Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns

Increased availability of high-resolution movement data has led to the development of numerous methods for studying changes in animal movement behavior. Path segmentation methods provide basics for detecting movement changes and the behavioral mechanisms driving them. However, available path segmentation methods differ vastly with respect to underlying statistical assumptions and output produced. Consequently, it is currently difficult for researchers new to path segmentation to gain an overview of the different methods, and choose one that is appropriate for their data and research questions. Here, we provide an overview of different methods for segmenting movement paths according to potential changes in underlying behavior. To structure our overview, we outline three broad types of research questions that are commonly addressed through path segmentation: 1) the quantitative description of movement patterns, 2) the detection of significant change-points, and 3) the identification of underlying processes or ‘hidden states’. We discuss advantages and limitations of different approaches for addressing these research questions using path-level movement data, and present general guidelines for choosing methods based on data characteristics and questions. Our overview illustrates the large diversity of available path segmentation approaches, highlights the need for studies that compare the utility of different methods, and identifies opportunities for future developments in path-level data analysis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40462-016-0086-5) contains supplementary material, which is available to authorized users

Assessing differences in connectivity based on habitat versus movement models for brown bears in the Carpathians

Context. Connectivity assessments typically rely on resistance surfaces derived from habitat models, assuming that higher-quality habitat facilitates movement. This assumption remains largely untested though, and it is unlikely that the same environmental factors determine both animal movements and habitat selection, potentially biasing connectivity assessments. Objectives. We evaluated how much connectivity assessments differ when based on resistance surfaces from habitat versus movement models. In addition, we tested how sensitive connectivity assessments are with respect to the parameterization of the movement models. Methods. We parameterized maximum entropy models to predict habitat suitability, and step selection functions to derive movement models for brown bear (Ursus arctos) in the northeastern Carpathians. We compared spatial patterns and distributions of resistance values derived from those models, and locations and characteristics of potential movement corridors. Results. Brown bears preferred areas with high forest cover, close to forest edges, high topographic complexity, and with low human pressure in both habitat and movement models. However, resistance surfaces derived from the habitat models based on predictors measured at broad and medium scales tended to underestimate connectivity, as they predicted substantially higher resistance values for most of the study area, including corridors. Conclusions. Our findings highlighted that connectivity assessments should be based on movement information if available, rather than generic habitat models. However, the parameterization of movement models is important, because the type of movement events considered, and the sampling method of environmental covariates can greatly affect connectivity assessments, and hence the predicted corridors