Choice of baseline climate data impacts projected species' responses to climate change

David J. Baker, Andrew J. Hartley, Stuart M. Butchart, Stephen G. Willis

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41 Citations (Scopus)

Abstract

Climate data created from historic climate observations are integral to most assessments of potential climate change impacts, and frequently comprise the baseline period used to infer species-climate relationships. They are often also central to downscaling coarse resolution climate simulations from General Circulation Models (GCMs) to project future climate scenarios at ecologically relevant spatial scales. Uncertainty in these baseline data can be large, particularly where weather observations are sparse and climate dynamics are complex (e.g. over mountainous or coastal regions). Yet, importantly, this uncertainty is almost universally overlooked when assessing potential responses of species to climate change. Here, we assessed the importance of historic baseline climate uncertainty for projections of species' responses to future climate change. We built species distribution models (SDMs) for 895 African bird species of conservation concern, using six different climate baselines. We projected these models to two future periods (2040–2069, 2070–2099), using downscaled climate projections, and calculated species turnover and changes in species-specific climate suitability. We found that the choice of baseline climate data constituted an important source of uncertainty in projections of both species turnover and species-specific climate suitability, often comparable with, or more important than, uncertainty arising from the choice of GCM. Importantly, the relative contribution of these factors to projection uncertainty varied spatially. Moreover, when projecting SDMs to sites of biodiversity importance (Important Bird and Biodiversity Areas), these uncertainties altered site-level impacts, which could affect conservation prioritization. Our results highlight that projections of species' responses to climate change are sensitive to uncertainty in the baseline climatology. We recommend that this should be considered routinely in such analyses.
Original languageEnglish
Pages (from-to)2392-2404
Number of pages13
JournalGlobal Change Biology
Volume22
Issue number7
DOIs
Publication statusPublished - 1 Jul 2016
Externally publishedYes

Keywords

  • Change factor method
  • Downscaling
  • General circulation models
  • Important bird and biodiversity areas
  • Satellite remote sensing
  • Species distribution model
  • Sub-Saharan Africa

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