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

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

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