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37 results for “trap density”
Supporting data for "Estimating animal density for a community of species using information obtained only from camera-traps"
<p>Data underlying a paper published in Methods in Ecology and Evolution (<a href="https://doi.org/10.1111/2041-210X.13930">https://doi.org/10.1111/2041-210X.13930</a>).</p> <p>These data are suitable for estimating animal density using the Random Encounter Model and include: i) detection counts for 35 species across 510 camera-trap locations; ii) movement speeds (estimated by tracking animal movements in camera-trap image sequences), iii) activity times (filtered so that records of the same species at the same location are > 60 minutes apart), and iv) measurements of the angular and radial distance from camera-traps for animals that were detected.</p>
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>
Long-Term Slough Crayfish (Procambarus fallax) Densities in the Florida Everglades from Throw Trap Sampling, Florida, USA, February 1996-December 2016
Densities of slough crayfish (Procambarus fallax) were collected throughout the Florida Everglades using 1-m2 throw-trapping samples from 1996–2016. Data were collected in four distinct regions within the Everglades (Shark River Slough, Taylor Slough, Water Conservation Area 3A and 3B) and during five seasonal periods each year. Each region consists of a series of sites, and each site consists of 3–5 sampling plots. The plot scale defines the unique sampling unit in the study, and 5–7 replicate throws were conducted at each plot. Corresponding hydrologic data were collected in conjunction with crayfish observations. These data describe antecedent average water depths, along with the duration of previous wet and dry conditions. Antecedent conditions were calculated by using both in situ measurements, as well as model output from the Everglades Depth Estimation Network (EDEN). Additional hydrologic data are provided in the LD_0 dataset, which describes daily depths measurements at sampling plots, as well as the length of antecedent complete drying events (LD_0). The length of antecedent dry conditions is assessed in R script FCE1267_01_LD_Depths, while the crayfish modeling code is described in FCE1267_02_Modeling_Code.
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. 3 in Captures of Stenoma catenifer (Lepidoptera: Depressariidae) are influenced by pheromone trap density in Hass avocado orchards
Fig. 3. Mean cumulative number by treatment of Stenoma catenifer (IC95) caught in traps baited with synthetic sex pheromones at different trap densities in Hass avocado orchards, Colima, Mexico, during the experiment in 2018. Means with the same lowercase letter are not significantly different from each other according to Tukey's test (X0.05). 1T2h = 0.5 traps per ha; 1Th = 1 trap per ha; 2Th = 2 traps per ha; 3Th = 3 traps per ha.
Fig. 5 in Captures of Stenoma catenifer (Lepidoptera: Depressariidae) are influenced by pheromone trap density in Hass avocado orchards
Fig. 5. Relationship between total number of Stenoma catenifer caught in different treatments in 4 Hass avocado orchards in Colima, Mexico, 2018. 1T2h = 0.5 traps per ha; 1Th = 1 trap per ha; 2Th = 2 traps per ha; 3Th = 3 traps per ha.
Fig. 2 in Captures of Stenoma catenifer (Lepidoptera: Depressariidae) are influenced by pheromone trap density in Hass avocado orchards
Fig. 2. Number of Stenoma catenifer caught in synthetic sex pheromone traps placed at different densities (1 T2h, 1Th, 2Th, and 3Th: treatments, number of traps per area) and in different Hass avocado orchards (1–4 of the Y right axis) in the municipalities of Comala and Cuauhtémoc, Colima, Mexico, 2018. The columns correspond to treatments and the rows to experimental orchards. 1T2h = 0.5 traps per ha; 1Th = 1 trap per ha; 2Th = 2 traps per ha; 3Th = 3 traps per ha.
Fig. 4 in Captures of Stenoma catenifer (Lepidoptera: Depressariidae) are influenced by pheromone trap density in Hass avocado orchards
Fig. 4. Nonparametric bootstrap sampling distribution of the total numbers of Stenoma catenifer caught in experimental plots (CI95%) (1–4) in the linear model of the different orchards. The black dot on each line indicates the mean value of the total for each of the treatments. 1T2h = 0.5 traps per ha; 1Th = 1 trap per ha; 2Th = 2 traps per ha; 3Th = 3 traps per ha.
Fig. 2 in Placement density and longevity of pheromone traps for monitoring of the citrus leafminer (Lepidoptera: Gracillariidae)
Fig. 2. Relationship between the proportional number of Phyllocnistis citrella captures per trap and day of aged lures with respect to unaged lures, and the number of weeks that each lure was exposed to field environmental conditions during spring 2013, for the 2 commercial brands of lures a) ISCA and b) AlphaScents.
Fig. 1 in Placement density and longevity of pheromone traps for monitoring of the citrus leafminer (Lepidoptera: Gracillariidae)
Fig. 1. Mean number of Phyllocnistis citrella adult male captures per trap and day (± standard error) from Apr 2012 to Dec 2013, at the 3 trap densities tested: high: approximately 1 trap per 0.40 ha (1 acre), medium: approximately 1 trap per 1.21 ha (3 acres), and low: approximately 1 trap per 2.02 ha (5 acres).
Fig. 4 in Placement density and longevity of pheromone traps for monitoring of the citrus leafminer (Lepidoptera: Gracillariidae)
Fig. 4. Relationship between the proportional number of Phyllocnistis citrella captures per trap and day of aged lures with respect to unaged lures, and the number of weeks that each lure was exposed to field environmental conditions, with data combined for ISCA and AlphaScents during spring and ISCA during summer/fall 2013.
Fig. 3 in Placement density and longevity of pheromone traps for monitoring of the citrus leafminer (Lepidoptera: Gracillariidae)
Fig. 3. Relationship between the proportional number of Phyllocnistis citrella captures per trap and day of aged lures with respect to unaged lures, and the number of weeks that each lure was exposed to field environmental conditions during summer/fall 2013, for the 2 commercial brands of lures a) ISCA and b) AlphaScents.
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 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>
Data belonging to "Successful invasion: camera trap distance sampling reveals higher density for invasive raccoon dog compared to native mesopredators"
<p>Data files (comma separated text files) containing the camera data (CameraData) containing the information on camera trap placements in the various sites and their operation time in days and aperture, the distance sampling data (DistanceData) containing the information on the species and distance detected for each 1s time interval in front of each camera, and the trigger data (TriggerData) containing the time stamps for the pictures taken of each species with each camera, collected in the years 2020 and 2021 in southern Finland. The repository further contains an R script "distanceSamplingScript" which uses the reposited above-described files for analysis reported in the publication "Successful invasion: camera trap distance sampling reveals higher density for invasive raccoon dog compared to native mesopredators" https://doi.org/10.1007/s10530-024-03323-4. The R script has been confirmed to run in R version 4.3.3 using packages "activity" vs 1.3.4 and "Distance" vs 1.0.9</p>
Fig. 1. Pherocon 1C wing trap with a in Captures of Stenoma catenifer (Lepidoptera: Depressariidae) are influenced by pheromone trap density in Hass avocado orchards
Fig. 1. Pherocon 1C wing trap with a commercial pheromone.
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>
Sitka Black-tailed Deer Camera trap data for density estimation from Afognak Island, Alaska
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Camera-trap data for fitting the random encounter model to estimate densities of coyotes and black-tailed jackrabbits in the Mojave Desert
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Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of "marked" wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of "unmarked" wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates.
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