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Fig. 3 in Fowler's Toad (Anaxyrus fowleri) occupancy in the southern mid-Atlantic, USA

Fig. 3. Accumulation curve showing the maximum agricultural pesticide application rates at occupied sites. The maximum amount of pesticides applied at 90% of occupied sites was 165 kg/km2.

Fig. 1 in Fowler's Toad (Anaxyrus fowleri) occupancy in the southern mid-Atlantic, USA

Fig. 1. Map of calling anuran surveys conducted in Maryland and Virginia. Closed circles indicate sites occupied by A. fowleri and open circles indicate unoccupied sites.

Fig. 2 in Fowler's Toad (Anaxyrus fowleri) occupancy in the southern mid-Atlantic, USA

Fig. 2. Accumulation curve showing the maximum proportions of habitat variables found at each occupied site. Ninety-percent of occupied sites were covered by less than 0.25 development, 0.35 hay, and 0.50 deciduous forest cover.

Fig. 4 in Fowler's Toad (Anaxyrus fowleri) occupancy in the southern mid-Atlantic, USA

Fig. 4. Mean site occupancy and confidence intervals of A. fowleri in Virginia and Maryland from 1999–2012. Time series analysis indicates a 53% decrease in site occupancy, from 55.3% in 1999 to 29.5% in 2012.

opencc-by-4.0Feb 2024View details →

Figure 2 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA

Figure 2. Mean posterior estimates of the probability of capturing grass carp eDNA from a site in a sample among sites (θ) from the model with the lowest WAIC score [ψ(Site)Θ(Site)p(.)]. Error bars represent 95% credible intervals. DR = Detroit River, HP = Hot Ponds, MB = Maumee Bay. All sites are located in western Lake Erie.

opencc-by-4.0Feb 2024View details →

Figure 1 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA

Figure 1. Map denoting all monthly grass carp eDNA sampling events in 2018 (A–C) and 2019 (D–F) aggregated at each sampling location (Hot Ponds, Detroit River, and North Maumee Bay) and acoustic receiver locations (grey circles) in the western basin of Lake Erie. Positive and negative eDNA detections, defined as at least one positive qPCR detection on one replicate among all markers (GCTM10, GCTM22, GCTM32) are denoted by orange crosses and pink triangles, respectively. The 3 grass carp captured from conventional gear (total sampling events = 451) in the Detroit River (October 2018), Hot Pond (July 2019) and North Maumee Bay (July 2019) are denoted by a yellow hexagon.

opencc-by-4.0Dec 2022View details →

Figure 2 in A rapid assessment of cave occupancy for Pacific sheath-tailed bats (fanihin ganas, Emballonura semicaudata rotensis) and Mariana swiftlets (chachaguak, Aerodramus bartschi) on Aguiguan, Mariana Islands

Figure 2: Locations of caves surveyed for Pacific sheath-tailed bat and Mariana swiftlet occupancy by the Commonwealth of the Northern Mariana Islands Division of Fish and Wildlife in 2021, with WorldView-2 satellite imagery (dated April 14, 2019) for reference. Surveyed caves are labeled: 1 – East Black Noddy Cave; 2 – Cliff Cave; 3 – Pillar Cave; 4 – Guano Cave; 5 – New Cave Complex; 6 – Southern Cave Complex; 7 – Crevice Cave

opencc-by-4.0Dec 2022View details →

Figure 1 in A rapid assessment of cave occupancy for Pacific sheath-tailed bats (fanihin ganas, Emballonura semicaudata rotensis) and Mariana swiftlets (chachaguak, Aerodramus bartschi) on Aguiguan, Mariana Islands

Figure 1: Map depicting the location of the Commonwealth of the Northern Mariana Islands in relation to the Asia-Pacific region and the location of Aguiguan (blue circle) within the archipelago. Sources: Basemaps: Esri, The General Bathymetric Chart of the Oceans, National Oceanic and Atmospheric Administration, National Geographic, DeLorme, HERE, Geonames.org, Garmin, United States Geological Survey, Earthstar Geographics.

opencc-by-4.0Mar 2017View details →

FIGURE 2 in Diet of Dendropsophus microcephalus and Scarthyla vigilans (Anura: Hylidae) at a locality in north-western Venezuela with notes on microhabitat occupation

FIGURE 2: Vertical distribution of individuals of Dendropsophus microcephalus and Scarthyla vigilans on emergent plants.

Fig. 7 in Using occupancy models to investigate the prevalence of ectoparasitic vectors on hosts: An example with fleas on prairie dogs

Fig. 7. Probabilities of flea occupancy (W) for black-tailed prairie dogs (Cynomys ludovicianus) in differing body condition during May–September 2011, at the Vermejo Park Ranch, New Mexico. The solid line depicts estimates of occupancy and dotted lines depict 95% confidence intervals.

Fig. 4 in Using occupancy models to investigate the prevalence of ectoparasitic vectors on hosts: An example with fleas on prairie dogs

Fig. 4. Model-averaged probabilities for detecting fleas (p) on a black-tailed prairie dog (Cynomys ludovicianus) during May–September 2011, at the Vermejo Park Ranch, New Mexico. Bars depict 95% confidence intervals.

Fig. 3 in Using occupancy models to investigate the prevalence of ectoparasitic vectors on hosts: An example with fleas on prairie dogs

Fig. 3. Indices for and estimates of flea prevalence on prairie dogs inside old colonies. The estimates are model-averaged values from occupancy models that accounted for imperfect detection of fleas. The naïve indices do not consider imperfect detection. Gains in precision (95% confidence interval) when estimating prevalence are depicted on the right. Confidence intervals for the estimates of prevalence during July–September are very small.

Fig. 2 in Using occupancy models to investigate the prevalence of ectoparasitic vectors on hosts: An example with fleas on prairie dogs

Fig. 2. The robust design for occupancy models of flea prevalence on black-tailed prairie dogs (Cynomys ludovicianus). Prairie dogs were sampled during primary occasions in different months of the year (May–September 2012). Each primary occasion comprised three secondary occasions (combings) during which fleas might be detected (p = probability of detection, given presence). A prairie dog was ''open'' to colonization by fleas between primary occasions. Once a prairie dog was colonized, it was occupied by fleas during all subsequent primary occasions (thus, the extinction probability, E, was fixed at zero, once a prairie dog was occupied by fleas). Closure was assumed during the secondary occasions, but we used behavioral covariates to account for removal of fleas from hosts during each secondary combing (REMOVAL1 and REMOVAL2, see text). In the example encounter history, a '1' indicates that at least one flea was detected during a combing event, and a '0' indicates that no fleas were detected.

Fig. 5 in Using occupancy models to investigate the prevalence of ectoparasitic vectors on hosts: An example with fleas on prairie dogs

Fig. 5. Model-averaged probabilities of flea occupancy (W) and flea colonization (γ) for black-tailed prairie dogs (Cynomys ludovicianus) in old and young colonies, and natural and translocation colonies during May–September 2011, at the Vermejo Park Ranch, New Mexico (see Fig. 1 and text for colony descriptions). Bars depict 95% confidence intervals. We do not report estimates of colonization for September, because few prairie dogs were sampled in that month.

Fig. 1 in Using occupancy models to investigate the prevalence of ectoparasitic vectors on hosts: An example with fleas on prairie dogs

Fig. 1. Map of the study area within the Vermejo Park Ranch, Colfax County, New Mexico, showing old and young, and natural and translocation colonies of black-tailed prairie dogs (Cynomys ludovicianus). Gray areas indicate extent of prairie dog colonies in 2009.

Fig. 6 in Using occupancy models to investigate the prevalence of ectoparasitic vectors on hosts: An example with fleas on prairie dogs

Fig. 6. Probabilities of flea occupancy (W) and flea colonization (γ) for black-tailed prairie dogs (Cynomys ludovicianus) in plots with differing densities of prairie dogs during May–September 2011, at the Vermejo Park Ranch, New Mexico. Solid lines depict estimates and dotted lines depict 95% confidence intervals.

opencc-by-4.0Aug 2014View details →

Fig. 1 in Toxoplasma gondii exposure in arctic-nesting geese: A multi-state occupancy framework and comparison of serological assays

Fig. 1. Comparison of seroprevalence estimates for Ross's Geese and Lesser Snow Geese generated by naïve and multi-state occupancy estimators (seroprevalence = Ψ1 × Ψ2).

opencc-by-4.0Aug 2019View details →

OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information

OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information This repository contains the official data from a USDOE-funded project at Carnegie Mellon University and Bosch Research Pittsburgh.  The primary goal of the project was to investigate the relationship between indoor commercial building occupant thermal comfort and various biometric and environmental predictors. We performed 77 individual comfort experiments, approved by our Institutional Review Board (IRB) and in satisfaction of participant consent guidelines. Our goal was to generate a dataset than enables comprehensive study of human thermal comfort preferences, in a commercial building environment, across a wide range of indoor environmental conditions. The data is comprised of the following feature groups: depth camera frames, biometrics sensor data, body shape information, subjective comfort data from the mobile device application, environmental sensor data from the commercial building HVAC system, and outdoor weather station data. This is the official dataset release for the following conference paper: <blockquote> Jonathan Francis*, Matias Quintana*, Nadine von Frankenberg, Sirajum Munir, and Mario Bergés. 2019. OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information. In BuildSys '19: ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, November 13–14, 2019, New York, NY. ACM, New York, NY, USA, 10 pages. </blockquote> To use this dataset, first download all the files. Next, issue the following commands on, e.g., Linux terminal: <pre><code class="language-bash">>$ cd /path/to/dataset/files >$ cat occutherm_dataset_v0-0-0.tar.gza* > archive.tar.gz >$ tar -xvzf archive.tar.gz</code></pre> Modeling and mobile application code are available in our project repository: <a href="https://github.com/jonfranc/occutherm">https://github.com/jonfranc/occutherm</a> If you find the repository or the dataset useful, please cite our paper: <pre><code>@inproceedings{francis_buildsys2019, author = {Francis, Jonathan and Quintana, Matias and von Frankenberg, Nadine and Munir, Sirajum and Berges, Mario}, title = {OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information}, booktitle = {Proceedings of the 6th International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation}, series = {BuildSys '19}, year = {2019}, isbn = {978-1-4503-7005-9/19/11}, location = {New York, NY}, numpages = {10}, acmid = {3360858}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Thermal Comfort, Human Studies, Machine Learning}, }</code></pre>

opencc-by-4.0Oct 2024View details →

Occupant Simulation Data based on Honda Accord 2024 Simplified Passenger Model and Full-factorial Sampling with 243 samples and VIRTHUMAN 5, 50, 95 Percentiles

Database with 729 Honda Accord 2014 passenger occupant simulations featuring VIRTHUMAN.