scholarly journals Author Correction: Camera trap placement for evaluating species richness, abundance, and activity

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Kamakshi S. Tanwar ◽  
Ayan Sadhu ◽  
Yadvendradev V. Jhala
Keyword(s):  
Species Richness ◽  
Camera Trap ◽  
Trap Placement

scholarly journals Camera trap placement for evaluating species richness, abundance, and activity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kamakshi S. Tanwar ◽  
Ayan Sadhu ◽  
Yadvendradev V. Jhala
Keyword(s):  
Species Richness ◽  
Group Size ◽  
Relative Abundance ◽  
Accumulation Rate ◽  
Camera Trap ◽  
Camera Traps ◽  
Parameter Estimates ◽  
Wild Ungulates ◽  
Trail Cameras ◽  
Trap Placement

AbstractInformation from camera traps is used for inferences on species presence, richness, abundance, demography, and activity. Camera trap placement design is likely to influence these parameter estimates. Herein we simultaneously generate and compare estimates obtained from camera traps (a) placed to optimize large carnivore captures and (b) random placement, to infer accuracy and biases for parameter estimates. Both setups recorded 25 species when same number of trail and random cameras (n = 31) were compared. However, species accumulation rate was faster with trail cameras. Relative abundance indices (RAI) from random cameras surrogated abundance estimated from capture-mark-recapture and distance sampling, while RAI were biased higher for carnivores from trail cameras. Group size of wild-ungulates obtained from both camera setups were comparable. Random cameras detected nocturnal activities of wild ungulates in contrast to mostly diurnal activities observed from trail cameras. Our results show that trail and random camera setup give similar estimates of species richness and group size, but differ for estimates of relative abundance and activity patterns. Therefore, inferences made from each of these camera trap designs on the above parameters need to be viewed within this context.

scholarly journals Random versus Game Trail-Based Camera Trap Placement Strategy for Monitoring Terrestrial Mammal Communities

PLoS ONE ◽  
2015 ◽  
Vol 10 (5) ◽  
pp. e0126373 ◽  
Author(s):  
Jeremy J. Cusack ◽  
Amy J. Dickman ◽  
J. Marcus Rowcliffe ◽  
Chris Carbone ◽  
David W. Macdonald ◽  
...  
Keyword(s):  
Camera Trap ◽  
Terrestrial Mammal ◽  
Mammal Communities ◽  
Trap Placement

scholarly journals Small mammal diversity along two neighboring Bornean mountains

2018 ◽  
Author(s):  
Melissa T. R. Hawkins ◽  
Miguel Camacho-Sanchez ◽  
Fred Tuh Yit Yuh ◽  
Jesus E Maldonado ◽  
Jennifer A Leonard
Keyword(s):  
Species Richness ◽  
Success Rate ◽  
Elevational Gradient ◽  
Camera Trap ◽  
High Turnover ◽  
Evolutionary Origins ◽  
Diversity Gradient ◽  
History Of ◽  
Climatic History ◽  
Mammal Diversity

Biodiversity across elevational gradients generally follows patterns, the evolutionary origins of which are debated. We trapped small non-volant mammals across an elevational gradient on Mount (Mt.) Kinabalu (4,101 m) and Mt. Tambuyukon (2,579 m), two neighboring mountains in Borneo, Malaysia. We also included visual records and camera trap data from Mt. Tambuyukon. On Mt. Tambuyukon we trapped a total of 299 individuals from 23 species in 6,187 trap nights (4.8% success rate). For Mt. Kinabalu we trapped a total 213 animals from 19 species, in 2,044 trap nights, a 10.4% success rate. We documented the highest diversity in the low elevations for both mountains, unlike previous less complete surveys which supported a mid-elevation diversity bulge on Mt. Kinabalu. Species richness decreased gradually towards the highlands to a more even community with different species (high turnover), less rich but with the highest levels of endemism. These patterns suggest that an interplay of topography and climatic history of the region were drivers of the diversity gradient, in addition to standing climatic and spatial hypothesis.

A camera trap appraisal of species richness and community composition of medium and large mammals in a Miombo woodland reserve

2021 ◽  
Author(s):  
Sally J. Reece ◽  
Frans G. T. Radloff ◽  
Alison J. Leslie ◽  
Rajan Amin ◽  
Craig J. Tambling
Keyword(s):  
Species Richness ◽  
Community Composition ◽  
Camera Trap ◽  
Large Mammals ◽  
Miombo Woodland

scholarly journals Wildlife trail or systematic? Camera trap placement has little effect on estimates of mammal diversity in a tropical forest in Gabon

2020 ◽  
Author(s):  
Davy Fonteyn ◽  
Cédric Vermeulen ◽  
Nicolas Deflandre ◽  
Daniel Cornelis ◽  
Simon Lhoest ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Camera Trap ◽  
Mammal Diversity ◽  
Trap Placement

scholarly journals Small mammal diversity along two neighboring Bornean mountains

2018 ◽  
Author(s):  
Melissa T. R. Hawkins ◽  
Miguel Camacho-Sanchez ◽  
Fred Tuh Yit Yuh ◽  
Jesus E Maldonado ◽  
Jennifer A Leonard
Keyword(s):  
Species Richness ◽  
Success Rate ◽  
Elevational Gradient ◽  
Camera Trap ◽  
High Turnover ◽  
Evolutionary Origins ◽  
Diversity Gradient ◽  
History Of ◽  
Climatic History ◽  
Mammal Diversity

Biodiversity across elevational gradients generally follows patterns, the evolutionary origins of which are debated. We trapped small non-volant mammals across an elevational gradient on Mount (Mt.) Kinabalu (4,101 m) and Mt. Tambuyukon (2,579 m), two neighboring mountains in Borneo, Malaysia. We also included visual records and camera trap data from Mt. Tambuyukon. On Mt. Tambuyukon we trapped a total of 299 individuals from 23 species in 6,187 trap nights (4.8% success rate). For Mt. Kinabalu we trapped a total 213 animals from 19 species, in 2,044 trap nights, a 10.4% success rate. We documented the highest diversity in the low elevations for both mountains, unlike previous less complete surveys which supported a mid-elevation diversity bulge on Mt. Kinabalu. Species richness decreased gradually towards the highlands to a more even community with different species (high turnover), less rich but with the highest levels of endemism. These patterns suggest that an interplay of topography and climatic history of the region were drivers of the diversity gradient, in addition to standing climatic and spatial hypothesis.

scholarly journals Camera trap placement and the potential for bias due to trails and other features

PLoS ONE ◽  
2017 ◽  
Vol 12 (10) ◽  
pp. e0186679 ◽  
Author(s):  
Joseph M. Kolowski ◽  
Tavis D. Forrester
Keyword(s):  
Camera Trap ◽  
Trap Placement

scholarly journals High performance machine learning models can fully automate labeling of camera trap images for ecological analyses

2020 ◽  
Author(s):  
Robin Whytock ◽  
Jędrzej Świeżewski ◽  
Joeri A. Zwerts ◽  
Tadeusz Bara-Słupski ◽  
Aurélie Flore Koumba Pambo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Species Richness ◽  
High Performance ◽  
Activity Patterns ◽  
Camera Trap ◽  
Ecological Crisis ◽  
List Type ◽  
Learning Models ◽  
Automated Labeling ◽  
Machine Learning Models

AbstractEcological data are increasingly collected over vast geographic areas using arrays of digital sensors. Camera trap arrays have become the ‘gold standard’ method for surveying many terrestrial mammals and birds, but these arrays often generate millions of images that are challenging to process. This causes significant latency between data collection and subsequent inference, which can impede conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve camera trap data processing speeds, but these models are not considered accurate enough for fully automated labeling of images.Here, we present a new approach to building and testing a high performance machine learning model for fully automated labeling of camera trap images. As a case-study, the model classifies 26 Central African forest mammal and bird species (or groups). The model was trained on a relatively small dataset (c.300,000 images) but generalizes to fully independent data and outperforms humans in several respects (e.g. detecting ‘invisible’ animals). We show how the model’s precision and accuracy can be evaluated in an ecological modeling context by comparing species richness, activity patterns (n = 4 species tested) and occupancy (n = 4 species tested) derived from machine learning labels with the same estimates derived from expert labels.Results show that fully automated labels can be equivalent to expert labels when calculating species richness, activity patterns (n = 4 species tested) and estimating occupancy (n = 3 of 4 species tested) in completely out-of-sample test data (n = 227 camera stations, n = 23868 images). Simple thresholding (discarding uncertain labels) improved the model’s performance when calculating activity patterns and estimating occupancy, but did not improve estimates of species richness.We provide the user-community with a multi-platform, multi-language user interface for running the model offline, and conclude that high performance machine learning models can fully automate labeling of camera trap data.

scholarly journals Medium and large sized mammals of the Boqueirão da Onça, North of Bahia State, Brazil

2019 ◽  
Vol 59 ◽  
pp. e20195912 ◽  
Author(s):  
Cláudia Bueno de Campos ◽  
Carolina Franco Esteves ◽  
Douglas De Matos Dias ◽  
Flávio Henrique Guimarães Rodrigues
Keyword(s):  
Species Richness ◽  
National Park ◽  
Camera Trap ◽  
Management Plan ◽  
Sampling Effort ◽  
Ecological Processes ◽  
Wild Mammals ◽  
The North ◽  
Negative Impacts ◽  
Few Data

The mosaic of protected areas of Boqueirão da Onça (8.636 km²), created in the north of Bahia state, is located in the Caatinga, an exclusively Brazilian biome, but exposed to a range of anthropic impacts that threaten its species and natural resources. Few data are available for various zoological groups in Caatinga, including mammals. In order to characterize the community of mammals of this region, considering species richness, we installed 80 camera-trap stations. With a sampling effort of 10,370 camera-days we recorded 28 species (22 wild and six domestic). Opportunistically, we recorded four mammals, resulting in a total richness of 32 species, five of which are included in the global list of endangered species, and seven in the national list. The results are significant, since the richness of wild mammals of the Boqueirão da Onça (S = 26) presented a high value when compared to other Caatinga localities. During the study we found evidence of human activities threatening the conservation of the region, such as poaching and deforestation. Therefore, there is an urgent need in the publication of the Management Plan of the recently created Boqueirão da Onça National Park, to minimize negative impacts on biodiversity and ensure the maintenance of ecological processes.

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