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In the field of biological conservation, mathematical modeling has been an indispensable tool to advance our understanding of population dynamics. Modeling rare and endangered species with complex ecophysiological tools can be challenging due to the constraints imposed by data availability. One strategy to overcome the mismatch between what we are trying to learn from a modeling exercise and the available empirical knowledge is to develop statistical models that tend to be more parsimonious. In the present study, we introduce a spatially explicit modeling framework to examine the strength and nature of the relationships of snow density and vegetation abundance with Peary caribou (Rangifer tarandus pearyi) populations. Peary caribou are vital to the livelihood and culture of High Arctic Inuit communities, but changing climatic conditions and anthropogenic disturbances may affect the integrity of this endemic species population. Owing to an estimated decline of over 35% during the last three generations, a recent assessment by the Committee on the Status of Endangered Wildlife in Canada assigned a Threatened status to Peary caribou in 2015. Recognizing the uncertainty typically associated with the selection of the best subset of explanatory variables and their optimal functional relationship with the response variable, we examined four models across six island complexes (Banks, Axel Heiberg, Melville, Bathurst, Mackenzie King, and Boothia) of the Arctic Archipelago and formulated two ensembles to synthesize their predictions into averaged Peary caribou population distributions. Our analysis showed that an ensemble strategy with region‐specific weights displayed the highest performance and most balanced error across the six island complexes. The causal linkages between snow, vegetation abundance, and Peary caribou did manifest themselves with the models examined, but the noise‐to‐signal ratios of the corresponding regression coefficients were generally high and there were instances where they were not discernible from zero. We also present a sensitivity analysis exercise that elucidates the influence of the observation/imputation errors on the model‐training phase, thereby highlighting the importance of assigning realistic error estimates that will not hamper the identification of important cause–effect relationships. Our study identifies critical augmentations of the available scientific knowledge that necessitate to design the optimal management actions of Peary caribou populations across the Canadian Arctic Archipelago.

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