A Pragmatic Approach for Determining Otter Distribution from Disparate Occurrence Records

Opportunistic records of animal occurrence may be problematic for inferring species distribution and habitat requirements because of unknown and uncontrolled sources of sampling variance. In this study, we used occurrence records for river otters (Lontra canadensis) derived from sign surveys, road kills, trapper bycatch, and opportunistic sightings (n=185 records collected 2001–2012) to assess the potential distribution and habitat relationships of otters across central and western New York, USA. To mitigate for obvious observation biases, we standardized observation intensity across regions a priori and restricted inference to readily accessible areas (i.e., ≤700m from the nearest road). Model selection, and the direction of covariate effects, proved robust to these sampling biases although effect sizes varied −7.1% to +48.0% after bias correction, with the coefficient for the proportion of available shoreline being the most unstable. Ultimately, the top bias-corrected model proved a reliable index for otter probability of occurrence given a strong, positive, and linear relationship with a withheld set of standardized survey records for otters collected in winter 2016–2017 (n=57; R²=0.90). This model indicated that approximately 20% of the study area represented high probability of otter occurrence. We demonstrated that reliable inference on wildlife habitat requirements can be obtained from disparate records of animal occurrence provided that data biases are known and effectively mitigated.