Accounting for inter‐annual variability alters long‐term estimates of climate suitability

Aim Species respond to environmental conditions and so reliable assessments of climate suitability are important for predicting how change could alter their distributions. Long-term average data often used evaluate the an area, but in these aggregated datasets, inter-annual variability is lost. Due non-linearity species’ biological responses climate, estimates long-term from may be biased differ derived annual over same period (average response). We investigate extent which such differences manifest a regional assessment 255 plant species across two 17-year time periods. Location Cornwall South-West England provides case study. Taxon Plantae. Methods run simple mechanistic model derive quantitative 1984–2000 2001–2017. For each period, we using representing monthly values that period. then year scores compare approaches. Results Average gave higher than response, suggesting bias against years poor temporally datasets. Differences between were larger areas high correlated with requirements, being small thermal niches narrow ranges precipitation tolerance. Main Conclusions Incorporating into or understanding might obscure this variance will predict reliably impacts on distributions should considered when distribution models.