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USGS2026

Appendix 6: Overview of iPOM 2.0: Updates to wolf abundance estimation in Montana

Montana Fish, Wildlife and Parks (MFWP) uses the integrated Patch Occupancy Model (iPOM) to estimate statewide and regional wolf abundance. During the early years of recovery, wolf abundance was tracked using minimum counts. As the population increased, limitations of this method became apparent, motivating development of a model-based approach for estimating abundance. iPOM development began in 2006, was operationalized in 2013, and has been used in its current form (iPOM 1.0) since 2021. Since its inception, iPOM has been developed and presented as an adaptive modeling framework intended to evolve as new data and analytical methods become available1. Here, we briefly present ongoing work to produce the next iteration of iPOM, hereafter iPOM 2.0, and summarize major model updates and resulting preliminary wolf abundance estimates. iPOM uses models for occupancy, territory size, and pack size, along with monitoring data collected by MFWP wolf specialists and potential wolf sightings reported by deer and elk hunters during the 5-week general rifle season each Nov-Dec. These monitoring data, wolf sightings, and covariates for habitat and survey effort are used to model occupancy of packs on a statewide grid of 600-km 2 cells. The area used by wolves is then estimated as each cell’s occupancy estimate multiplied by grid cell area. iPOM estimates pack abundance by dividing this area by predicted territory sizes, and in-pack wolf abundance by multiplying pack abundance by estimated group size. This is combined with a lone wolf rate to estimate total wolf abundance (see Sells et al. 1 for a full description of iPOM 1.0). This technical summary provides a high-level overview of the proposed iPOM updates and their implications for wolf abundance estimates. The estimates presented here represent results as of June 2026 and are provided to inform upcoming discussions surrounding harvest management and regulations (e.g., season dates, quotas, methods). Results are preliminary and remain subject to final model refinement and peer review.

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USGS2024

Wolf harvest management strategy evaluation: Annual Report, 2024

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.

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