Estimating the complex patterns of survey availability for loggerhead turtles. Issue 4 (24th March 2022)

• Record Type: Journal Article
• Authors: Hatch, Joshua M.; Haas, Heather L.; Sasso, Christopher R.; Patel, Samir H.; Smolowitz, Ronald J.
• Is Part Of: Journal of wildlife management. Volume 86:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-24
• Subjects: availability bias -- behavior -- Caretta caretta -- loggerhead turtles -- northwest Atlantic -- spatiotemporal regression; Wildlife management -- Periodicals; Zoology -- Periodicals; 333.954
• DOI: 10.1002/jwmg.22208
• Languages: English
• ISSNs: 0022-541X

Abstract

Abstract: Successful management strategies are important for conservation and allow accurate surveying and monitoring of populations for presence, abundance, and trend. This becomes challenging for cryptic, low‐density species, and for animals that have complicated life histories where not every stage of the life cycle can be surveyed effectively. We used information from animal‐borne data loggers to characterize the dive‐surfacing behavior of cryptic loggerhead turtles ( Caretta caretta ) in the northwest Atlantic from 2009–2018. Our data covered a large geographic area off the east coast of North America, and allowed us to present estimates for and variation in 3 metrics that can be used to assess availability bias affecting visual surveys: average dive duration, average surface duration, and the proportion of time at the surface. We used a stochastic partial differential equation approach to construct spatiotemporal regression models for the availability bias metrics. Model predictions showed pronounced individual, spatial, and spatiotemporal (seasonal) variation among the 245 turtles. Overall, we estimated an average dive duration of 14.5 ± 1.36 minutes (SE), an average surface duration of 15.1 ± 2.77 minutes, and an average proportion of time at the surface of 0.50 (95% CI = 0.41–0.59). We made predictions of the 3 availability bias metrics on a 20‐km × 20‐km grid and further used predictions to explore seasonal variations. Our results contribute new insights into loggerhead turtle behavior and provide information that enables survey counts to be translated into more accurate abundance estimates. Abstract : An important consideration in developing abundance estimates from line‐transect surveys is availability bias, where availability is the probability an animal is available for detection by the survey platform. Using spatiotemporal regression models and satellite tags, dive‐surfacing behavior was characterized to improve our understanding of vertical habitat use and to develop metrics of availability bias that can be used to improve population estimates of loggerhead turtles in the northwest Atlantic.