Landscape estimates of carrying capacity for grizzly bears using nutritional energy supply for management and conservation planning
Introduction
Wildlife populations are limited and regulated by a number of factors related to survival and productivity. Two elements influencing these factors are the availability and quality of food resources (Gordon, Hester, & Festa-Bianchet, 2004; Nijland, Nielsen, Coops, Wulder, & Stenhouse, 2014). Food resources and the nutrition they provide influence population performance and provide insight into the number of individuals a landscape can sustain (Chapman & Byron, 2018). This, in turn, has implications to wildlife managers for establishing population goals for management or, in the case of a threatened species, population recovery.
Carrying capacity is the theoretical maximum number of individuals of a species that can be sustained within a region given its environment (Verhulst, 1838; Whittaker, 1975). This measure can be used to determine the overall condition of an ecosystem (White & Gregovich, 2017), determine population thresholds to set goals and recommendations for management (Punt et al., 2020), measure the impact of anthropogenic disturbance (Punt et al., 2020, White & Gregovich, 2017), determine the length of time needed for population recovery (Russ & Alcala, 2004), and establish wildlife reintroduction thresholds (Doan & Guo, 2019). For threatened species, or those with small population sizes, knowledge of potential carrying capacity can be used to define and recommend recovery objectives (Lyons et al., 2018; Thapa & Kelly, 2017; Watari et al., 2013; Zerbini et al., 2019). While the International Union for Conservation of Nature (IUCN) has numerous criteria for determining whether a species is classified as threatened, such as whether a population has less than 1000 mature individuals (IUCN, 2012), these criteria are not species-specific (Hutchings & Kuparinen, 2014). For far-ranging, large-bodied mammals, population criteria are difficult and expensive to monitor (Proctor et al., 2010; Steenweg et al., 2016). Given this challenge, some have focused instead on “bottom-up” regulation of populations to acknowledge the linkage to habitat which ultimately can be managed (Nielsen, McDermid, Stenhouse, & Boyce, 2010, 2015; Nielsen, Larsen, Stenhouse, & Coogan, 2017). To help guide management actions, a more species-specific approach to determining carrying capacity involves estimating nutritional resources available to an animal’s energetic requirements.
Although carrying capacity can be determined in a number of ways and is inherently dynamic, a widely used approach for monitoring wildlife is to compare the total available digestible energy within an area to an individual animal’s energy requirement (Chapman & Byron, 2018; Lyons et al., 2018). Monitoring and measuring digestible energy has been employed in both marine and terrestrial ecosystems using field-based and remote sensing methods (Guyondet et al., 2015; Iijima & Ueno, 2016; Lyons et al., 2018; Perry & Schweigert, 2008). With rapid advances in remote sensing technologies and data, it is possible to estimate carrying capacity at increasingly fine spatial scales and over larger regions (Lyons et al., 2018). These fine-scale carrying capacity estimates can identify key areas where management or recovery efforts would be most likely to succeed (i.e., areas with high carrying capacity and low population size), and thus allow managers to focus conservation efforts and resources more efficiently.
Our goal in this study was to calculate and evaluate the carrying capacity of grizzly bears (Ursus arctos) for three management areas in Alberta, Canada, and to understand how two different analytic approaches (from complex to simple) influence carrying capacity estimates. We hypothesized that current landscape conditions in each management area would support a higher population of bears than already present, suggesting that top-down factors (mortality rates) limit local populations, but setting recovery targets requires knowing carrying capacity (habitat and food supply). We demonstrate the utility of our approach in a management context. Specifically, as higher road density has been associated with greater mortality risk among grizzly bears (Boulanger & Stenhouse, 2014), we compared the difference between carrying capacity estimates and observed population estimates to areas with high road densities to highlight places where conservation efforts may be the most effectively applied. Through this research, we demonstrate how carrying capacity can be used to support and guide management actions for the recovery of this provincially threatened species.
Section snippets
Study area
The study area is a 42,633 km2 region of west-central Alberta, Canada (Fig. 1) that encompasses core and secondary grizzly bear habitat based on road densities, secure habitat, and grizzly bear use as defined by Nielsen, Cranston, and Stenhouse (2009). The area is subdivided into watershed units approximately 500 km2 in size and covers three management areas managed as provincial, multiple use “crown” lands. These uses encompass industrial resource extraction activities, including forestry,
Spatial distribution of digestible energy
Total fruit and herbaceous kilocalorie contents were generally higher in the eastern part of the study area (Fig. 3), along with the central and northern portions of the Grande Cache management area. Average kilocalorie content per km2 for both fruit and herbs was highest in the Grande Cache management area and lowest in the Clearwater management area (Fig. 4a) that also corresponds to large-scale patterns in current population estimates. Total root kilocalorie content was higher in the
Discussion
We demonstrate a comprehensive method for estimating carrying capacity for a large-ranging omnivore using landscape predictions of digestible energy. We illustrate that even when considering just two key food resources results were similar to a more complex approach considering all major food items. Our management example highlights priority areas for implementation of management actions. Furthermore, the methods described here can be simplified and tailored to different management objectives
Funding source
This project was funded in part by the Grizzly-PAW project (NSERC File: CRDPJ 486175-15, Grantee: N.C. Coops, FRM, UBC). We would also like to thank the many funding partners who assisted in providing support to gather the needed data for this work, including the Forest Resource Improvement Association of Alberta, Alberta Environment and Parks, Parks Canada, and West Fraser Ltd.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We would like to acknowledge all of our partners and employees who have provided their time and energy over the past 20 years to accumulate the data set that we now have access to, and from which publications such as these are possible.
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