1. Arboreal camera trapping is a burgeoning method providing a novel and effective technique to answer research questions across a variety of ecosystems, and it has the capacity to improve our understanding of a wide range of taxa. However, while terrestrial camera trapping has received much attention, there is little guidance for dealing with the unique challenges of working in the arboreal realm.2. Our review draws on the expertise of researchers from six continents and the broader literature to investigate the advantages and disadvantages of arboreal camera trapping, and challenges to consider when using this technology. We also include mini-guides with detailed information on the current arboreal camera trap literature, mounts used to install arboreal cameras, tree climbing pointers and safety tips, methods for deploying cameras without climbing, and tips for managing interference with camera function.3. We find that arboreal camera traps have been most commonly used in the study of mammals in forests; however, there is potential for this method to be applied to a broad range of habitats including urban areas, and taxa such as birds, amphibians, invertebrates, and plants. Methods in arboreal camera trapping could be improved by developing a greater understanding of the factors affecting detection of species. The most common challenges of arboreal camera trapping are camera placement and camera site access. These can be overcome by understanding correct camera orientation, managing potential sources of interference in front of cameras, utilizing appropriate cameras mounts and training researchers properly. | 1769Methods in Ecology and Evoluঞon MOORE Et al.

Zero-deforestation commitments seek to decouple agricultural production and forest loss to improve prospects for biodiversity. However, the effectiveness of methods designed to meet these commitments is poorly understood. In a highly fragmented tropical landscape dominated by oil palm, we tested the capacity for the High Carbon Stock (HCS) Approach to prioritize forest remnants that sustain mammal diversity. Patches afforded high priority by HCS protocols (100 ha core area) provided important refuges for IUCN-threatened species and megafauna. However, patch-scale HCS area recommendations conserved only 35% of the mammal community. At least 3,000 ha would be required to retain intact mammal assemblages, with nearly 10 times this area needed if hunting pressure was high. While current HCS protocols will safeguard patches capable of sustaining biodiversity, highly fragmented tropical landscapes typical of zero-deforestation pledges will require thinking beyond the patch toward strategically configured forest remnants at the landscape level and enforcing strict controls on hunting. K E Y W O R D Sbiodiversity, camera-trapping, habitat fragmentation, High Carbon Stock Approach, land-use planning, occupancy modeling, oil palm, RSPO, Southeast Asia, tropical forest INTRODUCTIONTropical forests are important reservoirs for biodiversity, but are compromised by anthropogenic activities (Barlow et al., 2018). Over 200 million ha of tropical forest has been lostThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

1. Animal density is a fundamental parameter in ecology and conservation, and yet it has remained difficult to measure. For terrestrial mammals and birds, cameratraps have dramatically improved our ability to collect systematic data across a large number of species, but density estimation (except for species with natural marks) is still faced with statistical and logistical hurdles, including the requirement for auxiliary data and large sample sizes, and an inability to incorporate covariates.2. To fill this gap in the camera-trapper's statistical toolbox, we extended the existing Random Encounter Model (REM) to the multi-species case in a Bayesian framework. This multi-species REM can incorporate covariates and provides parameter estimates for even the rarest species. As input to the model, we used information directly available in the camera-trap data. The model outputs posterior distributions for the REM parameters-movement speed, activity level, the effective angle and radius of the camera-trap detection zone, and density-for each species. We applied this model to an existing dataset for 35 species in Borneo, collected across old-growth and logged forest. Here, we added animal position data derived from the image sequences in order to estimate the speed and detection zone parameters.3. The model revealed a decrease in movement speeds, and therefore day-range, across the species community in logged compared to old-growth forest, whilst activity levels showed no consistent trend. Detection zones were shorter, but of similar width, in logged compared to old-growth forest. Overall, animal density was lower in logged forest, even though most species individually occurred at higher density in logged forest. However, the biomass per unit area was substantially higher in logged compared to old-growth forest, particularly among herbivores and omnivores, likely because of increased resource availability at ground level. We also included body mass as a variable in the model, revealing that larger-bodied species were more active, had more variable speeds, and had larger detection zones.

Arboreal mammals form a diverse group providing ecologically important functions such as predation, pollination and seed dispersal. However, their cryptic and elusive nature, and the heights at which they live, makes studying these species challenging. Consequently, our knowledge of rainforest mammals is heavily biased towards terrestrial species, limiting our understanding of overall community structure and the possible impacts of human-induced disturbance. We undertook the first in-depth appraisal of an arboreal mammal community in Southeast Asia, using camera-traps set in unlogged and logged tropical rainforest in Sabah, Borneo. Using paired canopy and terrestrial camera-traps at 50 locations (25 in unlogged forest, 25 in logged), we assessed the effectiveness of camera-trapping at characterising the arboreal versus terrestrial community, and tested the influence of strata and forest type on community structure and composition. The paired design detected 55 mammal species across 15,817 camera-trap nights (CTNs), and additional canopy sampling in a subset of trees added a further two arboreal species to the inventory. In total, thirty species were detected exclusively by terrestrial camera-traps, eighteen exclusively by canopy camera-traps, and nine by units set at both heights, demonstrating significant differences between arboreal and terrestrial communities. This pattern was strongest in unlogged forest, reflecting greater structural diversity of this habitat, but held in logged forest as well. Species accumulation curves revealed that canopy camera-trapping significantly boosted species inventories compared to terrestrial-only sampling, and was particularly effective at detecting gliding mammals, rodents and primates. Canopy inventories took longer to reach an asymptote, suggesting that a greater sampling effort is required when deploying canopy camera-traps compared to those set on the ground. We demonstrate that arboreal mammals in Borneo’s rainforest form a diverse and distinct community, and can be sampled effectively using canopy camera-traps. However, the additional costs incurred by sampling in the canopy can be substantial. We provide recommendations to maximise sampling effectiveness, while bringing down costs, to help encourage further study into one of the last frontiers of tropical forest research.