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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.

© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 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.

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.

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. publishedVersion

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.

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. We thank the co-authors, field data collectors and funding agencies of the original data sources used in this study. We are also grateful to all curators of open-access databases from which we drew data for this study. In particular, we wish to thank Daniel Falster and Remko Duursma for compiling the biomass and allometry database (BAAD) for woody plants; Michael Keller and Maiza Nara for providing us access to data from the Sustainable Landscapes Brazil project; Kristina Anderson-Teixeira and her co-authors for archiving allometric data from the CTFS-ForestGEO forest dynamics plot at the Smithsonian Conservation Biology Institute (Virginia, USA); and KaDonna Randolph of the USDA forest service for her assistance in accessing the Forest Health Monitoring (FHM) database. We thank Bruno H?rault and an anonymous reviewer for their thoughtful and constructive comments on an earlier draft of our manuscript. T.J. was funded by NERC (grant number: NE/K016377/1). This work has benefited from ANR grants to J.C. (CEBA, ref. ANR-10-LABX-25-01 and TULIP, ref. ANR-10-LABX-0041). The Sustainable Landscapes Brazil project was supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, and USAID, and the US Department of State. Data collection for the UNECE ICP Forests PCC Collaborative Database was cofinanced by national or regional organizations and by the European Commission under regulations (EEC) No 2158/86, Forest Focus (EC) No 2152/200, FutMon (EC) LIFE07 ENV/D/218.