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Foundation Species Revisited: Citation Analysis of Ellison et al. 2005
Ecologists and environmental scientists often prioritize research efforts with conservation importance. Dominant, widespread, or locally abundant species at low risk of extinction receive relatively little attention unless they are invasive. Native foundation species create habitats and environmental conditions that support many associated species and modulate local-scale ecosystem processes, but the generally high local or regional abundance of foundation species may lead to less research about them. We used citation analysis (2005-2014) to examine research following from a suggestion to identify and study foundation species while they were still common and not threatened. We explored the use and expanding definition of the foundation species concept, as well as the trajectory and ecological focus of research on foundation species throughout the world in 378 papers published in this nine-year span. Contemporary authors who cite key papers defining a foundation species pay little attention to its actual definition and species studied in this context rarely were identified as foundation species. Although functions and roles of foundation species, such as creating unique microclimates or supporting dependent species, are being studied, less research is focused on identifying them before they are threatened or lost from the ecosystem that they otherwise define. Invasive species were identified as the most common threat to foundation species. Our citation analysis and synthesis provides a new conceptual framework linking identification of and research about foundation species with their functional roles and our ability to manage emerging threats to them.
Collection of figures to explore intra-regime weather variability of North Atlantic-European year-round weather regimes as Supplementary Dataset for Gerighausen et al. (2024)
<p>This is a supplementary dataset accompanying the publication <strong>Gerighausen et al. (2024) </strong>submitted to Meteorological Applications. It contains a collection of browsable figures, complementing selected regimes, seasons, and countries in the paper. The figures are provided as a zipped archive. The ZIP-File (1.2 GB) contains 4 subfolders and 4 auxiliary files as described in <strong>readme.md </strong>in the main folder. Once downloaded and unpacked, the .html navigation panels can be used in any browser to navigate through the plots. </p> <p>Data and methods used to generate the figures are explained in Gerighausen et al. (2024). In brief the analysis is based on ERA5 reanalysis 1979-2021 at 1° grid spacing and 6h temporal resolution aggregated to daily data. Anomalies are computed with respect to a 31-day running mean climatology. The figures are explained in the table below and in the navigation panel.</p> <p><strong>Gerighausen</strong>, J., J. Dorrington, M. Osman, and C. M. Grams, <strong>2024</strong>: Quantifying intra-regime weather variability for energy applications, <em>submitted to Meteorological Applications.</em> <a href="https://doi.org/10.48550/arXiv.2408.04302">doi:10.48550/arXiv.2408.04302</a></p>
Nabro volcano event catalogue from Lapins et al., 2021, JGR Solid Earth
<p>Catalogue of seismic events from Nabro volcano (Sep 2011 - Oct 2012). Data format is a csv file.</p> <p>Events were detected by U-GPD phase arrival picking model. See following paper for details on event detection and location procedure: <em>A Little Data Goes A Long Way Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning</em> by Lapins et al., 2021, <a href="https://doi.org/10.1029/2021JB021910">https://doi.org/10.1029/2021JB021910</a>).</p> <p>Original seismic waveforms are from the Nabro Urgency Array (Hammond et al., 2011; <a href="https://doi.org/10.7914/SN/4H_2011">https://doi.org/10.7914/SN/4H_2011</a>), which is publicly available through IRIS Data Services (<a href="http://service.iris.edu/fdsnws/dataselect/1/">http://service.iris.edu/fdsnws/dataselect/1/</a>). See Hammond et al. (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0025">2011</a>) for further details on waveform data access and availability.</p> <p>Full code to reproduce our U-GPD transfer learning model, perform model training, run the U-GPD model over continuous sections of data and use model picks to locate events in NonLinLoc (Lomax et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0044">2000</a>) are available at <a href="https://github.com/sachalapins/U-GPD">https://github.com/sachalapins/U-GPD</a>, with the release (v1.0.0) associated with this study also archived and available through Zenodo (Lapins, <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0036">2021</a>; <a href="https://doi.org/10.5281/zenodo.4558121">https://doi.org/10.5281/zenodo.4558121</a>).</p> <p> </p> <p>Dataset column key:</p> <p>time = Origin time of seismic event (UTC)</p> <p>lat = Hypocentre latitude in decimal degrees</p> <p>lon = Hypocentre longitude in decimal degrees</p> <p>depth = Hypocentre depth in km</p> <p>rms = RMS error for phase arrival picks and hypocentre (sec)</p> <p>erh = Estimate of horizontal Gaussian error (km)</p> <p>erz = Estimate of vertical Gaussian error (km)</p> <p>azgap = Azimuthal gap (maximum angle separating two adjacent seismic stations, measured from earthquake epicentre)</p> <p>cluster = HDBSCAN cluster number (see Chapter 6 of Lapins, 2021 doctoral thesis: <em>Detecting and characterising seismicity associated with volcanic and magmatic processes through deep learning and the continuous wavelet transform</em>. Persistent URL: <a href="https://hdl.handle.net/1983/ea90148c-a1b2-47ae-afad-5dd0a8b5ebbd">https://hdl.handle.net/1983/ea90148c-a1b2-47ae-afad-5dd0a8b5ebbd</a>)</p> <p>nab*_p_time = P-wave arrival time for station NAB* (UTC)</p> <p>nab*_p_prob = Maximum detection 'probability' around P-wave phase arrival from U-GPD model (between 0 and 1)</p> <p>nab*_s_time = S-wave arrival time for station NAB* (UTC)</p> <p>nab*_s_prob = Maximum detection 'probability' around S-wave phase arrival from U-GPD model (between 0 and 1)</p> <p> </p> <p>Station csv column key:</p> <p>Network = Seismic network name</p> <p>Station = Seismic station name</p> <p>Latitude = Latitude in decimal degrees</p> <p>Longitude = Longitude in decimal degrees</p> <p>Elevation_asl_km = Station elevation in km above sea level</p>
Perry et al. (2025) Data Package: Effects of diluted bitumen and remediation methods on lower trophic levels within boreal lake enclosures. Data were collected during 2019 at the IISD Experimental Lakes Area in Northwestern Ontario.
This data package corresponds to a research study by Perry et al. (2025) titled "The effects of diluted bitumen, the shoreline cleaner Corexit EC9580A, and bio-stimulation on the lower food web of a boreal lake, with a focus on natural phytoplankton communities." The study examines the effect of controlled spills of diluted bitumen and two remediation methods on lower trophic levels (phytoplankton, periphyton, zooplankton). The study was undertaken within shoreline enclosures within Lake 260 at the IISD Experimental Lakes Area during 2019. In addition to primary oil recovery using sorbent pads, the two secondary remediation methods: 1) enhanced monitoring natural recovery (eMNR) that included the biostimulation of microbial communities via a slow release nutrient fertilizer, and 2) a shoreline washing agent (SWA or SCA; Corexit 9580) used to increase oil removal from affected shorelines. This data package includes the response of perphyton and zooplankton.
Aggregated 30-minute soil water and temperature data from 9 NPP shrub sites at Jornada Basin LTER, 2013 - 2024 (for Pinos et al 2025 manuscript)
This is an aggregated dataset of 30-minute soil temperature and moisture data from 9 shrub-dominated NPP study sites at the Jornada Basin LTER site in southern New Mexico, U.S.A. Collection of soil volumetric water content data at all of the 15 Jornada LTER NPP sites, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Volumetric water content and soil temperature are measured every 30 minutes at an automated meteorological station installed at all 15 of the Jornada LTER program’s NPP sites. This dataset aggregates only the stations located at the 9 sites with shrub-dominated vegetation cover (creosotebush, tarbush, and mesquite dune sites). Measurements are made every 30 minutes at 10 cm, 20 cm, and 30 cm soil depths. This dataset is in support of the Pinos et al. 2025 manuscript.
Aggregated 30-minute meteorology data from 9 NPP shrub sites at Jornada Basin LTER, 2013 - 2024 (for Pinos et al 2025 manuscript)
This is an aggregated dataset of 30-minute summary meteorology data from 9 shrub-dominated NPP study sites at the Jornada Basin LTER site in southern New Mexico, U.S.A. Average air temperature, relative humidity, total precipitation, wind speed, wind direction, and solar radiation are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at all 15 of the Jornada LTER program’s NPP sites. This dataset aggregates only the stations located at the 9 sites with shrub-dominated vegetation cover (creosote, tarbush, and mesquite sites). Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. Solar radiation is measured at 3m. This dataset is in support of the Pinos et al. 2025 manuscript.
MCR LTER: Coral Reef: Modeling the effects of selectively fishing key functional groups of herbivores on coral resilience; data for Cook et al., 2023 Ecosphere
These data and code were generated in support of the manuscript: Cook DT, Schmitt RJ, Holbrook SJ, and HV Moeller, Ecosphere. To investigate the impacts of selectively harvesting functional groups of herbivorous fishes on coral resilience, we used a dynamic model that is grounded by the coral reef system in Moorea, French Polynesia. Our model simulates the fraction of a reef occupied through time by classes of key benthic spaceholders (coral, two stages of macroalgae, and turf). Benthic and fishing dynamics are linked through the harvesting of two functional groups of herbivorous fishes. We utilize data collected on the abundance of fishes on the reef and in the catch in Moorea, French Polynesia to inform our model and to empirically explore patterns of fishing selectivity. These data and code were published in Ecosphere and were a part of the thesis of D. Cook (2023). 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).
MCR LTER: Coral Reef: Dead coral skeletons impair key recovery processes following coral bleaching; data for Kopecky et al., 2024 Global Change Biology
The data included in this data package were collected on the North shore of Moorea, French Polynesia, from 2015-2023 to explore how dead coral skeletons (e.g,, left after coral bleaching events) influence critical processes tied to coral reef resilience. Together, these various datasets were used for analyses in the manuscript entitled "Changing disturbance regimes, material legacies, and stabilizing feedbacks: dead coral skeletons impair key recovery processes following coral bleaching", published in Global Change Biology. These data are in support of a publication Kopecky et al. (2024) Global Change Biology, and were a part of the thesis of K. Kopecky. The manuscript title and author list are as follows: Changing disturbance regimes, material legacies, and stabilizing feedbacks: dead coral skeletons impair key recovery processes following coral bleaching. Kai Kopecky, Russell J. Schmitt, Sally J. Holbrook. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 22-24354 (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 2005-2024). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Dataset to Manuscript: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Marcus Schiedung et al. (Biogeosciences)
<p>Dataset to manuscript: Schiedung, M., Bellè, S.-L., Sigmund, G., Kalbitz, K., and Abiven, S.: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Biogeosciences, https://doi.org/10.5194/bg-17-6457-2020, 2020.</p> <p>All parameters and variables are described in "Var_names" files.</p>
Dataset to Manuscript: Key drivers of pyrogenic carbon redistribution during a simulated rainfall event, Bellè et al. 2021 (Biogeosciences)
<p>Dataset to manuscript: Bellè, S-L., Berhe, A., Hagedorn, F., Santin, C., Schiedung, M., van Meerveld, I. and Abiven, S.: Key drivers of pyrogenic carbon redistribution during a simulated rainfall event, Biogeosciences, https://doi.org/10.5194/bg-2020-361, 2021. </p> <p>All parameters and variables are described in the "var_names" file.</p>
ECOBREED WP4 soybean data related to Randelovic et al. (2020)
<p>Data related to the publication of Randelovic et al. (2020) Agronomy 10, 1108. doi: 10.3390/agronomy10081108. Data include the following files: (a) Excel file with two sheets (2018 & 2019) including trial information (plot allocation) and number of plants per square meter; (b) RGB image of soybean trial 2018 taken at V4 stage (four unfolded trifoliolate leaves); (c) RGB image of soybean trial 2018 taken at R3 stage (beginning pod); (d) RGB image of soybean trial 2019 taken at V4 stage; (e) RGB image of soybean trial 2019 taken at R3 stage. [Growth stages according to Fehr WR, Caviness CE (1977) Stages of soybean development. Iowa State Univ. Cooperative Ext. Serv., Spec. Rep. 80.]</p>
Database of local seismicity registered on ocean bottom seismometers (OBS). Database related to Bornstein et al. (accepted in Earth and Space Science), PICKBLUE
<p>We assembled a database of Ocean Bottom Seismometer (OBS) waveforms and manual P and S picks from local seismicity, on which we trained PickBlue, a deep-learning picker, using the seismometer data and the hydrophone channel. The dataset belongs to Bornstein et al. (accepted 2023 in Earth and Space Science). The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records.</p><p>The complete database is also accessible with SEISBENCH: <br><a href="https://seisbench.readthedocs.io">https://seisbench.readthedocs.io</a><br>SEISBENCH on github:<br><a href="https://github.com/seisbench">https://github.com/seisbench</a></p><p>Related paper:</p><p>Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock., A., Barcheck, G., Grevemeyer, I., Tilmann, F. (accepted 2023 in Earth and Space Science). PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning, Earth and Space Science. </p>
Dataset to Schiedung et al. (2024): Millennial-aged pyrogenic carbon in high-latitude mineral soils
<p>Dataset to Schiedung et al. (2024, Communications Earth & Environment): Pyrogenic Carbon is Aged at Millennial Scale in High-Latitude Mineral Soils</p> <p>DOI: <a href="https://doi.org/10.1038/s43247-024-01343-5">10.1038/s43247-024-01343-5</a></p> <p>This repository includes the following files: </p> <p><strong><em>dd_all.csv</em> </strong>- Includes all data for the individual samples that are presented in the manuscript.</p> <p><strong><em>Var_names_dd_all.csv</em> </strong>- Describes all variables in <em>dd_all</em> with corresponding unit </p> <p><strong><em>dd_site_average.csv</em></strong> - Includes all data that has been determined on composite samples for each site or the average of all samples per site </p> <p><strong><em>Var_names_dd_site_average.csv</em></strong> - Describes all variables in <em>dd_site_average.csv</em> with corresponding unit</p> <p>All .csv use "," as separator. </p> <p>This data set is also connected to Schiedung et al. (2022, Catena <a href="https://doi.org/10.1016/j.catena.2022.106194"> https://doi.org/10.1016/j.catena.2022.106194</a> ) and the corresponding repository: <a href="../records/10609291">https://zenodo.org/records/10609291</a></p>
Scripts for Patton et al 2020; Science DOI: 10.1126/science.abb9772
<p>Scripts used for all data anaysis for Patton et al. 2020 <em>Science </em>3<span>70: eabb9772. </span><span>DOI: 10.1126/science.abb9772</span></p>
Figure 2a - Rossi et al. Design of Highly Efficient Semitransparent Perovskite/Organic Tandem Solar Cells RRL Solar (2022)
<p>The Data set is related to the<strong> figure 2a</strong> of the paper </p> <p>Design of Highly Efficient Semitransparent Perovskite/Organic Tandem Solar Cells by Daniele Rossi,Karen Forberich,Fabio Matteocci,Matthias Auf der Maur,Hans-Joachim Egelhaaf,Christoph J. Brabec,Aldo Di Carlo, Rapid Research Letter (2022) https://doi.org/10.1002/solr.202200242</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>
Four lipidomics datasets (mouse liver, mouse pancreatic islets, mouse soleus muscle and mouse visceral adipose tissue), generated for the publication Mehl et al., "A multiorgan map of metabolic, signalling, and inflammatory pathways that coordinately control fasting glycemia in mice"
<p>Mehl, Thorens et al present a multiomics study aimiing to<span> identify the pathways that are coordinately regulated in pancreatic </span><span>b</span><span>-cells, muscle, liver, and fat to control fasting glycemia we fed C57Bl/6, DBA/2 and Balb/c mice a regular chow or a high fat diet for 3, 10 and 30 days. We measured fasted glycemia, insulinemia and whole-body insulin resistance. Transcriptomic and lipidomic analysis were used in a data fusion approach to identify organ-specific pathways related to the glycemic levels across all conditions investigated. In pancreatic islets, constant insulinemia despite higher glycemic levels were associated with reduced expression of mRNAs encoding hormone and neurotransmitter receptors as well as OXPHOS, cadherins, integrins and gap junction proteins. Higher glycemia and whole-body insulin resistance were associated, in muscle, with reduced expression of mRNAs encoding insulin signaling proteins and enzymes of the glycolysis, Krebs’ cycle and OXPHOS pathways, as well as endocytosis and exocytosis proteins; in hepatocytes, with lower expression of mRNAs of the insulin signaling pathway, of branched chain amino acid catabolism and of OXPHOS; in adipose tissue, with increased expression of mRNAs of innate immunity and lipid catabolism. These data provide a map of the pathways that are coordinately recruited in the investigated tissues to control fasting glycemia and a resource for further studies of interorgan communication in glucose homeostasis. </span></p>
Imaging Data from Sub-Seasonal Variation in Neptune's Mid-Infrared Emission by Roman et al.
<p>Images of Neptune as used in <a href="https://arxiv.org/abs/2112.00033"><strong>Imaging Data from Sub-Seasonal Variation in Neptune's Mid-Infrared Emission </strong></a>by Michael T. Roman, Leigh N. Fletcher, Glenn S. Orton, Thomas K. Greathouse, Julianne I. Moses, Naomi Rowe-Gurney, Patrick G. J. Irwin, Arrate Antunano, James Sinclair, Yasumasa Kasaba, Takuya Fujiyoshi, Imke de Pater, Heidi B. Hammel.</p> <p>Data are from various observatories/telescope instruments, including:</p> <ul> <li>The European Southern Observatory's Very Large Telescope, VISIR</li> <li>Keck Observatory, LWS</li> <li>Subaru Observatory, COMICS</li> <li>Gemini North, Michelle and TEXES</li> <li>Gemini South, T-ReCS</li> </ul> <p>Data were acquired from observatory archives and flux calibrated, when possible, by comparison to standard stars, with spectral radiances expressed in units of W/m<sup>2</sup>/sr/micron. North is up in the images. The first extension (ext=0) is the image in native spatial resolution. The second extension (ext=1) features the disk normalized in size to that of the finest data (i.e., to a disk with an equatorial width of 51.8 pixels, as imaged by VLT-VISIR on August 13, 2018).</p> <p>Image file names and times correspond to approximate mid-time of combined image sequences, and will differ from original file headers.</p> <p>Questions concerning these data should be directed towards Michael Roman, m.t.roman@le.ac.uk or michael.thomas.roman@gmail.com</p> <p> </p> <p> </p>
Dissolved Cr concentration and stable isotope data presented in "Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ53Cr distributions in the ocean interior" (Janssen et al., 2021, EPSL).
<p>This dataset presents all of the dissolved Cr data included and discussed in “Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ<sup>53</sup>Cr distributions in the ocean interior” (Janssen et al., 2021, EPSL). Three primary datasets are included:</p> <ol> <li>Dissolved [Cr], [Cr(III)] and d53Cr in samples from shipboard particle regeneration incubations conducted in the subantarctic Southern Ocean.</li> <li>Dissolved [Cr] in porewater samples from a sediment core collected in the Tasman Sea in primarily calcareous sediments, along with [Cr] and δ<sup>53</sup>Cr in overlying bottom waters.</li> <li>3. A compilation of intermediate and deep water dissolved [Cr] and δ<sup>53</sup>Cr from seawater samples from the Southern, Pacific and Atlantic Oceans</li> </ol>
Water properties of Arco Lake, Budd Lake, Deming Lake, and Josephine Lake in Itasca State Park from 2006-2009 and 2019-et seq.
Depth profiles of water column chemical and physical properties were assessed with seasonal-scale frequency from four lakes in the Itasca State Park from 2006-2009 and from 2019-et seq. The data was used to assess the mixing status and major geochemical constituents within the lakes. Several parameters were routinely measured with deployable probes at meter or sub-meter resolution at the deepest location in each lake. Water samples were also collected for laboratory analysis. Bathymetry data collected in 2022 is supplied as rasters.
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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.