Can citizen science analysis of camera trap data be used to study reproduction? Lessons from Snapshot Serengeti program

Thel, Lucie; Chamaillé‐Jammes, Simon; Keurinck, Léa; Catala, Maxime; Packer, Craig; Huebner, Sarah; Bonenfant, Christophe

doi:10.2981/wlb.00833

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…However, since the study was conducted when few juvenile animals were present and most male deer still had their antlers making them easier to recognize, further analyses across seasons are needed to determine more precisely the ability of citizen scientist to identify age and sex of animals accurately from photo and video footage. It is likely that more citizen science projects will start requiring human observers to move beyond species classifications; provision of additional detail is already evident in projects asking participants to identify age and sex (Thel et al., 2021) and individual ID (McCarthy et al., 2021; Tagg et al., 2018). Further research is needed to establish optimum camera settings for accurate identification of these traits, although video appears to offer clear benefits over traditional photos.…”

Section: Discussionmentioning

confidence: 99%

Camera trapping with photos and videos: implications for ecology and citizen science

Green

Stephens

Whittingham

et al. 2022

Remote Sens Ecol Conserv

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Camera traps are increasingly used in wildlife monitoring and citizen science to address an array of ecological questions on a wide variety of species. However, despite the ability of modern camera traps to capture high-quality video, the majority of studies collect still images, in part because of concerns with video performance. We conducted a camera trap survey of a forested landscape in the UK, using a grid of paired camera traps, to quantify the impact of using video compared to photos on the outcomes of ecological research and for participation and engagement of citizen scientists. Ecological outputs showed no difference between photo and video datasets, but comparison between expert and citizen science classifications showed citizen scientists were able to classify videos more accurately (average accuracy of 95% for video, 86% for photo). Furthermore, citizen scientists were more likely to volunteer additional information on age (provided for 61% videos and 30% photos) and sex (provided for 63% videos and 45% photos) of animals in video footage. Concerns over slow trigger speeds for videos did not appear to affect our datasets or the inferences gained. When combined with citizen science, video datasets are likely to be of higher quality due to increased classification accuracy. Consequently, we encourage researchers to consider the use of video for future camera-trapping projects.

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Section: Discussionmentioning

confidence: 99%

Camera trapping with photos and videos: implications for ecology and citizen science

Green

Stephens

Whittingham

et al. 2022

Remote Sens Ecol Conserv

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Highly precise community science annotations of video camera‐trapped fauna in challenging environments

Arandjelovic,

Stephens,

Dieguez

et al. 2024

Remote Sens Ecol Conserv

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As camera trapping grows in popularity and application, some analytical limitations persist including processing time and accuracy of data annotation. Typically images are recorded by camera traps although videos are becoming increasingly collected even though they require much more time for annotation. To overcome limitations with image annotation, camera trap studies are increasingly linked to community science (CS) platforms. Here, we extend previous work on CS image annotations to camera trap videos from a challenging environment; a dense tropical forest with low visibility and high occlusion due to thick canopy cover and bushy undergrowth at the camera level. Using the CS platform Chimp&See, established for classification of 599 956 video clips from tropical Africa, we assess annotation precision and accuracy by comparing classification of 13 531 1‐min video clips by a professional ecologist (PE) with output from 1744 registered, as well as unregistered, Chimp&See community scientists. We considered 29 classification categories, including 17 species and 12 higher‐level categories, in which phenotypically similar species were grouped. Overall, annotation precision was 95.4%, which increased to 98.2% when aggregating similar species groups together. Our findings demonstrate the competence of community scientists working with camera trap videos from even challenging environments and hold great promise for future studies on animal behaviour, species interaction dynamics and population monitoring.

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Socially responsible facial recognition of animals

Roberts

2023

AI Ethics

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Can citizen science analysis of camera trap data be used to study reproduction? Lessons from Snapshot Serengeti program

Abstract: BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Cited by 5 publications

References 37 publications

Camera trapping with photos and videos: implications for ecology and citizen science

Camera trapping with photos and videos: implications for ecology and citizen science

Highly precise community science annotations of video camera‐trapped fauna in challenging environments

Socially responsible facial recognition of animals

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