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90 results for “species occurrence data”
Grasshopper species occurrence data in Mt. Kilimanjaro
<p>Species occurence data of grasshopper in 60 plots on the southern slope of Mt. Kilimanjaro.</p> <p>Orthoptera assemblages were recorded on all study sites by repeatedly walking for 1.5 h on parallel tracks (distance between transects ca. 1-1.5 m) and recording all sighted species. In forested study sites, trees and bushes in the understory vegetation were shaken for approximately 1.5 h. Insects falling from the vegetation were gathered on white canvas laid on the forest floor. Species which could not be identified during visits were collected and later identified. Study sites were also visited at night where Ensifera were registered acoustically. Additionally, two rounds of sweep net sampling were conducted on study sites to collect small species which may have remained undetected during transect walks. One round was conducted during the cool dry season (July to October) and one during the warm dry season (December to March). During each sweep netting round, 100 sweeps with a 30-cm diameter sweep were taken and all collected specimens were identified in the laboratory. Species accumulation curves for Caelifera and Ensifera on Mt. Kilimanjaro were published in , showing that more than 90% of the grasshopper, locust and bushcricket fauna for Mt. Kilimanjaro have been registered.</p> <p>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</p>
Global taxonomic occurrence grids using GBIF data for species distribution models.
<p>To achieve large geographic coverage, species occurrence databases that are composed of ad hoc species data collections such as that provided by the Global Biodiversity Information Facility (GBIF) are often used. A drawback to using these data is their geographic sampling bias, in which some regions are more intensively sampled than others, while other areas have very little to none reported sampling effort. Uneven sampling effort can mislead conclusions about biodiversity patterns and species distributions (Gotelli & Colwell, 2001; Lobo, 2008).</p> <p>Here we provide taxonomic occurrence grids to help mitigate the effects of sampling bias in species distribution modeling. These grids can be used to exclude areas of (a custom-defined) low sampling effort from the background when sampling for pseudo-absences’ (Phillips et al., 2009; Barbet-Massin et al.,2012). The occurrence grids have a 1 degree spatial resolution using WGS 84 as the geographic coordinate system. Each 1 degree grid cell contains the number of records present in GBIF corresponding to a specific taxonomic group: plants, mammals, reptiles, amphibians, birds and molluscs.</p> <p>To construct the occurrence grids, we used the 1- by 1-degree world latitude and longitude vector grid provided by ESRI (Redlands, California). It has a custom license which permits it reuse as long as ESRI is cited. It was downloaded from : <a href="https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7">https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7</a></p> <p>To map spatial sampling effort, the number of georeferenced occurrences corresponding to each taxonomic group contained by each 1- by 1-degree grid cell were counted. The grids were then converted to GeoTIFFs. The raster values correspond to the number of occurrences reported for the grid cells. For the purposes of the <a href="https://osf.io/7dpgr/">TrIAS project</a>, grid cells with fewer than 5 occurrences were removed. The TrIAS taxonomic occurrence grids are used as inputs to the TrIAS risk modelling and mapping workflow: https://github.com/trias-project/risk-modelling-and-mapping. Full (with all grid cells containing at least one occurrence) taxonomic occurrence grids are also provided.</p> <p>GBIF data for each taxonomic group were downloaded using the following criteria: “Basis of Record”: Observation, Machine Observation, Human Observation, Specimen, Material sample, Literature Occurrence, Unknown evidence., "HasCoordinate is true", "HasGeospatialIssue is false", "TaxonKey is Amphibia", "Year 1975-2005".</p> <p><strong>Raster Attributes</strong></p> <table> <tbody> <tr> <td> <p>Attribute</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>OID</p> </td> <td> <p>numeric row ID</p> </td> </tr> <tr> <td> <p>Value</p> </td> <td> <p>the number of records contained in the grid cell</p> </td> </tr> <tr> <td> <p>Count</p> </td> <td> <p>the number of times the value appears in the raster</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p>The extent of each taxonomic occurrence grid:</p> <ul> <li> <p>longitude -180.0; latitude -90.0 (southwest corner)</p> </li> <li> <p>longitude 180.0; latitude 90.0 (northeast corner)</p> </li> </ul> <p> </p> <p><strong>Files:</strong></p> <p>TrIAS taxonomic occurrence grids</p> <p>amphib_1deg_min5.tif</p> <p>birds_1deg_min5.tif</p> <p>mammals_1deg_min5.tif</p> <p>molluscs_1deg_min5.tif</p> <p>reptiles_1deg_min5.tif</p> <p> </p> <p>Raw taxonomic occurrence grids</p> <p>amphib_1deg_grid.tif</p> <p>birds_1deg_grid.tif</p> <p>mammals_1deg_grid.tif</p> <p>molluscs_1deg_grid.tif</p> <p>reptiles_1deg_grid.tif</p> <p><br> </p> <p> </p> <p> </p>
Occurrence data used to create species distribution models and apply an evaluation method
<p>These two files containing a table with three columns: species names, longitude, latitude. Each row of the tables represents a georeferenced presence record for the corresponding species. The original presence data were downloaded from the GBIF database and after going through a cleaning process, we ended with these records that passed all the tests.</p> <p>These datasets were used to create species distribution models (SDMs) that were then used to apply a new method to evaluate the performance of different SDMs. Jiménez & Soberón (2020)</p>
Data and code for the manuscript: "Varying richness need not imply non-random species co-occurrence: implications for specifying null models"
<p>Data and R code for the manuscript "Varying richness need not imply non-random species co-occurrence: implications for specifying null models".</p>
Data from: Integrated species distribution models to account for sampling biases and improve range wide occurrence predictions
<p><strong><span>Aim</span></strong></p> <p><span>Species distribution models (SDMs) that integrate presence-only and presence-absence data offer a promising avenue to improve information on species' geographic distributions. The use of such 'integrated SDMs' on a species range-wide extent has been constrained by the often-limited presence-absence data and by the heterogeneous sampling of the presence-only data. Here, we evaluate integrated SDMs for studying species ranges with a novel expert range map-based evaluation. We build a new understanding about how integrated SDMs address issues of estimation accuracy and data deficiency and thereby offer advantages over traditional SDMs.</span></p> <p><strong><span>Location</span></strong></p> <p><span>South and Central America.</span></p> <p><strong><span>Time period</span></strong></p> <p><span>1979-2017.</span></p> <p><strong><span>Major taxa studied</span></strong></p> <p><span>Hummingbirds.</span></p> <p><strong><span>Methods</span></strong></p> <p><span>We build integrated SDMs by linking two observation models – one for each data type – to the same underlying spatial process.</span> <span>We validate SDMs with two schemes: i) cross-validation with presence-absence data and ii) comparison with respect to the species' whole range as defined with IUCN range maps. We also compare models relative to the estimated response curves and compute the association between the benefit of the data integration and the number of presence records in each data set.</span></p> <p><strong><span>Results</span></strong></p> <p><span>The integrated SDM accounting for the spatially varying sampling intensity of the presence-only data was one of the top-performing models in both model validation schemes. Presence-only data alleviated overly large niche estimates, and data integration was beneficial compared to modelling solely presence-only data for species that had few presence points when predicting the species' whole range. On the community level, integrated models improved the species richness prediction.</span></p> <p><strong><span>Main conclusions</span></strong></p> <p><span>Integrated SDMs combining presence-only and presence-absence data are successfully able to borrow strengths from both data types and offer improved predictions of species' ranges. Integrated SDMs can potentially alleviate the impacts of taxonomically and geographically uneven sampling and to leverage the detailed sampling information in presence-absence data.</span></p>
Costa Rica mosquito community species occurrence and site environmental data, July - August 2017
<p>Land use change is an important driver of both biodiversity loss and zoonotic disease transmission in tropical countryside landscapes. Developing solutions for protecting biodiversity, public health, and livelihoods in working landscapes requires understanding the spatial scales at which habitat characteristics such as land cover shape biodiversity, especially for arthropods that transmit pathogens. A growing body of evidence shows that species richness for many taxa correlates with tree cover at small spatial scales of <100 m, indicating that local tree cover management is a promising conservation tool. To investigate whether mosquito species richness, community composition, and presence of specific disease vector species respond to tree cover—and if so, whether at spatial scales similar to other taxa—we surveyed mosquito communities along a tree cover gradient and across agricultural, residential, and forested land uses in rural southern Costa Rica. We found that tree cover was both positively correlated with mosquito species richness and negatively correlated with the presence of the common invasive dengue vector <em>Aedes albopictus</em>, particularly at small spatial scales of 80 – 200m<em>. </em>Beyond tree cover, land use type predicted community composition and <em>Ae. albopictus </em>presence, but not species richness. The results suggest that preservation and expansion of tree cover at local scales can protect biodiversity for a wide range of taxa and also confer protection against disease vector occurrence.</p>
State of biodiversity documentation in the Philippines: Metadata gaps, taxonomic biases, and spatial biases in the DNA barcode data of animal and plant taxa in the context of species occurrence data
<p>These files can be categorized into three groups: (1) raw datasets obtained from public databases (i.e., GBIF, BOLD, and GenBank), (2) manually edited files needed for parsing and analysis, and (3) supplementary files for spatial analysis. All are used in the examination of gaps and biases present in Philippine biodiversity data, which can direct research on the taxa and spatial regions that need more sampling.</p>
Linked collectors and determiners for: Literature based species occurrence data of birds of North-East India.
Natural history specimen data linked to collectors and determiners held within, "Literature based species occurrence data of birds of North-East India". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6115006c-ec2f-4bfd-9315-115879785446">https://bionomia.net/dataset/6115006c-ec2f-4bfd-9315-115879785446</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6115006c-ec2f-4bfd-9315-115879785446">https://gbif.org/dataset/6115006c-ec2f-4bfd-9315-115879785446</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Occurrence data for unidentified species and for species not yet listed in the species checklist of Seychelles.
Natural history specimen data linked to collectors and determiners held within, "Occurrence data for unidentified species and for species not yet listed in the species checklist of Seychelles". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/99ccf1cc-03e3-4bd4-8a78-50d46dee8cb7">https://bionomia.net/dataset/99ccf1cc-03e3-4bd4-8a78-50d46dee8cb7</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/99ccf1cc-03e3-4bd4-8a78-50d46dee8cb7">https://gbif.org/dataset/99ccf1cc-03e3-4bd4-8a78-50d46dee8cb7</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Danish Tingidae - Data for study on occurrence, temporal trends and potential species.
Natural history specimen data linked to collectors and determiners held within, "Danish Tingidae - Data for study on occurrence, temporal trends and potential species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/a968d7e9-5391-482d-8927-4b34b9863d56">https://bionomia.net/dataset/a968d7e9-5391-482d-8927-4b34b9863d56</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a968d7e9-5391-482d-8927-4b34b9863d56">https://gbif.org/dataset/a968d7e9-5391-482d-8927-4b34b9863d56</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Occurrences of Threatened Species included in the Third Edition of the Red Data Book of the Komi Republic (Russia).
Natural history specimen data linked to collectors and determiners held within, "Occurrences of Threatened Species included in the Third Edition of the Red Data Book of the Komi Republic (Russia)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/cf750a05-25f2-459b-8891-1a1fd23d7bf8">https://bionomia.net/dataset/cf750a05-25f2-459b-8891-1a1fd23d7bf8</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/cf750a05-25f2-459b-8891-1a1fd23d7bf8">https://gbif.org/dataset/cf750a05-25f2-459b-8891-1a1fd23d7bf8</a>. Formatted as a Frictionless Data package.
Evaluation of the shucking of certain species of scallops contaminated with domoic acid with a view to the production of edible parts meeting the safety requirements foreseen in the Union legislation - Summary statistics on occurrence and consumption data and exposure assessment results
<p>DomoicAcid_Raw_Occurrence_Data.CSV contains the raw occurrence dataset on Domoic Acid contaminant in scallops as extracted from EFSA DWH on the 9 June 2020, 16,369 samples presented in the opinion as described in its section 1.3.2. Occurrence data submitted to EFSA. The data is provided in .csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: outcome) to address the issue (e.g. delete record or update values in specific fields).</p> <p>The link to the catalogues of controlled terminologies can be found under "Related identifiers”.</p> <p><strong>Annex_</strong> DomoicAcid</p> <p>Table of contents</p> <p><br> Table A1</p> <p>Description of FoodEx2 codes used to describe scallop species and their anatomical parts</p> <p>Table A2</p> <p>Data cleaning steps applied to occurrence data on domoic acid in scallops</p> <p>Table A3</p> <p>Percentage of Left-Censored data and descriptive statistics for Limits of detection (LODs) and Limits of quantification (LOQs) for domoic acid in scallops (mg/kg)</p> <p>Table A4</p> <p>Descriptive statistics for domoic acid in scallops (mg/kg) as reported in the cleaned database (statistics weighted by number of units per sample)</p> <p>Table A5</p> <p>Descriptive statistics of body tissue weights (g) of scallops as submitted by data providers</p>
Text-fig. 4. Occurrence of P3 in maxillae from Deninger bears and cave bears, data after Table 1 (presence = P3 or alveoli observed, absence = no P3 developed, broken = caudal part of maxilla broken). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 4. Occurrence of P3 in maxillae from Deninger bears and cave bears, data after Table 1 (presence = P3 or alveoli observed, absence = no P3 developed, broken = caudal part of maxilla broken).
Text-fig. 2. Occurrence of P3 in maxillae from brown bears, data after Table 1 (presence = P3 or alveoli observed, absence = no P3 developed, broken = caudal part of maxilla broken). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 2. Occurrence of P3 in maxillae from brown bears, data after Table 1 (presence = P3 or alveoli observed, absence = no P3 developed, broken = caudal part of maxilla broken).
Text-fig. 8. Occurrence of p3 in mandibles from Deninger bears, data after Table 3 (presence = p3 or alveoli observed, absence = no p3 developed, broken = diastema fragmented). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 8. Occurrence of p3 in mandibles from Deninger bears, data after Table 3 (presence = p3 or alveoli observed, absence = no p3 developed, broken = diastema fragmented).
Text-fig. 6. Occurrence of p3 in mandibles from brown bears, data after Table 3 (presence = p3 or alveoli observed, absence = no p3 developed, broken = diastema fragmented). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 6. Occurrence of p3 in mandibles from brown bears, data after Table 3 (presence = p3 or alveoli observed, absence = no p3 developed, broken = diastema fragmented).
Text-fig. 5. Occurrence of p1 in mandibles from brown bears, data after Table 3 (presence = p1 or alveoli observed, absence = no p1 developed, broken = diastema fragmented). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 5. Occurrence of p1 in mandibles from brown bears, data after Table 3 (presence = p1 or alveoli observed, absence = no p1 developed, broken = diastema fragmented).
Text-fig. 1. Occurrence of P1 in maxillae from brown bears, data after Table 1 (presence = P1 or alveoli observed, absence = no P1 developed, broken = rostral part of maxilla broken). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 1. Occurrence of P1 in maxillae from brown bears, data after Table 1 (presence = P1 or alveoli observed, absence = no P1 developed, broken = rostral part of maxilla broken).
Text-fig. 3. Occurrence of P1 in maxillae from Deninger bears and cave bears, data after Table 1 (presence = P1 or alveoli observed, absence = no P1 developed, broken = rostral part of maxilla broken). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 3. Occurrence of P1 in maxillae from Deninger bears and cave bears, data after Table 1 (presence = P1 or alveoli observed, absence = no P1 developed, broken = rostral part of maxilla broken).
Text-fig. 7. Occurrence of p1 in mandibles from Deninger bears and cave bears, data after Table 3 (presence = p1 or alveoli observed, absence = no p1 developed, broken = diastema fragmented). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 7. Occurrence of p1 in mandibles from Deninger bears and cave bears, data after Table 3 (presence = p1 or alveoli observed, absence = no p1 developed, broken = diastema fragmented).
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OpenNeuro
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