Marina Observation of Sea Turtles: Establishing a Database of Intracoastal Waterway Green Sea Turtles in Northeast Florida

As conservation efforts regarding green sea turtles, Chelonia mydas, continue, it is imperative to document behaviors and foraging habits/habitats of understudied populations. We have conducted an 18-month study dedicated to photographing the local population feeding alongside floating docks within the Guana Tolomato Matanzas estuary to determine the capability of matching head scale patterns efficiently through a pattern matching program: HotSpotter. To date, 195 unique sea turtles have been identified between two different marinas located in St. Augustine, FL. Of these, 98 were spotted more than once, with 39 of them being "tracked" for longer than a year. Temperature trends were also monitored in conjunction, showing that more individuals appeared during the warmer months of the year. The evidence, overall, indicates that these locations host a resident population of green sea turtles, leading to the need for a discussion on potential threats originating from the usage of these marinas by humans.

Life-history constraints on maximum population growth for loggerhead turtles in the northwest Atlantic

Conservation planning for protected species often relies on estimates of life-history parameters. A commonly used parameter is the instantaneous maximum population growth rate (r max) that can be used to limit removals and design recovery targets. Estimation of r max can be challenging because of limited availability of species- and population-specific data and life-history information. We applied a method proposed by Neil and Lebreton, originally developed for birds, to loggerhead turtles. The method uses age-at-first-reproduction and adult survival to estimate r max. We used a variety of datasets and matrix population models to confirm an allometric assumption required by the method, and to generate estimates of age-at-first-reproduction and adult survival. A meta-analysis was applied to parameters from reported growth curves, which were then combined with the size distribution of neophyte nesters to derive estimates of age-at-first-reproduction. Adult survival rates were obtained from an existing matrix population model. Monte Carlo simulation was then used to combine the estimates of the allometric coefficients, age-at-first-reproduction, and adult survival to obtain a probability distribution of approximate r max values. Estimated annual maximum population growth rates averaged 0.024, with a mode of 0.017 and a 95% highest density interval of 0.006-0.047. These estimates were similar to values reported by others using different methods and captured the variability in positive, annual change estimates across nesting beach sites for the northwest Atlantic loggerhead population. The use of life-history parameters has a long history in wildlife and fisheries management and conservation planning. Our estimates of r max, while having some biases and uncertainty, encompassed values presently used in recovery planning for loggerhead turtles and offer additional information for the management of endangered and threatened species.

Fathoming sea turtles: monitoring strategy evaluation to improve conservation status assessments

Population monitoring must be accurate and reliable to correctly classify population status. For sea turtles, nesting beach surveys are often the only population-level surveys that are accessible. However, process and observation errors, compounded by delayed maturity, obscure the relationship between trends on the nesting beach and the population. We present a simulation-based tool, monitoring strategy evaluation (MoSE), to test the relationships between monitoring data and assessment accuracy, using green sea turtles, Chelonia mydas, as a case study. To explore this first application of MoSE, we apply different treatments of population impacts to virtual true populations, and sample the nests or nesters, with observation error, to test if the observation data can be used to diagnose population status accurately. Based on the observed data, we examine population trend and compare it to the known values from the operating model. We ran a series of scenarios including harvest impacts, cyclical breeding probability, and sampling biases, to see how these factors impact accuracy in estimating population trend. We explored the necessary duration of monitoring for accurate trend estimation and the probability of a false trend diagnosis. Our results suggest that disturbance type and severity can have important and persistent effects on the accuracy of population assessments drawn from monitoring nesting beaches. The underlying population phase, age classes disturbed, and impact severity influenced the accuracy of estimating population trend. At least 10 yr of monitoring data is necessary to estimate population trend accurately, and >20 yr if juvenile age classes were disturbed and the population is recovering. In general, there is a greater probability of making a false positive trend diagnosis than a false negative, but this depends on impact type and severity, population phase, and sampling duration. Improving detection rates to 90% does little to lower probability of a false trend diagnosis with shorter monitoring spans. Altogether, monitoring strategies for specific populations may be tailored based on the impact history, population phase, and environmental drivers. The MoSE is an important framework for analysis through simulation that can comprehensively test population assessments for accuracy and inform policy recommendations regarding the best monitoring strategies.