Since the 1980s, 3 major hurricanes have made landfall on Puerto Rico: Hugo in September 1989 (Saffir-Simpson scale, category 4), Georges in September 1998 (category 3) and María in September 2017 (category 4). María was the most devastating hurricane since the 3 major hurricanes that occurred in 1899-�1932. Major hurricanes can cause severe abundance declines and population bottlenecks by decreasing survival and reproductive rates and increasing predation and competition for limited resources. In April to June 1986-2021, we used distance sampling to estimate abundance and monitor the population dynamics of the endangered Puerto Rico plain pigeon Patagioenas inornata wetmorei and the abundant scaly-naped pigeon P. squamosa and red-tailed hawk Buteo jamaicensis. Here, we fit a Bayesian state-space logistic model with distance sampling abundance estimates to generate posterior estimates of maximum population growth rate and population carrying capacity, and predict abundance in April to June 2020-2030. In addition, we used N-mixture and 2-species models to assess association patterns in April to June 2015-2019. The scaly-naped pigeon and red-tailed hawk populations did not decline, or recovered faster from their declines than the plain pigeon population after the hurricanes. The association patterns between species were positive but variable for the 2 pigeon species and negative but variable for the plain pigeon and red-tailed hawk. At lowered abundance (i.e. mean ± SE estimates N̂ = 1043 ± 476 island-wide and N̂ = 522 ± 157 at the centre of abundance in the east-central region in April to June 2018-2021), the plain pigeon may become extinct if another hurricane with the path and intensity of María makes landfall on the island during the current decade.

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.