Optimizing monitoring of harvested moose (Alces alces) in Ontario, Canada

Monitoring of widely distributed wildlife species across multiple discrete management units presents challenges for the optimal allocation of monitoring effort. By balancing value in new information gained through monitoring with costs, monitoring effort can be optimally allocated to maximize benefit to wildlife management. The main research objective of this thesis was to identify factors affecting the optimal allocation of monitoring effort for moose (Alces alces) across multiple Wildlife Management Units (WMUs) in Ontario, Canada that have variable moose population densities and dynamics. Moose are a harvested species across their range in North America and require monitoring to ensure sustainable harvest and that population management objectives are met. The main approaches used to monitor moose in the study area included aerial surveys and hunter harvest information, and I used both sources of data collected by the Ontario Ministry of Natural Resources and Forestry. In this thesis, I determined (1) the utility of harvest data as a proxy of moose population abundance under a selective harvest system; (2) the role of synergistic climate-habitat relationships in shaping spatio-temporal variation in moose population dynamics; and (3) the monitoring design that optimized the use of aerial surveys to estimate population abundance, while balancing the needs and monitoring costs of multiple discrete WMUs. My findings revealed that restricted harvest of adult moose reflected spatial variability in moose abundance better than less restricted calf harvest; but this effect was impacted by high levels of both hunter effort and landscape disturbance that can influence the detectability of moose to hunters. Further, my work revealed that moose population response to climate was variable at local (i.e. WMU) scales and was mediated or exacerbated by habitat conditions that can alter ecological links, including parasite transmission and predation. I incorporated my findings of drivers of moose population variability into population models to evaluate how prioritizing alternative management criteria, in addition to using model-based estimates to replace information-gaps, impacted WMU-specific population and trend estimates. Also incorporated in the decision framework were WMU-specific costs and annual budget constraints. I further evaluated how the utility (based on minimizing population estimate uncertainty) of using a model-based estimate rather than conducting a survey was impacted by population density, severity of environmental stressors, and years since the last survey. My results showed that interval-based monitoring and incorporating model-based estimates that accounted for previous survey uncertainty captured population trends for the highest number of units across a 10-year period. The utility of conducting a survey increased with time since the last survey and was greater for low population densities when the severity of environmental stressors (i.e. winter severity) was high, while being greater for high population densities when winter severity was low. My thesis findings can be applied to other widely distributed and harvested species that are managed and monitored using multi-unit frameworks spanning environmental gradients that contribute to variability in population uncertainty.

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