The Use of Selfie Camera Traps to Estimate Home Range and Movement Patterns of Small Mammals in a Fragmented Landscape

The use of camera traps to track individual mammals to estimate home range and movement patterns, has not been previously applied to small mammal species. Our aim was to evaluate the use of camera trapping, using the selfie trap method, to record movements of small mammals within and between fragments of habitat. In a fragmented landscape, 164 cameras were set up across four survey areas, with cameras left to record continuously for 28 nights. Live trapping was performed prior to ear mark animals to facilitate individual identification on camera. Four small mammal species (sugar glider; Petaurus breviceps; brown antechinus; Antechinus stuartii, bush rat; Rattus fuscipes, and brown rat; Rattus norvigecus) were recorded on camera (N = 284 individuals). The maximum distance travelled by an individual sugar glider was 14.66 km, antechinus 4.24 km; bush rat 1.90 km and brown rat 1.28 km. Movements of both female and male sugar gliders in linear fragments were recorded at much higher rates than in larger patches of forest sampled in grids. Short term core homes ranges (50% KDE) of 34 sugar gliders ranged from 0.3 ha to 4.2 ha. Sugar glider core home ranges were on average 1.2 ha (±0.17) for females and 2.4 ha (±0.28) for males. The selfie trap is an efficient camera trapping method for estimating home ranges and movements due to its ability to obtain high recapture rates for multiple species and individuals. In our study landscape, linear strips of habitat were readily utilised by all small mammals, highlighting their importance as wildlife corridors in a fragmented landscape.

Training in the Dark: Using Target Training for Non-Invasive Application and Validation of Accelerometer Devices for an Endangered Primate (Nycticebus bengalensis)

Simple Summary

Recent advances in technology allow for the study of animal behaviours through indirect observations. This facilitates research on cryptic animals for which direct observations may miss a considerable portion of their activity. The validity of accelerometers in obtaining accurate animal behaviours, however, needs to be tested before collecting data in the wild. Modern zoos offer excellent opportunities for researchers to test field techniques in a safe setting. Here, we describe a non-invasive training program to attach an accelerometer to an individual Bengal slow loris at the Shaldon Wildlife Trust. This training took 39 15-min sessions and allowed for the attachment of the accelerometer for validation with reduced stress for the animal. We also collected videos to associate to accelerometer data to estimate the accuracy of accelerometers in identifying the behaviours of Bengal slow loris. The accuracy was above 80% with some of the behaviours that were clearly identified (e.g., resting: 99.8%), while others were more difficult to discern (e.g., suspensory walk, a locomotion behaviour, was discerned only 60.3% of times from other behaviours). The non-invasive training and accelerometer validation can be used on similar species before using accelerometers in the wild.

Abstract

Accelerometers offer unique opportunities to study the behaviour of cryptic animals but require validation to show their accuracy in identifying behaviours. This validation is often undertaken in captivity before use in the wild. While zoos provide important opportunities for trial field techniques, they must consider the welfare and health of the individuals in their care and researchers must opt for the least invasive techniques. We used positive reinforcement training to attach and detach a collar with an accelerometer to an individual Bengal slow loris (Nycticebus bengalensis) at the Shaldon Wildlife Trust, U.K. This allowed us to collect accelerometer data at different periods between January–June 2020 and January–February 2021, totalling 42 h of data with corresponding video for validation. Of these data, we selected 54 min where ten behaviours were present and ran a random forest model. We needed 39 15-min sessions to train the animal to wear/remove the collar. The accelerometer data had an accuracy of 80.7 ± SD 9.9% in predicting the behaviours, with 99.8% accuracy in predicting resting, and a lower accuracy (but still >75% for all of them apart from suspensory walk) for the different types of locomotion and feeding behaviours. This training and validation technique can be used in similar species and shows the importance of working with zoos for in situ conservation (e.g., validation of field techniques).