Collateral damage from agricultural netting to open-country bird populations in Thailand

Nets are used across a wide variety of food production landscapes to control avian pests typically resulting in deaths of entangled birds. However, the impact of nets on bird populations is a human–wildlife conflict that remains mostly unquantified. Here, we examined the scale of netting in the central plains of Thailand, a region dominated by ricefields, among which aquaculture ponds are increasingly interspersed. Nets/exclusion types, number of individual birds and species caught were recorded on 1312 road-survey transects (2-km length × 0.4-km width). We also interviewed 104 local farmers. The transect sampling took place in late- September 2020, and from December 2020 to April 2021. Each survey transect was visited only once. We found 1881 nets and barriers of parallel cords on 196 (15%) of the transects. Counts of nets and barriers were ~13 times higher than expected in aquaculture ponds based on their areal proportion, and vertical nets were the most commonly observed type (n = 1299). We documented 735 individuals of at least 45 bird species caught in the nets and parallel cords, including many species not regarded as pests. Approximately 20% of individuals caught in ricefields and 95% at aquaculture ponds were non-target bycatch. Our interviews suggested that 55% of respondents thought nets were ineffective while only 6% thought they were effective. We suggest imposing a ban on netting, considering other mitigation strategies to reduce conflicts such as promoting the use of parallel cords, and prioritizing conservation actions with community participation. Further studies should investigate the efficacy of less deleterious deterrents

Identifying conservation priorities for an understudied species in decline: Golden cats (Catopuma temminckii) in mainland Tropical Asia

Identifying conservation priorities for an understudied species can be challenging, as the amount and type of data available to work with are often limited. Here, we demonstrate a flexible workflow for identifying priorities for such data-limited species, focusing on the little-studied Asian golden cat (Catopuma temminckii) in mainland Tropical Asia. Using recent occurrence records, we modeled the golden cat's expected area of occurrence and identified remaining habitat strongholds (i.e., large intact areas with moderate-to-high expected occurrence). We then classified these strongholds by recent camera-trap survey status (from a literature review) and near-future threat status (based on publicly available forest loss projections and Bayesian Belief Network derived estimates of hunting-induced extirpation risk) to identify conservation priorities. Finally, we projected the species\u2019 expected area of occurrence in the year 2000, approximately three generations prior to today, to define past declines and better evaluate the species\u2019 current conservation status. Lower levels of hunting-induced extirpation risk and higher levels of closed-canopy forest cover were the strongest predictors of recent camera-trap records. Our projections suggest a 68% decline in area with moderate-to-high expected occurrence between 2000 and 2020, with a further 18% decline predicted over the next 20 years. Past and near-future declines were primarily driven by cumulatively increasing levels of hunting-induced extirpation risk, suggesting assessments of conservation status based solely on declines in habitat may underestimate actual population declines. Of the 40 remaining habitat strongholds, 77.5% were seriously threatened by forest loss and hunting. Only 52% of threatened strongholds had at least one site surveyed, compared to 100% of low-to-moderate threat strongholds, thus highlighting an important knowledge gap concerning the species\u2019 current distribution and population status. Our results suggest the golden cat has experienced, and will likely continue to experience, considerable population declines and should be considered for up-listing to a threatened category (i.e., VU/EN) under criteria A2c of the IUCN Red List

Identifying conservation priorities for an understudied species in decline: Golden cats (Catopuma temminckii) in mainland Tropical Asia

Abstract Identifying conservation priorities for an understudied species can be challenging, as the amount and type of data available to work with are often limited. Here, we demonstrate a flexible workflow for identifying priorities for such data-limited species, focusing on the little-studied Asian golden cat (Catopuma temminckii) in mainland Tropical Asia. Using recent occurrence records, we modeled the golden cat's expected area of occurrence and identified remaining habitat strongholds (i.e., large intact areas with moderate-to-high expected occurrence). We then classified these strongholds by recent camera-trap survey status (from a literature review) and near-future threat status (based on publicly available forest loss projections and Bayesian Belief Network derived estimates of hunting-induced extirpation risk) to identify conservation priorities. Finally, we projected the species' expected area of occurrence in the year 2000, approximately three generations prior to today, to define past declines and better evaluate the species' current conservation status. Lower levels of hunting-induced extirpation risk and higher levels of closed-canopy forest cover were the strongest predictors of recent camera-trap records. Our projections suggest a 68% decline in area with moderate-to-high expected occurrence between 2000 and 2020, with a further 18% decline predicted over the next 20 years. Past and near-future declines were primarily driven by cumulatively increasing levels of hunting-induced extirpation risk, suggesting assessments of conservation status based solely on declines in habitat may underestimate actual population declines. Of the 40 remaining habitat strongholds, 77.5% were seriously threatened by forest loss and hunting. Only 52% of threatened strongholds had at least one site surveyed, compared to 100% of low-to-moderate threat strongholds, thus highlighting an important knowledge gap concerning the species' current distribution and population status. Our results suggest the golden cat has experienced, and will likely continue to experience, considerable population declines and should be considered for up-listing to a threatened category (i.e., VU/EN) under criteria A2c of the IUCN Red List

Mapping threatened Thai bovids provides opportunities for improved conservation outcomes in Asia

Wild bovids provide important ecosystem functions as seed dispersers and vegetation modifiers. Five wild bovids remain in Thailand: gaur (Bos gaurus), banteng (Bos javanicus), wild water buffalo (Bubalus arnee), mainland serow (Capricornis sumatraensis) and Chinese goral (Naemorhedus griseus). Their populations and habitats have declined substantially and become fragmented by land-use change. We use ecological niche models to quantify how much potential suitable habitat for these species remains within protected areas in Asia and then specifically Thailand. We combined species occurrence data from several sources (e.g. mainly camera traps and direct observation) with environmental variables and species-specific and single, large accessible areas in ensemble models to generate suitability maps, using out-of-sample predictions to validate model performance against new independent data. Gaur, banteng and buffalo models showed reasonable model accuracy throughout the entire distribution (greater than or equal to 62%) and in Thailand (greater than or equal to 80%), whereas serow and goral models performed poorly for the entire distribution and in Thailand, though 5 km movement buffers markedly improved the performance for serow. Large suitable areas were identified in Thailand and India for gaur, Cambodia and Thailand for banteng and India for buffalo. Over 50% of suitable habitat is located outside protected areas, highlighting the need for habitat management and conflict mitigation outside protected areas

Where will the dhole survive in 2030? Predicted strongholds in mainland Southeast Asia

Dhole (Cuon alpinus) is threatened with extinction across its range due to habitat loss and prey depletion. Despite this, no previous study has investigated the distribution and threat of the species at a regional scale. This lack of knowledge continues to impede conservation planning for the species. Here we modeled suitable habitat using presence-only camera trap data for dhole and dhole prey species in mainland Southeast Asia and assessed the threat level to dhole in this region using an expert-informed Bayesian Belief Network. We integrated prior information to identify dhole habitat strongholds that could support populations over the next 50 years. Our habitat suitability model identified forest cover and prey availability as the most influential factors affecting dhole occurrence. Similarly, our threat model predicted that forest loss and prey depletion were the greatest threats, followed by local hunting, non-timber forest product collection, and domestic dog incursion into the forest. These threats require proactive resource management, strong legal protection, and cross-sector collaboration. We predicted <20% of all remaining forest cover in our study area to be suitable for dhole. We then identified 17 patches of suitable forest area as potential strongholds. Among these patches, Western Forest Complex (Thailand) was identified as the region's only primary stronghold, while Taman Negara (Malaysia), and northeastern landscape (Cambodia) were identified as secondary strongholds. Although all 17 patches met our minimum size criteria (1667 km(2)), patches smaller than 3333 km(2) may require site management either by increasing the ecological carrying capacity (i.e., prey abundance) or maintaining forest extent. Our proposed interventions for dhole would also strengthen the conservation of other co-occurring species facing similar threats. Our threat assessment technique of species with scarce information is likely replicable with other endangered species

CamTrapAsia: a dataset of tropical forest vertebrate communities from 239 camera trapping studies

Information on tropical Asian vertebrates has traditionally been sparse, particularly when it comes to cryptic species inhabiting the dense forests of the region. Vertebrate populations are declining globally due to land-use change and hunting, the latter frequently referred as “defaunation.” This is especially true in tropical Asia where there is extensive land-use change and high human densities. Robust monitoring requires that large volumes of vertebrate population data be made available for use by the scientific and applied communities. Camera traps have emerged as an effective, non-invasive, widespread, and common approach to surveying vertebrates in their natural habitats. However, camera-derived datasets remain scattered across a wide array of sources, including published scientific literature, gray literature, and unpublished works, making it challenging for researchers to harness the full potential of cameras for ecology, conservation, and management. In response, we collated and standardized observations from 239 camera trap studies conducted in tropical Asia. There were 278,260 independent records of 371 distinct species, comprising 232 mammals, 132 birds, and seven reptiles. The total trapping effort accumulated in this data paper consisted of 876,606 trap nights, distributed among Indonesia, Singapore, Malaysia, Bhutan, Thailand, Myanmar, Cambodia, Laos, Vietnam, Nepal, and far eastern India. The relatively standardized deployment methods in the region provide a consistent, reliable, and rich count data set relative to other large-scale pressence-only data sets, such as the Global Biodiversity Information Facility (GBIF) or citizen science repositories (e.g., iNaturalist), and is thus most similar to eBird. To facilitate the use of these data, we also provide mammalian species trait information and 13 environmental covariates calculated at three spatial scales around the camera survey centroids (within 10-, 20-, and 30-km buffers). We will update the dataset to include broader coverage of temperate Asia and add newer surveys and covariates as they become available. This dataset unlocks immense opportunities for single-species ecological or conservation studies as well as applied ecology, community ecology, and macroecology investigations. The data are fully available to the public for utilization and research. Please cite this data paper when utilizing the data

CamTrapAsia: A dataset of tropical forest vertebrate communities from 239 camera trapping studies

Information on tropical Asian vertebrates has traditionally been sparse, particularly when it comes to cryptic species inhabiting the dense forests of the region. Vertebrate populations are declining globally due to land‐use change and hunting, the latter frequently referred as “defaunation.” This is especially true in tropical Asia where there is extensive land‐use change and high human densities. Robust monitoring requires that large volumes of vertebrate population data be made available for use by the scientific and applied communities. Camera traps have emerged as an effective, non‐invasive, widespread, and common approach to surveying vertebrates in their natural habitats. However, camera‐derived datasets remain scattered across a wide array of sources, including published scientific literature, gray literature, and unpublished works, making it challenging for researchers to harness the full potential of cameras for ecology, conservation, and management. In response, we collated and standardized observations from 239 camera trap studies conducted in tropical Asia. There were 278,260 independent records of 371 distinct species, comprising 232 mammals, 132 birds, and seven reptiles. The total trapping effort accumulated in this data paper consisted of 876,606 trap nights, distributed among Indonesia, Singapore, Malaysia, Bhutan, Thailand, Myanmar, Cambodia, Laos, Vietnam, Nepal, and far eastern India. The relatively standardized deployment methods in the region provide a consistent, reliable, and rich count data set relative to other large‐scale pressence‐only data sets, such as the Global Biodiversity Information Facility (GBIF) or citizen science repositories (e.g., iNaturalist), and is thus most similar to eBird. To facilitate the use of these data, we also provide mammalian species trait information and 13 environmental covariates calculated at three spatial scales around the camera survey centroids (within 10‐, 20‐, and 30‐km buffers). We will update the dataset to include broader coverage of temperate Asia and add newer surveys and covariates as they become available. This dataset unlocks immense opportunities for single‐species ecological or conservation studies as well as applied ecology, community ecology, and macroecology investigations. The data are fully available to the public for utilization and research. Please cite this data paper when utilizing the data

A drone-based population survey of Delacour's langur (Trachypithecus delacouri) in the karst forests of northern Vietnam

The Critically Endangered Delacour’s langur (Trachypithecus delacouri) is presently found only in a few isolated karst areas in northern Vietnam, where conducting field surveys has proven challenging due to the difficult terrain. Accurate population estimates from a scientifically sound method are needed to inform management of the species. From October to December 2022, we used a drone equipped with optical and thermal cameras to survey the species in Kim Bang Forest, a critical site for the species. We estimated langur abundance from the resulting point count data using N-mixture models including abiotic and biotic variables. We also compared the effectiveness and efficiency of the drone method with two commonly used ground based methods. The drone survey recorded 16 groups with 104 individuals. We estimated a density of 0.87 groups per km2 and a population of 25 groups and 175 individuals. This estimate is 80-113% higher than previous ground-based estimates, attributed primarily to the higher area coverage by the drone survey. The estimate reaffirms the conservation importance of Kim Bang Forest for the species. The modelling also indicated that Delacour’s langur abundance was correlated negatively with Assamese macaque Macaca assamensis presence and positively with vegetation productivity. Other variables (elevation, terrain ruggedness and distance to forest edge) were much less important in explaining langur abundance. Compared to the ground based methods, the drone approach proved effective and resource-efficient for surveying Delacour’s langurs. We recommend the drone method for future Delacour’s langur surveys, with potential applicability to other arboreal mammals in difficult-to access karst forests