Abstract
AbstractAimSpecies respond to environmental conditions and so reliable assessments of climate suitability are important for predicting how climate change could alter their distributions. Long‐term average climate data are often used to evaluate the climate suitability of an area, but in these aggregated climate datasets, inter‐annual variability is lost. Due to non‐linearity in species’ biological responses to climate, estimates of long‐term climate suitability from average climate data may be biased and so differ from estimates derived from the average annual suitability over the same period (average response). We investigate the extent to which such differences manifest in a regional assessment of climate suitability for 255 plant species across two 17‐year time periods.LocationCornwall in South‐West England provides a case study.TaxonPlantae.MethodsWe run a simple mechanistic climate suitability model and derive quantitative estimates of climate suitability for 1984–2000 and 2001–2017. For each period, we run the model using climate data representing average monthly values for that period. We then run the model for each year using monthly climate data for that year and average the annual suitability scores across each period (average response). We compare estimates of climate suitability from these two approaches.ResultsAverage climate data gave higher estimates of suitability than the average response, suggesting bias against years of poor suitability in temporally aggregated climate datasets. Differences between suitability estimates were larger in areas of high climate variability and correlated with species’ environmental requirements, being larger for species with small thermal niches and narrow ranges of precipitation tolerance.Main ConclusionsIncorporating inter‐annual variability into climate suitability assessments or understanding the extent to which average climate data might obscure this variance will be important to predict reliably the impacts of climate change on species distributions and should be considered when using mechanistic species distribution models.
Highlights
Recent climate change has driven shifts in the geographic ranges of species (e.g. Kelly and Goulden, 2008, D’Andrea et al, 2009, Zorio, Williams and Aho, 2016) and further range shifts are expected as the climate continues to warm and weather patterns become more variable (Leemans and Solomon, 1993, Collins et al, 2011)
Differences between suitability estimates were larger in areas of high climate variability and correlated to species’ environmental requirements, being larger for species with small thermal niches and narrow ranges of precipitation tolerance
Recent trends in global warming and altered precipitation patterns will continue, regardless of any mitigation strategy to reduce anthropogenic greenhouse gas emissions (Collins et al, 2013), and it is timely that we enhance the ability to predict how future climate change may affect global biodiversity
Summary
Recent climate change has driven shifts in the geographic ranges of species (e.g. Kelly and Goulden, 2008, D’Andrea et al, 2009, Zorio, Williams and Aho, 2016) and further range shifts are expected as the climate continues to warm and weather patterns become more variable (Leemans and Solomon, 1993, Collins et al, 2011). Tools to predict how a changing climate might alter species distributions have been applied widely in studies of biogeography, ecology, and conservation biology and for species in both natural and cultivated systems Inter alia, this information has helped to suggest how habitat suitability may be altered (Bunn et al, 2015, Dyderski et al, 2018), the risks posed by invasive species (Paini et al, 2016, Petitpierre et al, 2016) and where conservation efforts may experience conflict with changing land uses, including agricultural production (Hannah et al, 2013). The reliability of these predictions has bearing on measures taken to limit biodiversity loss, ensure food security and maintain the ecosystem functions upon which human society depends
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