Data acquisition and management software for camera trap data: A case study from the TEAM Network

Fegraus, Eric; Lin, Kai; Ahumada, Jorge; Baru, Chaitan; Chandra, Surabhi; Youn, Choonhan

doi:10.1016/j.ecoinf.2011.06.003

Cited by 86 publications

(58 citation statements)

References 13 publications

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“…This protocol uses digital camera traps (60-90 camera traps points) at a density of 1 camera every 2 km² to monitor the status of species and changes in the community. Photographs are processed with an application called DeskTEAM (Fegraus et al, 2011). The data product is used to build annual occupancy and spatial occurrence models through sites.…”

Section: Camera Trapping: Team Protocol (P1)mentioning

confidence: 99%

CTFS‐ForestGEO: a worldwide network monitoring forests in an era of global change

Anderson‐Teixeira

Davies

Bennett

et al. 2014

Global Change Biology

503

439

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Global change is impacting forests worldwide, threatening biodiversity and ecosystem services including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long-term forest dynamics research sites (CTFS-ForestGEO) useful for characterizing forest responses to global change. Within very large plots (median size 25 ha), all stems ≥1 cm diameter are identified to species, mapped, and regularly recensused according to standardized protocols. CTFS-ForestGEO spans 25°S-61°N latitude, is generally representative of the range of bioclimatic, edaphic, and topographic conditions experienced by forests worldwide, and is the only forest monitoring network that applies a standardized protocol to each of the world's major forest biomes. Supplementary standardized measurements at subsets of the sites provide additional information on plants, animals, and ecosystem and environmental variables. CTFS-ForestGEO sites are experiencing multifaceted anthropogenic global change pressures including warming (average 0.61°C), changes in precipitation (up to AE30% change), atmospheric deposition of nitrogen and sulfur compounds (up to 3.8 g N m À2 yr À1 and 3.1 g S m À2 yr À1), and forest fragmentation in the surrounding landscape (up to 88% reduced tree cover within 5 km). The broad suite of measurements made at CTFS-ForestGEO sites makes it possible to investigate the complex ways in which global change is impacting forest dynamics. Ongoing research across the CTFSForestGEO network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forest diversity and dynamics in an era of global change.

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Section: Camera Trapping: Team Protocol (P1)mentioning

confidence: 99%

CTFS‐ForestGEO: a worldwide network monitoring forests in an era of global change

Anderson‐Teixeira

Davies

Bennett

et al. 2014

Global Change Biology

503

439

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“…Photos from BCNM and Soberania NP were processed in Desk-TEAM (Fegraus et al 2011), photos from Darién NP and San Lorenzo NP were annotated in Excel 2007 (Microsoft Corporation, Redmond WA, USA), and photos from the other sites in Central Panama were processed in the ForestGEO camera-trapping database (as in Kays et al 2011; http://www.forestGEO.si.edu). Photos were manually grouped into sequences that represented the same visit of an animal or group of animals (notably coatis Nasua narica and peccaries species Tayassu pecari and Pecari tajacu) in a 2 h-interval, which triggered the same camera one or multiple times depending on movement speed and residence time.…”

Section: Camera Trap Surveysmentioning

confidence: 99%

An assessment of the terrestrial mammal communities in forests of Central Panama, using camera-trap surveys

Meyer

Esser

Moreno

et al. 2015

Journal for Nature Conservation

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a b s t r a c tThe Isthmus of Panama, part of the planet's third largest megadiversity hotspot, and connecting the faunas of North and South America, has lost more than half of its forest due to agriculture and economic development. It is unknown to what degree the remaining forest, which is fragmented and subject to poaching, still supports the wildlife diversity found in intact forests. Here, we use camera-trap surveys to assess whether forests in Central Panama, the narrowest and most disturbed portion of the Isthmus, still have intact communities of medium-and large-bodied terrestrial mammals. During 2005-2014, we collected camera-trap survey data from 15 national parks and forest fragments on both sides of the Panama Canal, and compared these to similar data from two sites in the intact Darién National Park in Eastern Panama, the nearest available reference. We found that most sites in Central Panama -including some of the national parks -had lower mammal species richness and evenness than the reference sites, and less structurally-complex mammal communities. Forests in Central Panama had little or no apex predators and large terrestrial frugivores, with the exception of two sites directly connected to the reference site. Our results indicate that the terrestrial mammal community in forests of Central Panama is currently degraded, even inside national parks. These data provide a baseline for evaluating the success of conservation efforts to prevent the Panamanian Isthmus to become a bottleneck for movement of animals.

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“…Currently, no automatic approach is used to identify species from camera-trap images. Researchers analyze thousands or millions of photographs manually [3]. An automatic system that deals with this problem would accelerate the professionals' work, allowing them to focus on data analysis and important issues only.…”

Section: Introductionmentioning

confidence: 99%

Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks

Villa

Salazar

Vargas

2017

Ecological Informatics

222

103

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Abstract. Non intrusive monitoring of animals in the wild is possible using camera trapping framework, which uses cameras triggered by sensors to take a burst of images of animals in their habitat. However camera trapping framework produces a high volume of data (in the order on thousands or millions of images), which must be analyzed by a human expert. In this work, a method for animal species identification in the wild using very deep convolutional neural networks is presented. Multiple versions of the Snapshot Serengeti dataset were used in order to probe the ability of the method to cope with different challenges that camera-trap images demand. The method reached 88.9% of accuracy in Top-1 and 98.1% in Top-5 in the evaluation set using a residual network topology. Also, the results show that the proposed method outperforms previous approximations and proves that recognition in camera-trap images can be automated.

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Data acquisition and management software for camera trap data: A case study from the TEAM Network

Cited by 86 publications

References 13 publications

CTFS‐ForestGEO: a worldwide network monitoring forests in an era of global change

CTFS‐ForestGEO: a worldwide network monitoring forests in an era of global change

An assessment of the terrestrial mammal communities in forests of Central Panama, using camera-trap surveys

Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks

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