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40 results for “trapped populations”
Hubbard Brook Wildlife Monitoring Project: Assessing wildlife population presence, activity and habitat use through continual camera trap monitoring, 2018
Monitoring of wildlife at Hubbard Brook is essential to understand how these species are responding to forest and environmental condition over time, while also placing those wildlife species in the context of ecosystem structural and functional attributes. The presence and persistence of wildlife species common to an area can indicate suitable habitat conditions as well as refugia for less common species. Changes in species presence and activity, such as fewer to no sightings, may point to shifting conditions not suitable to the species missing from the area. Camera trap monitoring allows for continuous, non-obtrusive observation of many different species of wildlife and can be used as part of our understanding of current suitability of habitat condition. To better understand integrated forest condition, we established a camera trap network located at the Hubbard Brook Experimental Forest in the White Mountains of central New Hampshire. The cameras have logged over 1,500 wildlife observations, confirming the presence of many species, including those not previously reported (pine marten and river otter). A total of 15 mammal species have been detected and have also been effective at detecting some bird species, including the Northern Harrier. Natural history observations have provided insight into the lives of the species detected, including reproduction (Bull moose following cow during rut, moose calves, deer fawns), predation (red fox with snow-shoe hare) and presence of parasites (winter ticks on moose with hairless shoulders). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps
<p><strong>The dataset<br></strong></p> <p>The Pest Sticky Traps (PST) dataset is a collection of yellow chromotropic sticky trap pictures specifically designed for training/testing deep learning models to automatically count insects and estimate pest populations.</p> <p>Images were manually annotated by some experts of the Department of Agriculture, Food and Environment of the University of Pisa (Italy) by putting a dot over the centroids of each identified insect. Specifically, we labeled insects as belonging to the category “whitefly” considering two different species, i.e., the sweet potato whitefly (<em>Bemisia tabaci</em>) (Gennadius) and the greenhouse whitefly (<em>Trialeurodes vaporariorum</em>) (Westwood).</p> <p>The dataset comprises two subsets:<br>- a subset we suggest using for the training/validation phases (contained in the `train/` folder)<br>- a subset we suggest using for the test phase (contained in the `test/` folder)</p> <p>Annotations of the two subsets are contained in `train/annotations.csv` and `test/annotations.csv`, respectively. They have the following columns:<br>- *imageName* - filename of the image containing the whiteflies,<br>- *X,Y* - 2D coordinates of the whitefly in the image space,<br>- *class* - class index of the insect (always 0 in this dataset).</p> <p> </p> <p><strong>Citing our work</strong></p> <p>If you found this dataset useful, please cite the following paper</p> <blockquote> <pre>@inproceedings{CIAMPI2023102384,<br> title = {A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps},<br> journal = {Ecological Informatics},<br> volume = {78},<br> pages = {102384},<br> year = {2023},<br> issn = {1574-9541}, doi = {10.1016/j.ecoinf.2023.102384}, url = {https://www.sciencedirect.com/science/article/pii/S1574954123004132}, year = 2023, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, } </pre> </blockquote> <p>and this Zenodo Dataset</p> <blockquote> <pre>@dataset{ciampi_2023_7801239, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, title = {Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps}}, month = apr, year = 2023, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.7801239}, url = {<a href="https://doi.org/10.5281/zenodo.7801239">https://doi.org/10.5281/zenodo.6560823</a>} } </pre> </blockquote> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset or if you experience any issues downloading files, please contact us at <a href="mailto:mobdrone@isti.cnr.it">luca.ciampi@isti.cnr.it</a></p> <p> </p> <p> </p>
Figure 3 in Seasonal changes in the population structure of dominant planktonic copepods collected using a sediment trap moored in the western Arctic Ocean
Figure 3. Seasonal changes in sea ice concentration, surface chl. a (from satellite) and total mass flux (a), and daylight hours (b) at St. NAPt from October 2010 to September 2012.
Figure 8 in Seasonal changes in the population structure of dominant planktonic copepods collected using a sediment trap moored in the western Arctic Ocean
Figure 8. Seasonal changes in sea ice concentration, daylight hours, chl. a, and total mass flux from January to December (upper panel). The ecological characteristics of the five dominant copepods (lower panel). The open and solid bars indicate the high abundance and reproductive periods for each species, respectively.
Data from: Protection status, human disturbance, snow cover and trapping drive density of a declining wolverine population in the Canadian Rocky Mountains
<p>Protected areas are important in species conservation, but high rates of human-caused mortality outside their borders and increasing popularity for recreation can negatively affect wildlife populations. We quantified wolverine (<em>Gulo gulo</em>) population trends from 2011 to 2020 in >14 000 km2 protected and non-protected habitat in southwestern Canada. We conducted wolverine and multi-species surveys using non-invasive DNA and remote camera-based methods. We developed Bayesian integrated models combining spatial capture-recapture data of marked and unmarked individuals with occupancy data. Wolverine density and occupancy declined by 39 percent, with an annual population growth rate of 0.925. Density within protected areas was 3 times higher than outside and declined between 2011 (3.6 wolverines/1000 km2) and 2020 (2.1 wolverines/1000 km2). Wolverine density and detection probability increased with snow cover and decreased near development. Detection probability also decreased with human recreational activity. The annual harvest rate of 13% was above the maximum sustainable rate. We conclude that humans negatively affected the population through direct mortality, sub-lethal effects and habitat impacts. Our study exemplifies the need to monitor population trends for species at risk – within and between protected areas - as steep declines can occur unnoticed if key conservation concerns are not identified and addressed.</p>
Fig. 1. A in Monitoring coffee berry borer, Hypothenemus hampei (Coleoptera: Curculionidae), populations with alcoholbaited funnel traps in coffee farms in Colombia
Fig. 1. A red funnel trap used to capture CBB (Hypothenemus hampei) (A) and the methanol–ethanol lure (3: 1 ratio) enclosed in a semi-permeable membrane (B). CBB were collected in a plastic container containing 200 mL of soapy water. Photo Alex. E. Bustillo.
Fig. 2 in Monitoring coffee berry borer, Hypothenemus hampei (Coleoptera: Curculionidae), populations with alcoholbaited funnel traps in coffee farms in Colombia
Fig. 2. Number of CBB (Hypothenemus hampei) captured per trap per month (mean ± SEM) and corresponding berry infestation (%) on large coffee farms (≥ 20 ha) in Risaralda, Caldas, Colombia (A), and on small coffee farms (≤ 3 ha) in Viterbo, Caldas, Colombia (B) in 2003-2004. Note differences in axis scales.
Figures 5a–f in Trapping Records of Fruit Fly Pest Species (Diptera: Tephritidae) on Oahu (Hawaiian Islands): Analysis of Spatial Population Trends
Figures 5a–f. Mean (± S.E.) captures in different habitats for B. cucurbitae in cuelure and torula yeast (a, b), B. dorsalis in methyl eugenol and torula yeast (c, d) and C. capitata in trimedlure and torula yeast (e, f) traps. Units are flies per trap per day in male lure and per week in torula yeast traps.
Fig. 1 in Using hydrogen stable isotope ratios to trace the geographic origin of the population of Bactrocera dorsalis (Diptera: Tephritidae) trapped in northern China
Fig. 1. Implied relationship standard curve equation between Bactrocera dorsalis and precipitation based on a δ2H stable isotope (solid line indicates the linear regression and dash lines indicate the 95% confident intervals).
Data from: Protection status, human disturbance, snow cover and trapping drive density of a declining wolverine population in the Canadian Rocky Mountains
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Data from: using camera traps and N-mixture models to estimate population abundance: model selection really matters
<p>Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the data collection and statistical methods, sampling and ecological variation, and sample size. N-mixture models are a common method which has been applied to a wide range of taxa for estimating population abundance from non-invasive data representing the distribution of the species. We used population estimates from an aerial survey of moose and videos from camera traps to assess the sensitivity of N-mixture models to ecological conditions, the spatial scale at which they were measured, the criteria used to define independent detections, and model choice based on the common statistical criterion of parsimony. The most parsimonious N-mixture models were considerably biased, producing implausibly large and considerably imprecise estimates of the abundance of moose. Most of the other models produced estimates of abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N-mixture models were not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggest that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N-mixture model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps can provide valuable information on behavior of the target species as well as insights into multiple species in the community.</p>
Data from: Controlling trapping, overgrazing and invasive vegetation is key to saving Java's last population of the Black-winged Myna
<p><span><span>The Black-winged Myna (<em>Acridotheres melanopterus</em>) is an Endangered passerine endemic to the islands of Java and Bali, Indonesia. </span><span>Illegal trapping to supply the cage-bird trade has led to its near-total extinction, with the global population estimated to number fewer than 100 individuals. The only known population of Black-winged Mynas on Java occurs at Baluran National Park (BNP). </span>These data were generated to meet the two primary aims of the linked paper: the first was to estimate the Black-winged Myna <em>(Acridotheres</em> <em>melanopterus</em>) population size and range at BNP; and the second was to use species distribution modelling to evaluate the potential suitability of areas currently unoccupied by Black-winged Mynas across BNP to identify priorities for management intervention. We carried out line-transect distance sampling following the standard distance analysis method to estimate the population size. A total of 56 detections of groups of Black-winged Mynas were recorded along line transects and are included in the dataset. These records are associated with the land cover type for that segment of the transect, the perpendicular distance of the record to the transect, and the survey effort associated with each transect. To produce the species distribution model, we used three predictor raster layers along with all Black-winged Myna presences recorded </span>(not freely available, see below) and a generated set of pseudo-absences. The dataset contains both the raw data used to create the land cover classification as well as the final raster. The raw data used to produce the habitat classification contains classified points (n = 426) from across BNP, which were used as a training and test data for a random forest land cover classification.</p>
Using by-catch camera trapping data for estimating the population size of spotted hyena (Crocuta crocuta)
<p>Spotted hyenas (<em>Crocuta crocuta</em>) are an important carnivore species whose dual role of scavenger and predator is vital to trophic energy flows of systems in which they are found. Where populations of spotted hyenas are small, the environment has few cleaners and carcasses can remain unprocessed. Despite being largely characterized as scavengers, spotted hyenas actively hunt and take down live prey and at high densities can have depressing effects on fragile or choice ungulate populations. In addition, they can alter the structure and composition dynamics of the carnivore guild through direct conflict or indirectly through competition for food and space. Despite their importance to ecosystem function and balance, reliable estimates of spotted hyena densities are rare. This is because unlike lions and leopards, spotted hyenas are generally not regarded as a charismatic species and, as such, survey resources, which are costly, are seldom solely allocated towards surveying them. Nonetheless, being able to confidently estimate spotted hyena numbers is important for the effective management of carnivore and herbivore populations whose dynamics they influence.</p>
Data availability: Random encounter model is a reliable method for estimating population density of multiple species using camera traps
<p>Data of the paper entitled "Random encounter model is a reliable method for estimating population density of multiple species using camera traps" published on Remote Sensing in Ecology and Conservation</p>
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>
Data from: Evaluating predator control using two non-invasive population metrics: a camera trap activity index and density estimation from scat genotyping
<p>Includes datasets from the Wimmera and Mallee, Victoria, Australia:</p> <p>- Fox camera trap data used to model activity</p> <p>- Fox scat SECR capture and trap files used to model density</p>
Data from: Controlling trapping, overgrazing and invasive vegetation is key to saving Java's last population of the Black-winged Myna
Open the record for dataset details and reuse information.
Using by-catch camera trapping data for estimating the population size of spotted hyena (Crocuta crocuta)
Open the record for dataset details and reuse information.
Data from: using camera traps and N-mixture models to estimate population abundance: model selection really matters
Open the record for dataset details and reuse information.
Remote camera monitoring and arboreal trapping data for a reintroduced population of red-tailed phascogales (Phascogale calura)
Open the record for dataset details and reuse information.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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OpenNeuro
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