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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.
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).
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.
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>
Data for 'The value of shifting cultivation for biodiversity in Northeast India'
Shifting cultivation is a widespread land-use in many tropical countries that also harbours significant levels of biodiversity. Increasing frequency of cultivation cycles and expansion into old-growth forests have intensified the impacts of shifting cultivation on biodiversity and carbon sequestration. We assessed how bird diversity responds to shifting cultivation and the potential for co-benefits for both biodiversity and carbon in such landscapes to inform carbon-based payments for ecosystem service (PES) schemes. We conducted this study in Nagaland, Northeast India. We surveyed above-ground carbon stocks and bird communities across various stages of a shifting cultivation system and old-growth forest using composite carbon sampling plots and repeated point counts directly overlaying the carbon plots in both summer and winter. We assessed species diversity using species accumulation and rarefaction curves based on Hill numbers. We fitted a linear mixed-effect model to assess the relationship between species richness and fallow age. We also examined possible co-benefits between carbon and biodiversity from fallow regeneration in terms of relative community similarity to old-growth forest across carbons stocks. Farmland and secondary forests regenerating on fallowed land had similar bird species richness to old-growth forests in summer and relatively higher species richness in winter. Within regenerating fallows, we did not find any strong evidence that fallow age influenced bird species richness. Bird community resemblance to old-growth forest increased with secondary forest maturity, correlating also with carbon stocks in summer. However, bird community assemblage did not show a strong association with habitat types and carbon stocks during winter. This study underscores the important role of traditional non-intensive shifting cultivation in providing refuges for biodiversity within heterogeneous habitat mosaics. Effectively managing these landscapes is crucial f
Plant aboveground biomass data: BAC: Biodiversity and Climate (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cdr/386/8. The abstract below was extracted from the Level 0 data package and is included for context: Climate changes forecast for our region by GCM???s and shifts in biodiversity and composition each have the potential to alter ecosystem functioning; their interactive effects are unknown. The "BAC" experiment is designed to determine the direct and interactive effects of plant species numbers, plant community composition, temperature, and precipitation on 11 productivity, C and N dynamics, stability, and plant, microbe, and insect species abundances in CDR grassland ecosystems.
Plant species percent cover data: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
MCR LTER: Coral Reef: Biodiversity has a positive but saturating effect on imperiled coral reefs; data for Clements and Hay 2021, Science Advances
Species loss threatens ecosystems worldwide, but the ecological processes and thresholds that underpin positive biodiversity effects among critically important foundation species, such as corals on tropical reefs, remain inadequately understood. In field experiments, we manipulated coral species richness and intraspecific density to test whether, and how, biodiversity affects coral productivity and survival. Corals performed better in mixed species assemblages. Improved performance was unexplained by competition theory alone, suggesting that positive effects exceeded agonistic interactions during our experiments. Peak coral performance occurred at intermediate species richness and declined thereafter. Positive effects of coral diversity suggest that species’ losses on degraded reefs make recovery more difficult and further decline more likely. Harnessing these positive interactions may improve ecosystem conservation and restoration in a changing ocean. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as 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 2005-2022). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site. Datasets used in this study are available online from the BCO-DMO data system. Data for this paper can be found at (https://www.bco-dmo.org/project/837802).
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.
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>
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>
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>
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>
Data for Publication - Synergies and Trade-offs between Robusta Yield, Carbon Stocks and Biodiversity across Coffee Systems in the DR Congo
<p>Data used for the publication:</p> <p>"Synergies and Trade-offs between Robusta Yield, Carbon Stocks and Biodiversity across Coffee Systems in the DR Congo" - Ieben Broeckhoven, Jonas Depecker, Trésor Kasereka Muliwambene, Olivier Honnay, Roel Merckx and Bruno Verbist</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.