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991 results for “Species distribution data”
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>
Background data 'Effect of biotic dependencies in species distribution models: The future distribution of Thymallus thymallus under consideration of Allogamus auricollis'
<p>Background data of the paper 'Effect of biotic dependencies in species distribution models: The future distribution of Thymallus thymallus under consideration of Allogamus auricollis'</p>
Data set: Variations in water economy traits in two Sphagnum species across their distribution boundaries
<p><em>Sphagnum</em> trait data collected (2016-2017) across a climatic gradient in Sweden. Trait data for both shoot and canopy traits. Data for <em>Sphagnum cuspidatum</em> and <em>Sphagnum lindbergii</em>. Also contains data on species occurrence records in Sweden and output from speceis distribution modelling. See published paper for more information.</p> <p>Files contain (i) processed data ("calculated_trait_data...cvs"), (ii) raw data ("Campbell_etal_clim_traits_...cvs"), (iii) their readme files, and (iv) R-scripts to run the analyses. Note that you need the files in the zip-file to run the analyses in the R-script. The zip-file contains all raw data (climate, traits, species occurences), MaxEnt output, and raster files from photogrammetry.</p> <p>More info in paper: <a href="https://doi.org/10.1002/ajb2.16347" target="_blank" rel="noopener">https://doi.org/10.1002/ajb2.16347</a></p>
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
FIG. 3. — Navicordulia pascali n in The genus Navicordulia Machado & Costa, 1995 (Insecta, Odonata, Corduliidae s.str.): new species, identification key for males and data on ecology and distribution
FIG. 3. — Navicordulia pascali n. sp., holotype: A, S10 and anal appendages in dorsal view; B, part of S9, S10 and anal appendages in left lateral view. Scale bars: 1 mm.
FIG. 1. — Navicordulia pascali n in The genus Navicordulia Machado & Costa, 1995 (Insecta, Odonata, Corduliidae s.str.): new species, identification key for males and data on ecology and distribution
FIG. 1. — Navicordulia pascali n. sp., holotype: A, general habitus; B, head in dorsal view and part of thorax in right lateral view.Scale bars: A, 10 mm; B, 1 mm.
FIG. 6 in The genus Navicordulia Machado & Costa, 1995 (Insecta, Odonata, Corduliidae s.str.): new species, identification key for males and data on ecology and distribution
FIG. 6. — "Savane-roche" in the Barruol Mounts, locus typicus of Navicordulia pascali n. sp. Photo by Stéphane Brûlé.
Fig. 1 in New data on distribution and biology of the invasive species Hydrotaea aenescens (Wiedemann, 1830) (Diptera, Muscidae)
Fig. 1. Map showing the records of H. aenescens: 1 – Turkey, Antalya, near Side; 2 – Russia, Sochi region, near Veseloe; 3 – Russia, 60 km North of Narjan-Mar, 68.15N 53.65E. Countries where H.aenescens was previously recorded (Pont et al. 2007) are marked in green.
Bioclimatic data for species distribution modelling in the Amazon Basin
<p>In this dataset, bioclimatic data regarding the Amazon Basin, in the near of the cities of Manaus and Manacapuru are available. There are 11 environmental data variables, referring to temperature, atmospheric pressure, concentration of pollutants and aerosols, such as carbon monoxide, ozone, carbon dioxide, among others. These were collected by the G-159 Gulfstream aircraft during its two periods of operation (IOP1 and IOP2), available in the GOAmazon (Green Ocean Amazon) project's data repository. A spatial interpolation methodology (linear barycentric interpolation) was applied to each variable, in order to obtain a larger area of data. The species occurrence data were collected from the repositories of the ICMBio (Instituto Chico Mendes de Conservação da Biodiversidade) Portal da Biodiversidade and GBIF (Global Biodiversity Information Facility), referring to the same date and location of the environmental data. <br> </p>
Data and scripts for the analysis of fruit flies' species distributions in Reunion island
<p>Data and scripts supporting the analyses of the joint species distributions of eight Tephritids species in La Réunion island.</p>
Data from: Species distribution models of the Spotted Wing Drosophila (Drosophila suzukii, Diptera: Drosophilidae) in its native and invasive range reveal an ecological niche shift
<p>The Spotted Wing Drosophila (<em>Drosophila</em> <em>suzukii</em>) is native to Southeast Asia. Since its first detection in 2008 in Europe and North America, it has been a pest to the fruit production industry as it feeds and oviposits on ripening fruit. Here we aim to model the potential geographical distribution of <em>D. suzukii</em>. We performed an extensive literature review to map the current records. In total, 517 documented occurrences (96 native and 421 invasive) were identified spanning 52 countries. Next, we constructed three species distribution models (SDMs) based on occurrence records in: 1) the native range (SDMnative), 2) the invasive range in Europe (SDMEurope) and 3) a global model of all records (SDMglobal). The models aimed to investigate, whether this species will be able to occupy additional ecological niches beyond its native range and expand its current geographic distribution both globally and in Europe. The SDMs were generated using Maximum Entropy algorithms (Maxent) based on present occurrence records and bioclimatic variables (WorldClim). Predictions of habitat suitability vary greatly depending on the origins of occurrence records. According to all models, precipitation and low temperatures were key limiting factors for the distribution of <em>D. suzukii</em>, which suggests that this species requires a humid environment with mild winters in order to establish a permanent population in its invasive range. Several regions in the invasive range, not presently occupied by this species, were predicted highly suitable, especially in northern Europe, suggesting that <em>D. suzukii</em> is not occupying its full fundamental niche yet. Synthesis and applications. Based on these models of potential geographic distribution of the Spotted Wing Drosophila (<em>Drosophila</em> <em>suzukii</em>), we show a shift in the ecological niche in <em>D. suzukii</em> populations, emphasizing the importance of using presence and local environmental data. Further investigation regarding new occurrences is recommended to secure optimal pest management. Despite a continuing expansion, many countries still lack proper surveillance schemes, and we urge policymakers to initiate appropriate management programs.</p>
Data from: Complementary strengths of spatially-explicit and multi-species distribution models
<p><span><span><span><span><span><span><span><span><span><span><span> Species distribution models (SDMs) project the outcome of community assembly processes - dispersal, the abiotic environment, and biotic interactions - onto geographic space. Recent advances in SDMs account for these processes by simultaneously modeling the species that comprise a community in a multivariate statistical framework or by incorporating residual spatial autocorrelation in SDMs. However, the effects of combining both multivariate and spatially-explicit model structures on the ecological inferences and the predictive abilities of a model are largely unknown. We used data on eastern hemlock (<i>Tsuga canadensis</i>L.) and five additional co-occurring overstory tree species in 35,569 forest stands across Michigan, USA to evaluate how the choice of model structure, including spatial and non-spatial forms of univariate and multivariate models, affects ecological inference about the processes that shape community composition as well as model predictive ability.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span> Incorporating residual spatial autocorrelation via spatial random effects did not improve out-of-sample prediction for the six tree species, although in-sample model fit was higher in the spatial models. Spatial models attributed less variation in occurrence probability to environmental covariates than the non-spatial models for all six tree species, and estimated higher (more positive) residual co-occurrence values for most species pairs. The non-spatial multivariate model was better suited for evaluating habitat suitability and hypotheses about the processes that shape community composition. Environmental correlations and residual correlations among species pairs were positively related, perhaps indicating that residual correlations were due to shared responses to unmeasured environmental covariates. This work highlights the importance of choosing a non-spatial model formulation to address research questions about the species-environment relationship or residual co-occurrence patterns, and a spatial model formulation when within-sample prediction accuracy is the main goal.</span></span></span></span></span></span></span></span></span></span></p>
Data from: Discordant patterns of genetic and phenotypic differentiation in five grasshopper species co-distributed across a microreserve network
<p>Conservation plans can be greatly improved when information on the evolutionary and demographic consequences of habitat fragmentation is available for several co-distributed species. Here, we study spatial patterns of phenotypic and genetic variation among five grasshopper species that are co-distributed across a network of microreserves but show remarkable differences in dispersal-related morphology (body size and wing length), degree of habitat specialization and extent of fragmentation of their respective habitats in the study region. In particular, we tested the hypothesis that species with preferences for highly fragmented microhabitats show stronger genetic and phenotypic structure than co-distributed generalist taxa inhabiting a continuous matrix of suitable habitat. We also hypothesized a higher resemblance of spatial patterns of genetic and phenotypic variability among species that have experienced a higher degree of habitat fragmentation due to their more similar responses to the parallel large-scale destruction of their natural habitats. In partial agreement with our first hypothesis, we found that genetic structure, but not phenotypic differentiation, was higher in species linked to highly fragmented habitats. We did not find support for congruent patterns of phenotypic and genetic variability among any studied species, indicating that they show idiosyncratic evolutionary trajectories and distinctive demographic responses to habitat fragmentation across a common landscape. This suggests that conservation practices in networks of protected areas require detailed ecological and evolutionary information on target species in order to focus management efforts on those taxa that are more sensitive to the effects of habitat fragmentation.</p>
FIGURES 16 – 19 in New data on the distribution of species of Gastroserica Brenske, 1897, with descriptions of five new taxa from China and Laos (Coleoptera: Scarabaeidae: Sericini)
FIGURES 16 – 19: Habitus, 16 Gastroserica shaanxiana sp. n. (Holotype: China, S Shaanxi, road WanyuanZhenba 30 km S Zhenba); 17 G. huaphanensis sp. n. (Holotype: Laos, Hua Phan prov., Ban Saluei, Phu Phan Mt.); 18 G. contaminata sp. n. (Holotype: Laos, Hua Phan prov., Ban Saluei, Phu Phan Mt.); 19 G. stictica sp. n. (Holotype: Laos, Hua Phan prov., Ban Saluei, Phu Phan Mt.).
Supplementary material 5: Bombus spp trapped in Palmer Alaska, 2009 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
1040 specimens of fourteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 3: Bombus spp trapped in Fairbanks Alaska, 2009 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
2,131 specimens of fifteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 2: Bombus spp trapped in Delta Junction Alaska, 2010 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
1812 specimens of sixteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 1: Bombus spp trapped in Delta Junction Alaska, 2009 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
2,446 specimens of sixteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 4: Bombus spp trapped in Fairbanks Alaska, 2010 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
57 specimens of seven species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
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