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Data from: Thermal niches of wheat curl mite, Aceria tosichella (Acari: Eriophyidae): congruence between physiological and geographical distribution data
<p><strong>Filename: parm.csv</strong></p> <p>Population growth rate of the two wheat curl mite (WCM) lineages reared in different temperatures.</p> <ol> <li>lineage - mitochondrial lineage (MT-1 or MT-8)</li> <li>temp - the rearing temperature (ºC)</li> <li>n - no. of replications of the experiment</li> <li>r, r.lower, r.upper - estimated intrinsic population growth rate and its 95% confidence intervals</li> </ol> <p><strong>Filename: aceria.csv</strong></p> <p>Data on field sampling localities, WCM abundance and thermal niche suitabiity.</p> <ol> <li>julian - Julian date</li> <li>x, y - geodetic coordinates (EPSG: 2180)</li> <li>stems - no. of stems collected</li> <li>MT1, MT8- mitochondrial lineage (MT-1 or MT-8)</li> <li>TNS1, TNS8 - thermal niche suitability for lineages</li> </ol>
The Neanderthal Niche Space of Western Eurasia - Supplemental Material
<p>The data provided here are the supplemental information accompanying the journal article <strong>The Neanderthal Niche Space of Western Eurasia 145ka to 30ka ago</strong> by Yaworsky, Nielsen, & Nielsen. All analyses were performed in R v4.5.0 and are documented in the HTML document, <strong>Supplemental 9</strong>.</p> <p>List of Supplemental Files:</p> <ol> <li><strong>ROCEEH Archaeological Observations - File name: </strong><em><strong>FaunalData_52923_NOANIMALS.csv</strong></em> <ol> <li>Retrieved from <em>Role of Culture in Early Expansions of Humans Out-of Africa Database (</em>http://www.roceeh.net)</li> <li>Original PHP query is found in Supplemental 9. The zooarchaeological observations have been removed from the original PHP query.</li> </ol> </li> <li><strong>ROCEEH Dates Data - File name: </strong><em><strong>Dates_Geog.csv</strong></em> <ol> <li>Retrieved from <em>Role of Culture in Early Expansions of Humans Out-of Africa Database (</em>http://www.roceeh.net)</li> <li>Original PHP query is found in Supplemental 9. These are the Geolayer dates.</li> </ol> </li> <li><strong>ROCEEH Dates Data - File name: </strong><em><strong>Dates_Assem.csv</strong></em> <ol> <li>Retrieved from <em>Role of Culture in Early Expansions of Humans Out-of Africa Database (</em>http://www.roceeh.net)</li> <li>Original PHP query is found in Supplemental 9. These are the ArchLayer dates.</li> </ol> </li> <li><strong>ROCEEH Dates Data - File name: </strong><em><strong>Dates_ArchLayer.csv</strong></em> <ol> <li>Retrieved from <em>Role of Culture in Early Expansions of Humans Out-of Africa Database (</em>http://www.roceeh.net)</li> <li>Original PHP query is found in Supplemental 9. These are the Assemblage dates.</li> </ol> </li> <li><strong>Spatiotemporal Archaeological Observations - File name: </strong><em><strong>ArchaeologicalData_V1.csv</strong></em> <ol> <li>Derived from Supplemental 1 after removing observations that dated outside of the 145ka to 50ka year range, all NA values, and observations duplicated in time and space (1000-year range).</li> </ol> </li> <li><strong>Spatiotemporal Background Points - File name: </strong><em><strong>AbsencePointData.csv</strong></em> <ol> <li>Randomly generated background points. 100 random points were generated in each millennium.</li> </ol> </li> <li><strong>High-Resolution Spatiotemporal Predictions - File name: </strong><em><strong>SDM_MainGIF.mp4</strong></em> <ol> <li>High-resolution mp4 file showing the predictions of the Neanderthal niche space from 145ka to 30ka ago.</li> </ol> </li> <li><strong>Neanderthal Niche Space 145ka to 30ka ago- File name: </strong><em><strong>Human_Niche_Size.csv</strong></em> <ol> <li>Quantification of the Neanderthal niche space for each millennium.</li> </ol> </li> <li><strong>Analysis Markdown Document - File Name: </strong><em><strong>NeanderEdgeMD_v5.html</strong></em> <ol> <li>Markdown illustrating step-by-step the methods used to organize and analyze the data.</li> </ol> </li> <li><strong>Delta O18 Record - File name: </strong><em><strong>LisieckiRaymod18O.csv</strong></em> <ol> <li>Delta O18 Record from Lisiecki & Raymo, 2005.</li> <li>Used in Figure 1 of the publication to illustrate the relationship between Neanderthal niche size and Delta O18 values.</li> </ol> </li> <li><strong>Neanderthal Niche Space 350ka ago to Present - File name: </strong><em><strong>Human_Niche_Size_350k.csv</strong></em> <ol> <li>Neanderthal niche space estimates from 350ka to the present based on the model constructed around the 145ka to 50ka ago archaeological observations.</li> <li>Not discussed in the main publication.</li> </ol> </li> </ol> <p>The NeanderEDGE Project is funded by the Independent Research Fund Denmark (Danmarks Frie Forskningsfond) case number 9062-00027B.</p>
The Human Niche Space of Post-LGM Late Upper Paleolithic Europe - Supplemental Material
<p>The data provided here are the supplemental information accompanying the journal article <strong>The Human Niche Space of Post-LGM Late Upper Paleolithic Europe: The Effects of Climate and Population Growth on Human Land Use</strong> by Yaworsky, Hussain, & Riede. All analyses were performed in R v4.5.0 and are documented in the HTML document, <strong>Supplemental 4</strong>.</p> <p>Version 1.2 of the Analysis Markdown Document incoporates changes made to functions within the package ENMeval.</p> <p>List of Supplemental Files:</p> <ol> <li><strong>Spatiotemporal Archaeological Observations - File name: <em>Archaeologicaldata_v1.csv</em></strong> <ol> <li>Archaeological observations derived from Kretschmer (2015) and supplemented with additional observations (see main paper for details).</li> </ol> </li> <li><strong>Summed Probability Estimate for Population Estimation - File name: <em>Population_SPD2.csv</em></strong><br> <ol> <li>Summed probability distribution estimating changes in relative population size across Europe from 22ka ago to 9.1ka ago using data from the P3K14C database (Bird et al, 2022; see <strong>Supplemental 4</strong> for details).</li> </ol> </li> <li><strong>Spatiotemporal Background Points - File name: </strong><em><strong>AbsencePointData.csv</strong></em><br> <ol> <li>Randomly generated background points. 100 random points were generated in each millennium.</li> </ol> </li> <li><strong>Analysis Markdown Document - File Name: </strong><em><strong>CLIOARCH_MD_v1.2.html</strong></em><br> <ol> <li>Markdown illustrating step-by-step the methods used to organize and analyze the data.</li> </ol> </li> <li><strong>High-Resolution Spatiotemporal Predictions - File name: </strong><em><strong>SDM_MainGIF.mp4</strong></em><br> <ol> <li>High-resolution mp4 file showing the predictions of the potential climate niche space for humans from 22ka to 9.1ka ago.</li> </ol> </li> <li><strong>Potential Niche Space 22ka to 9.1ka ago- File name: </strong><em><strong>Human_Niche_Size.csv</strong></em> <ol> <li>Quantification of the potential climate niche space for each century.</li> </ol> </li> </ol> <p>The Climate data are not provided due to their size but are sourced from Karger et al (2023) and are accessible <a href="https://chelsa-climate.org/">here (https://chelsa-climate.org/)</a>.</p> <p> </p>
NICHE Flanders: reference values for the (a)biotic requirements of vegetation types in Flanders, Belgium
<p>This dataset contains site requirements/tolerance limits (or "reference values") for 28 vegetation types found in Flanders. It gives the lower and upper limits or the classes within which these vegetation types can occur, for 7 site factors that determine potential vegetation development. These reference values can be used to determine the potential distribution of the different vegetation types with the ecohydrological model NICHE Flanders (<a href="https://purews.inbo.be/ws/portalfiles/portal/5370206/Callebaut_etal_2007_NicheVlaanderen.pdf">Callebaut et al. 2007</a>, in Dutch).</p> <p>See the Technical info (available in English and Dutch) for more information.</p>
Data set: Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes
<h1>Repository for "Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes"</h1> <p>---</p> <p>These data scripts were used to perform analyses included in the research paper "Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes" </p> <p>Main questions for the study:</p> <p>1. What are the main axes of morphological variation?<br>2. Does variation in morphology among species correlate with their current environments? <br>3. Are lineages that occupy ecologically similar habitats morphologically convergent? <br>4. Is speciation predominantly allopatric or sympatric? <br>5. Do sister species have greater morphological and ecological niche overlap than expected relative to non-sister species pairs?</p> <h2>## Data structure</h2> <p>Contents in the data folder is archived as a zip and can be downloaded from Zenodo (for all versions see https://zenodo.org/doi/10.5281/zenodo.10397830). Once you unzip the zipped files, you will see three folders and some files that are no in any folders. </p> <p>/data/ - files that were manually created and the phylogeny</p> <p>/data/script_generated_data/ - A combination of processed data needed to run the analyses </p> <p>/data/dorsal/ - photographs of the head from the dorsal view. These photos were used for digitising landmarks and semilandmarks. </p> <p>/data/worldclim2_30s/ - cropped and merged annual temperature from WorldClim2 (Fick and Hijmans 2017), soil bulk density from <a href="https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData.html">Soil and Landscape Grid of Australia</a>, and Global Aridity Index from Zomer et al. (2022). <br><br>/DREaD/ - contains some files required to replicate DREaD analysis</p> <h2>## Code/Software</h2> <p>All scripts can be run using open source software. Scripts should be run in order to create necessary files that will be saved in /data/script_generated_data/ for further scripts. R is required to run R scripts (.R).</p> <h3>### /Code</h3> <p> - utility/*.R - scripts for custom functions. These are sourced in other scripts.<br> - DREaD/*.R - scripts associated with DREaD analyses<br> - 00_linear_measurement_shaperatio.R - script used to account for sexual dimorphism and calculate conventional PCA. Addresses Q1.<br> - 01_model_fitting.R - script used to address Q2 and plot visualisations.<br> - 02_convergence.R - this script calculates Ct1-4 and C5 scores. Addresses Q3.<br> - 02_convergence_model_fitting.R - this script evaluates fit of different evolutionary models to traits. Addresses Q3.<br> - 02_convergence_test_simulations.R - simulation studies to show that our phylogeny has sufficient power to detect convergence.<br> - 03_niche_enmtools_bias_account.R - calculates ecological niche models (ENMs) for each species using MAXENT. Runs Age-Overlap Correlation tests for geography and ENMs. Partially addresses Q4.<br> - 03_DREaD_Blindsnakes_AS.R - script to run DREaD analysis. <br> - 03_morpho_niche_overlap_plots.R - Runs Age-Overlap Correlation tests for body shape and snout shape. Plots AOCs. Partially addresses Q4. <br> - 04_pairwise_distance_test.R - Binomial tests between sister and non-sister pairs for ENMs and Geographic Range. Partially addresses Q5<br> - 04_morpho_pairwise.R - Binomial tests between sister and non-sister pairs for body shape and snout shape. Partially addresses Q5</p> <h2>## Contact</h2> <p>Should you have questions about these scripts or would like to request raw data, please do not hesitate to contact Sarin Tiatragul (contact information can be found in the paper) or on Github (https://github.com/stiatragul/blindsnakemorphoevo)</p> <h2>## References</h2> <p><a name="ref-fickWorldClim2017"></a>Fick, S. E., and R. J. Hijmans. 2017. <a href="https://doi.org/10.1002/joc.5086">WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas</a>. International Journal of Climatology 37:4302–4315.</p> <p><a name="ref-zomerVersion2022"></a>Zomer, R. J., J. Xu, and A. Trabucco. 2022. <a href="https://doi.org/10.1038/s41597-022-01493-1">Version 3 of the global aridity index and potential evapotranspiration database</a>. Scientific Data 9:409.</p>
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>
Ecological soil map NICHE Flanders - Ecologische NICHE bodemkaart Vlaanderen
<p>NL</p> <p><strong>ECOLOGISCHE NICHE BODEMKAART VLAANDEREN</strong></p> <p>De NICHE bodemkaart voor Vlaanderen is een ecologisch getinte vereenvoudigde bodemkaart die als input dient voor het ecohydrologisch model NICHE Vlaanderen (<a href="https://purews.inbo.be/ws/portalfiles/portal/5370206/Callebaut_etal_2007_NicheVlaanderen.pdf">Callebaut et al. 2007</a>). </p> <p>De bodem speelt een belangrijke rol voor standplaatscondities en het voorkomen van plantengemeenschappen. In NICHE Vlaanderen worden zogenaamde ‘ecologische bodemtypes’ gedefinieerd. De bodemkenmerken worden daarbij vereenvoudigd tot een paar ecologisch relevante kenmerken: de korrelgrootte en de aanwezigheid van organische stof, die bepalend zijn voor de vochtcondities, zuurgraad en het trofieniveau in de bodem.</p> <p>De NICHE bodemkaart onderscheidt 12 klassen:</p> <table> <tbody> <tr> <td>Cijfercode</td> <td>Lettercode</td> <td>Omschrijving</td> </tr> <tr> <td>2</td> <td>K1</td> <td>alluviale kleigronden, arm aan organisch materiaal</td> </tr> <tr> <td>3</td> <td>KV</td> <td>alluviale kleigronden, rijk aan organisch materiaal, venige klei, klei op veen</td> </tr> <tr> <td>5</td> <td>Le</td> <td>eolische leemgronden</td> </tr> <tr> <td>6</td> <td>MK</td> <td>maritieme klei</td> </tr> <tr> <td>7</td> <td>P</td> <td>trilveen</td> </tr> <tr> <td>8</td> <td>V</td> <td>veen</td> </tr> <tr> <td>11</td> <td>Z1</td> <td>humusarme zandgronden (dunne humuslaag), podzol</td> </tr> <tr> <td>12</td> <td>Z2</td> <td>humusrijke zandgronden (dikke humuslaag)</td> </tr> <tr> <td>13</td> <td>ZV</td> <td>venige zandgronden, moerige zandgronden, zandige veengronden</td> </tr> <tr> <td>14</td> <td>L1</td> <td>alluviale leemgronden, arm aan organisch materiaal</td> </tr> <tr> <td>15</td> <td>LV</td> <td>alluviale leemgronden, rijk aan organisch materiaal, venige leemgronden</td> </tr> <tr> <td>10</td> <td> </td> <td>gronden die niet in aanmerking komen voor NICHE Vlaanderen:</td> </tr> <tr> <td> </td> <td>NG</td> <td>niet gespecifieerd</td> </tr> <tr> <td> </td> <td>B</td> <td>bebouwde of sterk beïnvloede gronden</td> </tr> <tr> <td> </td> <td>D</td> <td>droge gronden</td> </tr> <tr> <td> </td> <td>W</td> <td>open water</td> </tr> </tbody> </table> <p>De NICHE bodemkaart is afgeleid van de digitale bodemkaart van Vlaanderen (Digitale versie van de Bodemkaart van Vlaanderen, uitgave 20/06/2017, Databank Ondergrond Vlaanderen). De stappen om een eenheid van de bodemkaart van Vlaanderen in een NICHE bodemtype om te zetten worden in detail beschreven in het NICHE rapport (Callebaut et al. 2007: hoofdstuk 3 pp 32-45 en bijlage 3.3).</p> <p>Als er terreingegevens beschikbaar zijn, kan er afgeweken worden van deze (NICHE) bodemkaart: hoofdstuk 3.4 van het NICHE rapport (Callebaut et al. 2007) licht toe hoe een NICHE bodemtype toegekend kan worden aan een bodemprofiel op basis van de textuur, de dikte en de opeenvolging van de verschillende horizonten.</p> <p><strong>Formaat</strong></p> <p>Vectoriële geografische informatie ter beschikking gesteld als:</p> <ul> <li>shapefile (.shp, .dbf, .shx) met projectiebeschrijving (.prj), ruimtelijke indexen (.sbn, . sbx), geospatial metadata in XML formaat (.shp.xml) en stijlen (ArcView Layer Format .lyr, Styled Layer Descriptor Format .sld)</li> <li>geopackage (.gpkg) met stijlen (Styled Layer Descriptor Format .sld)</li> </ul> <p>Geografische referentiesysteem: Belge 1972 / Belgian Lambert 72 (<a href="https://epsg.io/31370">EPSG-code 31370</a>)</p> <p>Hoogtereferentiesysteem: Tweede Algemene Waterpassing (<a href="https://www.ngi.be/website/tweede-algemene-waterpassing/">TAW</a>)</p> <p><strong>Attributen</strong></p> <ul> <li>fid- Id NICHE bodemkaart (geopackage)</li> <li>gid- Id digitale bodemkaart 2017</li> <li>Bodemtype- Bodemkaarteenheden volgens het Belgische bodemclassificatiesysteem (digitale bodemkaart 2017)</li> <li>Bodemser_c- Bodemserie bestaande uit 3 letters die staan voor textuur, drainage en profiel (digitale bodemkaart 2017)</li> <li>Bodemserie - Bodemserie: beschrijving voor textuur, drainage en profiel (digitale bodemkaart 2017)</li> <li>Unitype - Bodemkaarteenheden met zeepolders omgezet naar de bodemclassificatie van de rest van Vlaanderen (digitale bodemkaart 2017)</li> <li>Grove_leg - Gegeneraliseerde legende van de bodemkaart (digitale bodemkaart 2017)</li> <li>Substr_V_c - Substraten waarvan de lithologische aard verschilt van die van de oppervlakkige laag (lithologische discontinuiteit) (digitale bodemkaart 2017)</li> <li>Textuur_c - Grondsoort, aard van het moedermateriaal (digitale bodemkaart 2017)</li> <li>Drainage_c - Natuurlijke draineringsklasse, natuurlijke drainage (digitale bodemkaart 2017)</li> <li>Profontw_c - Profielontwikkeling (digitale bodemkaart 2017)</li> <li>Fase_c - Secundaire bodemkenmerken (digitale bodemkaart 2017)</li> <li>Varimoma_c - Variant van het moedermateriaal (digitale bodemkaart 2017)</li> <li>Variprof_c - Variant van de profielontwikkeling (digitale bodemkaart 2017)</li> <li>Streek - Landbouwstreek (digitale bodemkaart 2017)</li> <li>NICHE_let - Lettercode van het NICHE bodemtype</li> <li>NICHE_cijf - Cijfercode van het NICHE bodemtype</li> </ul> <p><strong>Wijzigingen sinds vorige versie</strong></p> <p>Sinds vorige versie (1.1):</p> <ul> <li>NICHE bodemkaart niet meer gebaseerd op versie 2001 van de bodemkaart, maar op versie 20/06/2017 incl. een update met enkele militaire domeinen (Beverlo, Kleine Brogel, Brasschaat) en met een unibodemtype voor de classificatie van de zeepolders, alsook een verbetering van verschillende fouten.</li> <li>namen van de velden veranderd</li> <li>cijfercode van de NICHE bodemtypes aangepast (2 t.e.m. 15 i.p.v. 20 000 t.e.m. 150 000)</li> <li>enkele verbeteringen van de vertaling tussen bodemtypes en NICHE bodemtypes: <ul> <li>sV, sV(o): ZV (i.p.v. V in v1.1)</li> <li>uvPep: LV (i.p.v. L1 in v1.1)</li> <li>GDa3x: LV (i.p.v. L1 in v1.1)</li> <li>v-Zdpb(z): Z1 (i.p.v. ZV in v1.1)</li> </ul> </li> </ul> <p><strong>Disclaimer</strong></p> <p>Deze kaart geeft de best beschikbare informatie maar is een vereenvoudiging van de werkelijkheid op terrein. Ten allen tijde geldt de reële situatie op terrein voor toepassing t.b.v. het beleidsmatig en wettelijk kader.</p> <p>EN</p> <p><strong>ECOLOGICAL SOIL MAP NICHE FLANDERS</strong></p> <p>The NICHE soil map for Flanders is an ecologically tinted simplified soil map that serves as input for the ecohydrological model NICHE Flanders (<a href="https://purews.inbo.be/ws/portalfiles/portal/5370206/Callebaut_etal_2007_NicheVlaanderen.pdf">Callebaut et al. 2007</a>).</p> <p>Soil plays an important role for habitat conditions and the occurrence of plant communities. In NICHE Flanders, so-called 'ecological soil types' are defined. The soil characteristics are thereby simplified to a few ecologically relevant characteristics: the grain size and the presence of organic matter, which determine the moisture conditions, acidity and the trophy level in the soil.</p> <p>The NICHE soil map distinguishes 12 classes:</p> <table> <tbody> <tr> <td>Numerical code</td> <td>Letter code</td> <td>Description</td> </tr> <tr> <td>2</td> <td>K1</td> <td>alluvial clay soils, poor in organic matter</td> </tr> <tr> <td>3</td> <td>KV</td> <td>alluvial clay soils, rich in organic matter, peaty clay, clay on peat</td> </tr> <tr> <td>5</td> <td>Le</td> <td>aeolian loamy soils</td> </tr> <tr> <td>6</td> <td>MK</td> <td>maritime clay</td> </tr> <tr> <td>7</td> <td>P</td> <td>quaking bog</td> </tr> <tr> <td>8</td> <td>V</td> <td>peat</td> </tr> <tr> <td>11</td> <td>Z1</td> <td>humus-poor sandy soils (thin humus layer), podzol</td> </tr> <tr> <td>12</td> <td>Z2</td> <td>humus-rich sandy soils (thick humus layer)</td> </tr> <tr> <td>13</td> <td>ZV</td> <td>peaty sandy soils, swampy sandy soils, sandy peat soils</td> </tr> <tr> <td>14</td> <td>L1</td> <td>alluvial loamy soils, poor in organic matter</td> </tr> <tr> <td>15</td> <td>LV</td> <td>alluvial loamy soils, rich in organic matter, peaty loams</td> </tr> <tr> <td>10</td> <td> </td> <td>grounds that do not qualify for NICHE Flanders:</td> </tr> <tr> <td> </td> <td>NG</td> <td>not specified</td> </tr> <tr> <td> </td> <td>B</td> <td>built-up or heavily influenced soils</td> </tr> <tr> <td> </td> <td>D</td> <td>dry soils</td> </tr> <tr> <td> </td> <td>W</td> <td>open water</td> </tr> </tbody> </table> <p>The NICHE soil map is derived from the digital soil map of Flanders (Digital version of the Soil map of Flanders, published on 20/06/2017, Database of the Subsoil in Flanders/Databank Ondergrond Vlaanderen). The steps to convert a unit of the soil map of Flanders into a NICHE soil type are described in detail in the NICHE report (Callebaut et al. 2007: chapter 3 pp 32-45 and appendix 3.3).</p> <p>If field data is available, it is possible to deviate from this (NICHE) soil map: chapter 3.4 of the NICHE report (Callebaut et al. 2007) explains how a NICHE soil type can be assigned to an observed soil profile based on the texture, the thickness and the succession of the different horizons.</p> <p><strong>Format</strong></p> <p>Vectorial geographic information provided as:</p> <ul> <li>shapefile (.shp, .dbf, .shx) with a description of the projection (.prj), spatial indexes (.sbn, .sbx), geospatial metadata in XML format (.shp.xml) and styles (ArcView Layer Format .lyr, Styled Layer Descriptor Format .sld)</li> <li>geopackage (.gpkg) with styles (Styled Layer Descriptor Format .sld)</li> </ul> <p>Geographical reference system: Belge 1972 / Belgian Lambert 72 (<a href="https://epsg.io/31370">EPSG code 31370</a>)</p> <p>Elevation Reference System: Second General Leveling (<a href="https://www.ngi.be/website/tweede-algemene-waterpassing/">Tweede Algemene Waterpassing - TAW</a>)</p> <p><strong>Attributes</strong></p> <ul> <li>fid - Id NICHE soil map (geopackage)</li> <li>gid - Id digital soil map 2017</li> <li>Bodemtype - Soil map units according to the Belgian soil classification system (digital soil map 2017)</li> <li>Bodemser_c - Core soil series: soil series consisting of 3 letters that stand for texture, drainage and profile (digital soil map 2017)</li> <li>Bodemserie - Core soil series: description for texture, drainage and profile (digital soil map 2017)</li> <li>Unitype - Soil map units with sea polders converted to the soil classification of the rest of Flanders (digital soil map 2017)</li> <li>Grove_leg - Generalized legend of the soil map (digital soil map 2017)</li> <li>Substr_V_c - Substrates whose lithological nature differs from that of the superficial layer (lithological discontinuity) (digital soil map 2017)</li> <li>Textuur_c - Soil type, nature of the parent material (digital soil map 2017)</li> <li>Drainage_c - Natural drainage class, natural drainage (digital soil map 2017)</li> <li>Profontw_c - Profile development (digital soil map 2017)</li> <li>Fase_c - Secondary soil features (digital soil map 2017)</li> <li>Varimoma_c - Variant of the parent material (digital soil map 2017)</li> <li>Variprof_c - Variant of the profile development (digital soil map 2017)</li> <li>Streek - Agricultural region (digital soil map 2017)</li> <li>NICHE_let - Letter code of the NICHE soil type</li> <li>NICHE_cijf - Numerical code of the NICHE soil type</li> </ul> <p><strong>Changes since previous version</strong></p> <p>Since previous version (1.1):</p> <ul> <li>NICHE soil map no longer based on version 2001 of the soil map, but on version 20/06/2017, including an update with some military domains (Beverlo, Kleine Brogel, Brasschaat) and with a uni-soil type for the classification of the sea polders, as well as the correction of various errors.</li> <li>names of the fields changed</li> <li>numerical code of the NICHE soil types changed (2-15 instead of 20 000-150 000)</li> <li>a few improvements to the translation between soil types and NICHE soil types: <ul> <li>sV, sV(o): ZV (instead of V in v1.1)</li> <li>uvPep: LV (instead of L1 in v1.1)</li> <li>GDa3x: LV (instead of L1 in v1.1)</li> <li>v-Zdpb(z): Z1 (instead of ZV in v1.1)</li> </ul> </li> </ul> <p><strong>Disclaimer</strong></p> <p>This map provides the best available information but is a simplification of the reality on the field. The real situation on the field prevails at all times over the NICHE soil map for all policy and legal applications.</p>
Stomach contents (1977-1981) and stable isotopes (1994) from the Everglades, Florida, USA from the publication "Fishes in a seasonally pulsed wetland show spatiotemporal shifts in diet and trophic niche but not shifts in trophic position"
Stomach contents of fishes (1977-1981) and stable isotopes of fishes, invertebrates, and basal resources (1994) were collected from spikerush marsh, sawgrass ridge, and alligator pond habitats in Shark River Slough, Everglades National Park, Florida, USA. These data were used to quantify diet, trophic niche area, trophic position, basal resource use and how these metrics vary among size classes, seasons, and habitats. Data collection is complete. These data support Flood et al. (2023). Associated R code will be made available through Peter Flood's GitHub: https://github.com/pjflood/historic_everglades_aquatic_food_web. References: Flood, Peter J., William F. Loftus, and Joel C. Trexler. "Fishes in a seasonally pulsed wetland show spatiotemporal shifts in diet and trophic niche but not shifts in trophic position." Food Webs 34 (2023): e00265. https://doi.org/10.1016/j.fooweb.2022.e00265
From Boston to Eden - or how to get systems that are really autonomous and sufficiently intelligent to survive in their niche
<p><a href="https://www.researchgate.net/project/Theoretical-artificial-intelligence/update/5e5f931a3843b0499fec8f6f?_iepl%5BviewId%5D=FMNsczWoobvAHwiOMdftISgB&_iepl%5Bcontexts%5D%5B0%5D=projectUpdatesLog&_iepl%5BinteractionType%5D=projectUpdateDetailClickThrough">From Boston to Eden - or how to get systems that are really autonomous and sufficiently intelligent to survive in their niche</a></p> <p>[lecture for the Dept. of AI, University of Groningen, Tuesday, March 3rd, 2020]</p> <p>As impressive as the robots of the Boston Dynamics company are (no AI involved) and as impressive the many results of deep learning are (no AI involved, either), the goal of creating autonomous, intelligent machines is as far away as it ever was. <br> In this presentation, I will give a brief overview of several deep-learning projects in our group. As a next step I will try to indicate <br> what may be missing, as regards 'real' AI. We may need a closer look at biological systems, i.e., the brain of animals. There exists a wide gap between the control systems at the low level of reflexive movement and the equilibria that need to be maintained ('Boston') versus the higher levels of processing, up to the levels of cognition and reasoning, which are very much upstairs ('Eden'). The missing middleware layer should not be underestimated: It contains the brain stem, up to the thalamus in animals and humans. <br> It corresponds to the 300-million year period before the 200 million years period where the neocortex was present. <br> What is this middleware doing? The conclusion may be that there is no autonomy without self protection, possible due to the presence of a separate and specialized valuation network that determines probability times utility (p*U), similar to what brain stem, midbrain and amygdala are doing in animals.</p>
The ecological niche and distribution of Neanderthals during the Last Interglacial [supplementary materials]]
<p>Supplementary material of the paper: Benito, B.M., Svenning, J.‐C., Kellberg‐Nielsen, T., Riede, F., Gil‐Romera, G., Mailund, T., Kjaergaard, P.C. and Sandel, B.S. (2017), The ecological niche and distribution of Neanderthals during the Last Interglacial. J. Biogeogr., 44: 51-61. <a href="https://doi.org/10.1111/jbi.12845">https://doi.org/10.1111/jbi.12845</a></p> <p>Files:</p> <p>- average_habitat_suitability.tif: geotif file describing the average habitat suitability (computed from a large ensemble of small models) for Homo neanderthalensis during the last interglacial.</p> <p>- standard deviation.tif: geotif with the standard deviation of the ensemble of small models mentioned above. Large values indicate a low consensus among models, while small values indicate a high consensus among models.</p> <p>- suitability_and_deviation.pdf: plot of the two previous maps blended through "whitening". Colors indicate habitat suitability, while "whitening" indicates higher standard deviation (lack of consensus).</p> <p>- neanderthal_eemian_sites_Appendix_I.xlsx: input dataset used to fit the models.</p>
Udayagiri, Madhya Pradesh. Cave 7, subsidiary niches.
<p>Udayagiri, Madhya Pradesh. Cave 7, subsidiary niches, located to the immediate east of the cave, with damaged figures of Durgā and Gaṇeśa, probably 5th century. As documented in 11/2012.</p>
Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia
<p>This repository contains data described in this study: Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia. <em>Ennis S et. al., iScience, 2023. DOI: </em><a href="https://doi.org/10.1016/j.isci.2023.106943">10.1016/j.isci.2023.106943</a><em>.</em></p> <p> </p> <p><strong>Contents:</strong></p> <ul> <li>bone_marrow.h5ad - AnnData file with the integrated dataset</li> <li>ref_model_final.tar.gz - A zipped folder containing the scVI model for the integrated dataset</li> <li>Supplemental_material.tar.gz - A zipped folder containing the supplemental figures and tables from the publication</li> </ul>
DATASET: TESTING NICHE EQUIVALENCE IN AMPHIDROMOUS FISH POPULATIONS
<p>Presence records of<em> Galaxias maculatus</em> were collected from 12 locations across five river basins in central-southern Chile during March, May, August, and November of 2019 as part of a study on fish sampling and processing (Ramírez-Álvarez et al. 2022. doi.org/10.1038/s41598-022-06936-8)</p> <p>Isotopic niches defined using a standard ellipse area (SEAc) in isotopic space, represented by a 2D ellipsoidal space (δ13C - δ15N) (see Supporting Information: Standard ellipse area functions - Ramírez-Álvarez et al. 2024. doi:10.1007/s10750-024-05738-5) - Empirical Bayesian Kriging</p> <p>Database of varibles used for niche modelling: isotopic niche and seven abiotic variables selected from 23 predictor variables: (1) 19 climate variables representing 1950–2000 climate averages from WorldClim (http://www.worldclim.org/). (2) Four spatially continuous topographic and hydrological variables from the EarthEnv Project adjusted to the HydroSHEDS river network (http://www.earthenv.org/) (Domisch et al. 2015). Selection of variables was accomplished by analysis of covariance and multicollinearity, using the ENMeval R package (Muscarella et al. 2014): (1) principal component analysis (PCA) to explore relationships among all predictors, evaluating the composition of components (component variables) that accounted for ≥65% of variance explained, (2) pairwise comparisons to detect pairs of variables with strong correlations (Pearson correlation coefficients <0.8), groups with a correlation of less than 0.8 were considered independent, and (3) variance inflation factor (VIF) <10, to reduce the effect of collinearity between predictors (Listed below). A VIF greater than 10 indicates collinearity problems in the model. The vifcor and vifstep functions were employed by calculating two different strategies to exclude highly collinear variables using a stepwise procedure (Muscarella et al. 2014).</p> <div><em>Variable description and ecological question associated, selected variables are marked in bold.</em></div> <div><em>Series 1: Temperature, temperature variations and interaction with precipitation.</em></div> <div>bio1: Annual Mean Temperature; Is the temperature usually suitable? </div> <div><strong>bio2: Mean Diurnal Range; Are the days too warm or too cold?</strong></div> <div>bio3: Isothermality; Do temperatures fluctuate greatly over the course of a month? </div> <div>bio4: Temperature Seasonality (standard deviation); Do temperatures fluctuate greatly over the course of a year?</div> <div>bio5: Min Temperature of Coldest Month; Is the maximum temperature too high?</div> <div>bio6: Min Temperature of Coldest Month; Is the temperature constantly too high?</div> <div><strong>bio7: Temperature Annual Range; Do temperatures fluctuate greatly over the course of a year?</strong></div> <div><strong>bio8: Mean Temperature of Wettest Quarter; Is it too cold or too warm during the rainy season?</strong></div> <div>bio9: Mean Temperature of Driest Quarter; Is it too cold or too warm during the dry season?</div> <div>bio10: Mean Temperature of Warmest Quarter; Are the warmer months too cold?</div> <div><strong>bio11: Mean Temperature of Coldest Quarter; Are the colder months too warm?</strong></div> <div><em>Series 2: Precipitation and Rainfall Patterns</em></div> <div>bio12: Annual Precipitation; Does it rain enough in a year?</div> <div>bio13: Precipitation of Wettest Month; Does it rain a lot during the wettest month?</div> <div><strong>bio14: Precipitation of Driest Month; Does it rain poorly during the driest month?</strong></div> <div>bio15: Precipitation Seasonality (Coefficient of Variation); Would rainfall fluctuate much between seasons?</div> <div>bio16: Precipitation of Wettest Quarter; Does it rain a lot in the rainy season?</div> <div>bio17: Precipitation of Driest Quarter; Is rainfall low during dry seasons?</div> <div>bio18: Precipitation of Warmest Quarter; Does it rain enough during the warmer months?</div> <div>bio19: Precipitation of Coldest Quarter; Does it rain enough during the colder months?</div> <div><em>Series 3: Topology and hydrology</em></div> <div><strong>dem: Average elevation; Does altitude play a role as a topological factor?</strong></div> <div><strong>slope_av: Average slope; Is the slope suitable for the accumulation of small ponds?</strong></div> <div>flow_ac_ac: Accumulation flow; Does enough water accumulate or does it drain too quickly?</div> <div>flow_ac_le: Accumulation flow direction; Does the flow direction support the formation of small ponds?</div> <p>Domisch, S., G. Amatulli & W. Jetz, 2015. Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Scientific Data 2(1):150073 doi:10.1038/sdata.2015.73.</p> <p>Muscarella, R., P. J. Galante, M. Soley‐Guardia, R. A. Boria, J. M. Kass, M. Uriarte & R. P. Anderson, 2014. ENM eval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in ecology and evolution 5(11):1198-1205</p>
Data inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al.
<p>This data packet provides inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al., currently in the submission process. This repository will eventually be updated to link to the published manuscript.</p> <p> </p> <p>===============================================================================<br>===============================================================================<br>Overview<br>===============================================================================<br>===============================================================================</p> <p>Data and model products associated with the manuscript "Mammal niches are not <br>conserved over continental scales" by Goldstein et al. </p> <p>Files are organized into two subdirectories. The first, "model_inputs/", <br>contains 8 data files intended to be used as part of the reproducible code <br>repository at https://github.com/dochvam/Mammal_SVCs_ISDM_reproducible. <br>The second subdirectory, "model_outputs/", contains modeled products giving<br>estimated spatially varying niche relationships and predictions of relative<br>abundance.</p> <p>Below, we describe the contents of each file type. See the main manuscript <br>for full methodology, data sources, and discussions of spatial scales.</p> <p>NOTE: Version 1 of this dataset contained some errors that have been corrected<br>in Version 2. Version 2 was used as the input dataset for the analyses in the<br>associated manuscript. Version 1 should not be used.</p> <p>===============================================================================<br>===============================================================================<br>Subdirectory 1: "model_inputs/"<br>===============================================================================<br>===============================================================================</p> <p>Two versions of each of four files are provided, corresponding to analyses <br>that do or do not consider ancient genetic lineages as potential sources of <br>spatial nonstationarity in mammal niches. Each file type is formatted the same,<br>and the versions are differentiated by either the suffix "nolineage" or <br>"lineage" in the filename. </p> <p>===============================================================================<br>File 1: gridcell_covars_lineage.csv and gridcell_covars_nolineage.csv<br>===============================================================================<br>These files are .csvs giving spatial covariate data for each scale 2 cell<br>in North America, summarized to 5000 m. All percentage values are given in <br>10ths of a percent (scale of 0-1000). The following columns are provided:</p> <p>- grid_cell: Scale 2 cell ID<br>- Arable: pct arable land (Jung et al. 2020)<br>- EVI_mean: mean enhanced vegetation index (Didan 2021)<br>- EVI_Q95: 95th quantile of EVI (Didan 2021)<br>- Forest: Pct forest cover (Jung et al. 2020)<br>- Grassland: pct grassland (Jung et al. 2020)<br>- Pastureland: pct pastureland (Jung et al. 2020)<br>- Pop_den: Human population density, from Gridded Population of the World (CIESIN 2018)<br>- Precipitation: avg annual precip. (Vega et al. 2017)<br>- Shrubland: pct shrubland (Jung et al. 2020)<br>- Temp_max: Average maximum daily temperature (Vega et al. 2017)<br>- Terrain_roughness (Amatulli et al. 2018)<br>- Wetlands: pct wetlands (Jung et al. 2020)<br>- is_land: Whether or not the cell is on land vs. ocean, used for filtering<br>- Agriculture: Pct. agricultural land (Jung et al. 2020)<br>- Pop_den_sqrt: Square root of human population density (CIESIN 2018)<br>- EVI_variability: Distance btw the 95% inner quantiles of EVI (Didan 2021)</p> <p> </p> <p>===============================================================================<br>File 2: inat_cts_lineage.csv and inat_cts_nolineage.csv<br>===============================================================================</p> <p>These files give summaries of iNaturalist sampling effort and detections<br>for target species. The following columns are provided:</p> <p>- grid_cell: Scale 3 cell ID<br>- n: Total iNaturalist effort in the cell (number of obs. of all mammals)<br>- The remaining columns are named for species. Each column gives the count<br> of observations of the species in the cell.</p> <p>===============================================================================<br>File 3: ct_datlist_lineage.RDS and ct_datlist_nolineage.RDS<br>===============================================================================</p> <p>The ct_datlist files contain R objects that are lists of lists. These objects ultimately<br>contain all of the camera detection histories and camera-level covariate data used<br>in modeling. We use the nice data type "unmarkedFrameOccu" from the unmarked R package<br>to organize these detection data.</p> <p>Each outer list is of length equal to the number of species. The ith element of each<br>list contains the following named slots:</p> <p>- species: a string giving the name of the ith species<br>- umf: an unmarkedFrameOccu object. This object has three important slots:<br> - y: a (# deployments) x (max # replicates) matrix giving 1s, 0s, or NAs indicating<br> whether the target species was observed in that 10-day window;<br> - siteCovs: a (# deployments) x 2 data frame with the following columns:<br> - site_ID: A unique ID of the exact location, shared by deployments with the same<br> coordinates<br> - subproject_ID: A unique ID indicating which camera array is associated <br> with this deployment<br> - obsCovs: a (# deployments * max # replicates) x 6 data frame with the following columns:<br> - year: the year of survey, relative to 2020 (zero-year is 2020)<br> - yday_scaled: the (scaled) Julian date of the beginning of the window<br> - yday_scaled_sq: yday_scaled^2, for use in estimating a quadratic effect<br> - log_roaddist_scaled: Scaled distance to nearest road (Meijer et al. 2018)<br> - Canopy_height_scaled: Scaled canopy height (Potapov et al. 2021)<br> - obs_len_scaled: Scaled duration of window, to account for some windows <br> being cut off at < 10 days<br>- coords: a data frame. Originally, this file gave the exact position for each camera,<br> but these exact locations have been scrubbed for privacy. See the original sources<br> cited in the manuscript for full details. This data frame contains the following column:<br> - scale2_grid_ID: the ID of the Scale-2 5000 m grid cell containing the camera</p> <p>===============================================================================<br>File 4: grid_translator_wspecs_nolineage.csv and grid_translator_wspecs_lineage.csv<br>===============================================================================</p> <p>These files are used for bookkeeping to track the relationships between the <br>three spatial scales in this study. Each row corresponds to a single "scale 2"<br>cell, giving the ID of the corresponding S3 and S4 grid and also an ID for each<br>species indicating whether and where it is in the species' range. </p> <p>Note that the scale names in the code don't match the manuscript. In the code,<br>"scale 1" is the level of an individual camera, "scale 2" is the 5 km intensity<br>grid, "scale 3" is the 50 km iNaturalist grid, and "scale 4" is the 100 km<br>SVC grid.</p> <p>The following columns are provided:<br>- scale2_grid_ID: unique ID for each cell in the 5 km intensity grid<br>- scale3_grid_ID: unique ID for each cell in the 50 km iNaturalist aggregation<br>- scale4_grid_ID: unique ID for each cell in the 100 km SVC grid<br>- GRID_ID_[species]: for each species, a column is provided on the S4 scale<br> counting each cell in the species' modeled range. NAs<br> indicate that the S2 cell defined in the row is not<br> included in the species' modeled range.</p> <p>===============================================================================<br>===============================================================================<br>Subdirectory 2: "model_outputs/"<br>===============================================================================<br>===============================================================================</p> <p>===============================================================================<br>File 1: svc_estimates.csv<br>===============================================================================</p> <p>This file gives an estimate of the effect of each covariate on each species'<br>intensity, and the uncertainty in that estimate, for each species/covariate<br>pair. Results correspond to lineage models for species with phylogeographies<br>and non-lineage species otherwise. Each row represents the effect of one <br>covariate on one species' relative intensity process within one 100 km cell g. <br>Note that many estimates of beta_g are uncertain even for strong spatial <br>effects---the model is often confident that a spatial process is supported <br>while estimates of the realized process are uncertain.</p> <p>The following columns are provided:<br>- x: the x-coordinate of the 100 km cell<br>- y: the y-coordinate of the 100 km cell<br>- species<br>- parname: the name of the covariate<br>- mean: the mean of the posterior samples of beta_g<br>- 2.5%: the 2.5th quantile of the posterior samples of beta_g<br>- 50%: the 50th quantile of the posterior samples of beta_g<br>- 97.5%: the 97.5th quantile of the posterior samples of beta_g</p> <p>The following spatial projection is used to define X/Y coordinates:<br>"+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83"</p> <p>===============================================================================<br>File 2: predicted_intensity.tif<br>===============================================================================</p> <p>This file contains a raster "brick" giving the predicted intensity surface <br>and uncertainty in this surface for each species. All predictions are generated<br>using models that do *not* account for lineage information---this means that <br>predictions for species with lineages are not from the models reported in the<br>main manuscript. The reason for this is that we found that lineages were <br>overall unsupported, so better predictions can be arrived at by excluding this<br>source of uncertainty in the underlying intensity process.</p> <p>The raster brick has 66 layers. Each layer provides either the mean predicted<br>log intensity in each grid cell across the species range or else provides<br>the standard error of that predicted log intensity. Layer names indicate output<br>type and species associated with each layer.</p> <p><br>===============================================================================<br>===============================================================================<br>References<br>===============================================================================<br>===============================================================================</p> <p>Camera data are obtained from the following sources, which can be consulted to<br>obtain the original raw camera data</p> <p>- Cove, Michael V., et al. "SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States." (2021): e03353.<br>- Kays, Roland, et al. "SNAPSHOT USA 2020: A second coordinated national camera trap survey of the United States during the COVID‐19 pandemic." (2022): e3775.<br>- Shamon, H., et al. “SNAPSHOT USA 2021: A third coordinated national camera trap survey of the United States.” Ecology, 105.6 (2024): e4318.<br>- Rooney, B., et al. “SNAPSHOT USA 2019–2023: The first five years of data from a coordinated camera trap survey of the United States.” In Press (2024).<br>- Kays, Roland, et al. "Does hunting or hiking affect wildlife communities in protected areas?." Journal of Applied Ecology 54.1 (2017): 242-252.<br>- Roberts, R. California Department of Fish and Wildlife, Bobcat Program Initiative. wildlifeinsights.org (2023).<br>- Lasky, Monica, et al. "CAROLINA CRITTERS: a collection of camera trap data from wildlife surveys across North Carolina." Ecology 102.7 (2021): e03372.<br>- Forrester, T. (2000). Urban to Wild Project. http://n2t.net/ark:/63614/w12004302. Accessed via wildlifeinsights.org on 2024-08-29.<br>- McMurry, S. et al. In review (2024).<br>- Forrester, T. (2011) Okaloosa S.C.I.E.N.C.E. Project. http://n2t.net/ark:/63614/w12004287. <br>- Myers, J. (2014) Tyson Research Center ForestGEO Project. http://n2t.net/ark:/63614/w12004295.<br>- McMurry, S., and Kays, R.(2023). Calloway Forest Preserve. http://n2t.net/ark:/63614/w12006449. Accessed via Wildlife Insights on 2024-08-29.<br>- McMurry, S., Parsons, A., Lasky, M., Luongo, K., Clark, J., McShea, W., Scher, L., Kays, R., Spurlin, J., Martin, G., Frech, G., Barajas-Salazar, K., Snider, M. (2022). Last updated October 2023. Calloway Forest Preserve. http://n2t.net/ark:/63614/w12004251. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Kays, R.. (2008). Last updated March 2024. Albany Area Camera Trapping Project. http://n2t.net/ark:/63614/w12003860. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Kays, R., Snider, M., McMurry, S., Alyetama, M. (2024). Last updated April 2024. Pilot Mountain Density 2024. http://n2t.net/ark:/63614/w12007160. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Malleshappa, V., Smithsonian, E., Kays, R., Schuttler, S. (2015). Last updated December 2022. Museums Connect Mexico. http://n2t.net/ark:/63614/w12004298. Accessed via wildlifeinsights.org on 2024-08-29.</p> <p>Covariate data are obtained from the following sources:<br>- Vega, G. C., Pertierra, L. R. & Olalla-Tárraga, M. Á. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data 4, 170078 (2017).<br>- Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7, 256 (2020).<br>- Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).<br>- Center For International Earth Science Information Network-CIESIN-Columbia University. Documentation for the Gridded Population of the World, Version 4 (GPWv4), Revision 11 Data Sets. (2018) doi:10.7927/H45Q4T5F.<br>- Didan, K. MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes Distributed Active Archive Center https://doi.org/10.5067/MODIS/MOD13A2.061 (2021).<br>- Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018).<br>- Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).<br>- Jensen, A. J. et al. Geographic barriers but not life history traits shape the phylogeography of North American mammals. Glob. Ecol. Biogeogr. e13875 (2024).</p> <p>iNaturalist data are obtained from inaturalist.org via the data exporter (see manuscript for details).</p>
R code for archaeological examples of calculating isotopic niche space and overlap using the rKIN package
<p>This R code was written to apply the tools of the rKIN package to calculate isotopic niche space and overlap for the three archaeological case studies for the manuscript Investigating Isotopic Niche Space: Using rKIN for Stable Isotope Studies in Archaeology published in the Journal of Archaeological Method and Theory. Raw data for the case studies are available in the supplemental Excel file.</p>
Data and script: Community size can affect the signals of ecological drift and niche selection on biodiversity
<p>Updated version of the code. Data files are the same. This is the final version of the code, associated with a manuscript published in Ecology (doi: 10.1002/ecy.3014). A preprint is also available: https://www.biorxiv.org/content/10.1101/515098v1.abstract</p> <p>This is a unique dataset on insect communities sampled identically in a total of 200 streams in climatically highly different regions (100 in Brazil and 100 in Finland). The sampling design included 5 streams (communities) per watershed and provided us replicates of metacommunities (watersheds). Data also include information on in-stream variables (such as current velocity (m/s), depth (cm), stream width (cm), % of sand (0.25-2 mm), gravel (2-16 mm), pebble (16-64 mm), cobble (64-256 mm), and boulder (256-1024 mm), % of canopy cover by riparian vegetation, pH, conductivity, total nitrogen, and total phosphorus) and catchment level variables (such as average slope, % of native forest cover, pasture, agriculture, planted forests, urban areas, mining, water bodies, bare soil, secondary forest cover, and mixed land uses).</p> <p>In addition to the dataset, here we also provide and R code used to investigate the relationship between beta diversity and community size. This code calculates 4 types of beta-diversity metric for each of 100 watersheds (5 streams) in Brazil and Finland. Beta diversity: Sorensen and Bray-Curtis dissimilarity between all pairs. Beta deviation from null models: Raup-Crick (vegan version) and Bray-Curtis beta-deviation (based on the scripts by Chris Catano and Jonathan Myers). These beta diversity metrics are modelled against community size, environmental heterogeneity and spatial extent.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.