Ecological Applications, 20(4), 2010, pp. 1173–1182 Ó 2010 by the Ecological Society of America Simultaneous modeling of habitat suitability, occupancy, and relative abundance: African elephants in Zimbabwe JULIEN MARTIN, 1,2,7 SIMON CHAMAILLE ´ -JAMMES, 3 JAMES D. NICHOLS, 2 HERVE ´ FRITZ, 3 JAMES E. HINES, 2 CHRISTOPHER J. FONNESBECK, 4 DARRYL I. MACKENZIE, 5 AND LARISSA L. BAILEY 6 1 Florida Cooperative Fish and Wildlife Research Unit, University of Florida, Gainesville, Florida 32611-0485 USA 2 Patuxent Wildlife Research Center, United States Geological Survey, 12100 Beech Forest Road, Laurel, Maryland 20708 USA 3 Universite ´ de Lyon, Universite ´ Lyon 1, CNRS, UMR 5558, Laboratoire de Biome ´trie et Biologie Evolutive, 43 Boulevard du 11 Novembre 1918, Villeurbanne F-69622 France 4 Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand 5 Proteus Wildlife Research Consultants, P.O. Box 5193, Dunedin, New Zealand 6 Department of Fish, Wildlife and Conservation Biology, 1474 Campus Delivery, Fort Collins, Colorado 80523 USA Abstract. The recent development of statistical models such as dynamic site occupancy models provides the opportunity to address fairly complex management and conservation problems with relatively simple models. However, surprisingly few empirical studies have simultaneously modeled habitat suitability and occupancy status of organisms over large landscapes for management purposes. Joint modeling of these components is particularly important in the context of management of wild populations, as it provides a more coherent framework to investigate the population dynamics of organisms in space and time for the application of management decision tools. We applied such an approach to the study of water hole use by African elephants in Hwange National Park, Zimbabwe. Here we show how such methodology may be implemented and derive estimates of annual transition probabilities among three dry-season states for water holes: (1) unsuitable state (dry water holes with no elephants); (2) suitable state (water hole with water) with low abundance of elephants; and (3) suitable state with high abundance of elephants. We found that annual rainfall and the number of neighboring water holes influenced the transition probabilities among these three states. Because of an increase in elephant densities in the park during the study period, we also found that transition probabilities from low abundance to high abundance states increased over time. The application of the joint habitat–occupancy models provides a coherent framework to examine how habitat suitability and factors that affect habitat suitability influence the distribution and abundance of organisms. We discuss how these simple models can further be used to apply structured decision-making tools in order to derive decisions that are optimal relative to specified management objectives. The modeling framework presented in this paper should be applicable to a wide range of existing data sets and should help to address important ecological, conservation, and management problems that deal with occupancy, relative abundance, and habitat suitability. Key words: adaptive resource management; African elephants; detection probabilities; Hwange National Park, Zimbabwe; joint habitat occupancy modeling; Loxodonta africana; multistate site occupancy models; structured decision making; surface water. INTRODUCTION Conservation of natural resources often requires managing abundance and spatial distribution of organ- isms by acting on their habitat (Williams et al. 2002, MacKenzie et al. 2006). One challenge is then to capture the features of the system that are most relevant to the management objectives while keeping the models as simple as possible (Clark and Mangel 2001, Nichols 2001). The ability to simplify a problem to its most critical components (i.e., develop a model) has often been viewed as an art essential to the advancement of science (Clark and Mangel 2001). In the context of management there are at least two additional arguments in favor of using simple models: (1) logistical difficulty of accumu- lating detailed information over large spatiotemporal scales; (2) computational limitations associated with structured decision-making tools for deriving decisions that are optimal relative to management objectives (Conroy and Moore 2001). The recent development of statistical models such as dynamic site occupancy models provides the opportunity to resolve fairly complex management and conservation problems with relatively simple models (MacKenzie et al. 2006, 2009). Manuscript received 19 February 2009; revised 27 July 2009; accepted 5 August 2009. Corresponding Editor: J. J. Millspaugh. 7 Present address: Fish and Wildlife Research Institute, 100 8th Avenue SE, St. Petersburg, Florida 33701 USA. E-mail: julienm@ufl.edu 1173