Abstract
Imperfect detection is an important problem when counting wildlife, but new technologies such as unmanned aerial systems (UAS) can help overcome this obstacle. We used data collected by a UAS and a Bayesian closed capture-mark-recapture model to estimate abundance and distribution while accounting for imperfect detection of aggregated Florida manatees (Trichechus manatus latirostris) at thermal refuges to assess use of current and new warmwater sources in winter. Our UAS hovered for 10 min and recorded 4 K video over sites in Collier County, FL. Open-source software was used to create recapture histories for 10- and 6-min time periods. Mean estimates of probability of detection for 1-min intervals at each canal varied by survey and ranged between 0.05 and 0.92. Overall, detection probability for sites varied between 0.62 and 1.00 across surveys and length of video (6 and 10 min). Abundance varied by survey and location, and estimates indicated that distribution changed over time, with use of the novel source of warmwater increasing over time. The highest cumulative estimate occurred in the coldest winter, 2018 (N = 158, CI 141–190). Methods here reduced survey costs, increased safety and obtained rigorous abundance estimates at aggregation sites previously too difficult to monitor.
Highlights
Imperfect detection is an important problem when counting wildlife, but new technologies such as unmanned aerial systems (UAS) can help overcome this obstacle
An important problem with counting any animal species is imperfect detection of individuals, where animals are missed by observers because of perception bias or they cannot be observed because they are under vegetation cover or water[1]
We show how to optimize the survey effort and to model UAS-collected data to estimate abundance and distribution while accounting for imperfect detection of a highly aggregated species, the Florida manatee (Trichechus manatus latirostris)
Summary
Imperfect detection is an important problem when counting wildlife, but new technologies such as unmanned aerial systems (UAS) can help overcome this obstacle. Some species gather in relatively large numbers in very tight spaces, which can prevent observers from detecting nearby conspecifics, and aquatic species often dive and resurface in a way that makes it difficult to tell individuals apart to get an accurate count. Numerous models have been developed to help estimate wildlife abundance while accounting for imperfect detection, large aggregations make the application of most existing protocols and models difficult or impossible to implement. Some of these models require scientists to be able to identify individual animals (e.g., spatial capture-mark-recapture), and others are affected by non-independence of detection or are just not appropriate to use when the data include multiple counts of the same animal. Mote Marine Laboratory, written communication 1/24/2020; FWC unpublished data), accurately estimating their numbers can be challenging
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