Data from: Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals

• Main Authors: Mäkinen, Jussi, Vanhatalo, Jarno
• Format: Other/Unknown Material
• Language: unknown
• Published: Zenodo 2019
• Subjects: hierarchical Bayesian modelling; Phoca hispida; Arctic marine mammals; Odobenus rosmarus rosmarus; Poisson point process; extensive transect survey; data integration; Ursus maritimus; Kara Sea; Odobenus rosmarus; walrus*
• Online Access: https://doi.org/10.5061/dryad.22c867m

Aim: Our aim involved developing a method to analyze spatiotemporal distributions of Arctic marine mammals (AMMs) using heterogeneous open source data, such as scientific papers and open repositories. Another aim was to quantitatively estimate the effects of environmental covariates on AMMs' distributions and to analyze whether their distributions have shifted along with environmental changes. Location: Arctic shelf area. The Kara Sea. Methods: Our literature search focused on survey data regarding polar bears (Ursus maritimus), Atlantic walruses (Odobenus rosmarus rosmarus) and ringed seals (Phoca hispida). We mapped the data on a grid and built a hierarchical Poisson point process model to analyze species' densities. The heterogeneous data lacked information on survey intensity and we could model only the relative density of each species. We explained relative densities with environmental covariates and random effects reflecting excess spatiotemporal variation and the unknown, varying sampling effort. The relative density of polar bears was explained also by the relative density of seals. Results: The most important covariates explaining AMMs' relative densities were ice concentration and distance to the coast, and regarding polar bears, also the relative density of seals. The results suggest that due to the decrease in the average ice concentration, the relative densities of polar bears and walruses slightly decreased or stayed constant during the 17-yearlong study period, whereas seals shifted their distribution from the Eastern to the Western Kara Sea. Main conclusions: Point process modelling is a robust methodology to estimate distributions from heterogeneous observations, providing spatially explicit information about ecosystems and thus serves advances for conservation efforts in the Arctic. In a simple trophic system, a distribution model of a top predator benefits from utilizing prey species' distributions compared to a solely environmental model. The decreasing ice cover seems to have led to changes ...