Find research datasets worth reusing

Effects of Warming on Tree Species Recruitment at Harvard Forest and Duke Forest since 2009

Climate change is restructuring forests of the United States, although the details of this restructuring are currently uncertain. Rising temperatures of 2 to 8 deg C and associated changes in soil moisture will shift the competitive balance between species that compete for light and water, changing their abilities to produce seed, germinate, grow, and survive. We are using large scale experiments to determine the effects of warming on the most sensitive stage of species distributions, i.e., recruitment, in mixed deciduous forests in southern New England and in the Piedmont region of North Carolina. Two questions organize our proposed research: (1) Might temperate tree species near the "warm" end of their range in the eastern United States decline in abundance during the coming century due to projected warming? and (2) Might trees near the "cool" end of their range in the eastern United States increase in abundance, or extend their range, during the coming 100 years because of projected warming? To explore these questions, we are exposing seedlings to air and soil warming experiments in two eastern deciduous forest sites; one at the Harvard Forest (HF) in central Massachusetts, and the other at the Duke Forest (DF) in the Piedmont region of North Carolina. We focus on tree species common to both Harvard and Duke Forests (such as red, black, and white oaks), those near northern range limits (black oak, tulip poplar), and those near southern range limits (yellow birch, sugar maple). At each site, we plant seeds in common gardens established in temperature-controlled, open-top chambers. The experimental design is replicated and fully factorial and involves three temperature regimes (ambient, +3 deg C and +5 deg C) and two light regimes (closed forest canopy (low light) and gap conditions (high light)). Measured variables include Fall/Spring responses to temperature and mid-Summer responses to low soil moisture. This research will advance our understanding of how the abu

Whole-Tree Nonstructural Carbohydrate Budgets in Five Species at Harvard Forest 2014

We measured nonstructural carbohydrate (NSC) concentrations throughout the year in the branches, stemwood, and roots of five temperate tree species. These NSC concentrations were used in two ways. First, we scaled up concentrations to the whole-tree level using allometric equations and compared NSC storage between these five species to determine the size and seasonal fluctuation of whole-tree total NSC budgets as well as the contribution of individual organs. Second, for four of these species, we assessed the radial patterns and seasonality of NSC concentrations in the stemwood based on contrasting wood anatomy (ring-porous vs. diffuse-porous).

Age of Nonstructural Carbohydrates in Four Tree Species at Harvard Forest 2015

We estimated the mean age of sugars within and between different organs of four temperate tree species using the radiocarbon (carbon-14) bomb spike approach. Radial patterns of carbon-14 in the stemwood and coarse roots showed that sugars tended to became older when moving towards the pith.

Nonstructural Carbon, Phenology and Wood Formation in Three Tree Species at Harvard Forest 2017-2019

This data set comprises various observations and measurements across the 2017 to 2019 growing season for seven red maple (Acer rubrum), eight red oak (Quercus rubra), and six white pine (Pinus strobus) in the Prospect Hill Tract of Harvard Forest. The observations include spring and fall leaf phenology and basic allometry, such as diameter at breast height and height. For the leaf phenology, we followed the protocol from John O’Keefe (HF003). Measurements include wood growth data from weekly microcores and a three time characterisation of growing season nonstructural carbon concentrations (soluble sugars and starch) for stems and leaves. Additionally, stem CO2 efflux was measured once a month for the 2018 growing season and weekly for the 2019 growing season.

openCC (other)Sep 2022View details →

edi56/100

Carbon Isotope and Ring Width Measurements from Tree Rings of Selected Canopy Species at Six Sites in the Eastern United States

Forest Water Use Efficiency (WUE) is defined as the ratio of carbon uptake per unit water vapor loss via transpiration. Micrometeorological measurements suggest that forest WUE has dramatically increased over the last two decades, in excess of what would be expected from increases in atmospheric carbon dioxide concentrations. Coinciding with observed trends in forest WUE have been marked decreases in acid deposition throughout much of North America and Europe. There is evidence that acid deposition may impact forest WUE, either by altering the availability of nutrients in forest soils or by directly affecting foliar physiology. Changes in WUE could also lead to changes in stream discharge from forested catchments. The hypothesized response of forests to changing levels of acid deposition is not currently considered in the land surface components of global climate models (GCMs). Since carbon dioxide and water vapor are the two most important greenhouse gases, it is vital to accurately model their land-atmosphere exchange. This research uses a catchment-based approach to investigate the effects of changing acid deposition on forest WUE. Tree ring carbon isotopes reconstruct historical WUE time series within six catchments that have been differentially impacted by acid deposition due to distinctions between their underlying bedrock mineralogy and geological histories. The research also capitalizes on experimental treatments that have altered soil biogeochemistry in paired catchment designs (Bear Brook, ME; Hubbard Brook, NH; and Fernow Experimental Forest, WV). Additional watersheds that vary in underlying bedrock chemistry are also used in this research to examine tree-ring WUE time series as natural experiments along a base-cation gradient. These watersheds include Sleepers River, VT; Hubbard Brook, NH; Cone Pond Watershed, NH; and Shenandoah National Park, VA.

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Functional Traits of Selected Tree Species in Harvard Forest, New Hampshire, and Southern Quebec 2015

Increasing evidence suggests that species' phenological responses may predict their performance with warming, but this work has generally ignored whether phenology is correlated with other traits known to drive plant performance. This is perhaps surprising given that interest in functional traits has also increased in recent decades, yet within the functional traits literature there has been an equally limited consideration of phenology, perhaps because robustly estimating it is time-intensive, and simple field estimates will show extreme variation across sites of different latitudes and climate regimes. Here we collected a suite of trait data on the same species for which we collected phenological data (see related dataset HF314, Leaf and Flower Phenology of Woody Plant Species at Harvard Forest and Southern Quebec 2015) to help address this gap. We focused on populations of trees in temperate forests in the Northeast face, which face different environmental conditions across their ranges. This project measured functional traits of trees at two to four sites, to provide a foundation for studies on the relationship between range shift, phenology, and functional traits.

openCC0Mar 2025View details →

zenodo52/100

Presence observations for six tree species prioritized for forest landscape restoration in Ethiopia

Description:Geolocations of presence occurrences for a selection of six species (Cordia africana, Croton macrostachyus, Eucalyptus globulus, Faidherbia albida, Grevillea robusta, Juniperus procera) sourced from databases (GBIF, RAINBIO) and from the scientific literature.Each record is associated with a DOI, link, or citation to the original source of the data. Observations were filtered using the R package CoordinatesCleaner (Zizka et al. 2019) with the clean_coordinates function to filter for errors that are common to biological collections.The breakdown of the number of observations by species is: Cordia africana (84); Croton macrostachyus (129); Eucalyptus globulus (20); Faidherbia albida (31); Grevillea robusta (350); Juniperus procera (115).

opencc-by-4.0Feb 2022View details →

edi52/100

Overwintering Fires from 2009-2010 Burns near Fairbanks, Alaska: Pre-fire Tree Species Density and Combustion Collected 2023

This dataset contains data from adjacent overwintering and single-season burn sites. For the overwintering fires, we targeted locations that had burned in the summers of 2009, smouldered through the winter months, and reignited in 2010. Adjacent to these overwintering sites, we identified single-season burn sites from within portions of the 2009 fires that were unaffected by overwintering. A total of seven overwintering fire sites and four single-season fire sites were sampled. Data inlcudes within plot measurments of post-fire seedling composition and density, residual SOL, burn depth estimated by black spruce adventitious roots, thaw depth, and pre-fire tree species composition and estimates of combustion. This is one of three packages from this project; this one contains the pre-fire tree species density and combustion data.

openOpenNov 2024View details →

edi52/100

Hubbard Brook Experimental Forest: Relations of the O-horizon with canopy tree species and hydropedologic soil types, 2021

As the interface between plants and soil, the organic horizon is the foundation of forest ecosystems. Two potential predictors of O-layer properties, vegetation and mineral soil type, are difficult to separate because they typically covary. We conducted a factorial study involving four canopy tree species and two soil types with distinctly different hydrology and topographic position to parse patterns in chemistry and microbiota of the O-layer in a north-temperate deciduous forest. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Jan 2025View details →

opencc-by-4.0Nov 2024View details →

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> 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. 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: Carpinus betulus, Corylus avellana, Frangula alnus, Populus tremula, Sorbus aucuparia, Tilia cordata, Tilia platyphyllos, and Ulmus laevis. The profiles were established using microsatellite markers (11 to 24 markers per species).  New genetic markers were developed for Carpinus betulus and Ulmus laevis. PCR products were run on an ABI 3500 Genetic Analyser (Thermo Fisher Scientific). <h2>Files</h2> 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.  <h3>Species_genotypes</h3> These files contain the genetic fingerprints of the parent trees of autochthonous Flemish seed orchards. Missing data is indicated as ‘MD’. For Carpinus betulus, 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. The next metadata is additionally given: - Species: the Latin name of the species - Seed_orchard: the name of the seed orchard in which the genotypes are located - 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) - Genotype: the fieldname given to the genotype - 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.  - Year_sampled: the year in which the genotypes were sampled in the respective seed orchard for genetic analysis. <h3>Species_binsets</h3> 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 Tilia platyphyllos and Tilia cordata, the same binsets were used.

opencc-by-4.0Feb 2022View details →

Tree ring width chronologies of four Pinaceae species in boreal forests in Yakutia in 2018

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. We processed samples of four species, Larix gmelinii (LAGM), Picea obovata (PIOB), Pinus sylvestris (PISY) and Pinus sibirica (PISI). These were recorded at a variety of locations: <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> Data format The data consists of one file in dendrochronological TUCSON format without header for each of the four tree species. Additional information 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 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 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

opencc-by-4.0Feb 2022View details →

Individual tree aboveground biomass of four Pinaceae species in boreal forests in Yakutia in 2018

Samples to estimate aboveground tree biomass for four boreal forest species (Larix gmelinii, Picea obovata, Pinus sylvestris, Pinus sibirica) 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). Data format The data consists of one table for each of the four species. The columns (N=13) contain the follwoing information: 1. TreeDataBaseID -> unique Tree Data Base identifier of the individual 2. Site     -> Sampling site name 3. SampleID -> Field name given to the individual 4. Species    -> Species name 5. Height_cm -> Height of the tree individual in cm 6. Vitality -> Estimate of the vitality state in 6 levels, ++ very good, + good, 0 mediocre, - bad, -- very bad, dead 7. NeedleWeight_g -> Dry weight of needles in g 8. StemWeight_g    -> Dry weight of the stem in g 9. BiomassBranchStatus -> 1 if branches are present and included in the biomass estimate or not 10. TotalWeightNonStem_g -> Dry weight of all parts but the stem, which are needles, branches and cones in g 11. DiameterBasal_cm -> Stem diameter at tree stem base (0 cm above ground) in cm 12. DiameterBreast_cm -> Stem diameter at breast height (130 cm above ground) in cm 13. CrownDiameter_cm -> Mean crown diameter in cm Additional information 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 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 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

opencc-by-4.0Jul 2024View details →

Potential Tree Species Richness in the Forests of New Caledonia

<h1>Description</h1> 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. 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). 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. <h1>Content</h1> 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. <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> 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. Additionally, due to a lack of occurrence data, only the Grande-Terre forest is covered in this raster.

opencc-by-4.0Oct 2024View details →

Data in: Reduced predation and energy flux in soil food webs by introduced tree species

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 (Fagus sylvatica), introduced Douglas fir (Pseudotsuga menziesii) and native range-expanding Norway spruce (Picea abies) 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.

opencc-by-4.0Nov 2024View details →

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

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.

opencc-by-4.0Apr 2021View details →

Topography drives microgeographic adaptations of closely-related species in two tropical tree species complexes

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 Symphonia (Clusiaceae), and Eschweilera (Lecythidaceae) with three species per complex and 385 and 257 individuals genotyped, respectively.

opencc-by-4.0Sep 2020View details →

Harmonized Tree Species Occurrence Points for Europe

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. The RDS file is created from an sf-object and suitable for fast reading in the R-programming environment. The CSV.GZ 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. The code producing this data set is <a href="https://gitlab.com/openlandmap/eu-forest-tree-point-data">publicly available on GitLab.</a> Data sets were last updated in September 2021. Variables: <ul> <li>id = unique point identifier</li> <li>easting = x coordinate</li> <li>northing = y coordinate</li> <li>country = ISO country code</li> <li>species = Latin species name</li> <li>genus = genus name</li> <li>scientific_name = long species name</li> <li>gbif_taxon_key = taxon key from GBIF</li> <li>gbif_genus_key = genus key from GBIF</li> <li>taxon_rank = species or genus</li> <li>year = year of observation</li> <li>accessed_through = database through which data was accessed (GBIF, LUCAS, EU-Forest)</li> <li>dataset_info = data set name (individual sub-data-set)</li> <li>citation = DOI citation of the individual data set</li> <li>license = distribution license</li> <li>location_accuracy = spatial accuracy of observation (meters)</li> <li>flag_location_issue = known location issues present</li> <li>flag_date_issue = known date issues present</li> <li>eoo = 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>dbh = 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>lc1 = <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>lc2 = <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>landmask_country = land mask overlay 30 meters (NA = not on land)</li> <li>corine = <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>nightlights = <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>canopy_height = <a href="https://zenodo.org/record/4057883#.X3sx4-2xW9I">canopy height</a> derived from GEDI waveform LiDAR point data</li> <li>natura_2000 = 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>freq_location = 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>geometry = point geometry in ETRS89 / LAEA Europe</li> </ul> See <a href="https://docs.google.com/spreadsheets/d/1WM0BIaVEKxTsCISEaF76RJ8F1iWiZlDfuyQCBiF2Sxw/edit?usp=sharing">this detailed documentation</a> for more insights into each variable and individual GBIF data set citations. If you would like to know more about the creation of this data set, see <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> 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.   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>).

opencc-by-4.0Feb 2023View details →

Data and code from "Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition"

#### Data description 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. Companion paper is found here: 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. European Journal of Soil Science. <a href="https://doi.org/10.1111/ejss.13350">https://doi.org/10.1111/ejss.13350</a> #### Metadata Soil sampling: July 2020 Maxwell_ShortComm_Data.csv data description ID: unique identifier per sample Block: numbered 1-6. Blocks 1,3,6 are control (unirrigated), Blocks, 2,4,5 are irrigated Plot: numbered plot according to the ORPHEE design. Plot 1 = BP, Plot 5 = PP, Plot 9 = BP_PP Espece: species ID. BP = pure birch (Betula pendula), PP = pure pine (Pinus pinaster), BP_PP (50% mixed birch-pine) Rep: sample replicate, 3 replicates per plot Sample name: long unique identifier per sample. Concatenation of Block, Plot, and Espece PD: gross protein depolymerization rates (micrograms nitrogen per grams dry soil per day = µg N g-1 d-1) AAU: gross free amino acid uptake rates (µg N g-1 d-1) Cmicrobial_ug_g: microbial biomass carbon (µg C g-1) Nmicrobial_ug_g: microbial biomass nitrogen (µg N g-1) MRT_FAA_hrs: mean residence time of free amino acids (hours) FAA_ugN_g: free amino acids (µg N g-1) Moisture_percent: soil moisture percent (%) N_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractable N (µg N g-1) C_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractableC (µg C g-1)