Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates

Abstract

Africa is predicted to be highly vulnerable to 21st century climatic changes. Assessing the impacts of these changes on Africa's biodiversity is, however, plagued by uncertainties, and markedly different results can be obtained from alternative bioclimatic envelope models or future climate projections. Using an ensemble forecasting framework, we examine projections of future shifts in climatic suitability, and their methodological uncertainties, for over 2500 species of mammals, birds, amphibians and snakes in sub-Saharan Africa. To summarize a priori the variability in the ensemble of 17 general circulation models, we introduce a consensus methodology that combines co-varying models. Thus, we quantify and map the relative contribution to uncertainty of seven bioclimatic envelope models, three multi-model climate projections and three emissions scenarios, and explore the resulting variability in species turnover estimates. We show that bioclimatic envelope models contribute most to variability, particularly in projected novel climatic conditions over Sahelian and southern Saharan Africa. To summarize agreements among projections from the bioclimatic envelope models we compare five consensus methodologies, which generally increase or retain projection accuracy and provide consistent estimates of species turnover. Variability from emissions scenarios increases towards late-century and affects southern regions of high species turnover centred in arid Namibia. Twofold differences in median species turnover across the study area emerge among alternative climate projections and emissions scenarios. Our ensemble of projections underscores the potential bias when using a single algorithm or climate projection for Africa, and provides a cautious first approximation of the potential exposure of sub-Saharan African vertebrates to climatic changes. The future use and further development of bioclimatic envelope modelling will hinge on the interpretation of results in the light of methodological as well as biological uncertainties. Here, we provide a framework to address methodological uncertainties and contextualize results.

Goal-oriented evaluation of species distribution models’ accuracy and precision: True Skill Statistic profile and uncertainty maps

“…Adding models into an ensemble could increase precision in SDM predictions 349 (Araújo & New 2007;Garcia et al 2012). However, not all models should be included on 350 the same ensemble or average (Knutti 2010), especially when any of the models is 351 particularly inaccurate.…”

Synergistic effects of climate and land-use change on representation of African bats in priority conservation areas

“…We performed a principal components analysis on these variables to control for autocorrelation between variables (Garcia et al, 2012;Schoeman et al 2013). Variables with the largest eigenvalues associated with the principal component axes were extracted (n = 10 variables), and compared using a correlation matrix.…”

Modelling Biome Shifts in the Indian Subcontinent under Scenarios of Future Climate Change

“…However, these earlier efforts have been either based on limited climate change scenarios 45 , or under scenarios where increases in temperature and proportional increases in rainfall are the same across the entire subcontinent 46 , or have modelled the potential distribution of vegetation types based on outputs from a single regional climate Model (e.g. HadRM3 43,44 ), thus not accounting for variability among predictions of future climates between different climate models 37,47,48 . Consequently, we lack measures of uncertainty associated with these forecasts attributable to differences in climate modelling approaches.…”

“…Processbased DGVMs on the other hand, simulate an array of ecological processes, including photosynthesis, plant carbon balance, phenology and fire to predict vegetation distribution 35,36 . While DGVMs are more biologically realistic and are capable of capturing transient dynamics in response to changing climates, they are also computationally complex and require detailed information on physiology and life-history traits of species 32,37 . Such data are often lacking, particularly in the tropics, and in these cases BEMs, with their lower data requirements, can be a valuable tool to explore climate-vegetation relationships and serve as a first approximation for understanding climate impacts on vegetation distribution 18,27,32,37,38 .…”

“…While DGVMs are more biologically realistic and are capable of capturing transient dynamics in response to changing climates, they are also computationally complex and require detailed information on physiology and life-history traits of species 32,37 . Such data are often lacking, particularly in the tropics, and in these cases BEMs, with their lower data requirements, can be a valuable tool to explore climate-vegetation relationships and serve as a first approximation for understanding climate impacts on vegetation distribution 18,27,32,37,38 . In this study, we adopt a bioclimatic envelope modelling approach to predict the future distribution of potential vegetation types across the Indian subcontinent under different scenarios of climate change.…”