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11 results for “vegetation class”
BgMA-ESy: Expert system for automatic classification of vegetation plots of subalpine tall-herb vegetation (class Mulgedio-Aconitetea) from Bulgaria
<p>*****</p> <p>BgMA-ESy is an expert system that classifies vegetation plots of the class <em>Mulgedio-Aconitetea</em> (<a href="https://doi.org/10.1111/avsc.12257">Mucina et al. 2016</a>) occurring in Bulgaria. The expert system can be run using the JUICE program (<a href="https://doi.org/10.1111/j.1654-1103.2002.tb02069.x">Tichý 2002</a>; <a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>).</p> <p>The aggregation of vascular plants included within the BgMA-ESy is adopted from EUNIS-ESy (<a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. 2020</a>; <a href="https://doi.org/10.5281/zenodo.4812736">https://doi.org/10.5281/zenodo.4812736</a>), and in a few cases, it is adjusted.</p> <p>*****</p> <p><strong>Specifications</strong></p> <p>The analyzed data (vegetation plots) cannot: </p> <ul> <li>include scrub vegetation (cover of tall shrub species > 8%; e.g., <em>Pinus mugo</em>, <em>Salix </em>spp.).</li> <li>contain tree species with cover > 1% (e.g., <em>Fagus sylvatica</em>, <em>Picea abies</em>).</li> <li>contain <em>Pteridium aquilinum </em>as a dominant species.</li> </ul> <p>The expert system was trained on vegetation plots with 5–100 m<sup>2</sup> area that occur above 1000 m a. s. l.</p> <p>* Exceptions from EUNIS-ESy aggregation:</p> <p>Heracleum sphondylium agg. does not include H. sphondylium subsp. verticillatum.</p> <p> </p> <p>*****</p> <p>When using this work, please cite:</p> <p>Szokala D., Kočí M. & Vassilev K. (2024): Subalpine tall-herb vegetation in Bulgaria: diversity and ecology. – Plant Biosystems 158: 490–510. <a href="https://doi.org/10.1080/11263504.2024.2327865">https://doi.org/10.1080/11263504.2024.2327865</a>.</p> <p>*****</p>
Figure 2 in Could Fidicina mannifera (Hemiptera: Cicadoidea: Fidicinini) promote a resource pulse in two Brazilian Cerrado vegetation classes?
Figure 2. Profile of lipids (mg g–1) in adult males and females of F. mannifera along emerging period, collected on the Agroecological Technology Center for Small Farmers (AGROTEC), Diorama, Goiás. Open diamonds demonstrate the amount of lipids present in females.
Figure 3 in Could Fidicina mannifera (Hemiptera: Cicadoidea: Fidicinini) promote a resource pulse in two Brazilian Cerrado vegetation classes?
Figure 3. Precipitation (mm) for the months sampled in 2013 in the areas of cerrado woodland (closed symbol) and gallery forest (open symbol) in the Agroecological Technology Center for Small Farmers (AGROTEC), Diorama, GO, Brazil.
Figure 1 in Could Fidicina mannifera (Hemiptera: Cicadoidea: Fidicinini) promote a resource pulse in two Brazilian Cerrado vegetation classes?
Figure 1. Proteins (mg g–1) in adult males and females of F. mannifera along the emerging period, collected on the Agroecological Technology Center for Small Farmers (AGROTEC), Diorama, Goiás. Open diamonds show the amount of protein present in females.
Eight Mile Lake Research Watershed, Thaw Gradient Extended sites: Vegetation data from land cover classes from an upland watershed undergoing permafrost thaw.
This data set contains meausrements ofpercent of ground cover (vegetation, water, bare soil) from sites throughout the wathershed within certain land cover types identified by an unsupervised landcover classification. The purpose was to see how land cover classes differed in soil properties and if we could detect diffences in classes undergoing permafrost thaw that results in thermokarst.
Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution
<p>Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution based on a compilation of data sets (Biome6000k, Geo-Wiki, LandPKS, mangroves soil database, and from various literature sources; total of about 65,000 training points). We used a comparable thematic legend used to produce the Dynamic Land Cover 100m: Version 2. Copernicus Global Land Operations product (Buchhorn et al. 2019), which is based on the UN FAO Land Cover Classification System (LCCS), so that users can compare actual (https://lcviewer.vito.be/) vs potential (this data set) land cover. Two classes not available in the LCCS were added: "subtropical/tropical mangrove vegetation" and "sub-polar or polar barren-lichen-moss, grassland". The map was created using relief and climate variables representing conditions the climate for the last 20+ years and predicted at 250 m globally using an Ensemble Machine Learning approach as implemented in the mlr package for R. Processing steps are described in detail <a href="https://github.com/Envirometrix/PNVmaps"><strong>here</strong></a>. Maps with "_sd_" contain estimated model errors per class. Antarctica is not included.</p> <p>Produced for the needs of the <a href="https://naturemap.earth/"><strong>NatureMap</strong></a> which is project run by the <strong>International Institute for Applied Systems Analysis</strong> (IIASA), the <strong>International Institute for Sustainability</strong> (IIS), the <strong>UN Environment Programme World Conservation Monitoring Centre</strong> (UNEP-WCMC), and the <strong>UN Sustainable Development Solutions Network</strong> (SDSN). NatureMap is funded by Norway’s International Climate Initiative (NICFI).</p> <p>Maps will also be made available via: <a href="https://OpenLandMap.org">OpenLandMap.org</a>. These are initial predictions for testing purposes only. A publication explaining all processing steps is pending.</p> <p>If you discover a bug, artifact or inconsistency in the predictions, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://github.com/Envirometrix/PNVmaps/issues">https://github.com/Envirometrix/PNVmaps/issues</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>pnv = theme: potential natural vegetation,</li> <li>potential.landcover = variable: potential land cover type (e.g. "open forest, evergreen needleleaf"),</li> <li>probav.lc100 = classification model: ProbaV-based land cover mapping legend (LCCS),</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: period 2000-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Data from: Vegetable phylloplane microbiomes harbour class 1 integrons in novel bacterial hosts and drive the spread of chlorite resistance
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Mapping the páramo land cover in the Northern Andes: Figure S4 Expert land-cover classification of the Andean páramo and distribution according to three groups: natural vegetation, natural abiotic and anthropogenic, and 12 classes
<p>The Andean páramo is a biodiverse and vulnerable tropical high-mountain region, whose spatio-ecological patterns remain understudied. The lack of general characterization of its overall extent, land-cover classes, and treeline spatial features hinders our capacity to understand its responses to human impacts and predict future land-system changes. To address this knowledge gap, we classified the land-cover of the páramo in the northern Andes. Moreover, we estimated 1) the páramo's total extent and distribution among countries, 2) the relative extent of 12 of its main land-cover classes, categorized into <i>natural vegetation, natural abiotic</i> and <i>anthropogenic </i>groups, and 3) the preliminary position and anthropogenic influence of its bordering treeline. Relying on Landsat 8 imagery, we performed hybrid manual-automated classifications using the Maximum Likelihood and Random Forest algorithms. The two resulting <i>final classifications</i> were manually checked for errors compared to Google Earth and VegPáramo data, and used to produce the <i>expert classification</i>. Finally, we delimited the treeline based on regional forest connectivity, and applied it to the expert classification to evaluate páramo elevations, surface areas and land-cover classes above the treeline. The páramo extent was estimated at 24,301 km<sup>2</sup>, distributed between Ecuador (47%), Colombia (43%), Venezuela (8%) and Peru (2%). Natural vegetation, especially shrublands, rosette plant communities and grasslands were dominant (altogether, 65%), whereas classes reflecting intense land-use covered 12% overall. The average treeline reached 3546 m and was bordered uphill at 16% with anthropogenic land-cover classes. The páramo's extent is smaller than previously suggested. It remains a (semi-) natural region, yet crop and pasture expansion towards high elevations is a critical concern for long-term sustainability. Future research can build on our findings to predict land-system changes and assess priority areas for conservation. We recommend for future research to focus on remnant forest patches and treeline connectivity in priority.</p>
Table ¹: Comparison of analysis of variance results for skull (occlusal view) and mandible (side view) shape in Rhipidomys mastacalis from three vegetation classes in Brazil. Object asymmetry and correspondence methods were employed to assess asymmetry for skulls and mandibles, respectively. in Morphological symmetry of Rhipidomys mastacalis (Mammalia, Rodentia, Cricetidae) in fragmented habitats of the Atlantic Forest in Northeastern Brazil: a study on the influence of the environment on an endemic species
<p><b>Table ¹:</b> Comparison of analysis of variance results for skull (occlusal view) and mandible (side view) shape in <i>Rhipidomys mastacalis</i> from three vegetation classes in Brazil.Object asymmetry and correspondence methods were employed to assess asymmetry for skulls and mandibles,respectively.</p><table><tbody><tr><th><b>Shape procrustes ANOVA</b></th></tr></tbody><tbody><tr><th><b>Effect Sum of squares</b></th><td><b>Mean squares</b></td><td><b>Degrees of freedom</b></td><td><i>F statistic</i></td><td><i>p -Value</i></td><td><b>Pillai tr.</b></td><td><i>p -Value</i></td></tr><tr><th><b>Skulls</b></th></tr><tr><th><b>Forested vegetation</b></th></tr><tr><th>Individual</th><td>0.19908517</td><td>0.0004253957</td><td>468</td><td>22.36</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Side</th><td>0.00366522</td><td>0.0002036232</td><td>18</td><td>10.70</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Individual × side</th><td>0.00890443</td><td>0.0000190266</td><td>468</td><td>2.24</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Error 1</th><td>0.00825565</td><td>0.0000084935</td><td>972</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Occupancy mosaics in forested areas</b></th></tr><tr><th>Individual</th><td>0.37829478</td><td>0.0003965354</td><td>954</td><td>18.57</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Side</th><td>0.00547536</td><td>0.0003041869</td><td>18</td><td>14.25</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Individual × side</th><td>0.02037065</td><td>0.0000213529</td><td>954</td><td>1.89</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Error 1</th><td>0.02201359</td><td>0.0000113239</td><td>1944</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Cocoa plantations</b></th></tr><tr><th>Individual</th><td>0.0645902300</td><td>0.0001302222</td><td>496</td><td>5.18</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Side</th><td>0.0113531900</td><td>0.0007095741</td><td>16</td><td>28.23</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Individual × side</th><td>0.0124666800</td><td>0.0000251344</td><td>496</td><td>1.88</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Error 1</th><td>0.0136608800</td><td>0.0000133407</td><td>1024</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Mandibles</b></th></tr><tr><th><b>Forested vegetation</b></th></tr><tr><th>Individual</th><td>0.70443879</td><td>0.0012579264</td><td>560</td><td>8.10</td><td><0.0001</td><td>14.16</td><td><0.0001</td></tr><tr><th>Side</th><td>0.00549957</td><td>0.0002749783</td><td>20</td><td>1.77</td><td>0.0207</td><td>0.0207</td><td>0.0069</td></tr><tr><th>Individual × side</th><td>0.08696012</td><td>0.0001552859</td><td>560</td><td>2.46</td><td><0.0001</td><td>10.75</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.07312665</td><td>0.0000387718</td><td>1160</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Occupancy mosaics in forested areas</b></th></tr><tr><th>Individual</th><td>1.19843989</td><td>0.0011984399</td><td>1000</td><td>8.16</td><td><0.0001</td><td>14.70</td><td><0.0001</td></tr><tr><th>Side</th><td>0.01169771</td><td>0.0005848855</td><td>20</td><td>3.98</td><td><0.0001</td><td>0.74</td><td>0.0001</td></tr><tr><th>Individual × side</th><td>0.14685738</td><td>0.0001468574</td><td>1000</td><td>3.03</td><td><0.0001</td><td>11.21</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.09880745</td><td>0.0000484350</td><td>2040</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Cocoa plantations</b></th></tr><tr><th>Individual</th><td>0.3269927600</td><td>0.0004808717</td><td>680</td><td>4.52</td><td><0.0001</td><td>14.14</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0143644400</td><td>0.0007182221</td><td>20</td><td>6.75</td><td><0.0001</td><td>0.86</td><td>0.0017</td></tr><tr><th>Individual × side</th><td>0.0723474900</td><td>0.0001063934</td><td>680</td><td>2.39</td><td><0.0001</td><td>10.41</td><td>0.0017</td></tr><tr><th>Error 1</th><td>0.0622041800</td><td>0.0000444316</td><td>1400</td><td>–</td><td>–</td><td>–</td><td>–</td></tr></tbody></table>
Table ²: Comparison of the results of analysis of variance on the shape of scapulae (occlusal view) and pelvis (side view) in Rhipidomys mastacalis from three vegetation classes in Brazil. Correspondence asymmetry was the only method used for asymmetry analysis. in Morphological symmetry of Rhipidomys mastacalis (Mammalia, Rodentia, Cricetidae) in fragmented habitats of the Atlantic Forest in Northeastern Brazil: a study on the influence of the environment on an endemic species
<p><b>Table ²:</b> Comparison of the results of analysis of variance on the shape of scapulae (occlusal view) and pelvis (side view) in <i>Rhipidomys mastacalis</i> from three vegetation classes in Brazil. Correspondence asymmetry was the only method used for asymmetry analysis.</p><table><tbody><tr><th><b>Shape procrustes ANOVA</b></th></tr></tbody><tbody><tr><th><b>Effect Sum of squares</b></th><td><b>Mean squares</b></td><td><b>Degrees of freedom</b></td><td><i>F statistic</i></td><td><i>p -Value</i></td><td><b>Pillai tr.</b></td><td><i>p -Value</i></td></tr><tr><th><b>Scapulae</b></th></tr><tr><th><b>Forested vegetation</b></th></tr><tr><th>Individual</th><td>0.0941373400</td><td>0.0010459705</td><td>90</td><td>3</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Side</th><td>0.0100439600</td><td>0.0010043960</td><td>2.88</td><td>0.0037</td><td>0.0003</td><td>–</td><td>–</td></tr><tr><th>Individual × side</th><td>0.0314069500</td><td>0.0003489662</td><td>90</td><td>5.89</td><td><0.0001</td><td>4.91</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0118544100</td><td>0.0000592721</td><td>200</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Occupancy mosaics in forested areas</b></th></tr><tr><th>Individual</th><td>0.2064168200</td><td>0.0010320841</td><td>200</td><td>4.82</td><td><0.0001</td><td>7.15</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0262808000</td><td>0.0026280796</td><td>10</td><td>12.28</td><td><0.0001</td><td>0.86</td><td>0.0022</td></tr><tr><th>Individual × side</th><td>0.0428160400</td><td>0.0002140802</td><td>200</td><td>2.68</td><td><0.0001</td><td>4.98</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0335675700</td><td>0.0000799228</td><td>420</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Cocoa plantations</b></th></tr><tr><th>Individual</th><td>0.2508635400</td><td>0.0009291242</td><td>270</td><td>4.07</td><td><0.0001</td><td>7.11</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0256608100</td><td>0.0025660812</td><td>10</td><td>11.24</td><td><0.0001</td><td>0.87</td><td><0.0001</td></tr><tr><th>Individual × side</th><td>0.0616394000</td><td>0.0002282941</td><td>270</td><td>3.10</td><td><0.0001</td><td>5.72</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0412323300</td><td>0.0000736292</td><td>560</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Pelvis</b></th></tr><tr><th><b>Forested vegetation</b></th></tr><tr><th>Individual</th><td>0.0543411200</td><td>0.0004312787</td><td>126</td><td>4.63</td><td><0.0001</td><td></td><td></td></tr><tr><th>Side</th><td>0.0043155600</td><td>0.0003082544</td><td>14</td><td>3.31</td><td>0.0002</td><td></td><td></td></tr><tr><th>Individual × side</th><td>0.0117297800</td><td>0.0000930935</td><td>126</td><td>2.31</td><td><0.0001</td><td>6.07</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0112943700</td><td>0.000040337</td><td>280</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Occupancy mosaics in forested areas</b></th></tr><tr><th>Individual</th><td>0.1059661700</td><td>0.0003440460</td><td>308</td><td>4.42</td><td><0.0001</td><td>9.69</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0049395300</td><td>0.0003528236</td><td>14</td><td>4.53</td><td><0.0001</td><td>0.85</td><td>0.0311</td></tr><tr><th>Individual × side</th><td>0.0239852500</td><td>0.0000778742</td><td>308</td><td>2.00</td><td><0.0001</td><td>6.64</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0251368400</td><td>0.0000390324</td><td>644</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Cocoa plantations</b></th></tr><tr><th>Individual</th><td>0.1292837500</td><td>0.0003420205</td><td>378</td><td>5.68</td><td><0.0001</td><td>10.51</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0043550500</td><td>0.0003110747</td><td>14</td><td>5.17</td><td><0.0001</td><td>0.84</td><td>0.0016</td></tr><tr><th>Individual × side</th><td>0.0227608400</td><td>0.0000602139</td><td>378</td><td>2.24</td><td><0.0001</td><td>6.17</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0210413800</td><td>0.0000268385</td><td>714</td><td>–</td><td>–</td><td>–</td><td>–</td></tr></tbody></table>
Mapping the páramo land cover in the Northern Andes: Figure S4 Expert land-cover classification of the Andean páramo and distribution according to three groups: natural vegetation, natural abiotic and anthropogenic, and 12 classes
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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)
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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.