Abstract
Citizen‐science databases have been used to develop species distribution models (SDMs), although many taxa may be only georeferenced to county. It is tacitly assumed that SDMs built from county‐scale data should be less precise than those built with more accurate localities, but the extent of the bias is currently unknown. Our aims in this study were to illustrate the effects of using county‐scale data on the spatial extent and accuracy of SDMs relative to true locality data and to compare potential compensatory methods (including increased sample size and using overall county environmental averages rather than point locality environmental data). To do so, we developed SDMs in maxent with PRISM‐derived BIOCLIM parameters for 283 and 230 species of odonates (dragonflies and damselflies) and butterflies, respectively, for five subsets from the OdonataCentral and Butterflies and Moths of North America citizen‐science databases: (1) a true locality dataset, (2) a corresponding sister dataset of county‐centroid coordinates, (3) a dataset where the average environmental conditions within each county were assigned to each record, (4) a 50/50% mix of true localities and county‐centroid coordinates, and (5) a 50/50% mix of true localities and records assigned the average environmental conditions within each county. These mixtures allowed us to quantify the degree of bias from county‐scale data. Models developed with county centroids overpredicted the extent of suitable habitat by 15% on average compared to true locality models, although larger sample sizes (>100 locality records) reduced this disparity. Assigning county‐averaged environmental conditions did not offer consistent improvement, however. Because county‐level data are of limited value for developing SDMs except for species that are widespread and well collected or that inhabit regions where small, climatically uniform counties predominate, three means of encouraging more accurate georeferencing in citizen‐science databases are provided.
Highlights
Species distribution models (SDMs) map the geographic distribution of empirically defined suitable environmental space for species of interest and as such are valuable tools in conservation (Franklin, 2009)
We asked whether any techniques could be employed that would allow county-scale data to generate species distribution models that were of comparable accuracy to those built with true localities; we examined whether using more samples or using county-scale environmental averages improved model performance
Distribution models built by taking county-wide environmental averages were compared to those built with county-centroid environmental values, and combinations of these
Summary
Species distribution models (SDMs) map the geographic distribution of empirically defined suitable environmental space for species of interest and as such are valuable tools in conservation (Franklin, 2009). For county-scale data that are common in citizen-science databases, the issue becomes whether to assign a coordinate, such as at a county’s centroid, at which to extract background environmental information, or to take an average value over the range of environmental conditions present at the scale (county) being used It is currently unknown how SDMs are affected by such coarsely scaled county data, and whether averaging approaches or increasing sample size can overcome the inherent limitations in such data. We asked whether any techniques could be employed that would allow county-scale data to generate species distribution models that were of comparable accuracy to those built with true localities; we examined whether using more samples or using county-scale environmental averages improved model performance
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