Coarse climate change projections for species living in a fine-scaled world

Christopher P Nadeau, Mark C Urban, Jon R Bridle

Research output: Contribution to journalArticle (Academic Journal)

27 Citations (Scopus)
462 Downloads (Pure)

Abstract

Accurately predicting biological impacts of climate change is necessary to guide policy. However, the resolution of climate data could be affecting the accuracy of climate change impact assessments. Here we review the spatial and temporal resolution of climate data used in impact assessments and demonstrate that these resolutions are often too coarse relative to biologically relevant scales. We then develop a framework that partitions climate into three important components: trend, variance, and autocorrelation. We apply this framework to map different global climate regimes and identify where coarse climate data is most and least likely to reduce the accuracy of impact assessments. We show that impact assessments for many large mammals and birds use climate data with a spatial resolution similar to the biologically relevant area encompassing population dynamics. Conversely, impact assessments for many small mammals, herpetofauna, and plants use climate data with a spatial resolution that is orders of magnitude larger than the area encompassing population dynamics. Most impact assessments also use climate data with a coarse temporal resolution. We suggest that climate data with a coarse spatial resolution likely reduces the accuracy of impact assessments the most in climates with high spatial trend and variance (e.g., much of western North and South America) and the least in climates with low spatial trend and variance (e.g., the Great Plains of the USA). Climate data with a coarse temporal resolution likely reduces the accuracy of impact assessments the most in the northern half of the northern hemisphere where temporal climatic variance is high. Our framework provides one way to identify where improving the resolution of climate data will have the largest impact on the accuracy of biological predictions under climate change.
Original languageEnglish
Pages (from-to)12-24
Number of pages13
JournalGlobal Change Biology
Volume23
Issue number1
Early online date3 Oct 2016
DOIs
Publication statusPublished - 1 Jan 2017

Keywords

  • autocorrelation
  • grid size
  • impact assessment
  • spatial resolution
  • spatial scaling
  • temporal resolution
  • trend
  • variance

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