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3,105 results for “Vegetation =”
Fig. 2 in Bat Diversity In The Vegetation Mosaic Around A Lowland Dipterocarp Forest Of Borneo
Fig. 2. Species accumulation curves indicating the cumulative number of species encountered relative to the total number of individuals captured in each vegetation type.
Fig. 1 in Bat Diversity In The Vegetation Mosaic Around A Lowland Dipterocarp Forest Of Borneo
Fig. 1. Study area and location of census points in four vegetation types in and around Lambir Hills National Park. Inset: the location of Lambir Hills National Park, Borneo, indicated by an arrow.
FIG. 6 in Dietary behaviour and competition for vegetal resources in two Early Miocene pecoran ruminants from Central Spain
FIG. 6. — Histograms showing the percentage of high and low occlusal relief and of each type of cusp shape for the Spanish taxa with living species and with fossil populations of Andegameryx Ginsburg, 1971 and Procervulus Gaudry, 1877 from Europe. Dental mesowear data of living taxa taken from Fortelius & Solounias (2000). Data of German taxa and fossil Procervulus ginsburgi Azanza, 1993 taken from Kaiser & RÖssner (2007) and DeMiguel et al. (2008), respectively. Abbreviations: see Material and methods.
FIG. 5 in Dietary behaviour and competition for vegetal resources in two Early Miocene pecoran ruminants from Central Spain
FIG. 5. — Mesowear features of selected teeth of: A, B, AAGR; C, D, PAGR; E, F, PMOR; G, H, PAB1. Scale bar: 1 cm. Abbreviations: see Material and methods.
FIG. 1 in Dietary behaviour and competition for vegetal resources in two Early Miocene pecoran ruminants from Central Spain
FIG. 1. — Location of the studied localities in the Iberian Chain (Central Spain). Ágreda and Moratilla are placed in the Calatayud basin, and Alto de Ballester 1 in the Rubielos de Mora basin.
FIG. 3 in Dietary behaviour and competition for vegetal resources in two Early Miocene pecoran ruminants from Central Spain
FIG. 3. — Histogram showing the density of scratches and pits for the Spanish taxa with living species and with Procervulus from other Spanish localities. Dental microwear data of living taxa and Procervulus taken from Solounias et al. (2000) and DeMiguel (2008, 2010), respectively. Abbreviations: see Material and methods.
FIG. 2 in Dietary behaviour and competition for vegetal resources in two Early Miocene pecoran ruminants from Central Spain
FIG. 2. — ESEM photomicrographs of share facets of the population of: A, AAGR; B, PAGR; C, PMOR; D, PAB1. Scale bars: 100 μm. Abbreviations: see Material and methods.
FIGURE 1. Collecting methods. A–D. Fogging. E. Beating vegetation. F. Berlese funnels. A in Taxonomic Revision Of The Jumping Goblin Spiders Of The Genus Orchestina Simon, 1882, In The Americas (Araneae: Oonopidae)
FIGURE 1. Collecting methods. A–D. Fogging. E. Beating vegetation. F. Berlese funnels. A. The sheets are disposed under the canopy approximately at 1 m above the ground. B. Each sheet was connected to each other with ropes and claspers. C. Setting the machine with a mixture of diesel and pyrethroids. D. The fog is finally directed to the canopy. E. Beating vegetation was the most frequent technique employed during this project. F. Occasionally, some specimens were captured in litter using Berlese funnels. A–D, F. Ecuador. E. Juan Fernández Islands, illustrative image, no specimens of Orchestina were collected there. Credits. C, D. Dimitri Forero. E. Jaime Pizarro.
Figure 1 in New mite records (Acari: Mesostigmata Trombidiformes) from soil and vegetation of some Syrian citrus agrosystems
Figure 1 Variations in the shape of dorsal body setae in the Syrian specimens ofPseudobryobia nikitensis: a – prodorsal setae; b –sc1 seta; c – c1 seta; d –d1 seta.
LT-Brazil: A database of leaf traits across biomes and vegetation types in Brazil
<p><span>Motivation: Leaf traits represent an important component of plant functional strategies, and those related to carbon fixation and nutrient acquisition form the leaf economics spectrum. However, observations of functional leaf traits are underrepresented in tropical regions in comparison with those in temperate areas. Brazil, a country with continental scale and vast biodiversity is a timely example, where many biomes are impacted by human activities and climate change. However, leaf traits relevant to understand vegetation responses to these impacts remain poorly quantified for many species found in the country. We compiled an extensive data set of four functional leaf traits for native woody species occurring in the Brazilian territory. In addition to trait observations, sampling dates and geo-references were compiled and climatic parameters and soil properties of each sampling site were extracted from several databases.</span></p> <p><span>Main types of variables contained: The LT-Brazil data set contains 3479, 1216, 775, and 775 clean observations of leaf mass per area, leaf nitrogen (N) concentration per unit mass, leaf phosphorus (P) concentration per unit mass, and leaf N : P ratio, respectively, from native woody species, encompassing information of biome, vegetation, taxonomic data, geographical coordinates, climatic parameters, as well as soil properties.</span></p> <p><span>Spatial location and grain: We compiled trait observations from 223 sites under native vegetation distributed in all main biomes (i.e., Amazônia, Caatinga, Cerrado, Mata Atlântica, Pampa, and Pantanal) across the Brazilian territory.</span></p> <p><span>Time period and grain: The data represent information published and/or sampled during the last 25 years.</span></p> <p><span>Major taxa and level of measurement: Our compilation was focused on trait data observed for native woody species, excluding monocots, palm trees, herbs, and hemiparasitic plants. Thus, 108, 478, and 1321 botanical families, genera, and species were included, covering <em>c.</em> 9% of the woody angiosperm flora of Brazil.</span></p> <p>Software format: Data are provided as comma-separated value (.csv) files.</p>
Fig. 3 in Vegetation of Paektu Mt. alpine tundra and changes of species composition in its ecotone
Fig. 3. Numerical classification of 42 relevés (20 ✕ 20m) of alpine tundra and adjacent larch forest vegetation from Paektu Mt. (North Korea). JACCARD's coefficient and β-flexible clustering method was used (β= -0.25). Explanations: see page 7–8.
Fig. 6 in Vegetation of Paektu Mt. alpine tundra and changes of species composition in its ecotone
Fig. 6. Light-park larch forest (Rhododendro aurei-Laricetum olgensis) – ground layer contents species of alpine tundra, e.g. Rhododendron aureum, Bupleurum euphorbioides, and Juniperus sibirica (photo I. Jarolímek).
Fig. 5 in Vegetation of Paektu Mt. alpine tundra and changes of species composition in its ecotone
Fig. 5. Timber-line on Paektu Mt. – contact zone of alpine tundra and light-park larch forest (Rhododendro aurei-Laricetum olgensis) (photo I. Jarolímek).
Vegetation cover fraction in each town block across Japan
<p>Although the percent of green space is the most frequently used index for quantifying urban green, it is currently unavailable in most cities in Japan. Here we provide an open dataset of the index for each town block across the country using Sentinel-2 satellite images in Google Earth Engine. The dataset is calibrated and validated with airborne datasets obtained in nine cities in Tokyo.</p> <p> </p> <p><strong>日本全国の町丁目別の緑被率の公開</strong></p> <ul> <li>都市の緑を定量化する指標としては、緑被率が最もよく使われていますが、現在、日本のほとんどの都市では緑被率を利用することができません。ここでは、Sentinel-2の衛星画像をGoogle Earth Engineを用いて、日本全国の町丁目別の緑被率をオープンデータとして提供しています。このデータセットは、東京都内の9区の航空機データ(各区のWEBサイトで公開)を用いて校正・検証されています。</li> <li>ライセンスに関してデータは<a href="https://creativecommons.org/licenses/by/4.0/deed.ja">CC BY 4.0</a>で公開しています。</li> <li>校正・検証に関しての詳細は、<a href="https://doi.org/10.3130/aijt.28.521">日本建築学会技術報告集</a>に掲載しています。 </li> <li>「FRAC_VEG」が緑被率です。</li> <li>境界データは「平成27年国勢調査」を用いています。</li> </ul> <p> </p> <p><strong>データ概要</strong></p> <p>年:2020</p> <p>空間分解能:町丁目(平成27年国勢調査)</p> <p> </p> <p><strong>引用方法</strong></p> <p>・データの利用のみの場合</p> <p>Kiyono Tomoki, Fujiwara Kunihiko, & Tsurumi Ryuta. (2021). Vegetation cover fraction in each town block across Japan (1.0.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5553516">https://doi.org/10.5281/zenodo.5553516</a></p> <p>・上記以外の場合(この取り組みそのものや,アルゴリズム等への言及)</p> <p>清野友規, 藤原邦彦, 鶴見隆太. Google Earth Engineを用いた町丁目別緑被率オープンデータ(全国版)の作成と評価, 日本建築学会技術報告集, 2022, 28 巻, 68 号, p. 521-526, https://doi.org/10.3130/aijt.28.521</p> <p> </p> <p><strong>問い合わせ</strong></p> <p>意見・要望・感想などお気軽にお問い合わせください。</p> <p>鶴見隆太(日建設計総合研究所) tsurumi.ryuta@nikken.jp</p> <p> </p> <p><strong>更新情報</strong></p> <p>v1.0.1 2021/10/7</p> <p>・エクセルでの文字化けを解消(文字コード:BOM付UTF-8)</p> <p>・町丁目のユニーク識別子であるKEY_CODE(11桁)を追加</p> <p>v0.1.0</p> <p>・初期バージョンをリリース</p> <p> </p> <p><strong>PI</strong></p> <p>Kiyono Tomoki</p>
Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds
<p>Our proposed dataset comprises 199 cylindrical plots of 10 m radius corresponding to typical pasture land parcels in South-Eastern France. Each plot contains between 3000 and 17000 3D points, and each point is attributed with a total of 10 features: (i) absolute 3D coordinates, (ii) RGB and Near-InfraRed reflectance obtained with aerial cameras, (iii) uncalibrated laser intensity, return number and number of returns provided by the aerial LiDAR.</p> <p>This dataset can be used as training data for our model deep learning model "Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds" available on <a href="https://github.com/ekalinicheva/plot_vegetation_coverage">https://github.com/ekalinicheva/plot_vegetation_coverage</a></p> <p> </p>
Data from: Cryogenic land surface processes shape vegetation biomass patterns in northern European tundra
<p>Tundra ecosystems have experienced changes in vegetation composition, distribution, and productivity over the past century due to climate warming. However, the increase in above-ground biomass (AGB) may be constrained by cryogenic land surface processes (LSP) that cause topsoil disturbance and variable microsite conditions. These effects have remained unaccounted for in tundra biomass models, although they can impact multiple opposing feedbacks between the biosphere and atmosphere, ecosystem functioning and biodiversity. Here, by using field-quantified data from northern Europe, remote sensing, and machine learning, we show that LSP substantially constrain AGB in tundra. The three surveyed LSP (cryoturbation, solifluction and nivation) collectively reduced AGB by an average of 123.0 g m<sup>-2 </sup>(-30.0%). This effect was significant over landscape positions and was especially pronounced in snowbed environments, where the mean reduction in AGB was 57.3%. Our results imply that LSP are pivotal in shaping future patterns of tundra biomass, as long as cryogenic ground activity is retained by climate warming.</p> <p>These are the key data and codes related from Aalto et al., (2021).</p> <p> </p>
Boreal forest tower-based remote sensing data (solar-induced fluorescence and reflectance-based vegetation indices)
<p>Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from August 2019-December 2021 at the Southern Old Black Spruce site in Saskatchewan Canada and the National Ecological Observatory Network (NEON) Delta Junction. We provide half-hourly averaged vegetation indices (NIRv, NDVI, PRI, CCI) and Solar-Induced Fluorescence (SIF) and for stand-representative targets. Additionally, we provide half-hourly Photosynthetically Active Radiation (PAR), a fraction of direct vs. diffuse radiation (Df), Air Temperature (Tair) and Gross Primary Productivity (GPP). </p>
Normalized Difference Vegetation Index (NDVI) data
<p> </p> <p>The raster data 'viCH_Landsat_NAfill.tif' represents Normalized Difference Vegetation Index (NDVI) data of Switzerland. Raster pixels have 250x250 m resolution and contain median values of the years 2013 to 2017. Data was collected by the Landsat satellite and was downloaded via Google Earth Engine. Missing values were closed with a local neighborhood average.</p> <p>The raster data 'viNE_Modis2018_NAfill.tif' represents NDVI data of North Eurasia, at 1x1 km resolution, and averaged over the growing season 2018. Data was collected by the Modis sensor and was downloaded via Google Earth Engine. Missing values were closed with a local neighborhood average.</p>
Cowpea GWAS drought stress in early vegetative stage
<p>The GWAS outputs for Cowpea (<em>Vigna unguiculata</em>) responses to drought stress at early vegetative stress. The cowpea seedlings (miniCore population) were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. The data was assembled and curated (https://rpubs.com/mjulkowska/Cowpea2022alltraits), and subsequently used for GWAS. The GWAS data was analyzed, and the most interesting associations were selected (https://rpubs.com/mjulkowska/Cowpea2022Gwas). </p> <p>The raw data was collected by Hayley Sussman, with help of Olga Khmelnitsky, while GWAS was performed by Magdalena Julkowska, using ASReml script developed by Arthur Korte (https://github.com/arthurkorte/GWAS), adapted for cowpea. </p>
Regional plot x species data for alpine vegetation
<p>Whether the distribution and assembly of plant species are adapted to current climates or legacy effects poses a problem for their conservation during ongoing climate change. The alpine regions of southern and central Europe (SACEU) are compared to those of the western US and Canada (WUSAC) because they differ in their geographies and histories. Individual-based simulation experiments disentangled the role of geography in species adaptations and legacy effects in four combinations: approximations of observed alpine geographies vs. regular lattices with the same number of regions (realistic and null representations), and virtual species with responses to either climatic or simple spatial gradients (adaptations or legacy effects). Additionally, dispersal distances were varied using five Gaussian kernels. Because the similarity of pairs of regional species pools indicated the processes of assembly at extensive spatiotemporal scales and is a measure of beta diversity, this output of the simulations was correlated to observed similarity for Europe and North America. In North America, correlations were highest for simulations with approximated geography and location-adapted species; those in Europe had their highest correlation with the lattice pattern and climate-adapted species. Only SACEU correlations were sensitive to dispersal limitation. The southern and central European alpine areas are more isolated and with more distinct climates to which species are adapted. In the western US and Canada, less isolation and more mixing of species from refugia has caused location to mask climate adaptation. Among continents, the balance of explanatory factors for the assembly of regional species pools will vary with their unique historical biogeographies, with isolation lessening disequilibria.</p>
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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.