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227 results for “tree planting”
Data from 'Tracability of Forest Reproductive Material with the quality label 'Plant van Hier': A DNA database with genetic profiles of native autochthonous tree and shrub species of Flanders, Belgium'
<h2>Background</h2> <p>Indigenous trees and shrubs play an important role in multifunctional forest management. They form a significant part of the biodiversity in our forests. Forest reproductive material (FRM) of autochthonous Flemish origin is sold under the quality label ‘Plant van Hier’, a certification mark of the Agency for Nature and Forests. To ensure the provenance of the seedlings, we developed a DNA-database of genetic profiles of potential parent trees, using species-specific genetic markers. This database enables the traceability of FRM of the ‘Plant van Hier’ label throughout the entire production chain; from seed harvesting and cultivation to planting by the end user.</p> <p>This database contains the genetic profiles of almost all possible parent trees present within 27 Flemish autochthonous seed orchards of eight ecologically important tree and shrub species: <em>Carpinus betulus</em>, <em>Corylus avellana</em>, <em>Frangula alnus</em>, <em>Populus tremula</em>, <em>Sorbus aucuparia</em>, <em>Tilia cordata</em>, <em>Tilia platyphyllos,</em> and <em>Ulmus laevis</em>. The profiles were established using microsatellite markers (11 to 24 markers per species). New genetic markers were developed for <em>Carpinus betulus</em> and <em>Ulmus laevis</em>. PCR products were run on an ABI 3500 Genetic Analyser (Thermo Fisher Scientific).</p> <h2>Files</h2> <p>The files will be updated when new genotypes are added to the seed orchards. The current data files contain data from genotypes collected in the period 2018-2023. </p> <h3>Species_genotypes</h3> <p>These files contain the genetic fingerprints of the parent trees of autochthonous Flemish seed orchards. Missing data is indicated as ‘MD’. For <em>Carpinus betulus</em>, an octoploid species, the allelic phenotype is given instead of the genotype as the number of times that an allele occurs on a specific locus is not known.</p> <p>The next metadata is additionally given:<br>- Species: the Latin name of the species<br>- Seed_orchard: the name of the seed orchard in which the genotypes are located<br>- Code_seed_orchard: the code of the seed orchard in which the genotypes are located as given in the Register of Flemish Forest Reproductive Material (‘Register bosbouwkundig uitgangsmateriaal’; inbo.be)<br>- Genotype: the fieldname given to the genotype<br>- Origin: the location where the genotype was collected in Flanders, Belgium. Genotypes were collected from natural stands which are assumed to have an autochthonous origin. When the specific location is unknown, the location ‘Flanders’ is given. <br>- Year_sampled: the year in which the genotypes were sampled in the respective seed orchard for genetic analysis.</p> <h3>Species_binsets</h3> <p>These files contain the binsets and allele names that are used to score the alleles of the genotypes in the programme Geneious Prime 2019.3.2 (<a href="https://www.geneious.com">https://www.geneious.com</a>). For <em>Tilia platyphyllos </em>and <em>Tilia cordata</em>, the same binsets were used.</p>
Fire-severity effects on plant-fungal interactions after a novel tundra wildfire disturbance: implications for arctic shrub and tree migration
Background-Vegetation change in high latitude tundra ecosystems is expected to accelerate due to increased wildfire activity. High-severity fires increase the availability of mineral soil seedbeds, which facilitates recruitment, yet fire also alters soil microbial composition, which could significantly impact seedling establishment. Results - We investigated the effects of fire severity on soil biota and associated effects on plant performance for two plant species predicted to expand into Arctic tundra. We inoculated seedlings in a growth chamber experiment with soils collected from the largest tundra fire recorded in the Arctic and used molecular tools to characterize root-associated fungal communities. Seedling biomass was significantly related to the composition of fungal inoculum. Biomass decreased as fire severity increased and the proportion of pathogenic fungi increased. Conclusions - Our results suggest that effects of fire severity on soil biota reduces seedling performance and thus we hypothesize that in certain ecological contexts fire-severity effects on plant-fungal interactions may dampen the expected increases in tree and shrub establishment after tundra fire.
Tree survey:Effects of Long Term Fertilization and Oak Canopy Cover on Plant Communities and Ecosystem Processes
In 1996 E142 was established in field D on top of the E004 macroplots. E004 was conducted in fields A, B, C and D by Dave Tilman. The purpose of E004 was to see what effect NH4NO3 addition has on large areas over a longer period of time with exposure to naturally-occurring levels of herbivory. The nutrient addition treatments in E004, E142 plots have been applied annually since 1982. These experiments, along with others at Cedar Creek, examine the community and ecosystem consequences of chronic nutrient loading.
Ground truth and raw hyperspectral files of olive trees for plant stress detection
<p>This dataset contains raw hyperspectral images from Cubert S-185 collected on 13 May 2021 from an olive field in Halkidiki, Northern Greece. Included is also a matrix containing the id of each recorded olive tree (the samples) that also appears in the hyperspectral images. QGIS (ver.3.28.0) software plugin 'zonal statistics multiband' was used to compute zonal statistics for each of the 138 spectral bands available for each sample. Accompanying each sample is also the ground truthing data recorded, which addresses the present stress of 3 stressors (<i>Verticillium dahliae, Pleospora herbarum </i>and 'other stressors').</p>
Trees of India Version 1: Standardization to Records in World Flora Online and the World Checklist of Vascular Plants, with matches in GlobalTreeSearch and GlobalUsefulNativeTrees
<p>The <strong>Trees of India (ToI, Version-I)</strong> includes data on 3708 tree species distributed across 35 states/union territories of India. The database is based on systematic review of 313 literature sources published from 1872-2022.This compendium is available via <a href="https://figshare.com/articles/dataset/ToI_Ver_-I_Trees_of_India_Version-I/23226281">Figshare</a> and was described by Mugal et al. <a href="https://link.springer.com/article/10.1007/s10531-023-02659-y">2023</a>:</p> <ul> <li>Khuroo, Anzar Ahmad; Mugal, Muzamil Ahmad; Wani, Sajad Ahmad (2023). ToI, Ver.-I : Trees of India, Version-I. figshare. Dataset. <a href="https://doi.org/10.6084/m9.figshare.23226281.v1">https://doi.org/10.6084/m9.figshare.23226281.v1</a></li> <li>Mugal, M.A., Wani, S.A., Dar, F.A. <em>et al.</em> Bridging global knowledge gaps in biodiversity databases: a comprehensive data synthesis on tree diversity of India. <em>Biodivers Conserv</em> <strong>32</strong>, 3089–3107 (2023). <a href="https://doi.org/10.1007/s10531-023-02659-y">https://doi.org/10.1007/s10531-023-02659-y</a></li> </ul> <p> </p> <p>Here I provide direct and fuzzy matches for taxa listed with accepted plant names in <strong>World Flora Online</strong> (<a href="https://www.worldfloraonline.org/downloadData">version 2023.03</a>; Borsch et al. <a href="https://doi.org/10.1002/tax.12373">2020</a>) and the <strong>World Checklist of Vascular Plants</strong> (WCVP <a href="https://doi.org/10.34885/nswv-8994">version 10</a>; Govaerts et al. <a href="https://www.nature.com/articles/s41597-021-00997-6">2021</a>). Matching was done in <em>R</em> through the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora</a> package (Kindt <a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">2020</a>). The taxonomic standardization process was similar to the one completed <a href="https://www.worldagroforestry.org/output/agroforestry-species-switchboard-30">during the preparation of the third major release</a> of the <a href="https://apps.worldagroforestry.org/products/switchboard">Agroforestry Species Switchboard</a> and when preparing the <strong>GlobalUsefulNativeTrees database</strong> (GlobUNT; <a href="https://worldagroforestry.org/output/globalusefulnativetrees">https://worldagroforestry.org/output/globalusefulnativetrees</a>).</p> <p>After matching species with the WCVP, information was compiled on the <strong>native distribution</strong> documented in the WCVP for level-3 units of the <a href="https://github.com/tdwg/wgsrpd">World Geographical Scheme for Recording Plant Distributions</a> that correspond to India, including India (IND), Assam (ASS), West Himalaya (WHM), East Himalaya (EHM), Laccadive Is. (LDV), Andaman Is. (AND) and Nicobar Is. (NCB). Also included after matching with the WCVP is information on the geographic area, lifeform and main biome. Similar information is available when searching for species from <a href="https://powo.science.kew.org/">Plants of the World Online</a>.</p> <p>Where a matching species was found in <strong>GlobalTreeSearch</strong> (Beech et al. <a href="https://www.tandfonline.com/doi/full/10.1080/10549811.2017.1310049">2017</a>; <a href="https://tools.bgci.org/global_tree_search.php">https://tools.bgci.org/global_tree_search.php</a>; accessed on 28th June 2023) filtered for India, the species name in GlobalTreeSearch is shown. Note that GlobalTreeSearch documents the <strong>native country distribution</strong> of tree species.</p> <p>Where a matching species was found in the <strong>GlobalUsefulNativeTrees</strong> database (GlobUNT, version 2023.11) filtered for India, the species name in the GlobUNT database is shown. GlobUNT has been described in the following publication: Kindt et al. (<a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) <strong>GlobalUsefulNativeTrees, a database of 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in restoration</strong>. <em>Sci Rep</em> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a>.</p> <p>See the metadata for information on versions.</p> <p> </p> <ul> <li>Borsch, T., Berendsohn, W., Dalcin, E., Delmas, M., Demissew, S., Elliott, A., Fritsch, P., Fuchs, A., Geltman, D., Güner, A., Haevermans, T., Knapp, S., le Roux, M.M., Loizeau, P.-A., Miller, C., Miller, J., Miller, J.T., Palese, R., Paton, A., Parnell, J., Pendry, C., Qin, H.-N., Sosa, V., Sosef, M., von Raab-Straube, E., Ranwashe, F., Raz, L., Salimov, R., Smets, E., Thiers, B., Thomas, W., Tulig, M., Ulate, W., Ung, V., Watson, M., Jackson, P.W. and Zamora, N. (2020), World Flora Online: Placing taxonomists at the heart of a definitive and comprehensive global resource on the world's plants. TAXON, 69: 1311-1341. <a href="https://doi.org/10.1002/tax.12373">https://doi.org/10.1002/tax.12373</a></li> <li>Govaerts, R., Nic Lughadha, E., Black, N. <em>et al.</em> The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. <em>Sci Data</em> <strong>8</strong>, 215 (2021). <a href="https://doi.org/10.1038/s41597-021-00997-6">https://doi.org/10.1038/s41597-021-00997-6</a></li> <li>E. Beech, M.Rivers, S. Oldfield & P. P. Smith (2017)GlobalTreeSearch: The first complete global database of tree species and country distributions, Journal of Sustainable Forestry, 36:5, 454-489, DOI: <a href="https://doi.org/10.1080/10549811.2017.1310049">10.1080/10549811.2017.1310049</a></li> <li>Kindt, R. 2020. WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data. <em>Applications in Plant Sciences</em> 8(9): e11388. <a href="https://doi.org/10.1002/aps3.11388">https://doi.org/10.1002/aps3.11388</a></li> </ul> <p> </p> <p>The developments of this dataset and GlobUNT were supported by the Darwin Initiative to project DAREX001 of <a href="https://www.darwininitiative.org.uk/project/DAREX001/"><em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em></a>.</p>
1-km forest tree height, cover, plant area index, and foliage height diversity for the CONUS
<p>Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at 1-km resolution for 2013-2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses.</p>
Data, Analytical Code, and Model Outputs From: "Green is the New Black: Outcomes of Post-Fire Tree Planting Across the Interior West, USA"
<p>This archive includes data (locations of tree plantings, one-year survival records, remotely sensed canopy cover change), statistical code, and model outputs from Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
Dalton and Nenana study site data including: invasive plant density estimates, invasive plant density, soil data, seedling estimates for dominant tree species and ground cover estimates for sites
This dataset contains invasive plant and stand level data for study sites along the Dalton and Parks highways in interior Alaska in the summer of 2012. Study sites were situated in burned and mature black spruce forests to compare invasive plant colonization patterns. Invasive plant density estimates along the road adjacent to each site are included, as well as invasive plant density within study sites. Other data includes ground cover estimates for dominant ground cover types, estimates of seedling abundance for dominant tree species, soil paramters (mineral soil pH and mineral soil moisture, residual organic layer/ organic layer depths, and active layer depths).
Measurement of Fuel load (organic biomass) Quantities of Organic Soils, Understory Plants and Trees at Sites within the Bonanza Creek LTER Regional Site Network in Interior Alaska, 2019
This dataset contains fuel load measurements for a subset of the BNZ LTER's RSN sites (n = 28). The data for each site separted by 16 fuel types (or plant functional types) including organic soil layers (fibric, mesic), vascular understory (evergreen shrub, short deciduous shrub, graminoid, forb, tall deciduous shrub, tree seedling/sapling, dead-downed wood), nonvascular understory (feather moss, Sphagnum moss, colonizer moss, lichen) and trees (evergreen tree, deciduous tree).
Figure 2 in Aculus taihangensis (Acari: Prostigmata: Eriophyidae), a potential biological control agent identified from the highly invasive pest plant, tree of heaven, in Türkiye
Figure 2. Aculus taihangensis. Prodorsal shield and part of dorsal opisthosoma: A. Protogyne, B. Deutogyne.
Figure 4 in Aculus taihangensis (Acari: Prostigmata: Eriophyidae), a potential biological control agent identified from the highly invasive pest plant, tree of heaven, in Türkiye
Figure 4. Aculus taihangensis – Male: A. Prodorsal shield and part of dorsal opisthosoma, B. Coxigenital region.
Figure 5 in Aculus taihangensis (Acari: Prostigmata: Eriophyidae), a potential biological control agent identified from the highly invasive pest plant, tree of heaven, in Türkiye
Figure 5. Dense aggregation of Aculus taihangensis along the midrib of a leaflet of the tree of heaven.
Figure 1 in Aculus taihangensis (Acari: Prostigmata: Eriophyidae), a potential biological control agent identified from the highly invasive pest plant, tree of heaven, in Türkiye
Figure 1. Map of Türkiye showing the provinces from which leaf samples were collected from the tree of heaven in 2022 and 2023 (* indicates the site in Çanakkale Province at which the eriophyid mite, Aculus taihangensis, was collected).
LegacyVegetation: Northern Hemisphere reconstruction of past plant cover and total tree cover from pollen archives of the last 14 ka
Open the record for dataset details and reuse information.
Figure 3 in Uvariopsis dicaprio (Annonaceae) a new tree species with notes on its pollination biology, and the Critically Endangered narrowly endemic plant species of the Ebo Forest, Cameroon
Figure 3 Uvariopsis dicaprio. (A) habit, cauliflorous inflorescences on trunk; (B) leafy branch, one season's growth; (C) inflorescence, showing pedicel articulations, bracts and bracteoles; (D) flower, with one petal removed to show the staminal dome; (E) detail of sparse hairs on abaxial petal surface; (F) stamen, different views; (G) junction of base of leaf with stem, showing dome-like axillary bud. All drawn from MacKinnon 51 (K) by MEG GRIFFITHS. Full-size DOI: 10.7717/peerj.12614/fig-3
Figure 4 in Uvariopsis dicaprio (Annonaceae) a new tree species with notes on its pollination biology, and the Critically Endangered narrowly endemic plant species of the Ebo Forest, Cameroon
Figure 4 Global distribution of Uvariopsis dicaprio, together with U. korupensis and U. submontana. Full-size DOI: 10.7717/peerj.12614/fig-4
Figure 1 in Uvariopsis dicaprio (Annonaceae) a new tree species with notes on its pollination biology, and the Critically Endangered narrowly endemic plant species of the Ebo Forest, Cameroon
Figure 1 Uvariopsis dicaprio. Cauliflorous inflorescences on trunk. Photo Lorna MacKinnon. Full-size DOI: 10.7717/peerj.12614/fig-1
Figure 2 in Uvariopsis dicaprio (Annonaceae) a new tree species with notes on its pollination biology, and the Critically Endangered narrowly endemic plant species of the Ebo Forest, Cameroon
Figure 2 Uvariopsis dicaprio. Trunk apex with cauliflorous flowers and canopy. Photo Lorna MacKinnon. Full-size DOI: 10.7717/peerj.12614/fig-2
Data used in manuscript Carbon sequestration potential of street tree plantings in Helsinki
<p>Data and model runs used in manuscript "Carbon sequestration potential of street tree plantings in Helsinki". This data set includes model runs for the Surface Urban Energy and Water balance Scheme (SUEWS) and soil carbon model Yasso.</p> <p><br> The data files are:</p> <p><strong>Met_Gapfilling</strong></p> <ul> <li>ConvertMeteorologyInput.m (MATLAB) is the main file and functions gapfilling.m (with other measurements) and gapfillingfill.m (with interpolations) are used in the gap filling</li> <li>Includes files for meteorological measurement data <ul> <li>Airport: Data from Helsinki-Vantaa airport; airportdata.m, where data is cleaned</li> <li>Precipitation: Data from multiple locations; Pres_Gap.m for gap filling precipitation and function PrecipitationGap.m</li> <li>Roof: Data from rooftop</li> <li>SMEARIII: Monthly meteorological data from Kumpula (2003-2016)</li> </ul> </li> <li>SUEWS_met file for the final gap filled meteorological files for SUEWS </li> </ul> <p><strong>Fits</strong></p> <ul> <li>Includes FitCO2_parameter.m for fitting CO2 parameters for SUEWS</li> <li>Includes functions Pho6.m and Resp0.m that have the function forms</li> <li>Includes data files for measurement data <ul> <li>CO2Data: Canopy photosynthesis and canopy respiration estimated with SPP model (KumpulaX.out for Tilia site and Kumpula2X.out for Alnus site)</li> <li>Met_2016: Meteorology from Kumpula for June to August in 2016</li> <li>SWCdata: Soil water content from two streets and three soil types</li> </ul> </li> </ul> <p><strong>ModelRuns</strong></p> <ul> <li>SUEWS model runs separately for Alnus and Tilia sites <ul> <li>Includes input and output files and model codes</li> <li>Alnus site includes both the Baserun and Finalrun</li> </ul> </li> <li>Yasso model runs <ul> <li>Model run in file yasso.f90</li> <li>Output files: DecRate...txt includes three soil types and values for each month from 2002 to 2016</li> <li>Yasso_meteorology_month.m creates meteorological input files for Yasso (Clim_month_xx.txt) using meteorology from SUEWS</li> <li>Lifetimerun: 30 year simulations that includes estimations for leaves and pruned branches</li> </ul> </li> </ul> <p><strong>FigCodes</strong></p> <ul> <li>Includes MATLAB codes for figures and statistics</li> <li>Includes measurement data for CO2, sap flow and SWC</li> </ul> <p> </p>
Great tits (Parus major) flexibly learn that herbivore-induced plant volatiles indicate prey location – an experimental evidence with two tree species
<p>1. When searching for food, great tits (Parus major) can use herbivore-induced plant volatiles (HIPVs) as an indicator of arthropod presence. Their ability to detect HIPVs was shown to be learned, and not innate, yet the flexibility and generalization of learning remains unclear. 2. We studied if, and if so how, naïve and trained great tits (Parus major) discriminate between herbivore-induced and non-induced saplings of Scotch elm (Ulmus glabra) and cattley guava (Psidium cattleyanum). We chemically analysed the used plants and showed that their HIPVs differed significantly and overlapped only in a few compounds. 3. Birds trained to discriminate between herbivore-induced and non-induced saplings preferred the herbivore-induced saplings of the plant species they were trained to. Naïve birds did not show any preferences. Our results indicate that the attraction of great tits to herbivore-induced plants is not innate, rather it is a skill that can be acquired through learning, one tree species at a time. 4. We demonstrate that the ability to learn to associate HIPVs with food reward is flexible, expressed to both tested plant species, even if the plant species has not coevolved with the bird species (i.e. guava). Our results imply that the birds are not capable of generalising HIPVs among tree species but suggest that they either learn to detect individual compounds or associate whole bouquets with food rewards.</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)
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DANDI Archive for NWB datasets
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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.