The Florida east coast terrapin (Malaclemys terrapin tequesta) is a rare and potentially endangered species that is difficult to survey because of poor detection probability and a patchy distribution. Like many rare species sampling programs, we apply a density and multistate occupancy sampling approach that considered the impacts of imperfect detection. We separated the detection process into availability of the animal within the sampling area (e.g., coming to surface) and perceptibility (actually seeing it). Our study employed a density estimation approach originally developed for birds, which combined time to detection and distance sampling within a Bayesian N-mixture model. Our study also estimated two abundance occupancy states (few and many terrapins). We were able to estimate large differences in terrapin densities between sites through functions of site-specific environmental covariates (water depth, distance to mangrove, and distance to land). The detection probability was poor (0.28) for the few terrapin occupancy state, but was much greater for many (0.75). The time to detection and distance-sampling approach for this aquatic animal should be useful for other aquatic organisms that regularly surface. Terrapins were generally available to be sighted within four minutes but detection declined rapidly to a low probability of terrapin detection >30 m. The approach of estimating density as a function of habitat covariates to identify habitat associations can provide an effective method that combined with adaptive sampling should be useful to investigate the distribution of terrapins in open water.

Seagrasses are the foundation of many coastal ecosystems and are in global decline because of anthropogenic impacts. For the Indian River Lagoon (Florida, U.S.A.), we developed competing multistate statistical models to quantify how environmental factors (surrounding land use, water depth, and time [year]) influenced the variability of seagrass state dynamics from 2003 to 2014 while accounting for time-specific detection probabilities that quantified our ability to determine seagrass state at particular locations and times. We classified seagrass states (presence or absence) at 764 points with geographic information system maps for years when seagrass maps were available and with aerial photographs when seagrass maps were not available. We used 4 categories (all conservation, mostly conservation, mostly urban, urban) to describe surrounding land use within sections of lagoonal waters, usually demarcated by land features that constricted these waters. The best models predicted that surrounding land use, depth, and year would affect transition and detection probabilities. Sections of the lagoon bordered by urban areas had the least stable seagrass beds and lowest detection probabilities, especially after a catastrophic seagrass die-off linked to an algal bloom. Sections of the lagoon bordered by conservation lands had the most stable seagrass beds, which supports watershed conservation efforts. Our results show that a multistate approach can empirically estimate state-transition probabilities as functions of environmental factors while accounting for state-dependent differences in seagrass detection probabilities as part of the overall statistical inference procedure.