Adaptive Harvest Management and Estimation of Recruitment for Wolves in Montana

PhD Dissertation, Allison Keever

Harvest is an important tool for managing wolf (Canis lupus) populations in Montana and Idaho. Harvest regulations are set to reach a desired population size or objective. To be successful in this endeavor requires that 1) the current population size is known and 2) the effects of harvest on the population can be accurately predicted. Both of these requirements, however, can be difficult to achieve. Monitoring could be used to determine current population size, and an understanding of population dynamics and a population model could be used to predict the effects of harvest. For wolves, however, monitoring can be challenging and the effects of harvest on wolf populations are still debated.

Montana estimates wolf abundance using patch occupancy models (POM). POM uses hunter observations of wolves and known locations of collared wolves to determine the area occupied by wolves and ultimately, wolf abundance. These estimates, however, are contingent on accurate estimates of territory and pack size which are likely affected by harvest. Accuracy of abundance estimates and the effects of harvest on wolf territory and group size is the focus of ongoing research at MTCWRU.

Predicting the effects of harvest on wolf populations is challenging because there is ecological uncertainty about wolf population dynamics. Population dynamics are simply the change in population size through time, and is primarily a function of births, immigration, deaths, and emigration. These processes can be explicitly expressed in the form of a population model that predicts future population size based on current population size, survival, and recruitment. Montana determines the number of breeding pairs (a male and female wolf with 2 surviving pups by December 31) via direct counts as a proxy for recruitment. The breeding pair metric, however, is an ineffective measure of recruitment, as it gives little insight into population growth rate or the level of harvest that could be sustained. One of my objectives is to develop an approach to estimate recruitment that is more tractable, cost effective, and biologically credible than the breeding pair metric.

Harvest decisions for wolves are further hindered by poor understanding of the effects of harvest on the wolf population. Not only is there a lot of variation in the reported levels of harvest that a wolf population can sustain, but there is also debate about whether harvest mortality is compensated for by an increase in survival or recruitment. Given uncertainty in wolf population dynamics and the effects of harvest on those dynamics, it is difficult to make informed harvest decisions. An adaptive harvest management (AHM) model for wolves could help guide harvest decisions in an adaptive framework, which would allow the formal assessment of harvest regimes in meeting objectives and determination of underlying biological processes. My second objective is to develop an AHM model for wolves in Montana and Idaho to help guide harvest decisions while learning about the effects of harvest on wolves via management and monitoring. This will not only be a useful tool for managers to guide harvest decisions for wolves, but also provide a means to learn about basic biological processes and improve decision making over time.