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

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.