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162 results for “habitat mapping”
A global map of terrestrial habitat types
<p>We provide a global spatially explicit characterization of 47 (version 001) terrestrial habitat types, as defined in the International Union for Conservation of Nature (IUCN) habitat classification scheme, which is widely used in ecological analyses, including for assessing species’ Area of Habitat. We produced this novel habitat map by creating a global decision tree that intersects the best currently available global data on land cover, climate and land use. The maps broaden our understanding of habitats globally, assist in constructing area of habitat (AOH) refinements and are relevant for broad-scale ecological studies and future IUCN Red List assessments. We hope that these data and outlined framework will spur further development of biodiversity-relevant habitat maps at global scales. An interactive interface helping to navigate the map can be found at on the Naturemap website ( https://explorer.naturemap.earth/map).</p> <p>Provided is the code to recreate the map (to made available soon), the global composite image at native -100m Copernicus resolution for level 1 and level 2 and layers of aggregated fractional cover (unit: [0-1] * 1000) at 1km for level 1 and level 2.</p> <p>Starting with version 004 there changemasks for the years 2016, 2017, 2018 and 2019 are supplied. Changemasks for the composite masks show the changed grid cells and their new values with earlier years being nested in later years, e.g. using the changemask for 2019 includes all changes up to 2019. For the fractional cover estimates at ~1km resolution, new fractional cover changemasks are supplied as subtraction (before - after) between the previous and current year (unit range: [-1 to 1] * 1000).</p> <p>We highlight that only changes in land cover are considered since most of the ancillary layers (e.g. pasture, forest management, climate, etc...) are static and thus not all changes in habitats can be found. We therefore recommend end users to continue using the 2015 dataset unless specific habitat updates to habitat are needed.</p> <p><strong>Citation:</strong></p> <p>Please cite the published paper and state the used version of the habitat map</p> <p>Jung, M., Dahal, P.R., Butchart, S.H.M., Donald, P.F., De Lamo, X., Lesiv, M., Kapos, V., Rondinini, C., Visconti, P., (2020). A global map of terrestrial habitat types. Sci. Data 7, 256. <a href="https://doi.org/10.1038/s41597-020-00599-8">https://doi.org/10.1038/s41597-020-00599-8</a></p>
May to July 2018 regions of interest (ROIs) of tidal marsh and tidal forest plant species to be used as ground reference data in habitat mapping
We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground reference data for habitat mapping. Regions of interest (ROIs) for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were generated near ground control points (GCP) by digitizing vegetation areas in ArcGIS 10.4 based on field maps.These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.
Redistribution of the Natura 2000 habitat map of Flanders (version 2023)
<p>This is a <i>redistribution</i> of a <i>subdataset</i> of the data source <a href="https://www.vlaanderen.be/DataCatalogRecord/9fffedd9-5076-4310-a366-198947717725">Biologische Waarderingskaart en Natura 2000 Habitatkaart - Toestand 2023</a>, originally published by the Research Institute for Nature and Forest (INBO) (De Saeger et al. 2023; see also De Saeger et al. 2017) and distributed by 'Digitaal Vlaanderen' under a CC-BY compatible license. It was redistributed in order to make it more easily findable and useful (in the long run) for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes.</p><p>The subdataset is a shapefile of geospatial polygons of BWK and Natura 2000 habitat types in the Flemish Region, identical to the shapefile 'BwkHab' in the original data source.</p>
Distribution and habitat suitability maps for Central European steppe plants
<p>This dataset contains distribution maps for Central European steppe plants and coordinates of species occurrence points used by Divíšek et al. (2022) to calibrate habitat suitability models. These models were projected onto past climates and the resulting habitat suitability maps for 10 periods since the Last Glacial Maximum (LGM) are also included. These maps were further used as input data for simulations of species migration from climatically suitable areas in the LGM to identify those that may have served as a source for colonisation of the species' current ranges. For each species, we present maps of climatically suitable areas during the LGM and mid-Holocene (for the latter period, only areas accessible from the LGM are shown), as well as maps of the "source areas" from which the species may have colonised the regions occupied today.</p>
Standardized map of habitat types and regionally important biotopes in Flanders
<p>The <code>habitatmap_stdized.gpkg</code> file is a processed version of the <a href="https://www.vlaanderen.be/datavindplaats/catalogus/biologische-waarderingskaart-en-natura-2000-habitatkaart-toestand-2023">Natura 2000 habitat map of Flanders</a> (De Saeger et al., 2023; see also De Saeger et al. 2017). It contains all polygons with Natura 2000 habitat types or regional important biotopes (RIB). This file is used as a basis for designing monitoring schemes in Flanders. </p> <p>In the original habitat map, every polygon can consist of maximum 5 different types (habitat (sub)types and regionally important biotopes). This information is stored in the columns <code>HAB1</code>, <code>HAB2</code>,..., <code>HAB5</code> of the attribute table. The fraction of each type within the polygons is stored in the columns <code>PHAB1</code>, <code>PHAB2</code>, ..., <code>PHAB5</code>.</p> <p>The <code>habitatmap_stdized.gpkg</code> file is a GeoPackage that contains:</p> <ul> <li><code>habitatmap_polygons</code>: a spatial layer with every habitat map polygon that contains a Natura 2000 habitat or RIB type.</li> <li><code>habitatmap_types</code>: a table with information on the habitat and RIB types (HAB1, HAB2,..., HAB5) that occur within each polygon of <code>habitatmap_polygons.</code></li> </ul> <p>The processing of the habitatmap_types table included following adjustments:</p> <ul> <li>For some polygons the type is uncertain, and the type code in the raw habitatmap data source consists of 2 or 3 possible types, separated with a ','. The different possible types are split up and one row is created for each of them, with <code>phab</code> for each new row simply set to the original value of <code>phab</code>. The variable <code>certain</code> will be <code>FALSE</code> if the original type code consists of 2 or 3 possible types, and <code>TRUE</code> if only one type is provided.</li> <li>Some polygons contain both a standing water habitat type and <code>rbbmr</code>: <ul> <li><code>3130_rbbmr</code>,</li> <li><code>3140_rbbmr</code>,</li> <li><code>3150_rbbmr</code>, and</li> <li><code>3160_rbbmr</code>.</li> </ul> </li> <li>Since <code>habitatmap_stdized_2020_v1</code>, the two types <code>31xx</code> and <code>rbbmr</code> are split up and one row is created for each of them, with <code>phab</code> for each new row simply set to the original value of <code>phab</code>. The variable certain in this case will be <code>TRUE</code> for both types.</li> <li>After those steps, a given polygon could contain the same type with the same value for <code>certain</code> repeated several times, e.g. when <code>31xx_rbbmr</code> is present with <code>phab</code> = yy% and <code>31xx</code> is present with <code>phab</code> = zz%. In that case the rows with the same <code>polygon_id</code>, <code>type</code> and <code>certain</code> were gathered into one row and the respective phab values were added up.</li> </ul> <p>The R-code for creating the <code>habitatmap_stdized</code> data source can be found in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/abf596e/src/generate_habitatmap_stdized">'n2khab-preprocessing' at commit abf596e</a>.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://github.com/inbo/n2khab">n2khab</a>.</p> <p>Attributes of <code>habitatmap_polygons</code>:</p> <ul> <li><code>polygon_id</code></li> <li><code>description_orig</code>: polygon description based on the original type codes in the raw habitatmap </li> </ul> <p>Attributes of <code>habitatmap_types</code>:</p> <ul> <li><code>polygon_id</code></li> <li><code>type</code>: the interpreted habitat or RIB type</li> <li><code>certain</code>: <code>TRUE</code> when type is certain and <code>FALSE</code> when type is uncertain</li> <li><code>code_orig</code>: original type code in raw habitatmap</li> <li><code>phab</code>: proportion of polygon covered by type, as a percentage.</li> </ul> <p>Since version <code>habitatmap_stdized_2020_v1</code>, rows are unique only by the combination of the <code>polygon_id</code>, <code>type</code> and <code>certain</code> columns.</p>
May to July 2018 ground control points GPS coordinates of tidal marsh and tidal forest plant species to be used as ground control points in habitat mapping.
We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground control points (GCP) and ground reference data for habitat mapping. GCPs for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were acquired. A real time kinematic (RTK) GPS survey of GPS coordinates and ground elevations for tidal marsh vegetation was carried out in May of June of 2018. A handheld GPS was used to collect GPS coordinates for tidal forest plant species in July of 2018. A total of 101 GCPs were collected in tidal habitats, with 26 in salt, 28 in brackish and 29 in tidal fresh marsh, and another 18 in tidal fresh forest. These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.
Redistribution of the Natura 2000 habitat map of Flanders, partim habitat type 3260 (version 2023)
<p>This is a redistribution of a subdataset of the data source '<a href="https://www.vlaanderen.be/datavindplaats/catalogus/biologische-waarderingskaart-en-natura-2000-habitatkaart-toestand-2023">Biologische Waarderingskaart en Natura 2000 Habitatkaart - Toestand 2023</a>', originally published by the Research Institute for Nature and Forest (INBO) and distributed by 'Digitaal Vlaanderen' under a CC-BY compatible license. It is redistributed for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes.</p><p>The subdataset is a shapefile of line segments of the Natura 2000 habitat type 3260 (Watercourses of plain to montane levels with the <i>Ranunculion fluitantis</i> and <i>Callitricho-Batrachion</i> vegetation) that correspond with its presence in watercourses in the Flemish Region, identical to the shapefile Hab3260 in the original data source.</p><p>The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, Department of Environment of the Flemish government).</p>
Map of standing water habitat types and regionally important biotopes in Flanders
<p>This map is a combination of the <a href="https://zenodo.org/records/13865531">standardized habitat map of Flanders</a> (version habitatmap_stdized_2023_v1) and <a href="https://zenodo.org/records/14203168">the watersurface map of Flanders</a> (version watersurfaces_2024). It contains standing water Natura 2000 habitat types (2190_a and 31xx) and regionally important biotopes (rbbah) in Flanders.</p> <p>The polygons with 2190_a habitat (dune slack ponds) are generated by selecting all watersurface polygons that overlap with dune habitat polygons (21xx) of the standardized habitat map.</p> <p>For each of the other aquatic habitat types (31xx and rbbah) we select the watersurface polygons that overlap with the selected habitat type polygons of the standardized habitat map. We also select polygons of the standardized habitat map containing standing water types but that do not overlap with polygons of the watersurface map.</p> <p>The <code>watersurfaces_hab.gpkg</code> file is a GeoPackage that contains:</p> <ul> <li><code>watersurfaces_hab_polygons</code>: a spatial layer with the selected polygons that contain standing water habitat types or regionally important biotopes. </li> <li><code>watersurfaces_hab_types</code>: a table with information on standing water habitat types and regionally important biotopes in each watersurface polygon.</li> </ul> <p>The R-code for creating the <code>watersurfaces_hab</code> data source can be found in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/58138a8/src/generate_watersurfaces_hab">'n2khab-preprocessing' at commit 58138a8</a>.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://github.com/inbo/n2khab">n2khab</a>.</p>
A Comparison of Recreational and Survey-Grade Side-Scan Sonar Systems in Mapping Reservoir Fish Habitat in 3 Southwest Ohio Reservoirs
Littoral zone aquatic habitat is thought to play an important driver of aquatic organism population dynamics, but historically has been difficult to obtain at the whole waterbody scale because it is costly and time-consuming to collect with traditional aquatic habitat sampling methods. Here we used side-scan sonar to quantification of habitat features over large areas using two levels of equipment: recreational (consumer-grade) and professional (survey-grade). Our goal was to compare performance of the different side-scan sonars by analyzing their ability to map shoreline habitat features (wood, vegetation, and substrate) in three southwest Ohio reservoirs that contain the range of habitat features of interest to fisheries biologists. We used a low-cost Lowrance Active Imaging 3-in-1 system (≈$2,000 USD) recreational sonar and an EdgeTech 6205 system (≈$150,000 USD) survey-grade sonar to collect imagery along the shoreline of three reservoirs in Ohio. Using imagery from each system, We manually delineated patches of submerged woody debris, standing timber, aquatic vegetation, and benthic substrate in GIS. We also compared the size of uniquely identifiable submerged wood from paired imagery to understand potential biases between the systems.
Figs 29–31. Maps and habitat. 29 in Mumetopia interfeles sp. nov., a new species of Anthomyzidae (Diptera) occurring en masse in an urban grassy habitat in Chile: its taxonomy, phylogeny and biology
Figs 29–31. Maps and habitat. 29. Map of Chile with position of type locality of Mumetopia interfeles Roháček sp. nov. (blue open square). 30. Satellite view of part of Valparaíso city, with collecting site indicated by red arrow. 31. Habitat of M. interfeles sp. nov., northern end of grassy area between houses visited by cats. Photo by J. von Tschirnhaus (31), other sources: Vemaps.com (29), Google Earth Pro (30).
Data from: Area of habitat maps and validated occurrences for neotropical birds of conservation concern
<p>Understanding species distributions is essential for advancing bird conservation, especially in the rapidly changing landscapes of the Neotropics, where habitat loss and degradation are accelerating. Area of Habitat (AOH) maps offer valuable spatial tools for illustrating species distributions by highlighting potentially suitable habitats within their geographic range. In this study, we generated AOH maps for 713 neotropical bird species of conservation concern, which includes species listed as globally or nationally threatened, endemic, or with restricted ranges. Using primary biodiversity data and a structured geospatial workflow, we refined approximately 2.5 million occurrence records through a flagging process and validated 50,743 records manually.<strong> </strong>This unparalleled effort led to the creation of high-quality AOH maps, along with altitude-corrected Extent of Occurrence (EOO-DEM) and Inverse Distance Weighted (IDW) range maps. Our AOH maps significantly improved species distribution predictions for 82% of species, over EOO-DEM maps. The validated occurrences and AOH maps produced in this study have wide-ranging applications, providing a valuable basis for the development of new species distribution models and for evaluating species’ natural history, extinction risk, and habitat threats. They also support the identification of priority areas for strategic conservation investments. Importantly, these maps played a key role in systematic conservation planning analyses for the Conserva Aves initiative, which is facilitating the creation of more than 80 new protected areas across Latin America, safeguarding 2 million hectares and improving the management of an additional 2 million hectares (<a href="https://conserva-aves.org/">https://conserva-aves.org/</a>).</p>
EUNIS Habitat Maps: Enhancing Thematic and Spatial Resolution for Europe through Machine Learning
<p>The EUNIS habitat classification is essential for categorising European habitats and supporting European policy on nature conservation and to implement the Nature Restoration Law. As such, to meet the growing demand for detailed and accurate habitat information, we provide spatial predictions for 260+ EUNIS habitat types at EUNIS level 3, together with validation and uncertainty analyses. </p> <p>More specifically, using ensemble machine learning models together with high-resolution satellite imagery and other climatic, terrain and soil variables, we produced an European habitat map at a 100-m resolution indicating the most likely EUNIS habitat at level 3 for every location across Europe. Predictions were validated for three independent countries, namely for France, the Netherlands and Austria. We also provide information on uncertainty and the most probable habitats at level 3 within each EUNIS level 1 formation. Products can be further refined with accurate and local land cover data. This product is thus likely to be particularly useful for restoration but also conservation purposes. </p> <p>Figure: <strong>Wall-to-wall map of EUNIS habitats at level 3 - (color coded at level 2 for visibility)</strong></p> <p></p>
Canopy Height Map of Los Angeles County Native Habitat Areas
<p>Using LARIAC4 (2016) LIDAR imagery this Canopy Height Model was derived using the lidR, terra and sf packages in R at a resolution of 1 meter. Native habitat areas were selected based on occurence of native flora taxa from iNaturalist in each LARIAC tile. Tiles with no or very few native plants were not included. Additionally, LA County north of the Santa Clara river and San Gabriel mountains was not included, please contact me if you need additional areas. Non-organic features such as power lines, houses, and some high altitude LIDAR noise are still present in the data, future efforts are needed to remove these. Processing took place on the Occidental College computing cluster using a machine with 192GB RAM and 32 cores and took approximately 3 days. The code used to generate this CHM is available here: https://gist.github.com/max-mapper/d52ad9df2f9ed4d191e67955f950e044. The CHM is available as Cloud Optimized Geotiff, which can be viewed in QGIS dynamically over HTTPS without requiring a full download.</p>
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae- in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae-
Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
<p>Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.</p>
Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2) in Structure and dynamics of the taxocenes of shrews in different habitats of the Norsky nature reserve
Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2)
Map B 1 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map B 1. number of records per UTM 10 x 10 km square in the dataset for the analysis of the phenology.
Map B 1 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map B 1. Number of records per UTM 10 x 10 km square in the dataset for the analysis of the habitat preferences.
EUNIS habitat suitability maps at 100m resolution
<p>Habitat suitability maps of 203 <a href="https://eunis.eea.europa.eu/">EUNIS</a> level 3 classes have been modeled at 100m resolution with <a href="https://biodiversityinformatics.amnh.org/open_source/maxent/">Maxent</a>. For the modeling plot observation from the <a href="http://euroveg.org/eva-database">European Vegetation Archive</a> have been used as observations for training and testing. As predictors various climate layers, soil layers, topographic layers and RS-enables EBVs have been used. Detailed information on the predictors can be found <a href="https://www.synbiosys.alterra.nl/nextgeoss/docs/Description_Abiotic_and_RSEBVs.pdf">here</a>.</p> <p>The 203 EUNIS habitat types comprise 8 groups according to the revised typology:</p> <ul> <li>Salt marsh (MA)</li> <li>Coastal habitat (N)</li> <li>Wetland (Q)</li> <li>Grassland (R)</li> <li>Shrub (S)</li> <li>Forest (T)</li> <li>Sparsely vegetated habitat (U)</li> <li>Man-made habitat (V)</li> </ul> <p><br> </p> <p> </p> <p> </p> <p> </p>
Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
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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)
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.