The population genomic structure of green turtles (Chelonia mydas) suggests a warm-water corridor for tropical marine fauna between the Atlantic and Indian oceans during the last interglacial van der Zee,

Green turtles Chelonia mydas and hawksbill turtles Eretmochelys imbricata are negatively impacted by natural and anthropogenic disturbances. Unknown numbers of turtles are killed annually in the coastal waters of Bonaire and Klein Bonaire, Caribbean Netherlands. We used N-mixture models, conventional distance sampling and the multiple Lincoln-Petersen method to estimate abundance from transect-count and net-capture surveys. Maximum likelihood and Bayesian generalised linear models were used to assess trends in annual abundance in 2003−2018, and a Bayesian state-space logistic model was developed to generate the posterior distributions of population parameters and make abundance predictions for 2019−2030. Mean ± SE annual abundance was 555 ± 149 green turtles (2.5 th and 97.5 th percentiles = 337, 943) and 70 ± 13 hawksbill turtles (49, 101), and there were no trends in western Bonaire and Klein Bonaire in 2003−2018. Mean annual abundance was 348 ± 135 green turtles (171, 731) and there was a positive trend inside Lac Bay, southeast Bonaire, 2003−2018. Green turtles have higher population growth rate and carrying capacity, and therefore can sustain higher human-induced mortality than hawksbill turtles. However, under low mortality rates (< 0.100), both species can fluctuate stably between the lower and upper limits of the carrying capacity. The methodology implemented can be adapted to estimate sea turtle abundance, monitor and model their population dynamics, and assess the negative impact of human-induced mortality in other Caribbean islands.

Abundance estimates corrected for changes in detection are needed to assess population trends. We used transect-count surveys and N-mixture models to estimate green turtle Chelonia mydas and hawksbill turtle Eretmochelys imbricata detection and total abundance at foraging grounds in Bonaire during 2003-2018, and we used these total abundance estimates to fit a Bayesian state-space logistic model and make abundance predictions for 2019-2030. During 2019-2022, we also recorded distance categories to estimate detection and total abundance using distance sampling and N-mixture models. In the present study, we focus on distance sampling to estimate observer detectability and total abundance, and to determine if total abundance increased, declined, or did not change during 2019-2022 and when compared with 2003-2018 estimates and 2019-2030 predictions. Detectability averaged 0.53 (SE = 0.02) for green turtles and 0.51 (SE = 0.06) for hawksbill turtles. Density (ind. km-2) and population size (individuals in the 4 km2 survey region) averaged 72.1 (SE = 17.3) and 288 (SE = 69) for green turtles and 21.8 (SE = 4.6) and 87 (SE = 18) for hawksbill turtles. Green turtle total abundance did not change during 2019-2022 (p > 0.05) but remained low when compared with 2003-2018 estimates and 2019-2030 predictions. Hawksbill turtle total abundance declined between 2020 and 2021 (z = 2.15, p = 0.03) and increased between 2021 and 2022 (z = -3.04, p = 0.002), but 2019-2022 estimates were similar to 2003-2018 estimates and 2019-2030 predictions. Our methodology can be used to monitor sea turtle populations at coastal foraging grounds in the Caribbean.