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Abstract Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap‐derived Big Data are becoming increasingly solvable with the help of scalable cyber‐infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media‐based and event‐based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in‐depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard.

ABSTRACT Peru holds a high mammalian diversity in its Amazonian region, with 326 species. However, our knowledge about the actual diversity is still considered incomplete, and the molecular information for those species in genetic databases is even less comprehensive. To assess the availability of genetic information for Peruvian Amazonian mammals relative to known diversity, we surveyed the Amazonian mammals with at least one molecular marker in the most widely used repositories for nucleotide sequences, GenBank and BOLD Systems. Our survey focused on widely used molecular markers in evolutionary biology-cytochrome b [cyt-b], cytochrome oxidase I [COI], 12S ribosomal RNA [12S], and the mitogenome [mit]-derived from Peruvian Amazon mammals. Additionally, to gain insights into the current mammalian sampling effort in Peruvian Amazonia, we generated a map of unique sampling localities and a heat map, utilizing 41951 records, which identified six major information gaps. This comprehensive analysis found 1597 genetic sequences corresponding to 180 mammalian species (55.2% of Peruvian Amazonian species): COI (38 species), cyt-b (167 species), 12S (56 species), and mitogenome (16 species). Taxonomically, Rodentia (53 species, four markers), Chiroptera (63 species, three markers), and Didelphimorphia (27 species, four markers) represented most molecular data, with a concentration of molecular markers in the orders Chiroptera (703) and Rodentia (499). Geographically, the Loreto department has the largest genetic information (530 records, 99 species). These results confirm a worrying underrepresentation of Peruvian Amazonian diversity in molecular databases. Consequently, we advocate for the use of scientific collections as an alternative source to systematically generate genetic information for the Amazonian mammal diversity in Peru to compensate for the current underrepresentation.

Because of their accessibility, freshwater ecosystems are particularly sensitive to deforestation and bank habitat fragmentation. Their responses to human activities in general, and specifically to small-scale extractive activities, are not well understood. Protected Areas (PAs) can be an effective tool to secure the conservation of freshwater animal assemblages. Given their ability to respond to changes in site quality and local resources, waterbirds could be suitable wetland ecosystem indicators. To examine the effectiveness of PAs in protecting neotropical biodiversity, we used repeated visual count surveys of freshwater and forest-associated birds, comparing species richness and abundance in protected oxbow lakes with lakes subject to gold mining and fishing activities. Bird community structure was predicted by the proportion of mined area in lake banks and water quality. Bird richness was reduced in unprotected oxbow lakes, and this pattern was stronger for aquatic species. Land protection was the most important predictor of bird abundance. Over half the species (53%) showed markedly reduced abundance in the unprotected sites. Our results provide evidence for the effectiveness of PAs in maintaining freshwater biodiversity and for the impacts of human activities on neotropical wetland bird assemblages. Our findings also suggest that water-associated species may be sensitive to the deterioration in hydrological processes promoted by these activities. By documenting the maintenance of elevated freshwater bird biodiversity within two PAs, our results inform the debate on their effectiveness. We recommend the extension of legal protections for freshwater ecosystems and their banks to prevent further degradation of essential habitats and animal communities, driven by expanding informal and illegal mining activities.

Abstract Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion‐activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best‐practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open‐grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.

Persecution by humans is one of the most pressing threats to jaguars (Panthera onca) throughout the Americas, yet few studies have examined the killing of jaguars outside cattle-ranching communities. Although over one-third of the jaguar's range is formally protected, relatively little is known about human-jaguar relationships within protected areas and indigenous territories. Protected land within the Bolivian Amazon, considered a stronghold for the jaguar, contains communities who differ economically, legally, and socially from previously-studied human populations living with jaguars. Using in-person structured interviews, we investigated attitudes and norms related to jaguars and jaguar killing, self-reported past killing of jaguars, and demographic variables in two protected areas and an indigenous territory: Integrated Management Area (IMA) of Santa Rosa del Abuná (Santa Rosa, n = 224), Indigenous Territory Tacana II (n = 137), and Manuripi National Amazon Wildlife Reserve (MNAWR, n = 169). Overall, people disliked (48.9%) or felt neutral (26.8%) toward jaguars. A relatively large number of people reported either being attacked or knowing someone who had been attacked by a jaguar: 15.45% in Santa Rosa, 14.20% in MNAWR, and 30.88% in Tacana II. Many respondents stated to have killed a jaguar, although the proportion differed among study areas: 20.39% of Santa Rosa, 55.47% of Tacana II, and 32.72% of MNAWR. People perceived jaguar persecution as relatively common: 44.9% of Santa Rosa, 90.8% of Tacana II, and 65.8% of MNAWR said their neighbors kill jaguars (i.e., descriptive norm). Also, 75.4% of Santa Rosa, 89.1% of Tacana II, and 69.1% of MNAWR said that some of their family members and neighbors thought jaguar killing was good (i.e., subjective norm). Descriptive and subjective norms positively influenced both attitudes toward killing and past killing of jaguars. This perception of jaguar killing being common and socially-accepted, combined with high rates of past killing and a growing illegal trade of jaguar parts, may create an atmosphere conducive to widespread jaguar persecution in the Bolivian Amazon. We recommend management strategies that focus on preventing jaguar depredation of small domestic animals, lessening the perception of carnivore encounters as dangerous to decrease safety-related fears, and making large carnivore killing socially unacceptable (e.g., through social marketing).

Determining the conservation status and populations trends for elusive predators has been impeded by discrepancies in how densities are calculated. Here, Luskin et al. introduce a means to standardize previous density estimates and assess the threats to the Sumatran tiger.

In the original version of the Article, reference 18 was incorrectly numbered as reference 30, and references 19 to 30 were incorrectly numbered as 18 to 29. These errors have now been corrected in the PDF and HTML versions of the manuscript.

Tropical peatlands are among the most carbon-dense terrestrial ecosystems yet recorded. Collectively, they comprise a large but highly uncertain reservoir of the global carbon cycle, with wide-ranging estimates of their global area (441 025–1700 000 km ^2 ) and below-ground carbon storage (105–288 Pg C). Substantial gaps remain in our understanding of peatland distribution in some key regions, including most of tropical South America. Here we compile 2413 ground reference points in and around Amazonian peatlands and use them alongside a stack of remote sensing products in a random forest model to generate the first field-data-driven model of peatland distribution across the Amazon basin. Our model predicts a total Amazonian peatland extent of 251 015 km ^2 (95th percentile confidence interval: 128 671–373 359), greater than that of the Congo basin, but around 30% smaller than a recent model-derived estimate of peatland area across Amazonia. The model performs relatively well against point observations but spatial gaps in the ground reference dataset mean that model uncertainty remains high, particularly in parts of Brazil and Bolivia. For example, we predict significant peatland areas in northern Peru with relatively high confidence, while peatland areas in the Rio Negro basin and adjacent south-western Orinoco basin which have previously been predicted to hold Campinarana or white sand forests, are predicted with greater uncertainty. Similarly, we predict large areas of peatlands in Bolivia, surprisingly given the strong climatic seasonality found over most of the country. Very little field data exists with which to quantitatively assess the accuracy of our map in these regions. Data gaps such as these should be a high priority for new field sampling. This new map can facilitate future research into the vulnerability of peatlands to climate change and anthropogenic impacts, which is likely to vary spatially across the Amazon basin.