Wildlife science inclusive of local priorities and knowledge co-production: moose habitat selection in the Adapted Forestry Regime of Eeyou Istchee, Northern Quebec

Inclusion of Indigenous knowledge about wildlife populations and their habitats can inform wildlife research, while also increasing local engagement and support for wildlife conservation decisions. Boreal forest land use and forestry practices have direct and indirect impacts on ecosystems and Indigenous communities. Eeyou Istchee, the Cree traditional territory in Northern Quebec, Canada, includes areas that are significantly impacted by forestry activities. Concerns have been raised about the impact of these forestry activities on moose, a wildlife species that is vitally important to Cree culture and food security. The Adapted Forestry Regime (AFR) was enacted in 2002 to better integrate Cree concerns and community participation in forestry practices and management. Included within this regime was the identification of Sites of Special Wildlife Interest to the Cree (25% areas), where forestry would be specially managed to reduce negative impacts of logging on wildlife, including moose. In this thesis I contribute a systematic review of the methods, successes, and limitations defining past attempts at experiential wildlife knowledge inclusion and through a case study evaluating the effects of an adapted forest regime on moose habitat selection informed by Cree knowledge. Chapter 1 presents a systematic review of methods reported in peer-reviewed literature to interweave local, expert, and Indigenous knowledge into quantitative modeling in wildlife analyses. This kind of knowledge interweaving can help to increase applicability, trust, and equity in wildlife science and management while also potentially increasing accuracy and transferability. We reviewed 49 articles and reported on the methodologies employed in knowledge holder selection, their stages of involvement, knowledge elicitation, modeling processes, bias and uncertainty management, and validation. We conclude with six key identified benefits, limitations, and recommended improvements for future analyses that interweave knowledge into quantitative science.Chapter 2 assesses moose habitat selection in the AFR informed by Cree expert knowledge retrieved from semi-structured interviews in the form of habitat relationships that were used to determine the variables explored in the model; land cover, elevation, distance to water, road density, and 25% areas were chosen for analysis based on recurring topics brought up by Cree experts that aligned with available data. We performed home range analysis, Generalized Linear Model analysis to assess habitat selection, and Resource Selection Function analyses to assess how moose used habitat features relative to availability. We ran models for mid-summer and mid-winter for 38 female moose fitted with GPS collars. Moose selected for 25% areas in both seasons. In summer, moose selected small islands, thinned forests (regenerating stands after forestry disturbance that have had brush cutting recently performed), coniferous forest with fir, and flood zones, while in winter moose selected mixedwood and deciduous forest. In both seasons, moose selected midland and upland terrain while avoiding lowlands. Moose tended to use sites regenerating post-forestry either similarly to, or more than sites regenerating from natural disturbance, although selection was less than for preferred intact stands. Through these analyses, I provide the first assessment of moose use of the 25% areas and quantify use of logged stands in the AFR, informed by and reflective of Cree Knowledge, highlighting the importance of a multi-season and multi-knowledge approach to assess the influence of an adapted forestry regime on the evolution of moose habitat quality. By illustrating how Cree knowledge can inform a quantitative analysis of moose habitat selection related to a local knowledge priority, this thesis represents a step towards a knowledge co-production approach that can improve the credibility, saliency, and legitimacy of research findings

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