Biodiversity monitoring is crucial in tackling defaunation in the Anthropocene, particularly in tropical ecosystems. However, field surveys are often limited by habitat complexity, logistical constraints, financing and detectability. Hence, leveraging drones technology for species monitoring is required to overcome the caveats of conventional surveys. We investigated prospective methods for wildlife monitoring using drones in four ecosystems. We surveyed waterbird populations in Pulau Rambut, a community of ungulates in Baluran and endemic non-human primates in Gunung Halimun-Salak, Indonesia in 2021 using a DJI Matrice 300 RTK and DJI Mavic 2 Enterprise Dual with additional thermal sensors. We then, consecutively, implemented two survey methods at three sites to compare the efficacy of drones against traditional ground survey methods for each species. The results show that drone surveys provide advantages over ground surveys, including precise size estimation, less disturbance and broader area coverage. Moreover, heat signatures helped to detect species which were not easily spotted in the radiometric imagery, while the detailed radiometric imagery allowed for species identification. Our research also demonstrates that machine learning approaches show a relatively high performance in species detection. Our approaches prove promising for wildlife surveys using drones in different ecosystems in tropical forests.

This study explores two large carnivores, Javan leopard (Panthera pardus melas) and dhole (Cuon alpinus) that are known to have an overlapping diet and are sympatric in the south-western part of West Java. We characterised their spatiotemporal habitat together with four medium-sized prey species. We used photographs collected along 39.420 night-traps from 108 camera-traps installed on the peninsula of Ujung Kulon National Park (UKNP). We are using maximum entropy modelling (MaxEnt) to investigate seasonal environmental factors. Concerning the dominant prey species, our results showed that distances to secondary forest edge were highly significant for both Javan leopard and dhole in wet and dry seasons. Javan leopard is mostly active at dawn and dusk, whereas spatially correlated with Javan mousedeer and red muntjac. Dhole exhibited diurnal activity and were avoiding Javan leopard to a certain extent. Its spatial use is overlapping with red muntjac, wild boar, and banteng. Using Spatially Explici...

Conservation of rare ungulates requires reliable population size estimates and distribution maps for prioritizing investments and assessing the effectiveness of conservation measures. We used both camera trapping and a random encounter model approach, and faecal pellet group counts, to update the range and population size of the Bawean deer Axis kuhlii in the Bawean Island Nature Reserve and Wildlife Sanctuary, Indonesia. We studied 2-month periods to fulfil the assumption of population closure. Both methods provided similar population density estimates (higher in the dry season) of c. 227–416 individuals. The estimated range of the species is significantly narrower than previously reported. The main threats (habitat loss as a result of illegal logging, and disturbance by dogs and hunters) are ongoing. Based on these results we suggest that the species should retain its Critically Endangered status on the IUCN Red List.

Tropical Rainforest Heritage sites of Sumatra are some of the most irreplaceable landscapes in the world for biodiversity conservation. These landscapes harbor many endangered Asiatic mammals all suffering multifaceted threats due to anthropogenic activities. Three charismatic mammals in Sumatra: Elephas maximus sumatranus, Pongo abelii, and Panthera tigris sumatrae are protected and listed as Critically Endangered (CR) within the IUCN Red List. Nevertheless, their current geographic distribution remains unclear, and the impact of environmental factors on these species are mostly unknown. This study predicts the potential range of those species on the island of Sumatra using anthropogenic, biophysical, topographic, and climatic parameters based on the ensemble machine learning algorithms. We also investigated the effects of habitat loss from current land use, ecosystem availability, and importance of Indonesian protected areas. Our predictive model had relatively excellent performance (Sørensen: 0.81–0.94) and can enhance knowledge on the current species distributions. The most critical environmental predictors for the distribution of the three species are conservation status and temperature seasonality. This study revealed that more than half of the species distributions occurred in non-protected areas, with proportional coverage being 83%, 72%, and 54% for E.m. sumatranus, P. abelii, and P.t. sumatrae, respectively. Our study further provides reliable information on places where conservation efforts must be prioritized, both inside and outside of the protected area networks, to safeguard the ongoing survival of these Indonesian large charismatic mammals.