Modeling heterothermic fitness landscapes in a marsupial hibernator using changes in body composition

Hibernation is an adaptive strategy that allows animals to enter a hypometabolic state, conserving energy and enhancing their fitness by surviving harsh environmental conditions. However, addressing the adaptive value of hibernation, at the individual level and in natural populations, has been challenging. Here, we applied a non-invasive technique, body composition analysis by quantitative magnetic resonance (qMR), to calculate energy savings by hibernation in a population of hibernating marsupials (Dromiciops gliroides). Using outdoor enclosures installed in a temperate rainforest, and measuring qMR periodically, we determined the amount of fat and lean mass consumed during a whole hibernation cycle. With this information, we estimated the daily energy expenditure of hibernation (DEEH) at the individual level and related to previous fat accumulation. Using model selection approaches and phenotypic selection analysis, we calculated linear (directional, β), quadratic (stabilizing or disruptive, γ) and correlational (ρ) coefficients for DEEH and fat accumulation. We found significant, negative directional selection for DEEH (βDEEH = - 0.58 ± 0.09), a positive value for fat accumulation (βFAT = 0.34 ± 0.07), and positive correlational selection between both traits (ρDEEH × FAT = 0.24 ± 0.07). Then, individuals maximizing previous fat accumulation and minimizing DEEH were promoted by selection, which is visualized by a bi-variate selection surface estimated by generalized additive models. At the comparative level, results fall within the isometric allometry known for hibernation metabolic rate in mammals. Thus, by a combination of a non-invasive technique for body composition analysis and semi-natural enclosures, we were characterized the heterothermic fitness landscape in a semi-natural population of hibernators.

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.

Integrative Physiological Responses to Acute Dehydration in the Rufous-Collared Sparrow: Metabolic, Enzymatic, and Oxidative Traits

Predictions indicate that birds worldwide will be affected by global warming and extreme climatic events which is especially relevant for passerines because the diurnal habits, small body size, and high mass-adjusted metabolic rates of this group make it particularly susceptible to increases in temperature and aridity. Some bird species respond to conditions that stress osmoregulation by increasing their rates of energy expenditure, nevertheless, the effect of dehydration on metabolic rates in birds has produced contrasting results. It also remains unknown whether hydration state may cause shifts in tissue-specific metabolic rates or modify tissue oxidative status. We used the rufous-collared sparrow (Zonotrichia capensis), to experimentally test the effect of dehydration on metabolic enzymes in erythrocytes, tissue oxidative status, basal metabolic rate (BMR), and total evaporative water loss. We found a significant increase in mass-adjusted BMR in water restricted (WR) birds compared to control birds (CT). Activity of cytochrome-c-oxidase (COX) in red blood cells (RBCs) was also significantly higher in the WR group relative to the CT group and this activity was positively correlated with mass-adjusted BMR. We found a moderate effect of water restriction on membrane damage of skeletal muscle. In a second set of individuals subjected to the same experimental conditions, lean mass and total water were tightly correlated and decreased by 10 and 12%, respectively, in birds in the WR group relative to the CT group. Decreases in total water and lean mass leads to an increase in mass-adjusted BMR in WR Z. capensis, suggesting that birds may simultaneously increase protein catabolism and production of metabolic water through oxidation. The significant positive relationship between BMR and COX in RBCs is a finding that requires additional research to determine whether erythrocyte metabolism is affected by dehydration per se and or it more generally reflects rates of energy expenditure in birds.