Dynamic wildlife habitat models: Seasonal foods and mortality risk predict occupancy-abundance and habitat selection in grizzly bears Scott E. Nielsen a, * , Greg McDermid b , Gordon B. Stenhouse c , Mark S. Boyce d a Department of Renewable Resources, University of Alberta, Edmonton, Canada T6G 2H1 b Department of Geography, University of Calgary, Calgary, Canada T2N 1N4 c Foothills Research Institute, Hinton, Alberta, Canada T7V 1X6 d Department of Biological Sciences, University of Alberta, Edmonton, Canada T6G 2E9 article info Article history: Received 21 January 2010 Received in revised form 6 April 2010 Accepted 7 April 2010 Keywords: Diet Habitat deficit Habitat quality Habitat selection Ursus arctos abstract Most current wildlife habitat models, such as resource selection functions, typically assume a static environment, extrapolate poorly in space and time, and often lack linkages to population processes. We submit that more mechanistic habitat models that directly consider bottom-up resources affecting growth and reproduction (i.e., food) and top-down limitations affecting survival are needed to effectively predict habitat quality, especially in the presence of rapid environmental change. Here we present a gen- eral model for estimating potential habitat quality (relating to growth and reproduction) and realised habitat quality (accounting for survival) using basic knowledge of the species’ seasonal diet, predicted locations of food resource patches and regional patterns in mortality risk. We illustrate our model for a threatened population of grizzly bears in west-central Alberta. Bi-monthly potential habitat quality suc- cessfully predicted habitat selection by radio-collared grizzly bears, while multi-seasonal realised habitat quality predicted patterns in occupancy-abundance as measured from unique bears at hair-snag sites. Bottom-up resources therefore predicted patterns of habitat selection, while top-down processes (sur- vival) were necessary to scale-up to population measures. We suggest that more direct measures of resources and environments that affect growth, reproduction and survival, as well as match the temporal scale of animal behaviour, be considered when developing wildlife habitat models. Ó 2010 Elsevier Ltd. All rights reserved. 1. Introduction In order to anticipate and manage the consequences of land- scape change to species habitat, knowledge of habitat require- ments are needed. Today most wildlife habitat models are based on patterns of animal use, occupancy or selection using radiote- lemetry or field survey data (Johnson et al., 2006). One of the most common approaches, called resource selection functions (RSFs, Manly et al., 2002), is to estimate habitat selection by comparing environmental characteristics at animal use locations with a set of available (random) locations. Habitat selection and occupancy- based estimates, however, may not relate to population measures such as density, questioning their utility for management of popu- lations (Nielsen et al., 2005; Johnson and Seip, 2008). Although environmental covariates describing habitat selection should re- late directly to those factors influencing survival (perceived risk) and reproduction or growth (food resource abundance) to ensure relations to population processes, most models are based on read- ily available surrogates of habitat such as Normalized Difference Vegetation Index (Wiegand et al., 2008) or cover types (Schloss- berg and King, 2009). Such models assume that the general distri- bution of animals (as opposed to population abundance or performance) is sufficient to define habitat quality and that surro- gates used to describe habitat adequately relate to food resource abundance. The use of habitat surrogates and habitat selection measures, however, diminish our understanding of critical regulat- ing factors of populations restricting our ability to target manage- ment actions. When in the presence of maladaptive habitat selection, the management for selected habitats may even hasten population decline by adding ecological traps/attractive sinks (Sch- laepfer et al., 2002; Nielsen et al., 2006, 2008). Knowledge of both top-down limitations to populations (survival) and bottom-up reg- ulation of populations (foods) are therefore needed to properly quantify habitat quality. Food resources (nutrients/energy) are often a critical regulating factor affecting individual growth of animals and abundance of populations (Miyashita, 1992; Carbone and Gittleman, 2002; Mattson et al., 2004; Brasher et al., 2007). This is particularly true 0006-3207/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2010.04.007 * Corresponding author. Address: 741 General Services Building, University of Alberta, Edmonton, Alberta, Canada T6G 2H1. Tel.: +1 780 492 1656; fax: +1 780 492 4323. E-mail addresses: scottn@ualberta.ca, scott.nielsen@ales.ualberta.ca (S.E. Nielsen). Biological Conservation 143 (2010) 1623–1634 Contents lists available at ScienceDirect Biological Conservation journal homepage: www.elsevier.com/locate/biocon