Leveraging location data of variable quality to reconstruct animal movements: application to a reintroduced island fox (Urocyon littoralis) population

Abstract Background: Despite significant advances in statistical approaches and data collection for analyzing wildlife movements over the last 50 years, there are limited analytical frameworks to be applied when spatial data are collected for purposes other than analyzing movement. Data collected for other purposes (e.g., sporadic captures or survival checks via telemetry) generally have lower temporal frequency or spatial precision than data collected to analyze fine-scale animal movement. The coarseness of the former renders them poorly suited for analysis using existing statistical tools. Methods: We propose a new way of estimating animal movement trajectories by integrating variable quality location data – including frequent but spatially coarse, irregularly-shaped polygon location data arising from VHF telemetry as well as less frequent, more spatially precise location data – using functional data analysis combined with a spatial resampling algorithm. We apply this method to analyze location data from the reintroduced Channel Island fox (Urocyon littoralis) subspecies population on Santa Rosa Island, California, which were collected from 2003-2012 for purposes other than movement analyses but provide an ideal case study to develop and test these novel methods. Results: By combining coarse-grained location knowledge, obtained through field notes and expert interpretation, with more precise location data, we reconstructed individual animal movement trajectories and demonstrated the utility of combining such data. Through the population ensemble of reconstructed trajectories, we learned that captive-born Channel Island foxes exhibited stronger seasonal movements than wild-born foxes, and most long-range movements occurred within the first two years of a fox’s time on the island. Conclusions: This methodology capitalizes on frequently overlooked but valuable spatial data, often found in field notes and expert knowledge, to reconstruct animal movements. Our approach has wide application to systems in which data of variable quality are collected for purposes other than studying movement and could have beneficial applications in species conservation, landscape ecology, disease management, and population monitoring.