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1. Quantifying the Autonomic Response to Stressors—One Way to Expand the Definition of “Stress” in Animals

   https://doi.org/10.1093/icb/icaa009  

   Gaidica, Matt ; Dantzer, Ben ( April 2020 , Integrative and Comparative Biology)

   null (Ed.)

   Abstract Quantifying how whole organisms respond to challenges in the external and internal environment (“stressors”) is difficult. To date, physiological ecologists have mostly used measures of glucocorticoids (GCs) to assess the impact of stressors on animals. This is of course too simplistic as Hans Seyle himself characterized the response of organisms to “noxious stimuli” using multiple physiological responses. Possible solutions include increasing the number of biomarkers to more accurately characterize the “stress state” of animal or just measuring different biomarkers to more accurately characterize the degree of acute or chronic stressors an animal is experiencing. We focus on the latter and discuss how heart rate (HR) and heart rate variability (HRV) may be better predictors of the degree of activation of the sympathetic–adrenal–medullary system and complement or even replace measures of GCs as indicators of animal health, welfare, fitness, or their level of exposure to stressors. The miniaturization of biological sensor technology (“bio-sensors” or “bio-loggers”) presents an opportunity to reassess measures of stress state and develop new approaches. We describe some modern approaches to gathering these HR and HRV data in free-living animals with the aim that heart dynamics will be more integrated with measures of GCs as bio-markers of stress state and predictors of fitness in free-living animals. 

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2. Behavior and detection method influence detection probability of a translocated, endangered amphibian

   https://doi.org/10.1111/acv.12645  

   Hammond, Talisin T. ; Curtis, Michelle J. ; Jacobs, Leah E. ; Tobler, Mathias W. ; Swaisgood, Ronald R. ; Shier, Debra M. ( October 2020 , Animal Conservation)

   Accurate estimates of survival are crucial for many management decisions in translocation programs. Maximizing detection probabilities and reducing sampling biases for released animals can aid in estimates of survival. One important source of sampling bias is an animal’s behavior. For example, individuals that are consistently more exploratory or active may be more likely to be detected visually. Behavioral traits can be related to survival after reintroduction, and because many pre‐release treatments aim to manipulate animal behavior, it is critical to tease apart relationships between behavior and detection probability. Here, we assessed the repeatability (intra‐individual consistency and inter‐individual variation) of behavioral traits for an endangered amphibian, the mountain yellow‐legged frog (Rana muscosa). Because new technological tools offer one potential solution for reducing sampling biases while increasing detection, we also tested whether a long‐range passive integrated transponder (PIT) tag reader could enhance surveys for these individuals after translocation into the wild. After confirming thatex situbredR. muscosaexhibit repeatable behavioral traits (repeatability = 0.25–0.41) and releasing these frogs (N = 196) into the wild, we conducted post‐release surveys visually and with the long‐range PIT tag reader. Integrating the long‐range reader into surveys improved detection probability four‐fold in comparison to visual surveys alone (~0.09 to ~0.36). Moreover, mark–recapture modeling revealed that tag reader detection probability was not biased toward detecting individuals of specific behavioral types, while visual detection was significantly related to behavioral traits. These results will enable a more accurate understanding of individual differences in post‐release success in translocations. This may be particularly important for amphibian species, which can be difficult to detect and are expected to increasingly be involved in human‐managed breeding and translocation programs due to their vulnerable conservation status.

    

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3. Behavioral variation, adaptation, and evolution

   https://doi.org/doi:10.1037/0000011-011  

   Shelton, D. S. ; Martins, E. P. ( January 2017 , APA)

   Individual animals behave differently from each other for myriad interrelated intrinsic and extrinsic reasons, and this behavioral variation is the raw substrate for evolutionary change. Behavioral varia- tion can both enhance and constrain long-term evolution (Foster, 2013), and it provides the basic materials on which natural and sexual selection can act. A rich body of historical experimental and conceptual foundations precedes many of the topics discussed. This classic literature is vast and impor- tant, and we encourage the reader to examine it in detail (e.g., Lehrman, 1953; Lorenz, 1971; Schnei- rla, 1966; Waddington, 1959) because we discuss more recent literature. For example, the study of the mechanisms that underlie behavioral variation has a divisive history, which involves carving out the relative contributions of genes and environment to a particular phenotype. Developmental systems and reaction-norm views challenged the issue of gene or environment by arguing that the interplay between genetic substrates and environmental inputs defined adaptive phenotypes across multiple contexts (Fos- ter, 2013; Gottlieb, 1991a, 1991b; Jablonka & Lamb, 2014). Identifying the interactional relationship between components permits researchers to under- stand how behavior becomes organized (Gottlieb, 1991a, 1991b) and can reveal links between indi- vidual variation and population-level persistence, species diversification (or stasis), and community dynamics (reviewed in Dingemanse & Wolf, 2013). Similarly, the study of individual differences has a rich history situated in the areas of behavioral genet- ics, sociobiology, behavioral ecology, developmen- tal psychology, personality theory, and studies of learning and cognition. Each area has its own goals, associated techniques, and levels of explanation. The study of behavioral variation during early develop- ment, for instance, has been documented primarily by psychologists studying proximate mechanisms in laboratory animal models, whereas the study of dif- ferent adult morphs using the adaptationist perspec- tive has been dominated by behavioral ecologists examining natural populations (Foster, 1995). A more complete description of individual differences requires an integrative study of the mechanisms (e.g., developmental, physiological) that guide intra- individual flexibility and the associated adaptive fine tuning of behavioral types. It is through this integra- tion that researchers can make predictions about the response of different individual phenotypes, groups, populations, and species to novel situations (e.g., captive and urban environments). 

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4. Studying animal locomotion with multiple data loggers: quantifying time drift between tags

   https://doi.org/10.1186/s40317-024-00363-4  

   White, Connor F. ; Lauder, George V. ( April 2024 , Animal Biotelemetry)

   Temporal accuracy is a fundamental characteristic of logging technology and is needed to correlate data streams. Single biologgers sensing animal movement (accelerometers, gyroscope, magnetometers, collectively inertial measurement unit; IMU) have been extensively used to study the ecology of animals. To better capture whole body movement and increase the accuracy of behavior classification, there is a need to deploy multiple loggers on a single individual to capture the movement of multiple body parts. Yet due to temporal drift, accurately aligning multiple IMU datasets can be problematic, especially as deployment duration increases. In this paper we quantify temporal drift and errors in commercially available IMU data loggers using a combination of robotic and animal borne experiments. The variance in drift rate within a tag is over an order of magnitude lower (σ = 0.001 s h⁻¹) than the variance between tags (σ = 0.015 s·h⁻¹), showing that recording frequency is a characteristic of each tag and not a random variable. Furthermore, we observed a large offset (0.54 ± 0.016 s·h⁻¹) between two groups of tags that had differing recording frequencies, and we observed three instances of instantaneous temporal jumps within datasets introducing errors into the data streams. Finally, we show that relative drift rates can be estimated even when deployed on animals displaying various behaviors without the tags needing to be simultaneously moved. For the tags used in this study, drift rates can vary significantly between tags, are repeatable, and can be accurately measured in the field. The temporal alignment of multiple tag datasets allows researchers to deploy multiple tags on an individual animal which will greatly increase our knowledge of movement kinematics and expand the range of movement characteristics that can be used for behavioral classification.

    

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5. Noninvasively Monitoring Orangutan Health Status: Determining Urine Concentrations

   Malbouf, Bryanna ; Kane, Erin E ; L Durgavich, Lara ; Knott, Cheryl D ( November 2019 , Fifth Annual Meeting of the Northeastern Evolutionary Primatologists)

   Biomarkers including reproductive hormones and indicators of energy balance can be used to analyze health status in wild animals. Non-invasive measures, analyzed through urine or feces, enable biomarker monitoring without interfering with organisms, important for critically endangered species like orangutans. A measure of urine concentration such as creatinine concentrations or specific gravity is necessary when analyzing urine samples. Here, we measure specific gravity in urine samples from three captive female orangutans using a digital hand-held urine specific gravity refractometer. We compare specific gravity to previously measured creatinine values for two orangutans, and assess the influence of time of collection and refractometer temperature on specific gravity for all three. We found a significant positive correlation between specific gravity and creatinine concentrations (N=1021, Pearson’s R=0.578, p<0.001). While we found no significant correlation between the time that samples were collected and specific gravity readings (N= 314, Pearson’s R = 0.079, p=0.17), readings from samples collected in the morning were slightly but significantly lower (N=255, mean=1.008) than samples collected in the afternoon (N=60, mean=1.009) (independent samples t-test, t312=-1.969, p=0.05). We found a significant negative correlation between specific gravity and the refractometer temperature (Pearson’s R=-0.23, p<0.001). In future studies, specific gravity can be used to determine urine concentration rather than creatinine, which is more costly and requires more time for lab work. Our future research will examine the correlation between specific gravity and creatinine concentrations as orangutans age, and the effects of aging on muscle wasting and reproductive status. 

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