Directness of resource use metrics affects predictions of bear body fat gain
- 265 Downloads
Many North American ursids rely on an annual hyperphagic period to obtain fat reserves necessary for winter survival and reproduction. Identifying causes of variation in body fat gain may improve understanding of how bear resource use affects body condition. We used data from southcentral Alaska to model changes in percentage body fat of adult female American black bears (Ursus americanus) in 1998 and 2000 and brown bears (Ursus arctos) in 2000. We used year, proportion of radio locations in different habitats, distance to streams containing salmon (Onchorynchus spp.), and degree of radio location clustering as predictors for black bears and elevation, distance to streams containing salmon, and degree of radio location clustering as predictors for brown bears. Degree of location clustering was the only predictor variable supported by parameter coefficients in black bear models, supporting our hypothesis that metrics of energetics perform better as predictors of body condition than habitat use. With every unit increase in location clustering black bear body fat increased 2 %. No predictor variables influenced variation in brown bear change in body fat. Some variables previously found useful for predicting bear presence (e.g., habitat) were not useful in predicting changes in body fat, an important biological outcome for these species. Rather than assuming fitness benefits of habitat-level selection, we recommend including metrics of energetics that might more directly influence biological outcomes.
KeywordsBears Body condition Habitat Resource use Salmon Ursus spp.
We thank Mississippi State University’s College of Forest Resources and the Forest and Wildlife Research Center and the University of Missouri for supporting this research. We thank Bruce Leopold for providing helpful comments to improve this manuscript. We thank the three anonymous reviewers and editor of Polar Biology for their helpful suggestions toward improving this manuscript.
- Belant JL, Follmann EH (2002) Sampling considerations for American black and brown bear home range and habitat use. Ursus 13:299–315Google Scholar
- Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New YorkGoogle Scholar
- Fretwell SD (1972) Populations in a seasonal environment. Princeton University Press, PrincetonGoogle Scholar
- Futuyma DJ (1998) Evolutionary biology, 3rd edn. Sinauer Associates, SunderlandGoogle Scholar
- Maletzke BT, Koehler GM, Wielgus RB, Aubry KB, Evans MA (2007) Habitat conditions associated with lynx hunting behavior during winter in northern Washington. J Wild Manag 72:1473–1478Google Scholar
- Marcoux M, Larocque G, Auger-Méthé M, Dutilleul P, Humphries MM (2010) Statistical analysis of animal observations and associated marks distributed in time using Ripley’s functions. Anim Behav 80(2):329–337Google Scholar
- McLellan BN (1993) Competition between black and grizzly bears as a natural population regulating factor. Proc West Black Bear Workshop 4:111–116Google Scholar
- Powell RA, Zimmerman JW, Seaman DE (1997) Important components of habitat for black bears. In: Putnam RJ (ed) Ecology and behaviour of North American black bears: home ranges, habitat and social organization. Chapman and Hall, London, pp 68–73Google Scholar
- R Development Core Team (2010) R: a language and environment for statistical computing. Vienna R Foundation for Statistical Computing. http://www.R-project.org. Accessed 20 May 2012
- Ricklefs RE, Miller GL (2000) Ecology. W. H. Freeman and Company, New YorkGoogle Scholar
- Wallace RA (1973) The ecology and evolution of animal behavior. Goodyear, Pacific PalisadesGoogle Scholar
- Webb SL, Riffell SK, Gee KL, Demarais S (2009) Using fractal analyses to characterize movement paths of white-tailed deer and response to spatial scale. J Wild Manag 90:1210–1217Google Scholar