Geology topics

Kevin M. Podruzny

Publications and source records attributed to Kevin M. Podruzny.

6 recordsLinked to original sources

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↗

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.

Montana↗

USGS2022

Economical defence of resources structures territorial space use in a cooperative carnivore

Ecologists have long sought to understand space use and mechanisms underlying patterns observed in nature. We developed an optimality landscape and mechanistic territory model to understand mechanisms driving space use and compared model predictions to empirical reality. We demonstrate our approach using grey wolves ( Canis lupus ). In the model, simulated animals selected territories to economically acquire resources by selecting patches with greatest value, accounting for benefits, costs and trade-offs of defending and using space on the optimality landscape. Our approach successfully predicted and explained first- and second-order space use of wolves, including the population's distribution, territories of individual packs, and influences of prey density, competitor density, human-caused mortality risk and seasonality. It accomplished this using simple behavioural rules and limited data to inform the optimality landscape. Results contribute evidence that economical territory selection is a mechanistic bridge between space use and animal distribution on the landscape. This approach and resulting gains in knowledge enable predicting effects of a wide range of environmental conditions, contributing to both basic ecological understanding of natural systems and conservation. We expect this approach will demonstrate applicability across diverse habitats and species, and that its foundation can help continue to advance understanding of spatial behaviour.

Montana↗

USGS2021

Evidence of economical territory selection in a cooperative carnivore

As an outcome of natural selection, animals are probably adapted to select territories economically by maximizing benefits and minimizing costs of territory ownership. Theory and empirical precedent indicate that a primary benefit of many territories is exclusive access to food resources, and primary costs of defending and using space are associated with competition, travel and mortality risk. A recently developed mechanistic model for economical territory selection provided numerous empirically testable predictions. We tested these predictions using location data from grey wolves ( Canis lupus ) in Montana, USA. As predicted, territories were smaller in areas with greater densities of prey, competitors and low-use roads, and for groups of greater size. Territory size increased before decreasing curvilinearly with greater terrain ruggedness and harvest mortalities. Our study provides evidence for the economical selection of territories as a causal mechanism underlying ecological patterns observed in a cooperative carnivore. Results demonstrate how a wide range of environmental and social conditions will influence economical behaviour and resulting space use. We expect similar responses would be observed in numerous territorial species. A mechanistic approach enables understanding how and why animals select particular territories. This knowledge can be used to enhance conservation efforts and more successfully predict effects of conservation actions.

Montana↗

USGS2013

Estimating occupancy and predicting numbers of gray wolf packs in Montana using hunter surveys

Reliable knowledge of the status and trend of carnivore populations is critical to their conservation and management. Methods for monitoring carnivores, however, are challenging to conduct across large spatial scales. In the Northern Rocky Mountains, wildlife managers need a time- and cost-efficient method for monitoring gray wolf (Canis lupus) populations. Montana Fish, Wildlife and Parks (MFWP) conducts annual telephone surveys of >50,000 deer and elk hunters. We explored how survey data on hunters' sightings of wolves could be used to estimate the occupancy and distribution of wolf packs and predict their abundance in Montana for 2007–2009. We assessed model utility by comparing our predictions to MFWP minimum known number of wolf packs. We minimized false positive detections by identifying a patch as occupied if 2–25 wolves were detected by ≥3 hunters. Overall, estimates of the occupancy and distribution of wolf packs were generally consistent with known distributions. Our predictions of the total area occupied increased from 2007 to 2009 and predicted numbers of wolf packs were approximately 1.34–1.46 times the MFWP minimum counts for each year of the survey. Our results indicate that multi-season occupancy models based on public sightings can be used to monitor populations and changes in the spatial distribution of territorial carnivores across large areas where alternative methods may be limited by personnel, time, accessibility, and budget constraints.

Montana↗

USGS2013

Pregnancy rates in central Yellowstone bison

Plains bison ( Bison b. bison ) centered on Yellowstone National Park are chronically infected with brucellosis ( Brucella abortus ) and culled along the park boundaries to reduce the probability of disease transmission to domestic livestock. We evaluated the relationship between pregnancy rates and age, dressed carcass weight, and serological status for brucellosis among bison culled from the central Yellowstone subpopulation during the winters of 1996–1997, 2001–2002, and 2002–2003. A model with only dressed carcass weight was the best predictor of pregnancy status for all ages with the odds of pregnancy increasing by 1.03 (95% CI = 1.02–1.04) for every 1-kg increase in weight. We found no effect of age or the serological status for brucellosis on pregnancy rates across age classes; however, we did find a positive association between age and pregnancy rates for bison ≥2 years old. Bison ≥2 years old had an overall pregnancy rate of 65% with markedly different rates in alternate ages for animals between 3 and 7 years old. Pregnancy rates were 0.50 (95% CI = 0.31–0.69) for brucellosis positive and 0.57 (95% CI = 0.34–0.78) for brucellosis negative 2- and 3-year-olds and 0.74 (95% CI = 0.60–0.85) in brucellosis positive and 0.69 (95% CI = 0.49–0.85) in brucellosis negative bison ≥4 years old. Only 1 of 21 bison <2 years old was pregnant. Our findings are important to accurately predict the effects of brucellosis on Yellowstone bison population dynamics. We review our results relative to other studies of Yellowstone bison that concluded serological status for brucellosis influences pregnancy rates.

Wyoming↗