Competition, prey, and mortalities influence gray wolf group size

Abstract

Group living is found in only 10-15% of carnivorans and can shape demographic processes. Sociality is associated with benefits including increased ability to acquire resources, decreased risk of mortality, and increased reproductive success.We hypothesized that carnivore group size is influenced by conditions related to competition, prey, and mortality risk, which should affect benefits and costs of sociality and resulting demographic processes. We evaluated our hypotheses with gray wolves (Canis lupus) using a 14-year dataset from a large, heavily managed population in the northern Rocky Mountains, USA. Annual mean group size ranged 4.86-7.03 and averaged 5.92 overall. Most groups were relatively small, with 80% containing ≤8 members. Groups were larger in areas with higher densities of conspecific groups, and smaller where prey availability was low. Group sizes remained largely stable while the population was unharvested or under low-intensity harvest but declined under high-intensity harvest. Results support the hypothesis that as habitat becomes saturated, inclusive fitness may become increasingly important such that subordinates delay dispersal. In addition to direct implications for birth and deaths, conditions related to prey and mortality risk may also influence dispersal decisions. Our work also provided a model to predict group size of wolves in our system, directly fulfilling a management need.

Integrating basic and applied research to estimate carnivore abundance

“…We demonstrate the integration of basic and applied research to meet management needs by developing a multimodel approach to estimate wolf abundance in Montana. This approach addresses important assumptions of existing methods for estimating wolf abundance by incorporating an occupancy model (Inman et al, 2019; Miller et al, 2013; Rich et al, 2013) and biologically based models for territory and pack size (Sells et al, 2021; Sells, Mitchell, Ausband, et al, 2022; Sells, Mitchell, Podruzny, et al, 2022). Our approach reduces monitoring needs while providing more accurate abundance estimates founded on the biology and behavior of wolves.…”

“…We used a wolf group size model (Sells, Mitchell, Podruzny, et al, 2022) to predict pack sizes in each 600 km 2 iPOM grid cell. The model, which was based on mechanisms hypothesized to influence wolf pack size, was developed using 14 years of wolf pack data and formalized as a generalized linear mixed effects model (family = Poisson) with variables on original scales: $$$$ {\mathrm{pack}}_{\mathrm{size}}=\exp \left(1.56+0.44\times \mathrm{pack}\ \mathrm{density}-67.28\times \mathrm{ruggedness}-0.06\times {\mathrm{harvest}\ \mathrm{intensity}}_{\mathrm{restricted}}-0.18\times \mathrm{harvest}\ {\mathrm{intensity}}_{\mathrm{liberal}}-0.03\times \mathrm{control}\ \mathrm{removals}-0.06\times {\mathrm{ecoregion}}_{\mathrm{IBE}}+0.04\times {\mathrm{ecoregion}}_{\mathrm{MRE}}+0.13\times {\mathrm{ecoregion}}_{\mathrm{CRE}}+0.03\times {\mathrm{ecoregion}}_{\mathrm{GLPE}}+0.00\times {\mathrm{ecoregion}}_{\mathrm{GRPE}}+\left(1|{\mathrm{Grid}}_{\mathrm{ID}}\right)\right) $$$$ Pack density was the mean pack density in the iPOM grid cell based on field monitoring from 2005 to 2018 (Sells, Mitchell, Podruzny, et al, 2022). Ruggedness was the terrain ruggedness in the iPOM grid cell (ranging 0–0.05 using Sappington et al, 2007's Vector Ruggedness Measure).…”

“…The group size model was developed using only good quality counts (i.e., pack size counts for which wolf specialists had a high degree of confidence, n = 660 pack‐years) and intended to minimize monitoring effort (Sells, Mitchell, Podruzny, et al, 2022). Whereas their initial group size model relied on annual estimates of pack density based on each pack's territory centroid, the predictive model used long‐term average pack density to eliminate this intensive monitoring requirement.…”

“…Whereas their initial group size model relied on annual estimates of pack density based on each pack's territory centroid, the predictive model used long‐term average pack density to eliminate this intensive monitoring requirement. This adaptation captured the long‐term trends in pack densities and still enabled the model to reliably estimate annual pack size (Sells, Mitchell, Podruzny, et al, 2022).…”

“…We used a mechanistic modeling approach that tested hypotheses about how and why wolves select particular territories and predicted territorial behavior across a full range of potential present and future conditions (Sells et al, 2021; Sells & Mitchell, 2020; Sells, Mitchell, Ausband, et al, 2022). We used an empirical model for group size that tested hypotheses about factors influencing pack sizes and enabled predicting patterns in pack sizes across Montana (Sells, Mitchell, Podruzny, et al, 2022). We present here the integration of these models.…”