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Ecologists are being challenged to predict how ecosystems will respond to climate changes. According to the Multi-Colored World (MCW) hypothesis, climate impacts may not manifest because consumers such as fire and herbivory can override the influence of climate on ecosystem state. One MCW interpretation is that climate determinism fails because alternative ecosystem states (AES) are possible at some locations in climate space. We evaluated theoretical and empirical evidence for the proposition that forest and savanna are AES in Africa. We found that maps which infer where AES zones are located were contradictory. Moreover, data from longitudinal and experimental studies provide inconclusive evidence for AES. That is, although the forest-savanna AES proposition is theoretically sound, the existing evidence is not yet convincing. We conclude by making the case that the AES proposition has such fundamental consequences for designing management actions to mitigate and adapt to climate change in the savanna-forest domain that it needs a more robust evidence base before it is used to prescribe management actions., (© 2024 The Author(s). New Phytologist © 2024 New Phytologist Foundation.)

Forecasting the risks of climate change for species and ecosystems is necessary for developing targeted conservation strategies. Previous risk assessments mapped the exposure of the global land surface to changes in climate. However, this procedure is unlikely to robustly identify priority areas for conservation actions because nonlinear physiological responses and colimitation processes ensure that ecological changes will not map perfectly to the forecast climatic changes. Here, we combine ecophysiological growth models of 135,153 vascular plant species and plant growth-form information to transform ambient and future climatologies into phytoclimates, which describe the ability of climates to support the plant growth forms that characterize terrestrial ecosystems. We forecast that 33% to 68% of the global land surface will experience a significant change in phytoclimate by 2070 under representative concentration pathways RCP 2.6 and RCP 8.5, respectively. Phytoclimates without present-day analogue are forecast to emerge on 0.3-2.2% of the land surface and 0.1-1.3% of currently realized phytoclimates are forecast to disappear. Notably, the geographic pattern of change, disappearance and novelty of phytoclimates differs markedly from the pattern of analogous trends in climates detected by previous studies, thereby defining new priorities for conservation actions and highlighting the limits of using untransformed climate change exposure indices in ecological risk assessments. Our findings suggest that a profound transformation of the biosphere is underway and emphasize the need for a timely adaptation of biodiversity management practices., (© 2024. The Author(s).)

Increasing rainfall variability is widely expected under future climate change scenarios. How will savanna trees and grasses be affected by growing season dry spells and altered seasonality and how tightly coupled are tree-grass phenologies with rainfall? We measured tree and grass responses to growing season dry spells and dry season rainfall. We also tested whether the phenologies of 17 deciduous woody species and the Soil Adjusted Vegetation Index of grasses were related to rainfall between 2019 and 2023. Tree and grass growth was significantly reduced during growing season dry spells. Tree growth was strongly related to growing season soil water potentials and limited to the wet season. Grasses can rapidly recover after growing season dry spells and grass evapotranspiration was significantly related to soil water potentials in both the wet and dry seasons. Tree leaf flushing commenced before the rainfall onset date with little subsequent leaf flushing. Grasses grew when moisture became available regardless of season. Our findings suggest that increased dry spell length and frequency in the growing season may slow down tree growth in some savannas, which together with longer growing seasons may allow grasses an advantage over C 3 plants that are advantaged by rising CO 2 levels., (© 2024 The Authors. New Phytologist © 2024 New Phytologist Foundation.)

One of the foundational premises of ecology is that climate determines ecosystems. This has been challenged by alternative ecosystem state models, which illustrate that internal ecosystem dynamics acting on the initial ecosystem state can overwhelm the influence of climate, and by observations suggesting that climate cannot reliably discriminate forest and savanna ecosystem types. Using a novel phytoclimatic transform, which estimates the ability of climate to support different types of plants, we show that climatic suitability for evergreen trees and C4 grasses are sufficient to discriminate between forest and savanna in Africa. Our findings reassert the dominant influence of climate on ecosystems and suggest that the role of feedbacks causing alternative ecosystem states is less prevalent than has been suggested.

Here we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits. Together, these traits -plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass - define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date., (© 2022. The Author(s).)

Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Here, we respond to Booth's criticism of our paper, "Predictive ability of a process-based versus a correlative species distribution model." Booth argues that our usage of the MaxEnt model was flawed and that the conclusions of our paper are by implication flawed. We respond by clarifying that the error Booth implies we made was not made in our analysis, and we repeat statements from the original manuscript which anticipated such criticisms. In addition, we illustrate that using BIOCLIM variables in a MaxEnt analysis as recommended by Booth does not change the conclusions of the original analysis. That is, high performance in the training data domain did not equate to reliable predictions in novel data domains, and the process model transferred into novel data domains better than the correlative model did. We conclude by discussing a hidden implication of our study, namely, that process-based SDMs negate the need for BIOCLIM-type variables and therefore reframe the variable selection problem in species distribution modeling., Competing Interests: None declared., (© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

A flexible use of the crassulacean acid metabolism (CAM) has been hypothesised to represent an intermediate stage along a C 3 to full CAM evolutionary continuum, when relative contributions of C 3 vs CAM metabolism are co-determined by evolutionary history and prevailing environmental constraints. However, evidence for such eco-evolutionary interdependencies is lacking. We studied these interdependencies for the leaf-succulent genus Drosanthemum (Aizoaceae, Southern African Succulent Karoo) by testing for relationships between leaf δ 13 C diagnostic for CAM dependence (i.e. contribution of C 3 and CAM to net carbon gain), and climatic variables related to temperature and precipitation and their temporal variation. We further quantified the effects of shared phylogenetic ancestry on CAM dependence and its relation to climate. CAM dependence is predicted by rainfall and its temporal variation, with high predictive power of rainfall constancy (temporal entropy). The predictive power of rainfall seasonality and temperature-related variables was negligible. Evolutionary history of the tested clades significantly affected the relationship between rainfall constancy and CAM dependence. We argue that higher CAM dependence might provide an adaptive advantage in increasingly unpredictable rainfall environments when the anatomic exaptation (succulence) is already present. These observations might shed light on the evolution of full CAM., (© 2021 The Authors New Phytologist © 2021 New Phytologist Foundation.)

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 CO 2 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 CO 2 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 CO 2 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 CO 2 changes are likely across Africa. Because of the large uncertainties in future projections, adaptation strategies must be highly flexible. Focused research on CO 2 effects, and improved model representations of these effects will be necessary to reduce these uncertainties., (© 2020 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Species distribution modeling is a widely used tool in many branches of ecology and evolution. Evaluations of the transferability of species distribution models-their ability to predict the distribution of species in independent data domains-are, however, rare. In this study, we contrast the transferability of a process-based and a correlative species distribution model. Our case study uses 664 Australian eucalypt and acacia species. We estimate models for these species using data from their native Australia and then assess whether these models can predict the adventive range of these species. We find that the correlative model-MaxEnt-has a superior ability to describe the data in the training data domain (Australia) and that the process-based model-TTR-SDM-has a superior ability to predict the distribution of the study species outside of Australia. The implication of this analysis, that process-based models may be more appropriate than correlative models when making projections outside of the domain of the training data, needs to be tested in other case studies., Competing Interests: We declare no conflict of interest., (© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

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 global-change 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, we map 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., (© 2020 The Authors New Phytologist © 2020 New Phytologist Trust.)

Understanding how and why rates of evolutionary diversification vary is a key issue in evolutionary biology, ecology, and biogeography. Evolutionary rates are the net result of interacting processes summarized under concepts such as adaptive radiation and evolutionary stasis. Here, we review the central concepts in the evolutionary diversification literature and synthesize these into a simple, general framework for studying rates of diversification and quantifying their underlying dynamics, which can be applied across clades and regions, and across spatial and temporal scales. Our framework describes the diversification rate ( d ) as a function of the abiotic environment ( a ), the biotic environment ( b ), and clade-specific phenotypes or traits ( c ); thus, d ~ a,b,c . We refer to the four components ( a - d ) and their interactions collectively as the "Evolutionary Arena." We outline analytical approaches to this framework and present a case study on conifers, for which we parameterize the general model. We also discuss three conceptual examples: the Lupinus radiation in the Andes in the context of emerging ecological opportunity and fluctuating connectivity due to climatic oscillations; oceanic island radiations in the context of island formation and erosion; and biotically driven radiations of the Mediterranean orchid genus Ophrys . The results of the conifer case study are consistent with the long-standing scenario that low competition and high rates of niche evolution promote diversification. The conceptual examples illustrate how using the synthetic Evolutionary Arena framework helps to identify and structure future directions for research on evolutionary radiations. In this way, the Evolutionary Arena framework promotes a more general understanding of variation in evolutionary rates by making quantitative results comparable between case studies, thereby allowing new syntheses of evolutionary and ecological processes to emerge., Competing Interests: None declared., (© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

There are two prominent and competing hypotheses that disagree about the effect of competition on diversification processes. The first, the bounded hypothesis, suggests that species diversity is limited (bounded) by competition between species for finite ecological niche space. The second, the unbounded hypothesis, proposes that innovations associated with evolution render competition unimportant over macroevolutionary timescales. Here we use phylogenetically structured niche modelling to show that processes consistent with both of these diversification models drive species accumulation in conifers. In agreement with the bounded hypothesis, niche competition constrained diversification, and in line with the unbounded hypothesis, niche evolution and partitioning promoted diversification. We then analyse niche traits to show that these diversification enhancing and inhibiting processes can occur simultaneously on different niche dimensions. Together these results suggest a new hypothesis for lineage diversification based on the multi-dimensional nature of ecological niches that can accommodate both bounded and unbounded evolutionary processes.

C3 plants can increase nutrient uptake by increasing transpiration, which promotes the flow of water with dissolved nutrients towards the roots. However, it is not clear if this mechanism of nutrient acquisition, termed 'mass flow', also operates in C4 plants. This is an important question, as differences in mass flow capacity may affect competitive interactions between C3 and C4 species. To test if mass flow can be induced in C4 species, we conducted an experiment in a semiarid seasonal savanna in South Africa. We grew six C4 grasses in nutrient-poor sand and supplied no nutrients, nutrients to the roots or nutrients spatially separated from the roots. We measured the rates of photosynthesis and transpiration, water-use efficiency (WUE), nitrogen gain and biomass. For all species biomass, N gain, photosynthesis and transpiration were lowest in the treatment without any nutrient additions. Responses to different nutrient positioning varied among species from no effect on N gain to a 50% reduction when nutrients were spatially separated. The ability to access spatially separated nutrients showed a nonsignificant positive relationship with both the response of transpiration and the response of WUE to spatial nutrient separation. This indicates that nutrient acquisition is not regulated by decreasing WUE in C4 grasses. Overall, our study suggests that under elevated CO2, when evaporative demand is lower, C4 species may be at a competitive disadvantage to C3 species when it comes to nutrient acquisition.

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able - for the first time - to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed - specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large-scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models., (© 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)

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., (© 2016 John Wiley & Sons Ltd.)

The extent of the savannah biome is expected to be profoundly altered by climatic change and increasing atmospheric CO2 concentrations. Contrasting projections are given when using different modelling approaches to estimate future distributions. Furthermore, biogeographic variation within savannahs in plant function and structure is expected to lead to divergent responses to global change. Hence the use of a single model with a single savannah tree type will likely lead to biased projections. Here we compare and contrast projections of South American, African and Australian savannah distributions from the physiologically based Thornley transport resistance statistical distribution model (TTR-SDM)-and three versions of a dynamic vegetation model (DVM) designed and parametrized separately for specific continents. We show that attempting to extrapolate any continent-specific model globally biases projections. By 2070, all DVMs generally project a decrease in the extent of savannahs at their boundary with forests, whereas the TTR-SDM projects a decrease in savannahs at their boundary with aridlands and grasslands. This difference is driven by forest and woodland expansion in response to rising atmospheric CO2 concentrations in DVMs, unaccounted for by the TTR-SDM. We suggest that the most suitable models of the savannah biome for future development are individual-based dynamic vegetation models designed for specific biogeographic regions.This article is part of the themed issue 'Tropical grassy biomes: linking ecology, human use and conservation'., (© 2016 The Author(s).)

Perennial grasses are a dominant component of grasslands, and provide important ecosystem services. However, most knowledge of grasslands' functioning and production comes from plot-level studies, and drivers of individual-level production remain poorly explored. Extrapolation from existing experiments is hampered by the fact that these are mostly concentrated on even-aged cohorts, and/or on the early stages of a plant's life cycle. Here we explored how local density regulates individual production in mono-specific natural grassland, focusing on adult individuals of a perennial savanna grass (Stipagrostis uniplumis). We found individual production to increase with individuals' size, but to decrease with neighbour abundance. A metric of neighbour abundance that considered size was superior to a metric based solely on the number of individuals. This finding is particularly important for studying competitive effects in natural populations, where plants are normally not even-sized. The inferred competition kernel, i.e. the function describing how competitive strength varies with spatial distance from a target plant, was hump-shaped, indicating strongest intraspecific competition at intermediate distances (10-30 cm). The spatial signature of competitive effects changed with time since fire; peak effects moved successively away from the target plant. Our results suggest that inferred competition kernels of long-lived plant populations may have shapes that differ from exponential or sigmoidal decreases. More generally, results underline that competition among neighbouring plants is dynamic. Studies that address density-dependent and density-independent (fire-related) population dynamics of perennial grasses in their fire-prone environment may thus shed new light on the functioning and production of grasslands.

Remote sensing studies suggest that savannahs are transforming into more tree-dominated states; however, progressive nitrogen limitation could potentially retard this putatively CO2-driven invasion. We analysed controls on nitrogen mineralization rates in savannah by manipulating rainfall and the cover of grass and tree elements against the backdrop of the seasonal temperature and rainfall variation. We found that the seasonal pattern of nitrogen mineralization was strongly influenced by rainfall, and that manipulative increases in rainfall could boost mineralization rates. Additionally, mineralization rates were considerably higher on plots with grasses and lower on plots with trees. Our findings suggest that shifting a savannah from a grass to a tree-dominated state can substantially reduce nitrogen mineralization rates, thereby potentially creating a negative feedback on the CO2-induced invasion of savannahs by trees., (© 2015 The Author(s).)

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 CO₂ fertilization 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., (© 2014 The Authors. New Phytologist © 2014 New Phytologist Trust.)

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.

Invasive species cost the global economy billions of dollars each year, but ecologists have struggled to predict the risk of an introduced species naturalizing and invading. Although carefully designed experiments are needed to fully elucidate what makes some species invasive, much can be learned from unintentional experiments involving the introduction of species beyond their native ranges. Here, we assess invasion risk by linking a physiologically based species distribution model with data on the invasive success of 749 Australian acacia and eucalypt tree species that have, over more than a century, been introduced around the world. The model correctly predicts 92% of occurrences observed outside of Australia from an independent dataset. We found that invasiveness is positively associated with the projection of physiological niche volume in geographic space, thereby illustrating that species tolerant of a broader range of environmental conditions are more likely to be invasive. Species achieve this broader tolerance in different ways, meaning that the traits that define invasive success are context-specific. Hence, our study reconciles studies that have failed to identify the traits that define invasive success with the urgent and pragmatic need to predict invasive success.

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., (© 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.)

Ecologists have long sought to understand the factors controlling the structure of savanna vegetation. Using data from 2154 sites in savannas across Africa, Australia, and South America, we found that increasing moisture availability drives increases in fire and tree basal area, whereas fire reduces tree basal area. However, among continents, the magnitude of these effects varied substantially, so that a single model cannot adequately represent savanna woody biomass across these regions. Historical and environmental differences drive the regional variation in the functional relationships between woody vegetation, fire, and climate. These same differences will determine the regional responses of vegetation to future climates, with implications for global carbon stocks.

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., (© 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.)

In this study, we explored how rainfall manipulation influenced competitive interactions between grasses and juvenile trees (small nonreproductive trees capable of resprouting) in savanna. To do this, we manipulated rainfall amount in the field using an incomplete factorial experiment that determined the effects of rainfall reduction, no manipulation, rainfall addition, and competition between grasses and trees on grass and tree growth. As response variables, we focused on several measures of tree growth and Disc Pasture Meter settling height as an estimate of grass aboveground biomass. We conducted the study over four years, at two sites in the Kruger National Park, South Africa. Our results show that rainfall manipulation did not have substantial effects on any of the measures of tree growth we considered. However, trees at plots where grasses had been removed grew on average 15 cm more in height and 1.3-1.7 times more in basal area per year than those in plots with grasses. Grass biomass was not influenced by the presence of trees but was significantly and positively influenced by rainfall addition. These findings were not fundamentally influenced by soil type or by prevailing precipitation, suggesting applicability of our results to a wide range of savannas. Our results suggest that, in savannas, increasing rainfall serves to increase the competitive pressure exerted by grasses on trees. The implication is that recruitment into the adult tree stage from the juvenile stage is most likely in drought years when there is little competition from grass for resources and grass fuel loads are low.

Aboveground and belowground biomass compartments of vegetation fulfil different functions and they are coupled by complex interactions. These compartments exchange water, carbon and nutrients and the belowground biomass compartment has the capacity to buffer vegetation dynamics when aboveground biomass is removed by disturbances such as herbivory or fire. However, despite their importance, root-shoot interactions are often ignored in more heuristic vegetation models. Here, we present a simple two-compartment grassland model that couples aboveground and belowground biomass. In this model, the growth of belowground biomass is influenced by aboveground biomass and the growth of aboveground biomass is influenced by belowground biomass. We used the model to explore how the dynamics of a grassland ecosystem are influenced by fire and grazing. We show that the grassland system is most persistent at intermediate levels of aboveground-belowground coupling. In this situation, the system can sustain more extreme fire or grazing regimes than in the case of strong coupling. In contrast, the productivity of the system is maximised at high levels of coupling. Our analysis suggests that the yield of a grassland ecosystem is maximised when coupling is strong, however, the intensity of disturbance that can be sustained increases dramatically when coupling is intermediate. Hence, the model predicts that intermediate coupling should be selected for as it maximises the chances of persistence in disturbance driven ecosystems.

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.

Large proportions of the Earth's land surface are covered by biomes dominated by C(4) grasses. These C(4)-dominated biomes originated during the late Miocene, 3-8 million years ago (Ma), but there is evidence that C(4) grasses evolved some 20 Ma earlier during the early Miocene/Oligocene. Explanations for this lag between evolution and expansion invoke changes in atmospheric CO(2), seasonality of climate and fire. However, there is still no consensus about which of these factors triggered C(4) grassland expansion. We use a vegetation model, the adaptive dynamic global vegetation model (aDGVM), to test how CO(2), temperature, precipitation, fire and the tolerance of vegetation to fire influence C(4) 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 C(3) and C(4) photosynthetic pathways cannot explain C(4) grass invasion into forests, but that fire is a crucial driver. Fire-promoting plant traits serve to expand the climate space in which C(4)-dominated biomes can persist. We propose that three mechanisms were involved in C(4) expansion: the physiological advantage of C(4) grasses under low atmospheric CO(2) allowed them to invade C(3) grasslands; fire allowed grasses to invade forests; and the evolution of fire-resistant savanna trees expanded the climate space that savannas can invade., (© 2012 The Authors. New Phytologist © 2012 New Phytologist Trust.)

Theories of plant allometry provide a general description of allometric scaling that is supposedly applicable across a wide array of environmental conditions. Scaling theories, however, ignore disturbances such as herbivory in their derivation. Here we examine the influence of herbivores on the scaling of height and diameter of two common African savanna tree species. Using Bayesian piecewise regressions, we show that herbivores modify tree allometry. We also show that the pattern of allometric modification contains information regarding herbivore foraging behavior and the resultant alteration of plant architecture. Interpreting realized allometries in the light of theoretical predictions based on assumptions of zero disturbances may help reveal the degree of herbivore impacts. However, predictions of plant form and function that fail to include disturbances such as herbivory may struggle to find general applicability.

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.

A classic problem in coexistence theory is how grasses and trees coexist in savannas. A popular deterministic model of savannas, the rooting niche separation model, is based on an assumption that is not empirically supported in many savannas. Alternative models that do not rely on the rooting niche assumption invoke intricate stochastic mechanisms that limit their attractiveness as general models of savannas. In this article we develop an alternative deterministic model of grass-tree interactions and use it to analyze the conditions under which grass-tree coexistence is possible. The novel feature of this model is that it partitions aboveground and belowground competition and simulates the fact that fire and herbivory remove only aboveground biomass. The model predicts that stable coexistence of grasses and trees is possible, even when grasses and trees do not have separate rooting niches. We show that when aboveground competition is intense, grasses can be excluded by trees; under such conditions, fire can prevent grasses from exclusion and induce a stable savanna state. The model provides a general framework for exploring the interactive effects of competition, herbivory, and fire on savanna systems.

The amount of carbon stored in savannas represents a significant uncertainty in global carbon budgets, primarily because fire causes actual biomass to differ from potential biomass. We analyzed the structural response of woody plants to long-term experimental burning in savannas. The experiment uses a randomized block design to examine fire exclusion and the season and frequency of burn in 192 7-ha experimental plots located in four different savanna ecosystems. Although previous studies would lead us to expect tree density to respond to the fire regime, our results, obtained from four different savanna ecosystems, suggest that the density of woody individuals was unresponsive to fire. The relative dominance of small trees was, however, highly responsive to fire regime. The observed shift in the structure of tree populations has potentially large impacts on the carbon balance. However, the response of tree biomass to fire of the different savannas studied were different, making it difficult to generalize about the extent to which fire can be used to manipulate carbon sequestration in savannas. This study provides evidence that savannas are demographically resilient to fire, but structurally responsive.

Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover, but perceptions differ on which of these are the primary drivers of savanna structure. Here we show, using data from 854 sites across Africa, that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than approximately 650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered 'stable' systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of approximately 650 mm, savannas are 'unstable' systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics.

Models of plant migration based on estimates of biological parameters severely underestimate the rate of spread when compared to empirical estimates of plant migration rates. This is disturbing, since an ability to predict migration and colonization rates is needed for predicting how native species will distribute themselves in response to habitat loss and climate change and how rapidly invasive species will spread. Part of the problem is the difficulty of formally including rare long-distance dispersal events in spread models. In this article, we explore the process of making predictions about plant migration rates. In particular, we examine the links between data, statistical models, and ecological predictions. We fit mixtures of Weibull distributions to several dispersal data sets and show that statistical and biological criteria for selecting the most appropriate statistical model conflict. Fitting a two-component mixture model to the same data increases the spread-rate prediction by an average factor of 4.5. Data limit our ability to fit more components. Using simulations, we show that a small proportion (0.001) of seeds moving long-distances (1-10 km) can lead to an order of magnitude increase in predicted spread rate. The analysis also suggests that most existing data sets on dispersal will not resolve the problem; more effort needs to be devoted to collecting data on long-distance dispersal. Although dispersal had the strongest effect on the predicted spread rate, we showed that dispersal interacts strongly with plant life history, disturbance, and habitat loss in influencing the predicted rate of spread. The importance of these interactions means that an approach that integrates local and long-distance dispersal with plant life history, disturbance, and habitat availability is essential for predicting migration rates.