Ecological Monographs, 79(1), 2009, pp. 77–108 Ó 2009 by the Ecological Society of America Incorporating ecological drivers and uncertainty into a demographic population viability analysis for the island fox VICTORIA J. BAKKER, 1,9 DANIEL F. DOAK, 2 GARY W. ROEMER, 3 DAVID K. GARCELON, 4 TIMOTHY J. COONAN, 5 SCOTT A. MORRISON, 6 COLLEEN LYNCH, 7 KATHERINE RALLS, 8 AND REBECCA SHAW 6 1 Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, California 95064 USA 2 Department of Zoology and Physiology, University of Wyoming, Laramie, Wyoming 82071 USA 3 Department of Fishery and Wildlife Sciences, New Mexico State University, P.O. Box 30003, Las Cruces, New Mexico 88003 USA 4 Institute for Wildlife Studies, P.O. Box 1104, Arcata, California 95518 USA 5 National Park Service, Channel Islands National Park, 1901 Spinnaker Drive, Ventura, California 93001 USA 6 The Nature Conservancy, 201 Mission Street, Fourth Floor, San Francisco, California 94105 USA 7 University of South Dakota, Department of Biology, Vermillion, South Dakota 57069 USA 8 Center for Conservation and Ecological Genetics, Smithsonian National Zoological Park, Washington, D.C. 20008 USA Abstract. Biometricians have made great strides in the generation of reliable estimates of demographic rates and their uncertainties from imperfect field data, but these estimates are rarely used to produce detailed predictions of the dynamics or future viability of at-risk populations. Conversely, population viability analysis (PVA) modelers have increased the sophistication and complexity of their approaches, but most do not adequately address parameter and model uncertainties in viability assessments or include important ecological drivers. Merging the advances in these two fields could enable more defensible predictions of extinction risk and better evaluations of management options, but only if clear and interpretable PVA results can be distilled from these complex analyses and outputs. Here, we provide guidance on how to successfully conduct such a combined analysis, using the example of the endangered island fox (Urocyon littoralis), endemic to the Channel Islands of California, USA. This more rigorous demographic PVA was built by forming a close marriage between the statistical models used to estimate parameters from raw data and the details of the subsequent PVA simulation models. In particular, the use of mark–recapture analyses and other likelihood and information-theoretic methods allowed us to carefully incorporate parameter and model uncertainty, the effects of ecological drivers, density dependence, and other complexities into our PVA. Island fox populations show effects of density dependence, predation, and El Nin˜o events, as well as substantial unexplained temporal variation in survival rates. Accounting not only for these sources of variability, but also for uncertainty in the models and parameters used to estimate their strengths, proved important in assessing fox viability with different starting population sizes and predation levels. While incorporating ecological drivers into PVA assessments can help to predict realistic dynamics, we also show that unexplained process variance has important effects even in our extremely well-studied system, and therefore must not be ignored in PVAs. Overall, the treatment of causal factors and uncertainties in parameter values and model structures need not result in unwieldy models or highly complex predictions, and we emphasize that future PVAs can and should include these effects when suitable data are available to support their analysis. Key words: Aquila chrysaetos; density dependence; ecological drivers; Golden Eagle; island fox; mark– recapture; population viability analysis, PVA; process variance; stochasticity; uncertainty; Urocyon littoralis. INTRODUCTION Modeling the possible trajectories of rare and declining populations to predict future viability and identify management options has become a mainstay of conservation biology. Referred to as population viability analysis, or PVA, this approach has provided insights into some of the most controversial issues in conserva- tion biology (e.g., Crouse et al. 1987, Lande 1988). These mathematical descriptions of population behavior re- place simple statistical analyses and expert opinions, which are usually only indirectly or unquantifiably linked to current and future population dynamics. While PVA models can make direct quantitative predictions of stochastic population futures, uncertainties inherent to any model can decrease the reliability of these predic- tions if not accounted for properly (Taylor 1995, Beissinger and Westphal 1998, White 2000, Coulson et al. 2001b, Ellner et al. 2002, Doak et al. 2005). These uncertainties arise from many sources: field methods Manuscript received 17 May 2007; revised 4 February 2008; accepted 25 March 2008; final version received 6 May 2008. Corresponding Editor: K. L. Cottingham. 9 E-mail:vjbakker@gmail.com 77