Evaluating trends in abundance of immature green turtles, Chelonia mydas, in the Greater Caribbean

Many long-lived marine species exhibit life history traits. that make them more vulnerable to overexploitation. Accurate population trend analysis is essential for development and assessment of management plans for these species. However, because many of these species disperse over large geographic areas, have life stages inaccessible to human surveyors, and/or undergo complex developmental migrations, data on trends in abundance are often available for only one stage of the population, usually breeding adults. The green turtle (Chelonia mydas) is one of these long-lived species for which population trends are based almost exclusively on either numbers of females that emerge to nest or numbers of nests deposited each year on geographically restricted beaches. In this study, we generated estimates of annual abundance for juvenile green turtles at two foraging grounds in the Bahamas based on long-term capture-mark-recapture (CMR) studies at Union Creek (24 years) and Conception Creek (13 years), using a two-stage approach. First, we estimated recapture probabilities from CMR data using the Cormack-Jolly-Seber models in the software program MARK; second, we estimated annual abundance of green turtles. at both study sites using the recapture probabilities in a Horvitz-Thompson type estimation procedure. Green turtle abundance did not change significantly in Conception Creek, but, in Union Creek, green turtle abundance had successive phases of significant increase, significant decrease, and stability. These changes in abundance resulted from changes in immigration, not survival or emigration. The trends in abundance on the foraging grounds did not conform to the significantly increasing trend for the major nesting population at Tortuguero, Costa Rica. This disparity highlights the challenges of assessing population-wide trends of green turtles and other long-lived species. The best approach for monitoring population trends may be a combination of (1) extensive surveys to provide data for large-scale trends in relative population abundance, and (2) intensive surveys, using CMR techniques, to estimate absolute abundance and evaluate the demographic processes' driving the trends

Green turtle somatic growth model: evidence for density dependence.

Abstract. The green turtle, Chelonia mydas, is a circumglobal species and a primary herbivore in marine ecosystems. Overexploitation as a food resource for human populations has resulted in drastic declines or extinction of green turtle populations in the Greater Caribbean. Attempts to manage the remaining populations on a sustainable basis are hampered by insufficient knowledge of demographic parameters. In particular, compensatory responses resulting from density-dependent effects have not been evaluated for any sea turtle population and thus have not been explicitly included in any population models. Growth rates of immature green turtles were measured during an 18-yr study in Union Creek, a wildlife reserve in the southern Bahamas. We have evaluated the growth data for both straight carapace length (SCL) and body mass with nonparametric regression models that had one response variable (absolute growth rate) and five potential covariates: sex, site, year, mean size, and recapture interval. The SCL model of size-specific growth rates was a good fit to the data and accounted for 59% of the variance. The body-mass model was not a good fit to the data, accounting for only 26% of the variance. In the SCL model, sex, site, year, and mean size all had significant effects, whereas recapture interval did not. We used results of the SCL model to evaluate a density-dependent effect on somatic growth rates. Over the 18 yr of our study, relative population density underwent a sixfold increase followed by a threefold decrease in Union Creek as a result of natural immigration and emigration. Three lines of evidence support a density-dependent effect. First, there is a significant inverse correlation between population density and mean annual growth rate. Second, the condition index (mass/(SCL) 3 ) of green turtles in Union Creek is positively correlated with mean annual growth rates and was negatively correlated with population density, indicating that the green turtles were nutrient limited during periods of low growth and high population densities. Third, the population in Union Creek fluctuated around carrying capacity during our study and thus was at levels likely to experience densitydependent effects that could be measured. We estimate the carrying capacity of pastures of the seagrass Thalassia testudinum, the major diet plant of the green turtle, as a range from 122 to 4439 kg green turtles/ha or 16-586 million 50-kg green turtles in the Caribbean. Because green turtle populations are probably regulated by food limitation under natural conditions, carrying capacity can serve as a baseline to estimate changes in green turtle populations in the Caribbean since preColumbian times and to set a goal for recovery for these depleted populations. Finally, we compare the growth functions for green turtle populations in the Atlantic and Pacific oceans. Not only does the form of the size-specific growth functions differ between the two regions (monotonic declining in the Atlantic and nonmonotonic in the Pacific), but also small juvenile green turtles in the Atlantic have substantially higher growth rates than those in the Pacific. Research is needed to evaluate the causes of these differences, but our results indicate that demographic parameters between ocean basins should only be extrapolated with great caution

Global Conservation Priorities for Marine Turtles

Where conservation resources are limited and conservation targets are diverse, robust yet flexible priority-setting frameworks are vital. Priority-setting is especially important for geographically widespread species with distinct populations subject to multiple threats that operate on different spatial and temporal scales. Marine turtles are widely distributed and exhibit intra-specific variations in population sizes and trends, as well as reproduction and morphology. However, current global extinction risk assessment frameworks do not assess conservation status of spatially and biologically distinct marine turtle Regional Management Units (RMUs), and thus do not capture variations in population trends, impacts of threats, or necessary conservation actions across individual populations. To address this issue, we developed a new assessment framework that allowed us to evaluate, compare and organize marine turtle RMUs according to status and threats criteria. Because conservation priorities can vary widely (i.e. from avoiding imminent extinction to maintaining long-term monitoring efforts) we developed a “conservation priorities portfolio” system using categories of paired risk and threats scores for all RMUs (n = 58). We performed these assessments and rankings globally, by species, by ocean basin, and by recognized geopolitical bodies to identify patterns in risk, threats, and data gaps at different scales. This process resulted in characterization of risk and threats to all marine turtle RMUs, including identification of the world's 11 most endangered marine turtle RMUs based on highest risk and threats scores. This system also highlighted important gaps in available information that is crucial for accurate conservation assessments. Overall, this priority-setting framework can provide guidance for research and conservation priorities at multiple relevant scales, and should serve as a model for conservation status assessments and priority-setting for widespread, long-lived taxa

Regional Management Units for Marine Turtles: A Novel Framework for Prioritizing Conservation and Research across Multiple Scales

Background: Resolving threats to widely distributed marine megafauna requires definition of the geographic distributions of both the threats as well as the population unit(s) of interest. In turn, because individual threats can operate on varying spatial scales, their impacts can affect different segments of a population of the same species. Therefore, integration of multiple tools and techniques - including site-based monitoring, genetic analyses, mark-recapture studies and telemetry - can facilitate robust definitions of population segments at multiple biological and spatial scales to address different management and research challenges. Methodology/Principal Findings: To address these issues for marine turtles, we collated all available studies on marine turtle biogeography, including nesting sites, population abundances and trends, population genetics, and satellite telemetry. We georeferenced this information to generate separate layers for nesting sites, genetic stocks, and core distributions of population segments of all marine turtle species. We then spatially integrated this information from fine-to coarse-spatial scales to develop nested envelope models, or Regional Management Units (RMUs), for marine turtles globally. Conclusions/Significance: The RMU framework is a solution to the challenge of how to organize marine turtles into units of protection above the level of nesting populations, but below the level of species, within regional entities that might be on independent evolutionary trajectories. Among many potential applications, RMUs provide a framework for identifying data gaps, assessing high diversity areas for multiple species and genetic stocks, and evaluating conservation status of marine turtles. Furthermore, RMUs allow for identification of geographic barriers to gene flow, and can provide valuable guidance to marine spatial planning initiatives that integrate spatial distributions of protected species and human activities. In addition, the RMU framework - including maps and supporting metadata - will be an iterative, user-driven tool made publicly available in an online application for comments, improvements, download and analysis

Recovery trend over 32 years at the Hawaiian green turtle rookery of French frigate shoals

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Temperature-dependent bioaccumulation of copper in an estuarine oyster

Bioaccumulation models are an important and widely-used tool for assessing ecosystem health with regards to heavy metal contamination. However, these models do not usually account for the potentially significant effect of temperature-dependency in metal uptake. In this study, we explored the role of temperature-dependency in heavy metal bioaccumulation by developing and comparing two kinetic-based copper bioaccumulation models for a common estuarine oyster (Saccostrea glomerata): (i) a standard first-order model that ignores temperature effects; and (ii) a modified first-order model that uses a standard temperature function to account for the temperature-dependency of the uptake rate constant. The models were calibrated within a Bayesian framework so that parameters could be treated as random variables and any uncertainty propagated through to the model output. A 12-month biomonitoring study was carried out within Moreton Bay, Queensland, Australia to provide time-series data for the modelling. Results of the modelling showed that the two bioaccumulation models provided comparable fits of the biomonitoring field data. However, dependent on the time of year and monitoring period selected, the copper uptake rate would vary dramatically due to temperature effects, which could result in an overestimation or underestimation of the copper uptake rate. Finally by calibrating the bioaccumulation models within a Bayesian framework, these models were able to utilize prior knowledge of the model parameters as part of the calibration process and also account for the uncertainty and variability in the bioaccumulation predictions. The ability to account for uncertainty and variability is an important consideration when undertaking environmental risk assessments especially in coastal waterways where there are strong seasonal variations