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Aerial imagery and video recordings of animals are used for many areas of research such as animal behaviour, behavioural neuroscience and field biology. Many automated methods are being developed to extract data from such high-resolution videos. Most of the available tools are developed for videos taken under idealised laboratory conditions. Therefore, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. Methods that are useful for field conditions are often difficult to implement and thus remain inaccessible to empirical researchers. To address this gap, we present an open-source package called Multi-Object Tracking in Heterogeneous environments (MOTHe), a Python-based application that uses a basic convolutional neural network for object detection. MOTHe offers a graphical interface to automate the various steps related to animal tracking such as training data generation, animal detection in complex backgrounds and visually tracking animals in the videos. Users can also generate training data and train a new model which can be used for object detection tasks for a completely new dataset. MOTHe doesn't require any sophisticated infrastructure and can be run on basic desktop computing units. We demonstrate MOTHe on six video clips in varying background conditions. These videos are from two species in their natural habitat-wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track individuals in all these videos. MOTHe is available as an open-source GitHub repository with a detailed user guide and demonstrations at: https://github.com/tee-lab/MOTHe-GUI.

Manual analysis of large amount of wildlife image data collected through camera-trapping is time consuming, expensive, prone to errors and bias, and laborious. It also constrains geographical area, size and duration of wildlife studies, and delays decision making. Recent advancements in deep learning (DL) have provided promising solutions for automating such analyses, and addressed many such drawbacks. However, the existence of many object detectors with varied performance on different datasets may complicate the choice of appropriate object detector for a particular task. This study deployed experimental approach to achieve two goals; firstly, to compare predictive performance and processing speed of Faster R-CNN and single shot multibox detector (SSD) across four feature extractors namely ResNet50, ResNet101, ResNet152 and Inception ResNet on MS COCO, vis-à-vis their performance on camera-trap image dataset (CTID) with 11,019 images. Secondly, to assess and compare performance of the same object detectors when trained on the same CTID. We found that object detectors demonstrate a smaller range in mean average precision (mAP) of 2.72% in our CTID than in MS COCO dataset (8.4%) and that detectors' performance on MS COCO and CTID does not match. We also found that all detectors attained similar predictive performance (accuracy) for each animal class on CTID, and that they all performed well in detecting hyena, giraffe, warthog, lion, and guineafowl, but poorly on baboon, buffalo and wildebeest. Lastly, detectors performed better on some smallbodied species like guineafowl and hartebeest than on large-bodied species like zebra and elephant, respectively, despite the large-bodied species having more training data than small-bodied ones. Similarly, all object detectors performed poorly on zebra with the largest training data (1351 images) than on hyena (931), giraffe (920), warthog (979), lion (862), guineafowl (931), hartebeest (479) and elephant (744). Similarly, elephant was outperformed on hartebeest by all detectors. [ABSTRACT FROM AUTHOR]

Statistical models use observations of animals to make inferences about the abundance and distribution of species. However, the spatial distribution of animals is a complex function of many factors, including landscape and environmental features, and intra‐ and interspecific interactions. Modelling approaches often have to make significant simplifying assumptions about these factors, which can result in poor model performance and inaccurate predictions.Here, we explore the implications of complex spatial structure for modelling the abundance of the Serengeti wildebeest, a gregarious migratory species. The social behaviour of wildebeest leads to a highly aggregated distribution, and we examine the consequences of omitting this spatial complexity when modelling species abundance. To account for this distribution, we introduce a multi‐latent framework that uses two random fields to capture the clustered distribution of wildebeest.Our results show that simplifying assumptions that are often made in spatial models can dramatically impair performance. However, by allowing for mixtures of spatial models accurate predictions can be made. Furthermore, there can be a non‐monotonic relationship between model complexity and model performance; complex, flexible models that rely on unfounded assumptions can potentially make highly inaccurate predictions, whereas simpler more traditional approaches involve fewer assumptions and are less sensitive to these issues.We demonstrate how to develop flexible spatial models that can accommodate the complex processes driving animal distributions. Our findings highlight the importance of robust model checking protocols, and we illustrate how realistic assumptions can be incorporated into models using random fields. [ABSTRACT FROM AUTHOR]