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

Morelle K., Bunnefeld N., Lejeune P., Oswald S.A. [2017] From animal tracks to fine-scale movement modes: a straightforward approach for identifying multiple spatial movement patterns. Methods in Ecology and Evolution. doi: 10.1111/2041-210X.12787

• 1. Thanks to developments in animal tracking technology, detailed data on the movement tracks of individual
  animals are now attainable for many species. However, straightforwardmethods to decompose individual tracks
  into high-resolution, spatialmodes 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 separatemovement 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 equation to 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 4 months using GPS collars. We defined five candidate movement modes based on previously published studies of short-termmovements: encamped, ranging, round trips (complete and partial) and wandering.
• 3. Our approach successfully classified over 80% of the tracks into thesemovement modes lasting between 5 and 54 h and covering between 300 m to 20 km. Repeated analyses of GPS data resampled at different rates indicated that one positional fix every 3–4 h 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 fromspatial 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.

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