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

Abstract

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.

Integrating basic and applied research to estimate carnivore abundance

“…We used a recently developed mechanistic territory model to predict territory size (Sells et al, 2021; Sells & Mitchell, 2020; Sells, Mitchell, Ausband, et al, 2022). The territory model was a spatially explicit, agent‐based model representing the hypothesis that wolves are adapted to select economical territories that maximize food benefits and minimize costs of travel, competition, and mortality risk.…”

“…During simulations, packs were added to a gridded landscape simulated to represent Montana. Each 1 km 2 grid cell had a food benefit based on an ungulate density index and a measure for terrain ruggedness and human density (Sells, Mitchell, Ausband, et al, 2022). Each time a pack was added, it selected a starting location for a territory center and then calculated cell values around itself by discounting each nearby cell's food benefits by its associated costs of ownership.…”

“…Each time a pack was added, it selected a starting location for a territory center and then calculated cell values around itself by discounting each nearby cell's food benefits by its associated costs of ownership. Food benefits accounted for ungulate densities, based on a spatial index generated from 10 year average densities at hunting district levels using the best available data for deer, elk, and moose (Sells et al, 2021; Sells, Mitchell, Ausband, et al, 2022). Ownership costs accounted for terrain ruggedness, distance from the territory center, number of other packs already in the area, and human density.…”

“…Whereas estimates of area occupied through POM are reliable (Miller et al, 2013), the reliability of abundance estimates hinge on assumptions about territory size, territory overlap, and pack size (Inman et al, 2019). Assumptions of a fixed territory size with minimal overlap are simplistic; in reality, territories vary spatiotemporally (Sells et al, 2021; Sells & Mitchell, 2020; Sells, Mitchell, Ausband, et al, 2022; Uboni et al, 2015). Furthermore, estimates of mean territory size were largely derived preharvest and at a smaller population size (Rich et al, 2012).…”

“…We developed an Integrated POM (iPOM) that employs mechanistic and empirical models to incorporate current knowledge about wolf behavior and available data. 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).…”