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585 results for “Camera trap”
DoeDat Camera Trap Project 4073095 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 1266766 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 2002316 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 2002326 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 764058 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
Figure 2 in Camera traps capture images of predators of Caiman crocodilus yacare eggs (Reptilia: Crocodylia) in Brazil's Pantanal wetlands
Figure 2. Mammalian predators of the eggs of the Pantanal caiman (Caiman crocodilus yacare) – (A) crab-eating fox (Cerdocyon thous); (B) coati (Nasua nasua); (C) tayra (Eira barbara); (D) feral pig (Sus scrofa).
DoeDat Camera Trap Project 6375552 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 6494189 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
Data from: Density-dependent space use affects interpretation of camera trap detection rates
<p>Camera-traps (CTs) are an increasingly popular tool for wildlife survey and monitoring. Estimating relative abundance in unmarked species is often done using detection rate as an index of relative abundance, which assumes a positive linear relationship with true abundance. This assumption may be violated if movement behavior varies with density, but the degree to which movement is density-dependent across taxa is unclear. The potential confounding of population-level relative abundance indices by movement depends on how regularly, and by what magnitude, movement rate and home-range size vary with density. We conducted a systematic review and meta-analysis to quantify relationships between movement rate, home range size, and density, across terrestrial mammalian taxa. We then simulated animal movements and CT sampling to test the effect of contrasting movement scenarios on CT detection rates. Overall, movement rate and home range size were negatively correlated with density and positively correlated with one another. The strength of the relationships varied significantly between taxa and populations. In simulations, detection rates were related to true abundance but underestimated change, particularly for slower moving species with small home ranges. In situations where animal space use changes markedly with density, we estimate that up to thirty percent of a true change in abundance may be missed due to the confounding effect of movement, making trend estimation more difficult. The common assumption that movement remains constant across densities is therefore violated across a wide range of mammal species. When studying unmarked species using CT detection rates, researchers and managers should consider that such indices of relative abundance reflect both density and movement. Practitioners interpreting changes in detection rates should be aware that observed differences may be biased low relative to true changes in abundance, and that further information on animal movement may be required to make robust inferences on population trends.</p>
Data from: Under the snow: a new camera trap opens the white box of subnivean ecology
Snow covers the ground over large parts of the world for a substantial portion of the year. Yet very few methods are available to quantify biotic variables below the snow, with most studies of subnivean ecological processes relying on comparisons of data before and after the snow cover season. We developed a camera trap prototype to quantify subnivean small mammal activity. The trap consists of a camera that is attached facing downward from the ceiling of a box, which is designed to function as a snow-free tunnel. We tested it by placing nine traps with passive infrared sensors in a subarctic habitat where snow cover lasted for about 6 months. The traps were functional for the whole winter, permitting continuous data collection of site-specific presence and temporal activity patterns of all three small mammal species present (the insectivorous common shrew, Sorex araneus, the herbivorous tundra vole, Microtus oeconomus, and the carnivorous stoat, Mustela erminea) as well as abiotic conditions (presence/absence of snow cover and subnivean temperature). Based on their successful functioning (only 6% of the photographs appeared empty or were of poor quality, whereas ca 80% were of small mammals and the remaining of birds and invertebrates), we discuss how the new camera trap can enable subnivean studies of small mammal communities. This greatly increases the temporal resolution and extent of data collection and thereby provides unpreceded opportunities to understand population and food web dynamics in ecosystems with snow cover.
Data from: Estimating the intensity of use by interacting predators and prey using camera traps
Understanding how organisms distribute themselves in response to interacting species, ecosystems, climate, human development and time is fundamental to ecological study and practice. A measure to quantify the relationship among organisms and their environments is intensity of use: the rate of use of a specific resource in a defined unit of time. Estimating the intensity of use differs from estimating probabilities of occupancy or selection, which can remain constant even when the intensity of use varies. We describe a method to evaluate the intensity of use across conditions that vary in both space and time. We demonstrate its application on a large mammal community where linear developments and human activity are conjectured to influence the interactions between white‐tailed deer (Odocoileus virginianus) and wolves (Canis lupus) with possible consequences on threatened woodland caribou (Rangifer tarandus caribou). We collect and quantify intensity of use data for multiple, interacting species with the goal of assessing management efficacy, including a habitat restoration strategy for linear developments. We test whether blocking linear developments by spreading logs across a 200‐m interval can be applied as an immediate mitigation to reduce the intensities of use by humans, predator and prey species in a boreal caribou range. We deployed camera traps on linear developments with and without restoration treatments in a landscape exposed to both timber and oil development. We collected a three‐year dataset and employed spatial recurrent event models to analyse intensity of use by an interacting human and large mammal community across a range of environmental and climatic conditions. Spatial recurrent event models revealed that intensity of use by humans influenced the intensity of use by all five large mammal species evaluated, and the intensities of use by wolves and deer were inextricably linked in space and time. Conditions that resist travel on linear developments had a strong negative effect on the intensity of human and large mammal use. Mitigation strategies that resist, or redirect, animal travel on linear developments can reduce the effects of resource development on interacting human and predator–prey interactions. Our approach is easily applied to other continuous time point‐based survey methodologies and shows that measuring the intensity of use within animal communities can help scientists monitor, mitigate and understand ecological states and processes.
Sherlock Camera Trap Dataset
<h2>Data</h2><p>Base dataset containing the camera trap images used for the 'small' and 'paired' tests of Sherlock, andSherlock code.</p><p>Camera trap images used for the larger 'main' test can be found in the following datasets:</p><p>https://zenodo.org/uploads/10023354</p><p>https://zenodo.org/uploads/10026321</p><p>https://zenodo.org/uploads/10036325</p><p>https://zenodo.org/uploads/10039718</p><p>https://zenodo.org/uploads/10044242</p><p>https://zenodo.org/uploads/10047672</p><p>https://zenodo.org/uploads/10048102</p><p>https://zenodo.org/uploads/10048378</p><h2><a href="https://github.com/mpenn114/Sherlock#sherlock">Sherlock</a></h2><p>This repository contains a code package, Sherlock, which provides an easy-to-use tool for processing camera trapping images.</p><p>Its aim is to remove false positive images - that is, principally, images where the camera has been triggered by a very small disturbances, such as a plant blowing in the wind. If images containing a specific species is being targeted, it also allows for the user to easily customise parameters to set the colour of the deisred animals. This can help to filter out images containing other animals, and thus increase the accuracy of the code.</p><p>This code is aimed to be usable by someone with no prior coding experience. To help this, a guide to installing Python (the language which Sherlock is written in) is provided at the end of this readme.</p><h3><a href="https://github.com/mpenn114/Sherlock#-input-format-">Input Format</a></h3><p>This code can process JPG images (NB: it should be possible to edit the code to accept any image format). These images should be stored in folders/subfolders, with a single "master folder" containing all of these folders. Examples of acceptable folder structures are below:</p><p>[Master Folder] -> Images (that is, an image would have the path .../MasterFolder/0001.JPG)</p><p>[Master Folder] -> [Location Folders] -> Images (e.g. .../MasterFolder/Aberystwyth/0001.JPG)</p><p>[Master Folder] -> [Location Folders] -> [Sub-location Folders] -> Images (e.g. .../MasterFolder/Aberystwyth/Constitution Hill/0001.JPG)</p><p>[Master Folder] -> [Location Folders] -> [Sub-location Folders] > [Camera Number] -> Images (e.g. .../MasterFolder/Aberystwyth/Constitution Hill/Camera1/0001.JPG)</p><p>In all of these cases, it is simply necessary to specify the master folder. Note that it is also possible to have a mixture of these cases (e.g. some locations may not have sub-locations)</p><p>It is important that the JPG images in each image folder are named consecutively as 0001.JPG, 0002.JPG, ... (note, the number of leading zeros is not important).</p><h3><a href="https://github.com/mpenn114/Sherlock#-output-format-">Output Format</a></h3><p>The code can produce different kinds of outputs, depending on the needs of the user.</p><p>The primary output is, for each folder of images, a folder containing CSV files, labelled as "CSV_Outputs[Runcode]". These files are "Potential_Animals" (a list of all images that the code believes may contain animals); "Unlikely_Animals" (a list of all images that the code believes do not contain an animal); "Errors" (a list of images that could not be processed); "Close to Animals" (a list of images such that images close to them - in terms of image number and time - were identified as potential animals); "Overall Animals" (the combination of the lists in "Potential_Animals" and "Close_to_Animals") and "False_Negatives" (if the code is in "testing mode", explained below, a list of all the false negatives)</p><p>It is also possible to get the code to write any potential animal images into a new folder, called "PotentialAnimals[Runcode]" where any of the images that are identified as animals, along with those that are close to them, are re-written with red boxes indicating the locations in which animals were thought to be. Note that this does not edit the original images in any way. However, it can be turned off if desired (as these images will be reasonably large files)</p><p>Finally, the code can be put into "testing mode". This can be done by changing one of the parameters (explained at the start of the code file), and seeks to compare the results of the code with human-inputted results. A list of image numbers containing animals should be created, called "Animaldata.csv", and a list of image numbers not containing animals should be created, called "nonAnimaldata.csv". These should then be saved in the same folder as the images, and their inclusion will allow the code to create CSV outputs that compare the two sets of results. The code can also compare results according to a number of different characteristics of the image, such as time and location, provided that the format matches that of the example CSV which is included in this Github.</p><h3><a href="https://github.com/mpenn114/Sherlock#-installing-python-anaconda-and-jupyter-lab-">Installing Python, Anaconda and Jupyter Lab</a></h3><p>Anaconda (and thus, Python) can be installed by visiting:</p><p>Windows: <a href="https://docs.anaconda.com/anaconda/install/windows/">https://docs.anaconda.com/anaconda/install/windows/</a></p><p>Mac: <a href="https://docs.anaconda.com/anaconda/install/mac-os/">https://docs.anaconda.com/anaconda/install/mac-os/</a></p><p>Linux: <a href="https://docs.anaconda.com/anaconda/install/linux/">https://docs.anaconda.com/anaconda/install/linux/</a></p><p>Once Anaconda has been installed, you should be able to find "Anaconda Prompt", and open it to get a command window. Type</p><p>conda install -c conda-forge jupyterlab</p><p>into this window and press enter to install Jupyter Lab</p><h3><a href="https://github.com/mpenn114/Sherlock#-opening-jupyter-lab-">Opening Jupyter Lab</a></h3><p>To open Jupyter Lab, open Anaconda Prompt, type in</p><p>jupyter lab</p><p>and then press enter. It should open in your web browser (note: you do not need an Internet connection to do this, or to run any of this code, except the section immediately following)</p><h3><a href="https://github.com/mpenn114/Sherlock#-opening-sherlock-">Opening Sherlock</a></h3><p>You can copy the code for Sherlock onto your computer by opening a new notebook (by clicking the "Python 3" button below "Notebook" in the right hand window. Then, there should be a textbox with your cursor inside it. If you have Git installed on your computer (which you can install from here <a href="https://github.com/git-guides/install-git">https://github.com/git-guides/install-git</a>), you can copy the code for Sherlock by copying</p><p>!git clone <a href="https://github.com/mpenn114/Sherlock">https://github.com/mpenn114/Sherlock</a></p><p>into this textbox, and then pressing the run button (which is a button in the row of buttons above the textbox - it looks like a "play" button). You should then be able to see a folder called Sherlock on the left hand side of the screen. Double-click on this folder to open it, and then double-click on the file "Sherlock.ipynb" to open the notebook for Sherlock. This should then appear on the right hand window.</p><p>Otherwise, you can download the code from this repository as a zip file. After extracting this code (right click on the zip file and click "Extract All"), you then will need to copy the notebook "Sherlock.ipynb" to the folder that Python opens with when you start Jupyter Lab. On Windows, this will generally be "C:/Users/[Your username]".</p><p>This file then contains all the information needed to run the code at the top. The actual code is below this initial text and, once you are happy with the inputs and parameters, you can run it by pressing the clicking somewhere on it, and then pressing the "run" button.</p><p>Note: If you are using an old Mac operating system (iOS 13 or earlier) then you may have an error when running the code. This can be fixed by removing the line</p><p>!pip install opencv-python</p><p>from the code and replacing it with the two lines</p><p>!pip uninstall opencv-python --y</p><p>!pip install opencv-python==4.4.0.46</p><p>The code should then run without any errors.</p><p>If you are using a camera with an unusual metadata format, then you may need to use the Sherlock_legacy.ipynb file instead. This manually detects whether an image was taken during the day or at night, rather than using the image metadata. Do feel free to log this as an issue if the main version doesn't work and we will seek to add a fix!</p>
Assessing environmental DNA metabarcoding and camera trap surveys as complementary tools for biomonitoring of remote desert water bodies
<p>Biodiversity assessments are indispensable tools for planning and monitoring conservation strategies. Camera traps (CT) are widely used to monitor wildlife and have proven their usefulness. Environmental DNA (eDNA)-based approaches are increasingly implemented for biomonitoring, combining sensitivity, high taxonomic coverage and resolution, non-invasiveness and easiness of sampling, but remain challenging for terrestrial fauna. However, in remote desert areas where scattered water bodies attract terrestrial species, which release their DNA into the water, this method presents a unique opportunity for their detection. In order to identify the most efficient method for a given study system, comparative studies are needed. Here, we compare CT and DNA metabarcoding of water samples collected from two desert ecosystems, the Trans-Altai Gobi in Mongolia and the Kalahari in Botswana. We recorded with CT the visiting patterns of wildlife and studied the correlation with the biodiversity captured with the eDNA approach. The aim of the present study was threefold: a) to investigate how well waterborne eDNA captures signals of terrestrial fauna in remote desert environments, which have been so far neglected in terms of biomonitoring efforts; b) to compare two distinct approaches for biomonitoring in such environments and c) to draw recommendations for future eDNA-based biomonitoring. We found significant correlations between the two methodologies and describe a detectability score based on variables extracted from CT data and the visiting patterns of wildlife. This supports the use of eDNA-based biomonitoring in these ecosystems and encourages further research to integrate the methodology in the planning and monitoring of conservation strategies.</p>
Camera trap data: Density dependence of daily activity in three ungulate species
<p><span><span><span><span><span><span><span><span><span><span><span>Daily activity in herbivores reflects a balance between finding food and safety. The safety-in-numbers theory predicts that living in higher population densities increases safety, which should affect this balance. High-density populations are thus expected to show a more even distribution of activity – i.e. spread – and higher activity levels across the day. We tested these predictions for three ungulate species; red deer (<i>Cervus elaphus</i>), roe deer (<i>Capreolus capreolus</i>) and wild boar (<i>Sus scrofa</i>). We used camera traps to measure the level and spread of activity across ten forest sites at the Veluwe, the Netherlands, that widely range in ungulate density. Food availability and hunting levels were included as covariates. Daily activity was more evenly distributed when population density was higher for all three species. Both deer species showed relatively more feeding activity in broad daylight and wild boar during dusk. Activity level increased with population density only for wild boar. Food availability and hunting showed no correlation with activity patterns. These findings indicate that ungulate activity is to some degree density dependent. However, while these patterns might result from larger populations feeling safer as the safety-in-numbers theory states, we cannot rule out that they are the outcome of greater intraspecific competition for food, forcing animals to forage during suboptimal times of the day. Overall, this study demonstrates that wild ungulates adjust their activity spread and level based on their population size.</span></span></span></span></span></span></span></span></span></span></span></p>
Camera traps for monitoring insects - supporting information
<p>Insect and pollinator populations are vitally important to the health of ecosystems, food production, and economic stability, but are declining worldwide. New, cheap, and simple monitoring methods are necessary to inform management actions and should be available to researchers around the world.</p> <p>Here we evaluate the efficacy of commercially available, close-focus automated camera traps to monitor insect-plant interactions. We compared two video settings—scheduled and motion-activated—to a traditional human observation method.</p> <p>Our results show that camera traps with scheduled video settings detected more insects overall than humans, but relative performance varied by insect order. Scheduled cameras significantly outperformed motion-activated cameras, detecting more insects of all orders and size classes.</p> <p>We conclude that scheduled camera traps are an effective and relatively inexpensive tool for monitoring interactions between plants and insects of all size classes, and their ease of accessibility and set-up allows for the potential of widespread use. The digital format of video also offers the benefits of recording, sharing, and verifying observations.</p>
Supplementary material 1 from: Premate E, Fišer Ž, Kuralt Ž, Pekolj A, Trajbarič T, Milavc E, Hanc Ž, Kostanjšek R (2022) Behavioral observations of the olm (Proteus anguinus) in a karst spring via direct observations and camera trapping. Subterranean Biology 44: 69-83. https://doi.org/10.3897/subtbiol.44.87295
Figure S1
Figures 1-4 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Figures 1-4 Predators of bird nests recorded with camera traps in an area of Atlantic rainforest. (1) the Collared Forest-falcon, Micrastur semitorquatus, depredating a nest of the White-necked Thrush, Turdus albicollis. (2) the Red-breasted Toucan, Ramphastos dicolorus, depredating a nest of the Ruddy Quail-dove, Geotrygon montana. (3) the Ocelot, Leopardus pardalis, depredating a nest of the Gray-hooded Flycatcher, Mionectes rufiventris. (4) the Gray Slender Mouse Opossum, Marmosops incanus depredating a nest of the Royal Flycatcher, Onychorhynchus swainsoni.
Figure 2 from: Pyšková K, Kauzál O, Storch D, Horáček I, Pergl J, Pyšek P (2018) Carnivore distribution across habitats in a central-European landscape: a camera trap study. ZooKeys 770: 227-246. https://doi.org/10.3897/zookeys.770.22554
Figure 2 Habitat types studied. A wetland B floodplain forest C mixed forest D shrubby grassland (see text for description). Photo credits: Klára Pyšková
Figure 3 from: Pyšková K, Kauzál O, Storch D, Horáček I, Pergl J, Pyšek P (2018) Carnivore distribution across habitats in a central-European landscape: a camera trap study. ZooKeys 770: 227-246. https://doi.org/10.3897/zookeys.770.22554
Figure 3 Habitat preferences of the carnivores studied; the figures are percentages of the total number of standardized daily records as recorded in each habitat.
Figure 1 from: Pyšková K, Kauzál O, Storch D, Horáček I, Pergl J, Pyšek P (2018) Carnivore distribution across habitats in a central-European landscape: a camera trap study. ZooKeys 770: 227-246. https://doi.org/10.3897/zookeys.770.22554
Figure 1 Location of the study area in central Bohemia, western part of the Czech Republic (black rectangle).
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.