A Hierarchical Covariate Model for Detection, Availability and Abundance of Florida Manatees at a Warm Water Aggregation Site

Abstract

We constructed a Bayesian hierarchical model for estimating the population size and associated probabilities of availability and conditional detection for Florida manatees aggregating during winter, based on a series of monitoring flights over 3 years, 2001–2003. Building upon the findings of Edwards et al. (2007), our approach combines four sources of monitoring data in a single integrated modeling framework to estimate all model parameters simultaneously. Population size was modeled as a function of availability and detection, which in turn were estimated with covariate models consisting of environmental predictor variables. Previous work estimating manatee abundance from aerial surveys have either serially combined parameters estimated in separate models (Edwards et al. 2007), modeled availability and detection jointly (Craig and Reynolds 2004) or ignored detection bias altogether. Time-specific estimates of availability were high, with some variation among flight series, while estimates of conditional detection were extremely variable from one survey to the next. We obtained improved precision in our estimates of population size relative to Edwards et al. (2007). Our results emphasize the consequences of ignoring detection bias when interpreting survey counts. We hope that this research will be influential in the design of a new state-wide aerial survey monitoring program for Florida manatees.