Energy gains predict the distribution of plains bison across populations and ecosystems

Abstract

Developing tools that help predict animal distribution in the face of environmental change is central to understanding ecosystem function, but it remains a significant ecological challenge. We tested whether a single foraging currency could explain bison (Bison bison) distribution in dissimilar environments: a largely forested environment in Prince Albert National Park (Saskatchewan, Canada) and a prairie environment in Grasslands National Park (Saskatchewan, Canada). We blended extensive behavioral observations, relocations of radio-collared bison, vegetation surveys, and laboratory analyses to spatially link bison distribution in the two parks and expected gains for different nutritional currencies. In Prince Albert National Park, bison were more closely associated with the distribution of plants that maximized their instantaneous energy intake rate (IDE) than their daily intake of digestible energy. This result reflected both bison's intensity of use of individual meadows and their selection of foraging sites within meadows. On this basis, we tested whether IDE could explain the spatial dynamics of bison reintroduced to Grasslands National Park. As predicted, bison distribution in this park best matched spatial patterns of plants offering rapid IDE rather than rapid sodium intake, phosphorus intake, or daily intake of digestible energy. Because the two study areas have very different plant communities, a phenomenological model of resource selection developed in one area could not be used to predict animal distribution in the other. We were able, however, to successfully infer the distribution of bison from their foraging objective. This consistency in foraging currency across ecosystems and populations provides a strong basis for forecasting animal distributions in novel and dynamic environments.

Risk perception by endangered European bison Bison bonasus is context (condition) dependent

“…Nutrition quality was the primary determinant of hay consumption when resources were scarce at the end of winter (supporting hypothesis 4), however there was no evidence of compensatory drinking to cope with poorer quality forage (refuting hypothesis 5), although water from snow may be taken despite the energetic costs associated with this during cold periods. Distance from home range centre affected haystack selection and consumption, but opposite that predicted based on optimality theory of minimising energy expenditure (refuting hypothesis 6), illustrating that bison were so short of forage within the forest that they would travel further and then consume more once they selected an optimal patch (Courant and Fortin 2010;Babin et al 2011;Kowalczyk et al 2011). From spring to summer, European bison remain within forested habitats in Białowie_ za.…”

“…In contrast, less fibrous forage will be processed faster and will yield a higher digestible content and energy ingestion (i.e. lower GUDs) (Courant and Fortin 2010;Babin et al 2011).…”

Straight from the caribou’s (Rangifer tarandus) mouth: detailed observations of tame caribou reveal new insights into summer–autumn diets

“…Forage resources (i.e., vegetation consumed by herbivores) are a fundamental driver of ungulate ecology, influencing distributions, habitat use, and seasonal migrations within populations (Albon and Langvatn 1992;Mahoney and Schaefer 2002;Pettorelli et al 2007;Owen-Smith et al 2010;Babin et al 2011). For individuals, assimilation of nutrients from high-quality nutritional resources in summer is essential for the accretion of fat and protein reserves for pregnancy and overwinter survival (Cook et al 2004(Cook et al , 2013Dale et al 2008;Parker et al 2009;Hurley et al 2014).…”

Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions

“…Numerical likelihood maximization, which we conducted in the application to bison in the next section, is only feasible for models involving few random effects; the other parameters are assumed to be common to all individuals. Monte Carlo EM (expectation-maximization) has been suggested when many random effects are included (Altman 2007 (Babin et al 2011), but for simplicity we restricted the present analysis to the winter of 2005/2006. The bison behavior varies with the season, but within one winter, homogeneity over time can reasonably be assumed.…”