A Spatio-Temporally Explicit Random Encounter Model for Large-Scale Population Surveys.

ABSTRACT: Random encounter models can be used to estimate population abundance from indirect data collected by non-invasive sampling methods, such as track counts or camera-trap data. The classical Formozov-Malyshev-Pereleshin (FMP) estimator converts track counts into an estimate of mean population density, assuming that data on the daily movement distances of the animals are available. We utilize generalized linear models with spatio-temporal error structures to extend the FMP estimator into a flexible Bayesian modelling approach that estimates not only total population size, but also spatio-temporal variation in population density. We also introduce a weighting scheme to estimate density on habitats that are not covered by survey transects, assuming that movement data on a subset of individuals i

SUBMITTER: Jousimo J 

PROVIDER: S-EPMC5017679 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature