Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth

Abstract

AbstractAimMuseum and herbarium specimen records are frequently used to assess the conservation status of species and their responses to climate change. Typically, occurrences with imprecise geolocality information are discarded because they cannot be matched confidently to environmental conditions and are thus expected to increase uncertainty in downstream analyses. However, using only precisely georeferenced records risks undersampling of the environmental and geographical distributions of species. We present two related methods to allow the use of imprecisely georeferenced occurrences in biogeographical analysis.InnovationOur two procedures assign imprecise records to the (1) locations or (2) climates that are closest to the geographical or environmental centroid of the precise records of a species. For virtual species, including imprecise records alongside precise records improved the accuracy of ecological niche models projected to the present and the future, especially for species with c. 20 or fewer precise occurrences. Using only precise records underestimated loss of suitable habitat and overestimated the amount of suitable habitat in both the present and the future. Including imprecise records also improves estimates of niche breadth and extent of occurrence. An analysis of 44 species of North American Asclepias (Apocynaceae) yielded similar results.Main conclusionsExisting studies examining the effects of spatial imprecision typically compare outcomes based on precise records against the same records with spatial error added to them. However, in real‐world cases, analysts possess a mix of precise and imprecise records and must decide whether to retain or discard the latter. Discarding imprecise records can undersample the geographical and environmental distributions of species and lead to mis‐estimation of responses to past and future climate change. Our method, for which we provide a software implementation in the enmSdmX package for R, is simple to use and can help leverage the large number of specimen records that are typically deemed “unusable” because of spatial imprecision in their geolocation.