Haematological and biochemical reference intervals for wild green turtles (Chelonia mydas): a Bayesian approach for small sample sizes

Colour Preference of Post Hatchling Hawksbill (Eretmochelys imbricata) and Green (Chelonia mydas) Sea Turtles in Captivity

Variations in the ecological roles of sea turtle species may lead to differentiations in ocular design and visual sensitivity to the colour spectrum. Behavioural colour preference studies in air and in water on hatchling and post-hatchling green turtles found evidence of a blue hue attractiveness when given a choice between blue, red, and yellow. This paper assessed and compared the colour preferences to singular colours via the behavioural responses of eleven hawksbill turtles and twelve green turtles at 15 months of age and at 22 months of age. Turtles were presented with one coloured water balloon per day (purple (400-450 nm), dark blue (450-490 nm), cyan (490-520 nm), green (520-560 nm), yellow (560-590 nm), orange (590-635 nm), and red (635-700 nm)). Time to contact balloons with beak and behaviours exhibited by turtles were recorded. Hawksbill turtles had the greatest level of interactions across both phases to shorter wavelengths with hue preference being between 450 and 490 nm. Green turtles consistently had the greatest level of interaction to longer wavelengths with a yellow (560-590 nm) hue preference. The results of this study support behavioural differences between two co-occurring turtle species that may reflect an adaptive preference for colour wavelengths associated with the optimal foraging niche for each.

Inter- and intraspecific blood-related biomarkers and chemical exposure in confined and free-living sea turtles

The relevance of recovery centers and head-starting programs for rescue, rehabilitation, rearing, and conservation of sea turtles is recognized worldwide. In addition, these centers contribute to generating biochemical and physiological data needed to identify health markers and provide baseline values. Because of the marine ecosystems' deterioration, biomarker identification is a global priority for sea turtle conservation; nevertheless, information on specific endpoints, such as neurotoxicity and mutagenesis, is still limited in sea turtles. This study aimed to contrast a set of non-invasive blood biomarkers with ecotoxicological and clinical applications in confined green sea turtles (Chelonia mydas) compared with free-living ones from the Mexican Caribbean. Additionally, interspecific (green, hawksbill (Eretmochelys imbricata), loggerhead (Caretta caretta) turtles) differences were also evaluated. Plasmatic organochlorine pesticides (OCs) and polychlorinated biphenyl (PCBs) were also determined. The concentration ranges of uric acid, total proteins, lipids (cholesterol and triglycerides), and thyroxine of both confined and free-living green turtles fell outside the reference intervals for the species. Additionally, confined green turtles had the highest number of erythrocytic nuclear abnormalities (ENA) and elevated levels of hemoglobin, lipid peroxidation, and activity of glutathione S-transferase, glutathione peroxidase, and carboxylesterase (CE). Contrasts among confined species identified hawksbill turtles with the lowest glutathione reductase activity, green turtles with the lowest ENA frequency and CE activity, and loggerhead turtles with the highest plasmatic concentrations of PCBs and OCs. The information here provided can be used as information in health monitoring programs and for conservation and management policies at regional, national, and international level.

Blood analyte reference intervals and correlations with trace elements of immature and adult Eastern Pacific green turtles (Chelonia mydas) in coastal lagoons of Baja California Sur, México

Sea turtles can bioaccumulate high concentrations of potentially toxic contaminants. To better understand trace element effects on sea turtles' health, we established reference intervals for hematological and plasma biochemical analytes in 40 in-water, foraging immature and adult Eastern Pacific green turtles (Chelonia mydas) from two coastal lagoons in Baja California Sur, quantified whole blood concentrations of eight trace elements, and assessed their correlations. Rank-order trace element concentrations in both immature and adult turtles was zinc > selenium > nickel > arsenic > copper > cadmium > lead > manganese. Immature turtles had significantly higher copper and lower nickel and zinc concentrations. Additionally, a number of relationships between trace elements and blood analytes were identified. These data provide baseline information useful for future investigations into this population, or in other geographic regions and various life-stage classes.

Field-based adipose tissue quantification in sea turtles using bioelectrical impedance spectroscopy validated with CT scans and deep learning

Loss of adipose tissue in vertebrate wildlife species is indicative of decreased nutritional and health status and is linked to environmental stress and diseases. Body condition indices (BCI) are commonly used in ecological studies to estimate adipose tissue mass across wildlife populations. However, these indices have poor predictive power, which poses the need for quantitative methods for improved population assessments. Here, we calibrate bioelectrical impedance spectroscopy (BIS) as an alternative approach for assessing the nutritional status of vertebrate wildlife in ecological studies. BIS is a portable technology that can estimate body composition from measurements of body impedance and is widely used in humans. BIS is a predictive technique that requires calibration using a reference body composition method. Using sea turtles as model organisms, we propose a calibration protocol using computed tomography (CT) scans, with the prediction equation being: adipose tissue mass (kg) = body mass - (-0.03 [intercept] - 0.29 * length2/resistance at 50 kHz + 1.07 * body mass - 0.11 * time after capture). CT imaging allows for the quantification of body fat. However, processing the images manually is prohibitive due to the extensive time requirement. Using a form of artificial intelligence (AI), we trained a computer model to identify and quantify nonadipose tissue from the CT images, and adipose tissue was determined by the difference in body mass. This process enabled estimating adipose tissue mass from bioelectrical impedance measurements. The predictive performance of the model was built on 2/3 samples and tested against 1/3 samples. Prediction of adipose tissue percentage had greater accuracy when including impedance parameters (mean bias = 0.11%-0.61%) as predictor variables, compared with using body mass alone (mean bias = 6.35%). Our standardized BIS protocol improves on conventional body composition assessment methods (e.g., BCI) by quantifying adipose tissue mass. The protocol can be applied to other species for the validation of BIS and to provide robust information on the nutritional and health status of wildlife, which, in turn, can be used to inform conservation decisions at the management level.