Detecting and monitoring rodents using camera traps and machine learning versus live trapping for occupancy modeling

Abstract

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Determining best methods to detect individuals and monitor populations that balance effort and efficiency can assist conservation and land management. This may be especially true for small, non-charismatic species, such as rodents (Rodentia), which comprise 39% of all mammal species. Given the importance of rodents to ecosystems, and the number of listed species, we tested two commonly used detection and monitoring methods, live traps and camera traps, to determine their efficiency in rodents. An artificial-intelligence machine-learning model was developed to process the camera trap images and identify the species within them which reduced camera trapping effort. We used occupancy models to compare probability of detection and occupancy estimates for six rodent species across the two methods. Camera traps yielded greater detection probability and occupancy estimates for all six species. Live trapping yielded biasedly low estimates of occupancy, required greater effort, and had a lower probability of detection. Camera traps, aimed at the ground to capture the dorsal view of an individual, combined with machine learning provided a practical, noninvasive, and low effort solution to detecting and monitoring rodents. Thus, camera trapping with machine learning is a more sustainable and practical solution for the conservation and land management of rodents.

Keywords

• detection
• occupancy
• machine learning
• effort
• camera trapping
• live trapping