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Monday, January 25 • 11:20am - 11:40am
Inferences On Demographic Rates Using State-Space Models

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AUTHORS: Elise Zipkin*, Michigan State University; Sam Rossman, Michigan State University

ABSTRACT: Understanding a population’s spatial and temporal dynamics requires unbiased, precise estimation of demographic rates, such as reproduction, survival, and movement. Yet estimating these quantities can be difficult, requiring years of intensive data collection. Often this is accomplished through the capture and recapture of individual animals, which is generally only feasible at a limited number of locations. In contrast, recently developed models using a state-space formulation allow for the estimation of abundance and spatial variation in abundance from count data alone for both closed (e.g., N-mixture model) and open (Dail-Madsen model, structured population count model) populations. The modeling framework uses a discrete distribution to estimate local abundance (e.g., Poisson or negative binomial), and a binomial distribution to account for imperfect detectability of individuals during sampling. This is in contrast to traditional state-space models which typically employ normal distributions for both process and sampling error. This approach requires repeated survey events during a time period when the population is closed and thus detection errors can be explicitly attributed to false-negatives in the data (e.g., failure to detect an individual when it is present). We review recent advances in state-space modeling for estimating demographic rates in populations using unmarked data. We also demonstrate how detection/nondetection (e.g., occupancy) data can be used either separately or in conjunction with count data to more precisely estimate recruitment, survivorship, colonization, and extinction rates. We discuss the data requirements (e.g., number of survey locations, years, and replicate sampling events) for both accurate and precise estimates of the parameters of interest.

Monday January 25, 2016 11:20am - 11:40am EST
Vandenberg B