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107 results for “Camera trap data”
Data from: Revealing kleptoparasitic and predatory tendencies in an African mammal community using camera traps: a comparison of spatiotemporal approaches
Camera trap data are increasingly being used to characterise relationships between the spatiotemporal activity patterns of sympatric mammal species, often with a view to inferring inter-specific interactions. In this context, we attempted to characterise the kleptoparasitic and predatory tendencies of spotted hyaenas Crocuta crocuta and lions Panthera leo from photographic data collected across 54 camera trap stations and two dry seasons in Tanzania's Ruaha National Park. We applied four different methods of quantifying spatiotemporal associations, including one strictly temporal approach (activity pattern overlap), one strictly spatial approach (co-occupancy modelling), and two spatiotemporal approaches (co-detection modelling and temporal spacing at shared camera trap sites). We expected a kleptoparasitic relationship between spotted hyaenas and lions to result in a positive spatiotemporal association, and further hypothesised that the association between lions and their favourite prey in Ruaha, the giraffe Giraffa camelopardalis and the zebra Equus quagga, would be stronger than those observed with non-preferred prey species (the impala Aepyceros melampus and the dikdik Madoqua kirkii). Only approaches incorporating both the temporal and spatial components of camera trap data resulted in significant associative patterns. The latter were particularly sensitive to the temporal resolution chosen to define species detections (i.e. occasion length), and only revealed a significant positive association between lion on spotted hyaena detections, as well as a tendency for both species to follow each other at camera trap sites, during the dry season of 2013, but not that of 2014. In both seasons, observed spatiotemporal associations between lions and each of the four herbivore species considered provided no convincing or consistent indications of any predatory preferences. Our study suggests that, when making inferences on inter-specific interactions from camera trap data, due regards should be given to the potential behavioural and methodological processes underlying observed spatiotemporal patterns.
Data from: Identifying drivers of spatial variation in occupancy with limited replication camera trap data
Occupancy models are widely used in camera trap studies to analyze species presence, abundance, and geographic distribution, among other important ecological quantities. These models account for imperfect detection using a latent variable to distinguish between true presence/absence and observed detection of a species. Under certain experimental setups, parameter estimation in a latent variable framework can be challenging. Several studies have issued guidelines on the number of independent replicated observations (surveys) needed for each unchanging occupancy field (season) to ensure reliable estimation. In this paper we present a spatio-temporal occupancy model, and show through a simulation study that it can be fit to data obtained from a \textit{single} survey per season, so long as the number of seasons is sufficiently large. We include an application using camera-trap data on the Thomson's gazelle in the Serengeti in Tanzania.
Linking camera-trap data to taxonomy: Identifying photographs of morphologically similar chipmunks
<p>Remote cameras are a common method for surveying wildlife and recently have been promoted for implementing large-scale regional biodiversity monitoring programs. The use of camera-trap data depends on the correct identification of animals captured in the photographs, yet misidentification rates can be high, especially when morphologically similar species co-occur, and this can lead to faulty inferences and hinder conservation efforts. Correct identification is dependent on diagnosable taxonomic characters, photograph quality, and the experience and training of the observer. However, keys rooted in taxonomy are rarely used for the identification of camera-trap images and error rates are rarely assessed, even when morphologically similar species are present in the study area. We tested a method for ensuring high identification accuracy using two sympatric and morphologically similar chipmunk (<i>Neotamias</i>) species as a case study. We hypothesized that the identification accuracy would improve with use of the identification key, and with observer training, resulting in higher levels of observer confidence and higher levels of agreement among observers. We developed an identification key and tested identification accuracy based on photographs of verified museum specimens. Our results supported predictions for each of these hypotheses. In addition, we validated the method in the field by comparing remote camera data with live-trapping data. We recommend use of these methods to evaluate error rates and to exclude ambiguous records in camera-trap datasets. We urge that ensuring correct and scientifically defensible species identifications is incumbent on researchers and should be incorporated into the camera-trap workflow.</p>
Dataset from: Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring
<p>Estimating habitat and spatial associations for wildlife is common across ecological studies, and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inferences in an effort to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eularian) data for seven species collected simultaneously in eastern Washington (2018 – 2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differ depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, though specific habitat relationships differed. We hypothesized the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.</p>
Data from: A camera trap based assessment of climate-driven phenotypic plasticity of seasonal moulting in an endangered carnivore
<p>For many species, the ability to rapidly adapt to changes in seasonality is essential for long-term survival. In the Arctic, seasonal moulting is a key life history event that provides year-round camouflage and thermal protection. However, increased seasonal variability can lead to phenological mismatch. In this study, we investigated whether winter-white (white morph) and winter-brown (blue morph) Arctic foxes (<em>Vulpes lagopus</em>) could adjust their winter-to-summer moult to match local environmental conditions. We used camera trap images spanning an eight-year period to quantify the timing and rate of fur change in a polymorphic subpopulation in south-central Norway. Seasonal snow cover duration and temperature governed the phenology of the spring moult. We observed a later onset and longer moulting duration with decreasing temperature and longer snow season. Additionally, white foxes moulted earlier than blue in years with shorter periods of snow cover and warmer temperatures. These results suggest that phenotypic plasticity allows Arctic foxes to modulate the timing and rate of their spring moult as snow conditions and temperatures fluctuate. With the Arctic warming at an unprecedented rate, understanding the capacity of polar species to physiologically adapt to a changing environment is urgently needed in order to develop adaptive conservation efforts. Moreover, we provide the first evidence for variations in the moulting phenology of blue and white Arctic foxes. Our study underlines the different intraspecific selective pressures that can exist in populations where several morphs co-occur, and illustrates the importance of integrating morph-based differences in future management strategies of such polymorphic species.</p>
Data availability: Clustered and rotating designs as a strategy to obtain precise detection rates in camera trapping studies
<p>Manuscript data "Clustered and rotating designs as a strategy to obtain precise detection rates in camera trapping studies" published in Journal of Applied Ecology. R code to replicate the simulations can be found in the supplementary materials of the manuscript.</p>
WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models
<p>#############</p> <h1>WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models</h1> <p>#############</p> <p>Authors: Valentin Gabeff, Marc Russwurm, Devis Tuia & Alexander Mathis</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the article: <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6">https://link.springer.com/article/10.1007/s11263-024-02026-6</a></p> <p>--------------------------------</p> <p>WildCLIP is a fine-tuned CLIP model that allows to retrieve camera-trap events with natural language from the Snapshot Serengeti dataset. This project intends to demonstrate how vision-language models may assist the annotation process of camera-trap datasets.</p> <p>Here we provide the processed Snapshot Serengeti data used to train and evaluate WildCLIP, along with two versions of WildCLIP (model weights).</p> <p>Details on how to run these models can be found in the project <a href="https://github.com/amathislab/wildclip">github repository</a>.</p> <h2>Provided data (images and attribute annotations): </h2> <p>The data consists of 380 x 380 image crops corresponding to the MegaDetector output of Snapshot Serengeti with a confidence threshold above 0.7. We considered only camera trap images containing single individuals.</p> <p>A description of the original data can be found on LILA <a href="https://lila.science/datasets/snapshot-serengeti">here</a>, released under the <a href="https://cdla.dev/permissive-1-0/" rel="nofollow">Community Data License Agreement (permissive variant)</a>.</p> <p>We warmly thank the authors of LILA for making the MegaDetector outputs publicly available, as well as for structuring the dataset and facilitating its access.</p> <h2>Adapted CLIP model (model weights): </h2> <p>WildCLIP models provided:</p> <ul> <li><strong>[New] WildCLIP_vitb16_t1.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>[New] WildCLIP_vitb16_t1_lwf.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1, and with the additional VR-LwF loss. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>WildCLIP_vitb16_t1_base.pth:</strong> CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1.pth</em>)</li> <li><strong>WildCLIP_vitb16_t1t7_lwf_base.pth</strong>: CLIP model with the ViT-B/16 visual backbone trained on data with captions following templates 1 to 7, and with the additional VR-LwF loss. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1t7_lwf.pth</em>)</li> </ul> <p>We also provide the CSV files containing the train / val / test splits. The train / test splits follow camera split from LILA (https://lila.science/datasets/snapshot-serengeti). The validation split is custom, and also at the camera level.</p> <ul> <li><strong>train_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Train set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>val_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Validation set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>test_dataset_crops_single_animal_template_captions_T1T8T10.csv</strong>: Test set with captions from templates 1, 8, 9 and 10 (columns "all captions")</li> </ul> <p>Details on how the models were trained can be found in the associated <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6" target="_blank" rel="noopener">publication</a>.</p> <h2>References: </h2> <p>If you find our code, or weights, please cite:</p> <pre>@article{gabeff2024wildclip, title={WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models}, author={Gabeff, Valentin and Ru{\ss}wurm, Marc and Tuia, Devis and Mathis, Alexander}, journal={International Journal of Computer Vision}, pages={1--17}, year={2024}, publisher={Springer} }</pre> <p>If you use the adapted Snapshot Serengeti data please also cite their article:</p> <pre>@article{swanson2015snapshot, title={Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna}, author={Swanson, Alexandra and Kosmala, Margaret and Lintott, Chris and Simpson, Robert and Smith, Arfon and Packer, Craig}, journal={Scientific data}, volume={2}, number={1}, pages={1--14}, year={2015}, publisher={Nature Publishing Group} }</pre>
Camera trap data of mammals from Baluran National Park
<p>Dholes (<em>Cuon alpinus</em>) are endangered large carnivores found in scattered populations in Asia. One of the main threats to dholes is the decreasing prey availability throughout their distribution range. In the present study we used camera trap data collected over six years to investigate the temporal activity patterns of dholes and their putative prey species in Baluran National Park in Java, Indonesia. We also explored the overlap in activity between dholes and the park's other remaining large carnivore the Javan leopard (<em>Panthera pardus melas</em>), as well as humans. Furthermore, we investigated potential differences in activity patterns between dholes in packs and dholes roaming in pairs or alone. We found a high temporal overlap between dholes and their wild ungulate prey species (ranging from D=0.66–0.90), with the lowest overlap observed between dholes and bantengs (<em>Bos javanicus</em>) (D=0.66), and the highest between dholes and muntjacs (<em>Muntiacus muntjak</em>) (D=0.90). A very low overlap was found between dholes and domestic cattle (<em>Bos indicus</em>) (D=0.27) whereas a moderately high overlap was found between dholes and leopards (D=0.70) and dholes and humans (D=0.62). We found a significant difference in activity patterns between dholes in packs and dholes roaming alone or in pairs (D=0.78, p=0.01). Single/pairs of dholes were more active both during the day and at night, whereas packs were predominantly active around sunrise and sunset. The high overlap with humans potentially has a negative effect on dhole activity, particularly for dispersing individuals, and the low overlap with domestic species questions the extent to which dholes are considered to predate on them.</p>
Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna
Camera traps can be used to address large-scale questions in community ecology by providing systematic data on an array of wide-ranging species. We deployed 225 camera traps across 1,125 km2 in Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics. The cameras have operated continuously since 2010 and had accumulated 99,241 camera-trap days and produced 1.2 million sets of pictures by 2013. Members of the general public classified the images via the citizen-science website www.snapshotserengeti.org. Multiple users viewed each image and recorded the species, number of individuals, associated behaviours, and presence of young. Over 28,000 registered users contributed 10.8 million classifications. We applied a simple algorithm to aggregate these individual classifications into a final 'consensus' dataset, yielding a final classification for each image and a measure of agreement among individual answers. The consensus classifications and raw imagery provide an unparalleled opportunity to investigate multi-species dynamics in an intact ecosystem and a valuable resource for machine-learning and computer-vision research.
Data from: The challenges of recognising individuals with few distinguishing features: identifying red foxes Vulpes vulpes from camera-trap photos
Over the last two decades, camera traps have revolutionised the ability of biologists to undertake faunal surveys and estimate population densities, although identifying individuals of species with subtle markings remains challenging. We conducted a two-year camera-trapping study as part of a long-term study of urban foxes: our objectives were to determine whether red foxes could be identified individually from camera-trap photos, and highlight camera-trapping protocols and techniques to facilitate photo identification of species with few or subtle natural markings. We collected circa 800,000 camera-trap photos over 4945 camera days in suburban gardens in the city of Bristol, UK: 152,134 (19 %) included foxes, of which 13,888 (9 %) contained more than one fox. These provided 174,063 timestamped capture records of individual foxes; 170,923 were of foxes ≥ 3 months old. Younger foxes were excluded because they have few distinguishing features. We identified the individual (192 different foxes: 110 males, 49 females, 33 of unknown sex) in 168,417 (99 %) of these capture records; the remainder could not be identified due to poor image quality or because key identifying feature(s) were not visible. We show that carefully designed survey techniques facilitate individual identification of subtly-marked species. Accuracy is enhanced by camera-trapping techniques that yield large numbers of high resolution, colour images from multiple angles taken under varying environmental conditions. While identifying foxes manually was labour-intensive, currently available automated identification systems are unlikely to achieve the same levels of accuracy, especially since different features were used to identify each fox, the features were often inconspicuous, and their appearance varied with environmental conditions. We discuss how studies based on low numbers of photos, or which fail to identify the individual in a significant proportion of photos, risk losing important biological information, and may come to erroneous conclusions.
Agouti camera trap data Paul Braun
<p>Personal Agouti camera trap data collected by Paul Braun.</p>
Data from: Zooming in on mechanistic predator-prey ecology: integrating camera traps with experimental methods to reveal the drivers of ecological interactions
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Automated location invariant animal detection in camera trap images using publicly available data sources
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Data from: Camera trap placement and the potential for bias due to trails and other features
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Data from: Arboreal camera trapping: taking a proven method to new heights
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Data from: Identifying drivers of spatial variation in occupancy with limited replication camera trap data
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Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna
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Data from: Distance and size matters: a comparison of six wildlife camera traps and their usefulness for wild birds
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Data from: Revealing kleptoparasitic and predatory tendencies in an African mammal community using camera traps: a comparison of spatiotemporal approaches
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Data from: Distance sampling with camera traps
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OpenNeuro
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