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Durham University

Department of Biosciences

Profile

Publication details for Professor Stephen G Willis

Baker, D.J., Hartley, A.J., Burgess, N.D., Butchart, S.H.M., Carr, J.A., Smith, R.J., Belle, E. & Willis, S.G. (2015). Assessing climate change impacts for vertebrate fauna across the West African protected area network using regionally appropriate climate projections. Diversity and Distributions 21(9): 991-1003.

Author(s) from Durham

Abstract

Aim
We conduct the first assessment of likely future climate change impacts for biodiversity across the West African protected area (PA) network using climate projections that capture important climate regimes (e.g. West African Monsoon) and mesoscale processes that are often poorly simulated in general circulation models (GCMs).

Location
West Africa.

Methods
We use correlative species distribution models to relate species (amphibians, birds, mammals) distributions to modelled contemporary climates, and projected future distributions across the PA network. Climate data were simulated using a physically based regional climate model to dynamically downscale GCMs. GCMs were selected because they accurately reproduce important regional climate regimes and generate a range of regional climate change responses. We quantify uncertainty arising from projected climate change, modelling methodology and spatial dependency, and assess the spatial and temporal patterns of climate change impacts for biodiversity across the PA network.

Results
Substantial species turnover across the network is projected for all three taxonomic groups by 2100 (amphibians = 42.5% (median); birds = 35.2%; mammals = 37.9%), although uncertainty is high, particularly for amphibians and mammals, and, importantly, increases across the century. However, consistent patterns of impacts across taxa emerge by early to mid-century, suggesting high impacts across the Lower Guinea forest.

Main conclusions
Reducing (e.g. using appropriate climate projections) and quantifying uncertainty in climate change impact assessments helps clarify likely impacts. Consistent patterns of high biodiversity impacts emerge in the early and mid-century projections, while end-of-century projections are too uncertain for reliable assessments. We recommend that climate change adaptation should focus on earlier projections, where we have most confidence in species responses, rather than on end-of-century projections that are frequently used. In addition, our work suggests climate impact should consider a broad range of species, as we simulate divergent responses across taxonomic groups.