230 H. D. Slater et al.


FIG. 1 Monitoring locations in Sikundur and Aras Napal and the Sikundur region within the Leuser Ecosystem in Sumatra, Indonesia.


Microclimate


At each monitoring location, we recorded below-canopy hourly ambient temperature (°C) and light intensity (lux) using Onset HOBO UA-002-08 8 K Pendant Waterproof Temperature & Light Intensity Loggers (Onset, Bourne, USA). Because of a software issue, we were only able to col- lect microclimate data for 49 out of the 60 days. We placed the loggers in a shaded location to minimize the risk of greenhouse effects within the sensor casing from direct sunlight; despite this, there were still some instances of elevated temperature and light intensity measurements. To minimize the effect of these records in the final dataset, we removed data points for which the recorded light inten- sity exceeded 32,000 lux (level of direct sunlight; Hiscocks, 2011, inMarsh et al., 2022). Wealso excluded data points for which temperature increased by.5 °C between consecutive hourly recordings, and the two data points immediately fol- lowing them (to ensure loggers had sufficient time to return to ambient temperatures). Overall, 223 data points were re- moved from the analysis. Finally, we summarized climate data for the entire sampling period (including both night- time and daytime temperatures) and for each 24-h period.


Mammal occurrence


We used mammal detections to determine the occurrence of detectable terrestrial mammal species at each sampling location. At each location, we deployed one SpyPoint Force-Dark remote trail camera (Eurohunt, Harztor, Germany), using 20 cameras in total. These cameras fea- ture 42 no-glow light-emitting diodes and take infrared images at night, thereby avoiding the disturbance of wild- life with camera flashes. We secured cameras at a height


of c. 0.5 m from the ground, facing towards animal signs or probable trails. We removed any vegetation directly in front of the cameras to provide a clear field of view. We did not bait camera locations to avoid influencing animal movements. We set the cameras to take one image followed by a 30-s video when triggered by movement. At the end of themonitoring period,we collected the cam-


eras and identified captured species in individual images using the Integrated Taxonomic Information System data- base (ITIS, 2021).We attached a metadata tag with the stan- dardized common name of the identified species to each image.We extracted image metadata and tabulated allmam- mal detection events with the camtrapR package in R 4.0.0 (Niedballa et al., 2016;R Core Team, 2020). We applied a minimum delta time (i.e. the time between two subsequent detection events of the same species at the same location) of 1 h to prevent captures of the same individual being counted multiple times at one location. For each location, we counted the total number of mammal detections and the number of detections grouped by mammal order. We calculated the naïve occupancy (i.e. the proportion of sites at which a species was detected) of each detected mammal species or family. We checked that the sampling effort was sufficient to capture all detectable families using a species accumulation curve plotted with the vegan package in R (Oksanen et al., 2019). We did this at the family level as some smallmammals could not be identified to species level.


Data analysis


We pooled data from all locations and grouped them by distance from the forest edge (0.0, 0.5, 1.0, 1.5 and 2.0 km). We estimated mammal species richness by calculating the


Oryx, 2024, 58(2), 228–239 © The Author(s), 2023. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605323000212


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