Latrine ecology of nilgai antelope

Abstract 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.</jats:p

CameraTrapDetectoR: Automatically detect, classify, and count animals in camera trap images using artificial intelligence

AbstractMotion-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.</jats:p

CameraTrapDetectoR General Model

This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR general model versions 1 and 2. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts mammals and birds in camera trap images, and includes categories for humans, vehicles, and a background class. Additional information about the R package and the training data can be found in the package's Github repository: https://github.com/CameraTrapDetectoR/CameraTrapDetectoR List of Resources: general_v1.zip is a folder containing the model architecture, model weights, and class label dictionary used to deploy the general model version 1 in CameraTrapDetectoR general_v2.zip is a folder containing the model architecture, model weights, and class label dictionary used to deploy the general model version 2 in CameraTrapDetectoR general_v2_cl.zip is a folder containing the all information to deploy the general v2 model via Python script from the command line. Full instructions for set up and use may be found at https://github.com/CameraTrapDetectoR/model_training </p

CameraTrapDetectoR Species Model

CameraTrapDetectoR is an R package that uses deep learning computer vision models to automatically detect, count, and classify common North American domestic and wild species in camera trap images. Data for all versions of the taxonomic species model are located in this dataset. This data is automatically downloaded, extracted, and deployed in the tool's deploy_model function. Additional information about the R package and the training data can be found in the package's Github repository: https://github.com/CameraTrapDetectoR/CameraTrapDetectoR This research used resources provided by the SCINet project and the AI Center of Excellence of the USDA Agricultural Research Service, ARS project number 0500-00093-001-00-D. List of Resources: species_v1.zip is a folder containing the model weights, model architecture, and class label dictionary for the first version of the species model. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. species_v2.zip is a folder containing the model weights, model architecture, and class label dictionary for the second version of the species model. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone, trained on the ARS SCINet Atlas cluster. This model identifies and counts 78 North American species in camera trap images, including humans vehicles and a background class. The training dataset contains 169,352 unique images, with an average of 2199 images per class excluding background class. The (min, max) range of images count per class is (107, 7027); this class imbalance was addressed with a suite of data augmentations and weighted random sampling. Images were acquired from a total of 26 databases across North America. species_v2_cl.zip is a folder containing the all information to deploy the species v2 model via Python script from the command line. Full instructions for set up and use may be found at https://github.com/CameraTrapDetectoR/model_training </p

CameraTrapDetectoR Pig Model

This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR pig-only model version 1. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts wild pigs in camera trap images. It also includes categories for non-pig detections (animal, human, or vehicle), and a background class. Additional information about the R package and the training data can be found in the package's Github repository: https://github.com/CameraTrapDetectoR/CameraTrapDetectoR List of Resources: pig_v1.zip is a folder containing the model architecture, model weights, and class label dictionary used to deploy the pig-only model version 1 in CameraTrapDetectoR </ul

CameraTrapDetectoR Family Model

This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR family model version 1. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts animals from 33 North American taxonomic families in camera trap images, including humans vehicles and a background class. Additional information about the R package and the training data can be found in the package's Github repository: https://github.com/CameraTrapDetectoR/CameraTrapDetectoR Resources in this dataset: family_v1.zip is a folder containing the model architecture, model weights, and class label dictionary required to deploy the first generation family model in CameraTrapDetectoR family_v2.zip is a folder containing the model architecture, model weights, and class label dictionary required to deploy the family model version 2 in CameraTrapDetectoR family_v2_cl.zip is a folder containing the all information to deploy the family v2 model via Python script from the command line. Full instructions for set up and use may be found at https://github.com/CameraTrapDetectoR/model_training </p