The iWildCam 2019 Challenge Dataset

Camera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population density of animal species. The computer vision community has been making strides towards automating the species classification challenge in camera traps, but as we try to expand the scope of these models from specific regions where we have collected training data to different areas we are faced with an interesting problem: how do you classify a species in a new region that you may not have seen in previous training data? In order to tackle this problem, we have prepared a dataset and challenge where the training data and test data are from different regions, namely The American Southwest and the American Northwest. We use the Caltech Camera Traps dataset, collected from the American Southwest, as training data. We add a new dataset from the American Northwest, curated from data provided by the Idaho Department of Fish and Game (IDFG), as our test dataset. The test data has some class overlap with the training data, some species are found in both datasets, but there are both species seen during training that are not seen during test and vice versa. To help fill the gaps in the training species, we allow competitors to utilize transfer learning from two alternate domains: human-curated images from iNaturalist and synthetic images from Microsoft's TrapCam-AirSim simulation environment

Testing the consistency of wildlife data types before combining them: the case of camera traps and telemetry.

Wildlife data gathered by different monitoring techniques are often combined to estimate animal density. However, methods to check whether different types of data provide consistent information (i.e., can information from one data type be used to predict responses in the other?) before combining them are lacking. We used generalized linear models and generalized linear mixed-effects models to relate camera trap probabilities for marked animals to independent space use from telemetry relocations using 2 years of data for fishers (Pekania pennanti) as a case study. We evaluated (1) camera trap efficacy by estimating how camera detection probabilities are related to nearby telemetry relocations and (2) whether home range utilization density estimated from telemetry data adequately predicts camera detection probabilities, which would indicate consistency of the two data types. The number of telemetry relocations within 250 and 500 m from camera traps predicted detection probability well. For the same number of relocations, females were more likely to be detected during the first year. During the second year, all fishers were more likely to be detected during the fall/winter season. Models predicting camera detection probability and photo counts solely from telemetry utilization density had the best or nearly best Akaike Information Criterion (AIC), suggesting that telemetry and camera traps provide consistent information on space use. Given the same utilization density, males were more likely to be photo-captured due to larger home ranges and higher movement rates. Although methods that combine data types (spatially explicit capture-recapture) make simple assumptions about home range shapes, it is reasonable to conclude that in our case, camera trap data do reflect space use in a manner consistent with telemetry data. However, differences between the 2 years of data suggest that camera efficacy is not fully consistent across ecological conditions and make the case for integrating other sources of space-use data

Monitoring wild animal communities with arrays of motion sensitive camera traps

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location -specific information on movement and behavior. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans

Limitations to recording larger mammalian predators in savannah using camera traps and spoor

Traditionally, spoor (tracks, pug marks) have been used as a cost effective tool to assess the presence of larger mammals. Automated camera traps are now increasingly utilized to monitor wildlife, primarily as the cost has greatly declined and statistical approaches to data analysis have improved. While camera traps have become ubiquitous, we have little understanding of their effectiveness when compared to traditional approaches using spoor in the field. Here, we a) test the success of camera traps in recording a range of carnivore species against spoor; b) ask if simple measures of spoor size taken by amateur volunteers is likely to allow individual identification of leopards and c) for a trained tracker, ask if this approach may allow individual leopards to be followed with confidence in savannah habitat. We found that camera traps significantly under-recorded mammalian top and meso-carnivores, with camera traps more likely under-record the presence of smaller carnivores (civet 64%; genet 46%, Meller’s mongoose 45%) than larger (jackal sp. 30%, brown hyena 22%), while leopard was more likely to be recorded by camera trap (all recorded by camera trap only). We found that amateur trackers could be beneficial in regards to collecting presence data; however the large variance in measurements of spoor taken in the field by volunteers suggests that this approach is unlikely to add further data. Nevertheless, the use of simple spoor measurements in the field by a trained field researcher increases their ability to reliably follow a leopard trail in difficult terrain. This allows researchers to glean further data on leopard behaviour and habitat utilisation without the need for complex analysis

Terrestrial camera traps: essential tool for the detection and future monitoring of the Critically Endangered Sira curassow Pauxi koepckeae

The only known population of Sira curassow Pauxi koepckeae resides within the Sira Communal Reserve, a chain of isolated and high-elevation outcrops of the Peruvian Andes. The species has previously been detected on just a handful of occasions, is thought to number less than 400 adult individuals and is Critically Endangered according to the International Union for Conservation of Nature Red List. As such, evaluating potential monitoring techniques to study the Sira curassow is of crucial importance to best inform future management strategies. We performed a preliminary assessment of camera traps to detect and collect novel ecological information on the Sira curassow. We used 17 cameras placed at regular altitudinal intervals (either 50 or 100 m) between 800 and 1800 m above sea level, 2 cameras placed at important habitat features, and 2 additional cameras placed on trails to assess hunting activity. Cameras were left in situ for 6 mo (March-September 2015). Sira curassows were detected at 26% of survey locations, totalling 19 independent detections. This resulted in an overall occupancy estimate of 0.25 across the whole transect and 0.55 across the current known elevational range. All records occurred between 1150 and 1500 m. Finally, we detail new ecological information obtained from the camera trap footage, readdress current threats to the species and provide recommendations regarding future monitoring

The Diversity of Terrestrial Mammals Surrounding Waterfall at Billy Barquedier National Park

Billy Barquedier is a National Park located in the Stann Creek district of Belize that contains Neotropical vegetation and wildlife. This study was performed to provide a baseline inventory and appearance frequency patterns of the terrestrial mammals located within Zone 1 of the park near a waterfall and to gain a greater understanding of the biodiversity and activity patterns of terrestrial mammals within the park. The methods included camera traps, small Sherman live traps, large live traps, and tracking methods. A non-random sampling method of placing camera traps and live traps on or near human-made or animal-made trails was used to identify the maximum amount of species possible within the eight-week study period. Bait including the local fruit Mamey Apple (Pouteria sapota) was used to attract wildlife to the study area. The hypothesis was at least eight species would be identified during the eight-week study period. The results indicated eleven species were identified, therefore the null hypothesis less than eight species would be identified was rejected and the alternative hypothesis that at least eight species would be identified was accepted. The non- random sampling method introduced bias into the data. Consequently, definite conclusions about relative density and abundance of animals in the area cannot be drawn by this study alone. However, chi-squared tests revealed statistically significant evidence animals appeared more frequently in the central region of the study site, during the first three days the cameras were set out, and during the nighttime hours (2000 to 0459)

Single-atom trapping in holographic 2D arrays of microtraps with arbitrary geometries

We demonstrate single-atom trapping in two-dimensional arrays of microtraps with arbitrary geometries. We generate the arrays using a Spatial Light Modulator (SLM), with which we imprint an appropriate phase pattern on an optical dipole trap beam prior to focusing. We trap single $^{87}{\rm Rb}$ atoms in the sites of arrays containing up to $\sim100$ microtraps separated by distances as small as $3\;\mu$m, with complex structures such as triangular, honeycomb or kagome lattices. Using a closed-loop optimization of the uniformity of the trap depths ensures that all trapping sites are equivalent. This versatile system opens appealing applications in quantum information processing and quantum simulation, e.g. for simulating frustrated quantum magnetism using Rydberg atoms.Comment: 9 pages, 10 figure