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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.

Montana↗

USGS2019

Brood size affects future reproduction in a long-lived bird with precocial young

Estimation of trade-offs between current reproduction and future survival and fecundity of long-lived vertebrates is essential to understanding factors that shape optimal reproductive investment. Black brant geese (Branta bernicla nigricans) fledge more goslings, on average, when their broods are experimentally enlarged to be greater than the most common clutch size of four eggs. Thus, we hypothesized that the lesser frequency of brant clutches exceeding four eggs results, at least partially, from a future reduction in survival, breeding probability, or clutch size for females tending larger broods. We used an eight-year mark-recapture dataset (Barker robust design) with five years of clutch and brood manipulations to estimate long-term consequences of reproductive decisions in brant. We did not find evidence of a trade-off between reproductive effort and true survival or future initiation date and clutch size. Rather, future breeding probability was maximized (0.92 ± 0.03 [se]) for manipulated females tending broods of four goslings and lower for females tending smaller (one gosling; 0.63 ± 0.09 [se]) or larger broods (seven goslings; 0.52 ± 0.15 [se]). Our results suggest that demographic trade-offs for female brant tending large broods may reduce the fitness value of clutches larger than four and, therefore, contribute to the paucity of larger clutches. The lack of a trade-off between reproductive effort and survival provides evidence that survival, to which fitness is most sensitive in long-lived animals, is buffered against temporal variation in brant.

American Naturalist↗