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A novel method to reduce time investment when processing videos from camera trap studies.

ABSTRACT: Camera traps have proven very useful in ecological, conservation and behavioral research. Camera traps non-invasively record presence and behavior of animals in their natural environment. Since the introduction of digital cameras, large amounts of data can be stored. Unfortunately, processing protocols did not evolve as fast as the technical capabilities of the cameras. We used camera traps to record videos of Eurasian beavers (Castor fiber). However, a large number of recordings did not contain the target species, but instead empty recordings or other species (together non-target recordings), making the removal of these recordings unacceptably time consuming. In this paper we propose a method to partially eliminate non-target recordings without having to watch the recordings, in order to reduce workload. Discrimination between recordings of target species and non-target recordings was based on detecting variation (changes in pixel values from frame to frame) in the recordings. Because of the size of the target species, we supposed that recordings with the target species contain on average much more movements than non-target recordings. Two different filter methods were tested and compared. We show that a partial discrimination can be made between target and non-target recordings based on variation in pixel values and that environmental conditions and filter methods influence the amount of non-target recordings that can be identified and discarded. By allowing a loss of 5% to 20% of recordings containing the target species, in ideal circumstances, 53% to 76% of non-target recordings can be identified and discarded. We conclude that adding an extra processing step in the camera trap protocol can result in large time savings. Since we are convinced that the use of camera traps will become increasingly important in the future, this filter method can benefit many researchers, using it in different contexts across the globe, on both videos and photographs.

SUBMITTER: Swinnen KR

PROVIDER: S-EPMC4053333 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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A novel method to reduce time investment when processing videos from camera trap studies.

Swinnen Kristijn R R KR Reijniers Jonas J Breno Matteo M Leirs Herwig H

PloS one 20140611 2

Camera traps have proven very useful in ecological, conservation and behavioral research. Camera traps non-invasively record presence and behavior of animals in their natural environment. Since the introduction of digital cameras, large amounts of data can be stored. Unfortunately, processing protocols did not evolve as fast as the technical capabilities of the cameras. We used camera traps to record videos of Eurasian beavers (Castor fiber). However, a large number of recordings did not contain ...[more]

PMID: 24918777

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Project description:Regular monitoring of wild animal populations through the collection of behavioral and demographic data is critical for the conservation of endangered species. Identifying individual Asian elephants (Elephas maximus), for example, can contribute to our understanding of their social dynamics and foraging behavior, as well as to human-elephant conflict mitigation strategies that account for the behavior of specific individuals involved in the conflict. Wild elephants can be distinguished using a variety of different morphological traits-e.g., variations in ear and tail morphology, body scars and tumors, and tusk presence, shape, and length-with previous studies identifying elephants via direct observation or photographs taken from vehicles. When elephants live in dense forests like in Thailand, remote sensing photography can be a productive approach to capturing anatomical and behavioral information about local elephant populations. While camera trapping has been used previously to identify elephants, here we present a detailed methodology for systematic, experimenter differentiation of individual elephants using data captured from remote sensing video camera traps. In this study, we used day and night video footage collected remotely in the Salakpra Wildlife Sanctuary in Thailand and identified 24 morphological characteristics that can be used to recognize individual elephants. A total of 34 camera traps were installed within the sanctuary as well as crop fields along its periphery, and 107 Asian elephants were identified: 72 adults, 11 sub-adults, 20 juveniles, and four infants. We predicted that camera traps would provide enough information such that classified morphological traits would aid in reliably identifying the adult individuals with a low probability of misidentification. The results indicated that there were low probabilities of misidentification between adult elephants in the population using camera traps, similar to probabilities obtained by other researchers using handheld cameras. This study suggests that the use of day and night video camera trapping can be an important tool for the long-term monitoring of wild Asian elephant behavior, especially in habitats where direct observations may be difficult.

Project description:Camera trapping to study wildlife allows for data collection, without the need to capture animals. Traditionally, camera traps have been used to target larger terrestrial mammal species, though recently novel methods and adjustments in procedures have meant camera traps can be used to study small mammals. The selfie trap (a camera trapping method) may present robust sampling and ecological study of small mammals. This study aimed to evaluate the selfie trap method in terms of its ability to detect species and estimate population density. To address this aim, standard small mammal live trapping was undertaken, immediately followed by camera trapping using the selfie trap. Both methods were set to target the arboreal sugar glider (Petaurus breviceps) and semi-arboreal brown antechinus (Antechinus stuartii). The more ground-dwelling bush rat (Rattus fuscipes) was also live trapped and recorded on camera. Across four survey areas, the probability of detection for each of the three species was higher for selfie traps than for live trapping. Spatially explicit capture-recapture models showed that selfie traps were superior at estimating density for brown antechinus and sugar gliders, when compared to simulated live trapping data. Hit rates (number of videos per various time intervals) were correlated with abundance. When correlating various hit rate intervals with abundance, the use of 10-min hit rate was best for predicting sugar glider abundance (R2 = 0.94). The abundance of brown antechinus was estimated from selfie traps using a 24-h hit rate as a predictor (R2 = 0.85). For sugar gliders, the selfie trap can replace live trapping as individuals can be identified through their unique facial stripes and natural ear scars, and thus used in capture-recapture analysis. This method may be useful for monitoring the abundance of other small mammal species that can also be individually recognized from photographs.Supplementary informationThe online version contains supplementary material available at 10.1007/s13364-022-00643-5.

Dataset Information

A novel method to reduce time investment when processing videos from camera trap studies.

Publications

A novel method to reduce time investment when processing videos from camera trap studies.

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