Global taxonomic occurrence grids using GBIF data for species distribution models. Article Swipe
Amy J.S. Davis
,
Diederik Strubbe
,
Quentin Groom
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.7556851
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.7556851
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). 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. 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 : https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7 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 TrIAS project, 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. 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". Raster Attributes Attribute Description OID numeric row ID Value the number of records contained in the grid cell Count the number of times the value appears in the raster The extent of each taxonomic occurrence grid: longitude -180.0; latitude -90.0 (southwest corner) longitude 180.0; latitude 90.0 (northeast corner) Files: TrIAS taxonomic occurrence grids amphib_1deg_min5.tif birds_1deg_min5.tif mammals_1deg_min5.tif molluscs_1deg_min5.tif reptiles_1deg_min5.tif Raw taxonomic occurrence grids amphib_1deg_grid.tif birds_1deg_grid.tif mammals_1deg_grid.tif molluscs_1deg_grid.tif reptiles_1deg_grid.tif
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https://openalex.org/W4393429531Canonical identifier for this work in OpenAlex
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https://doi.org/10.5281/zenodo.7556851Digital Object Identifier
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Global taxonomic occurrence grids using GBIF data for species distribution models.Work title
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datasetOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-01-21Full publication date if available
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Amy J.S. Davis, Diederik Strubbe, Quentin GroomList of authors in order
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greenOpen access status per OpenAlex
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https://doi.org/10.5281/zenodo.7556851Direct OA link when available
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Distribution (mathematics), Mathematics, Species distribution, Statistics, Geography, Biology, Ecology, Mathematical analysis, HabitatTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Machine | 325 |
| abstract_inverted_index.Unknown | 334 |
| abstract_inverted_index.achieve | 1 |
| abstract_inverted_index.appears | 372 |
| abstract_inverted_index.corner) | 388, 394 |
| abstract_inverted_index.effects | 91 |
| abstract_inverted_index.effort, | 217 |
| abstract_inverted_index.effort. | 62 |
| abstract_inverted_index.exclude | 105 |
| abstract_inverted_index.false", | 341 |
| abstract_inverted_index.license | 196 |
| abstract_inverted_index.mapping | 290 |
| abstract_inverted_index.mislead | 67 |
| abstract_inverted_index.numeric | 352 |
| abstract_inverted_index.others, | 51 |
| abstract_inverted_index.permits | 198 |
| abstract_inverted_index.plants, | 163 |
| abstract_inverted_index.present | 154 |
| abstract_inverted_index.provide | 83 |
| abstract_inverted_index.records | 153, 359 |
| abstract_inverted_index.regions | 45 |
| abstract_inverted_index.sample, | 331 |
| abstract_inverted_index.sampled | 49 |
| abstract_inverted_index.spatial | 134, 215 |
| abstract_inverted_index.species | 5, 14, 73, 96 |
| abstract_inverted_index.system. | 143 |
| abstract_inverted_index.(Gotelli | 75 |
| abstract_inverted_index.1-degree | 180, 233 |
| abstract_inverted_index.Colwell, | 77 |
| abstract_inverted_index.Facility | 26 |
| abstract_inverted_index.Material | 330 |
| abstract_inverted_index.composed | 10 |
| abstract_inverted_index.contains | 149 |
| abstract_inverted_index.counted. | 237 |
| abstract_inverted_index.drawback | 32 |
| abstract_inverted_index.latitude | 182, 385, 391 |
| abstract_inverted_index.mammals, | 164 |
| abstract_inverted_index.mitigate | 89 |
| abstract_inverted_index.patterns | 71 |
| abstract_inverted_index.project, | 265 |
| abstract_inverted_index.provided | 20, 187 |
| abstract_inverted_index.purposes | 261 |
| abstract_inverted_index.removed. | 274 |
| abstract_inverted_index.reported | 60, 254 |
| abstract_inverted_index.sampling | 40, 61, 64, 93, 111, 117, 216 |
| abstract_inverted_index.specific | 160 |
| abstract_inverted_index.“Basis | 321 |
| abstract_inverted_index."TaxonKey | 342 |
| abstract_inverted_index.(Phillips | 120 |
| abstract_inverted_index.Attribute | 349 |
| abstract_inverted_index.GeoTIFFs. | 244 |
| abstract_inverted_index.Specimen, | 329 |
| abstract_inverted_index.construct | 171 |
| abstract_inverted_index.contained | 228, 360 |
| abstract_inverted_index.converted | 242 |
| abstract_inverted_index.coverage, | 4 |
| abstract_inverted_index.criteria: | 320 |
| abstract_inverted_index.databases | 7 |
| abstract_inverted_index.following | 319 |
| abstract_inverted_index.longitude | 184, 383, 389 |
| abstract_inverted_index.modeling. | 98 |
| abstract_inverted_index.modelling | 288 |
| abstract_inverted_index.molluscs. | 169 |
| abstract_inverted_index.provided. | 308 |
| abstract_inverted_index.reptiles, | 165 |
| abstract_inverted_index.taxonomic | 84, 161, 226, 277, 303, 313, 380, 397, 406 |
| abstract_inverted_index.workflow: | 291 |
| abstract_inverted_index.(Redlands, | 190 |
| abstract_inverted_index.(northeast | 393 |
| abstract_inverted_index.(southwest | 387 |
| abstract_inverted_index.Amphibia", | 344 |
| abstract_inverted_index.Literature | 332 |
| abstract_inverted_index.Record”: | 323 |
| abstract_inverted_index.al.,2012). | 126 |
| abstract_inverted_index.background | 115 |
| abstract_inverted_index.containing | 298 |
| abstract_inverted_index.coordinate | 142 |
| abstract_inverted_index.correspond | 248 |
| abstract_inverted_index.downloaded | 209, 316 |
| abstract_inverted_index.evidence., | 335 |
| abstract_inverted_index.geographic | 3, 39, 141 |
| abstract_inverted_index.occurrence | 6, 85, 128, 173, 278, 304, 381, 398, 407 |
| abstract_inverted_index.resolution | 135 |
| abstract_inverted_index.1975-2005". | 346 |
| abstract_inverted_index.Description | 350 |
| abstract_inverted_index.Information | 25 |
| abstract_inverted_index.Occurrence, | 333 |
| abstract_inverted_index.amphibians, | 166 |
| abstract_inverted_index.collections | 16 |
| abstract_inverted_index.conclusions | 68 |
| abstract_inverted_index.intensively | 48 |
| abstract_inverted_index.occurrence) | 302 |
| abstract_inverted_index.occurrences | 222, 253, 272 |
| abstract_inverted_index.Biodiversity | 24 |
| abstract_inverted_index.California). | 191 |
| abstract_inverted_index.Observation, | 324, 326, 328 |
| abstract_inverted_index.biodiversity | 70 |
| abstract_inverted_index.distribution | 97 |
| abstract_inverted_index.Barbet-Massin | 124 |
| abstract_inverted_index.corresponding | 157, 223 |
| abstract_inverted_index.distributions | 74 |
| abstract_inverted_index.georeferenced | 221 |
| abstract_inverted_index."HasCoordinate | 336 |
| abstract_inverted_index.<strong>Raster | 347 |
| abstract_inverted_index.custom-defined) | 109 |
| abstract_inverted_index.pseudo-absences’ | 119 |
| abstract_inverted_index."HasGeospatialIssue | 339 |
| abstract_inverted_index.Attributes</strong> | 348 |
| abstract_inverted_index.birds_1deg_grid.tif | 410 |
| abstract_inverted_index.birds_1deg_min5.tif | 401 |
| abstract_inverted_index.amphib_1deg_grid.tif | 409 |
| abstract_inverted_index.amphib_1deg_min5.tif | 400 |
| abstract_inverted_index.mammals_1deg_grid.tif | 411 |
| abstract_inverted_index.mammals_1deg_min5.tif | 402 |
| abstract_inverted_index.molluscs_1deg_grid.tif | 412 |
| abstract_inverted_index.molluscs_1deg_min5.tif | 403 |
| abstract_inverted_index.reptiles_1deg_grid.tif | 413 |
| abstract_inverted_index.reptiles_1deg_min5.tif | 404 |
| abstract_inverted_index.<strong>Files:</strong> | 395 |
| abstract_inverted_index.https://github.com/trias-project/risk-modelling-and-mapping. | 292 |
| abstract_inverted_index.https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7 | 212 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |