Linear infrastructures, especially roads, affect the integrity of natural habitats worldwide. Roads act as a barrier to animal movement, cause mortality, decrease gene flow and increase the probability of local extinctions, particularly for arboreal species. Arboreal wildlife bridges increase connectivity of fragmented forests by allowing wildlife to safely traverse roads. However, the majority of studies about such infrastructure are from Australia, while information on lowland tropical rainforest systems in Meso and South America remains sparse. To better facilitate potential movement between forest areas for the arboreal wildlife community of Costa Rica’s Osa Peninsula, we installed and monitored the early use of 12 arboreal wildlife bridges of three different designs (single rope, double rope, and ladder bridges). We show that during the first 6 months of monitoring via camera traps, 7 of the 12 bridges were used, and all bridge designs experienced wildlife activity (mammals crossing and birds perching). A total of 5 mammal species crossing and 3 bird species perching were observed. In addition to preliminary results of wildlife usage, we also provide technical information on the bridge site selection process, bridge construction steps, installation time, and overall associated costs of each design. Finally, we highlight aspects to be tested in the future, including additional bridge designs, monitoring approaches, and the use of wildlife attractants.

The payload size and commercial availability of thermal infrared cameras mounted on drones has initiated a new wave in the potential for conservationists and researchers to survey, count and detect wildlife, even the most complex of habitats such as forest canopies. However, several fundamental design and methodological questions remain to be tested before standardized monitoring approaches can be broadly adopted. We test the impact of both the speed of drone flights and diel flight period on tropical rainforest canopy wildlife detections. Detection and identification rates differ between both flight speeds and diel time. Overall ~ 36% more detections were made during slower flight speeds, along with a greater ability to categorize taxonomic groups. Flights conducted at 3am resulted in ~ 67% more detections compared to flights conducted at 7am (the diel period with the lowest detection rate). However, 112% more detections could be identified to taxonomic group in 7am flights compared with 3am flights – due to the types of wildlife being identified and the assistance of the RGB camera. Although, this technology holds great promise for carrying out surveys in structurally complex and poorly known ecosystems like forest canopies, there is more to do in further methodological testing, and building automated post-processing systems. Our results suggest that drone studies in the same habitat types, with the same animal densities, could be off by multiples if flown during different times and/or at different speeds. The difference could be an alarming 5-6x variation in animal detections or identification depending on changes in these two factors alone.