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299 results for “Niche Modelling”
Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species
<p>Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.</p>
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution
<p>Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Long time-series ecological niche modelling using archaeological settlement data.
<p><strong>CR_settlement_niche_[N]_[Yr]_[BC/AD].tif</strong></p> <p>Ecological niche models in GeoTIFF format generated with the MaxEnt software based using prehistoric settlement evidence as training data and environmental layers (elevation, mean annual precipitation, mean annual temperature, landscape water balance, soil types) as background data. Raster values represent the probability of presence of a settlement.<br> <strong>N</strong> - chronological ordering<br> <strong>Yr, BC/AD</strong> - calendar years BC or AD</p> <p> </p> <p><strong>CR_settlement_niche_combined.tif</strong></p> <p>All models combined by averaging.</p> <p> </p> <p><strong>CR_settlement_archeo.zip</strong></p> <p>Archaeological data used to train the MaxEnt models in ESRI SHP format with the following fields:</p> <p><strong>Site_Type:</strong> Cemetery or Settlement</p> <p><strong>Archeo_Dat:</strong> Archaeological dating (culture or period)</p> <p><strong>Source:</strong> Source dataset (AMCR or LONGWOOD)</p> <p>AMCR: Archeologická mapa České republiky – Archaeological Map of the Czech Republic. Retrieved from https://digiarchiv.aiscr.cz/.</p> <p>LONGWOOD: Kolář, J., Tkáč, P., Macek, M., & Szabó, P. (2016). Archaeology and Historical Ecology: the Archaeological Database of the LONGWOOD ERC Project. Archäologisches Korrespondenzblatt 46/4, 539-554.</p> <p><strong>Yrs_BP_Avg:</strong> Average dating in calendar years BP (based on the archaeological dating)</p> <p><strong>Yrs_BP_Unc:</strong> Temporal uncertainty of the dating (half of the culture or period's duration)</p> <p><strong>Loc_Accur:</strong> Spatial accuracy derived from the recorded degree of the accuracy of location (radius in meters around the center point)</p>
Spatial dataset for ecological niche and spatial distribution modeling of Herichthys bartoni (Cichliformes: Cichlidae) in the Media Luna spring, Mexico
<p>Dataset for the endangered endemic cichlid <em>Herichthys bartoni</em> in the Media Luna spring, Mexico. This data includes occurrences records by species life stage (adult, juvenile and fry), in three field sessions corresponding to the summer period, in the years 1999, 2009 and 2019.</p> <p>For more information about the codes where the previous datasets could be used, visit the following repository with URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Likewise, the UC and WDp variables used to run the ecological niche and spatial distribution model, by summer period, can be found in the following repository wirh URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p>
Ecological niche models for American black bear, Rafinesque's big-eared bat, and timber rattlesnake
<p>This data set contains rasters that are predictive environmental suitability maps for three wildlife species: the American black bear (<i>Ursus americanus</i>), Rafinesque's big-eared bat (<i>Corynorhinus rafinesquii</i>), and Timber rattlesnake (<i>Crotalus horridus</i>). Rasters for each species include: individual prediction maps for each of 5 ENMs (GBM: generalized boosting model, GLM: generalized linear model, MARS: multivariate adaptive regression spline, MX: maximum entropy, and RF: random forest), as well as the ensemble prediction map from all five ecological niche models (ENMs).</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>
Model, data, and analysis for Negative Niche Construction Favors the Evolution of Cooperation
<p>This repository contains the model, data, and analysis corresponding to <em>Negative Niche Construction Favors the Evolution of Cooperation</em> as submitted for review by Brian D. Connelly, Katherine J. Dickinson, Sarah P. Hammarlund, and Benjamin Kerr. Contents are released to the public domain under the Creative Commons CC0 License.</p>
Fig. 5. The marginal response curve for the explanatory variable Bio14 in Modelling The Bioclimatic Niche And Distribution Of The Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), In Ukraine
Fig. 5. The marginal response curve for the explanatory variable Bio14 (Precipitation of driest week). (HS — habitat suitability).
Fig. 1 in Modelling The Bioclimatic Niche And Distribution Of The Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), In Ukraine
Fig. 1. Occurrences of Mus spicilegus in Ukraine and neighbouring areas used for creating the ENM. [Data collected before (triangles) and after (circles) 1990.]
Fig. 4. The marginal response curve for the explanatory variable Bio09 in Modelling The Bioclimatic Niche And Distribution Of The Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), In Ukraine
Fig. 4. The marginal response curve for the explanatory variable Bio09 (Mean temperature of driest quarter). (HS — habitat suitability).
Fig. 7. 0.5 in Modelling The Bioclimatic Niche And Distribution Of The Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), In Ukraine
Fig. 7. 0.5 oC isotherms for Bio09 (Mean temperature of driest quarter) for different time periods: 1 — 1980s; 2 — 2000s; 3 — contemporary; 4 — predicted for 2030.
Fig. 6. A in Modelling The Bioclimatic Niche And Distribution Of The Steppe Mouse, Mus Spicilegus (Rodentia, Muridae), In Ukraine
Fig. 6. A current climate habitat suitability map for the Steppe mouse (Mus spicilegus) in Ukraine. Darker shades of gray denote areas of higher predicted habitat suitability probabilities (≥ 0.5) and lighter shades correspond to lower (≥ 0.311 and <0.5). [Administrative regions in Ukraine: 1 — Chernihiv Region; 2 — Kyiv Region; 3 — Ternopil Region; 4 — Ivano-Frankivsk Region.]
Fig. 4 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Reptiles
Fig. 4. Areas (polygons) in Western Podillya (Ukraine), where there is a predicted probability for the accommodation 9, 8 or 7 reptile species (gradient from dark gray — 9 species to light — 7 species). Districts numbered as in fig. 3.
Fig. 3 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Reptiles
Fig. 3. Areas (downward diagonal filled polygons) in Western Podillya (Ukraine), where the average predicted habitat suitability for reptile species exceeds 0.5 (Districts: 1 — Terebovlianskyi, 2 —Husiatynskyi, 3 — Buchatskyi, 4 — Chortkivskyi, 5 — Chemerovetskyi, 6 — Horodenkivskyi, 7 — Zalishchytskyi, 8 — Borshchivskyi, 9 — Kamianets-Podilskyi, 10 — Zastavnivskyi, 11 — Khotynskyi).
Fig. 4 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 4. Two upper categories ("Moderate" and "High") collapsed to identify areas of predicted presence (dark gray shading) for B. variegata in the study area.
Fig. 1 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 1. Response of Bombina variegata to Bio 11: x-axis — mean temperature of coldest quarter (°C x 10); y- axis— logistic output (probability of presence).
Fig. 3 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 3. Response of Triturus cristatus to the Human Footprint: x-axis — Human Footprint; y-axis — logistic output (probability of presence).
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.