Using Plasma Vitellogenin in Loggerhead Sea Turtles to Assess Reproductive Maturation and Estrogen-Like Contaminant Exposure

Vitellogenin (VTG), an egg yolk precursor, is abnormally produced by male and juvenile oviparous species after exposure to estrogens. Plasma VTG in loggerhead sea turtles (Caretta caretta) helped us understand their reproductive maturation and investigate it as a biomarker of contaminant exposure. The presence of VTG was screened in plasma from 404 loggerheads from the northwestern Atlantic Ocean using a freshwater turtle antibody in western blots. The concentrations of VTG were semiquantified using band intensities calibrated to results from a loggerhead antibody enzyme-linked immunoassay. The detection and concentrations of VTG were in (from highest to lowest): nesting females, in-water adult females, subadult females, smaller females, unknown sex, and males. Loggerheads from this region begin vitellogenesis at ≅77 cm straight carapace length. We classified VTG expression as abnormal in nine male or juvenile turtles. Organochlorine contaminant (OC) concentrations were measured in blood and/or fat biopsies of some turtles. One abnormal VTG female had the second highest fat polychlorinated biphenyl (PCB) and 4,4'-dichlorodiphenyldichloroethylene concentrations compared among 43 VTG-negative juveniles. The nine VTG-abnormal turtles had average blood PCB concentrations 8.5% higher, but not significantly different, than 46 VTG-negative juveniles (p = 0.453). In turtles less than 77 cm, blood PCB concentrations were significantly, but weakly, correlated with semiquantified VTG concentrations (tau = 0.1, p = 0.004). Greater blood OC concentrations were found in adult females than in males, which motivated the creation of a conceptual model of OC, VTG, and hormone concentrations across a reproductive cycle. A decision tree is also provided incorporating VTG as a sexing tool. Abnormal VTG expression cannot conclusively be linked to endocrine disruption caused by these OC concentrations. Studies should further investigate causes of abnormal VTG expression in wild sea turtles. Environ Toxicol Chem 2023;00:1-18. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Classifying Goliath Grouper (Epinephelus itajara) Behaviors from a Novel, Multi-Sensor Tag

Inertial measurement unit sensors (IMU; i.e., accelerometer, gyroscope and magnetometer combinations) are frequently fitted to animals to better understand their activity patterns and energy expenditure. Capable of recording hundreds of data points a second, these sensors can quickly produce large datasets that require methods to automate behavioral classification. Here, we describe behaviors derived from a custom-built multi-sensor bio-logging tag attached to Atlantic Goliath grouper (Epinephelus itajara) within a simulated ecosystem. We then compared the performance of two commonly applied machine learning approaches (random forest and support vector machine) to a deep learning approach (convolutional neural network, or CNN) for classifying IMU data from this tag. CNNs are frequently used to recognize activities from IMU data obtained from humans but are less commonly considered for other animals. Thirteen behavioral classes were identified during ethogram development, nine of which were classified. For the conventional machine learning approaches, 187 summary statistics were extracted from the data, including time and frequency domain features. The CNN was fed absolute values obtained from fast Fourier transformations of the raw tri-axial accelerometer, gyroscope and magnetometer channels, with a frequency resolution of 512 data points. Five metrics were used to assess classifier performance; the deep learning approach performed better across all metrics (Sensitivity = 0.962; Specificity = 0.996; F₁-score = 0.962; Matthew’s Correlation Coefficient = 0.959; Cohen’s Kappa = 0.833) than both conventional machine learning approaches. Generally, the random forest performed better than the support vector machine. In some instances, a conventional learning approach yielded a higher performance metric for particular classes (e.g., the random forest had a F₁-score of 0.971 for backward swimming compared to 0.955 for the CNN). Deep learning approaches could potentially improve behavioral classification from IMU data, beyond that obtained from conventional machine learning methods.

Biomarkers of reproduction in endangered green sea turtles (Chelonia mydas) nesting at Tortuguero, Costa Rica

Understanding the timing of vitellogenesis is essential for identifying threats to the reproductive success of endangered oviparous vertebrate species, such as sea turtles. We measured concentrations of testosterone (T) and vitellogenin (VTG) in green sea turtles (Chelonia mydas) nesting at Tortuguero, Costa Rica, as biomarkers of ovarian development. Testosterone concentration increased from the first to second month and VTG concentration increased at the third week of sampling. These results show that Tortuguero green sea turtles were still producing both biomarkers early into the nesting season. VTG concentration was negatively correlated with female weight, suggesting that larger females start nesting earlier at Tortuguero and that we may have sampled larger females further into their reproductive cycle.