Abundance estimation from multiple data types for groupliving animals: An example using dhole (Cuon alpinus)

Large carnivores are declining globally and require baseline population estimates for management, however large-scale population estimation is problematic for species without unique natural marks. We used camera trap records of dhole Cuon alpinus, a group-living species, from three national parks in Thailand as a case study in which we develop integrated likelihood models to estimate abundance incorporating two different data sets, count data and detection/non-detection data. We further investigated relative biases of the models using different proportions of data with lower versus higher quality and assessed parameter identifiability. The simulations indicated that the relative bias on average across 24 tested scenarios was 2% with a 95% chance that the simulated data sets obtained the true animal abundances. We found that bias was high (\u3e10%) when sampling 60 sites with only 5 sampling occasions. We tested four additional scenarios with varying proportions of count data. Our model tolerated the use of relatively low proportions of the higher quality count data, but below 10% the results began to show bias (\u3e6%). Data cloning indicated that the parameters were identifiable with all posterior variances shrinking to near zero. Our model demonstrates the benefits of combining data from multiple studies even with different data types. Furthermore, the approach is not limited to camera trap data. Detection/non-detection data from track surveys or counts from transects could also be combined. Particularly, our model is potentially useful for assessing populations of rare species where large amounts of by-catch datasets are available

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

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

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

Pangolins in global camera trap data: Implications for ecological monitoring

Despite being heavily exploited, pangolins (Pholidota: Manidae) have been subject to limited research, resulting in a lack of reliable population estimates and standardised survey methods for the eight extant species. Camera trapping represents a unique opportunity for broad-scale collaborative species monitoring due to its largely non-discriminatory nature, which creates considerable volumes of data on a relatively wide range of species. This has the potential to shed light on the ecology of rare, cryptic and understudied taxa, with implications for conservation decision-making. We undertook a global analysis of available pangolin data from camera trapping studies across their range in Africa and Asia. Our aims were (1) to assess the utility of existing camera trapping efforts as a method for monitoring pangolin populations, and (2) to gain insights into the distribution and ecology of pangolins. We analysed data collated from 103 camera trap surveys undertaken across 22 countries that fell within the range of seven of the eight pangolin species, which yielded more than half a million trap nights and 888 pangolin encounters. We ran occupancy analyses on three species (Sunda pangolin Manis javanica, white-bellied pangolin Phataginus tricuspis and giant pangolin Smutsia gigantea). Detection probabilities varied with forest cover and levels of human influence for P. tricuspis, but were low (<0.05) for all species. Occupancy was associated with distance from rivers for M. javanica and S. gigantea, elevation for P. tricuspis and S. gigantea, forest cover for P. tricuspis and protected area status for M. javanica and P. tricuspis. We conclude that camera traps are suitable for the detection of pangolins and large-scale assessment of their distributions. However, the trapping effort required to monitor populations at any given study site using existing methods appears prohibitively high. This may change in the future should anticipated technological and methodological advances in camera trapping facilitate greater sampling efforts and/or higher probabilities of detection. In particular, targeted camera placement for pangolins is likely to make pangolin monitoring more feasible with moderate sampling efforts

Pangolins in Global Camera Trap Data: Implications for Ecological Monitoring

Despite being heavily exploited, pangolins (Pholidota: Manidae) have been subject to limited research, resulting in a lack of reliable population estimates and standardised survey methods for the eight extant species. Camera trapping represents a unique opportunity for broad-scale collaborative species monitoring due to its largely non-discriminatory nature, which creates considerable volumes of data on a relatively wide range of species. This has the potential to shed light on the ecology of rare, cryptic and understudied taxa, with implications for conservation decision-making. We undertook a global analysis of available pangolin data from camera trapping studies across their range in Africa and Asia. Our aims were (1) to assess the utility of existing camera trapping efforts as a method for monitoring pangolin populations, and (2) to gain insights into the distribution and ecology of pangolins. We analysed data collated from 103 camera trap surveys undertaken across 22 countries that fell within the range of seven of the eight pangolin species, which yielded more than half a million trap nights and 888 pangolin encounters. We ran occupancy analyses on three species (Sunda pangolin Manis javanica, white-bellied pangolin Phataginus tricuspis and giant pangolin Smutsia gigantea). Detection probabilities varied with forest cover and levels of human influence for P. tricuspis, but were low (M. javanica and S. gigantea, elevation for P. tricuspis and S. gigantea, forest cover for P. tricuspis and protected area status for M. javanica and P. tricuspis. We conclude that camera traps are suitable for the detection of pangolins and large-scale assessment of their distributions. However, the trapping effort required to monitor populations at any given study site using existing methods appears prohibitively high. This may change in the future should anticipated technological and methodological advances in camera trapping facilitate greater sampling efforts and/or higher probabilities of detection. In particular, targeted camera placement for pangolins is likely to make pangolin monitoring more feasible with moderate sampling efforts

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

Abundance estimation from multiple data types for groupliving animals: An example using dhole (Cuon alpinus)

Large carnivores are declining globally and require baseline population estimates for management, however large-scale population estimation is problematic for species without unique natural marks. We used camera trap records of dhole Cuon alpinus, a group-living species, from three national parks in Thailand as a case study in which we develop integrated likelihood models to estimate abundance incorporating two different data sets, count data and detection/non-detection data. We further investigated relative biases of the models using different proportions of data with lower versus higher quality and assessed parameter identifiability. The simulations indicated that the relative bias on average across 24 tested scenarios was 2% with a 95% chance that the simulated data sets obtained the true animal abundances. We found that bias was high (\u3e10%) when sampling 60 sites with only 5 sampling occasions. We tested four additional scenarios with varying proportions of count data. Our model tolerated the use of relatively low proportions of the higher quality count data, but below 10% the results began to show bias (\u3e6%). Data cloning indicated that the parameters were identifiable with all posterior variances shrinking to near zero. Our model demonstrates the benefits of combining data from multiple studies even with different data types. Furthermore, the approach is not limited to camera trap data. Detection/non-detection data from track surveys or counts from transects could also be combined. Particularly, our model is potentially useful for assessing populations of rare species where large amounts of by-catch datasets are available

The spatial and temporal displacement of native species by domestic dogs

Domestic dogs have been predicted to be a high risk to 10 % of mammal and 11 % of bird species across mainland Southeast Asia. Within Thailand their population is estimated at over 12 million and 80 % live in rural areas where they adopt a free-ranging lifestyle. This lifestyle enables them to enter protected forests without restrictions. To access the spatial and temporal impacts domestic dogs have on local wildlife a two-year camera trap study was undertaken in a fragmented forest complex in Northern Thailand. Co-occurrence modelling was used to estimate the impacts of domestic dogs on a native predator (golden jackal) and prey (green peafowl) specie’s occurrence probability. Temporal segregation was accessed using activity pattern overlaps and compared to Huai Kha Khaeng, a more protected and unfragmented forest complex. Although the results from the co-occurrence models did not find any spatial segregation, it was found that temporal avoidance was occurring in the protected areas with domestic dogs and golden jackal having a clear temporal niche, this temporal separation was lessened in the unfragmented forest. Additionally, over 3x more humans were independently photographed than any other species and 2.5x more domestic dogs were independently photographed than golden jackal in the fragmented protected areas. Ultimately, working in partnership with the local community on approaches that will reduce domestic dogs presence in the forest is essential along with a stringent population management plan in order to lower the number of free-ranging dogs in the area