Divergent effects of lure on multi-species camera-trap detections and quality of photos

The use of attractants to increase detection of target species, such as carnivores, in camera trap studies must be tested for its effectiveness and be carefully planned, as it can lead to misleading comparisons among species. We analyzed a five-year multi-species camera trap dataset of lured and control stations in a protected area in the Amazon rainforest. We aimed to identify the lure effect on a wider range of species and assess whether its use is an efficient strategy to increase the number and the quality of carnivore records. From the 14 vertebrate species analyzed, we found that the use of lures had a negligible effect on nine species, and did not improve the number of records or the detection probability of the carnivores. On the other hand, lured stations attracted omnivores and scavengers (common opossum, black-and-white tegu, and turkey vulture) while had the opposite effect on potential prey species (Black-capped capuchin and Northern Amazon squirrel). We detected a stronger effect of the lure when considering the number of records (relative abundance models) than the probability of detection (occupancy models). The lure increased the proportion of high-quality photos, suitable for individual recognition of jaguars, but only for the first weeks, when the lure was fresh. Therefore, we suggest that the sardine and egg-based lures should be refreshed every-two weeks to ensure greater effectiveness in the quality of photos for jaguar individualization. However, it is important to consider that lure renewing will imply a significant increase in field-related costs and it’s likely to bias other species studies. Thus, we advise that lures should only be used if researchers are certain that the focus is only to increase carnivore data at the expense of using non-target species. Camera traps survey design must be carefully planned a priori and the cost-benefit of lure use and refreshment should be weighed in the study context

Wild dogs at stake: deforestation threatens the only Amazon endemic canid, the short-eared dog (Atelocynus microtis)

The persistent high deforestation rate and fragmentation of the Amazon forests are the main threats to their biodiversity. To anticipate and mitigate these threats, it is important to understand and predict how species respond to the rapidly changing landscape. The short-eared dog Atelocynus microtis is the only Amazon-endemic canid and one of the most understudied wild dogs worldwide. We investigated short-eared dog habitat associations on two spatial scales. First, we used the largest record database ever compiled for short-eared dogs in combination with species distribution models to map species habitat suitability, estimate its distribution range and predict shifts in species distribution in response to predicted deforestation across the entire Amazon (regional scale). Second, we used systematic camera trap surveys and occupancy models to investigate how forest cover and forest fragmentation affect the space use of this species in the Southern Brazilian Amazon (local scale). Species distribution models suggested that the short-eared dog potentially occurs over an extensive and continuous area, through most of the Amazon region south of the Amazon River. However, approximately 30% of the short-eared dog's current distribution is expected to be lost or suffer sharp declines in habitat suitability by 2027 (within three generations) due to forest loss. This proportion might reach 40% of the species distribution in unprotected areas and exceed 60% in some interfluves (i.e. portions of land separated by large rivers) of the Amazon basin. Our local-scale analysis indicated that the presence of forest positively affected short-eared dog space use, while the density of forest edges had a negative effect. Beyond shedding light on the ecology of the short-eared dog and refining its distribution range, our results stress that forest loss poses a serious threat to the conservation of the species in a short time frame. Hence, we propose a re-assessment of the short-eared dog's current IUCN Red List status (Near Threatened) based on findings presented here. Our study exemplifies how data can be integrated across sources and modelling procedures to improve our knowledge of relatively understudied species

AMAZONIA CAMTRAP: a dataset of mammal, bird, and reptile species recorded with camera traps in the Amazon forest

The Amazon forest has the highest biodiversity on earth. However, information on Amazonian vertebrate diversity is still deficient and scattered across the published, peer-reviewed and grey literature and in unpublished raw data. Camera traps are an effective non-invasive method of surveying vertebrates, applicable to different scales of time and space. In this study, we organized and standardized camera trap records from different Amazon regions to compile the most extensive dataset of inventories of mammal, bird and reptile species ever assembled for the area. The complete dataset comprises 154,123 records of 317 species (185 birds, 119 mammals and 13 reptiles) gathered from surveys from the Amazonian portion of eight countries (Brazil, Bolivia, Colombia, Ecuador, French Guiana, Peru, Suriname and Venezuela). The most frequently recorded species per taxa were: mammals - Cuniculus paca (11,907 records); birds - Pauxi tuberosa (3,713 records); and reptiles - Tupinambis teguixin (716 records). The information detailed in this data paper opens-up opportunities for new ecological studies at different spatial and temporal scales, allowing for a more accurate evaluation of the effects of habitat loss, fragmentation, climate change and other human-mediated defaunation processes in one of the most important and threatened tropical environments in the world. The dataset is not copyright restricted; please cite this data-paper when using its data in publications and we also request that researchers and educators inform us of how they are using this data