Multilevel multinomial models as a tool to extract population spatial networks from capture-recapture data

O_LIEstimating the impacts of anthropogenic disturbances requires an understanding of the habitat use patterns of individuals within a population. This is especially the case when disturbances are localized within a populations spatial range, as variation in habitat-use within a population can drastically alter the distribution of impacts. C_LIO_LIHere, we illustrate the potential for multilevel multinomial models to generate spatial networks from capture-recapture data, a common data source use in wildlife studies to monitor population dynamics and habitat use. These spatial networks capture which regions of a populations spatial distribution share similar/dissimilar individual usage patterns, and can be especially useful for detecting structured habitat use within the populations spatial range. C_LIO_LIUsing simulations and 18 years of capture-recapture data from St. Lawrence Estuary (SLE) beluga, we show that this approach can successfully estimate the magnitude of similarities/dissimilarities in individual usage patterns across sectors, and identify sectors that share similar individual usage patterns that differ from other sectors, i.e., structured habitat use. In the case of SLE beluga, this method identified multiple clusters of individuals, each preferentially using restricted areas within their summer range of the SLE. C_LIO_LISynthesis and applications. Multilevel multinomial models can be effective at estimating spatial structure in habitat use within wildlife populations sampled by capture-recapture of individuals. Our finding of a structured habitat use within the SLE beluga summer range has direct implications for estimating individual exposures to localized stressors, such as underwater noise from shipping or other activities. C_LI