From animal tracks to fine-scale movement modes: a straightforward approach for identifying multiple spatial movement patterns

1. Thanks to developments in animal tracking technology, detailed data on the movement tracks of individual animals are now attainable for many species. However, straightforward methods to decompose individual tracks into high-resolution, spatial modes are lacking but are essential to understand what an animal is doing. 2. We developed an analytical approach that combines separately validated methods into a straightforward tool for converting animal GPS tracks into short-range movement modes. Our three-step analytical process comprises: (i) decomposing data into separate movement segments using behavioural change point analysis; (ii) defining candidate movement modes and translating them into nonlinear or linear equations between net squared displacement (NSD) and time and (iii) fitting each candidate equationto NSD segments and determining the best-fitting modes using Concordance Criteria, Akaike's Information Criteria and other fine-scale segment characteristics. We illustrate our approach for three sub-adults, male wild boar Sus scrofa tracked at 15-min intervals over 4months using GPS collars. We defined five candidate movement modes based on previously published studies of short-term movements: encamped, ranging, round trips (complete and partial) and wandering. 3. Our approach successfully classified over 80% of the tracks into these movement modes lasting between 5 and 54h and covering between 300m to 20km. Repeated analyses of GPS data resampled at different rates indicated that one positional fix every 3-4h was sufficient for >70% classification success. Classified modes were consistent with published observations of wild boar movement, further validating our method. 4. The proposed approach advances the status quo by permitting classification into multiple movement modes (where these are adequately discernable from spatial fixes) facilitating analyses at high temporal and spatial resolutions, and is straightforward, largely objective, and without restrictive assumptions, necessary parameterizations or visual interpretation. Thus, it should capture the complexity and variability of tracked animal movement mode for a variety of taxa across a wide range of spatial and temporal scales.