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585 results for “Camera trap”
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>
Supplementary material 5 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Video of a nest of the Gray-hooded Flycatcher, Mionectes rufiventris, containing two nestlings, being depredated by Three-striped Short-tailed Opossum, Monodelphis americana. Note that the small mammal has climbed the closed nest, found its entrance, and has invaded the interior of the nest. :
Supplementary material 4 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Video of an Ocelot, Leopardus pardalis, depredating a nest of Gray-hooded Flycatcher, Mionectes rufiventris. The video shows the moment in which a nestling try to leave the nest and is captured in the air by the Ocelot. :
Supplementary material 3 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Video of a nest of the Blue Manakin, Chiroxiphia caudata, being depredated by The Red-breasted Toucan, Ramphastos dicolorus. The video shows a young in late nestling stage being removed from the nest and being carried away by the toucan. :
Supplementary material 2 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Video of the Red-breasted Toucan, Ramphastos dicolorus, consuming an egg of the Ruddy Quail-dove, Geotrygon montana. Seven days later, a second egg present in this nest was also consumed by a Red-breasted Toucan. :
Supplementary material 1 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Video of the Collared Forest-falcon, Micrastur semitorquatus, depredating a nest of White-necked Thrush, Turdus albicollis, containing three eggs. In this cloudy day, the infrared LEDs were activated. :
Figure 1 in Recording potential predators of herpetofauna in southern Mexico using camera traps and realistic models
Figure 1. Location of the Area de Proteccion y Desarrollo de Ceratozamia (APDC) in south-east Veracruz, Mexico. Black circles show the positions of the 13 camera traps within the study area. Land use layer obtained from Karra et al. (2021) with a resolution of 10 m.
Figure 2 in Recording potential predators of herpetofauna in southern Mexico using camera traps and realistic models
Figure 2. Colouration in life of Craugastor loki (a) and Ninia sebae (c) and their respective painted models (b and d) with embedded piece of wire.
Figure 1 in First record of albinism in long-nosed mongoose Xenogale naso documented with camera traps in the Yoko Council Forest, Centre Cameroon
Figure 1: Map showing the camera trap stations with mongoose species in the Yoko Council Forest, Cameroon.
Figure 8 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 8: Estimated detection probability with 95 % confidence interval for models run using each combination of cameras for black-bellied pangolins. For example, with 1 camera there are 6 combinations, either only camera 1, 2, 3, 4, 5, or 6. Red X's at 0 represent models that did not have enough detections for model convergence.
Figure 6 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 6: Map showing the location of where black-bellied pangolin were detected during the study. Sites with a black dot were sites where cameras were placed but no black-bellied pangolin were detected, aqua are sites where 1 of the 6 cameras at the site detected the pangolin, and yellow are sites where 3 of the 6 cameras at the site detected pangolin.
Figure 7 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 7: Relationship between the linear (height) effect of camera height (A), the quadratic (height + height2) effect of camera height (B), the linear (zone) effect of camera zone (C), and the quadratic (zone + zone2) effect of camera zone (D) on detection probability for black-bellied pangolin.
Figure 2 in First record of albinism in long-nosed mongoose Xenogale naso documented with camera traps in the Yoko Council Forest, Centre Cameroon
Figure 2: The recorded albino long-nosed mongooses in a grassland savannah (a) and in a swamp dominated by raffia palms (b and c) of Yoko Council Forest, Cameroon.
Figure 4 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 4: Relationship between camera zone and detection probability for white-bellied pangolin using the model zone + zone2 on detection probability.
Figure 5 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 5: Estimated detection probability with 95 % confidence interval for models run using each combination of cameras for white-bellied pangolins. For example, with 1 camera there are 6 combinations, either only camera 1, 2, 3, 4, 5, or 6. Red X's at 0 represent models that did not have enough detections for model convergence.
Figure 3 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 3: Map showing white-bellied pangolin detection locations during the study. Black dot are sites where cameras were placed but no white-bellied pangolin were detected,aqua are sites where 1 of the 6 cameras at the site detected the pangolin, and yellow are sites where 3 of the 6 cameras at the site detected pangolin.
Figure 1 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 1: Location of camera trap survey locations for 2022 shown with yellow dots, as well as previous camera trap locations, shown with black dots.The map also shows rivers, roads, as well as the central, buffer, and transition zones of the park.
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International Brain Laboratory public data
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OpenNeuro
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