Improving Peary Caribou Presence Predictions in MaxEntUsing Spatialized Snow Simulations

In this article, the researchers 1) investigate whether a snow model adapted for the Antarctic (SNOWPACK) can produce snow simulations relevant to Canadian High  Arctic  conditions,  and  2)  test  snow  model  outputs  to  determine  their  utility  in  predicting  Peary  caribou  occurrence  with MaxEnt modelling software. They model Peary caribou occurrence across three seasons: July–October (summer forage and rut), November–March (fall movement and winter forage), and April–June (spring movement and calving). Results of snow simulations using the Antarctic SNOWPACK model demonstrated that both top and bottom density values were greatly improved when compared to simulations using the original version developed for alpine conditions. Results were also more consistent with field measurements using the Antarctic model, though it underestimated the top layer density compared to on-site measurements. Modelled outputs including snow depth and CT350 (cumulative thickness of snow layers surpassing the critical density value of 350 kg·m-3; a density threshold relevant to caribou) proved to be important predictors of Peary caribou space use in each of the top seasonal models along with vegetation and elevation. All seasonal models were robust in that they were able to predict reasonably well the occurrence of Peary caribou outside the period used to develop the models. This work highlights the need for continued monitoring of snow conditions with climate change to understand potential impacts to the species’ distribution.