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22,710 results for “Plant”
Plant-flower visitor network from Avon Gorge, UK
<table> <tbody> <tr> <td>Abstract</td> <td>This dataset gathers information on interactions between plants<br>and their flower visitors collected throughout 2004 (11 surveys covering local flowering season) the Avon Gorge (England), an iconic field site well known for its rare plant populations. The study area (1480 m2 ) included a broad<br>range of flowering plants, and overall the dataset shows information for 260 species (81 plant species, 179 insect species and morphospecies).</td> </tr> <tr> <td>Classification System</td> <td>all taxa were identified by specialist taxonomists</td> </tr> <tr> <td>Sampling Description</td> <td>A total of 11 survey visits were carried out from 10 May to 27 September 2004, this covering the main period of insect activity. Flower and insect surveys took place approximately every 14 days under dry conditions. In each flower abundance survey, a stratified random design was used to select 1 m2 quadrats in the study area. The area was divided into nine sub-areas based on habitat type and accessibility. Each sub-area was divided into 1 m2 quadrats and 2·5% (37) of these were randomly selected per sampling occasion. In each quadrat, the number of floral units of each plant species was recorded, defined as the distance that a small bee (c.1 cm length) would fly, rather than walk (Saville 1993). For example, in the Asteraceae, a flower unit is the entire inflorescence while in the Rosaceae, a flower unit is a single flower. Thus, the floral unit is defined from the bee’s perspective rather than by flower anatomy. Rare flowers which were missed using this method were included in the food web data as rare species with an abundance of two flower units (which was the lowest number of units observed in the plot for any species).<br>In the insect surveys, an observation point was chosen for each flowering plant species by randomly selecting one of the quadrats where the species was present. All the flowering units that could be surveyed by a single observer (approximately a semi-circle with 1-m radius) were observed for 20 min. On consecutive sampling occasions, plant species were rotated through three time slots, the morning (09.00–12.00 h), early afternoon (12.00–15.00 h) and late afternoon (15.00–18.00 h), to allow each species to be observed equally over time. At least two floral units were observed per plant species per sample. All flower–visitor interactions were recorded, and all visitors observed were collected for identification. To estimate the overall abundance of each plant species, the average number of flower units per 1 m 2 quadrat was multiplied by the total area of the study site. To estimate the interaction frequency for each visitor–plant species pair, we divided the total number of visits recorded by the number of flower units observed (per 20 min) and then multiplied by the total number of floral units in the study plot. By collecting the insects, we did not allow for repeated visits by the same individual; hence, some visitation frequencies may be underestimated. However, collecting specimens is essential for identification of most visitor species. Hymenoptera, Diptera, and Coleoptera were identified by taxonomists either to species or to morphospecies. Lepidoptera were identified to species by the authors and Heteroptera and parasitoids were morphotyped by the authors.</td> </tr> </tbody> </table>
Leaf samples of three common plant species collected in seven LandKlif quadrants
<p><span>Leaf samples of Acer pseudoplatanus, Dactylis glomerata and Potentilla reptans were collected in seven LandKlif quadrants along a climate gradient in summer 2020. In each quadrant, leaves were sampled in two habitats (forest and open landscape). In each habitat, three leaves from seven individuals of each species were collected. Specific leaf area (SLA) and leaf dry matter content (LDMC) of each leaf sample were determined in the lab. In addition, nitrogen content was measured at the level of individuals. This dataset contains the mean SLA and LDMC of the leaves sampled from each individual, as well as information on the site where they were collected.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Plant diversity and indicator values within 200m radius of the Landklif plots
<p><span>Species numbers of vascular plants as assessed in vegetation surveys inside and within 200m radius of the Landklif plots. Vegetation inside the plots was sampled between mid-May and end of July 2019 (seven subplots, 10m2 sampling area per plot). Cover values for each species were estimated following the Braun-Blanquet scale. Species pools within 200m radius around the plot were assessed between mid-May and begin of August 2020 by standardized transect walks (walking time proportional to area percentages of dominant habitat types within 200m radius, 60 minutes total walking time in each circle). The dataset contains average species numbers of the subplots, total species numbers of the plots, and total species numbers within 200m radius of the plots, as well as mean Ellenberg indicator values on plot and 200m scale.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p> <p> </p>
Data of emission of floral volatiles and damage induced emissions of plants
<p>The file Emissiondata_plants_GCIMS contains a broad data set on the emissions of plant volatile organic compounds from different taxa. This allows cross species comparison of recorded emission pattern. Furthermore, distribution of identified (or unidentified) compounds can be traced for diferent species. </p> <p>All measurements were conducted performed with a mobile ppq-tec-GC-IMS (ION-GAS GmbH, Dortmund, Germany) based on hardware provided by STEP GmbH (Pockau-Lengefeld, Germany). GC pre-separation was performed under isothermal conditions (80°C) for 1500 s on a MXT-200 capillary column (30 m x 0.53 mm, 1.5 µm coating) with a carrier gas flow (filtered air from internal gas circuit) of 21 mL min<sup>-1</sup>. Ionization of pVOCs for mobility separation was performed using a tritium source of β-radiation (100 MBq). Mobility separation and subsequent detection were performed with a drift-tube IMS (drift length of 5.61 cm) at 70 °C and at a field strength of 300 V cm<sup>-1</sup>.</p> <p> </p> <p>The dataset contains 14 Variables and a total of 1866 observations (status: 10/06/2023, Version 1.0.0)</p> <p>4 variables describe the plant material. This includes the variables Species, Genus, Family and Order </p> <p>4 variables describe the sampled species. This includes plant part (flower or leaves), plant status (damaged or undamaged), the sample location and the Accession (only if the sample was provided by the Bonn University Botanical Gardens)</p> <p>6 variables describe the recorded emission patterns. Compound refers to the substance (unidentified compounds are abbreviated with UNK-n), retention time and relative ion mobility (parameters that allow cross species comparison and identification of substances), signal type (for some substances, ion clusters can be observed at higher concentrations. signal type refers to these ion clusters), and signal intensity (semi-quantitative measure for the abundance of a substance) and relative abundance (rel_abund; proportional contribution of a substance within a species, where the strogest signal is 1). </p> <p> </p> <p>The reference_compounds table is added to this. This contains information on substances that have already been identified (CAS, mass weight, retention time and relative ion mobility, dimerisation). These data were collected by direct injection of pure substances into the GC-IMS used. </p> <p> </p> <p> </p>
Discovery of South African plant-based biomarkers as potential flagships for SARS- CoV-2 receptor
<h1><span>Table S1: </span><span>Distinguished metabolites in the extracts of <em><span>Artemisia annua </span></em><span>and <em>Artemisia afra </em></span>using UPLC-MS/MS set in positive ionization mode.</span></h1> <h1><span>Table S2: Identified and docked compound-based biomarkers from <em><span>Artemisia annua </span></em><span>and <em>Artemisia afra </em></span>(ESI+ scan).</span></h1>
[Dataset] Plant–pollinator in a highly intensive agricultural landscape LTSER Zone Atelier Plaine & Val de Sèvre
<p>We built bipartite networks formed by pollinators and the flowers they forage on, using data collected in the Long Term Socio-Ecological Research site "Zone Atelier Plaine & Val de Sèvre" (Bretagnolle et al. 2028). We compiled a six-year monitoring dataset of plant–pollinator interactions, sampling by sweep-nets along transects in the main crop types of this intensive agricultural plain. </p> <p>The dataset contained all the "pollinator-plant" pair observed in each crop samples.</p>
global interpreted planted forest, natural forests validation samples
<p>This dataset provided the global validation dataset including planted forest, natural forest, and non-forest in 2015. This dataset was visually interpreted using the high spatial resolution (<1 m) images from Google Earth, and combining the spatial distribution of planted forets, and forest gain map.</p> <p>1 denotes planted forest, 2 denotes the natural forest, and 3 denotes the nonforest.</p> <p>Detailed information about how the global validation dataset was visually interpreted can be seen in the following reference:</p> <p>Xu, H., He, B., Guo, L., Yan, X., Zeng, Y., Yuan, W., et al. (2024). Global forest plantations mapping and biomass carbon estimation. Journal of Geophysical Research: Biogeosciences, 129, e2023JG007441.</p>
Plant Available Water Content (PAWC)
<p>Plant Available Water Capacity (PAWC) provides the maximum amount of water a soil profile can hold for a given plant to use. The PAWC data are available for RethinkAction's case studies and have beend derived using as a basis ISRIC’s (International Soil Reference and Information Centre) “SoilGrids250m" product.</p>
Plant metabolites modulate animal social networks and lifespan
<p><span>Social interactions influence disease spread, information flow, and resource allocation across species, yet heterogeneity in social interaction frequency and its fitness consequences remain poorly understood. Additionally, animals can utilize plant metabolites for purposes beyond nutrition, but whether that shapes social networks is unclear. Here, we investigated how non-nutritive plant metabolites impact social interactions and the lifespan of the turnip sawfly, <em>Athalia rosae</em>. Adult sawflies acquire neo-clerodane diterpenoids ('clerodanoids') from non-food plants, showing intraspecific variation in natural populations and laboratory-reared individuals. Clerodanoids can also be transferred between conspecifics, leading to increased agonistic social interactions. Network analysis indicated increased social interactions <span>in sawfly groups where some or all individuals had prior access to clerodanoids</span>. Social interaction frequency varied with clerodanoid status, with fitness costs including reduced lifespan resulting from increased interactions. Our findings highlight the role of intraspecific variation in the acquisition of non-nutritional plant metabolites in shaping social networks, with fitness implications on individual social niches.</span></p>
Data for :Nitrogen availability in digestates from full-scale biogas plants following soil application as affected by operation parameters and input feedstocks
<p>This archive contains data for the paper "Nitrogen availability in digestates from full-scale biogas plants following soil application as affected by operation parameters and input feedstocks". Obtained from a soil incubation experiment for 80 days.</p><p> </p>
Lobelia appendiculata var. gattingeri (Campanulaceae) - whole plant - in flower - general view
Image of Lobelia appendiculata var. gattingeri (Campanulaceae) - whole plant - in flower - general view
Lobelia appendiculata var. gattingeri (Campanulaceae) - whole plant - in flower - general view
Image of Lobelia appendiculata var. gattingeri (Campanulaceae) - whole plant - in flower - general view
Delphinium carolinianum ssp. calciphilum (Ranunculaceae) - whole plant - in flower - general view
Image of Delphinium carolinianum ssp. calciphilum (Ranunculaceae) - whole plant - in flower - general view
Medicago lupulina (Fabaceae) - herbaceous angiosperms - whole plant - in flower - general view
Image of Medicago lupulina (Fabaceae) - herbaceous angiosperms - whole plant - in flower - general view
Lobelia appendiculata var. gattingeri (Campanulaceae) - whole plant - in flower - general view
Image of Lobelia appendiculata var. gattingeri (Campanulaceae) - whole plant - in flower - general view
Modeled tritium in precipitation from Fukushima Daiichi Nuclear Power Plant accident simulations with MIROC5-iso
<p>This data set contains modeled tritium in precipitation values from different simulations of Fukushima Daiichi Nuclear Power Plant (FDNPP) accident produced with MIROC5-iso. The simulations are for the period 2011-20121 and were with different anthropogenic tritium source functions. A complete description can be found in Cauquoin, A., Gusyev, M., Bong, H., Okazaki, A., and Yoshimura, K.: Modeling tritium release to the atmosphere during the Fukushima Daiichi Nuclear Power Plant accident and application to estimating post-accident water system transit times, <em>Environ. Sci. Pollut. Res.</em>, <a href="https://doi.org/10.1007/s11356-025-35919-1" target="_blank" rel="noopener">https://doi.org/10.1007/s11356-025-35919-1</a>, 2025. </p> <p>The simulations are named fukushima_accident_{jra55, era5}_total_gas_{div100, div200, div500, div1000}, with {jra55, era5} describing a nudging to JRA-55 or ERA5 reanalyses, and with {div100, div200, div500, div1000} describing the anthropogenic tritium input function used in DatasetS1_table_tritium_release_atm_fukushima_input.csv.</p> <p>The modeled values of tritium in Hiso river water, Minamisoma spring and artesian groundwater, calculated using MIROC5-iso tritium in monthly precipitation in Fukushima, scaled Tokyo GNIP data, and tritium measurements in preciptation at Fukushima as input of the TracerLPM model, are included too. </p> <p>The model data can be downloaded as netcdf, csv or xlsx files:</p> <ul> <li>*_daymean.prcpTU.nc: daily mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_monmean.prcpTU.nc: monthly mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_daymean.prcp.nc: daily precipitation over the period 2011-2021, expressed in mm/day;</li> <li>*_monmean.prcp.nc: monthly precipitation over the period 2011-2021, expressed in mm/month;</li> <li>*_prcp_daymean.remapnn.csv: daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in mm/day;</li> <li>*_prcp_monmean.remapnn.csv: montly mean precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in mm/month;</li> <li>*_prcpTU_daymean.remapnn.csv: tritium in daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in TU;</li> <li>*_prcpTU_monmean.remapnn.csv: tritium in montly precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in TU;</li> <li>DatasetS1_table_tritium_release_atm_fukushima_input.csv: Table of anthropogenic tritium daily release, based on reconstructed iodine-131 total gas emissions from <a href="https://doi.org/10.5194/acp-15-1029-2015" target="_blank" rel="noopener">Katata et al. (2015)</a>, used as inputs for MIROC5-iso.</li> <li>TracerLPM_fukushima_with_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to JRA-55 was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_without_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation ctrl nudged to JRA-55 (without FDNPP peak) was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_with_peak_era5.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to ERA5 was used for constructing Cin(t).</li> </ul>
The Effects of Plant-Microbe-Environment Interactions on Mineral Weathering Patterns in a Granular Basalt
<p>Data used in the Milici et al. <em>Geobiology </em>article "The Effects of Plant-Microbe-Environment Interactions on Mineral Weathering in Granular Basalt". The data result from a greenhouse experiment in which 14 genotypes of Alfalfa <em>(Medicago</em> sativa) were grown in an unweathered granular basaltic tephra, exposed to an early successional soil microbial community, and replicated across three different soil moisture treatments. This experiment seeks to identify the roles of vascular plants and soil microbes on mineral weathering. Please see the article for full project description. </p> <p>General File Descriptions:</p> <p>"AllPerformanceGeochem.csv" contains both the performance and geochemistry data associated with each plant grown in the experiment and is used for the majority of the analyses.</p> <p>"FullCensusTimeSeries.csv" contains the growth and survival data for the plants across the entire 3 month duration of the experiment and is used only to calculate survival rate and growth rate.</p> <p>"pottingsoilmass.csv" contains the data for alfalfa grown in potting soil and is used to compare how much the basalt limited plant growth relative to a potting soil control. </p> <p>These data are cleaned and formatted for analysis via the code in the github repository linked to this data repository. </p> <p> </p>
Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"
<p>This dataset results from an experiment on young oil palm plants (<em>Elaeis guineensis</em>) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), <em>i.e.</em>, a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%).</p> <p>Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.</p>
Concentrating solar power (CSP) plants AI-training dataset for flux density measurements.
<p>In this dataset, the tools required for the training of a neural net in the context of flux density measurements in concentrating solar power (CSP) plants are included. An Excel file with 931 meteorological conditions and the positions of the power plant and the receiver is included, as well as 15928 pairs of images resulting from ray-tracing in Solarturm Juelich (STJ) each of these conditions with 17 different combinations of heliostats. <br> <br>This dataset is part of the WP1 of TOPCSP european project (funded by HORIZON MSCA Doctoral Network, Project number 101072537).</p>
Catalogue of Plants 2021. Royal Botanic Garden Edinburgh (data)
<p>This is a static snapshot of the Royal Botanic Garden Edinburgh (RBGE) Living Collection Catalogue held in the Living Collections Management System (CMS) it is produced as a record of what was being grown at the RBGE on 10/09/2021. </p> <p>The data includes the specialist gardens of Benmore Botanic Garden (BBG), Dawyck Botanic Garden (DBG), Logan Botanic Garden (LBG), Sites of the International Conifer Conservation Programme (ICCP) and the Scottish Native Plants Project.</p> <p>The dynamic (up to date) data available at</p> <p><a href="https://data.rbge.org.uk/search/livingcollection/">https://data.rbge.org.uk/search/livingcollection/</a></p> <p> </p> <p><strong>How to Use the Catalogue Spreadsheet</strong></p> <p><em>By Benedict Lyte</em></p> <p>The catalogue is divided into nine sections:</p> <ol> <li>Bryophytes</li> <li>Fern allies</li> <li>Ferns</li> <li>Gnetophytes</li> <li>Conifers</li> <li>Ginkgophytes</li> <li>Cycads</li> <li>Dicotyledons</li> <li>Monocotyledons</li> </ol> <p> </p> <p>The listing is organised alphabetically by family following APGIV (<a href="http://www.mobot.org/MOBOT/research/APweb">http://www.mobot.org/MOBOT/research/APweb</a>) and then accession number.</p> <p>The taxon data uses World Flora Online (<a href="http://www.worldfloraonline.org">http://www.worldfloraonline.org</a>) for verification.</p> <p> </p> <p><strong>Accession number</strong></p> <p>RBGE accession numbers are eight digits long: the first four digits represent the year of the accession and are followed by the sequential number of that accession in that year.</p> <p> </p> <p><strong>The location where the accession is alive</strong></p> <p>BBG = Benmore Botanic Garden</p> <p>DBG = Dawyck Botanic Garden</p> <p>InvI = Inverleith, under glass</p> <p>InvO = Inverleith, outdoors</p> <p>LBG = Logan Botanic Garden</p> <p>Ext = External site that is part of the International Conifer Conservation Programme or the Scottish Native Plants Project</p> <p> </p> <p><strong>Country of origin</strong></p> <p>Names of the countries follow those given by the International Organization for Standardization standard 3166 (ISO, 2011).</p> <p> </p> <p><strong>Collection info</strong></p> <p>Collection ID is a code allocated by RBGE.</p> <p>Collector is the name(s) of the individual(s) on the collection trip with that ID.</p> <p>Collection number is the number assigned to the accession when it was collected.</p>
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OpenNeuro
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