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Estimating Detection Probabilities for Fishers Using Non-Invasive Methods and Implications for Survey Protocols 29 January 2009 Final Report Prepared for: U.S. Fish and Wildlife Service, Area Field
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3 Oregon Department of Forestry, U.S. Department of Agriculture. National Center for Ecological Analysis and Synthesis, Corvallis, Oregon. . The Fisher Group (a statistical consulting firm) is a world-leading research organization specialized in developing statistical methods for ecological studies of natural and human-induced systems. Fisher is one of the world's most prominent scholars and practitioners of computer-aided statistical analysis of ecological systems, whose work has been recognized since 1974. Fisher's scientific work has focused on estimating detection probabilities that are applicable to biological and environmental data such as stream flow, water and soil temperatures, habitat composition, animal and plant populations, and other ecological systems. Fisher has also developed novel statistical methods for estimating detection probabilities with regard to detection and avoidance of other types of hazards (e.g., wind noise, fire, and floods). The Fisher Group has developed software (PAP) to make use of a new method for estimating detection probabilities for fisheries and other ecological monitoring schemes called Fisher's Hidden Variable Method (HIM). In this paper, the Fisher Group describes how HIM can be used to study detection probabilities and how it is used by both fishery ecologists using non-invasive methods and fishers in surveys using invasive methods. Fisher's Hidden Variable Model The Fisher Group has developed the Fisher's Hidden Variable Model (HFS) to estimate detection probabilities for non-invasive ecological monitoring schemes. HFS uses a process called Hidden Variable Optimization (HIT) to estimate the probability of detection of a given variable from a set of other variables. HIT can be used to estimate detection probabilities for both species and traits that are relatively common, as well as for rarer traits, such as age at reproductive maturity. HIT uses the Fisher's Hidden Variable Method to incorporate the variable of interest into a mathematical function that estimates the detection probability for a different subset of all variables. Thus, the model requires little work to estimate the probabilities of different environmental variables that are more common and rarer in a given environment. In addition, using a relatively small number of additional variables, HFS can provide more accurate (more robust) estimates of detection probabilities than other non-invasive methods (e.g., sampling of fish).

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Estimating detection probabilities is for determining the likelihood of detecting specific events or items.
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