Open AccessImproved spatial estimates of climate predict patchier species distributions
Author(s) -
Storlie C. J.,
Phillips B. L.,
VanDerWal J. J.,
Williams S. E.
Publication year - 2013
Publication title -
diversity and distributions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.918
H-Index - 118
eISSN - 1472-4642
pISSN - 1366-9516
DOI - 10.1111/ddi.12068
Subject(s) - spatial distribution , environmental science , spatial ecology , climate change , climatology , climate model , spatial analysis , species distribution , geography , ecology , geology , habitat , remote sensing , biology
Aim Correlative species distribution models ( SDM s) combined with spatial layers of climate and species' localities represent a frequently utilized and rapid method for generating spatial estimates of species distributions. However, an SDM is only as accurate as the inputs upon which it is based. Current best‐practice climate layers commonly utilized in SDM (e.g. ANUCLIM ) are frequently inaccurate and biased spatially. Here, we statistically downscale 30 years of existing spatial weather estimates against empirical weather data and spatial layers of topography and vegetation to produce highly accurate spatial layers of weather. We proceed to demonstrate the effect of inaccurately quantified spatial data on SDM outcomes. Location The Australian Wet Tropics. Methods We use B oosted R egression T rees ( BRT s) to generate 30 years of spatial estimates of daily maximum and minimum temperature for the study region and aggregate the resultant weather layers into ‘accu CLIM ’ climate summaries, comparable with those generated by current best‐practice climate layers. We proceed to generate for seven species of rainforest skink comparable SDM s within species; one model based on ANUCLIM climate estimates and another based on accu CLIM climate estimates. Results Boosted Regression Trees weather layers are more accurate with respect to empirically measured temperature, particularly for maximum temperature, when compared to current best‐practice weather layers. ANUCLIM climate layers are least accurate in heavily forested upland regions, frequently over‐predicting empirical mean maximum temperature by as much as 7°. Distributions of the focal species as predicted by accu CLIM were more fragmented and contained less core distributional area. Conclusion Combined these results reveal a source of bias in climate‐based SDM s and indicate a solution in the form of statistical downscaling. This technique will allow researchers to produce fine‐grained, ground‐truthed spatial estimates of weather based on existing estimates, which can be aggregated in novel ways, and applied to correlative or process‐based modelling techniques.