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25 results for “remote cameras”
Data and code from: Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras.
<p>Detection data from American black bears and code used in "Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras" published in the Journal of Applied Ecology (Evans & Rittenhouse 2018). Unmarked detection data was collected using remote cameras in northwest Connecticut in 2014, and individual detection data was determined from unique genotypes obtained from non-invasive hair snares constructed at camera sampling locations.</p> <p>EN14.rds contains detection data as an R list:</p> <p>$y (num): J (sites) x K (occasions) matrix containing detection counts</p> <p>$X (int): 2 x J matrix of site coordinates</p> <p>$xlims (num): bounding x-coordinates</p> <p>$ylims (num): bounding y-coordinates</p> <p>$M (int): upper bound for super population of individuals</p> <p>$nTraps (int): number of sampling sites (J)</p> <p>$nReps (int): number of MCMC interations</p> <p>$forest (num): vector of site-specific covariates</p> <p>$mark (int): K x I matrix storing site numbers at which individual (i) was detected on occasion k</p> <p>FullModel.R provides functions used to fit constant density models to unmarked detections incorporating covariates of detection probability.</p> <p>partialID.R provides functions and code used to estimate density from mixtures of marked and unmarked detection data</p> <p>VariableDensity.R provides functions and code used to fit variable density models to unmarked detection data incorporating spatial covariates of density.</p> <p> </p>
Raspberry Pi nest cameras – an affordable tool for remote behavioural and conservation monitoring of bird nests
<p><span><span><span><span><span><span><span><span><span><span><span>1. Bespoke (custom-built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioural ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>2. Here we describe a novel, Raspberry Pi-based camera system that is fully portable and yet weatherproof – especially to humidity and salt spray. The camera was paired with a passive infra-red sensor, to create a movement-triggered camera capable of recording videos over a 24-hr period. We describe an example deployment involving "retro-fitting" these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviours and interspecific interactions of two sympatric species of breeding storm-petrel (Monteiro's storm-petrel <i>Hydrobates monteiroi</i> and Madeiran storm-petrel <i>Hydrobates castro</i>) during their chick-rearing periods.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Of the 138 deployments, 70% of all deployments were deemed to be "Successful" (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30 s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as "Partial" or "Failure" deployments were specifically due to the weather/humidity), and we make further trouble-shooting suggestions to mitigate additional weather-related failures.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Here we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently the camera-build methods and scripts could be applied easily to many different species that also utilise cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species-specific daily cycles of behaviours and interspecies interactions.</span></span></span></span></span></span></span></span></span></span></span></p>
Raspberry Pi nest cameras – an affordable tool for remote behavioural and conservation monitoring of bird nests
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Remote camera monitoring and arboreal trapping data for a reintroduced population of red-tailed phascogales (Phascogale calura)
<p>Effective monitoring methods are required to evaluate the success of wildlife reintroduction programs. To improve the threat status of the Vulnerable red-tailed phascogale (<em>Phascogale calura</em>), the Australian Wildlife Conservancy reintroduced the species to a fenced reserve at Mt. Gibson Wildlife Sanctuary. After trialing a variety of post-release monitoring methods, remote camera monitoring and arboreal trapping with an extensive period of pre-luring provided the most information with which to evaluate the success of the reintroduction. To date, reintroduced red-tailed phascogales have increased in both occupancy and population size following releases which began at Mt. Gibson in 2017. Other managers of red-tailed phascogale populations may find the described methods useful, particularly in the context of multi-species reintroductions where trap saturation can reduce capture rates of smaller species, such as phascogales.</p>
Stroke Team Remote Evaluation Using a Digital Observation Camera- Long Term Outcomes(STRokE DOC-LTO)
ClinicalTrials.gov study NCT00936455. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Stroke Team Remote Evaluation Using a Digital Observation Camera
ClinicalTrials.gov study NCT00283868. IPD Sharing: Not stated. Countries: 1. Publications: 11.
Remote camera monitoring and arboreal trapping data for a reintroduced population of red-tailed phascogales (Phascogale calura)
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Data from: Through the eye of a Gobi khulan – application of camera collars for ecological research of far-ranging species in remote and highly variable ecosystems
The Mongolian Gobi-Eastern Steppe Ecosystem is one of the largest remaining natural drylands and home to a unique assemblage of migratory ungulates. Connectivity and integrity of this ecosystem are at risk if increasing human activities are not carefully planned and regulated. The Gobi part supports the largest remaining population of the Asiatic wild ass (Equus hemionus; locally called "khulan"). Individual khulan roam over areas of thousands of square kilometers and the scale of their movements is among the largest described for terrestrial mammals, making them particularly difficult to monitor. Although GPS satellite telemetry makes it possible to track animals in near-real time and remote sensing provides environmental data at the landscape scale, remotely collected data also harbors the risk of missing important abiotic or biotic environmental variables or life history events. We tested the potential of animal born camera systems ("camera collars") to improve our understanding of the drivers and limitations of khulan movements. Deployment of a camera collar on an adult khulan mare resulted in 7,881 images over a one-year period. Over half of the images showed other khulan and 1,630 images showed enough of the collared khulan to classify the behaviour of the animals seen into several main categories. These khulan images provided us with: i) new insights into important life history events and grouping dynamics, ii) allowed us to calculate time budgets for many more animals than the collared khulan alone, and iii) provided us with a training dataset for calibrating data from accelerometer and tilt sensors in the collar. The images also allowed to document khulan behaviour near infrastructure and to obtain a day-time encounter rate between a specific khulan with semi-nomadic herders and their livestock. Lastly, the images allowed us to ground truth the availability of water by: i) confirming waterpoints predicted from other analyses, ii) detecting new waterpoints, and iii) compare precipitation records for rain and snow from landscape scale climate products with those documented by the camera collar. We discuss the added value of deploying camera collars on a subset of animals in remote, highly variable ecosystems for research and conservation.
Using global remote camera data of a "solitary" species complex to evaluate the drivers of group formation
<p>The social system of animals involves a complex interplay between physiology, natural history, and the environment. Long relied upon discrete categorizations of "social" and "solitary" inhibit our capacity to understand species, and their interactions with the world around them. Here, we use a globally distributed camera trapping dataset to test the drivers of aggregating into groups in a species complex (martens and relatives, family <em>Mustelidae</em>, Order <em>Carnivora</em>) assumed to be obligately solitary. We use a simple quantification, the probability of being detected in a group, that was applied across our globally derived camera trap dataset. Using a series of binomial generalized mixed-effects models applied to a dataset of 16,483 independent detections across 17 countries on four continents we test explicit hypotheses about potential drivers of group formation. We observe a wide range of probabilities of being detected in groups within the "solitary" model system, with the probability of aggregating in groups varying by more than an order of magnitude. We demonstrate that a species' proclivity towards aggregating in groups is underpinned by a range of resource-related factors, primarily the distribution of resources, with increasing patchiness of resources facilitating group formation, as well as interactions between environmental conditions (resource constancy/winter severity) and physiology (energy storage capabilities). Combined these factors explain observed variance in context-dependent tendencies towards grouping. The wide variation in propensities to aggregate with conspecifics observed here highlights how continued failure to recognise complexities in the social behaviours of apparently "solitary" species limits our understanding not only of the individual species, but also the causes and consequences of group formation.</p>
Validating the use of stereo-video cameras to conduct remote measurements of sea turtles
<p>Stereo-Video Camera Systems (SVCSs) are a promising tool to remotely measure body size of wild animals without the need for animal handling. Here, we assessed the accuracy of SVCSs for measuring straight carapace length (SCL) of sea turtles. To achieve this, we hand captured and measured 63 juvenile, sub-adult, and adult sea turtles across three species, greens, <i>Chelonia mydas </i>(n = 52), loggerheads, <i>Caretta caretta </i>(n = 8), and<i> </i>Kemp's ridley, <i>Lepidochelys kempii </i>(n = 3) in the waters off Eleuthera, The Bahamas and Crystal River, Florida, U.S.A. between May - November 2019. Upon release, we filmed these individuals with the SVCS. We performed photogrammetric analysis to extract stereo SCL measurements (eSCL), which were then compared to the (manual) capture measurements (mSCL). mSCL ranged from 25.9 – 89.2 cm, while eSCL ranged from 24.7 – 91.4 cm. Mean percent bias of eSCL ranged from -0.61% (± 0.11 SE) to -4.46% (± 0.31 SE) across all species and locations. We statistically analyzed potential drivers of measurement error, including distance of the turtle to the SVCS, turtle angle, image quality, turtle size, capture location, and species.Using a linear mixed effects model, we found that the distance between the turtle and the SVCS was the primary factor influencing measurement error. Our research suggests that stereo-video technology enables high-quality measurements of sea turtle body size collected <i>in situ</i> without the need for hand-capturing individuals. This study contributes to the growing knowledge base that SVCS are accurate for body size measurements independent of taxonomic clade.</p>
Figure 4 in Use of remote cameras to evaluate ocelot (Leopardus pardalis) population parameters in seasonal tropical dry forests of central-western Mexico
Figure 4: Relationship between estimated ocelot density and precipitation in tropical rain forests (TRF) and tropical seasonal ecosystems (TSE). Ocelot density in tropical rain forest was the closest to show a significant increase with annual precipitation (R2 = 0.2463, p = 0.071).
Figure 3 in Use of remote cameras to evaluate ocelot (Leopardus pardalis) population parameters in seasonal tropical dry forests of central-western Mexico
Figure 3: Estimated ocelot density in tropical rainforest sites (TRF) and tropical seasonal ecosystems (TSE). Thick horizontal lines correspond to median values. The upper and lower extremes of the boxes correspond to the first and third quartiles, whiskers correspond to 1.5 times the interquartile range of the data and empty circles are outliers.
Figure 2 in Use of remote cameras to evaluate ocelot (Leopardus pardalis) population parameters in seasonal tropical dry forests of central-western Mexico
Figure 2: Examples of markings employed for individual recognition of ocelots. (A) and (B) Photographic recapture of same individual in the locality of El Naranjal. (C) and (D) Different individuals recorded in the locality of Playa del Venado. The oval indicates an example of a set of unique spot and stripes patterns employed for individual identification.
Stroke DOC Arizona TIME - Stroke Team Remote Evaluation Using a Digital Observation Camera
ClinicalTrials.gov study NCT00623350. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Data from: Through the eye of a Gobi khulan – application of camera collars for ecological research of far-ranging species in remote and highly variable ecosystems
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Validating the use of stereo-video cameras to conduct remote measurements of sea turtles
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Using global remote camera data of a “solitary” species complex to evaluate the drivers of group formation
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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>
Assessing environmental DNA metabarcoding and camera trap surveys as complementary tools for biomonitoring of remote desert water bodies
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Data from: Large-scale assessment of intra- and inter-annual breeding success using a remote camera network
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.