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Precision livestock management incorporates new technologies, including bio-loggers, to remotely monitor livestock health and behavior. Despite the potential benefits in extensive cattle systems, limited adoption of these sensors has occurred potentially due to cost, technical, or processing challenges. We resampled high-resolution GPS and accelerometer data across multiple epochs (spanning 10 s to 15 min) to evaluate which combinations of devices, data features, and epochs might be considered optimal for assessing resting, grazing, travel, and rumination behavior in free-ranging cattle. We used random forest models to predict cattle behavior across the growing season to assess how variations in model accuracy were reflected in inference of activity budgets. Classification accuracy was greatest (>0.90) when GPS information was combined with at least one accelerometer metric. Epochs of 30–90 s provided the greatest classification accuracy, although epochs up to 300 s had similar classification accuracies, but with increased variability in accuracy. Classification accuracies decreased when we included rumination, but similarly had the greatest performance when both GPS and a full suite of accelerometer features was used (accuracy of ∼0.90). Average daily grazing time (8.3 h day-1) was within 2 h across devices, epochs, and behavioral schemes. Rumination time was again similar across devices and epochs, averaging 6.5 h day-1. Daily travel distance decreased by ∼4 km as the GPS fix interval increased from 10 s to 15 min. This study provides guidance for balancing fine-scale data collection with data processing and battery limitations for assessing cattle behaviors in extensive rangelands.

Monitoring wildlife is fundamental to managing the health of rangelands but challenging due to the extensive and dynamic nature of these ecosystems. The black-tailed prairie dog (Cynomys ludovicianus) is considered both a keystone species of conservation concern and an agricultural pest. This animal is an example of a wildlife species for which detailed monitoring is both high priority and difficult to accomplish cost-effectively using ground-based methods. In this study, we conducted a robust evaluation of the potential to use deep learning to detect prairie dog burrows from remotely sensed imagery acquired from unoccupied aerial systems (UAS). We processed UAS imagery to create RGB, topographic position index (TPI) and normalized difference vegetation index (NDVI) products at varying spatial resolutions (2–30 cm). We then evaluated the minimum set of inputs and image resolution required to train a deep convolutional neural network (CNN) for burrow detection and scale this up to identify entire colonies. We validated results at the scale of individual burrows, sub-colony burrow density and range-wide colony area using ground and digitized observations. We found the 2 cm imagery proved computationally impractical for scaling, but performance did not decline between 2 and 5 cm imagery, and models performed well up to 10–15 cm. The top models always included TPI and the combination of RGB + TPI tended to perform best across spatial resolutions. Adding NDVI generally did not improve model performance. At 5 cm resolution, the top models achieved high precision and recall for detecting individual burrows (F-score 0.84–0.87) and burrow density was strongly correlated with validation data (r = 0.94–0.95). In pastures with active colonies, overlap between predicted and ground delineated colonies was high (60–94%). The CNN-based approach could not distinguish between currently active colonies and a colony that had recently become inactive due to a sylvatic plague (Yersinia pestis) epizootic. However, further analysis showed that CNN-derived burrow density was related to colony age and satellite-derived vegetation conditions in active colonies, and that the plague-affected colony deviated from expected vegetation trends. We conclude that a deep learning algorithm can accurately detect prairie dog burrows from UAS imagery acquired at 5–10 cm resolution, and that scaling from individual burrows to entire colonies is achievable but warrants further research. Combining CNN-derived burrow density maps with satellite-derived vegetation conditions may help identify recent colony abandonment, despite ongoing presence of burrows.