The use of scent for communication is widespread in mammals, yet the role of scent-marking in the social system of many species is poorly understood. Nilgai antelope (Boselaphus tragocamelus) are native to India, Nepal, and Pakistan. They were introduced to Texas rangelands in the United States during the 1920s to 1940s, and have since expanded into much of coastal South Texas and northern Mexico. The nilgai social system includes the use of latrines or repeated defecation at a localized site. We quantified and described physical and behavioral characteristics of nilgai latrine ecology to investigate drivers of latrine use at three sites in South Texas, during April 2018 to March 2019. Latrines were abundant (2.6–8.7 latrines/ha on unpaved roads, 0.4–0.9 latrines/ha off-roads), with no evidence for selection as to vegetation communities; latrines were dynamic in persistence and visitation rates. We found higher densities of latrines in Spring surveys, just after the peak of nilgai breeding activity, compared to Autumn surveys. Density of nilgai latrines was 3–10 times greater than estimated population densities, indicating individual nilgai must use multiple latrines. Camera traps and fecal DNA analysis revealed latrines were mainly (70%) visited by bulls and defecated on by bulls (92% in photos, 89% for DNA samples). The greatest frequency of visits occurred during the peak in the nilgai breeding season, from December–February; latrines were visited every 2–3 days on average. Body characteristics of photographed individuals and genetic analysis of feces indicated repeated visits from the same individuals. Nilgai cows occasionally used latrines; their use was sometimes followed by bulls showing flehmen responses after a female defecated or urinated on the latrine. We propose that dominant bulls use latrines for territory demarcation to display social dominance to both cows in estrus and subordinate bulls. Cows likely use latrines to communicate reproductive status. This study is the first intensive assessment focused on latrine ecology in nilgai. Our results directly contradict anecdotal descriptions of latrine use and behavior in nilgai but are consistent with predictions of antelope social systems based on body size, feeding type, and group dynamics.

Motion-activated wildlife cameras, or camera traps, are widely used in biological monitoring of wildlife. Studies using camera traps amass large numbers of images and analyzing these images can be a large burden that inhibits research progress. We trained deep learning computer vision models using data for 168 species that automatically detect, count, and classify common North American domestic and wild species in camera trap images. We provide our trained models in an R package, CameraTrapDetectoR. Three types of models are available: a taxonomic class model classifies objects as mammal (human and non-human) or avian; a taxonomic family model that recognizes 31 mammal, avian, and reptile families; a species model that recognizes 75 domestic and wild species including all North American wild cat species, bear species, and Canid species. Each model also includes a category for vehicles and empty images. The models performed well on both validation datasets and out-of-distribution testing datasets as mean average precision values ranged from 0.80 to 0.96. CameraTrapDetectoR provides predictions as an R object (a data frame) and flat file and provides the option to create plots of the original camera trap image with the predicted bounding box and label. There is also the option to apply models using a Shiny Application, with a point-and-click graphical user interface. This R package has the potential to facilitate application of deep learning models by biologists using camera traps.

Net‐wire fencing built to confine livestock is common on rangelands in the Southwestern USA, yet the impacts of livestock fencing on wildlife are largely unknown. Many wildlife species cross beneath fences at defined crossing locations because they prefer to crawl underneath rather than jump over fences. Animals occasionally become entangled jumping or climbing over fences, leading to injury or death. More commonly, repeated crossings under net‐wire fencing by large animals lead to fence damage, though the damage is often tolerated by landowners until the openings affect the ability to enclose livestock. The usage, placement, characteristics, and passage rates of fence crossings beneath net‐wire fencing are poorly understood. We monitored 20 randomly selected fence crossings on net‐wire livestock fencing across two study sites on rangelands in South Texas, USA, from April 2018 to March 2019. We assessed the characteristics of fence‐crossing locations (openings beneath the fence created by animals to aid in crossing) and quantified crossing rates and the probability of crossing by all species of animals via trail cameras. We documented 10,889 attempted crossing events, with 58% ( n = 6271) successful. Overall, 15 species of medium‐ and large‐size mammals and turkey ( Meleagris gallopavo ) contributed to crossing events. Crossing locations received 3–4 crossing attempts per day on average, but the number of attempts and probability of successful crossing varied by location and fence condition. The probability of crossing attempts was most consistently influenced by the opening size of the crossing and season; as crossing size (opening) increased, the probability of successful crossing significantly increased for all species. Peaks in crossing activity corresponded with species' daily and seasonal movements and activity. The density and size of fence‐crossing locations were dependent on fence maintenance and not associated with vegetation communities or habitat variables. However, crossing locations were often re‐established in the same locations after fence repairs. This is one of the few studies to monitor how all animal species present interacted with net‐wire livestock fencing in rangelands. Our results will help land managers understand the impact of net‐wire livestock fencing on animal movement.