Advances in tracking technology in the past decades strongly enhanced our ability to track animal movements, and generated an ever increasing body of literature which gave birth to the field of Movement Ecology. The level of statistical analysis in ecology journals is considerable higher nowadays, developing and applying numerous models for animal movement data, particularly to identify behavioral patterns through movement; e.g. independent mixture models, state-space models and correlated random walk models to name but a few. Movement models are usually introduced to the community as reproducible (i.e. with the same data, shared in the paper, the same results should be expected) and replicable (i.e. the method could be used for similar questions but different data). With a variety of models to study animal movement, we revisited the most popular methods used to identify behavioral patterns in animal movement. We selected frequently cited papers applying these methods, for which the data was publicly available. We then assessed the correctness of the statistical models (e.g. the data was in the right format, the assumptions were verified and respected), the reproducibility of the results, and ultimately the appropriateness of the method for the ecological questions tackled in these studies.
For this poster, we present results from seven papers that were suitable for reproducibility review. We attempted to obtain study data from online repositories or by directly contacting the authors. Of the six papers for which we were able to get the data, only one one them was fully reproducible. Moreover, in some cases where authors mentioned making data available upon request, there were no response from the authors despite several contact attempts. There is a general need to improve communication among wildlife professionals. Although additional training in data management and documentation would certainly help advance reproducibility, it may not be enough to resolve the reproducibility crisis without a stronger commitment to data sharing.