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A key assumption in species distribution modeling (SDM) with presence‐background (PB) methods is that sampling of occurrence localities is unbiased and that any sampling bias is proportional to the background distribution of environmental covariates. This assumption is rarely met when SDM practitioners rely on federated museum records from natural history collections for geo‐located occurrences due to inherent sampling bias found in these collections. We use a simulation approach to explore the effectiveness of three methods developed to account for sampling bias in SDM with PB frameworks. Two of the methods rely on careful filtering of observation data—geographic thinning (G‐Filter) and environmental thinning (E‐Filter)—while a third, FactorBiasOut, creates selection weights for background data to bias locations toward areas where the observation dataset was sampled. While these methods have been assessed previously, evaluation has emphasized spatial predictions of habitat potential. Here, we dig deeper into the effectiveness of these methods by exploring how sampling bias not only affects predictions of habitat potential, but also our understanding of niche characteristics such as which explanatory variables and response curves best represent species–environment relationships. We simulate 100 virtual species ranging from generalist to specialist in their habitat preferences and introduce geographic and environmental bias at three intensity levels to measure the effectiveness of each correction method to (1) predict true probability of occurrence across a study area, (2) recover true species–environment relationships, and (3) identify true explanatory variables. We find that the FactorBiasOut most often showed the greatest improvement in recreating known distributions but did no better at correctly identifying environmental covariates or recreating species–environment relationships than G‐Filter or E‐Filter methods. Narrow niche species are most problematic for biased calibration datasets, such that correction methods can, in some cases, make predictions worse.

AIMS: To investigate spatial congruence between ecological niches and genotype in two allopatric species of desert tortoise that are species of conservation concern. LOCATION: Mojave and Sonoran Desert ecoregions; California, Nevada, Arizona, Utah, USA. METHODS: We compare ecological niches of Gopherus agassizii and Gopherus morafkai using species distribution modelling (SDM) and then calibrate a pooled‐taxa distribution model to explore local differences in species–environment relationships based on the spatial residuals of the pooled‐taxa model. We use multiscale geographically weighted regression (MGWR) applied to those residuals to estimate local species–environment relationships that can vary across the landscape. We identify multivariate clusters in these local species–environment relationships and compare them against models of (a) a geographically based taxonomic designation for two sister species and (b) an environmental ecoregion designation, with respect to their ability to predict a genotype association index for these two species. RESULTS: We find non‐identical niches for these species, with differences that span physiographic and vegetation niche dimensions. We find evidence for two distinct clusters of local species–environment relationships that when mapped, predict an index of genotype association for the two sister taxa better than did either the geographically based taxonomic designation or an environmental ecoregion designation. MAIN CONCLUSIONS: Exploring local species–environment relationships by coupling SDM and MGWR can benefit studies of biogeography and conservation. We find that niche separation in habitat selection conforms to genotypic differences between sister taxa of tortoise in a recent secondary contact zone. This result may inform decision making by agencies with regulatory or land management authority for the two sister taxa addressed here.

The distribution and abundance of human-caused disturbances vary greatly through space and time and are cause for concern among land stewards in natural areas of the southwestern border-lands between the USA and Mexico. Human migration and border protection along the international boundary create Unauthorized Trail and Road (UTR) networks across National Park Service lands and other natural areas. UTRs may cause soil erosion and compaction, damage to vegetation and cultural resources, and may stress wildlife or impede their movements. We quantify the density and severity of UTR disturbances in relation to soils, and compare the use of previously established targeted trail assessments (hereafter — targeted assessments) against randomly placed transects to detect trail densities at Coronado National Memorial in Arizona in 2011. While trail distributions were similar between methods, targeted assessments estimated a large portion of the park to have the lowest density category (0–5 trail encounters...