Foraging and habitat use of common duikers,Sylvicapra grimmia, in a heterogeneous environment within the Soutpansberg, South Africa

Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning

Using machine learning (ML) to automate camera trap (CT) image processing is advantageous for time-sensitive applications. However, little is currently known about the factors influencing such processing. Here, we evaluate the influence of occlusion, distance, vegetation type, size class, height, subject orientation towards the CT, species, time-of-day, colour, and analyst performance on wildlife/human detection and classification in CT images from western Tanzania. Additionally, we compared the detection and classification performance of analyst and ML approaches. We obtained wildlife data through pre-existing CT images and human data using voluntary participants for CT experiments. We evaluated the analyst and ML approaches at the detection and classification level. Factors such as distance and occlusion, coupled with increased vegetation density, present the most significant effect on DP and CC. Overall, the results indicate a significantly higher detection probability (DP), 81.1%, and correct classification (CC) of 76.6% for the analyst approach when compared to ML which detected 41.1% and classified 47.5% of wildlife within CT images. However, both methods presented similar probabilities for daylight CT images, 69.4% (ML) and 71.8% (analysts), and dusk CT images, 17.6% (ML) and 16.2% (analysts), when detecting humans. Given that users carefully follow provided recommendations, we expect DP and CC to increase. In turn, the ML approach to CT image processing would be an excellent provision to support time-sensitive threat monitoring for biodiversity conservation.

Zooming in on mechanistic predator-prey ecology: Integrating camera traps with experimental methods to reveal the drivers of ecological interactions

Camera trap technology has galvanized the study of predator-prey ecology in wild animal communities by expanding the scale and diversity of predator-prey interactions that can be analysed. While observational data from systematic camera arrays have informed inferences on the spatiotemporal outcomes of predator-prey interactions, the capacity for observational studies to identify mechanistic drivers of species interactions is limited. Experimental study designs that utilize camera traps uniquely allow for testing hypothesized mechanisms that drive predator and prey behaviour, incorporating environmental realism not possible in the laboratory while benefiting from the distinct capacity of camera traps to generate large datasets from multiple species with minimal observer interference. However, such pairings of camera traps with experimental methods remain underutilized. We review recent advances in the experimental application of camera traps to investigate fundamental mechanisms underlying predator-prey ecology and present a conceptual guide for designing experimental camera trap studies. Only 9% of camera trap studies on predator-prey ecology in our review use experimental methods, but the application of experimental approaches is increasing. To illustrate the utility of camera trap-based experiments using a case study, we propose a study design that integrates observational and experimental techniques to test a perennial question in predator-prey ecology: how prey balance foraging and safety, as formalized by the risk allocation hypothesis. We discuss applications of camera trap-based experiments to evaluate the diversity of anthropogenic influences on wildlife communities globally. Finally, we review challenges to conducting experimental camera trap studies. Experimental camera trap studies have already begun to play an important role in understanding the predator-prey ecology of free-living animals, and such methods will become increasingly critical to quantifying drivers of community interactions in a rapidly changing world. We recommend increased application of experimental methods in the study of predator and prey responses to humans, synanthropic and invasive species, and other anthropogenic disturbances.

Too much of a good thing? A landscape-of-fear analysis for collared peccaries (Pecari tajacu) reveals hikers act as a greater deterrent than thorny or bitter food

To study how wildlife perceive recreating humans, we studied the habitat selection of a human commensalist, the collared peccary (Pecari tajacu (Linnaeus, 1758)). We measured peccary activity patterns in an area of high human activity (Tumamoc Hill Desert Laboratory in Tucson, Arizona, USA) using a landscape-of-fear analysis. We examined whether the perception of risk from human activity interacted with the chemical (tannin) and mechanical (thorns) antipredator mechanisms of local plant species. The peccaries avoided food stations near a hiking trail. The population foraged less near houses, i.e., moderate human activity, than in the perceived safety of a small wadi. Plant defence treatments impacted the harvesting of food only in the safe zone, suggesting that risk trumps food selectivity. The strong effect of the hiking trail on habitat selection in this disturbance-loving species is an indicator of a much larger impact on sensitive species in conservation areas.