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6,246 results for “Biodiversity”
2020 Stakeholder Survey of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) - Quantitative Dataset
<p>This dataset is the outcome of a survey of IPBES stakeholders that was conducted in May-June 2020. The aim of this survey is to better understand stakeholder engagement with IPBES, to improve implementation of the IPBES stakeholder engagement strategy (decision IPBES-3/4 presented in document IPBES/3/18), and to further increase the inclusivity and effectiveness of the IPBES work programme. Results will help, among others, to better align communication and outreach, and to strengthen collaborative processes within the IPBES work programme.</p> <p><br> This dataset presents only the quantitative data of the complete dataset of responses. It has been anonymised and all personal comments in response to open questions have been removed. For information, the full anonymised dataset has been published on Zenodo, with restricted access (see DOI: <a href="http://doi.org/10.5281/zenodo.4121916">10.5281/zenodo.4121916</a>).</p> <p>This dataset is under restricted access and embargoed until after the eighth session of IPBES Plenary. For any inquiry, please contact IPBES Head of Communications (stakeholders@ipbes.net). </p>
Biodiversity Hotspots Map (no text)
<p>This map displays the global <a href="https://zenodo.org/record/3261807#.X8_HgNhKg2x">Biodiversity Hotspots 2016.1 dataset</a>. The colors assigned to the hotspots are only used to distinguish adjacent hotspots and have no other meaning. The biodiversity hotspots represent terrestrial biodiversity only. The offshore lines are a cartographic device to group and highlight islands that are part of the same hotspots (e.g. Polynesia-Micronesia, Indo-Burma). The background image is from Natural Earth. This version is without labels; a version with the hotspots labelled in English is available: 10.5281/zenodo.4311850</p> <p>There are currently <a href="https://www.cepf.net/node/1996">36 recognized biodiversity hotspots</a>. These are Earth’s most biologically rich—yet threatened—terrestrial regions.</p> <p>To qualify as a biodiversity hotspot, an area must meet two strict criteria:</p> <ul> <li>Contain at least 1,500 species of vascular plants found nowhere else on Earth (known as "endemic" species).</li> <li>Have lost at least 70 percent of its primary native vegetation.</li> </ul> <p>Many of the biodiversity hotspots exceed the two criteria. For example, both the Sundaland Hotspot in Southeast Asia and the Tropical Andes Hotspot in South America have about <strong>15,000</strong> endemic plant species. The loss of vegetation in some hotspots has reached a startling <strong>95</strong> percent.</p>
Data licences and organization type of contributors to the Global Biodiversity Information Facility as of 19 January 2016
<p>Data from the Global Biodiversity Information Facility were extracted using R (version 3.2.0) on 9 July 2015 using the rgbif package (version 0.9.0) (Chamberlain, S., Ram, K., Barve, V. & Mcglinn, D. (2015) Package ‘rgbif’: Interface to the Global 'Biodiversity' Information Facility 'API' http://cran.r-project.org/web/packages/rgbif/rgbif.pdf). The ‘rights’ statements was extracted for all occurrence datasets with one or more observations. A total of 12,458 datasets were extracted, but only about 11% of the datasets have an explicit data-useage-rights statement at the dataset level. However, some datasets use the occurrence level ‘rights’ and ‘accessRights’ fields. To extract these data the rights information was obtained from the first record of each dataset where a rights statement was missing at the dataset level.</p> <p>The datasets were categorized into 13 different types depending on the origin of the observations.</p> <ol> <li>Biodiversity Information Facility or data centre</li> <li>Botanical Garden or Herbarium</li> <li>Citizen science</li> <li>Commercial</li> <li>Data publisher</li> <li>Educational</li> <li>Government</li> <li>Museum</li> <li>Network</li> <li>Parks Authority or Nature Reserve</li> <li>Research institution</li> <li>Society</li> <li>Foundations</li> </ol>
Supplementary material 3: World Spider Catalog Bibliographic Data: Treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
List of journal/publisher by ranked by treatment count exported from the World Spider Catalog 14 October 2014 with total treatments by source, cumulative treatments, and cumulative proportion of treatments.
Supplementary material 2: World Spider Catalog Bibliographic Data: Publications from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Ranked list of journal/publisher exported from the World Spider Catalog 14 October 2014 with total articles by source, cumulative articles, and cucmulative proportion of articles.
Ecological filtering shapes the impacts of agricultural deforestation on biodiversity
<p>This dataset and associated code are for the manuscript titled "Ecological filtering shapes the impacts of agricultural deforestation on biodiversity", which is due to be published in the journal Nature Ecology & Evolution (accepted on September 20, 2023). The abstract of this manuscript is as follows:</p><p> </p><p>The biodiversity impacts of agricultural deforestation vary widely across regions. Previous efforts to explain this variation have focused exclusively on the landscape features and management regimes of agricultural systems, neglecting the potentially critical role of ecological filtering in shaping deforestation tolerance of extant species assemblages at large geographical scales via selection for functional traits. Here we provide a large-scale test of this role using a global database of species abundance ratios between matched agricultural and native forest sites that comprises 71 avian assemblages reported in 44 primary studies, and a companion database of ten functional traits for all 2,647 species involved. Using meta-analytic, phylogenetic, and multivariate methods, we show that beyond agricultural features, filtering by the extent of natural environmental variability and the severity of historical anthropogenic deforestation shapes the varying deforestation impacts across species assemblages. For assemblages under greater environmental variability – proxied by drier and more seasonal climates under greater disturbance regime – and longer deforestation histories, filtering has attenuated the negative impacts of current deforestation by selecting for functional traits linked to stronger deforestation tolerance. Our study provides a heretofore largely missing piece of knowledge in understanding and managing the biodiversity consequences of deforestation by agricultural deforestation.</p>
Land, carbon and biodiversity data for supply chain impact calculations
<p>Monitoring, halting and reversing land conversion is fundamental to meeting international biodiversity and climate targets, and agriculture is the major driver of land conversion. We present an open access set of global data for calculating land use change impacts of agricultural supply chains. These data, originally prepared for the LandGriffon service, include indicators of deforestation, conversion of natural ecosystems, greenhouse gas emissions, and loss of intact or high integrity ecosystems following international standards and guidelines for reporting and target setting in the agriculture, forestry, and land use sector. In order to assign impacts to agricultural production, we prepare data using a spatial adaptation of the statistical Land Use Change (sLUC) accounting approach distributing impact to human activities across the local area using a 50km radius. The results are high resolution global maps of impact per hectare of land occupation. These can then be combined with land footprint data, cropland extent, or productivity maps to calculate land use change related impacts for specific crop volumes sourced from specific regions. Carbon and deforestation results are validated against FAO statistics at the national level.</p>
The location of solar farms within England's ecological landscape: implications for biodiversity conservation
<p>Data associated to the article entitled 'The location of solar farms within England's ecological landscape: implications for biodiversity conservation'. </p> <p> </p>
Opportunities and barriers for promoting biodiversity in Danish beef production
<p>Code and data used in the paper "Opportunities and barriers for promoting biodiversity in Danish beef production" published in iScience.</p> <p>Pre-proof available at: <span>https://doi.org/10.1016/j.isci.2024.111422</span></p>
Data and script: Community size can affect the signals of ecological drift and niche selection on biodiversity
<p>Updated version of the code. Data files are the same. This is the final version of the code, associated with a manuscript published in Ecology (doi: 10.1002/ecy.3014). A preprint is also available: https://www.biorxiv.org/content/10.1101/515098v1.abstract</p> <p>This is a unique dataset on insect communities sampled identically in a total of 200 streams in climatically highly different regions (100 in Brazil and 100 in Finland). The sampling design included 5 streams (communities) per watershed and provided us replicates of metacommunities (watersheds). Data also include information on in-stream variables (such as current velocity (m/s), depth (cm), stream width (cm), % of sand (0.25-2 mm), gravel (2-16 mm), pebble (16-64 mm), cobble (64-256 mm), and boulder (256-1024 mm), % of canopy cover by riparian vegetation, pH, conductivity, total nitrogen, and total phosphorus) and catchment level variables (such as average slope, % of native forest cover, pasture, agriculture, planted forests, urban areas, mining, water bodies, bare soil, secondary forest cover, and mixed land uses).</p> <p>In addition to the dataset, here we also provide and R code used to investigate the relationship between beta diversity and community size. This code calculates 4 types of beta-diversity metric for each of 100 watersheds (5 streams) in Brazil and Finland. Beta diversity: Sorensen and Bray-Curtis dissimilarity between all pairs. Beta deviation from null models: Raup-Crick (vegan version) and Bray-Curtis beta-deviation (based on the scripts by Chris Catano and Jonathan Myers). These beta diversity metrics are modelled against community size, environmental heterogeneity and spatial extent.</p> <p> </p>
UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC) Data Archive and Biodiversity Dataset Graph hash://md5/10663911550bb52a0f5741993f82db9d hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c
<p>A biodiversity dataset graph: UCSB-IZC</p> <p>The intended use of this archive is to facilitate (meta-)analysis of the UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC). UCSB-IZC is a natural history collection of invertebrate zoology at Cheadle Center of Biodiversity and Ecological Restoration, University of California Santa Barbara.</p> <p>This dataset provides versioned snapshots of the UCSB-IZC network as tracked by Preston [2,3] between 2021-10-08 and 2021-11-04 using [preston track "https://api.gbif.org/v1/occurrence/search/?datasetKey=d6097f75-f99e-4c2a-b8a5-b0fc213ecbd0"].</p> <p>This archive contains 14349 images related to 32533 occurrence/specimen records. See included sample-image.jpg and their associated meta-data sample-image.json [4].</p> <p>The images were counted using:</p> <p>$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\<br> | grep -o -P ".*depict"\<br> | sort\<br> | uniq\<br> | wc -l</p> <p>And the occurrences were counted using:</p> <p>$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\<br> | grep -o -P "occurrence/([0-9])+"\<br> | sort\<br> | uniq\<br> | wc -l</p> <p>The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance files and data files. Only two index and provenance files are included and have been individually included in this dataset publication. Index files provide a way to links provenance files in time to establish a versioning mechanism.</p> <p>To retrieve and verify the downloaded UCSB-IZC biodiversity dataset graph, first download preston-*.tar.gz. Then, extract the archives into a "data" folder. Alternatively, you can use the Preston [2,3] command-line tool to "clone" this dataset using:</p> <p>$ java -jar preston.jar clone --remote https://archive.org/download/preston-ucsb-izc/data.zip/,https://zenodo.org/record/5557670/files,https://zenodo.org/record/5660088/files/</p> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <p>$ java -jar preston.jar history<br> <urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .<br> <hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c> <http://purl.org/pav/previousVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .</p> <p>To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.</p> <p>$ java -jar preston.jar verify<br> hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c file:/home/jhpoelen/ucsb-izc/data/ce/1d/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c OK CONTENT_PRESENT_VALID_HASH 66438 hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c<br> hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 file:/home/jhpoelen/ucsb-izc/data/f6/8d/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 OK CONTENT_PRESENT_VALID_HASH 4093 hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844<br> hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef file:/home/jhpoelen/ucsb-izc/data/3e/70/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef OK CONTENT_PRESENT_VALID_HASH 5746 hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef<br> hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b file:/home/jhpoelen/ucsb-izc/data/99/58/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b OK CONTENT_PRESENT_VALID_HASH 6147 hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b</p> <p>Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".</p> <p>Files in this data publication:</p> <p>--- start of file descriptions ---</p> <p>-- description of archive and its contents (this file) --<br> README</p> <p>-- executable java jar containing preston [2,3] v0.3.1. --<br> preston.jar</p> <p>-- preston archive containing UCSB-IZC (meta-)data/image files, associated provenance logs and a provenance index --<br> preston-[00-ff].tar.gz</p> <p>-- individual provenance index files --<br> 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a</p> <p>-- example image and meta-data --<br> sample-image.jpg (with hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c)<br> sample-image.json (with hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844)</p> <p>--- end of file descriptions ---</p> <p><br> References</p> <p>[1] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-11-04 as indexed by the Global Biodiversity Informatics Facility (GBIF) with provenance hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36 hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c.<br> [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .<br> [3] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101132<br> [4] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-10-08. https://www.gbif.org/occurrence/3323647301 . hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c</p>
Dataset for: Multi-scale approach to biodiversity proxies of biological control service in European farmlands
<p>Dataset for the BiodivERsA COFUND Woodned project. Information on which spatio-temporal factors are simultaneously affecting crop pests and their natural enemies is required to improve conservation biological control practices. The study was conducted in 80 winter wheat crop fields distributed in three regions of North-western Europe (Brittany, Hauts-de-France and Wallonia), along intra-regional gradients of landscape complexity. Five taxa : aphids, slugs, spiders, carabids, and parasitoids were sampled for two consecutive years. We analysed the influence of regional, landscape and local factors on the abundance and species richness of crop-dwelling organisms, as proxies of the service/disservice they provide. Firstly, there was higher biocontrol potential in areas with mild winter climatic conditions. Secondly, natural enemy communities were less diverse and had lower abundances in landscapes with high crop and wooded continuities, contrary to slugs and aphids. Finally, field boundaries with grass strips were more favourable to spiders and carabids than boundaries formed by hedges, while the opposite was found for crop pests, with the latter being less abundant towards the centre of the fields. These results are quite unexpected because they show that hedgerows and woodlots should not be the unique cornerstones of agro-ecological landscape design strategies. We point out that combining woody and grassy habitats to take full advantage of the features and ecosystem services they both provide may promote sustainable agricultural ecosystems. It may be possible to both reduce pest pressure and promote natural enemies by accounting for taxa-specific antagonistic responses to multi-scale environmental characteristics.</p>
Data and outputs for chapter 'The impacts of the 2019-20 wildfires on Australian fungi ' in 'Australia's Megafires: Biodiversity Impacts and Lessons from 2019-2020
<p><strong>Dataset includes raw data downloaded from the following sources: fungi_data.csv</strong></p> <ul> <li>Atlas of Living Australia occurrence download: https://doi.org/10.26197/ala.9e0ca388-9da2-4096-b1a3-26e2aaa51d8a. Accessed 2021-09-16. GBIF.org (16 September 2021)</li> <li>GBIF Occurrence Download https://doi.org/10.15468/dl.secenk</li> <li>Fungimap (https://fungimap.org.au/ (data obtained directly from Fungimap Inc.)</li> <li>MycoPortal (https://mycoportal.org/portal/index.php)</li> <li>iNaturalist (https://www.inaturalist.org/home)</li> </ul> <p><strong>Output files from point and polygon overlap with fire layer:</strong></p> <ul> <li>Fungi and fire analysis point overlap.xlsx</li> <li>Fungi and fire analysis polygon overlap.xlsx</li> </ul> <p> </p> <p> </p>
Estimating the carbon footprint of citizen science biodiversity monitoring
<p>Datasets used in the production of the paper Gillings, S. & Harris, S.J. 2022. Estimating the carbon footprint of citizen science biodiversity monitoring. People & Nature.</p> <p>The dataset comprises a) the estimated round-trip distances from approximate locations of observers to survey locations for the UK Breeding Bird Survey and b) questionnaire responses concerning mode of travel used to access survey locations. Data have been anonymised and locations have been coarsened to preserve anonymity.</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p> <p> </p> <p> </p>
GC impacts on soil biodiversity
<p>Dataset for the meta-analysis "Global change and their environmental stressors have a significant impact on soil biodiversity – a meta-analysisGlobal change and their environmental stressors have a significant impact on soil biodiversity – a meta-analysis"</p> <p>R Code for the data cleaning and analysis can be found here: https://github.com/helenphillips/GCimpactsSB</p>
Data for: Systematic and highly resolved modelling of biodiversity in inherently rare groundwater amphipods (Journal of Biogeography, 2024)
<p>Original research article:</p> <p>Knüsel, M., Alther, R., Locher, N., Ozgul, A., Fišer, C. & Altermatt, F. (2024). Systematic and highly resolved modelling of biodiversity in inherently rare groundwater amphipods. <em>Journal of Biogeography</em>, https://doi.org/10.1111/jbi.14975.</p>
Biogeographic data for "The marine biodiversity impact of the Late Miocene Mediterranean salinity crisis"
<p>Lists of species that were present in the Mediterranean Sea both in the pre-evaporitic Messinian and the Zanclean (based on https://doi.org/<a href="../doi/10.5281/zenodo.10782428">10.5281/zenodo.10782428</a>), biogeographic information on their presence outside the Mediterranean, and accordingly their status as either "possible endemic" to the Mediterranean or "non-endemic" if they were also found outside the basin.</p> <p>In this version, we added also the list of species present in the Mediterranean Sea in the pre-evaporitic Messinian that can be considered possible endemics, based on the same rule, and the indication if they survived the MSC.</p>
A plant biodiversity effect resolved to a single genetic locus - datasets
<p>Despite extensive evidence that biodiversity promotes plant community productivity, progress towards understanding the mechanistic basis of this effect remains slow, impeding the development of predictive ecological theory and agricultural applications<em>. </em>Here, we analysed non-additive interactions between genetically divergent Arabidopsis accessions in experimental plant communities. By combining methods from ecology and genetics, we identified a major effect locus that promotes complementarity amongst genotypes and above-ground productivity in mixed communities. In experiments with near-isogenic lines, we show that this diversity effect can act independently of other genomic regions and be resolved to a single locus representing less than 0.3% of the genome. Using plant-soil-feedback experiments, we demonstrate that allelic diversity also causes genotype-specific soil legacy responses in a subsequent growing period. Our work thus shows that positive diversity effects can be linked to single Mendelian factors, and that a range of complex community properties, some of which manifest themselves even after the original community has disappeared<strong>, </strong>can have a simple, single cause. This may pave the way to novel breeding strategies, focussing on phenotypic properties that manifest themselves beyond isolated individuals, i.e. at a higher level of biological organisation.</p>
Herbarium specimen image of Melilotus altissimus Thuill., part of the collection of Naturalis Biodiversity Center
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.
Herbarium specimen image of Acer buergerianum Miq., part of the collection of Naturalis Biodiversity Center
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.
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.