Estimating ungulate migration corridors from sparse movement data

Abstract

Many ungulates migrate between distinct summer and winter ranges, and identifying, mapping, and conserving these migration corridors have become a focus of local, regional, and global conservation efforts. Brownian bridge movement models (BBMMs) are commonly used to empirically identify these seasonal migration corridors; however, they require location data sampled at relatively frequent intervals to obtain a robust estimate of an animal’s movement path. Fitting BBMMs to sparse location data violates the assumption of conditional random movement between successive locations, overestimating the area (and width) of a migration corridor when creating individual and population-level occurrence distributions, and precluding the use of low-frequency, or sparse, data in mapping migration corridors. In an effort to expand the utility of BBMMs to include sparse global positioning system (GPS) data, we propose an alternative approach to model migration corridors from sparse GPS data. We demonstrate this method using GPS data collected every 2 hours from four mule deer (Odocoileus hemionus) and four elk (Cervus canadensis) herds within Wyoming and Idaho. First, we used BBMMs to estimate a baseline corridor for the 2-hour data. We then subsampled the 2-hour data to one location every 12 hours (a proxy for sparse data) and fitted BBMMs to the 12-hour data using a fixed motion variance (FMV) value, instead of estimating the Brownian motion variance empirically. A range of FMV values was tested to identify the value that best approximated the baseline migration corridor. FMV values within a species-specific range (mule deer: 400–1,200 m2; elk: 600–1,600 m2) successfully delineated migration corridors similar to the 2-hour baseline corridors; overall, lower values delineated narrower corridors and higher values delineated wider corridors. Optimal FMV values of 800 m2 (mule deer) and 1,000 m2 (elk) decreased the inflation of the 12-hour corridors relative to the 2-hour corridors from traditional BBMMs. This FMV approach thus enables using sparse movement data to approximate realistic migration corridor dimensions, providing an important alternative when movement data are collected infrequently. This approach greatly expands the number of datasets that can be used for migration corridor mapping—a useful tool for management and conservation across the globe.