Giving the Forest Eyes: The Benefits of Using Camera Traps to Study Unhabituated Chimpanzees (Pan troglodytes verus) in Southeastern Senegal

Ontl, Kelly Boyer; Pruetz, Jill D.

doi:10.1007/s10764-014-9783-3

Cited by 29 publications

(35 citation statements)

References 26 publications

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“…Such methodology provides advantages in gathering data with limited direct observation [15,16] and offers an alternative to habituation of wild primates [17]. We hope that researchers interested in primate behavioural ecology will be inspired by the 'end to end' process that we have described here.…”

Section: Resultsmentioning

confidence: 99%

Identification of behaviours from accelerometer data in a wild social primate

O’Riain²,

et al. 2017

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Background: The use of accelerometers in bio-logging devices has proved to be a powerful tool for the quantification of animal behaviour. While bio-logging techniques are being used on wide range of species, to date they have only been seldom used with non-human primates. This is likely due to three main factors: the long tradition of direct field observations, a difficulty of attaching bio-logging devices to wild primates and the challenge of deciphering acceleration signals in species' with remarkable locomotor and behavioural diversity. Here, we overcome these aforementioned obstacles and provide methodology for identification of behaviours from accelerometer data of wild chacma baboons (Papio ursinus) in Cape Town, South Africa. Results:We apply machine learning techniques to process complex accelerometer data, collected by bespoke tracking collars to quantify a range of behaviours (focusing on locomotion and foraging behaviour). We successfully identify six broad state behaviours that represent 93.3% of the time budget of the baboons. Resting, walking, running and foraging were all identified with high recall and precision representing the first classification of multiple behavioural states from accelerometer data for a wild primate. Conclusion:Our 'end to end' process-from collar design and build to the collection and quantification of acceleration data-provides advantages over gathering data by traditional observation, not least because it affords data collection without the presence of an observer which may affect an animal's behaviour. Furthermore, our methodology and findings open new possibilities for the fine-scale study of movement and foraging ecology in wild primates, and in particular our baboon study population which is in conflict with people.

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Section: Resultsmentioning

confidence: 99%

Identification of behaviours from accelerometer data in a wild social primate

O’Riain²,

et al. 2017

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“…Agricultural practices, hunting, poaching and gold mining are potential threats to the conservation of chimpanzees and their habitats at Diaguiri, similar to other areas of the species' range across Africa [Kormos et al, 2004]. Today, the local human population of Diaguiri is composed mainly of Muslims who do not eat primates, but the hunting of other wild animals constitutes a threat to the chimpanzees [Carter et al, 2003;Boyer-Ontl and Pruetz, 2014]. The Diaguiri chimpanzee community has at least 11 individuals, based on camera trap images of identified chimpanzees and on the maximum number of fresh nests located at a single site on the same day [Ndiaye P.I., unpubl.…”

Section: Study Site and Subjectsmentioning

confidence: 99%

Nest-Building Behaviour by Chimpanzees (Pan troglodytes verus) in the Non-Protected Area of Diaguiri (Kedougou, Senegal): Implications for Conservation

Ndiaye¹,

Badji²,

Lindshield³

et al. 2018

IJFP

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Diaguiri is a non-protected area situated in south-eastern Senegal. Chimpanzees (Pan troglodytes verus) have been living here since 1999 but information on the characteristics of their nest sites have been lacking until now. Furthermore, we implemented nest survey methods recently standardised to arid landscapes during a national chimpanzee survey. Variations in nest decay rates are poorly understood in savanna-woodland mosaic habitats. This study aims to describe in Diaguiri: (1) the distribution of chimpanzee nests and the location of their core sleeping area, (2) the tree species used for nest building, (3) the association between nests and nesting tree heights, and (4) differences in nest decay rates between habitats. We surveyed 43 km of chimpanzee nesting habitats and recorded 871 nests in gallery forest and woodland habitats between March 2015 and June 2016. Diaguiri chimpanzees used at least 17 tree species for nest building, and 58.8% of nests were in only 3 tree species: Pterocarpus erinaceus, Anogeissus leiocarpus and Diospyros mespiliformis. Nest decay rate was faster in gallery forests than in woodlands. These data are of great value for understanding habitat use by a Critically Endangered subspecies of savanna chimpanzee, for population density estimates of the species and for conservation programmes in this region and in savanna biomes.

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“…It has great potential to provide unique and valuable data on the impacts of conservation threats [71], sociodemographics [23], behavioural plasticity [32,55,3], disease mapping and screening [9], species interactions [23], habitat use [53,24], feeding ecology [24,51,59], activity patterns [32,55], and ranging patterns [48].…”

Section: Monitoring In Ecology Todaymentioning

confidence: 99%

“…Freytag et al offer their dataset 2 as well as a working implementation of their identification pipeline 3 on GitHub. The package contains identification, age and gender estimation components, but also a face detection component.…”

Section: Deep Facial Chimpanzee Identificationmentioning

confidence: 99%

Towards Automated Visual Monitoring of Individual Gorillas in the Wild

Brust

Burghardt

Groenenberg

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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In this paper we report on the context and evaluation of a system for an automatic interpretation of sightings of individual western lowland gorillas (Gorilla gorilla gorilla) as captured in facial fieldVisual data acquisition in the field often captures sufficient information to establish encounters with individual gorillas. However, relevant information is locked within the pixel patterns measured, usually requiring expert knowledge and time-consuming efforts for identification. Computer vision can help to extract gorilla identities by performing automated species detection, followed by individual facial identification. We show that standard deep learning models combined with a traditional SVM classifier can be used for this task. To assist encounter processing, predictions can be presented graphically with known population information as shown.

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Giving the Forest Eyes: The Benefits of Using Camera Traps to Study Unhabituated Chimpanzees (Pan troglodytes verus) in Southeastern Senegal

Cited by 29 publications

References 26 publications

Identification of behaviours from accelerometer data in a wild social primate

Identification of behaviours from accelerometer data in a wild social primate

Nest-Building Behaviour by Chimpanzees (Pan troglodytes verus) in the Non-Protected Area of Diaguiri (Kedougou, Senegal): Implications for Conservation

Towards Automated Visual Monitoring of Individual Gorillas in the Wild

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