Correction of location errors for presence‐only species distribution models

Summary Species distribution models ( SDM s) for presence‐only data depend on accurate and precise measurements of geographical and environmental covariates that influence presence and abundance of the species. Some data sets, however, may contain both systematic and random errors in the recorded location of the species. Environmental covariates at the recorded location may differ from those at the true location and result in biased parameter estimates and predictions from SDM s. Regression calibration is a well‐developed statistical method that can be used to correct the bias in estimated coefficients and predictions from SDM s when the recorded geographical location differs from the true location for some, but not all locations. We expand the application of regression calibration methods to SDM s and provide illustrative examples using simulated data and opportunistic records of whooping cranes ( Grus americana ). We found we were able to successfully correct the bias in our SDM parameters estimated from simulated data and opportunistic records of whooping cranes using regression calibration. When modelling species distributions with data that have geographical location errors, we recommend researchers consider the effect of location errors. Correcting for location errors requires that at least a portion of the data have locations recorded without error. Bias correction can result in an increase in variance; this increase in variance should be considered when evaluating the utility of bias correction.