Integrating basic and applied research to estimate carnivore abundance

Abstract

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.

Estimating wolf abundance with unverified methods

“…In particular, three states in the USA have recently changed methods for estimating wolves across large areas, as both the cost of the task has grown and the sociopolitical scrutiny of policy has intensified. In 2021, the Fish, Wildlife & Parks agency of Montana, USA (MT) changed from monitoring intensive efforts and a patch occupancy model (POM) [16,17] to predicting population size by combining predictions on area occupied with predictive models of home range size and pack size (called an "integrated population occupancy model" iPOM) [18]. In 2021, the Fish & Game agency of Idaho (ID) changed to space-to-event models with stationary cameras [19,20].…”