Special Issue "The Application of Camera Trap Technology in Field Research"

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Wildlife".

Deadline for manuscript submissions: closed (31 August 2019).

Special Issue Editor

Dr. Andrew W. Claridge
E-Mail Website
Guest Editor
University of New South Wales (UNSW) Australia, Environmental and Mathematical Sciences, Sydney, Australia
Interests: cryptic wildlife; survey tools; wildlife management; wildlife monitoring; wildlife survey

Special Issue Information

Dear Colleagues,

Camera trapping is used extensively around the world for survey, monitoring, and management-related activities relating to wildlife. As the technology improves so too does the rate at which raw data are collected. This presents some interesting logistical and technical challenges for researchers as they return from the field and attempt to make sense of pattern in their datasets. This Special Issue of Animals will explore some of the major emerging themes in the field of modern-day camera trapping, including (i) innovative ways in which the technology is being used in a field situation; (ii) the various ways in which camera traps can be optimally set in the field to collect data; (iii) the use of automated intelligence in identifying wildlife species from image files, and; (iv) the role of citizen science in assisting interpretation of images.

Dr. Andrew W. Claridge
Guest Editor

Manuscript Submission Information

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Keywords

  • Camera Trap
  • Infrared
  • Management
  • Monitoring
  • Technology
  • Wildlife

Published Papers (9 papers)

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Research

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Open AccessArticle
ClassifyMe: A Field-Scouting Software for the Identification of Wildlife in Camera Trap Images
Animals 2020, 10(1), 58; https://doi.org/10.3390/ani10010058 - 27 Dec 2019
Cited by 1
Abstract
We present ClassifyMe a software tool for the automated identification of animal species from camera trap images. ClassifyMe is intended to be used by ecologists both in the field and in the office. Users can download a pre-trained model specific to their location [...] Read more.
We present ClassifyMe a software tool for the automated identification of animal species from camera trap images. ClassifyMe is intended to be used by ecologists both in the field and in the office. Users can download a pre-trained model specific to their location of interest and then upload the images from a camera trap to a laptop or workstation. ClassifyMe will identify animals and other objects (e.g., vehicles) in images, provide a report file with the most likely species detections, and automatically sort the images into sub-folders corresponding to these species categories. False Triggers (no visible object present) will also be filtered and sorted. Importantly, the ClassifyMe software operates on the user’s local machine (own laptop or workstation)—not via internet connection. This allows users access to state-of-the-art camera trap computer vision software in situ, rather than only in the office. The software also incurs minimal cost on the end-user as there is no need for expensive data uploads to cloud services. Furthermore, processing the images locally on the users’ end-device allows them data control and resolves privacy issues surrounding transfer and third-party access to users’ datasets. Full article
(This article belongs to the Special Issue The Application of Camera Trap Technology in Field Research)
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Open AccessArticle
Activity Rhythms of Coexisting Red Serow and Chinese Serow at Mt. Gaoligong as Identified by Camera Traps
Animals 2019, 9(12), 1071; https://doi.org/10.3390/ani9121071 - 02 Dec 2019
Abstract
Surveying the activity rhythms of sympatric herbivorous mammals is essential for understanding their niche ecology, especially for how they partition resources and their mechanisms of coexistence. Over a five-year period, we conducted infrared camera-trapping to monitor the activity rhythms of coexisting red serow [...] Read more.
Surveying the activity rhythms of sympatric herbivorous mammals is essential for understanding their niche ecology, especially for how they partition resources and their mechanisms of coexistence. Over a five-year period, we conducted infrared camera-trapping to monitor the activity rhythms of coexisting red serow (Capricornis rubidus) and Chinese serow (C. milneedwardsii milneedwardsii) in the remote mountainous region of Pianma, Mt. Gaoligong, Yunnan, China. Cameras captured images of red serow and Chinese serow on 157 and 179 occasions, respectively. We used circular kernel density models to analyze daily activity rhythms and how temporal variations in activity ensure their co-existence. Although their overall activity levels and patterns were similar, temporal activity and behavior partitioning among the two species occurred during the wet season. Compared with Chinese serows, red serows exhibited less variable daily activity levels, patterns, as well as feeding and vigilance behaviors between seasons. When the two species occasionally ranged together, red serows tended to alter their activity pattern while Chinese serows significantly increased their activity level. Red serow and Chinese serow are exploitative competitors but coexist by altering their daily activity rhythms when in contact and changing activity patterns during the wet season, enabling their coexistence. Full article
(This article belongs to the Special Issue The Application of Camera Trap Technology in Field Research)
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Open AccessArticle
Spider Monkeys Rule the Roost: Ateline Sleeping Sites Influence Rainforest Heterogeneity
Animals 2019, 9(12), 1052; https://doi.org/10.3390/ani9121052 - 01 Dec 2019
Abstract
The sleeping site behavior of Ateline primates has been of interest since the 1980s, yet limited focus has been given to their influence upon other rainforest species. Here, we use a combination of arboreal and terrestrial camera traps, and dung beetle pitfall traps, [...] Read more.
The sleeping site behavior of Ateline primates has been of interest since the 1980s, yet limited focus has been given to their influence upon other rainforest species. Here, we use a combination of arboreal and terrestrial camera traps, and dung beetle pitfall traps, to characterize spider monkey sleeping site use and quantify the impact of their associated latrines on terrestrial vertebrate and dung beetle activity. We also characterize the physical characteristics of the sleeping sites and the floristic and soil composition of latrines beneath them. Spider monkey activity at sleeping sites peaked at dawn and dusk and group composition varied by sex of the adults detected. The habitat-use of terrestrial fauna (vertebrates and dung beetles) differed between latrine sites and non-latrine controls, underpinned by species-specific changes in the relative abundance of several seed-dispersing species (such as paca and great curassow). Seedling density was higher in latrines than in non-latrine controls. Although most soil properties were similar between latrines and controls, potassium and manganese concentrations were different. These results suggest that spider monkey sleeping site fidelity leads to a hotspot of ecological activity in latrines and downstream impacts on rainforest floristic composition and diversity. Full article
(This article belongs to the Special Issue The Application of Camera Trap Technology in Field Research)
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Open AccessArticle
Elucidating Patterns in the Occurrence of Threatened Ground-Dwelling Marsupials Using Camera-Traps
Animals 2019, 9(11), 913; https://doi.org/10.3390/ani9110913 - 03 Nov 2019
Abstract
Establishing trends in endangered fauna against management efforts is a key but often challenging enterprise. Camera-traps offer a new and literal window into monitoring many different mammalian species. Getting it right demands seeking baseline information about how often target species interact with these [...] Read more.
Establishing trends in endangered fauna against management efforts is a key but often challenging enterprise. Camera-traps offer a new and literal window into monitoring many different mammalian species. Getting it right demands seeking baseline information about how often target species interact with these devices, prior to setting a long-term monitoring strategy. We used a camera-trap array to collect detection data on three species of threatened ground-dwelling marsupials in south-eastern mainland Australia. Over a four-year period, occupancy estimates for two species of bandicoot (southern brown bandicoot Isoodon obesulus and long-nosed bandicoot Perameles nasuta) and a single species of rat-kangaroo (long-nosed potoroo Potorous tridatylus) were generated. These estimates were variously robust depending on visitation history, but nevertheless indicated persistence of these rare and otherwise under threat species. Detection probability for each species differed between study areas, type of management and with complexity of ground and shrub vegetation cover. The relationship between detection and vegetation structure dictated that survey effort was only robust where conditions were optimal for a given species. Outside of that further survey effort would be required to have confidence in survey outcome. In the future this would demand a different sampling strategy, be that through lengthening survey time or adding additional camera units at sites. Full article
(This article belongs to the Special Issue The Application of Camera Trap Technology in Field Research)
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Open AccessArticle
A Study of Population Size and Activity Patterns and Their Relationship to the Prey Species of the Eurasian Lynx Using a Camera Trapping Approach
Animals 2019, 9(11), 864; https://doi.org/10.3390/ani9110864 - 25 Oct 2019
Abstract
Revealing the behavioral relationships between predators and their prey is fundamental in understanding the community structure and ecosystem functions of such animals. This study aimed at detecting the population size and activity patterns of Eurasian lynx (Lynx lynx) (along with its [...] Read more.
Revealing the behavioral relationships between predators and their prey is fundamental in understanding the community structure and ecosystem functions of such animals. This study aimed at detecting the population size and activity patterns of Eurasian lynx (Lynx lynx) (along with its prey) by camera trapping monitoring from 2014 to 2017 at the Saihanwula nature reserve in central Inner Mongolia. The total effective trapping days were 29,892 and 20 lynx were identified from 343 trapping photos based on the inner side patterns of their forelimbs. The daily activity rhythms of the lynx overlapped with those of different prey in different seasons. The yearly activity pattern of the lynx was influenced by its main prey’s biology. In conclusion, this study reveals that the activity patterns of the top predator matched those of its prey in different time periods. Habitat management strategies promoting the restoration of prey communities would benefit the lynx in maintaining a stable community structure. Full article
(This article belongs to the Special Issue The Application of Camera Trap Technology in Field Research)
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Open AccessCommunication
Leopard Density Estimation within an Enclosed Reserve, Namibia Using Spatially Explicit Capture-Recapture Models
Animals 2019, 9(10), 724; https://doi.org/10.3390/ani9100724 - 25 Sep 2019
Cited by 1
Abstract
The establishment of enclosed conservation areas are claimed to be the driving force for the long-term survival of wildlife populations. Whilst fencing provides an important tool in conservation, it simultaneously represents a controversial matter as it stops natural migration processes, which could ultimately [...] Read more.
The establishment of enclosed conservation areas are claimed to be the driving force for the long-term survival of wildlife populations. Whilst fencing provides an important tool in conservation, it simultaneously represents a controversial matter as it stops natural migration processes, which could ultimately lead to inbreeding, a decline in genetic diversity and local extinction if not managed correctly. Thus, wildlife residing in enclosed reserves requires effective conservation and management strategies, which are strongly reliant on robust population estimates. Here, we used camera traps combined with the relatively new class of spatially explicit capture-recaptured models (SECR) to produce the first reliable leopard population estimate for an enclosed reserve in Namibia. Leopard density was estimated at 14.51 leopards/100 km2, the highest recorded density in Namibia to date. A combination of high prey abundance, the absence of human persecution and a lack of top-down control are believed to be the main drivers of the recorded high leopard population. Our results add to the growing body of literature which suggests enclosed reserves have the potential to harbour high densities and highlight the importance of such reserves for the survival of threatened species in the future. Full article
(This article belongs to the Special Issue The Application of Camera Trap Technology in Field Research)
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Open AccessArticle
An Evaluation of Systematic Versus Strategically-Placed Camera Traps for Monitoring Feral Cats in New Zealand
Animals 2019, 9(9), 687; https://doi.org/10.3390/ani9090687 - 16 Sep 2019
Abstract
We deploy camera traps to monitor feral cat (Felis catus) populations at two pastoral sites in Hawke’s Bay, North Island, New Zealand. At Site 1, cameras are deployed at pre-determined GPS points on a 500-m grid, and at Site 2, cameras [...] Read more.
We deploy camera traps to monitor feral cat (Felis catus) populations at two pastoral sites in Hawke’s Bay, North Island, New Zealand. At Site 1, cameras are deployed at pre-determined GPS points on a 500-m grid, and at Site 2, cameras are strategically deployed with a bias towards forest and forest margin habitat where possible. A portion of cameras are also deployed in open farmland habitat and mixed scrub. We then use the abundance-induced heterogeneity Royle–Nichols model to estimate mean animal abundance and detection probabilities for cameras in each habitat type. Model selection suggests that only cat abundance varies by habitat type. Mean cat abundance is highest at forest margin cameras for both deployment methods (3 cats [95% CI 1.9–4.5] Site 1, and 1.7 cats [95% CI 1.2–2.4] Site 2) but not substantially higher than in forest habitats (1.7 cats [95% CI 0.8–3.6] Site 1, and 1.5 cats [95% CI 1.1–2.0] Site 2). Model selection shows detection probabilities do not vary substantially by habitat (although they are also higher for cameras in forest margins and forest habitats) and are similar between sites (8.6% [95% CI 5.4–13.4] Site 1, and 8.3% [5.8–11.9] Site 2). Cat detections by camera traps are higher when placed in forests and forest margins; thus, strategic placement may be preferable when monitoring feral cats in a pastoral landscape. Full article
(This article belongs to the Special Issue The Application of Camera Trap Technology in Field Research)
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Open AccessArticle
Improving Terrestrial Squamate Surveys with Camera-Trap Programming and Hardware Modifications
Animals 2019, 9(6), 388; https://doi.org/10.3390/ani9060388 - 25 Jun 2019
Cited by 2
Abstract
Camera-traps are used widely around the world to census a range of vertebrate fauna, particularly mammals but also other groups including birds, as well as snakes and lizards (squamates). In an attempt to improve the reliability of camera-traps for censusing squamates, we examined [...] Read more.
Camera-traps are used widely around the world to census a range of vertebrate fauna, particularly mammals but also other groups including birds, as well as snakes and lizards (squamates). In an attempt to improve the reliability of camera-traps for censusing squamates, we examined whether programming options involving time lapse capture of images increased detections. This was compared to detections by camera-traps set to trigger by the standard passive infrared sensor setting (PIR), and camera-traps set to take images using time lapse in combination with PIR. We also examined the effect of camera trap focal length on the ability to tell different species of small squamate apart. In a series of side-by-side field comparisons, camera-traps programmed to take images at standard intervals, as well as through routine triggering of the PIR, captured more images of squamates than camera-traps using the PIR sensor setting alone or time lapse alone. Similarly, camera traps with their lens focal length set at closer distances improved our ability to discriminate species of small squamates. With these minor alterations to camera-trap programming and hardware, the quantity and quality of squamate detections was markedly better. These gains provide a platform for exploring other aspects of camera-trapping for squamates that might to lead to even greater survey advances, bridging the gap in knowledge of this otherwise poorly known faunal group. Full article
(This article belongs to the Special Issue The Application of Camera Trap Technology in Field Research)
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Review

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Open AccessReview
Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence
Animals 2020, 10(1), 132; https://doi.org/10.3390/ani10010132 - 14 Jan 2020
Abstract
Camera trapping has become an increasingly reliable and mainstream tool for surveying a diversity of wildlife species. Concurrent with this has been an increasing effort to involve the wider public in the research process, in an approach known as ‘citizen science’. To date, [...] Read more.
Camera trapping has become an increasingly reliable and mainstream tool for surveying a diversity of wildlife species. Concurrent with this has been an increasing effort to involve the wider public in the research process, in an approach known as ‘citizen science’. To date, millions of people have contributed to research across a wide variety of disciplines as a result. Although their value for public engagement was recognised early on, camera traps were initially ill-suited for citizen science. As camera trap technology has evolved, cameras have become more user-friendly and the enormous quantities of data they now collect has led researchers to seek assistance in classifying footage. This has now made camera trap research a prime candidate for citizen science, as reflected by the large number of camera trap projects now integrating public participation. Researchers are also turning to Artificial Intelligence (AI) to assist with classification of footage. Although this rapidly-advancing field is already proving a useful tool, accuracy is variable and AI does not provide the social and engagement benefits associated with citizen science approaches. We propose, as a solution, more efforts to combine citizen science with AI to improve classification accuracy and efficiency while maintaining public involvement. Full article
(This article belongs to the Special Issue The Application of Camera Trap Technology in Field Research)
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