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6,246 results for “Biodiversity”
Plant community responses to functional group and species removals along biodiversity experiment vegetation transects at the Jornada Basin LTER site, 1997-2002
This dataset contains vegetative cover data of plots that have had various plant functional groups or species experimentally removed at the Jornada Basin LTER site in southern New Mexico, USA. This data was collected with the objective to distinguish the differential effects of plant community biomass, functional groups, and biodiversity within functional groups on ecosystem and plant community function. To make these distinctions, treatments were established by the selective removal of plant species or functional groups within experimental plots. There are eight treatments: control (C, no removals); four functional group removal treatments (PG, perennial grass removed; S, shrubs removed; SSh, subshrubs removed; Succ, succulents removed), and three species richness manipulation treatments. Richness manipulations included a simplified treatment (Simp), where only the single most abundant species of each growth form is preserved and all other species in the growth form are removed, a reduced‐Larrea treatment (rL), where the Larrea is assumed to be the dominant and is removed while minority components remain, and a reduced-Prosopsis treatment (rP), where Prosopis rather than Larrea is removed as the shrub dominant. Following treatments, vegetative data was collected by sampling each plot along three transects twice a year (Spring and Fall) for 5 years from 1997-2002 (no data collected in 1998). This data set consists of the date of collection, plot number, treatment type, transect number, quadrat number, species codes, two diameters, height, condition, count, record IDs, and error codes. This study is complete.
Soil Temperature and Water Content in Macrosystems Biodiversity Project at Harvard Forest 2011-2012
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. Readings of soil temperature and soil moisture were taken with HOBO sensors from November 2011 to November 2012. These sensors were installed at five experimental tree growth plots installed by the Enquist Lab (PI, Brian Enquist) from the University of Arizona as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
Soil Chemistry and Moisture in Macrosystems Biodiversity Project at Harvard Forest 2012
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. Soil chemistry (TN, TC, NH4-N, NO3-N, and pH) and moisture measurements were taken from soil cores from an array of 21 1m2 subplots and processed by the University of Oklahoma Institute for Environmental Genomics as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
Soil Bacteria and Archaea in Macrosystems Biodiversity Project at Harvard Forest 2012
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. This field experiment focused on soil microbes. DNA was extracted and purified from soil cores from an array of 21 1m2 subplots. The V4 region of the 16S rRNA genes for bacteria and archaea were amplified and sequenced using Illumina MiSeq by the University of Oklahoma Institute for Environmental Genomics as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
Soil Invertebrate Species in Macrosystems Biodiversity Project at Harvard Forest 2012
Leaf litter invertebrates and soil microbes were sampled in an array of 21 1m2 subplots by the Kaspari Ant Lab at the University of Oklahoma as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
Tree Growth in Macrosystems Biodiversity Project at Harvard Forest 2011-2013
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. This dataset contains annual growth measurements of trees along a series of transects using the measures of diameter at breast height and/or diameter and ground height at the five Gentry plots set up at Harvard Forest. These plots were set up by the Enquist Lab (PI, Brian Enquist) from the University of Arizona as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
Biomass removal at the initiation of a biodiversity experiment at the Jornada Basin LTER site in 1995
This dataset contains data on vegetation biomass removed from treatment plots during the establishment of a biodiversity experiment at the Jornada Basin LTER site in southern New Mexico, USA. In fall of 1995, various combinations of plant functional groups or species were experimentally removed from 25 x 25 meter plots with the objective to distinguish the differential effects of plant community biomass, functional groups, and biodiversity within functional groups on ecosystem and plant community function. Eight different treatments were established with selective removal of species or functional groups: control (C, no removals); four functional group removal treatments (PG, perennial grass removed; S, shrubs removed; SSh, subshrubs removed; Succ, succulents removed), and three species richness manipulation treatments. Richness manipulations included a simplified treatment (Simp), where only the single most abundant species of each growth form is preserved and all other species in the growth form are removed, a reduced‐Larrea treatment (rL), where the Larrea is assumed to be the dominant and is removed while minority components remain, and a reduced-Prosopsis treatment (rP), where Prosopis rather than Larrea is removed as the shrub dominant. The amount of plant material removed during the establishment of these treatments was recorded for later use as covariate or measure of disturbance. Removed fresh material was weighed in the field by species, then converted to dry mass using a subset of removed plot vegetation that was oven-dried and weighed in the lab. Variables in this file summarize the dry mass of plants removed by growth form (functional group, i.e. shrub, subshrub, perennial grass, succulent), and by total live dry mass, for each plot in the biodiversity experiment. Also provided are masses of dead material collected from plots (same groups as live material removed for each treatment) and total dry mass, live plus dead. Species-level data are available upo
Plant species-level responses to functional group and species removals in biodiversity experiment plots at the Jornada Basin LTER site, 1999
This dataset contains individual species size data in vegetation plots that have had various plant functional groups or species experimentally removed at the Jornada Basin LTER site in southern New Mexico, USA. This data was collected with the objective to distinguish the differential effects of plant community biomass, functional groups, and biodiversity within functional groups on ecosystem and plant community function. To make these distinctions, treatments were established by the selective removal of plant species or functional groups within experimental plots. There are eight treatments: control (C, no removals); four functional group removal treatments (PG, perennial grass removed; S, shrubs removed; SSh, subshrubs removed; Succ, succulents removed), and three species richness manipulation treatments. Richness manipulations included a simplified treatment (Simp), where only the single most abundant species of each growth form is preserved and all other species in the growth form are removed, a reduced‐Larrea treatment (rL), where the Larrea is assumed to be the dominant and is removed while minority components remain, and a reduced-Prosopsis treatment (rP), where Prosopis rather than Larrea is removed as the shrub dominant. In 1999, this pilot study attempted to assess individual species responses of representative individuals in these treatments. Ten randomly selected individuals of eight plant species were measured in each experimental plot, and this dataset reports volumetric data (diameters and height) for each. The study was designed as an individual-based complement to the transect data in EDI dataset knb-lter-jrn.210121001 but was not continued past 1999. This dataset is complete.
Data from: Invasion timing affects multiple scales, metrics and facets of biodiversity outcomes in ecological restoration experiments (Missouri, 2009-2016)
Vegetation responses to experimental ecological restoration treatments at Tyson Research Centre of Washington University in Missouri, USA. These data include species-level cover responses to various factorial restoration treatments. Treatments were applied starting in 2009 and were measured in 2016. Treatment responses reflect these long term responses, but the dataset is comprised to one time point.
Plantation Biodiversity Plots at Harvard Forest since 2007
Plantations have been part of forestry at Harvard Forest since its beginning. Plantations were established from 1911-1944, usually on open or cut-over land. The objectives for plantation forests at Harvard Forest were to: increase the amount of productive forest land; examine the suitability of various species and cultivars to New England conditions; and test planting and tending methods. At first, mainly small plantings (less than 1 to 4 acres) were established primarily on "blank" lands. Some larger plantations (20+ acres) were established on cut-over land around 1930. The bulk of the plantings were red pine, white pine and spruce (white and Norway), but several other conifers were also planted. The maximum amount of land in plantations came to about 270 acres (less than 10% of Harvard Forest’s land base). Tending of the plantations, especially weeding in the decade following establishment, but also in-filling, pruning and thinning, was conducted and carefully documented. However, tending of plantations ceased about 1950. Since then, the fate of the plantations has been mixed. Some plantations were out-competed by native forest with only scattered planted trees remaining. A few acres of plantations were harvested in the 1950s. About 40 acres of plantations were harvested in the 1990s. As of 2007, approximately 135 acres remained. These stands range in age from 60-90 years old. Some stands have substantial areas blown down. We developed a management plan to harvest about 80 acres of mature plantation forests beginning in 2008 in order to terminate these long term experiments, to regenerate a diversity of native tree species and restore native forests to these sites, and to initiate a new suite of long term experiments. For the next 10-15 years, the harvested areas will provide early successional habitat for a variety of wildlife species. A suite of permanent vegetation plots was established throughout the plantations to assess vegetation structure in plantations an
Climate Change Impacts on Forest Biodiversity at Harvard Forest since 2011
Climate change is rapidly transforming forests over much of the globe in ways that are not anticipated by current science. Large-scale forest diebacks, apparently linked to interactions involving drought, warm winters, and other species, are becoming alarmingly frequent. Models of biodiversity and climate have not provided guidance on if/where/when such responses will occur. Instead models often predict potential numbers of extinctions, but these forecasts not are linked in any mechanistic way to the processes that could cause them. Both modeling and field studies rely on aggregate metrics of species presence/absence or relative abundance at regional scales, but climate affects individuals. Aggregation of individual data to the species level, hides or even qualitatively changes climate effects. By sampling and analysis at the individual scale across continental variation in climate, this study can link the individual scale processes to regional responses. This study will exploit existing research sites and the new NEON platform of sites for synthesis of models and data to determine when and where predicting climate impacts on biodiversity is a plausible goal, understand where surprises are likely to occur, and attribute those predictions back to individual tree health and vulnerability to climate risk factors. The study will provide climate vulnerability forecasts for forest biodiversity that are directly linked to the process scale. Our goal is provide probabilistic forecasts for the joint distribution of forest responses to climate change, including growth, reproduction, and mortality risk. For scientists, US Forest Service researchers, and policy makers predictions will anticipate combined risks of increasing drought and longer growing seasons. Methods developed under this project will be disseminated through training workshops for postdoctoral associates at other universities and resource managers.
MCR LTER: Coral Reef: 3D photogrammetry improves measurement of growth and biodiversity patterns in branching corals; data for Curtis 2023, Coral Reefs
These data and code were generated in support of the manuscript: Curtis JS, Galvan JW, Primo A, Osenberg CW, and AC Stier, Coral Reefs. We collected manual and photogrammetry-based measurements of coral size and volume to examine which method best described short-term coral growth and links between coral habitat and biodiversity of CAFI (coral-associated fishes and invertebrates). This study was completed between August and December 2019 on an experimental array located in the back reef off the south shore of Moorea, French Polynesia. These data were published in Coral Reefs, analyses and full methods descriptions of this model can be found in the manuscript “3D photogrammetry improves measurement of growth and biodiversity patterns in branching corals”. This manuscript uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023).
Organized actors at the biodiversity science-policy-society interface
<p>This database was developed in the context of the Deliverable 2.1 of the BioAgora project 'Developing the Science Service for European Research and Biodiversity Policymaking' (<a href="https://bioagora.eu/)">https://bioagora.eu/)</a>. BioAgora is a collaborative European project funded by the Horizon Europe programme (Horizon Europe research and innovation programme, grant agreement No. 101059438). The project's main outcome is intended to be the development of a Science Service for Biodiversity, the principal EU mechanism to connect research and knowledge on biodiversity to the needs of policy making through a continuous dialogue. The ultimate goal of BioAgora and of the Science Service is to support the implementation of the Biodiversity Strategy for 2030, and more broadly the sustainability transition required by the EU Green Deal. The BioAgora project was launched in July 2022 for a duration of 5 years. It gathers a Consortium of 22 partners, from 13 European countries, led the Finnish Environment Institute (Syke). Partners represent a diversity of actors coming from academia, public authorities, SMEs, and associations. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. </p> <p>In order to develop the database, a thorough desk search was conducted to compile an extensive, albeit not exhaustive, list of organizations operating at the science-policy-society interface in the context of biodiversity and sustainability. In collecting the list, we focused on actors operating at EU level, although we also included particularly relevant international, regional or national organized actors. The desk search built upon the work already developed in the context of two pan-European projects, funded by the Seventh framework programme of the European Community: ‘Developing a Knowledge Network for European Expertise on biodiversity and ecosystem services to inform policy making and economic sectors (KNEU, 2010-2014, grant 265299) and ‘Establishing a European Knowledge and Learning Mechanism to Improve the Policy-Science-Society Interface on Biodiversity and Ecosystem Services’ (Eklipse, 2016-2020, grant 690474). The two above-mentioned projects preceded the BioAgora project in that they aimed at understanding and improving the effectiveness of the biodiversity science-policy(-society) interface in Europe. Such projects had thus already compiled extensive databases of relevant organizations in Europe (including national and international actors, in addition to EU level actors), and quantified the relevance of such organizations based on votes cast by project members and based on interviews with key organizations. The database developed through the desk search conducted was further refined with suggestions for relevant organizations provided by BioAgora’s participants and by the representatives of the organizations interviewed during the other steps of the data collection. The data collection processes started in September 2022 and was updated until June 2024. Note that the categories for network types (Columns E-F) are not mutually exclusive. For further details about the development of the database please see Deliverable 2.1 (<a href="https://bioagora.eu/deliverables/">https://bioagora.eu/deliverables/</a>). </p>
A biodiversity dataset graph: Biological Associations in TaxonWorks hash://sha256/e4a47c067d6c125da60c9a1b92b5eecdea539cb8666cd3aed99db347ae5b8ed0 hash://md5/686007de79cc2a49ab23fd3debe56e3f
<p>The intended use of this archive is to facilitate (meta-)analysis of Biological Associations captured in TaxonWorks [1]. TaxonWorks is an integrated web-based workbench for taxonomists and biodiversity scientists. It allows you to capture, organize, and enrich your data; share it with collaborators; and package it for analysis and publication. </p> <p>This dataset provides versioned snapshots of the TaxonWorks network as tracked by Preston [2,3,4] during 2024-05-07 using:</p> <pre><code>preston track -u https://sfg.taxonworks.org</code></pre> <p>. In addition, this dataset provides a processed version of the biological associations using the "preston tw-stream" command as generated by the following bash script:</p> <pre><code>#!/bin/bash # # Generates GloBI interaction JSON Lines from provided provenance log as generated by preston tw-stream. # /usr/local/bin/preston cat hash://sha256/c1b081afa6ea0f60570c24cca85c4d9acd91eeefe36b9cacd1fe53b6893ea154\ | /usr/local/bin/preston tw-stream </code></pre> <p><br>The script itself was executed using:</p> <pre><code>cat transform.sh | preston bash </code></pre> <p>The execution of this transform.sh script (with content id hash://sha256/6dfe3c4ebf877bed73aebbe88c7d388bf894c569578ed7b28ca68e57a6afe43b), as well as their results, is captured within this datasets also. A rdf/quads formatted machine readable version of the workflow execution description can be found via:</p> <pre><code>preston cat hash://sha256/e4a47c067d6c125da60c9a1b92b5eecdea539cb8666cd3aed99db347ae5b8ed0 </code></pre> <p>And, the resulting JSON Lines file has content id (or signature) hash://sha256/4c2b8642251ced5985660d63c565efa6e5a9bf3d12b3b0c0d9ac577905f5e897 and is also included as interactions.json to facilitate access. </p> <p>The first json record can be generated using:</p> <pre><code>preston cat hash://sha256/4c2b8642251ced5985660d63c565efa6e5a9bf3d12b3b0c0d9ac577905f5e897\ | head -n1\ | jq . </code></pre> <p>or, provided that the interactions.json has content id starting with hash://sha256/4c2b86...</p> <pre><code>cat interactions.json\ | head -n1\ | jq . </code></pre> <p>This produces the following (formatted) json object:</p> <pre><code>{<br> "http://www.w3.org/ns/prov#wasDerivedFrom": "hash://sha256/fdbf13dc5f3d9c5afbc03db62699e2ce2724c499b7d91d8b0bf31e39409b153a",<br> "http://www.w3.org/1999/02/22-rdf-syntax-ns#type": "application/vnd.taxonworks+json",<br> "referenceId": "https://sfg.taxonworks.org/api/v1/sources/213218",<br> "interactionId": "https://sfg.taxonworks.org/api/v1/biological_associations/227664",<br> "taxonRootsResolved": 2,<br> "referenceResolved": true,<br> "referenceCitation": "@article{213218,\n author = {Monzen, Kota},\n journal = {Annual Report of the Gakugei Faculty of the Iwate University},\n pages = {24-38},\n title = {Revision of the Japanese gall wasps with the descriptions of new genus, subgenus, species and subspecies (II). Cynipidae (Cynipinae) Hymenoptera.},\n volume = {6},\n year = {1954}\n}\n",<br> "interactionTypeId": "gid://taxon-works/BiologicalRelationship/69",<br> "interactionTypeName": "gall",<br> "sourceTaxonName": "Neuroterus hakonensis",<br> "sourceTaxonId": "gid://taxon-works/TaxonName/1174121",<br> "sourceTaxonRank": "species",<br> "sourceTaxonAuthorship": "Ashmead, 1904",<br> "sourceTaxonPath": "Root | Cynipidae | Neuroterus | Neuroterus hakonensis",<br> "sourceTaxonPathIds": "gid://taxon-works/TaxonName/623170 | gid://taxon-works/TaxonName/1170060 | gid://taxon-works/TaxonName/1170097 | gid://taxon-works/TaxonName/1174121",<br> "sourceTaxonPathNames": "nomenclatural rank | family | genus | species",<br> "targetTaxonName": "Quercus",<br> "targetTaxonId": "gid://taxon-works/TaxonName/1173543",<br> "targetTaxonRank": "genus",<br> "targetTaxonAuthorship": "",<br> "targetTaxonPath": "Root | Fagaceae | Quercus",<br> "targetTaxonPathIds": "gid://taxon-works/TaxonName/623170 | gid://taxon-works/TaxonName/1173542 | gid://taxon-works/TaxonName/1173543",<br> "targetTaxonPathNames": "nomenclatural rank | family | genus"<br>}<br></code></pre> <p>In this example, a claim is made that, according to https://sfg.taxonworks.org/api/v1/sources/213218 [6] Neuroterus hakonensis (a gall wasp) has a primary host in the genus of Quercus (oak tree). </p> <p>In total, 237,068 such claims can be found in the generated resource with alias interactions.json and content id starting with hash://sha256/4c2b86... .</p> <p>In addition, the archive preston.tar.gz to allow for batch download. The archive contains three types of files: index files, provenance logs and data files. In addition, index files have been individually included in this dataset publication to facilitate remote access. Index files provide a way to links provenance files in time to establish a versioning mechanism. Provenance files describe how, when, what and where the TaxonWorks content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 . </p> <p>To retrieve and verify the downloaded TaxonWorks biodiversity dataset graph, download preston.tar.gz. Then, extract the archive into a "data" folder. Alternatively, you can use the preston[2] command-line tool to "clone" this dataset using:</p> <pre><code>java -jar preston.jar clone --remote https://zenodo.org/record/11151783/files </code></pre> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <pre><code> java -jar preston.jar history --log tsv</code></pre> <p>to be:</p> <pre><code>hash://sha256/e4a47c067d6c125da60c9a1b92b5eecdea539cb8666cd3aed99db347ae5b8ed0 http://www.w3.org/ns/prov#wasDerivedFrom hash://sha256/c1b081afa6ea0f60570c24cca85c4d9acd91eeefe36b9cacd1fe53b6893ea154 </code><br><code>hash://sha256/c1b081afa6ea0f60570c24cca85c4d9acd91eeefe36b9cacd1fe53b6893ea154 http://www.w3.org/ns/prov#wasDerivedFrom hash://sha256/a4d651aac5220487835e6178511886e98b845b2d98cb7c5447fb2b042e0654d2hash://sha256/a4d651aac5220487835e6178511886e98b845b2d98cb7c5447fb2b042e0654d2 http://www.w3.org/ns/prov#wasDerivedFrom hash://sha256/ab7550368905e7c919e70a306efbb97719a1edbba2cfe4c4515f635ebc0be4bb hash://sha256/a4d651aac5220487835e6178511886e98b845b2d98cb7c5447fb2b042e0654d2 http://www.w3.org/ns/prov#wasDerivedFrom hash://sha256/ab7550368905e7c919e70a306efbb97719a1edbba2cfe4c4515f635ebc0be4bb</code><br><code>hash://sha256/ab7550368905e7c919e70a306efbb97719a1edbba2cfe4c4515f635ebc0be4bb http://www.w3.org/ns/prov#wasDerivedFrom hash://sha256/ff5e709305e593c87711e897b6341b94e775e2f312aa6d4ae5ed6120babd6f5e urn:uuid:0659a54f-b713-4f86-a917-5be166a14110 http://purl.org/pav/hasVersion hash://sha256/ff5e709305e593c87711e897b6341b94e775e2f312aa6d4ae5ed6120babd6f5e </code></pre> <p><br>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> <pre><code>java -jar preston.jar verify</code></pre> <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 (a rendition of this file) --<br>README</p> <p>-- biological associations indexed from TaxonWorks expressed in a GloBI [5] compatible JSON Lines file --<br>interactions.json</p> <p>-- first 10 biological associations indexed from TaxonWorks expressed in a GloBI [5] compatible JSON Lines file --<br>interactions-10.json</p> <p>-- executable java jar containing preston [2,3,4] v0.8.5-SNAPSHOT. --<br>preston.jar</p> <p>-- preston archive containing TaxonWorks data files, associated provenance logs and a provenance index --<br>preston.tar.gz</p> <p>-- individual provenance index files --</p> <p>1fed32bf78298d7ecc3d9f36d106f1d7d7773a8b9a5e47af6632f36c1f82adb5<br>29306c5c144c3d7fd21be344d8b6b554b6f6efa3b8f8f5c0b27cdf0e88785652<br>2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a<br>d31ff1ef1dea88c5952181a4f30e7ea7862873aa5f66430451275aa6d08d329e<br>deb84d69224af488da585186f88cafc58e978db5f9897de624cc9b02c0c83742<br>e9c34683f1e826f68f841f3419bd5ee9c0fa18be04713a6fd3364f226c7c5f2f<br>f98d36a9dc7bd833c93b3b61130865628f7bc2f7bb0920e95afcd16fba3dc6a8<br>ffb41d48979ceb964fbfbeb68cb60b584b759950087fdcc012521b866249bc39</p> <p>--- end of file descriptions ---</p> <p>This work is funded in part by grant NSF OAC 1839201, NSF DBI 1901932, NSF DBI 1901926, and NSF DBI 2102006 from the National Science Foundation.<br> </p>
Supporting Data for Crawford et al. 2024, Effects of Cropland Abandonment on Biodiversity
<p><strong>This archive contains derived and supporting data products to support:</strong></p> <blockquote>Crawford CL*, Wiebe RA, Yin H, Radeloff VC, and Wilcove DS. 2024. Effects of cropland abandonment on biodiversity. <em>Nature Sustainability.</em> In press.</blockquote> <p>*Contact Christopher L. Crawford at ccrawford@alumni.princeton.edu with any questions.</p> <p>A public Zenodo archive of the Github repository containing analysis scripts developed for this project (https://github.com/chriscra/biodiversity_abandonment) can be found here: <a href="https://doi.org/10.5281/zenodo.13777205">10.5281/zenodo.13777205</a></p> <p>This analysis builds on: Crawford, C. L., Yin, H., Radeloff, V. C. & Wilcove, D. S. Rural land abandonment is too ephemeral to provide major benefits for biodiversity and climate. <em>Science Advances </em>8, 1–13 (2022). Data and scripts from Crawford et al. 2022 are archived and publicly available at Zenodo (https://doi.org/10.1126/sciadv.abm8999).</p> <p>The annual land cover maps (1987-2017, 30 meter resolution) that underlie our analysis were developed on Google Earth Engine using publicly available Landsat satellite imagery (Yin et al. 2020, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2020.111873).<br>These annual land cover maps, along with other derived data that were produced by Crawford et al. 2022, are archived and publicly available at Zenodo (https://doi.org/10.5281/zenodo.5348287).</p> <p>This archive includes important derived data products created for Crawford et al. 2024. Note that these and other project data are described in detail in **util/_util_files.R** (https://github.com/chriscra/biodiversity_abandonment). This is a convenience script that loads many of the relevant input and derived data that are used throughout the project. The primary required data files for reproducing this work are archived here, but "_util_files.R" also includes information about where additional files can be accessed (if external, e.g., https://doi.org/10.5281/zenodo.5348287) or created across the various .R and .Rmd files in this repository (e.g., "habitats.Rmd" chunk {r land-cover-of-abn-pixels}).</p> <p>Naming conventions for sites and raster files follow Crawford et al. 2022, as described here: https://doi.org/10.5281/zenodo.5348287</p> <p><strong>Site file names correspond to the following geographic locations:</strong><br>belarus = Vitebsk, Belarus / Smolensk, Russia<br>bosnia_herzegovina = Bosnia & Herzegovina<br>chongqing = Chongqing, China<br>goias = Goiás, Brazil<br>iraq = Iraq<br>mato_grosso = Mato Grosso, Brazil<br>nebraska = Nebraska / Wyoming, USA<br>orenburg = Orenburg, Russia / Uralsk, Kazakhstan<br>shaanxi = Shaanxi/Shanxi, China<br>volgograd = Volgograd, Russia<br>wisconsin = Wisconsin, USA</p> <h1><strong>This archive includes the following files:</strong></h1> <ul> <li>site_df.csv</li> <li>crop_to_abn_iucn_observed.zip</li> <li>crop_to_abn_iucn_potential.zip</li> <li>max_abn_lcc_iucn.zip</li> <li>max_abn_lcc_iucn_potential.zip</li> <li>lcc_iucn_habitat.zip</li> <li>lcc_iucn_habitat_potential.zip</li> <li>frag_df.csv</li> <li>frag_hypo_no_abn_2017_df.csv</li> <li>iucn_lc_crosswalk.csv</li> <li>habitat_age_req_coded.csv</li> <li>centroids_df.csv</li> <li>aoh_l.parquet</li> <li>aoh_feols.parquet</li> <li>aoh_start_end_l.parquet</li> <li>aoh_change_df.parquet</li> <li>aoh_est_change_tmp_all.csv</li> <li>aoh_obs_change_tmp_all.csv</li> <li>taxonomy_df.parquet</li> <li>final_species_list.csv</li> <li>trait_mod_df_modx1.rds</li> </ul> <h3>site_df.csv</h3> <p>A list of site names and related metadata describing our study sites, taken from https://zenodo.org/records/5348287</p> <h2>Derived habitat rasters:</h2> <h3>crop_to_abn_iucn_observed.zip (Calculation 1a)<br>crop_to_abn_iucn_potential.zip (Calculation 1b)<br>max_abn_lcc_iucn.zip (Calculation 2a)<br>max_abn_lcc_iucn_potential.zip (Calculation 2b)<br>lcc_iucn_habitat.zip (Calculation 3a)<br>lcc_iucn_habitat_potential.zip (Calculation 3b)</h3> <p>These maps show IUCN Level 2 habitat types (Jung et al. 2020) interpolated onto the land cover classes in the Yin et al. (2020) abandonment maps at multiple spatial and temporal extents, which serve as inputs for the three primary calculations in our manuscript. Accompanying each calculation is a corresponding map for a scenarios in which no abandoned croplands were recultivated over the course of the time series (marked as "potential"). Each .zip file contains maps for each of 11 sites.</p> <p><strong>Calculation 1. </strong>This calculation isolates the direct effect of abandonment on habitat availability, by comparing the habitat provided before and after abandonment. These "crop_to_abn_iucn" maps show IUCN Level 2 habitats in cropland pixels that experienced abandonment, including the abandonment period as well as the immediately preceding period of cultivation (to allow for a proper before and after comparison). As a result, these maps show only habitat provided by croplands when they were actively cultivated, abandoned, or, where appropriate, recultivated, which allows for a proper before and after comparison. These maps are created in the script "cluster/noncrop_precrop_mask.R".</p> <p><strong>Calculation 2.</strong> This calculation considered changes in habitat that took place exclusively in pixels that experienced abandonment at some point during the time series (following Calculation 1), but expanded to track changes across our entire time series, from 1987 through 2017, in order to account for any land cover that was cleared for agriculture prior to abandonment. These "max_abn_lcc_iucn" maps therefore show IUCN Level 2 habitat types for each pixel that was abandoned at any point during the time series, across the full time series. These maps were created in the script "habitats.Rmd" code chunks {r mask-lcc-iucn-habitat-to-abn} and {r *potential_max}. </p> <p><strong>Calculation 3. </strong>This calculation tracks habitat area provided by every pixel throughout the entire spatial and temporal extent (1987-2017), in order to place abandonment into the context of broader land-cover change dynamics like ongoing cropland expansion taking place alongside of abandonment. These "lcc_iucn" maps therefore show the IUCN Level 2 habitat types for each pixel at each site in each year of our time series. These maps were created in the script "habitats.Rmd" code chunks {r lcc-iucn-habitat-composite} and {r *potential-lcc-full} and the script "cluster/potential_full_iucn.R".</p> <p>Some analyses require these .tif files (manipulated as SpatRasters using {terra}, https://rspatial.org/terra/) to be converted to tabular format (data.tables, via {data.table} (https://rdatatable.gitlab.io/data.table/) and saved as .parquet files (via {arrow}, https://arrow.apache.org/docs/r/). This can be accomplished via scripts "cluster/save_spatraster_as_dt.R" and "cluster/save_parquet.R."</p> <h3><br>frag_df.csv<br>frag_hypo_no_abn_2017_df.csv</h3> <p>These tabular files contain derived fragmentation statistics calculated using the {landscapemetrics} R package (https://r-spatialecology.github.io/landscapemetrics/). The second file contains metrics for a scenario in which no croplands were abandoned through the year 2017, in order to assess the effect cropland abandonment on landscape configuration. Each file contains 11 columns: </p> <ol> <li>"layer" -- the spatial raster layer for which the metric is calculated, corresponding to a year.</li> <li>"level" -- the level at which the metric is calculated, in our case, the land cover "class."</li> <li>"class" -- corresponding the to land cover class for which the metric is calculated (1 = non-vegetation, 2 = woody vegetation [i.e., forest], 3 = cropland, and 4 = herbaceous vegetation [i.e., grassland]).</li> <li>"id" -- An unused field containing NA values.</li> <li>"metric" -- the specific term used for each metric by {landscapemetrics} ("area_mn", "clumpy", or "para_mn").</li> <li>"value" -- the numerical value of the statistic.</li> <li>"name" -- the name of the landscape metric being calculated ("patch area," "clumpiness index," or "perimeter-area ratio").</li> <li>"type" -- the broad type of metric being calculated ("area and edge metric," "aggregation metric," or "shape metric").</li> <li>"function_name" -- the name of the {landscapemetrics} function used to calculate the statistic.</li> <li>"site" -- the site (out of 11 study sites) for which this statistic was calculated.</li> <li>"year" -- the year corresponding to the metric statistic, between 1987-2017 (including 1986-2018 for Nebraska and 1987-2018 for Wisconsin)<br>Additional details on these metrics can be found at https://r-spatialecology.github.io/landscapemetrics/.</li> </ol> <p>The spatial IUCN data underlying our analyses (species range maps) are available upon request from BirdLife International (http://datazone.birdlife.org/species/requestdis) and IUCN (https://www.iucnredlist.org/resources/spatial-data-download). Tabular species assessment data (including habitat and elevation preferences) are freely available from IUCN (https://www.iucnredlist.org/). Here we share three IUCN-related data files that serve as important inputs throughout our analyses:</p> <h3>iucn_lc_crosswalk.csv</h3> <p>This tabular file outlines the crosswalk between the 4 land cover classes in Yin et al. 2020 and the IUCN Level 2 habitat types mapped by Jung et al. 2020. It contains five columns:</p> <ol> <li>"map_code" -- the habitat code corresponding to Jung et al. (2020).</li> <li>"Coarse_Name" -- the broad Level 1 habitat grouping.</li> <li>"lc" -- the corresponding land cover type from Yin et al. (2020) (1 = non-vegetation, 2 = woody vegetation [i.e., forest], 3 = cropland, and 4 = herbaceous vegetation [i.e., grassland]).</li> <li>"IUCNLevel" -- the full IUCN Level 2 habitat type name. </li> <li>"code" -- the IUCN Level 2 habitat code. </li> </ol> <h3>habitat_age_req_coded.csv</h3> <p>This tabular file lists whether each species was determined (by R. Alex Wiebe [AW] and Christopher L. Crawford [CLC]) to be a "mature forest obligate" (i.e., requiring forest older than 30 years, our time series length) or not. Species determined to be "mature forest obligate" species were excluded from our final analysis. The file includes 11 columns: </p> <ol> <li>"vert_class" -- Vertebrate class ("bird" or "mam" [mammal])</li> <li>"binomial" -- Species' binomial scientific name containing genus and species.</li> <li>"common_names" -- Species' common names listed by IUCN.</li> <li>"mature_forest_obl" -- Whether a species is determined to be a "mature forest obligate" species (1) or not (0). Some species are marked as 0.9, 0.75, 0.25, or 0.1 as an indication of some uncertainty, but these were rounded to the nearest integer for the final analysis.</li> <li>"water_obl" -- Whether a species is determined to be a "water obligate" species (1) or not (0). Some species were marked as 0.9, 0.75, 0.25, or 0.1 as an indication of some uncertainty. Note: this field was not used in the analysis.</li> <li>"habitat" -- The description of the species' habitat, drawn from individual IUCN assessments (see https://www.iucnredlist.org/).</li> <li>"site_presence" -- Where each species is present across our 11 study sites.</li> <li>"suitable_habitats" -- A list of IUCN Level 2 habitat types consider suitable habitat by each species.</li> <li>"major_habitats" -- A list of IUCN Level 2 habitat types listed as having "Major Importance" for that species.</li> <li>"coder" -- The author that assigned the mature forest obligate and water obligate codes ("AW" = R. Alex Wiebe, "CLC" = Christopher L. Crawford).</li> <li>"Chris_notes" -- A text field contains notes on coding process.</li> </ol> <h3>centroids_df.csv</h3> <p>This is a simple tabular dataset containing the longitude and latitude of the centroid of each bird and mammal species' range that overlaps with one of my sites. Columns include "binomial," which lists each species binomial scientific name, "centroid_longitude," and centroid_latitude." Centroid positions were calculated in QGIS using species range files from IUCN and BirdLife International.</p> <h3><br>aoh_l.parquet</h3> <p>This tabular file contains the raw AOH results produced using the script "cluster/aoh.R." This file contains the area of each suitable IUCN Level 2 habitat for each bird and mammal species at each site in each year of our time series (1987-2017), calculated across a range of calculations and scenarios. This file includes the primary data that serve as inputs for much of the rest of the analysis. The overall area of habitat for each species in each year at each site (a tabular data file named "aoh") summed across suitable habitat types and filtered to include or exclude passage areas for migratory birds, is calculated from "aoh_l" in the "AOH.Rmd" script in code chunks "filter-aoh-suitability-by-season" and "**calculate-aoh" (similarly to other derived datasets that serve as inputs for various parts of the analysis). This "aoh" file provides input data for the linear models used to extract AOH trends and test for significance. "aoh_l.parquet" includes 20 columns: </p> <ol> <li>"aoh_type" -- A label indicating the temporal and spatial scale at which AOH is calculated: "crop_abn_iucn" (Calc. 1a), "crop_abn_potential_iucn" (Calc. 1b), "max_abn_iucn" (Calc. 2a), "max_potential_abn_iucn" (Calc. 2b), "full_iucn" (Calc. 3a), and "full_potential_iucn" (Calc. 3b). "abn_iucn" and "potential_abn_iucn" correspond to calculations that only capture habitat following abandonment (i.e., not including habitat provided by croplands prior to abandonment); these calculations are not included in our final analysis.</li> <li>"vert_class" -- Vertebrate class ("amp," amphibians; "bird," birds; or "mam," mammals). Note that only birds and mammals were included in our final analysis.</li> <li>"site" -- One of our 11 study sites (see above).</li> <li>"binomial" -- Species binomial scientific name.</li> <li>"year" -- Year for which AOH is calculated (1987-2017).</li> <li>"map_code" -- Code indicating the IUCN Level 2 habitat associated with the area statistic. See "iucn_lc_crosswalk.csv."</li> <li>"season" -- Seasonal code indicating the season in which a species considers the habitat to be suitable, drawn from IUCN. Codes are: 1 ("Resident"), 2 ("Breeding") (2), "Non-breeding Season" (3), Passage (4), and Seasonal Occurrence Uncertain (5)</li> <li>"area" -- Area of Habitat, in hectares (ha).</li> <li>"mature_forest_obl" -- Whether a species is determined to be a "mature forest obligate" species (1) or not (0), drawn directly from "habitat_age_req_coded.csv" (see above). Some species are marked as 0.9, 0.75, 0.25, or 0.1 as an indication of some uncertainty, but these were rounded to the nearest integer for the final analysis.</li> <li>"redlistCategory" -- IUCN Red List Category: "Extinct," "Extinct in the Wild," "Critically Endangered," "Endangered," "Vulnerable," "Near Threatened," "Least Concern," "Data Deficient," or "Not Evaluated."</li> <li>"IUCN_aoh_ha" -- [Unused] A preliminary summation of all habitat area for each species in each year, prior to filtering. We did not use this field in our analysis. Our final AOH calculation involved first filtering out mismatched season and habitat suitability combinations.</li> <li>"time" -- The time required for the area of habitat calculation (in seconds).</li> <li>"className" -- Vertebrate class: "AMPHIBIA," "AVES," or "MAMMALIA."</li> <li>"category" -- Duplicate field for IUCN Red List Category, unused.</li> <li>"core_index" -- An index used to assign specific AOH calculations to run in parallel across multiple computing cores on Princeton's High-Performance Computing Cluster.</li> <li>"total_range_area" -- The species total range area, in square kilometers (km^2), calculated across all range polygons for each species provided by IUCN and BirdLife International. See "cluster/calc_range_area.R."</li> <li>"range_size_quantile" -- A numerical index representing global species range size quantiles, within each class. Values range from 0 (the smallest global range within a class) to 1 (the largest global range within a class). These quantiles are used to define "small-ranged species," as species with global range sizes smaller than the median global range size in their class. See "cluster/calc_range_area.R."</li> <li>"water_obl" -- Whether a species is determined to be a "water obligate" species (1) or not (0). Some species were marked as 0.9, 0.75, 0.25, or 0.1 as an indication of some uncertainty. Note: this field was not used in the analysis. Drawn directly from "habitat_age_req_coded.csv" (see above).</li> <li>"coder" -- The author that assigned the mature forest obligate and water obligate codes ("AW" = R. Alex Wiebe, "CLC" = Christopher L. Crawford). Drawn directly from "habitat_age_req_coded.csv" (see above).</li> <li>"common_names" -- Species' common names listed by IUCN, drawn directly from "habitat_age_req_coded.csv" (see above).</li> </ol> <h3><br>aoh_feols.parquet</h3> <p>This tabular data contains the results of linear regressions predicting area of habitat as a function of time. We parameterized models for each species in each site for each of the 6 AOH calculation types described above and in Crawford et al. 2024 (Calculations 1a, 1b, 2a, 2b, 3a, and 3b). We used the R package {fixest} to parameterize these ordinary least squares (OLS) linear regressions, using the Newey-West estimator to calculate standard errors. We used the R package {broom} to extract ("tidy") the model coefficient estimates and statistics. See "AOH.Rmd" chunk {r **feols}. This file includes 20 columns:</p> <ol> <li>"term" -- The name of the regression term: "(Intercept)" or slope ("year0").</li> <li>"estimate" -- The estimated value of the regression term.</li> <li>"std.error" -- The standard error of the regression term.</li> <li>"statistic" -- The value of a T-statistic to use in a hypothesis that the regression term is non-zero.</li> <li>"p.value" -- The two-sided p-value associated with the observed statistic.</li> <li>"conf.low" -- Lower bound on the confidence interval for the estimate (in our case 5%).</li> <li>"conf.high" -- Upper bound on the confidence interval for the estimate (in our case, 95%).</li> <li>"aoh_type" -- A label indicating the temporal and spatial scale at which AOH is calculated: "crop_abn_iucn" (Calc. 1a), "crop_abn_potential_iucn" (Calc. 1b), "max_abn_iucn" (Calc. 2a), "max_potential_abn_iucn" (Calc. 2b), "full_iucn" (Calc. 3a), and "full_potential_iucn" (Calc. 3b). "abn_iucn" and "potential_abn_iucn" correspond to calculations that only capture habitat following abandonment (i.e., not including habitat provided by croplands prior to abandonment); these calculations are not included in our final analysis.</li> <li>"run_index" -- An index used to easily pull observations for each model run. There is one index for each unique species at each site, in each of the aoh_types, calculated including and excluding passage areas.</li> <li>"vert_class" -- Vertebrate class ("amp," amphibians; "bird," birds; or "mam," mammals). Note that only birds and mammals were included in our final analysis.</li> <li>"site" -- One of our 11 study sites (see above).</li> <li>"binomial" -- Species binomial scientific name.</li> <li>"n_obs" -- The number of observations included in the model run.</li> <li>"n_unique_obs" -- The number of unique observations included in the model run (used to exclude species with constant AOH).</li> <li>"redlistCategory" -- IUCN Red List Category: "Extinct," "Extinct in the Wild," "Critically Endangered," "Endangered," "Vulnerable," "Near Threatened," "Least Concern," "Data Deficient," or "Not Evaluated."</li> <li>"mature_forest_obl" -- Whether a species is determined to be a "mature forest obligate" species (1) or not (0), drawn directly from "habitat_age_req_coded.csv" (see above). Some species are marked as 0.9, 0.75, 0.25, or 0.1 as an indication of some uncertainty, but these were rounded to the nearest integer for the final analysis.</li> <li>"common_names" -- Species' common names listed by IUCN, drawn directly from "habitat_age_req_coded.csv" (see above).</li> <li>"start_year" -- The first year for which this species has area of habitat at this site (i.e., the first observation included in the model).</li> <li>"end_year" -- The last year for which this species has area of habitat at this site (i.e., the last observation included in the model).</li> <li>"passage_type" -- Whether a model run includes passage areas ("include_passage") or does not include passage areas ("exclude_passage") when calculating area of habitat (AOH) for migratory birds.</li> </ol> <p><br><strong>Two files contain model effect sizes for AOH models:</strong></p> <h3>aoh_start_end_l.parquet</h3> <p>This tabular data file contains observed effect sizes: the observed change in AOH for each species at each site, in each calculation, derived directly from observations from the start and end of the time series. These data are calculated in "AOH.Rmd" chunk: {r observed-change-in-aoh-by-window-size}. This data serves as direct input for the file "aoh_obs_change_tmp_all" (see below), which is the primary input for the traits linear models in our analysis (see "traits.Rmd", "_util_files.R"). This file contains 24 columns:</p> <ol> <li>"run_index" -- An index used to easily pull observations for each model run. There is one index for each unique species at each site, in each of the aoh_types, calculated including and excluding passage areas.</li> <li>"start" -- The mean area of habitat (AOH), in hectares (ha), at the "start" of the time series, as calculated across the number of years specified in "window_size."</li> <li>"start_year" -- The year of the first AOH observation.</li> <li>"end" -- The mean area of habitat (AOH), in hectares (ha), at the "end" of the time series, as calculated across the number of years specified in "window_size."</li> <li>"end_year" -- The year of the last AOH observation.</li> <li>"window_size" -- The number of years across which "start" and "end" AOH values are averaged (e.g., if "window_size" is 5, "start" is then the mean AOH across the first 5 years of observations, and "end" is the mean AOH across the last 5 years of observations).</li> <li>"abs_change" -- The absolute change in AOH, calculated as the difference between the mean AOH at the end of the time series and the mean AOH at the start of the time series (i.e., end - start).</li> <li>"prop_change" -- The proportional change in AOH, calculated as the absolute change in AOH divided by the AOH value at the start of the time series (i.e., abs_change/start).</li> <li>"percent_change" -- The percent change in AOH, calculated as 100 times the proportional change in AOH (i.e., 100 * prop_change).</li> <li>"ratio" -- The ratio of the mean AOH at the end of the time series to the mean AOH at the start of the time series (i.e., end/start).</li> <li>"ratio_mod" -- A modified ratio of the ending AOH to the starting AOH, for which ratio values less than 1 are replaced by additive inverse of the reciprocal value (i.e., 1/ratio * -1). Ratios greater than 1 are left the same.</li> <li>"abs_change_as_prop_site_area" -- The absolute change in AOH as a proportion of site area (i.e., abs_change / total_site_area_ha_2017).</li> <li>"aoh_type" -- A label indicating the temporal and spatial scale at which AOH is calculated: "crop_abn_iucn" (Calc. 1a), "crop_abn_potential_iucn" (Calc. 1b), "max_abn_iucn" (Calc. 2a), "max_potential_abn_iucn" (Calc. 2b), "full_iucn" (Calc. 3a), and "full_potential_iucn" (Calc. 3b). "abn_iucn" and "potential_abn_iucn" correspond to calculations that only capture habitat following abandonment (i.e., not including habitat provided by croplands prior to abandonment); these calculations are not included in our final analysis.</li> <li>"vert_class" -- Vertebrate class ("amp," amphibians; "bird," birds; or "mam," mammals). Note that only birds and mammals were included in our final analysis.</li> <li>"site" -- One of our 11 study sites (see above).</li> <li>"binomial" -- Species binomial scientific name.</li> <li>"passage_type" -- Whether a model run includes passage areas ("include_passage") or does not include passage areas ("exclude_passage") when calculating area of habitat (AOH) for migratory birds.</li> <li>"common_names" -- Species' common names listed by IUCN, drawn directly from "habitat_age_req_coded.csv" (see above).</li> <li>"redlistCategory" -- IUCN Red List Category: "Extinct," "Extinct in the Wild," "Critically Endangered," "Endangered," "Vulnerable," "Near Threatened," "Least Concern," "Data Deficient," or "Not Evaluated."</li> <li>"mature_forest_obl" -- Whether a species is determined to be a "mature forest obligate" species (1) or not (0), drawn directly from "habitat_age_req_coded.csv" (see above). Some species are marked as 0.9, 0.75, 0.25, or 0.1 as an indication of some uncertainty, but these were rounded to the nearest integer for the final analysis.</li> <li>"total_site_area_ha_2017" -- The total site area (ha) in 2017. (Drawn directly from "area_summary_df," from https://zenodo.org/records/5348287)</li> <li>"area_ever_abn_ha" -- The total area of those pixels that were abandoned at least once during the time series (corresponding to the area of potential abandonment, as of 2017). (Drawn directly from "area_summary_df," from https://zenodo.org/records/5348287)</li> <li>"trend" -- The overall trend in AOH ("gain," "loss," or "no trend"), determined by the sign of slope coefficients and statistical significance at p < 0.05.</li> <li>"factor_change" -- The factor change in AOH, calculated as either the proportional change in AOH (i.e., prop_change) for values greater than 0, or as the reciprocal of the proportional change in AOH (i.e., 1/prop_change) for values greater than 0.</li> </ol> <h3>aoh_change_df.parquet</h3> <p>This tabular data file contains effect sizes estimated from linear regression coefficients (i.e., slopes and intercepts), calculated in "AOH.Rmd" chunk {r estimated-changes-aoh-change-df}. This file contains 29 columns:</p> <ol> <li>"run_index" -- An index used to easily pull observations for each model run. There is one index for each unique species at each site, in each of the aoh_types, calculated including and excluding passage areas.</li> <li>"est_type" -- The estimate type, whether the estimated model slope ("estimate") or the lower ("conf.low") or upper ("conf.high") bounds of the 95% confidence interval around the slope estimate.</li> <li>"vert_class" -- Vertebrate class ("amp," amphibians; "bird," birds; or "mam," mammals). Note that only birds and mammals were included in our final analysis.</li> <li>"site" -- One of our 11 study sites (see above).</li> <li>"start_year" -- The year of the first AOH observation.</li> <li>"end_year" -- The year of the last AOH observation.</li> <li>"slope" -- The model estimated slope value.</li> <li>"intercept" -- The model estimated intercept value.</li> <li>"aoh_type" -- A label indicating the temporal and spatial scale at which AOH is calculated: "crop_abn_iucn" (Calc. 1a), "crop_abn_potential_iucn" (Calc. 1b), "max_abn_iucn" (Calc. 2a), "max_potential_abn_iucn" (Calc. 2b), "full_iucn" (Calc. 3a), and "full_potential_iucn" (Calc. 3b). "abn_iucn" and "potential_abn_iucn" correspond to calculations that only capture habitat following abandonment (i.e., not including habitat provided by croplands prior to abandonment); these calculations are not included in our final analysis.</li> <li>"passage_type" -- Whether a model run includes passage areas ("include_passage") or does not include passage areas ("exclude_passage") when calculating area of habitat (AOH) for migratory birds.</li> <li>"binomial" -- Species binomial scientific name.</li> <li>"mature_forest_obl" -- Whether a species is determined to be a "mature forest obligate" species (1) or not (0), drawn directly from "habitat_age_req_coded.csv" (see above). Some species are marked as 0.9, 0.75, 0.25, or 0.1 as an indication of some uncertainty, but these were rounded to the nearest integer for the final analysis.</li> <li>"total_site_area_ha_2017" -- The total site area (ha) in 2017. (Drawn directly from "area_summary_df," from https://zenodo.org/records/5348287)</li> <li>"area_ever_abn_ha" -- The total area of those pixels that were abandoned at least once during the time series (corresponding to the area of potential abandonment, as of 2017). (Drawn directly from "area_summary_df," from https://zenodo.org/records/5348287)</li> <li>"trend" -- The trend in AOH experienced by the species at this site for this aoh_type calculation ("gain," "loss," or "no trend"), determined by the sign of slope coefficients and assigning statistical significance when p < 0.05.</li> <li>"n_trends" -- The number of distinct trends in AOH experienced by the species across all of the sites overlapping with its range, including this site.</li> <li>"trend_types" -- The types of trends in AOH experienced by this species across all sites overlapping with its range (some combination of "gain", "loss", and/or "no trend").</li> <li>"overall_trend" -- The overall trend in AOH experienced by this species across all sites overlapping with its range ("gain" - experiencing "gain" trends at all occurring sites; "loss" - experiencing "loss" trends at all occurring sites; "no trend" - experiencing "no trend" at all occurring sites; "weak gain" - experiencing "gain" trends at some sites and "no trend" at others; "weak loss" - experiencing "loss" trends at some sites and "no trend" at others; or "context dependent" - experienced "gain" trends at some sites and "loss" trends at other sites [referred to as "mixed" effects in Crawford et al. 2024])</li> <li>"trend_direction" -- The general direction of the trend in AOH for the species across all occurring sites ("gain" when overall_trend is either "gain" or "weak_gain"; "loss" when overall_trend is either "loss" or "weak_loss"; "context dependent" when overall_trend is "context dependent" [i.e., "mixed" effects]; and "no trend" when overall_trend is "no trend").</li> <li>"trend_consistency" -- An indication of how consistent the trend in AOH is across all occurring sites ("consistent" if overall_trend is "gain" or "loss"; "weak" if "weak_gain" or "weak_loss"; and "opposite" if "context dependent" [i.e., "mixed" effects]).</li> <li>"time_range" -- The number of years for which the species has AOH observations at this site for this aoh_type calculations.</li> <li>"aoh_start_est" -- The estimated AOH at the start of the time series, calculated from linear regression slope and intercept coefficients.</li> <li>"aoh_end_est" -- The estimated AOH at the end of the time series, calculated from linear regression slope and intercept coefficients.</li> <li>"abs_change" -- The absolute change in estimated AOH over the course of the time series (i.e., aoh_end_est - aoh_start_est).</li> <li>"abs_change_as_prop_site_area" -- The absolute change in estimated AOH as a proportion of site area (i.e., abs_change / total_site_area_ha_2017).</li> <li>"ratio_change" -- The ratio of the estimated AOH at the end of the time series to the estimated AOH at the start of the time series (i.e., aoh_end_est / aoh_start_est).</li> <li>"prop_change" -- The proportional change in estimated AOH, calculated as the absolute change in estimated AOH divided by the estimated AOH value at the start of the time series (i.e., abs_change / aoh_start_est).</li> <li>"factor_change" -- The factor change in estimated AOH, calculated as either the proportional change in estimated AOH (i.e., prop_change) for values greater than 0, or as the reciprocal of the proportional change in estimated AOH (i.e., 1/prop_change) for values greater than 0.</li> <li>"percent_change" -- The percent change in estimated AOH, calculated as 100 times the proportional change in estimated AOH (i.e., 100 * prop_change).</li> </ol> <h3>taxonomy_df.parquet</h3> <p>This tabular data file contains basic taxonomic information used in the analysis, including 10 columns:</p> <ol> <li>"vert_class" -- Vertebrate class ("bird," birds; or "mam," mammals).</li> <li>"binomial" -- Species binomial scientific name, drawn from IUCN or BirdLife International.</li> <li>"redlistCategory" -- IUCN Red List Category: "Extinct," "Extinct in the Wild," "Critically Endangered," "Endangered," "Vulnerable," "Near Threatened," "Least Concern," "Data Deficient," or "Not Evaluated."</li> <li>"order" -- Taxonomic order.</li> <li>"family" -- Taxonomic family.</li> <li>"n_sp_in_family_sample" -- The number of species contained in the family included in our analysis.</li> <li>"order_common" -- A common name to refer to the order.</li> <li>"family_common" -- A common name to refer to the family.</li> <li>"n_in_family" -- The total number of species contained in the family globally.</li> <li>"threatened" -- Whether a species is considered threatened with extinction (i.e., is listed as "Critically Endangered," "Endangered," or "Vulnerable" on the IUCN Red List).</li> </ol> <h3><br>aoh_obs_change_tmp_all.csv<br>aoh_est_change_tmp_all.csv</h3> <p>These two tabular data files contain data used as inputs for the linear models involved in our traits analysis exploring how species' responses to cropland abandonment are affected by habitat suitabilities and other traits. The key variables are the response variables for our models ("binary_gain_v_loss", "abs_change_percent_site", and "log(ratio)") and predictor variables c("forest_occ", "savanna_occ", "shrubland_occ", "grassland_occ", "wetlands_occ", "rocky_occ", "caves_occ", "desert_occ", "urban_occ", "arable_occ", "n_suitable_habitats_lvl2", "vert_class", "threatened", "Trophic_level", "log10(Body_mass_g)", "log10(total_range_area)", "abs(centroid_latitude)", and "max_abn_ext_percent_site"). Further details are contained in "traits.Rmd"</p> <p>These two files are developed from "aoh_start_end_l" and "aoh_change_df," but filtered to include only birds and mammals, to exclude passage areas from AOH calculations, to exclude mature forest obligate species, and to use only a window_size of 5 years (for "aoh_obs_change_tmp_all") and model estimates (rather than 95% confidence interval bounds, for "aoh_est_change_tmp_all"). </p> <p><strong>aoh_obs_change_tmp_all.csv contains 63 columns.</strong></p> <ul> <li>Columns 1-24 match "aoh_start_end_l". </li> <li>Columns 25-31 match "taxonomy_df" columns 3 through 10.</li> <li>Columns 32-34: "Body_mass_g" (species body mass, in grams), "Trophic_level" (whether a species is a "Carnivore", a "Herbivore," or an "Omnivore"), and "Habitat_breadth_IUCN" (the number of IUCN Level 2 habitats a species can occupy) were taken from from Etard et al. 2020 (https://doi.org/10.1111/geb.13184)</li> <li>Column 35: "total_range_area" -- drawn from "aoh_l," see above</li> <li>Columns 36-37: "centroid_longitude" and "centroid_latitude" are drawn from "centroids_df," see above.</li> <li>Columns 38-50 are Boolean variables that indicate whether a species can occupy a specific IUCN Level 1 habitat type (i.e., whether IUCN lists that Level 1 habitat as suitable for the species). These variables are as follows, with the IUCN Level 1 habitat code listed in brackets: "forest_occ" [1], "savanna_occ" [2], "shrubland_occ" [3], "grassland_occ" [4], "wetlands_occ" [5], "rocky_occ" [6], "caves_occ" [7], "desert_occ" [8], "marine_intertidal_occ"[12], "marine_coastal_occ" [13], "artificial_terrestrial_occ" [14], "artificial_aquatic_occ" [15], and "introduced_occ" [16].</li> <li>Columns 51-52 represent the number of IUCN Level 1 ("n_suitable_habitats") and IUCN Level 2 ("n_suitable_habitats_lvl2") habitats a species has listed as suitable habitats by IUCN, respectively.</li> <li>Columns 53-56 are Boolean variables indicating whether a species can occupy a subset of IUCN Level 2 habitats, which are listed in brackets: "arable_occ" [14.1 Arable Land]; "farmland_occ" [14.1 Arable Land, 14.2 Pastureland, or 14.4 Rural Gardens]; "ag_occ" (duplicate of "farmland_occ"); "urban_occ" [14.5 Urban Areas].</li> <li>Columns 57-58 represent the maximum spatial extent of abandonment at a give site (i.e., the area of all lands that were abandoned at least once during the time series), whether divided by site area ("max_abn_extent_div_site_area," i.e,. area_ever_abn_ha / total_site_area_ha_2017) or as a percent of site area ("max_abn_ext_percent_site").</li> <li>Column 59 is "abs_change_percent_site," calculated as 100 * abs_change_as_prop_site_area.</li> <li>Columns 60-63 are binary values (1 or 0) indicating the whether the species experienced statistically significant gains in AOH ("binary_trend_gain"), statistically significant losses in AOH ("binary_trend_loss"), no trend in AOH ("binary_trend_no_trend"). Column 63 ("binary_gain_v_loss") is a binary value assigning a value of 1 for gains, 0 for losses, and NA for other values.</li> </ul> <p><br><strong>aoh_est_change_tmp_all.csv contains 70 columns:</strong></p> <ul> <li>Columns 1-29 match "aoh_change_df".</li> <li>Columns 30-37 match "taxonomy_df" columns 3 through 10.</li> <li>Columns 38-40: "Body_mass_g" (species body mass, in grams), "Trophic_level" (whether a species is a "Carnivore", a "Herbivore," or an "Omnivore"), and "Habitat_breadth_IUCN" (the number of IUCN Level 2 habitats a species can occupy) were taken from from Etard et al. 2020 (https://doi.org/10.1111/geb.13184)</li> <li>Column 41: "total_range_area" -- drawn from "aoh_l," see above</li> <li>Columns 42-43: "centroid_longitude" and "centroid_latitude" are drawn from "centroids_df," see above.</li> <li>Columns 44-56 are Boolean variables that indicate whether a species can occupy a specific IUCN Level 1 habitat type (i.e., whether IUCN lists that Level 1 habitat as suitable for the species). These variables are as follows, with the IUCN Level 1 habitat code listed in brackets: "forest_occ" [1], "savanna_occ" [2], "shrubland_occ" [3], "grassland_occ" [4], "wetlands_occ" [5], "rocky_occ" [6], "caves_occ" [7], "desert_occ" [8], "marine_intertidal_occ"[12], "marine_coastal_occ" [13], "artificial_terrestrial_occ" [14], "artificial_aquatic_occ" [15], and "introduced_occ" [16].</li> <li>Columns 57-58 represent the number of IUCN Level 1 ("n_suitable_habitats") and IUCN Level 2 ("n_suitable_habitats_lvl2") habitats a species has listed as suitable habitats by IUCN, respectively.</li> <li>Columns 59-62 are Boolean variables indicating whether a species can occupy a subset of IUCN Level 2 habitats, which are listed in brackets: "arable_occ" [14.1 Arable Land]; "farmland_occ" [14.1 Arable Land, 14.2 Pastureland, or 14.4 Rural Gardens]; "ag_occ" (duplicate of "farmland_occ"); "urban_occ" [14.5 Urban Areas].</li> <li>Columns 63-64 represent the maximum spatial extent of abandonment at a give site (i.e., the area of all lands that were abandoned at least once during the time series), whether divided by site area ("max_abn_extent_div_site_area," i.e,. area_ever_abn_ha / total_site_area_ha_2017) or as a percent of site area ("max_abn_ext_percent_site").</li> <li>Columns 65-68 are binary values (1 or 0) indicating the whether the species experienced statistically significant gains in AOH ("binary_trend_gain"), statistically significant losses in AOH ("binary_trend_loss"), no trend in AOH ("binary_trend_no_trend"). Column 63 ("binary_gain_v_loss") is a binary value assigning a value of 1 for gains, 0 for losses, and NA for other values.</li> <li>Column 69, "slope_prop_site", is the estimated linear regression coefficient, or slope, as a proportion of site area, calculated as slope / total_site_area_ha_2017. </li> <li>Column 70 is "abs_change_percent_site," calculated as 100 * abs_change_as_prop_site_area.</li> </ul> <h3><br>final_species_list.csv</h3> <p>The final list of bird and mammal species included in our analysis, including the vertebrate class ("vert_class") and binomial species scientific name ("binomial") along with the overall response to cropland abandonment ("overall_trend"), the sites where that species had AOH affected by cropland abandonment ("sites"), the IUCN Red List Category ("redlistCategory"), the "obligate_type" (i.e., whether a species is a mature forest obligate, or not), the "range_size_quantile" (ranking species by global geographic range size), and "common_names". Note that mature forest obligates were excluded from our final results. These columns match the definitions included above.</p> <h3>trait_mod_df_modx1.rds</h3> <p>This R data file contains the results of our regression models run in "traits.Rmd" code chunk "*many-models", which is where our three traits linear regression models are run. These data are contained in the form of a nested tibble, or a set of tibbles nested within columns of a tibble (see: https://tidyr.tidyverse.org/articles/nest.html). These data include the input data ("data"), resulting models ("model"), model coefficients ("tidy"), regression tables ("gt"), and diagnostic statistics ("glance") for our many model runs across different response variables ("response") and AOH calculations ("aoh_type"). See "traits.Rmd" code chunk "*many-models" for more information.</p>
FAB 1: Forests and Biodiversity Experiment - High density diversity experiment: carbon budget data
This data represents changes in above and belowground C pools in young stands six years after the initiation of the Forests and Biodiversity experiment (FAB1) in 2013, consisting of high density plots of one, two, five, or 12 tree species planted in a common garden. Trees were planted to represent a range of native functional diversity, including needle-leaf conifer and broadleaf deciduous species as well as ectomycorrhizal and arbuscular mycorrhizal species. We quantified the effects of species richness, phylogenetic diversity, and functional diversity on aboveground C accumulation, as well as on soil C accumulation, fine root C, and soil aggregation. To assess the role of the microbial community in mediating these effects, we further compared changes in soil C pools to phospholipid fatty acids (PLFAs) profiles collected in 2016.
Agronomic Yields on the Biodiversity Gradient Experiment at the Kellogg Biological Station, Hickory Corners, MI (2000 to 2020)
Dataset AbstractAgronomic yields have been measured on the Biodiversity Gradient Experiment since 2000. Samples are collected from the corn, soy and winter wheat plots before harvest by harvesting a subplot with a plot combine. Initial soil moisture from the Biodiversity Gradient baseline sampling is also available for corn, soy and wheat plots.original data source http://lter.kbs.msu.edu/datasets/35
CETAF-DiSSCo/COVID19-TAF biodiversity-related knowledge hub working group: indexed biotic interactions and review summary
<p>This data publication originated as part of developing a biodiversity-related knowledge hub on COVID-19 via COVID19-TAF - Communities Taking Action (https://cetaf.org/covid19-taf-communities-taking-action), a community-rooted initiative raised jointly by the Consortium of European Taxonomic Facilitaties (CETAF, https://cetaf.org) and Distributed Systems of Scientific Collections (DiSSCo, https://www.dissco.eu/).</p> <p>This archive contains the biodiversity datasets of interest identified in period 14 April-6 October 2020 through COVID19-TAF activities and subsequently indexed by Global Biotic Interactions (GloBI, https://globalbioticinteractions.org). GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, virus-host, parasite-host) by combining existing open datasets using open source software.</p> <p>These identified datasets (see references and reviews below) add to a growing collection of open species interaction datasets already indexed by GloBI. So, this data publication only includes a small subset of indexed datasets and include only datasets that were added as a direct consequence of COVID19-TAF activities of the biodiversity-related knowledge hub working group.</p> <p>If you have questions or comments about this publication, please open an issue at https://github.com/ParasiteTracker/tpt-reporting or contact the authors by email.</p> <p>Funding:<br> The creation of this archive was made possible in part by reporting software developed as part of the National Science Foundation award "Collaborative Research: Digitization TCN: Digitizing collections to trace parasite-host associations and predict the spread of vector-borne disease," Award numbers DBI:1901932 and DBI:1901926 . Also, this material is based upon work supported by the National Science Foundation under Grant No. DGE-1545433 .</p> <p>References:<br> Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.</p> <p>GloBI Data Review Report</p> <p>Datasets under review:<br> - Geiselman, Cullen K. & Sarah Younger. 2020. Bat Eco-Interactions Database. www.batbase.org accessed via https://github.com/globalbioticinteractions/batbase/archive/9c65cfeee1a054f9db8cd8bf6892017fd1b3c840.zip on 2020-10-04T22:53:45.576Z<br> - Geiselman, Cullen K. and Tuli I. Defex. 2015. Bat Eco-Interactions Database. www.batplant.org accessed via https://github.com/globalbioticinteractions/batplant/archive/a2e1b57052244d5251d17e96ea61f58bea88975e.zip on 2020-10-04T22:54:28.727Z<br> - Daniel Becker, Gregory F Albery, Anna R Sjodin, Timothee Poisot, Tad Dallas, Evan A. Eskew, Maxwell J. Farrell, Sarah Guth, Barbara A Han, Nancy B Simmons, Colin J Carlson. 2020. Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization. bioRxiv 2020.05.22.111344; doi: https://doi.org/10.1101/2020.05.22.111344 accessed via https://github.com/globalbioticinteractions/becker2020/archive/47c6ad28e1c5058f3c13ca69a59fdf21229e8d7f.zip on 2020-10-04T22:54:46.723Z<br> - Chen L, Liu B, Yang J, Jin Q, 2014. DBatVir: the database of bat-associated viruses. Database (Oxford). 2014:bau021. doi:10.1093/database/bau021 accessed via https://github.com/globalbioticinteractions/dbatvir/archive/a906d76e362484d3ca1edbe9683f672838ab70b0.zip on 2020-10-04T22:56:13.913Z<br> - Chen L, Liu B, Wu Z, Jin Q, Yang J, 2017. DRodVir: A resource for exploring the virome diversity in rodents. J Genet Genomics. 44(5):259-264. accessed via https://github.com/globalbioticinteractions/drodvir/archive/0346c0e8d4d66c6400e9965bd6a6aeed24cd7586.zip on 2020-10-04T23:06:04.368Z<br> - Agosti, Donat. 2020. Transcription of Linné, C. von, 1758. Systema naturae per regna tria naturae secundum classes, ordines, genera, species, cum characteribus, differentiis, synonymis, locis. Available at: http://dx.doi.org/10.5962/bhl.title.542 . accessed via https://github.com/globalbioticinteractions/linnaeus1758/archive/a818060080fa04a88dac6df1ae5b897304ae8877.zip on 2020-10-05T00:46:04.852Z<br> - Mollentze, Nardus, & Streicker, Daniel G. (2019). Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3516613 accessed via https://github.com/globalbioticinteractions/mollentze2019/archive/ad12dc74d03c3d992618f16c37cafb7f7ffd9d01.zip on 2020-10-05T00:50:55.878Z<br> - Eneida L. Hatcher, Sergey A. Zhdanov, Yiming Bao, Olga Blinkova, Eric P. Nawrocki, Yuri Ostapchuck, Alejandro A. Schäffer, J. Rodney Brister, Virus Variation Resource – improved response to emergent viral outbreaks, Nucleic Acids Research, Volume 45, Issue D1, January 2017, Pages D482–D490, https://doi.org/10.1093/nar/gkw1065 . accessed via https://github.com/globalbioticinteractions/ncbi-virus/archive/531a8d743d7adcf1153a19087e5d3c5b76750e3e.zip on 2020-10-05T00:53:53.646Z<br> - Olival, K. J., Hosseini, P. R., Zambrana-Torrelio, C., Ross, N., Bogich, T. L., & Daszak, P. (2017). Host and viral traits predict zoonotic spillover from mammals. Nature, 546(7660), 646–650. doi:10.1038/nature22975 accessed via https://github.com/globalbioticinteractions/olival2017/archive/f61070a5339d0e6c6e76d7eb4e2102decb52317d.zip on 2020-10-05T00:56:43.356Z<br> - Pensoft Darwin Core Archives with associateTaxa columns accessed via https://github.com/globalbioticinteractions/pensoft-dwca/archive/ee8831a2a391203f4fa8c05a0ddd927202b234bf.zip on 2020-10-05T00:56:51.868Z<br> - Pensoft Darwin Core Archives available via Integrated Publication Toolkit accessed via https://github.com/globalbioticinteractions/pensoft-ipt/archive/4ad4b47978324681289e36f8c2b247b1bcc97b1a.zip on 2020-10-05T00:58:01.912Z<br> - De Rojas M, Doña J, Dimov I (2020) A comprehensive survey of Rhinonyssid mites (Mesostigmata: Rhinonyssidae) in Northwest Russia: New mite-host associations and prevalence data. Biodiversity Data Journal 8: e49535. https://doi.org/10.3897/BDJ.8.e49535 accessed via https://github.com/globalbioticinteractions/pensoft-table/archive/3488e0397ca4e083d5eca6949951e426a75713e3.zip on 2020-10-05T00:58:03.647Z<br> - Marcus Guidoti, Tatiana Ruschel, Donat Agosti. 2020. Corona virus related biotic associations manually extracted from literature. Plazi. accessed via https://github.com/globalbioticinteractions/plazi-covid19/archive/326578b0d9f974760dcd2e962d86636a6487a6c0.zip on 2020-10-05T00:58:08.025Z<br> - Shaw, LP, Wang, AD, Dylus, D, et al. The phylogenetic range of bacterial and viral pathogens of vertebrates. Mol Ecol. 2020; 29: 3361– 3379. https://doi.org/10.1111/mec.15463 accessed via https://github.com/globalbioticinteractions/shaw2020/archive/bb9ab857b7fdbb4e931752d01b43d37b3ada77cf.zip on 2020-10-05T01:05:23.554Z<br> - OpenBiodiv. 2020. Annotated biotic interaction tables from Pensoft publications. accessed via https://github.com/pensoft/pensoft-interaction-tables/archive/bb7d1dc9f2eba220a61502e06e6114053fd30788.zip on 2020-10-05T03:03:23.372Z<br> - Quentin J. Groom. 2020. Bat interation data manually extracted from literature. accessed via https://github.com/qgroom/batinterations/archive/70108945f9014aa0ac1db920191867f7e151c793.zip on 2020-10-05T03:04:11.533Z</p> <p>Generated on:<br> 2020-10-06</p> <p>by:<br> GloBI's Elton 0.10.2<br> (see https://github.com/globalbioticinteractions/elton).</p> <p> </p> <p>Note that all files ending with .tsv are files formatted<br> as UTF8 encoded tab-separated values files.</p> <p>https://www.iana.org/assignments/media-types/text/tab-separated-values</p> <p><br> Included in this review archive are:</p> <p>README:<br> This file.</p> <p>review_summary.tsv:<br> Summary across all reviewed collections of total number of distinct review comments.</p> <p>review_summary_by_collection.tsv:<br> Summary by reviewed collection of total number of distinct review comments.</p> <p>indexed_interactions_by_collection.tsv:<br> Summary of number of indexed interaction records by institutionCode and collectionCode.</p> <p>review_comments.tsv.gz:<br> All review comments by collection.</p> <p>indexed_interactions_full.tsv.gz:<br> All indexed interactions for all reviewed collections.</p> <p>indexed_interactions_simple.tsv.gz:<br> All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.</p> <p>datasets_under_review.tsv:<br> Details on the datasets under review.</p> <p>elton.jar:<br> Program used to update datasets and generate the review reports and associated indexed interactions.</p> <p><br> datasets.zip:<br> source datasets collected by elton in process of executing the generate_report.sh script.</p> <p>generate_report.sh:<br> program used to generate the report</p> <p>generate_report.log:<br> log file generated as part of running the generate_report.sh script</p>
Data belonging to: Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.
<p>Data belonging to the paper Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.</p> <p>Data includes analysis scripts (R Language) and all used data files. Data is composed of location information of the different sites, environmental conditions on site and vegetation composition.</p>
Biodiversity atlas data
<p><strong>Introdution</strong><br> This dataset contains six sets of biodiversity atlas data that have been used as a test of occupancy downscaling methods. They are derived from two taxonomic groups, vascular plants and birds and are either regional or national atlases. The original atlas have already been published and the references are below. Should these data be used these citations should be used.</p> <p><strong>File contents</strong><br> Each zip file contains a set of text files, one for each taxa. Each text file has three tab seperated columns, longitude, latitude and presence. The presence column indicates whether the grid cell was occupied (1) or unoccupied (0). The longitude and latitude columns are the grid references of each grid cell surveyed. For Ireland the Irish grid system is used (EPSG:29903); for the UK the Ordnance Survey Grid (EPSG:27700) and for Belgian the Lambert 72 system (EPSG:31370). The grid cell area for all plant datasets is 4 km<sup>2</sup>. Whereas the grid areas for the Flemish birds is 25 km<sup>2</sup> and for the Irish breeding birds is 100 km<sup>2</sup>. </p> <p><strong>References</strong></p> <ol> <li>Evans, P., Evans, I. & Rothero, G. (2002) Flora of Assynt: fowering plants and ferns. P.A. Evans and I.M. Evans. ISBN 0954181301.</li> <li>Forbes, R.S. & Northridge, R.H. (2012) The Flora of County Fermanagh. National Museums Northern Ireland. ISBN 1905989288</li> <li>Halliday, G. (1997) A Flora of Cumbria. Centre for North-West Regional Studies, University of Lancaster. ISBN 1862200203.</li> <li>National Biodiversity Data Centre (2011) The Second Atlas of Breeding Birds in Britain and Ireland: 1988-1991. URL https://doi.org/10.15468/pkhsnb</li> <li>Shropshire Ecological Data Network (2017) Shropshire Ecological Data Network database. Occurrence Dataset. URL https://doi.org/10.15468/5v5pvk</li> <li>Lockton, A.J. & Whild, S.J. (2015) The Flora and Vegetation of Shropshire. Shropshire Botanical Society. ISBN 0953093727.</li> <li>Vermeersch, G., Anselin, A., Devos, K., Herremans, M., Stevens, J., Gabriëls, J., Van Der Krieken, B., Brosens, D. & Desmet, P. (2014) Broedvogels - Atlas of the breeding birds in Flanders 2000-2002. v1.5. URL http://doi.org/10.15468/sccg5a</li> </ol>
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.