Screening Radiolocation Datasets for Movement Strategies With Time Series Segmentation

  Classical home range analysis is tailored to meet requirements of data with few points per individual with relatively large intervals between observations. The swift rise in Global Positioning System (GPS)‐based studies requires the development of new analytical approaches because GPS data allow for more detailed analysis in time and space. The amount of data derived from GPS studies enhances the potential to more accurately separate movement strategies. We present a general, simple, conceptual approach to using large movement datasets to automatically screen and delimit spatial and temporal home ranges of individuals and movement strategies using time series segmentation. We used GPS data for moose ( Alces alces ) from a boreal Swedish population as an example. We tested predictions that our screening method could separate seasonal migration from dispersal and nomadic strategies by the movement profile, which includes several dimensions. Our analysis showed that broad strategies were detected using our simple analytical approach, which speeds up use of GPS data for management and research because the method can be used to calculate more objective spatial and temporal activity ranges in relation to movement strategies. Our examples illustrate the importance of using the time stamp on location data in describing home ranges and movements.