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Abstract Background Triaxial accelerometers have revolutionized wildlife research by providing an unprecedented understanding of the behavior of free-living animals. Machine learning is often applied to acceleration data to classify diverse animal behaviors across taxa. However, the high frequency, continuous data collection typically favored for behavioral classification studies often generates very large data sets, which may inhibit remote data acquisition and make data storage challenging. Coarse-frequency sampling or non-continuous bursts of acceleration data reduce these problems. To analyze such data, a suite of variables that summarize key features of the behavior of interest can be generated. These variables can then be used in numerous classification approaches, accommodating variation in data collection methods or sampling regimes. We demonstrate the potential for non-continuous accelerometer data to identify long-duration behavior and employ machine learning to classify the nesting behaviors of the critically endangered eastern Santa Cruz giant tortoise (Chelonoidis donfaustoi). Results We field validated 112 nesting events from 21 giant tortoises. We then derived summary statistics based on accelerometry (e.g., overall dynamic body acceleration, metrics comparing acceleration before and after the probable event) and used them as inputs for Random Forest and Boosted Regression Tree classification algorithms. Our models produced a harmonic mean of precision and sensitivity (F1-score) of 0.91. We tested the generality of our model and found that the model performs well when applied to both novel individuals and years. The most important variable in accurately classifying data sequences was the proportion of acceleration data bursts above an activity threshold followed by the average overall dynamic body acceleration value of the bursts. Conclusions These results demonstrate the feasibility and efficacy of using non-continuous accelerometer data to identify prolonged, biologically relevant behaviors in free-living wildlife. By using summary variables that do not require continuous sampling, this approach facilitates long-term monitoring of animal behavior. Similar methodology has potential to inform priority questions in ecology and conservation, such as predicting wildlife responses to climate change and identifying critical habitats, with applications across diverse species and behaviors.

Background: Triaxial accelerometers have revolutionized wildlife research by providing an unprecedented understanding of the behavior of free-living animals. Machine learning is often applied to acceleration data to classify diverse animal behaviors across taxa. However, the high frequency, continuous data collection typically favored for behavioral classification studies often generates very large data sets, which may inhibit remote data acquisition and make data storage challenging. Coarse-frequency sampling or non-continuous bursts of acceleration data reduce these problems. To analyze such data, a suite of variables that summarize key features of the behavior of interest can be generated. These variables can then be used in numerous classification approaches, accommodating variation in data collection methods or sampling regimes. We demonstrate the potential for non-continuous accelerometer data to identify long-duration behavior and employ machine learning to classify the nesting behaviors of the critically endangered eastern Santa Cruz giant tortoise (Chelonoidis donfaustoi). Results: We field validated 112 nesting events from 21 giant tortoises. We then derived summary statistics based on accelerometry (e.g., overall dynamic body acceleration, metrics comparing acceleration before and after the probable event) and used them as inputs for Random Forest and Boosted Regression Tree classification algorithms. Our models produced a harmonic mean of precision and sensitivity (F1-score) of 0.91. We tested the generality of our model and found that the model performs well when applied to both novel individuals and years. The most important variable in accurately classifying data sequences was the proportion of acceleration data bursts above an activity threshold followed by the average overall dynamic body acceleration value of the bursts. Conclusions: These results demonstrate the feasibility and efficacy of using non-continuous accelerometer data to identify prolonged, biologically relevant behaviors in free-living wildlife. By using summary variables that do not require continuous sampling, this approach facilitates long-term monitoring of animal behavior. Similar methodology has potential to inform priority questions in ecology and conservation, such as predicting wildlife responses to climate change and identifying critical habitats, with applications across diverse species and behaviors. [ABSTRACT FROM AUTHOR]

The reproductive success of polar bears (Ursus maritimus) in U.S. zoos is poor and the underlying causes of reproductive failure remain elusive. It is evident that new methodologies for evaluating fertility are needed, along with baseline information about species-specific reproductive anatomy and physiology. Between 2012 and 2024, 25 examinations of 17 female polar bears were conducted opportunistically, and included vulvar measurements, vaginal cytology, vaginoscopy, hysteroscopy with endometrial biopsy, and/or ultrasonography. Additionally, reproductive tracts (n = 10) harvested post-mortem were examined shortly after collection, allowing for inspection of anatomy ex vivo. The outcomes of these activities yielded detailed measurements and descriptions of reproductive anatomy. Vaginal cytology patterns demonstrated shifts in vaginal epithelial cell type associated with estrus and vaginoscopy enabled systematic evaluation of internal reproductive structures, including the vestibule, vestibulo-vaginal junction, urethral os, vaginal canal, and cervix. Hysteroscopy allowed visualization of the uterine endometrium and facilitated endometrial biopsy collection. Additionally, ultrasonography was utilized to identify ovaries in several bears, offering insights into ovarian dynamics and follicular development. Several abnormalities were noted during examinations of both in vivo and ex vivo tracts; these included clitoral hypertrophy, vaginal strictures, uterine polyps, endometritis, and cystic hyperplasia, although it is unclear if these aberrations significantly impacted fertility. Findings contribute to a better understanding of polar bear reproductive anatomy, highlight the utility of diagnostic techniques for monitoring reproduction, and elucidate potential causes of reproductive failure in polar bears.

Antecedentes: el monitoreo hormonal no invasivo (MHNI) a partir de muestras fecales constituye una herramienta para el estudio de la biología reproductiva de una especie. Su aplicación permite el desarrollo e implementación de estrategias de conservación más efectivas, las cuales contribuyen a incrementar la población de las especies que se encuentran en peligro de extinción. Objetivo: determinar el perfil reproductivo de las hembras de manatí antillano mediante la cuantificación de hormonales esteroides a través del uso de MHNI. Metodología: se recolectaron muestras de materia fecal de cuatro manatís durante 45 días, las cuales fueron sometidas a un proceso de extracción de esteroides, empleando una solución de metanol al 90 %. Para la cuantificación se aplicaron kits inmunoenzimáticos de la marca DetectX®, diseñados para medir cuantitativamente progesterona y estradiol en extractos de heces. Resultados: la utilización de los kits mencionados, así como la técnica de extracción de esteroides propuesta, permitieron la cuantificación de progesterona y estradiol, encontrando valores de 334,3 y 109,9 ng/g en heces, máximo y mínimo respectivamente para la progesterona, y para el estradiol los valores fueron de 90,9 a 12,2 ng/g de heces respectivamente. Conclusiones: los resultados obtenidos servirán como punto de partida para establecer los valores de esteroides fecales de la especie, los cuales podrán ser empleados en próximas investigaciones del ciclo reproductivo de las hembras. [ABSTRACT FROM AUTHOR]

Reproductive success in females relies primarily upon better understanding of fundamental ovarian processes to facilitate captive reproductive management and ultimately the development of assisted reproductive technologies as an alternative tool for conservation breeding of endangered wildlife species. Wild ungulates (cervids and non-domestic bovids) are an extraordinarily diverse group of mammals with remarkable diversity in reproductive biology (anatomy, behaviour, physiology and seasonality). This indicates a clear need, and indeed a big challenge, for acquiring such basic reproductive knowledge in severely threatened ungulates before attempting even a simpler technology such as artificial insemination. Despite a few sporadic successes of artificial insemination technology in ungulates, there is much to learn, including optimizing estrus synchronization protocols and an animal-friendly fixed-time insemination procedure that would maximize breeding success with minimal stress to the animal. Recent advances in non-invasive methodologies for monitoring endocrine profiles and assessment of ovarian function in wildlife species enable us to understand and characterize basic reproductive events in a species. This review discusses non-invasive approaches being used for monitoring endocrine and ovarian activity in wild ungulates. Further, it reviews the effectiveness of different methodologies for control of estrus and ovulation in non-domestic bovids and cervids. Additionally, the challenges regarding their application in different ungulates as a standard routine practice like in livestock are addressed. [ABSTRACT FROM AUTHOR]

We measured metabolites of progesterone (progestins) in fecal samples collected from captive Arabian oryx (Oryx leucoryx) females in postpartum (n=8), nonpregnant (n=9), and pregnant (n=8) reproductive stages between 1996 and 1998. We analyzed progestins using enzyme-immunoassays for pregnanediol and 20-oxo-pregnanes, respectively. Progestin concentrations were elevated for 3 days after parturition and then decreased to basal anestrous concentrations. Ovarian cyclicity resumed 25±2.4 days after parturition in 5 of the 8 females monitored. In nonpregnant females, excretion of fecal progestins followed a cyclic pattern increasing 6- to 12-fold from the follicular to the luteal phase. Fecal progestin concentrations allowed discrimination between pregnant and nonpregnant females after 3 months of gestation (P<0.01), mean concentration of the tested hormone metabolites being at least 3 times higher during mid and later stages of gestation (>3 months) than during early pregnancy (0-3 months). These data were subsequently used to set criteria for designation of a cow as pregnant in 55 free-ranging Arabian oryx in the reserve of Mahazat as-Sayd, Saudi Arabia sampled in 1998-1999 and 2003. The proportion of pregnant and nonpregnant oryx correctly identified by the test was 81% and 83%, respectively, when using both progestin assays. Despite a limited sample size, our results provide evidence that fecal progestin analysis is a reliable non- invasive method to determine the reproductive status of captive Arabian oryx and that it also can provide reasonably accurate physiological indices of pregnancy status in free- ranging specimens. [ABSTRACT FROM AUTHOR]