Landscape-level Models to Assess Risk to Important Wildlife Species

Decision Support Systems to Evaluate Flow Regime Tradeoff in the Upper Snake River

Management of water flows from Jackson Lake dam has the potential to affect the riparian plant community, wildlife, and fisheries, and as a consequence, is a major concern for managers within Grand Teton National Park. The potential for conflict among the various interests of stakeholder groups is high. We will use structural equation modeling methods (SEM) (Pugesek et al. 2003) to generate causal models of the structure and function of the aquatic and riparian ecosystems. The SEM model is a statistical model in the same sense as analysis of variance or multiple regression models. SEM model results can be incorporated into decision support systems. Model parameters can be modified by user inputs and the system-wide consequences evaluated by the user.

Landscape-level Modeling and Decision Support for Wildlife Management

• Use of logistic regression in wildlife habitat-selection modeling. Logistic regression is one of the most widely used statistical tools for modeling wildlife-habitat relationships. However, frequent misapplication of this method by wildlife scientists reflects an inadequate understanding of the logistic model, its interpretation, and the influence of sampling design. To encourage proper application, the correct use and interpretation of logistic regression was reviewed for 3 common sampling designs, and guidelines for applying logistic regression were offered for each. A particularly controversial finding was that logistic regression is generally inappropriate for modeling habitat selection in studies that employ a use–availability design, whereby 2 random samples are drawn independently, one from habitats available to the animal, another from habitats used by the animal. Because this is perhaps the most common sampling design in wildlife-habitat research, results of this study underscore the need for further work to develop credible statistical methods for modeling habitat selection in the use-availability setting.
• Robust methods for modeling probability of use. Habitat selection models have become an important wildlife conservation tool that, ideally, allow one to estimate expected relative densities of animal use over a landscape, and to forecast likely effects of habitat change. Unfortunately, no generally robust method for building such models has yet been proposed. Working from first principles, a simple conceptual model of the habitat-selection process suggests that nonparametric methods may offer important advantages for modeling probability of use. In this study, the accuracy and precision of selected nonparametric approaches is being examined to test this prediction.

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