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43 results for “distance sampling”
CBP01 Variable distance line-transect sampling of bird population numbers in different habitats on Konza Prairie
Records of bird species based on line transect sampling, giving perpendicular distance of sighting from the transect line on 16 separate transects. Bird surveys were conducted 2-4 times per year in January, April, June, and October for a 29-year period from 1981 to 2009. Transects were designed to determine bird communities and population numbers associated with tallgrass prairie habitats with different experimental treatments (fire frequency, grazed by bison vs. ungrazed), riparian habitats on forest edge, and gallery forests dominated by oak woodland.
CBP01 Variable distance line-transect sampling of bird population numbers in different habitats on Konza Prairie (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-knz/26/11. The abstract below was extracted from the Level 0 data package and is included for context: Records of bird species based on line transect sampling, giving perpendicular distance of sighting from the transect line on 16 separate transects. Bird surveys were conducted 2-4 times per year in January, April, June, and October for a 29-year period from 1981 to 2009. Transects were designed to determine bird communities and population numbers associated with tallgrass prairie habitats with different experimental treatments (fire frequency, grazed by bison vs. ungrazed), riparian habitats on forest edge, and gallery forests dominated by oak woodland.
Digital Elevation Model (DEM) of northern Brøggerhalvøya (Svalbard, Norway) with Ground Sampling Distance (GSD) of 50 cm
<p>HRSC is a multisensor pushbroom instrument with 9 CCD line sensors mounted in parallel that has been in orbit around Mars since January 2004 on ESA’s Mars Express spacecraft (Gwinner et al., 2016). It simultaneously obtains high-resolution stereo, multicolor, and multiphase images. Digital photogrammetric techniques are used to reconstruct the topography on the basis of five stereo channels, which provide five different views of the ground.</p> <p>An airborne version of the HRSC was used for the acquisition of stereo and color images in Svalbard. Since 1997, different airborne versions of HRSC have been developed. The principles of HRSC-AX data processing are described by Gwinner et al. (2006). The orientation data of the camera are reconstructed from a global positioning system inertial navigation system (GPS INS). HRSC-AX has been applied in diverse technical and scientific applications (e.g., Gwinner et al., 1999, 2000; Hauber et al., 2001; Otto et al., 2007) and has also been successfully used to investigate rock glacier activity (Roer and Nyenhuis, 2007). The flight campaign in July–August 2008 covered a total of seven regions in Svalbard: (1) Longyearbyen and the surroundings of Adventfjorden, (2) large parts of Adventdalen, (3) large parts of the Brøggerhalvøya (halvøya = peninsula) in western Spitsbergen (this dataset), (4) the Bockfjorden area in northern Spitsbergen, (5) the northeastern shore of the Palanderbukta and the margin of the adjacent ice cap in Nordaustlandet, (6) an area on Prins Karls Forland, and (7) the area of the abandoned Russian mining settlement of Pyramiden together with the nearby Ebbedalen. </p> <p>This dataset is a Digital Elevation Model (DEM) derived from HRSC-AX stereo images. The elevations recorded in the DEM are ellipsoid heights; i.e., they are not computed with respect to a geoid but to a mathematically defined reference surface, which is a<br>rotational ellipsoid with the equatorial A and B axes both having a radius of 6378.14 km and the polar<br>C axis having a radius of 6356.75 km. This results in an offset of about 36.5m with respect to geoid<br>heights; i.e., sea level in the HRSC-AX DEM is not at 0 m, but at ~36.5 m.</p> <p><strong>References</strong></p> <p>Gwinner, K., Hauber, E., Hoffmann, H., Scholten, F., Jaumann, R., Neukum, G.,<br>Coltelli, M., and Puglisi, G., 1999, The HRSC-A experiment on high reso-<br>lution imaging and DEM generation at the Aeolian Islands, in Proceedings<br>of the 13th International Conference on Applied Geologic Remote Sens-<br>ing: Ann Arbor, Michigan, ERIM International, v. I, p. 560–569.</p> <p>Gwinner, K., Hauber, E., Jaumann, R., and Neukum, G., 2000, High-resolution,<br>digital photogrammetric mapping: A tool for earth science: Eos<br>(Transactions, American Geophysical Union), v. 81, no. 44, p. 513–520,<br>doi:10.1029/00EO00364.</p> <p>Gwinner, K., Coltelli, M., Flohrer, J., Jaumann, R., Matz, K.-D., Marsella, M.,<br>Roatsch, T., Scholten, F., and Trauthan, F., 2006, The HRSC-AX Mt.<br>Etna Project: High-Resolution Orthoimages and 1 m DEM at Regional<br>Scale: International Archives of Photogrammetry and Remote Sensing,<br>v. XXXVI, Part 1, http://isprs.free.fr/documents/Papers/T05-23.pdf.</p> <p>Gwinner, K., Scholten, F., Spiegel, M., Schmidt, R., Giese, B., Oberst,<br>J., Heipke, C., Jaumann, R., and Neukum, G., 2009, Derivation and<br>validation of high-resolution digital elevation models from Mars Express<br>HRSC data: Photogrammetric Engineering and Remote Sensing, v. 75,<br>no. 9, p. 1127–1142.</p> <p>Gwinner, K., Jaumann, R., Hauber, E., et al., 2016, The High Resolution Stereo Camera (HRSC) of Mars Express and its<br>approach to science analysis and mapping for Mars and its satellites: Planetary and Space Science, v. 126, p. 93–138. http://dx.doi.org/10.1016/j.pss.2016.02.014</p> <p>Hauber, E., Slupetzky, H., Jaumann, R., Wewel, F., Gwinner, K., and Neukum,<br>G., 2001, Digital and automated high resolution stereo mapping of the<br>Sonnblick glacier: EARSeL eProceedings, v. 1, no. 1, p. 246–254.</p> <p>Jaumann, R., Neukum, G., Behnke, T., Duxbury, T.C., Eichentopf, K., Flohrer,<br>J., van Gasselt, S., Giese, B., Gwinner, K., Hauber, E., Hoffmann, H., Hoff-<br>meister, A., Köhler, U., Matz, K.-D., McCord, T.B., Mertens, V., Oberst,<br>J., Pischel, R., Reiss, D., Ress, E., Roatsch, T., Saiger, P., Scholten, F.,<br>Schwarz, G., Stephan, K., Wählisch, M., and the HRSC Co-Investigator<br>Team, 2007, The high-resolution stereo camera (HRSC) experiment on<br>Mars Express: instrument aspects and experiment conduct from interplan-<br>etary cruise through the nominal mission: Planetary and Space Science, v.<br>55, p. 928–952, doi:10.1016/j.pss.2006.12.003.</p> <p>Otto, J.-C., Kleinod, K., König, O., Krautblatter, M., Nyenhuis, M., Roer,<br>I., Schneider, M., Schreiner, B., and Dikau, R., 2007, HRSC-A data:<br>A new high-resolution data set with multipurpose applications in physi-<br>cal geography: Progress in Physical Geography, v. 31, no. 2, p. 179–197,<br>doi:10.1177/0309133307076479.</p> <p>Roer, I., and Nyenhuis, M., 2007, Rockglacier activity studies on a regional<br>scale: Comparison of geomorphological mapping and photogrammetric<br>monitoring: Earth Surface Processes and Landforms, v. 32, p. 1747–1758,<br>doi:10.1002/esp.1496.</p>
CBP01 Variable distance line-transect sampling of bird population numbers in different habitats on Konza Prairie (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/339/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-knz/26/11. The abstract below was extracted from the Level 0 data package and is included for context: Records of bird species based on line transect sampling, giving perpendicular distance of sighting from the transect line on 16 separate transects. Bird surveys were conducted 2-4 times per year in January, April, June, and October for a 29-year period from 1981 to 2009. Transects were designed to determine bird communities and population numbers associated with tallgrass prairie habitats with different experimental treatments (fire frequency, grazed by bison vs. ungrazed), riparian habitats on forest edge, and gallery forests dominated by oak woodland.
Data for: Tip of the Red Giant Branch Distances with JWST. II. I−band Measurements in a Sample of Hosts of 10 SN Ia Match HST Cepheids
<p>Data for: "Tip of the Red Giant Branch Distances with JWST. II. I−band Measurements in a Sample of Hosts of 10 SN Ia Match HST Cepheids". The photometry provided is after DOLPHOT quality cuts, foreground extinction corrections, and spatial cuts.</p>
French Guianan mammal and bird population densities with spatial-capture recapture, line transect distance sampling, and 'unmarked' density models
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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>
biodiego88/Publicacion_densidad_Aspurrelli_PNNUtria: Distance Sampling Atelopus spurrelli
<p>Código para el análisis de datos de distancias perpendiculares con el modelo Multinomial-Poisson mixture para estimar la densidad de Atelopus spurrelli en el Parque Nacional Natural Utría</p>
Pooling robustness in distance sampling: Avoiding bias when there is unmodelled heterogeneity
<p>Data from a two-visit line transect survey of four songbird species gathered in spring 2004. Study area size was 33.2 ha of woodland and parkland on the Montrave Estate near Leven in Fife, Scotland.</p>
The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics
<p>Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.</p>
The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics
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Pooling robustness in distance sampling: Avoiding bias when there is unmodelled heterogeneity
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Wildlife density estimation by distance sampling: A novel technique with movement compensation
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Data from: Methods to account for incomplete viewsheds in distance sampling
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Black-tailed deer distance sampling on Blakely Island (WA), 2007 - 2021
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Data from: Estimating waterfowl breeding pair and brood densities using distance sampling with uncrewed aerial systems
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Distance sampling: Comparing walked transects and road transects for rock ptarmigan densities and population trends
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Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output.
Data from: Relationship type affects the reliability of dispersal distance estimated using pedigree inferences in partially sampled populations: a case study involving invasive American mink in Scotland
Estimating dispersal—a key parameter for population ecology and management—is notoriously difficult. The use of pedigree assignments, aided by likelihood-based software, has become popular to estimate dispersal rate and distance. However, the partial sampling of populations may produce false assignments. Further, it is unknown how the accuracy of assignment is affected by the genealogical relationships of individuals and is reflected by software-derived assignment probabilities. Inspired by a project managing invasive American mink (Neovison vison), we estimated individual dispersal distances using inferred pairwise relationships of culled individuals. Additionally, we simulated scenarios to investigate the accuracy of pairwise inferences. Estimates of dispersal distance varied greatly when derived from different inferred pairwise relationships, with mother–offspring relationship being the shortest (average = 21 km) and the most accurate. Pairs assigned as maternal half-siblings were inaccurate, with 64%–97% falsely assigned, implying that estimates for these relationships in the wild population were unreliable. The false assignment rate was unrelated to the software-derived assignment probabilities at high dispersal rates. Assignments were more accurate when the inferred parents were older and immigrants and when dispersal rates between subpopulations were low (1% and 2%). Using 30 instead of 15 loci increased pairwise reliability, but half-sibling assignments were still inaccurate (>59% falsely assigned). The most reliable approach when using inferred pairwise relationships in polygamous species would be not to use half-sibling relationship types. Our simulation approach provides guidance for the application of pedigree inferences under partial sampling and is applicable to other systems where pedigree assignments are used for ecological inference.
Data from: Hierarchical distance sampling to estimate population sizes of common lizards across a desert ecoregion
1) Multi-species wildlife monitoring across large geographical regions is important for effective conservation planning in response to expected impacts from climate change and land use. Unlike many species of birds, mammals, and amphibians which can be efficiently sampled using automated sensors including cameras and sound recorders, reptiles are often much more challenging to detect, in part because of their typically cryptic behavior and generally small body sizes. Although many lizard species are more active during the day which makes them easier to detect using visual encounter surveys, they may be unavailable for sampling during certain periods of the day or year due to their sensitivity to temperature. 2) In recognition of these sampling challenges, we demonstrate application of a recent innovation in distance sampling that adjusts for temporary emigration between repeat survey visits. We used transect surveys to survey lizards at 229 sites throughout the Mojave Desert in California, USA, 2016. 3) We estimated a total population size of 80 million (90% CI: 64–97 million) for the three most common species of lizards across this 66,830 km2 ecoregion. We mapped how density at the 1-km2 scale was predicted to vary with vegetation cover and human development. We validated these results against independent surveys from the southern portion of our study area. 4) Our methods and results demonstrate how multi-species monitoring programs spanning arid ecoregions can better incorporate information about reptiles.
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