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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>
Tree ring width chronologies of four Pinaceae species in boreal forests in Yakutia in 2018
<p>Tree cores and discs were collected during fieldwork in Yakutia in 2018 by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia (Kruse et al., 2019). The samples were dried, sanded, digitized and further processed by identifying the ring layers end exporting the tree ring width for each year. The site chronologies were established by cross-dating all samples to each other, which helped coping with small ring sizes but especially with missing rings, and frost rings.<br> We processed samples of four species, <em>Larix gmelinii </em>(LAGM), <em>Picea obovata </em>(PIOB), <em>Pinus sylvestris </em>(PISY) and <em>Pinus sibirica </em>(PISI). These were recorded at a variety of locations:</p> <ul> <li>LAGM from Lake Khamra sites EN18079, -80, -81, -83 (N59.974919° E112.958985°, N59.977106° E112.961379°, N59.970583° E112.987096°, N59.974714° E113.002874°)</li> <li>PIOB from Lake Khamra sites EN18079, -81, -83 (59.974919° E112.958985°, 59.970583° E112.987096°, 59.974714° E113.002874°)</li> <li>PISI from Lake Khamra site EN18080 (N59.977106° E112.961379°)</li> <li>PISY from different sites between EN18061 (N62.076376° E129.618586°) and EN18077 (N61.892568° E114.288623°)</li> </ul> <p><strong>Data format</strong><br> The data consists of one file in dendrochronological TUCSON format without header for each of the four tree species.</p> <p><strong>Additional information</strong><br> This data is linked to further information about individual trees and their sites as published in: van Geffen, Femke; Schulte, Luise; Geng, Rongwei; Heim, Birgit; Pestryakova, Luidmila A; Herzschuh, Ulrike; Kruse, Stefan (2021): Tree height and crown diameter during fieldwork expeditions that took place in 2018 in Central Yakutia and Chukotka, Siberia. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.932817<br> and an extension to: Shevtsova, Iuliia; Kruse, Stefan; Herzschuh, Ulrike; Brieger, Frederic; Schulte, Luise; Stuenzi, Simone Maria; Pestryakova, Luidmila A; Zakharov, Evgenii S (2020): Individual tree and tall shrub partial above-ground biomass of central Chukotka in 2018. PANGAEA, https://doi.org/10.1594/PANGAEA.923784<br> Information about the expedition in 2018 in: Kruse, Stefan; Bolshiyanov, Dimitry Yu; Grigoriev, Mikhail N; Morgenstern, Anne; Pestryakova, Ludmila A; Tsibizov, Leonid; Udke, Annegret (2019): Russian-German Cooperation: Expeditions to Siberia in 2018. Berichte zur Polar- und Meeresforschung = Reports on Polar and Marine Research, 734, 257 pp, https://doi.org/10.2312/BzPM_0734_2019</p> <p> </p>
Individual tree aboveground biomass of four Pinaceae species in boreal forests in Yakutia in 2018
<p>Samples to estimate aboveground tree biomass for four boreal forest species (<em>Larix gmelinii</em>, <em>Picea obovata</em>, <em>Pinus sylvestris</em>, <em>Pinus sibirica</em>) were collected during fieldwork in Yakutia in 2018 by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia (Kruse et al., 2019). From each of the visited site, three living trees (a small, a medium-sized and the talles tree) per each site were cut down after estimating the quantity of the different types to be sampled, namely branches, needles, cones, making up the tree. Further, to estimate the stem weight, tree discs were taken. The discs were taken at the base of a tree (0 cm, disc A), breast height (130 cm, disc B) and top/close to the top of a tree (260 cm, disc C). If the tree was small with <1.3 m, its stem is included as woody biomass in the branch sample. To estimate each tree's stem biomass, the stem was assumed to have a cone shape. Dead trees were also sampled, if present. All harvested samples were weighed fresh in the field and subsampled. The dry weight of all subsamples was recorded after oven drying (60 °C, 48 h for needle and branch samples, up to one week for tree stem discs). A detailed protocol for total tree and shrub AGB estimation can be found in Shevtsova, et al. (2020).</p> <p><strong>Data format</strong><br> The data consists of one table for each of the four species. The columns (N=13) contain the follwoing information:<br> 1. TreeDataBaseID -> unique Tree Data Base identifier of the individual<br> 2. Site -> Sampling site name<br> 3. SampleID -> Field name given to the individual<br> 4. Species -> Species name<br> 5. Height_cm -> Height of the tree individual in cm<br> 6. Vitality -> Estimate of the vitality state in 6 levels, ++ very good, + good, 0 mediocre, - bad, -- very bad, dead<br> 7. NeedleWeight_g -> Dry weight of needles in g<br> 8. StemWeight_g -> Dry weight of the stem in g<br> 9. BiomassBranchStatus -> 1 if branches are present and included in the biomass estimate or not<br> 10. TotalWeightNonStem_g -> Dry weight of all parts but the stem, which are needles, branches and cones in g<br> 11. DiameterBasal_cm -> Stem diameter at tree stem base (0 cm above ground) in cm<br> 12. DiameterBreast_cm -> Stem diameter at breast height (130 cm above ground) in cm<br> 13. CrownDiameter_cm -> Mean crown diameter in cm</p> <p><strong>Additional information</strong><br> This data is linked to further information about individual trees and their sites as published in: van Geffen, Femke; Schulte, Luise; Geng, Rongwei; Heim, Birgit; Pestryakova, Luidmila A; Herzschuh, Ulrike; Kruse, Stefan (2021): Tree height and crown diameter during fieldwork expeditions that took place in 2018 in Central Yakutia and Chukotka, Siberia. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.932817<br> Information about the expedition in 2018 in: Kruse, Stefan; Bolshiyanov, Dimitry Yu; Grigoriev, Mikhail N; Morgenstern, Anne; Pestryakova, Ludmila A; Tsibizov, Leonid; Udke, Annegret (2019): Russian-German Cooperation: Expeditions to Siberia in 2018. Berichte zur Polar- und Meeresforschung = Reports on Polar and Marine Research, 734, 257 pp, https://doi.org/10.2312/BzPM_0734_2019<br> Aboveground estimation protocol and further data in: Shevtsova, Iuliia; Kruse, Stefan; Herzschuh, Ulrike; Brieger, Frederic; Schulte, Luise; Stuenzi, Simone Maria; Pestryakova, Ludmila A; Zakharov, Evgenii S (2020): Total above-ground biomass of 39 vegetation sites of central Chukotka from 2018. PANGAEA, https://doi.org/10.1594/PANGAEA.923719</p>
Potential Tree Species Richness in the Forests of New Caledonia
<h1>Description</h1> <p>This dataset aims to represent, in geographic space, the potential distribution of biological tree richness in New Caledonian forests according to a 1 ha grid based on the observed distribution of 148,085 occurrences for 1112 tree species.</p> <p>For each species, we constructed the environmental niche based on 7 abiotic variables (rainfall, slope, elevation, compound topographic index, substrate, sunshine index, distance to the east coast; cf. Pouteau et al., 2015, 2019 for details). We used species distribution models (SDM) and stacked species distribution models (S-SDM) through the R-package SSDM (Schmitt et al., 2017).</p> <p>According to the S-SDM model, the potential richness ranges between 18 and 355 tree species per hectare in New Caledonia. We adjusted this range to the richness observed on 24 1 ha plots from the Permanent Plant Inventory Network of New Caledonia (NC-PIPPN), which ranges between 35 and 121 tree species per hectare. Finally, we clipped the resulting raster with the forest map of New Caledonia (version 2024, Birnbaum et al., 2024) to produce the raster of potential distribution of biological tree richness in the New Caledonian forests at 1 ha resolution.</p> <h1>Content</h1> <p>This dataset was produced, analyzed, and verified using a combination of open-source software, including QGIS, PostgreSQL, PostGIS, Python, R, and the GDAL library, all running on Linux.</p> <ul> <li>amap_raster_forest_richness.tif is a GeoTIFF utilizing the WGS84 international coordinate system and consists of a single band with graduated values ranging from 35 to 121 potential tree species per hectare. The NoData value was set to 0.</li> <li>amap_raster_forest_richness.png is a image illustrating the spatial distribution of the data and values</li> </ul> <h1>Limitations</h1> <p>This dataset is strictly based on the relationship between a few environmental variables and a limited set of tree species occurrences. While it provides a valuable overview of potential tree species richness, it represents only a part of the complex biotic and abiotic interactions that lead to the effective presence or absence of a species in the environment. Consequently, the projection of these probabilities onto the geographical space provides only an overview of the potential richness of forest fragments, which should not be considered as the observed diversity.</p> <p>Additionally, due to a lack of occurrence data, only the Grande-Terre forest is covered in this raster.</p>
Landsat bands (cloud free), tree cover (2000, 2010), bare-ground and surface water occurrence at 250 m based on GlobalForestWatch and USGS
<p>Landsat bands (cloud free) and tree cover (2000) based on Hansen et al. (2013), global surface water occurrence based on Pekel at al. (2016), and tree cover and bare-ground cover (2010) based the USGS land cover mapping projects (University of Maryland, Department of Geographical Sciences and USGS). All layers resampled to spatial resolution 1/480 d.d. (about 250 m) using gdalwarp with "average" resampling. Antarctica is not included. Original layers are available at 30 m resolution.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>lcv = theme: land cover,</li> <li>bareground = variable: occurrence of bareground,</li> <li>landsat.usgs = determination method: Landsat landcover at 30 m resolution project (https://landcover.usgs.gov/glc/),</li> <li>p = probability or fraction,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2010..2010 = time reference: year 2010,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Tree-covered and intact forest landscapes BC1000, 1995, 2000, 2005, 2010, 2013, 2016 at 250 m
<p>Based on the <a href="http://www.unep-wcmc.org/resources-and-data/generalised-original-and-current-forest">UNEP historic forest cover map</a>, ESA land cover time series and <a href="http://www.intactforests.org/data.ifl.html">intact forest landscape (IFL 2000, 2013 and 2016) data</a>. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>ldg = theme: land degradation,</li> <li>forest.cover = variable: forest / tree cover,</li> <li>esacci.ifl = determination method: combination of ESA land cover and IFL maps,</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>1995 = time reference: year 1995,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Trees (Comune di Napoli)
<p>STL and Urban Atlas based data subset, where every Urban Atlas element with CODE 31000 as well as all STL elements were extracted as tree elements with the next combined information:</p> <p>gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint hillshade_green_fraction real</p> <p>This data is an input for local effects calculation.</p>
IPTIS (individual pomelo tree image sample) datasets
<p>These datasets include 480 clip images from two study sites (i.e., Site A and B) totally. The origional UAV-based images were captured with DJI drones on four different dates, i.e., 3 Debruary 2021, 12 March 2021, 12 April 2021, and 16 January 2022. They were proceeded into four separate datasets according to the date and one in total. They were used for the study on Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5 by Y Xiong, X Zeng, W Lai, J Liao, Y Chen, M Zhu, and K Huang, which was published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 7554 - 7576, 2024(22 March 2024). https://doi.org/10.1109/JSTARS.2024.3379522.<br>They were named IPTIS (individual pomelo tree image sample) datasets for short.</p>
Data for estimating spruce tree health using drone-based RGB and multispectral imagery
<p>The dataset contains multispectral and RGB orthomosaics (.tif), and photogrammetric point clouds (.laz) of four study areas (about 25 ha each), where bark beetle-related decline of Norway spruce has been observed in Helsinki, Finland. The filenames refer to Area 1 (Männikkötie), Area 2 (Maunulanmaja), Area 3 (Hakuninmaa), and Area 4 (Paloheinä), described in detail in Junttila et al. 2022. Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season, Remote Sensing 14(4), 909: <a href="https://doi.org/10.3390/rs14040909">https://doi.org/10.3390/rs14040909</a> </p> <p>The image data was acquired between 11th and 14th September 2020.</p> <p>RE = Red-Edge M multispectral data<br>RGB = RGB data (Phantom 4 Pro)<br>Altum = Altum multispectral data</p> <p>The ground sampling distances (GSD) were approximately 3 cm, 5 cm, and 8 cm for RGB, Altum, and RedEdge, respectively.</p> <p>The field reference data file contains 556 geolocated trees assessed in the field (between 11.9. and 17.9.2020), of which 203 were dead and 353 were alive. The data is in polygon format, representing the crown delineation done during the data processing. The file includes tree heights estimated from airborne laser scanning data, dbh (for a subset of trees), discoloration, defoliation, resin flow, bark structural damage, and canopy size estimates. More details are in the journal article mentioned above.</p> <p>Key for Field Reference:</p> <p>Z = tree height<br>dbh = diameter-at-breast-height (cm)<br>vari = Discoloration (score 0-5)<br>harsu = Defoliation (score 0-4)<br>pihka = Resin flows (score 0-2)<br>runko = Stem/bark structural damage (score 0-2)<br>latvus = Significantly decreased canopy size (score 0-1)</p>
Academic Family Tree Data Export
<p>The Academic Family Tree is a live, crowdsourced project, that documents academic mentoring relationships across many fields. Data are updated continually. This snapshot was taken on 2024-10-18.</p> <p>We welcome inquiries about this dataset and are interested in learning about any new results you uncover. Contact: <a href="mailto:davids@ohsu.edu?subject=Academic%20Family%20Tree%20%2F%20Zenodo%20dataset">davids@ohsu.edu</a>.</p> <p>This dataset contains key tables from the Academic Family Tree, including information on names/institutions of academic mentoring relationships and semi-automated links of authors to publications and US grants (NSF, NIH only). A subset of publication and grant links have been validated by human users. To save space, only unique identifiers are included for publications (PMID, DOI) and grants (federal project number), without other metadata (author, title, journal, principle investigator, etc). These identifiers should be adequate to link to other databases. Also note, author-publication links are broken into several separate files. These files should be concatenated into a single table to generate a complete dataset. Some additional information is available here: <a href="https://academictree.org/export.php">https://academictree.org/export.php</a>.</p> <p>These data are associated with Liénard, J.F., Achakulvisut, T., Acuna, D.E. <em>et al.</em> Intellectual synthesis in mentorship determines success in academic careers. <em>Nature Communications</em> <strong>9, </strong>4840 (2018) (<a href="https://doi.org/10.1038/s41467-018-07034-y">https://doi.org/10.1038/s41467-018-07034-y</a>). Please cite this publication in work that uses this dataset.</p> <p>Funded by NSF Award 1933675.</p>
Data in: Reduced predation and energy flux in soil food webs by introduced tree species
<p>The introduction of non-native tree species has become a global concern and may disruptnative communities and related ecosystem functions. Soil food webs regulate organic matter decomposition and nutrient cycling in forests with their feeding activities, butevaluating consequences of tree species introduction on soil invertebrates is challengingdue to the complex trophic structure and wide range in body size of soil invertebrates. Here, we employed an energetic food web approach, and estimated the energy flux in soil food webs using a four-node model including soil meso- and macrofauna decomposers and predators. We examined pure and mixed stands of native European beech (<em>Fagus sylvatica</em>), introduced Douglas fir (<em>Pseudotsuga menziesii</em>) and native range-expanding Norway spruce (<em>Picea abies</em>) across site conditions. Compared to native forests, introduced tree species reduced total mass of macrofauna predators by 92% at sandy sites but not that of decomposers, suggesting trophic downgrading in soil food webs by Douglas fir. The energy flux in mixed forests was intermediate between respective monocultures, suggesting that tree mixtures mitigate potential negative impacts of introduced tree species on food web functioning. Across size classes, soil macrofauna responded more sensitively to changes in environmental conditions than soil mesofauna. Despite the lower total mass, the energy flux through mesofauna outweighed that through macrofauna when consideringenergy loss to predators, highlighting the importance of mesofauna for decomposition processes in forest soil food webs. Additionally, total energy flux positively correlated with species richness, pointing to the significance of soil biodiversity for trophic functionality. Overall, the study emphasizes the critical role of tree species composition, site conditionsand soil biodiversity in driving energy flux through soil food webs and maintaining forest ecosystem functions.</p>
Data, Analytical Code, and Model Outputs From: Restoration Treatments Enhance Tree Growth and Alter Climatic Constraints During Extreme Drought
<p>This archive includes data (forest inventories, tree ring measurements, climate variables), statistical code, model outputs, and a preprint copy of 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>
Datasets for phylogenetic analyses and phylogenetic trees for: Genetic barcodes for species identification and phylogenetic estimation in ghost spiders (Araneae: Anyphaenidae: Amaurobioidinae). Invertebrate Systematics, 2024
<p>We combined the COI sequence data with legacy multigene sequence data to create a new, taxon-rich phylogeny for the Amaurobioidinae. We used sequences for four loci that have been used in previous studies on the subfamily: two mitochondrial loci, COI (658bp) and ribosomal subunit 16S (16S, 410bp); and two nuclear loci, Histone H3 (H3, 327bp) and ribosomal subunit 28S (28S, 839bp). We complemented the Amaurobioidinae data with sequences from several non-amaurobioidine anyphaenids and two clubionids as outgroups. Sequence alignment was performed using the MAFFT (ver. 7.308) plugin in Geneious, allowing MAFFT to automatically select an appropriate alignment strategy based on the properties of each locus, or with the online MAFFT server (https://mafft.cbrc.jp), which consistently selected the L-INS-i algorithm. Finally, alignments of the four loci were concatenated to construct a 2234 bp multigene sequence matrix containing 692 taxa, with about 55% missing/gap data (“full” matrix henceforth). To ensure that excessive missing data did not affect the resulting topology, we also constructed a reduced matrix by removing additional COI-only specimens so that each species and morphotype was represented by just one or two specimens for which all loci were available (where possible). After realignment, this reduced matrix was 2235 bp long, included 167 taxa, and had about 22% missing/gap data (“reduced” matrix henceforth). Phylogenetic analyses under maximum likelihood, including model selection, were then conducted with IQ-TREE 2. We performed phylogenetic analyses on both concatenated matrices (the full matrix and the reduced matrix) and on each individual locus. For model selection, we provided an initial scheme that partitioned the matrix by locus, and further partitioned the protein-coding loci (COI and H3) by codon position. We used ModelFinder and searched for the best partition scheme, all in IQ-TREE. The best models (partitions) for the full dataset were: GTR+F+I+G4 (16S), GTR+F+I+I+R4 (28S), TVM+F+I+I+R2 (COI-1), TIM2+F+R4 (COI-2), GTR+F+R5 (COI-3), TVMe+G4 (H3-1-H3-2), SYM+G4 (H3-3); and for the reduced dataset: GTR+F+I+G4 (16S), GTR+F+I+G4: (28S), GTR+F+I+G4: (COI-2), GTR+F+I+G4: (COI-3), TVM+F+I+G4: (COI-1, H3-2), GTR+F+I+G4: (H3-1), GTR+F+I+G4: (H3-3). For each dataset, once the best models and partitions were defined, we executed 10 independent replicates of tree calculations followed by 1000 ultrafast bootstrap replicates, and the replicate reaching the maximum likelihood was chosen. Phylogenetic analyses under parsimony were made with TNT, under equal weights, using the “new technology” search with default values, asking for 10 independent hits to the minimal length, and submitting the resulting trees to a round of TBR branch swapping. </p>
Multi-year measurements of tree motion from an accelerometer on a spruce tree near Niwot Ridge, Colorado
<p>This repository includes 12 Hz three-axis acceleration data from an accelerometer mounted to the bole of a <em>Picea engelmannii</em> (engelmann spruce) next to the C-1 Ameriflux tower at Niwot Ridge LTER, Colorado, USA. The data were recorded from November 2014 through August 2020. More information on the installation can be found in Raleigh et al. (in review, Water Resources Research).</p> <p>The data are stored in netCDF files, chunked based on the collection date when the data were downloaded from the accelerometer.</p> <p><strong>File metadata:</strong></p> <p>Filename</p> <p>GCDC_L01_Raw_Data_Niwot_TreeXX_collection_YYYYMMDD.nc</p> <p>where</p> <p>XX = tree number (01 = spruce, 02 = fir)</p> <p>YYYYMMDD = year (YYYY), month (MM), and day (DD) of data collection</p> <p> </p> <p>Each netCDF includes four variables:</p> <p>1. serial_date = time increment (fractional days), as defined by Matlab: "A serial date number represents the whole and fractional number of days from a fixed, preset date (January 0, 0000) in the proleptic ISO calendar." The serial dates are in mountain standard time (MST) with no adjustments for daylight savings.</p> <p>2. Ax = acceleration in the vertical direction (counts)</p> <p>3. Ay = acceleration in the east-west direction (counts)</p> <p>4. Az = acceleration in the north-south direction (counts)</p> <p>To convert the "counts" unit to gravitational units (g), divide Ax, Ay, and Az each by 2048, as explained in the manufacturer's user manual.</p> <p> </p> <p> </p>
Multi-year measurements of tree motion from an accelerometer on a fir tree near Niwot Ridge, Colorado
<p>This repository includes 12 Hz three-axis acceleration data from an accelerometer mounted to the bole of an <em>Abies lasiocarpa</em> (subalpine fir) next to the C-1 Ameriflux tower at Niwot Ridge LTER, Colorado, USA. The data were recorded from November 2014 through August 2020. More information on the installation can be found in Raleigh et al. (in review, Water Resources Research).</p> <p>The data are stored in netCDF files, chunked based on the collection date when the data were downloaded from the accelerometer.</p> <p><strong>File metadata:</strong></p> <p>Filename</p> <p>GCDC_L01_Raw_Data_Niwot_TreeXX_collection_YYYYMMDD.nc</p> <p>where</p> <p>XX = tree number (01 = spruce, 02 = fir)</p> <p>YYYYMMDD = year (YYYY), month (MM), and day (DD) of data collection</p> <p> </p> <p>Each netCDF includes four variables:</p> <p>1. serial_date = time increment (fractional days), as defined by Matlab: "A serial date number represents the whole and fractional number of days from a fixed, preset date (January 0, 0000) in the proleptic ISO calendar." The serial dates are in mountain standard time (MST) with no adjustments for daylight savings.</p> <p>2. Ax = acceleration in the vertical direction (counts)</p> <p>3. Ay = acceleration in the east-west direction (counts)</p> <p>4. Az = acceleration in the north-south direction (counts)</p> <p>To convert the "counts" unit to gravitational units (g), divide Ax, Ay, and Az each by 2048, as explained in the manufacturer's user manual.</p> <p> </p>
Topography drives microgeographic adaptations of closely-related species in two tropical tree species complexes
<p>Combining LiDAR-derived topography, tree inventories, and single nucleotide polymorphisms (SNPs) from gene capture experiments, we explored genome-wide population genetic structure, covariation of environmental variables, and genotype-environment association to assess microgeographic adaptations to topography within the species complexes <em>Symphonia</em> (Clusiaceae), and <em>Eschweilera</em> (Lecythidaceae) with three species per complex and 385 and 257 individuals genotyped, respectively.</p>
Harmonized Tree Species Occurrence Points for Europe
<p>This data set is a harmonized collection of existing data from GBIF, the EU-Forest project and the LUCAS survey. It has about 3 million observations and is supplemented by variables (e.g. location accuracy, land cover type, canopy height, etc.) which enable precise filtering for specific user applications.</p> <p>The <em>RDS </em>file is created from an sf-object and suitable for fast reading in the R-programming environment. The <em>CSV.GZ</em> file contains records as a table with Easting and Northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.</p> <p><strong>The code producing this data set is <a href="https://gitlab.com/openlandmap/eu-forest-tree-point-data">publicly available on GitLab.</a></strong></p> <p>Data sets were last updated in September 2021.</p> <p>Variables:</p> <ul> <li><strong>id</strong> = unique point identifier</li> <li><strong>easting</strong> = x coordinate</li> <li><strong>northing </strong>= y coordinate</li> <li><strong>country </strong>= ISO country code</li> <li><strong>species </strong>= Latin species name</li> <li><strong>genus </strong>= genus name</li> <li><strong>scientific_name </strong>= long species name</li> <li><strong>gbif_taxon_key </strong>= taxon key from GBIF</li> <li><strong>gbif_genus_key </strong>= genus key from GBIF</li> <li><strong>taxon_rank </strong>= species or genus</li> <li><strong>year </strong>= year of observation</li> <li><strong>accessed_through </strong>= database through which data was accessed (GBIF, LUCAS, EU-Forest)</li> <li><strong>dataset_info </strong>= data set name (individual sub-data-set)</li> <li><strong>citation </strong>= DOI citation of the individual data set</li> <li><strong>license </strong>= distribution license</li> <li><strong>location_accuracy </strong>= spatial accuracy of observation (meters)</li> <li><strong>flag_location_issue </strong>= known location issues present</li> <li><strong>flag_date_issue </strong>= known date issues present</li> <li><strong>eoo </strong>= Extent of occurrence (applying the concept of natural geographical range used for the EU-Forest data set (<a href="https://www.nature.com/articles/sdata2016123">Mauri et al., 2017</a>) to all other data points. 1 = point inside species range; 0 = point outside; NA = EOO polygon not available for this species)</li> <li><strong>dbh </strong>= Diameter Breast Height (only recorded for observations from the EU-Forest data set (<a href="https://www.nature.com/articles/sdata2016123">Mauri et al., 2017</a>))</li> <li><strong>lc1 </strong>= <a href="https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2018">LUCAS</a> land cover type 1 (only recorded for observations from LUCAS data)</li> <li><strong>lc2 </strong>= <a href="https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2018">LUCAS </a>land cover type 2 (only recorded for observations from LUCAS data)</li> <li><strong>landmask_country </strong>= land mask overlay 30 meters (NA = not on land)</li> <li><strong>corine </strong>= <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc2018">CORINE 2018</a> land cover type (extracted from the 100 meter raster data set)</li> <li><strong>nightlights</strong> = <a href="https://eogdata.mines.edu/download_dnb_composites.html">light pollution</a> observed by VIIRS (proxy for remoteness / distance to human structures)</li> <li><strong>canopy_height </strong>= <a href="https://zenodo.org/record/4057883#.X3sx4-2xW9I">canopy height</a> derived from GEDI waveform LiDAR point data</li> <li><strong>natura_2000 </strong>= Natura 2000 site code (if a point falls inside a protected area (<a href="https://www.eea.europa.eu/data-and-maps/data/natura-11">GIS-layer</a>) this variable contains the site identification code; all sites can be explored on an <a href="https://natura2000.eea.europa.eu/">interactive map</a>)</li> <li><strong>freq_location </strong>= number of points with identical location (in some cases one location has multiple observation, differing in species and/or year. This may lead to difficulties in certain modeling tasks)</li> <li><strong>geometry </strong>= point geometry in ETRS89 / LAEA Europe</li> </ul> <p>See <strong><a href="https://docs.google.com/spreadsheets/d/1WM0BIaVEKxTsCISEaF76RJ8F1iWiZlDfuyQCBiF2Sxw/edit?usp=sharing">this detailed documentation</a></strong> for more insights into each variable and individual GBIF data set citations.</p> <p>If you would like to know more about the creation of this data set, see</p> <ol> <li>the R-Markdown documenting the process (<a href="https://gitlab.com/openlandmap/eu-forest-tree-point-data">GitLab repository</a>)</li> <li>the talk at OpenGeoHub Summer School 2020 (<a href="https://www.youtube.com/watch?v=5HhmLGcqXLs&list=PLXUoTpMa_9s0Ea--KTV1OEvgxg-AMEOGv&index=40">Youtube</a>)</li> </ol> <p>Some advice: This data set is a puzzle with pieces from many different sources. Take some time to explore before including it in your work. Use summary statistics to see which variables have NAs and how many. Choose your filtering criteria wisely. For example, some points with the highest location accuracy have no record for the year of observations. You would exclude these, if "year > 1990" was your criteria.</p> <p> </p> <p>This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (<a href="https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095">https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095</a>).</p>
International benchmark for ALS individual tree segmentation
<p>This upload aims to provide an international benchmark dataset for airborne LiDAR-based individual tree segmentation algorithm comparison and development.</p> <p> </p> <p> </p>
Wind energy production in forests conflicts with tree - roosting bats
<p>Many countries are investing heavily in wind power generation,<sup>1</sup> triggering a high demand for suitable land. As a result, wind energy facilities are increasingly being installed in forests,<sup>2,3</sup> despite the fact that forests are crucial for the protection of terrestrial biodiversity.<sup>4</sup> This green-green dilemma is particularly evident for bats, as most species at risk of colliding with wind turbines roost in trees.<sup>2</sup> With some of these species reported to be declining,<sup>5-8</sup> we see an urgent need to understand how bats respond to wind turbines in forested areas, especially in Europe where all bat species are legally protected. We used miniaturized global positioning system (GPS) units to study how European common noctule bats (<em>Nyctalus noctula</em>), a species that is highly vulnerable at turbines,<sup>9</sup> respond to wind turbines in forests. Data from 60 tagged common noctules yielded a total of 8129 positions, of which 2.3% were recorded at distances <100 m from the nearest turbine. Bats were particularly active at turbines <500 m near roosts, which may require such turbines to be shut down more frequently at times of high bat activity to reduce collision risk. Beyond roosts, bats avoided turbines over several kilometers, supporting earlier findings on habitat loss for forest-associated bats.<sup>10</sup> This habitat loss should be compensated by developing parts of the forest as refugia for bats. Our study highlights that it can be particularly challenging to generate wind energy in forested areas in an ecologically sustainable manner with minimal impact on forests and the wildlife that inhabit them.</p>
Data and code from "Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition"
<p>#### Data description<br> Data from large scale, long-term tree diversity experiment in southwestern France (<a href="https://sites.google.com/view/orpheeexperiment/home">ORPHEE</a>), additionally manipulating water contraint. Variables presented are soil nitrogen cycling rates measured using isotope pool dilutions.</p> <p>Companion paper is found here:</p> <p>Maxwell TL, Augusto L, Tian Y, Wanek W & Fanin N (2023). Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition. <em>European Journal of Soil Science</em>. <a href="https://doi.org/10.1111/ejss.13350">https://doi.org/10.1111/ejss.13350</a></p> <p>#### Metadata<br> Soil sampling: July 2020<br> Maxwell_ShortComm_Data.csv data description</p> <p>ID: unique identifier per sample<br> Block: numbered 1-6. Blocks 1,3,6 are control (unirrigated), Blocks, 2,4,5 are irrigated<br> Plot: numbered plot according to the ORPHEE design. Plot 1 = BP, Plot 5 = PP, Plot 9 = BP_PP<br> Espece: species ID. BP = pure birch (<em>Betula pendula</em>), PP = pure pine (<em>Pinus pinaster</em>), BP_PP (50% mixed birch-pine)<br> Rep: sample replicate, 3 replicates per plot<br> Sample name: long unique identifier per sample. Concatenation of Block, Plot, and Espece<br> PD: gross protein depolymerization rates (micrograms nitrogen per grams dry soil per day = µg N g-1 d-1)<br> AAU: gross free amino acid uptake rates (µg N g-1 d-1)<br> Cmicrobial_ug_g: microbial biomass carbon (µg C g-1)<br> Nmicrobial_ug_g: microbial biomass nitrogen (µg N g-1)<br> MRT_FAA_hrs: mean residence time of free amino acids (hours)<br> FAA_ugN_g: free amino acids (µg N g-1)<br> Moisture_percent: soil moisture percent (%)<br> N_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractable N (µg N g-1)<br> C_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractableC (µg C g-1)</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.