Abstract
AbstractAimOur aim involved developing a method to analyse 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 analyse whether their distributions have shifted along with environmental changes.LocationArctic shelf area. The Kara Sea.MethodsOur 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 analyse 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.ResultsThe 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‐year‐long study period, whereas seals shifted their distribution from the Eastern to the Western Kara Sea.Main conclusionsPoint 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 in AMMs’ distributions in the marginal Arctic region.
Highlights
The decline of the sea ice has changed the Arctic landscape and the habitats of the marine species in the Arctic marginal seas (Durner et al, 2009; Gaston, Gilchrist, & Hipfner, 2005; Laidre et al, 2015)
We modelled the effect of seals’ relative density on polar bears’ log relative density with a Michaelis-Menten function, f(xs,t) = axs,t/(b + xs,t), which is commonly used in ecology for responses that first increase or decrease and saturate
Even though the effects of the relative density of seals and ice concentration cannot be fully disentangled, as seals are heavily dependent on ice, the results indicate that the relative density of seals has a clear positive effect on the relative density of polar bears
Summary
The decline of the sea ice has changed the Arctic landscape and the habitats of the marine species in the Arctic marginal seas (Durner et al, 2009; Gaston, Gilchrist, & Hipfner, 2005; Laidre et al, 2015). Accurate information on species’ distributions helps to assess species’ vulnerability to changes in their habitats (Laidre et al, 2008) and to prevent their exposure to human caused hazards (Helle, Jolma, & Venesjärvi, 2016). By inferring how habitat characteristics (sea ice, depth, distance to the coast and hydrography) correlate with the abundances of AMMs, we can predict the distributions of AMMs. The predictions can be utilized in planning conservation actions and in assessing the risks of different species-human interactions (Wilson, Regehr et al, 2017; Wilson, Trukhanov et al, 2017), MÄKINEN and VANHATALO whereas habitat utilization functions can underpin spatially explicit demographic analysis for better population assessments (Kearney & Porter, 2009; Lunn et al, 2016). In areas with high survey costs and little designed survey data, flawed population assessments may lead to improper conservation or harvest actions (Regehr, Wilson, Rode, Runge, & Stern, 2017)
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