Modeling drought mortality and resilience of savannas and forests in tropical Asia - Simon Scheiter; Dushyant Kumar; Mirjam Pfeiffer; Liam Langan (Ecological Modelling, 2024)

Biome classification influences current and projected future biome distributions - Simon Scheiter; Dushyant Kumar; Mirjam Pfeiffer; Liam Langan (Global Ecology and Biogeography, 2024)
Aim Biome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence biome patterns, and vegetation models can be used to assess the susceptibility of biomes to experience transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences current and projected future biome patterns. Location Africa, Australia, Tropical Asia. Time period 2000–2099. Major taxa studied Tropical vegetation. Methods We used the adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of functional types, (2) a cluster analysis based on the cover of functional types and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation‐based biome maps and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes. Results As expected, biome patterns were strongly related to the scheme used for biome classification. The highest data‐model agreement was derived for a cluster analysis using 21 simulated traits. Traits related to size were most important for classification. Considering all classification schemes, the area projected to undergo biome transitions under climate change varied between 16.5% and 32.1%. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes. Main conclusions Studies projecting biome patterns and transitions under current and future climate should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.

Combined impacts of future climate‐driven vegetation changes and socioeconomic pressures on protected areas in Africa - Carola Martens; Simon Scheiter; Guy F. Midgley; Thomas Hickler (Conservation Biology, 2022)
Africa's protected areas (PAs) are the last stronghold of the continent's unique biodiversity, but they appear increasingly threatened by climate change, substantial human population growth, and land‐use change. Conservation planning is challenged by uncertainty about how strongly and where these drivers will interact over the next few decades. We investigated the combined future impacts of climate‐driven vegetation changes inside African PAs and human population densities and land use in their surroundings for 2 scenarios until the end of the 21 st century. We used the following 2 combinations of the shared socioeconomic pathways (SSPs) and representative greenhouse gas concentration pathways (RCPs): the “middle‐of‐the‐road” scenario SSP2–RCP4.5 and the resource‐intensive “fossil‐fueled development” scenario SSP5–RCP8.5. Climate change impacts on tree cover and biome type (i.e., desert, grassland, savanna, and forest) were simulated with the adaptive dynamic global vegetation model (aDGVM). Under both scenarios, most PAs were adversely affected by at least 1 of the drivers, but the co‐occurrence of drivers was largely region and scenario specific. The aDGVM projections suggest considerable climate‐driven tree cover increases in PAs in today's grasslands and savannas. For PAs in West Africa, the analyses revealed climate‐driven vegetation changes combined with hotspots of high future population and land‐use pressure. Except for many PAs in North Africa, future decreases in population and land‐use pressures were rare. At the continental scale, SSP5–RCP8.5 led to higher climate‐driven changes in tree cover and higher land‐use pressure, whereas SSP2–RCP4.5 was characterized by higher future population pressure. Both SSP–RCP scenarios implied increasing challenges for conserving Africa's biodiversity in PAs. Our findings underline the importance of developing and implementing region‐specific conservation responses. Strong mitigation of future climate change and equitable development scenarios would reduce ecosystem impacts and sustain the effectiveness of conservation in Africa.

Assessing protected area vulnerability to climate change in a case study of South African national parks - Kevin M. Coldrey; Jane K. Turpie; Guy Midgley; Simon Scheiter; Lee Hannah; Patrick R. Roehrdanz; Wendy B. Foden (Conservation Biology, 2022)
Climate change is challenging the ability of protected areas (PAs) to meet their objectives. To improve PA planning, we developed a framework for assessing PA vulnerability to climate change based on consideration of potential climate change impacts on species and their habitats and resource use. Furthermore, the capacity of PAs to adapt to these climate threats was determined through assessment of PA management effectiveness, adjacent land use, and financial resilience. Users reach a PA‐specific vulnerability score and rank based on scoring of these categories. We applied the framework to South Africa's 19 national parks. Because the 19 parks are managed as a national network, we explored how resources might be best allocated to address climate change. Each park's importance to the network's biodiversity conservation and revenue generation was estimated and used to weight overall vulnerability scores and ranks. Park vulnerability profiles showed distinct combinations of potential impacts of climate change and adaptive capacities; the former had a greater influence on vulnerability. Mapungubwe National Park emerged as the most vulnerable to climate change, despite its relatively high adaptive capacity, largely owing to large projected changes in species and resource use. Table Mountain National Park scored the lowest in overall vulnerability. Climate change vulnerability rankings differed markedly once importance weightings were applied; Kruger National Park was the most vulnerable under both importance scenarios. Climate change vulnerability assessment is fundamental to effective adaptation planning. Our PA assessment tool is the only tool that quantifies PA vulnerability to climate change in a comparative index. It may be used in data‐rich and data‐poor contexts to prioritize resource allocation across PA networks and can be applied from local to global scales.

Large uncertainties in future biome changes in Africa call for flexible climate adaptation strategies - Carola Martens; Thomas Hickler; Claire Davis‐Reddy; Francois Engelbrecht; Steven I. Higgins; Graham P. von Maltitz; Guy F. Midgley; Mirjam Pfeiffer; Simon Scheiter (Global Change Biology, 2021)
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.

Climate change promotes transitions to tall evergreen vegetation in tropical Asia - Simon Scheiter; Dushyant Kumar; Richard T. Corlett; Camille Gaillard; Liam Langan; Ralph Sedricke Lapuz; Carola Martens; Mirjam Pfeiffer; Kyle W. Tomlinson (Global Change Biology, 2020)
Vegetation in tropical Asia is highly diverse due to large environmental gradients and heterogeneity of landscapes. This biodiversity is threatened by intense land use and climate change. However, despite the rich biodiversity and the dense human population, tropical Asia is often underrepresented in global biodiversity assessments. Understanding how climate change influences the remaining areas of natural vegetation is therefore highly important for conservation planning. Here, we used the adaptive Dynamic Global Vegetation Model version 2 (aDGVM2) to simulate impacts of climate change and elevated CO 2 on vegetation formations in tropical Asia for an ensemble of climate change scenarios. We used climate forcing from five different climate models for representative concentration pathways RCP4.5 and RCP8.5. We found that vegetation in tropical Asia will remain a carbon sink until 2099, and that vegetation biomass increases of up to 28% by 2099 are associated with transitions from small to tall woody vegetation and from deciduous to evergreen vegetation. Patterns of phenology were less responsive to climate change and elevated CO 2 than biomes and biomass, indicating that the selection of variables and methods used to detect vegetation changes is crucial. Model simulations revealed substantial variation within the ensemble, both in biomass increases and in distributions of different biome types. Our results have important implications for management policy, because they suggest that large ensembles of climate models and scenarios are required to assess a wide range of potential future trajectories of vegetation change and to develop robust management plans. Furthermore, our results highlight open ecosystems with low tree cover as most threatened by climate change, indicating potential conflicts of interest between biodiversity conservation in open ecosystems and active afforestation to enhance carbon sequestration.

Global ecosystems and fire: Multi‐model assessment of fire‐induced tree‐cover and carbon storage reduction - Gitta Lasslop; Stijn Hantson; Sandy P. Harrison; Dominique Bachelet; Chantelle Burton; Matthias Forkel; Matthew Forrest; Fang Li; Joe R. Melton; Chao Yue; Sally Archibald; Simon Scheiter; et al. (Global Change Biology, 2020)
In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001–2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems.

Linking scales and disciplines: an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management - Christian Berger; Mari Bieri; Karen Bradshaw; Christian Brümmer; Thomas Clemen; Thomas Hickler; Werner Leo Kutsch; Ulfia A. Lenfers; Carola Martens; Guy F. Midgley; Kanisios Mukwashi; Victor Odipo; et al. (Climatic Change, 2019)

African shrub distribution emerges via a trade‐off between height and sapwood conductivity - Camille Gaillard; Liam Langan; Mirjam Pfeiffer; Dushyant Kumar; Carola Martens; Steven I. Higgins; Simon Scheiter (Journal of Biogeography, 2018)
Aim Shrubs are multistemmed woody plants and are a successful growth form in many ecosystems globally. Ecosystems such as the Fynbos in South Africa or mediterranean shrublands are dominated by a dense shrub cover. In savannas, shrubs are a major vegetation component. Despite the importance of shrubs at the global scale, they are, in contrast to trees, often understudied both in empirical and modelling studies. We define shrub and tree strategies by a trade‐off between water uptake capacity and height growth, and aim to explore if this trade‐off allows us to explain shrub distribution. Location Sub‐Saharan Africa Methods We improve a dynamic vegetation model, the adaptive Dynamic Global Vegetation Model version 2 ( aDGVM 2), to simulate shrubs as multistemmed woody plants, based on a trade‐off between rapid height growth in single‐stemmed trees and efficient water uptake in multistemmed shrubs. Results We show that, in aDGVM 2, (a) the implemented trade‐off allows a multistemmed shrub strategy to emerge and is sufficient to simulate the broad distribution of shrubs in African savannas; (b) fire and aridity promote shrubs at the expense of trees and grasses; and (c) the presence of shrubs influences competitive interactions between grasses and woody vegetation. Main conclusion We provide a novel approach to simulate shrubs in a dynamic vegetation model. This approach enhances our understanding of the distribution of shrubs , but further work is required for arid and mediterranean shrublands. We conclude that introducing fundamental trade‐offs between growth forms into vegetation models can improve vegetation representation.

Climate‐biomes, pedo‐biomes or pyro‐biomes: which world view explains the tropical forest–savanna boundary in South America? - Liam Langan; Steven I. Higgins; Simon Scheiter (Journal of Biogeography, 2017)
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.

Fire in Australian savannas: from leaf to landscape - Jason Beringer; Lindsay B. Hutley; David Abramson; Stefan K. Arndt; Peter Briggs; Mila Bristow; Josep G. Canadell; Lucas A. Cernusak; Derek Eamus; Andrew C. Edwards; Bradley J. Evans; Benedikt Fest; et al. (Global Change Biology, 2015)
Savanna ecosystems comprise 22% of the global terrestrial surface and 25% of Australia (almost 1.9 million km 2 ) and provide significant ecosystem services through carbon and water cycles and the maintenance of biodiversity. The current structure, composition and distribution of Australian savannas have coevolved with fire, yet remain driven by the dynamic constraints of their bioclimatic niche. Fire in Australian savannas influences both the biophysical and biogeochemical processes at multiple scales from leaf to landscape. Here, we present the latest emission estimates from Australian savanna biomass burning and their contribution to global greenhouse gas budgets. We then review our understanding of the impacts of fire on ecosystem function and local surface water and heat balances, which in turn influence regional climate. We show how savanna fires are coupled to the global climate through the carbon cycle and fire regimes. We present new research that climate change is likely to alter the structure and function of savannas through shifts in moisture availability and increases in atmospheric carbon dioxide, in turn altering fire regimes with further feedbacks to climate. We explore opportunities to reduce net greenhouse gas emissions from savanna ecosystems through changes in savanna fire management.

Connecting dynamic vegetation models to data – an inverse perspective - Florian Hartig; James Dyke; Thomas Hickler; Steven I. Higgins; Robert B. O’Hara; Simon Scheiter; Andreas Huth (Journal of Biogeography, 2012)
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.

A physiological analogy of the niche for projecting the potential distribution of plants - Steven I. Higgins; Robert B. O’Hara; Olga Bykova; Michael D. Cramer; Isabelle Chuine; Eva‐Maria Gerstner; Thomas Hickler; Xavier Morin; Michael R. Kearney; Guy F. Midgley; Simon Scheiter (Journal of Biogeography, 2012)
Aim To develop a physiologically based model of the plant niche for use in species distribution modelling. Location Europe. Methods We link the Thornley transport resistance (TTR) model with functions which describe how the TTR’s model parameters are influenced by abiotic environmental factors. The TTR model considers how carbon and nutrient uptake, and the allocation of these assimilates, influence growth. We use indirect statistical methods to estimate the model parameters from a high resolution data set on tree distribution for 22 European tree species. Results We infer, from distribution data and abiotic forcing data, the physiological niche dimensions of 22 European tree species. We found that the model fits were reasonable (AUC: 0.79–0.964). The projected distributions were characterized by a false positive rate of 0.19 and a false negative rate 0.12. The fitted models are used to generate projections of the environmental factors that limit the range boundaries of the study species. Main conclusions We show that physiological models can be used to derive physiological niche dimensions from species distribution data. Future work should focus on including prior information on physiological rates into the parameter estimation process. Application of the TTR model to species distribution modelling suggests new avenues for establishing explicit links between distribution and physiology, and for generating hypotheses about how ecophysiological processes influence the distribution of plants.

The stability of African savannas: insights from the indirect estimation of the parameters of a dynamic model - Steven I. Higgins; Simon Scheiter; Mahesh Sankaran (Ecology, 2010)
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.

Impacts of climate change on the vegetation of Africa: an adaptive dynamic vegetation modelling approach - SIMON SCHEITER; STEVEN I. HIGGINS (Global Change Biology, 2009)
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 CO 2 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 CO 2 fertilization effect is not saturated at ambient CO 2 levels and will strongly increase in response to further increases in CO 2 levels. The model provides a general and flexible framework for describing vegetation response to the interactive effects of climate and disturbances.