or coldest part of the year, and (iii) vegetation height. Although biomes produced on the basis of this classification show a strong spatial coherence, they show little congruence with existing biome classification schemes. Our biome map provides an alternative classification scheme for comparing the biogeochemical rates of terrestrial ecosystems. We use this new biome classification scheme to analyse the patterns of biome change observed over recent decades. Overall, 13% to 14% of analysed pixels shifted in biome state over the 30‐year study period. A wide range of biome transitions were observed. For example, biomes with tall vegetation and minimum vegetation activity in the cold season shifted to higher productivity biome states. Biomes with short vegetation and low seasonality shifted to seasonally moisture‐limited biome states. Our findings and method provide a new source of data for rigorously monitoring global vegetation change, analysing drivers of vegetation change and for benchmarking models of terrestrial ecosystem function.
The distribution and abundance of plant species are intimately related to their reproductive success, which in turn is affected by a large number of environmental variables. Yet, reproductive success ...is rarely taken into account in species distribution models (SDMs). In this paper we examine the extent to which consideration of the reproduction niche and its relationship with temperature could improve SDMs. We review the literature on plant reproductive responses to temperature and the influence of these relationships on species range delimitation. We define the reproduction niche and discuss how temperature influences several stages of the reproductive process. Furthermore, we review examples that illustrate how the reproduction niche influences species distributions and discuss how aspects of the reproduction niche could be considered in SDMs. We show that the reproduction niche fundamentally influences species distributions and that in principle it is easy to include aspects of the reproduction niche in SDMs, although sufficient data are only available for a restricted number of species. Bayesian methods and inverse parameterization may be the most efficient way to use existing data.
Biomes are constructs for organising knowledge on the structure and functioning of the world’s ecosystems, and serve as useful units for monitoring how the biosphere responds to anthropogenic ...drivers, including climate change. The current practice of delimiting biomes relies on expert knowledge. Recent studies have questioned the value of such biome maps for comparative ecology and globalchange research, partly due to their subjective origin. Here we propose a flexible method for developing biome maps objectively. The method uses range modelling of several thousands of plant species to reveal spatial attractors for different growth-form assemblages that define biomes. The workflow is illustrated using distribution data from 23 500 African plant species. In an example application, we create a biome map for Africa and use the fitted species models to project biome shifts. In a second example,wemap gradients of growth-form suitability that can be used to identify sites for comparative ecology. This method provides a flexible framework that (1) allows a range of biome types to be defined according to user needs and (2) enables projections of biome changes that emerge purely from the individualistic responses of plant species to environmental changes.
Biomes are globally distributed, structurally and functionally similar vegetation units defined without reference to plant species composition. The boundaries between biomes are presumed to ...correspond with species turnover and changes in biogeochemical cycling. Determining the controls of biome distributions is thus critical for anticipating global change impacts. Historically, climate and soils have been understood to adequately explain the global distribution of biomes. Convergent evolution and environmental filtering are assumed to be pervasive, ultimately resulting in deterministic patterns of vegetation structure and function in relation to prevailing environmental conditions. Recent studies have highlighted significant problems with this view of biomes. Firstly, systematic structural and functional divergence within biomes has been identified when comparing environmentally similar, yet floristically distinct regions. Secondly, climatic determinism is being further undermined by evidence suggesting multiple stable biome states are possible for some combinations of climatic drivers. We argue that biomes remain useful and necessary constructs for organizing our knowledge of how ecosystems function and for predicting how they might respond to change. However, biome concepts should acknowledge the limits to predictability from environmental conditions alone and the influence of historical processes on modern vegetation patterns. We discuss how direct mapping of plant structure and function, the incorporation of insights into biome evolution and a new generation of vegetation models will lead to improvements in the concept of what biomes are, where they occur, and efforts to predict their distribution.
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.
Ongoing and predicted global change makes understanding and predicting species' range shifts an urgent scientific priority. Here, we provide a synthetic perspective on the so far poorly understood ...effects of interspecific interactions on range expansion rates. We present theoretical foundations for how interspecific interactions may modulate range expansion rates, consider examples from empirical studies of biological invasions and natural range expansions as well as process-based simulations, and discuss how interspecific interactions can be more broadly represented in process-based, spatiotemporally explicit range forecasts. Theory tells us that interspecific interactions affect expansion rates via alteration of local population growth rates and spatial displacement rates, but also via effects on other demographic parameters. The best empirical evidence for interspecific effects on expansion rates comes from studies of biological invasions. Notably, invasion studies indicate that competitive dominance and release from specialized enemies can enhance expansion rates. Studies of natural range expansions especially point to the potential for competition from resident species to reduce expansion rates. Overall, it is clear that interspecific interactions may have important consequences for range dynamics, but also that their effects have received too little attention to robustly generalize on their importance. We then discuss how interspecific interactions effects can be more widely incorporated in dynamic modeling of range expansions. Importantly, models must describe spatiotemporal variation in both local population dynamics and dispersal. Finally, we derive the following guidelines for when it is particularly important to explicitly represent interspecific interactions in dynamic range expansion forecasts: if most interacting species show correlated spatial or temporal trends in their effects on the target species, if the number of interacting species is low, and if the abundance of one or more strongly interacting species is not closely linked to the abundance of the target species.
Current rates of climate and atmospheric change are likely higher than during the last millions of years. Even higher rates of change are projected in CMIP5 climate model ensemble runs for some ...Representative Concentration Pathway (RCP) scenarios. The speed of ecological processes such as leaf physiology, demography or migration can differ from the speed of changes in environmental conditions. Such mismatches imply lags between the actual vegetation state and the vegetation state expected under prevailing environmental conditions. Here, we used a dynamic vegetation model, the adaptive Dynamic Global Vegetation Model (aDGVM), to study lags between actual and expected vegetation in Africa under a changing atmospheric CO2 mixing ratio. We hypothesized that lag size increases with a more rapidly changing CO2 mixing ratio as opposed to slower changes in CO2 and that disturbance by fire further increases lag size. Our model results confirm these hypotheses, revealing lags between vegetation state and environmental conditions and enhanced lags in fire-driven systems. Biome states, carbon stored in vegetation and tree cover in Africa are most sensitive to changes in CO2 under recent and near-future levels. When averaged across all biomes and simulations with and without fire, times to reach an equilibrium vegetation state increase from approximately 242 years for 200 ppm to 898 years for 1000 ppm. These results have important implications for vegetation modellers and for policy making. Lag effects imply that vegetation will undergo substantial changes in distribution patterns, structure and carbon sequestration even if emissions of fossils fuels and other greenhouse gasses are reduced and the climate system stabilizes. We conclude that modelers need to account for lag effects in models and in data used for model testing. Policy makers need to consider lagged responses and committed changes in the biosphere when developing adaptation and mitigation strategies.
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.
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