Wolf harvest season setting is complicated and controversial. State law requires Montana Fish, Wildlife and Parks (MFWP) to both reduce the wolf population and avoid federal relisting under the Endangered Species Act (Montana Fish, Wildlife and Parks, 2002). Disparate stakeholder groups each have different objectives for wolf management. For instance, big game advocates want to see improved big game populations and hunting opportunities in northwest Montana, while wolf advocates want to see regulations that minimize wolf mortality. Decision making about season setting tries to balance these objectives. Wolf hunting and trapping season decisions are made by the Montana Fish and Wildlife Commission and are informed by annual wolf abundance estimates from an integrated patch occupancy model (iPOM, Sells et al., 2022c) as well as the predictions of wolf abundance into the future under potential constant harvest levels. Parametric uncertainty (uncertainty surrounding the value of a parameter) from the iPOM estimates is propagated through to future projections, providing the Commission with plausible and worst-case outcomes of different levels of public harvest over the short term, i.e., five years into the future, on the wolf population in Montana (Parks et al., 2024).

An alternative approach to inform wolf management and harvest decisions is through adaptive management. Adaptative management is appropriate for decisions that are made iteratively and when monitoring data are collected to learn about the outcomes from decisions, where monitoring data help to reduce critical uncertainties regarding ecosystem function or management outcomes (Walters, 1986; Williams, 2011). Management strategy evaluation (MSE) is one way to develop an adaptive management framework. MSE was developed by fisheries managers and scientists to more accurately and fully incorporate various forms of uncertainty, consider long-term time horizons, and add more transparency in a fisheries context (Punt et al., 2016). It has been used routinely and has become a standard approach for complicated and contentious marine fisheries management situations, yet it has been underutilized in wildlife management (but see Bunnefeld et al., 2013, 2011).

MSE is a forward simulation approach for testing prospective management options or strategies over a wide range of possible states (Punt et al., 2016). A MSE framework captures the ‘truth’ or what is happening in the system (termed the ‘the operating model’) and the information available to the decision makers (termed ‘the estimation model’ or ‘management strategy’). More precisely, there are four main processes modeled. First, models are constructed based on current understanding and data to represent ‘truth’. Second, the collection of monitoring data is simulated from the ‘truth’ model. Third, the simulated monitoring data are fit to an estimation model and the next time step’s population metrics are predicted from the estimated parameters. Fourth, based on the estimation model results and the predictions, the decision-making process is simulated following a management strategy, whereby a decision is made and the implementation of this decision feeds back into the ‘truth’ model (Figure 1). This process continues through time. Additionally, each simulation through time is repeated to capture the full range of stochasticity and uncertainty.