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9,204 results for “tree”
L4E - Maximum tree height extracted from LiDAR transects over the Brazilian Amazon
<p>Between 2016 and 2018, the EBA airborne missions (conducted by the Brazilian National Institute for Space Research (INPE) and funded by Amazon Fund) collected airborne lidar transects of 375 ha (12.5 x 0.3 km) each. A majority of the transects were flown over randomly selected locations of old growth and second growth as forests defined by the PRODES and TerraClass databases (PRODES, INPE, 2016; TerraClass, INPE, 2014). PRODES separates forests from non-forest while TerraClass identifies second growth forest and other land covers.</p>
Data for investigating structural complexity of individual Scots pine trees
<p>Tree functional traits together with processes such as forest regeneration, growth, and mortality affect forest and tree structure. Forest management inherently impacts these processes. Moreover, forest structure, biodiversity, resilience, and carbon uptake can be sustained and enhanced with forest management activities. To assess structural complexity of individual trees, comprehensive and quantitative measures are needed, and they are often lacking for current forest management practices. Fractal analysis and a single scale, independent metric called box dimension offer means for assessing structural complexity of individual trees. Terrestrial laser scanning (TLS) point clouds provide three-dimensional (3D) information on trees that can be utilized in generating the box dimension metric. This data set includes information needed for generating the box dimension from 741 individual Scots pine (<em>Pinus sylvestris</em> L.) trees from 9 sample plots with different thinning treatments located in southern boreal forests. The thinning treatments include two intensities of thinning and control treatment (i.e., no thinning treatment since the establishment). The data set can be used in characterizing structural complexity of individual Scots pine trees of various size as well as assessing effects of various thinning treatments on it.</p> <p>Please see the data descriptor for more information on the data structure and its possibilities.</p> <p>Please keep the designated corresponding author informed of any plans to use the data. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the data must include proper acknowledgement.</p>
Supplementary Material: "A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data"
<p><strong>Supplementary Material</strong></p> <p>This material regards the paper entitled "<em>A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data</em>".</p> <p>The Readme.txt file explains all the contents of the data package, which consists of the data supporting the paper and the MATLAB script for the Individual Tree Detection and Measurement (ITDM).</p> <p>Please cite the related article if using the data or the script.</p> <p>Latella, M., Sola, F., & Camporeale, C. (2021). A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data. Remote Sensing, 13(2), 322.</p>
Data from: Choosy beetles: how host trees and southern boreal forest naturalness may determine dead wood beetle communities
<p>See methods section of paper for detailed information on dataset and sources; briefly, these .csv files includes numbers of each beetle species captured at all sites used in the project, as well as information about each site and about each species.</p> <p> </p> <p>Data from:</p> <p><strong>Choosy beetles: how host trees and southern boreal forest naturalness may determine dead wood beetle communitie</strong><strong>s</strong></p> <p>Ryan C. Burner, Tone Birkemoe, Jörg G. Stephan, Lukas Drag, Jörg Muller, Otso Ovakainen, Mária Potterf, Olav Skarpaas, Tord Snall, Anne Sverdrup-Thygeson</p> <p>Forest Ecology and Management, 2021</p> <p> </p> <p>From abstract of paper:</p> <p>Wood-living beetles make up a large proportion of forest biodiversity, and contribute to important ecosystem services, including decomposition. Beetle communities in managed southern boreal forests are less species rich than in natural and near-natural forest stands. In addition, many beetle species rely primarily on specific tree species. Yet, the associations between individual beetle species, forest management category, and tree species are seldom quantified, even for red-listed beetles. We compiled a beetle capture dataset from flight intercept traps placed in Norway spruce (<em>Picea abies</em>), oak (<em>Quercus sp.</em>), and Eurasian aspen (<em>Populus tremulae</em>) trees in 413 sites in mature managed forest, near-natural forest, and clear-cuts in southeastern Norway. We used joint species distribution models to estimate the strength of associations for 368 saproxylic beetle species (including 20 vulnerable, endangered, or critical red-listed species) for each forest management category and tree species. Tree species on which traps were mounted had the largest effect on beetle communities; oaks had the most highly associated beetle species, including most of the red-listed species, followed by Norway spruce and Eurasian aspen. Most beetle species were more likely to be captured in near-natural than in mature managed forest. Our estimated associations were compatible – for many species – with categorical classifications found in several existing databases of saproxylic beetle preferences. These quantitative beetle-habitat associations will improve future analyses that have typically relied on categorical classifications. Our results highlight the need to prioritize conservation of near-natural forests and oak trees in Scandinavia to protect the habitat of many red-listed species in particular. Furthermore, we underline the importance of carefully considering the species of trees on which traps are mounted in order to representatively sample beetle communities in forest stands.</p>
Terrestrial laser scanning - RIEGL VZ-1000, individual tree point clouds and cylinder models, Belgian hedgerows and tree rows
<p>Terrestrial laser scans were acquired for 69 trees (<em>Quercus robur</em>: 39 trees; <em>Alnus glutinosa</em>: 19 trees; <em>Betula pendula: </em>11 trees) in hedgerows and tree rows in agricultural lands in Flanders, Belgium. We used a RIEGL VZ-1000 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH, Austria) with a beam divergence of 0.35 mrad operating in the infrared (wavelength 1550 nm) with a range up to 1000 m. We scanned leaf-off and all recorded variables are valid for overbark measurements. Individual trees were manually extracted from the co-registered point cloud in RiSCAN PRO software (provided by RIEGL). To the extracted trees, quantitative structure models (QSM) were fitted. We used the QSMs to derive branch length (m), total wood volume (m³) and merchantable wood volume (m³, using only cylinders with diameter > 7 cm). From the point clouds, we extracted the tree structural features such as crown projection (m²), maximum crown diameter (m) and tree height (m). Biomass expansion factors (BEF) were calculated by dividing total tree volume to merchantable tree volume. We expressed the age dependency of the BEF values via non-linear regression models. See Van Den Berge et al. (2021) for further information (DOI: 10.1007/s12155-021-10250-y).</p>
Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation
<p>Big, time-scaled phylogenies are fundamental to connecting evolutionary processes to modern biodiversity patterns. Yet inferring reliable phylogenetic trees for thousands of species involves numerous trade-offs that have limited their utility to comparative biologists. To establish a robust evolutionary timescale for all ~6000 living species of mammals, we developed credible sets of trees that capture root-to-tip uncertainty in topology and divergence times. Our 'backbone-and-patch' approach to tree-building applies a newly assembled 31-gene supermatrix to two levels of Bayesian inference: (i) backbone relationships and ages among major lineages, using fossil node- or tip-dating; and (ii) species-level 'patch' phylogenies with non-overlapping in-groups that each correspond to one representative lineage in the backbone. Species unsampled for DNA are either excluded ('DNA-only' trees) or imputed within taxonomic constraints using branch lengths drawn from local birth-death models ('completed' trees). Joining time-scaled patches to backbones results in species-level trees of extant Mammalia with all branches estimated under the same modeling framework, thereby facilitating rate comparisons among lineages as disparate as marsupials and placentals. We compare our phylogenetic trees to previous estimates of mammal-wide phylogeny and divergence times, finding that (i) node ages are broadly concordant among studies, and (ii) recent (tip-level) rates of speciation are estimated more accurately in our study than in previous 'supertree' approaches where unresolved nodes led to branch length artifacts. Credible sets of mammalian phylogenetic history are now available for download at <a href="http://vertlife.org/phylosubsets">http://vertlife.org/phylosubsets</a>, enabling investigations of long-standing questions in comparative biology.</p>
Image Repository Decision Tree - Where do I deposit my imaging data
<p>Depositing data in quality data repositories is one crucial step towards FAIR (Findable, Accessible, Interoperable, and Reusable) data. Accordingly, Euro-BioImaging strongly encourages sharing scientific imaging data in established, thematic repositories. </p> <p>To guide you in the selection of appropriate repositories, we have created an overview of available repositories for different types of image data, including their scope and requirements. This decision tree guides you through questions about your data and directs you to the correct repository, and/or provides instructions for further processing to meet the critera of the repositories. </p> <p>Three seperate trees are provided for different classes of imaging data: open bioimage data, preclinical data, and human imaging data. These versions with three trees can be used for web-view. Update: also the editable versions in powerpoint format (.pptx) are now provided. Please be aware that opening the versions with another program might lead to shifted formatting.</p> <p>Update: we now also provide ready-to-print versions designed to be printed on A3 format. One page shows the open bioimaging data tree and one page combines the preclinical and human imaging data trees. Also the editable versions of these are provided.</p>
Tree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India
<p><strong>TITLE</strong></p><p><strong>Tree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India</strong><br> </p><p><strong>DESCRIPTION</strong></p><p>This dataset contains point-centred quarter (PCQ) data on trees and habitat structure measurements data from rainforest fragments and some coffee plantations in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. The data were gathered to quantity habitat parameters for bird and small carnivorous mamm community studies. Data were gathered mainly by T. R. Shankar Raman and Divya Mudappa (2000 to 2003), Hari Sridhar (2005), and Akshay Surendra (2019).</p><p><strong>Publications</strong></p><p>Specific portions of the dataset have been used in the following publications:</p><ul><li>Mudappa, D. 2001. <a href="https://hdl.handle.net/10603/101890">Ecology of the brown palm civet <i>Paradoxurus jerdoni</i> in the tropical rainforests of the Western Ghats, India</a>. Ph. D. thesis, Bharathiar University, Coimbatore. https://hdl.handle.net/10603/101890</li><li>Raman, T. R. S. 2001. <a href="https://archive.org/details/raman-2001-ph-d-thesis-iisc">Community ecology and conservation of mid-elevation tropical rainforest bird communities in the southern Western Ghats, India</a>. PhD thesis, Indian Institute of Science, Bangalore. https://archive.org/details/raman-2001-ph-d-thesis-iisc</li><li>Raman, T.R.S. 2006. <a href="https://doi.org/10.1007/s10531-005-2352-5">Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India</a>. <i>Biodiversity and Conservation</i> 15: 1577–1607. https://doi.org/10.1007/s10531-005-2352-5</li><li>Sridhar, H., & Sankar, K. 2008. <a href="https://doi.org/10.1017/S0266467408004823">Effects of habitat degradation on mixed-species bird flocks in Indian rain forests</a>. <i>Journal of Tropical Ecology</i> 24: 135-147. https://doi.org/10.1017/S0266467408004823</li><li>Surendra, A. & Raman, T. R. S. 2022. <a href="https://doi.org/10.1101/2022.10.22.513365">Forest bird decline and community change over 19 years in long-isolated South Asian tropical rainforest fragments</a>. Preprint. <i>BioRxiv</i> 2022.10.22.513365. https://doi.org/10.1101/2022.10.22.513365<br> </li></ul><p>A related dataset is the following:<br>Raman, T. R. S. (2020). Data from: Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India. <i>Dryad Dataset.</i> https://doi.org/10.5061/dryad.4mw6m907q<br> </p><p><strong>Curation and corrections</strong></p><p>Data were collated, curated, and corrected before this upload. Besides addition of new columns, explanations of metadata, and other corrections included few related to canopy measurements, effective girth of multi-stem trees, and species identification.</p><p><strong>Acknowledgements</strong></p><p>We are grateful to P. Jeganathan and P. R. Shankar for assistance with data collection in 2000. Others who assisted with field research, and funding agencies related to the specific studies, are acknowledged in the above publications. The data compilation and publication was carried out as part of a grant from Fondation Franklinia to NCF.</p><p><br><strong>CONTACTS</strong><br> </p><p>CONTACT #1<br>1. Name: T. R. Shankar Raman<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: trsr@ncf-india.org<br>5. ORCID: https://orcid.org/0000-0002-1347-3953</p><p>CONTACT #2<br>1. Name: Divya Mudappa<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: divya@ncf-india.org<br>5. ORCID: https://orcid.org/0000-0001-9708-4826</p><p>CONTACT #3<br>1. Name: Hari Sridhar<br>2. Work Address: Wildlife Institute of India, Post Bag #18, Chandrabani, Dehradun – 248001, Uttarakhand, India; Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: harisridhar1982@gmail.com<br>5. ORCID: https://orcid.org/0000-0003-3286-0120</p><p>CONTACT #4<br>1. Name: Akshay Surendra<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India; School of the Environment, Yale University, New Haven, CT – 06511, USA; New York Botanical Garden, 2900 Southern Blvd, Bronx, NY 10458<br>3. Work Phone: +91 821 2515601<br>4. Email address: akshaysurendra1@gmail.com<br>5. ORCID: https://orcid.org/0000-0003-2719-7432<br> </p><p><br><strong>GEOGRAPHIC COVERAGE</strong></p><p>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India</p><p>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p><p><br><strong>TEMPORAL COVERAGE</strong></p><p>1. Begins: 2000-01-01 (Year, Month, Day)<br>2. Ends: 2019-12-31 (Year, Month, Day)</p><p><br><strong>METHODS</strong></p><p>Methods involved are described in the publications listed above. The vegetation sampling methods are briefly described below.</p><p>PCQ data: Trees ≥30cm girth at breast height (gbh, at 1.3 m) were sampled in replicate point-centred quarter (PCQ) points in each of the sites (fragments or coffee plantations).</p><p>All trees in the PCQ plots were identified to species, or in a few cases to genus, using available field guides. Using a tape measure, distance from plot centre to the middle of the bole and GBH were recorded for each tree. At each of the PCQ plots, circular plots were laid to enumerate shrubs and cut trees and record presence or absence of lianas, cane, Lantana etc as described in the metadata. Canopy and leaf litter variables were measured at replicate points, spaced 25 to 50 m apart, in each site. Elevation readings were also taken at these points using an altimeter or handheld GPS. Canopy height was measured using a rangefinder. Percentage canopy cover was measured using a spherical densiometer at each of the 25 points in each site. Vertical stratification was assessed by noting presence or absence of foliage in the following height intervals (in metres): 0–1, 1–2, 2–4, 4–8, 8–16, 16–24, 24–32, and > 32, directly above and in a 0.5 m radius around each point. Leaf litter depth on the forest floor was measured using a calibrated wooden probe at each point. Where ground vegetation and litter were disturbed along trails, the samples were taken away from trails in the forest floor.</p><p><br><strong>FILES INCLUDED</strong><br>Besides the 00_README.txt file that contains this metadata, the dataset includes the following 7 files, whose details and contents are explained below. (Wherever used in the various files, NA implies not available.)<br> </p><p><strong>01) sites.csv -- Details of study sites</strong><br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>decimalLongitude: Longitude in decimal degrees North (WGS 84 datum)<br>decimalLatitude: Latitude in decimal degrees East (WGS 84 datum)<br>habitat: Habitat type as mature tropical rainforest, tropical rainforest fragment, or coffee plantation<br>Description: Description of the place<br> </p><p><strong>02) allpcqdata.csv -- Tree data from point-centred quarter (PCQ) surveys</strong><br>Year: Year of survey for bird and vegetation study<br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>Point_name: Name ID of point-centred quarter (PCQ) point as used within a survey year<br>pointID: Unique ID of point-centred quarter (PCQ) point including year of survey<br>Tree_no: Tree number ID given to the four trees in each PCQ plot (T1 to T4)<br>verbatimIdentification: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name as currently identified under updated taxonomy<br>nativeAlien: Category indicating whether species is native or alien to the region/country<br>kingdom: Taxonomic Kingdom<br>phylum: Taxonomic Phylum<br>Distance_eff: Distance in metres from centre of PCQ plot to centre of tree trunk<br>Girth_eff: Girth in centimetres (cm) at breast height (1.3 m) of the tree after correction (using appropriate formula) in the case of multi-stemmed individuals<br>locationRemarks: Code for site name as originally used<br>SpCode: Species code as originally used during data entry<br>TreeHeight: Tree height in metres (only available in 2019 survey)<br>identificationRemarks: Notes related to identification if available<br>occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and note on one possibly errorneous girth<br> </p><p><strong>03) pcqlocations.csv -- Locations of sample PCQ points</strong><br>pointID: Unique ID of point-centred quarter (PCQ) point including year of survey<br>note: Site name code<br>decimalLatitude: Latitude in decimal degrees East (WGS 84 datum)<br>decimalLongitude: Longitude in decimal degrees North (WGS 84 datum)<br>coordinateUncertaintyInMeters: Approximate uncertainty of the location in metres<br> </p><p><strong>04) allhabitat.csv -- Data on habitat structure variables</strong><br>Year: Year of survey for bird and vegetation study<br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>Point: ID of replicate survey point within the Fragment<br>0-1m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 0-1 m above ground<br>1-2m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 1-2 m above ground<br>2-4m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 2-4 m above ground<br>4-8m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 4-8 m above ground<br>8-16m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 8-16 m above ground<br>16-24m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 16-24 m above ground<br>24-32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 24-32 m above ground<br>over32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band greater than 32 m above ground<br>VertStrata: Number of vertical strata with foliage (sum of preceding 8 columns)<br>CanopyHeight: Canopy height in metres<br>CanopyOpenness: Canopy openness in percentage as measured using a spherical densiometer<br>CanopyCover: Canopy cover (closure) in percentage as measured using a spherical densiometer<br>CanopyOverlap: Canopy overlap rank: 0-open sky above; 1-branches above barely touching; 2-overlapping branches above, sky visible; 3-overlapping branches, sky not visible<br>UC: Canopy overlap rank as above, for understorey vegetation only<br>MC: Canopy overlap rank as above, for the midstorey only<br>CC: Canopy overlap rank as above, for the upper canopy only<br>Altitude: Altitude above sea leavel in metres, measued from hand-held altimeter or GPS device<br>RfShrub: Number of shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Coffee: Number of coffee bushes (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Maesopsis: Number of alien Maesopsis eminii stems (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Strobilanthes: Number of Strobilanthes shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>TotalShrub: Total number of shrubs within 2 m radius of point<br>Liana: Presence (1) or absence (0) of woody lianas within 5 m radius of point<br>Cane: Presence (1) or absence (0) of cane (Calamus sp.) within 2 m radius of point<br>Lantana: Presence (1) or absence (0) of Lantana camara shrubs within 2 m radius of point<br>Bamboo: Presence (1) or absence (0) of bamboo culms within 2 m radius of point<br>LeafLitter: Depth of leaf litter in cm (to 0.5 cm accuracy) measured using a calibrated wooden probe<br>CutTrees: Number of cut trees within 5 m radius of point<br> </p><p><strong>05) gbifnames.csv -- Results of GBIF name matching tool</strong><br>sno: Serial number<br>verbatimScientificName: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name after matching with Global Biodiversity Information Facility (GBIF) database to lowest taxonomic level<br>sciNameWithAuthor: Scientific name with author as provided by GBIF name matching tool<br>key: GBIF key as provided by GBIF name matching tool<br>matchType: Type of match as provided by GBIF name matching tool<br>confidence: Confidence as provided by GBIF name matching tool<br>status: Status as accepted name or synonym as provided by GBIF name matching tool<br>rank: Taxonomic rank as provided by GBIF name matching tool<br>kingdom: Kingdom as provided by GBIF name matching tool<br>phylum: Phylum as provided by GBIF name matching tool<br>class: Class as provided by GBIF name matching tool<br>order: Order as provided by GBIF name matching tool<br>family: Family as provided by GBIF name matching tool<br>genus: Genus as provided by GBIF name matching tool<br>species: Species as provided by GBIF name matching tool<br>canonicalName: Canonical name as provided by GBIF name matching tool<br>authorship: Author of name as provided by GBIF name matching tool<br> </p><p><strong>06) plots2000.csv -- Data from 5 m radius circular plots in select sites</strong><br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>PlotID: ID of 5 m radius plot<br>Treeno: Serial number of tree in the plot<br>verbatimIdentification: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name as currently identified under updated taxonomy<br>Girth_eff: Girth in centimetres (cm) at breast height (1.3 m) of the tree after correction (using appropriate formula) in the case of multi-stemmed individuals<br>nativeAlien: Category indicating whether species is native or alien to the region/country<br>kingdom: Kingdom as provided by GBIF name matching tool<br>phylum: Phylum as provided by GBIF name matching tool<br>occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and identification</p><p> </p><p><strong>07) anampcqs4gbif.rmd -- Text file with code in the R statistical and programming language</strong> </p><p>This R code was used for converting data in this Zenodo dataset into Darwin Core occurrence dataset for upload to the Global Biodiversity Information Facility (GBIF, https://www.gbif.org). The published dataset can now be accessed at: https://doi.org/10.15468/cmsveh</p><p> </p><p><strong>Changes in Version 2</strong></p><p>In sites.csv, changed habitat from "Rainforest" to "Tropical rainforest fragment" for Puthuthottam</p><p>Added the anampcqs4gbif.rmd file with R code</p>
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>
Extracted raw data from: Global dominance of lianas over trees is driven by forest disturbance, climate, and topography
<p>In a meta-analysis, we use an unprecedented dataset, representing 556 unique locations worldwide, distributed across 44 countries and six continents to show for the first time that lianas (woody vines) thrive relatively better than trees when forests are disturbed, temperature increase, precipitation decrease, and particularly in tropical lowlands. We demonstrate that liana dominance can persist for decades post-disturbance and hinder the recovery of disturbed forests, especially when climate favours lianas. With implications for the global carbon sink, our findings suggest that degraded tropical forests with environmental conditions favouring lianas should be the highest priority to consider for restoration management.</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>
Global patterns of tree wood density
<p>Wood density is a fundamental property related to tree biomechanics and hydraulic function while playing a crucial role in assessing vegetation carbon stocks by linking volumetric retrieval and a mass estimate. This study provides a high-resolution map of the global distribution of tree wood density at the 0.01º (~1 km) spatial resolution, derived from four decision trees machine learning models using a global database of 28,822 tree-level wood density measurements. An ensemble of four top-performing models, combined with eight cross-validation strategies shows great consistency, providing wood density patterns with pronounced spatial heterogeneity. The global pattern shows lower wood density values in northern and northwestern Europe, Canadian forest regions, and slightly higher values in Siberia forests, western USA, and southern China. In contrast, tropical regions, especially wet tropical areas, exhibit high wood density. Climatic predictors explain 49~63% of spatial variations, followed by vegetation characteristics (25~31%) and edaphic properties (11~16%). Notably, leaf type (evergreen vs. deciduous) and leaf habit type (broadleaved vs. needleleaved) are the most dominant individual features among all selected predictive covariates. Wood density tends to be higher for angiosperm broadleaf trees compared to gymnosperm needleleaf trees, particularly for evergreen species. The distributions of wood density categorized by leaf types and leaf habit types have good agreement with the features observed in wood density measurements. This global map quantifying wood density distribution can help improve accurate predictions of forest carbon stocks, providing deeper insights into ecosystem functioning and carbon cycling such as forest vulnerability to hydraulic and thermal stresses in the context of future climate change. </p> <h3>Research Funding</h3> <ul> <li>GlobBiomass DUE Project. Grant Number: <span>4000113100/14/I-NB</span></li> <li>German Federal Ministry for Economic Affairs and Climate Action. Grant Number: <span>50EE1904</span></li> <li>ESM2025</li> <li>H2020 European Research Council. Grant Number: <span>855187</span></li> <li>International Max Planck Research School for Biogeochemical Cycles</li> <li>ESA IFBN project. Grant Number: <span>4000114425/15/NL/FF/gp</span></li> <li>ESA FRM4BIOMASS. Grant Number: <span>4000142684/23/I-EF-bgh</span></li> <li>Poland National Centre for Research and Development REMBIOFOR project. Grant Number: <span>BIOSTRATEG1/267755/4/NCBR/2015</span></li> </ul>
How is tree growth rate linked to root functional traits in phylogenetically related poplar hybrids?
<p>Fine roots play a crucial role in soil nutrient and water acquisition, significantly contributing to tree growth. Fine roots with a high specific root length (SRL) and small diameter are often considered to help trees grow fast. However, inconsistencies in the literature do not provide a clear basis on the effect of root functional traits, such as SRL or root mass density (RMD), on tree growth rate in phylogenetically related trees. Our aim was to examine relationships between tree growth rate and root functional traits, using clones displaying different growth rates in a hybrid poplar plantation located in New Liskeard, ON, Canada. Fine roots (diameter < 2 mm) samples were collected using soil cores at depths of 0–20, 20–40 and 40–60 cm, and analyzed for morphological, chemical and architectural traits. High SRL and thin fine roots were associated with the least productive clones, which is not consistent with the root economics spectrum (RES) theory. However, the most productive clone had larger fine root diameter and higher root lignin concentrations, probably reducing root construction and maintenance costs and C losses. Therefore, at the 0–20 and 20–40 cm depths, tree growth rates showed positive correlations with root diameter and root lignin concentrations, but negative correlations with SRL and root soluble compounds concentration. Increasing RMD at the 0–20 cm depth promoted tree growth rates, showing the importance of soil exploration in the topsoil for tree growth. We conclude that fine root variation does not always follow the RES hypothesis and argue that the rapid growth rate of trees may also be driven by fine root growth in diameter and mass in phylogenetically related trees.</p>
Adult baobab trees's distribution map across Sahel
<p><span>The baobab tree (<em>Adansonia digitata</em> <em>L.</em>) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we employ sub-meter resolution satellite imagery to identify nearly 3</span><span> million baobab trees in the Sahel, a dryland region of 1.5 million km<sup>2</sup>. This achievement is considered an essential step towards improving valuable woody species' management and monitoring system. To prevent mismanagement of this specific tree species, we aggregated every single adult baobab tree map to<span> 5 <span>× </span>5 km grids. We also classified the baobab trees using the tree crown diameters( small: 3-9m; medium 9m-13m; large: >13m). The baobab tree count map is also available for this three different size classes.</span></span></p>
Analysis of a complex role of trees in street canyon using LES model (experiment: Terronska)
<h1>README</h1> <p>This is a companion dataset to the paper <em>Analysis of a complex role of trees in street canyon using LES</em> model by <em>Řezníček et al.</em>, to be submitted to <em><span>Quarterly</span> <span>Journal</span> of the Royal Meteorological Society</em>. All the supplementary data needed for the reproduction of the experiment described in the manuscript are provided on this ZENODO repository. The supplementary data includes the following:</p> <p>1. <em>01_palm_source_code.zip</em> contains the source code for the current version of the PALM model used for this experiment</p> <p>2. <em>02_inputs-configs.zip</em> which contains:</p> <ul> <li>static driver files (for cases 01 = full-trees, 02 = half-trees, 03 = no-strees)</li> <li>dynamic driver files (for different winds directions W = west, SW = southwest, S = south and stratifications C = convective, N = neutral + stable)</li> <li>configuration files for the first PALM run (p3d), and the configuration files for the restart runs (p3dr) for each of the performed simulations</li> <li>the files with N02 are apllied for child domain</li> </ul> <p>3. 03_maps-GIS contains maps in gis or png format with one hour averages outputs: </p> <ul> <li>the cases are terC/N_W/SW/S_01/02/03 for the stratifications, wind direcrions and trees-scenario (see the legend above)</li> <li>abs for absolute values, diff for differences from no-tree scenario, 01h = 1 hour average</li> <li>variables are bio_UTCI - universal thermal climate index [deg C], kc_PM10 = PM10 concentration in 2m or 10m height [<span>μ</span>/m^3], theta_2m = temperature in 2m [deg C], wspeed_10m = wind-speed in 10m, tsurf = surface temperature [deg C], rad_sw_in = incoming shortwave radiation flux [W/m^2] and rad_lw_out = outgoing longwave radiation flux [W/m^2]</li> </ul> <p>4. 04_cuts contains svg and png plots with vertical and horizontal (xy) cuts </p> <ul> <li>the cases are terC/N_W_01/02/03 for the stratifications and trees-scenario (see the legend above) and west winds</li> <li>jugp-ciirc = the vertical cut for (JugP) street (near the ciirc-CTU building), terr-street = the vertical cut for (Terr) street</li> </ul> <h1>PALM MODEL INSTALLATION AND USAGE GUIDE</h1> <h2>A. Installation</h2> <p>1. First, make sure to satisfy the Software Requirements. On Debian-based Linux Distributions, this can be achieved by the following command:</p> <p><code>sudo apt-get install gfortran g++ make cmake coreutils libopenmpi-dev openmpi-bin libnetcdff-dev netcdf-bin libfftw3-dev python3-pip python3-pyqt5 flex bison ncl-ncarg</code></p> <p>2. Also, some additional python dependencies are needed, which can be installed using pip. In case you want to use a virtual environment for these dependencies, please make sure to create one first. Afterwards, you can install the python dependencies by executing the following command:</p> <p><code>python3 -m pip install -r requirements.txt</code></p> <p>3. Now the PALM model system can be installed with the following commands (please replace with the desired installation directory):</p> <p><code>export install_prefix=""</code><br><code>bash install -p ${install_prefix}</code><br><code>export PATH=${install_prefix}/bin:${PATH}</code></p> <p>4. The following optional command permanently adds this installation to your bash environment:</p> <p><code>echo "export PATH=${install_prefix}/bin:\${PATH}" >> ~/.bashrc</code></p> <p>5. Type <code>bash install -h</code> to get all available options of the install script. During installation, the script calls the respective install script of all packages in this repository and installs them to the chosen directory. Therefore, it is not necessary to manually install any of the packages.</p> <p>You can test your installation with the following commands:</p> <p><code>palmtest --cases urban_environment_restart --cores 4</code></p> <h2>B. Usage</h2> <p>After a successful installation, the executables for all packages have been linked into the directory <code>/bin</code> and a default PALM configuration file can be found at <code>/.palm.config.default</code>. In case you have installed the python dependencies inside a virtual environment, that environment needs to be active whenever you wand to use PALM. For usage of each of the packages, please refer to their individual documentation. Next, you need to create your first PALM setup in order to start a simulation. To get a simple preconfigured setup and start your first PALM simulation, please execute the following sequence of commands:</p> <p><code>mkdir -p "${install_prefix}/JOBS/example_cbl/INPUT"</code><br><code>cp "packages/palm/model/tests/cases/example_cbl/INPUT/example_cbl_p3d" "${install_prefix}/JOBS/example_cbl/INPUT/"</code><br><code>cd ${install_prefix}</code><br><code>palmrun -r example_cbl -c default -a "d3#" -X 4 -v -z</code></p> <h1>ACKNOWLEDGEMENT</h1> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic<br>georisks.</p> <p>The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
MATEdb2, a Collection of High-Quality Metazoan Proteomes across the Animal Tree of Life to Speed Up Phylogenomic Studies
<p>Recent advances in high-throughput sequencing have exponentially increased the number of genomic data available for animals (Metazoa) in the last decades, with high-quality chromosome-level genomes being published almost daily. Nevertheless, generating a new genome is not an easy task due to the high cost of genome sequencing, the high complexity of assembly, and the lack of standardized protocols for genome annotation. The lack of consensus in the annotation and publication of genome files hinders research by making researchers lose time in reformatting the files for their purposes but can also reduce the quality of the genetic repertoire for an evolutionary study. Thus, the use of transcriptomes obtained using the same pipeline as a proxy for the genetic content of species remains a valuable resource that is easier to obtain, cheaper, and more comparable than genomes. In a previous study, we presented the Metazoan Assemblies from Transcriptomic Ensembles database (MATEdb), a repository of high-quality transcriptomic and genomic data for the two most diverse animal phyla, Arthropoda and Mollusca. Here, we present the newest version of MATEdb (MATEdb2) that overcomes some of the previous limitations of our database: (i) we include data from all animal phyla where public data are available, and (ii) we provide gene annotations extracted from the original GFF genome files using the same pipeline. In total, we provide proteomes inferred from high-quality transcriptomic or genomic data for almost 1,000 animal species, including the longest isoforms, all isoforms, and functional annotation based on sequence homology and protein language models, as well as the embedding representations of the sequences. We believe this new version of MATEdb will accelerate research on animal phylogenomics while saving thousands of hours of computational work in a plea for open, greener, and collaborative science.</p>
Tree Annotation Vocabulary (TAV) - Knowledge Graph and Annotated Dataset
<p>This dataset contains all the files used in developing the Tree-KG, the knowledge graph to capture the tree annotations in the works of Vladimir Nabokov. </p> <p>In the Annotated Dataset folder, 6 spreadsheets in excel (.xlsx) format are provided. They are numbered. Note that annotated data are all in English as the consulted works are the English translations of the literary works of Nabokov.</p> <p>(1) contains the tree annotations from the novels originally written in Russian by Vladimir Nabokov.</p> <p>(2) contains the tree annotations from the novels originally written in English by Vladimir Nabokov.</p> <p>(3) contains the tree annotations from the short stories originally written in Russian and English by Vladimir Nabokov.</p> <p>(4) is the knowledge base (KB) developed to link the annotated trees to Wikidata and DBPedia.</p> <p>(5) is the benchmarking results of some entity recognition tools. It includes the relevant passages from Nabokov's novels that were used in the experiments as well as the prompts used in getting the results.</p> <p>(6) represents the complete bibliographic details of the works of Vladimir Nabokov (https://thenabokovian.org/abbreviations).</p> <p>In the Ontology Versions folder, four ontology (TAV) files in turtle (.ttl) format are provided. They are all numbered and dated to represent their different versions. Some sample SPARQL queries are provided in a .txt file. The KG was developed on Protégé. </p> <p>(1) contains the essential schema for the TAV vocabulary.</p> <p>(2) contains the schema for TAV vocabulary with links to external vocabularies (Schema.Org; Open Annotation, etc.). </p> <p>(3) contains the Tree-KG in so far it reflects data from three novels (Mary; King, Queen, Knave; Glory).</p> <p>(4) contains the entire Tree-KG based on all the works mentioned in the excel sheets (20 books).</p> <p>(5) contains some sample SPARQL queries (.txt) file.</p>
Large-scale 3D building and tree datasets constructed from airborne LiDAR point clouds in Glasgow, UK
<p>This is the updated version of building 3D model data. The revision includes appending attributes to the lod1 and lod2 shapefile and creating cityjson file for each 3D building model. All 3D building models are available in mesh (.obj), multipath shapefile, and cityjson (.json) now.</p> <p><strong>IMPORTANT NOTE: We suggest using the building footprint, lod1, and lod2 data of this version (Version v4).</strong></p> <p>Urban Big Data Centre of the University of Glasgow generates 3D city models via the airborne LiDAR point clouds acquired between 2020-2021 on behalf of Glasgow City Council. It is a large-scale 3D city model containing 3D information on terrain, trees, and buildings in Glasgow City. This dataset comprises terrain, tree canopy, and building products derived from high-density airborne LiDAR point clouds. </p> <p>The terrain products include Digital Terrain Model (DTM), Digital Surface Model (DSM), and normalized Digital Surface Model (nDSM) in 0.5 m spatial resolution. The DTM and DSM rasters were provided by the vendor and nDSM rasters were obtained by subtracting DTM from DSM. Terrain products are provided in 5 km by 5 km GeoTIF format raster.</p> <p>The tree canopy products are composed of canopy height models (CHM) and tree top locations. Classified tree point clouds were applied with pit-free algorithm to generate CHM in 0.5 m grid raster in GeoTIF format [1]-[2]. Treetop locations were identified by using Local Maximum Filter based on CHM and are recorded as points in Shapefile format. The tree canopy products are provided in 5 km by 5 km tiles.</p> <p>Building 3D model products include footprint polygons with building height attributes and 3D mesh of building models in LoD1 and LoD2 levels. A series of processes such as converting building point clouds to building height models (BHM), converting BHM to polygons, and polygon regularization were conducted to obtain the building footprint polygons. Building height attributes were calculated from BHM for each footprint. The building footprint data are provided in Shapefile format. LoD1 models were generated based on the footprint and average height of the building. LoD2 models were constructed based on footprint and building point cloud with City3D tool[3]. LoD1 and LoD2 models are provided in OBJ and shapefile format. Building 3D model products are provided in 5 km by 5 km tiles. The RMSE of Euclidean distances between each point in the point cloud to the reconstructed model was calculated to evaluate the LoD2 model construction. A table of RMSE and a note for a few problematic models are provided.</p>
Point clouds from terrestrial laser scanning from crowns of individual Scots pine trees
<p>Trees adapt to their growing conditions by regulating the sizes of their parts and their relationships. For example, removal or death of adjacent trees increases the growing space and the amount of light received by the remaining trees enabling their crowns to expand. Knowledge about the effects of silvicultural practices on crown size and shape as well as about the quality of branches affecting the shape of a crown is, however, still limited. Laser scanning (or Light detecting and ranging LiDAR) has provided new opportunities for characterizing trees in more detail in three-dimensional space. Especially terrestrial laser scanning (TLS) has increasingly been used in producing a variety of tree attributes. This data set includes 3D reconstruction of crowns of Scots pine (<em>Pinus sylvestris</em> L.) trees from sample plots with different thinning treatments. The thinning treatments include two intensities of thinning, three thinning types as well as control (i.e. no thinning treatment since the establishment). This data set can be used in developing point cloud processing algorithms for single tree crown characterization and for investigating variation in crown size and shape as well as the effects of various thinning treatments on crown size and shape of Scots pine trees grown in boreal forests.</p>
Tree diameter growth and increment core δ13C data from a recently thinned forestry-drained site (Lettosuo) in southern Finland.
<p>Dataset includes increment core data from Lettosuo drained peatland forest site. The study site locates in the Tammela municipality in southern Finland (60° 38’ 31’’ N, 23° 57’ 35’’ E). Increment cores were analysed for the ring widths for dominant and suppressed Norway spruce trees, and for the ring δ<sup>13</sup>C values from suppressed Norway spruce trees. Data was collected as a part of BiBiFe (”Biogeochemical and biophysical feedbacks from forest harvesting to climate change”) consortium that is funded by the Academy of Finland. </p> <p> </p> <p>Sampling for increment cores was done during October 2020 for sample trees (10 in total, of which 5 were suppressed trees from thinned area and 5 suppressed trees from control area) and additional sampling was conducted for annual diameter increment for 3 tree groups to increase sample size for diameter growth (suppressed trees in thinned area [n=20], dominant trees in thinned area [n=22] and suppressed trees in control area[n=20]) during March 2021. </p> <p> </p> <p><strong>Tree </strong><strong>ring carbon isotope data</strong></p> <p> </p> <p>Laser ablation IRMS method was applied in the Stable Isotope Laboratory of Luke (SILL) to quantify δ<sup>13</sup>C values in 10 increment cores for the time period 2010–2020, following principles of Schulze et al. (2004) and described in Lehtonen et al (manuscript). Up to 11 evenly spaced “spots” for each annual tree ring were measured to obtain information on the intra-annual variation of δ<sup>13</sup>C of the samples. </p> <p> </p> <p>(1) File: Lettosuo_d13C.xls</p> <p>File includes d13C measurements</p> <p> </p> <p><strong>Data column description below for isotope data: </strong></p> <p> </p> <p><strong>id</strong> stands for tree id [id includes tree identity, year and also spot number]</p> <p><strong>year</strong> is the year of the tree ring</p> <p><strong>nr</strong> is an index for data </p> <p><strong>tree</strong> indicates tree identity "C" for control and "H" for harvest</p> <p><strong>treatment</strong> indicates the treatment of the sampling area (control / harvest)</p> <p><strong>d13C</strong> gives the measured d13C value based on the LA-IRMS measurements</p> <p><strong>season </strong>indicates whether observation originated from the earlywood (EW) or latewood (LW) period, where 1 is EW and 2 is LW</p> <p> </p> <p><strong>Tree ring width measurements</strong></p> <p> </p> <p>In addition to the δ<sup>13</sup>C values, also the ring widths were measured. Here, also additional dominant trees were measured. </p> <p> </p> <p>(3) Files:</p> <p>controlRW.csv</p> <p>dominantRW.csv</p> <p>thinningRW.csv</p> <p> </p> <p>Files include increment core data (in micrometers) from isotope sample trees and additional increment core trees from the control area and harvested area of the site. Dominant trees were measured only from the thinned area. </p> <p> </p> <p>In the .csv files individual columns are for ring widths for individual trees. In the controlRW.csv and thinningRW.csv files first 5 columns include diameter increments from sample trees (those that have also d13C measurements).</p> <p> </p> <p><strong>References:</strong></p> <p> </p> <p>Lehtonen A, Leppä K, Sahlstedt E, Schiestl-Aalto P, Heikkinen J, Young G, Korkiakoski M, Peltoniemi M, Rinne-Garmston K, Sarkkola S, Lohila A, Mäkipää R (manuscript). Fast recovery of Norway spruce trees after thinning from above on a drained peatland forest site.</p> <p> </p> <p>Korkiakoski M, Ojanen P, Penttilä T, Minkkinen K, Sarkkola S, Rainne J, Laurila T, Lohila A (2020) Impact of partial harvest on CH<sub>4</sub> and N<sub>2</sub>O balances of a drained boreal peatland forest. Agric For Meteorol 295:108168.</p> <p> </p> <p>Schulze B, Wirth C, Linke P, Brand WA, Kuhlmann I, Horna V, Schulze E-D (2004) Laser ablation-combustion-GC-IRMS--a new method for online analysis of intra-annual variation of 13C in tree rings. Tree Physiol 24:1193–1201.</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.