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

By R. A. Garcia, N. Burgess, M. Cabeza, C. Rahbek, Miguel B. Araújo. Global Change Biology, 2011

176 citations recorded by the source corpus.

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.

Subfields: Ecological Modeling, Ecology, Genetics

View Paper: https://www.semanticscholar.org/paper/914121e5e25a8bab1c29341fd31df9e1ea8b54d4

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Citations in the corpus

Climate change vulnerability assessment of species (2018, methodology): “…& New, 2006; Heikkinen et al., 2010; Sánchez-Fernández, Lobo, & Hernández-Manrique, 2011; Settele et al., 2008) and vertebrates, including birds (Garcia, Burgess, Cabeza, Rahbek, & Araújo, 2012; Gregory et al., 2009; Hole et al., 2011), mammals (Hughes, Satasook, Bates, Bumrungsri, & Jones,…”
Uncertainty associated with survey design in Species Distribution Models (2014, methodology): “…to improve the performance of ensemble forecasting approaches it is necessary to modify the building-up of the consensus, either removing poor models or weighting individual models based on a previously selected performance metric (Ara ujo & New, 2007; Marmion et al., 2009; Garcia et al., 2012).”
Uses and misuses of bioclimatic envelope modeling. (2012, unknown): “2009, Marmion et al. 2009, Garcia et al. 2012).”
sdm: a reproducible and extensible R platform for species distribution modelling (2016, methodology): “Furthermore, several communitybased models (Baselga and Araújo 2009) and consensus techniques (Garcia et al. 2012), derived from fitting multiple (i.e. ensembles) of models (Araújo and New 2007), are implemented in sdm.”
Species' intrinsic traits inform their range limitations and vulnerability under environmental change (2015, unknown): “We chose not to utilize presence–absence/background species distribution modelling due to the large effects of modelling technique and possible false absences on results, which are especially apparent when modelling species across a broad range of geographical range sizes (Garcia et al., 2012).”
Conserving the Brazilian semiarid (Caatinga) biome under climate change (2012, background): “Nonetheless, HADCM3 presented the same contrasting patterns of gains and losses of species climatic suitability of the others AOGCMs, and the interpretation of conservation efforts remains similar as for CCCMA and CSIRO.”
Measuring the relative effect of factors affecting species distribution model predictions (2014, methodology): “The individual effects of different factors on SDM predic- tions have been the object of many previous studies, but, to our knowledge, only Dormann et al. (2008), Diniz-Filho et al. (2009) and Garcia et al. (2012) considered the relative effects of factors on SDMs jointly.”