such data are sparse at best making any statements of viability notoriously imprecise (Boyce, 1992). Confidence intervals for extinction, for instance, often overlap 0 and 1 shortly into the future, making their usefulness for predictions of actual extinction questionable (Ludwig, 1999; Fieberg and Ellner, 2000). Year-to-year variation in demographic data, often due to fluctuating food resources (Ostfeld and Keesing, 2000), common for grizzly bears (Pease and Mattson, 1999), make uncertainty around predictions even greater. For example, if one were to assume relatively small year-toyear variation in demographic processes, between 5 and 10 times as many years of count data would be required to estimate the probability of quasi-extinction with reasonable precision (Fiebert and Ellner, 2000). Following this rule of thumb, annual counts of Yellowstone grizzly bears since 1959, would only provide a reasonably accurate prediction of extinction risk for 9 years into the future (Morris and Doak, 2002). Gathering the necessary data to parameterize a quantitative PVA for accurate predictions therefore presents a ‘catch-22’ situation, where the amount of time required to collect necessary data may end up exceeding the time to extinction for some species at grave risk. Given such limitations, many have argued instead for a more qualitative role for PVA where risk of extinction is ranked among management scenarios (Boyce, 1992; Beissinger and Westphal, 1998; Morris and Doak, 2002). In fact, ranks of population risk among management scenarios appear to be rather robust (McCarthy et al., 2003), although an adaptive management approach should be sought where various management strategies are implemented for spatially segregated populations (Noon et al., 1999). Using such an approach, PVA provides a supporting tool for management decisions, rather than a formal estimation of extinction risk (Possingham et al., 2002). As Lacy and Miller (2002) point out, analyses should strive to integrate human activities more directly into PVA models and ask questions such as, “What happens to the probability of population persistence (or some other measure of viability) if humans do not change in number, distribution, or activity patterns over time?” Management models relying on a habitat-based PVA make the assumption that changes in the amount and distribution of habitat are driving population numbers (Noon et al., 1999). Although demographic rates can be associated with habitats and simulated
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