Integrating basic and applied research to estimate carnivore abundance

A clear connection between basic research and applied management is often missing or difficult to discern. We present a case study of integration of basic research with applied management for estimating abundance of gray wolves (Canis lupus) in Montana, USA. Estimating wolf abundance is a key component of wolf management but is costly and time intensive as wolf populations continue to grow. We developed a multimodel approach using an occupancy model, mechanistic territory model, and empirical group size model to improve abundance estimates while reducing monitoring effort. Whereas field-based wolf counts generally rely on costly, difficult-to-collect monitoring data, especially for larger areas or population sizes, our approach efficiently uses readily available wolf observation data and introduces models focused on biological mechanisms underlying territorial and social behavior. In a three-part process, the occupancy model first estimates the extent of wolf distribution in Montana, based on environmental covariates and wolf observations. The spatially explicit mechanistic territory model predicts territory sizes using simple behavioral rules and data on prey resources, terrain ruggedness, and human density. Together, these models predict the number of packs. An empirical pack size model based on 14 years of data demonstrates that pack sizes are positively related to local densities of packs, and negatively related to terrain ruggedness, local mortalities, and intensity of harvest management. Total abundance estimates for given areas are derived by combining estimated numbers of packs and pack sizes. We estimated the Montana wolf population to be smallest in the first year of our study, with 91 packs and 654 wolves in 2007, followed by a population peak in 2011 with 1252 wolves. The population declined ~6% thereafter, coincident with implementation of legal harvest in Montana. Recent numbers have largely stabilized at an average of 191 packs and 1141 wolves from 2016 to 2020. This new approach accounts for biologically based, spatially explicit predictions of behavior to provide more accurate estimates of carnivore abundance at finer spatial scales. By integrating basic and applied research, our approach can therefore better inform decision-making and meet management needs.

Evaluating the importance of wolverine habitat predictors using a machine learning method

In the conterminous United States, wolverines (Gulo gulo) occupy semi-isolated patches of subalpine habitats at naturally low densities. Determining how to model wolverine habitat, particularly across multiple scales, can contribute greatly to wolverine conservation efforts. We used the machine-learning algorithm random forest to determine how a novel analysis approach compared to the existing literature for future wolverine conservation efforts. We also determined how well a small suite of variables explained wolverine habitat use patterns at the second- and third-order selection scale by sex. We found that the importance of habitat covariates differed slightly by sex and selection scales. Snow water equivalent, distance to high-elevation talus, and latitude-adjusted elevation were the driving selective forces for wolverines across the Greater Yellowstone Ecosystem at both selection orders but performed better at the second order. Overall, our results indicate that wolverine habitat selection is, in large part, broadly explained by high-elevation structural features, and this confirms existing data. Our results suggest that for third-order analyses, additional fine-scale habitat data are necessary.

A framework for collaborative wolverine connectivity conservation

Maintaining connectivity between high-elevation public lands is important for wolverines and other species of conservation concern. This work represents the first effort to prioritize wolverine connectivity under future climate conditions using a systematic conservation planning framework. We optimized 10, 15, 20, and 50% of habitat features for wolverines using integer linear programming. We identified 369 privately owned areas in the 10% solution, 572 in the 15% solution, 822 in the 20% solution, and 3,996 in the 50% solution where voluntary landowner easements would improve the long-term landscape functionality for wolverine connectivity. The median estimated easements ranged from $8,762 to $12,220 across the four solutions (total costs $14,874,371 to $196,346,714). Overall, this effort demonstrates the utility of optimization problems for conserving connectivity, provides a proactive tool to engage potential collaborators, identifies easements that will likely protect various subalpine species, and offers a framework for the conservation of additional species.

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.

Home range size and resource use by swift foxes in northeastern Montana

Swift foxes (Vulpes velox) are endemic to the Great Plains of North America, but were extirpated from the northern portion of their range by the mid-1900s. Despite several reintroductions to the Northern Great Plains, there remains a ~350 km range gap between the swift fox population along the Montana and Canada border and that in northeastern Wyoming and northwestern South Dakota. A better understanding of what resources swift foxes use along the Montana and Canada border region will assist managers to facilitate connectivity among populations. From 2016 to 2018, we estimated the home range size and evaluated resource use within the home ranges of 22 swift foxes equipped with Global Positioning System tracking collars in northeastern Montana. Swift fox home ranges in our study were some of the largest ever recorded, averaging (± SE) 42.0 km² ± 4.7. Our results indicate that both environmental and anthropogenic factors influenced resource use. At the population level, resource use increased by 3.3% for every 5.0% increase in percent grasslands. Relative probability of use decreased by 7.9% and 7.4% for every kilometer away from unpaved roads and gas well sites, respectively, and decreased by 3.0% and 11.3% for every one-unit increase in topographic roughness and every 0.05 increase in normalized difference vegetation index (NDVI), respectively. Our study suggests that, to reestablish connectivity among swift fox populations in Montana, managers should aim to maintain large corridors of contiguous grasslands at a landscape scale, a process that likely will require having to work with multiple property owners.

Landscape genetics of wolverines (Gulo gulo): scale-dependent effects of bioclimatic, topographic, and anthropogenic variables

Climate change can have particularly severe consequences for high-elevation species that are well-adapted to long-lasting snow conditions within their habitats. One such species is the wolverine, Gulo gulo, with several studies showing a strong, year-round association of the species with the area defined by persistent spring snow cover. This bioclimatic niche also predicts successful dispersal paths for wolverines in the contiguous United States, where the species shows low levels of genetic exchange and low effective population size. Here, we assess the influence of additional climatic, vegetative, topographic, and anthropogenic, variables on wolverine genetic structure in this region using a multivariate, multiscale, landscape genetic approach. This approach allows us to detect landscape-genetic relationships both due to typical, small-scale genetic exchange within habitat, as well as exceptional, long-distance dispersal among habitats. Results suggest that a combination of snow depth, terrain ruggedness, and housing density, best predict gene flow in wolverines, and that the relative importance of variables is scale-dependent. Environmental variables (i.e., isolation-by-resistance, IBR) were responsible for 79% of the explained variation at small scales (i.e., up to ~230 km), and 65% at broad scales (i.e., beyond ~420 km). In contrast, a null model based on only space (i.e., isolation-by-distance, IBD) accounted only for 17% and 11% of the variation at small and broad scales, respectively. Snow depth was the most important variable for predicting genetic structures overall, and at small scales, where it contributed 43% to the variance explained. At broad spatial scales, housing density and terrain ruggedness were most important with contributions to explained variation of 55% and 25%, respectively. While the small-scale analysis most likely captures gene flow within typical wolverine habitat complexes, the broad-scale analysis reflects long-distance dispersal across areas not typically inhabited by wolverines. These findings help to refine our understanding of the processes shaping wolverine genetic structure, which is important for maintaining and improving functional connectivity among remaining wolverine populations.

Winter movement behavior by swift foxes (Vulpes velox) at the northern edge of their range

Winter can be a limiting time of year for many temperate species, who must access depressed prey resources to meet energetic demands. The swift fox (Vulpes velox (Say, 1823)) was extirpated from Canada and Montana (USA) by 1969, but was reintroduced in the 1980s to Canada, and subsequently spread into northern Montana. Swift foxes in this region are at the current northern range edge where winter conditions are harsher and persist longer than in their southern range (i.e., Colorado (USA) to Texas (USA)). We collected fine-scale locational data from swift foxes fitted with global positioning system collars to examine movement and resource-use patterns during winter of 2016–2017 in northeastern Montana. Our results suggest that swift foxes displayed three distinct movement patterns (i.e., resting, foraging, and travelling) during the winter. Distance to road decreased relative probability of use by 39%–46% per kilometre across all movement states and individuals, whereas the influence of topographic roughness and distance to crop field varied among movement states and individuals. Overall, while our findings are based on data from three individuals, our study suggests that across movement states during the critical winter season, swift foxes are likely using topography and areas near roads to increase their ability to detect predators.