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Reprocessing of the dataset "Plasma Proteome Profiling Reveals the Effects of Weight Loss on the Apolipoprotein Family and Systemic Inflammation Status"
<p>Reprocessing of the MassIVE repository MSV000080596, originally generated to investigate the dynamic changes in the plasma proteomes of a cohort of individuals with obesity following weight loss and maintenance. The reprocessing included all samples from 52 individuals taken right after the weight-loss process and during the weight maintenance phase of the study (Weeks 0, 4, 13, 26, 39, and 52).</p> <p>We used the sequence database generated by ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) representing all populations from the 1000 Genomes Project (doi.org/10.5281/zenodo.10149277). For the search, SearchGUI version 4.3.1 and PeptideShaker version 3.0.0 were used with the X!Tandem and Tide search engines. The modification settings specified were carbamidomethylation of C as fixed and oxidation of M, deamidation of N and Q, Pyrrolidone of E and Q, and acetylation of protein N-terminus as variable modifications. The maximum peptide length was set to 40 amino acids and the precursor and fragment ion tolerances were set to 7 and 20 ppm, respectively. Resulting PSMs were processed as described in (doi.org/10.1021/acs.jproteome.3c00243) using Percolator version 3.5 provided with features based on peptide retention time (DeepLC version 1.1.2) and fragmentation predictors (MS2PIP version 3.9.0), and filtered at a 1% estimated FDR.</p> <p>The attached file contains all the peptide-spectrum matches identified at 1% FDR. The peptides have been annotated with transcripts, genes, and alleles using the ProHap Peptide Annotator v1.1 (<a href="https://github.com/ProGenNo/ProHap_PeptideAnnotator">https://github.com/ProGenNo/ProHap_PeptideAnnotator</a>).</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>
Microclimate temperature effects propagate across scales in forest ecosystems, Berchtesgaden National Park, Bavaria, Germany
Context: Forest canopies shape subcanopy environments, affecting biodiversity and ecosystem processes. Empirical forest microclimate studies are often restricted to local scales and short-term effects, but forest dynamics unfold at landscape scales and over long time periods. Objectives: We developed the first explicit and dynamic implementation of microclimate temperature buffering in a forest landscape model and investigated effects on simulated forest dynamics and outcomes. Methods: We adapted the individual-based forest landscape and disturbance model iLand to use microclimate temperature for three processes [decomposition, bark beetle (Ips typographus L.) development, and tree seedling establishment]. We simulated forest dynamics with or without microclimate temperature buffering in a temperate European mountain landscape under historical climate and disturbance conditions.
Nutrient amendment effects on phytoplankton, water chemistry, and cyanotoxins in the 2018 Large-Scale Mesocosm Experiment at the University of Kansas Field Station
This dataset includes water physicochemical parameters, phytoplankton community composition, and cyanobacteria metabolites collected during a 21-day nutrient amendment experiment conducted from 23 July to 13 August 2018 at the University of Kansas Biological Station, Lawrence, KS, United States (39.049674°N, 95.190777°W). The experiment was performed using 18 large-scale, closed-bottom fiberglass tanks (volume: 11,000 L; height: 1.25 m; diameter: 3 m). Three tanks served as ambient controls (CON), while the others received one of the following nutrient treatments: nitrogen only (280 µM) as either ammonium chloride (NH4) or sodium nitrate (NO3); nitrogen (280 µM) plus phosphorus (200 µM) as either ammonium chloride + dipotassium phosphate (NHP) or sodium nitrate + dipotassium phosphate (NOP); and phosphorus only (200 µM) as dipotassium phosphate (P). Each tank received an initial nutrient dose on Day 0.5, followed by weekly additions of 20% of the initial amendment to maintain treatment conditions. All data were quality controlled to correct basic errors and to remove measurements outside the manufacturer’s standard operational ranges.
Data from: Cascading effects of apex predator recovery on rodent foraging activity and seed predation
This dataset was collected to examine the effects of apex predator presence on post-dispersal seed predation and rodent foraging behavior in Mediterranean ecosystems of southern Spain. The study focused on the Iberian lynx (Lynx pardinus) as a top predator capable of altering mesopredator and small mammal communities through cascading interactions. We used the fleshy-fruited tree Pyrus bourgaeana as a model species and conducted a seed predation experiment in two areas with and without lynx presence. A total of 1152 seeds were placed in 144 seed depots across forest and open habitats and three microhabitat types (rock, shrub, and open ground). Rodent activity and foraging behavior were monitored using 36 camera traps installed at a subset of seed depots, and rodent abundance was estimated with live trapping one week later. The dataset includes seed predation counts, camera-trap records of rodent visits, live-trapping results, and vegetation cover estimates. These data allow investigation of how predation risk and habitat structure influence rodent activity and post-dispersal seed predation dynamics in Mediterranean landscapes.
Soil nitrogen availability and acidity: effects on aboveground production and belowground carbon allocation in mid- and late-successional mixed temperate forests (2009-2021)
In 2011, an experimental nitrogen x pH manipulation study was initiated in mid- and late-successional mixed temperate forests in central New York, USA to disentangle the often-confounded roles of nitrogen (N) and soil pH in driving various ecosystem processes. This data package contains forest productivity (wood, litterfall, and aboveground net primary production), total belowground carbon flux (TBCF), and leaf litterfall and fine root chemistry (C and N concentration) data collected from all experimental plots. It also includes plot-level, species-weighted estimates of measured and modeled photosynthesis (Anet) for the late-successional stands. Wood production, litterfall production, and litterfall chemistry data were collected between 2009 and 2019. Aboveground net primary production data are reported for a pre-treatment interval (2009-2011) and the interval including years 6-9 of experimental treatment (2016-2019). All other properties were measured between years 9 and 11 of the experiment (2019-2021).
Effects of factorial nitrogen, phosphorus, and potassium with micronutrient addition and Host Community on Fungal Endophyte Diversity at Cedar Creek Ecosystem Reserve, Minnesota, USA, 2014
The microbes contained within free-living organisms can alter host growth, reproduction, and interactions with the environment. In turn, processes occurring at larger scales determine the local biotic and abiotic environment of each host that may affect the diversity and composition of the microbiome community. Here, we examine variation in the diversity and composition of the foliar fungal microbiome in the grass host, Andropogon gerardii, across a factorial nitrogen, phosphorus, and potassium addition experiment in Minnesota, USA. We found limited evidence of direct effects of nutrients on endophyte diversity. Instead, the effects of nutrients on endophyte diversity appeared to be mediated by accumulation of plant litter and plant diversity loss. Specifically, nitrogen addition is associated with a 40% decrease in plant diversity and an 11% decrease in endophyte richness. Although nitrogen, phosphorus, and potassium addition increased aboveground live biomass and decreased relative Andropogon cover, endophyte diversity did not covary with live plant biomass or Andropogon cover. Our results suggest that fungal endophyte diversity within this focal host is determined in part by the diversity of the surrounding plant community and its potential impact on immigrant propagules and dispersal dynamics. Our results suggest that elemental nutrients reduce endophyte diversity indirectly via impacts on the local plant community, not direct response to nutrient addition.
Effects of shading on tundra vegetation senescence at Toolik Lake, Coldfoot, Sagwon - Alaska 2016
Data on the effects of shading tundra vegetation from the sun when it is low in on the horizon in the north. If light quality was altered through shading, phenology might be affected. Senescence (color change) was measured for the common tundra species.
Effects of 2015 experimental burn on Eriophorum vaginatum at Toolik Lake Field Station, Alaska 2016
This was an experimental burn conducted in the summer of 2015 to provide sites for an experiment to see whether seeds of Eriophorum vaginatum from different ecotypes could establish in recently burned areas. It consisted of ten 2 meter X 2 meter plots along with a similar number of control plots. There was little seedling establishment but other data have been collected on the plots.
Modeling the effect of explicit vs implicit representation of grazing on ecosystem carbon and nitrogen cycling in response to elevated carbon dioxide and warming in arctic tussock tundra, Alaska - Dataset A
We use a simple model of coupled carbon and nitrogen cycles in terrestrial ecosystems to examine how explicitly representing grazers versus having grazer effects implicitly aggregated in with other biogeochemical processes in the model alters predicted responses to elevated carbon dioxide and warming. The aggregated approach can affect model predictions because grazer-mediated processes can respond differently to changes in climate from the processes with which they are typically aggregated. We use small-mammal grazers in arctic tundra as an example and find that the typical three-to-four-year cycling frequency is too fast for the effects of cycle peaks and troughs to be fully manifested in the ecosystem biogeochemistry. We conclude that implicitly aggregating the effects of small-mammal grazers with other processes results in an underestimation of ecosystem response to climate change relative to estimations in which the grazer effects are explicitly represented. The magnitude of this underestimation increases with grazer density. We therefore recommend that grazing effects be incorporated explicitly when applying models of ecosystem response to global change.
Modeling the effect of explicit vs implicit representation of grazing on ecosystem carbon and nitrogen cycling in response to elevated carbon dioxide and warming in arctic tussock tundra, Alaska - Dataset B
We use a simple model of coupled carbon and nitrogen cycles in terrestrial ecosystems to examine how explicitly representing grazers versus having grazer effects implicitly aggregated in with other biogeochemical processes in the model alters predicted responses to elevated carbon dioxide and warming. The aggregated approach can affect model predictions because grazer-mediated processes can respond differently to changes in climate from the processes with which they are typically aggregated. We use small-mammal grazers in arctic tundra as an example and find that the typical three-to-four-year cycling frequency is too fast for the effects of cycle peaks and troughs to be fully manifested in the ecosystem biogeochemistry. We conclude that implicitly aggregating the effects of small-mammal grazers with other processes results in an underestimation of ecosystem response to climate change relative to estimations in which the grazer effects are explicitly represented. The magnitude of this underestimation increases with grazer density. We therefore recommend that grazing effects be incorporated explicitly when applying models of ecosystem response to global change.
Model Simulations of The Effects of Shifts in High-frequency Weather Variability (No Long-term Weather Trend) Control Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122
Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations without a long term weather trend.
Model Simulations of The Effects of Shifts in High-frequency Weather Variability (With a Long-term Trend) on Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122
Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations with a long-term weather trend.
Effects of Nitrogen Fertilization on Litter and Soil Decomposition: Gravimetric Soil Moisture
The influence of inorganic nitrogen (N) inputs on decomposition is poorly understood. Some prior studies suggest that N may reduce the decomposition of substrates with high concentrations of lignin via inhibitory effects on the activity of lignin-degrading enzymes, although such inhibition has not always been demonstrated. The purpose of E145 was to study the effects of nitrogen (N) addition on decomposition of seven substrates ranging in initial lignin concentrations (from 7.4 - 25.6%) over five years in eight different grassland and forest sites in central Minnesota.
Effects of ectomycorrhizal fungi on pine litter decomposition in temperate pine forests in California, Florida, and Minnesota
This experiment is designed to assess the generality of the effect of ECM fungi on leaf litter decomposition in temperate pine forests. To assess ECM fungal effects on decomposition, we established and ECM fungal knockdown experiment (via trenching) in nine temperate pine forests in California, Florida, and Minnesota. In litter bags incubated (July 2021-July 2022) in paired trenched and untrenched plots at each site we compared leaf litter decomposition (of native pine litter and a common Pinus strobus litter), fungal community composition (via high throughput sequencing), fungal abundance (via qPCR), decomposition enzyme expression, and soil nutrient availability. Contrary to widely cited theory and other results from a subset of our field sites, we found that ECM fungi either increased or did not impact pine litter decomposition in temperate pine forests.
Greenhouse experiment (FCE) in April and August 2001: Responses of neotropical mangrove saplings to the combined effect of hydroperiod and salinity/Biomass
A greenhouse experiment was performed for 13.5 months to evaluate the effect of salinity and hydroperiod on seedling growth rates of 2 mangrove species( Laguncularia racemosa and Rizhophora mangle). Data analyses are currently being performed.
Greenhouse mixed culture experiment from August 2002 to April 2003 (FCE): Evaluate the effect of salinity and hydroperiod on interspecific mangrove seedlings growth rate (mixed culture) / Morphometric variables
A greenhouse experiment (mixed culture experiment) was performed for 8 months to evaluate the effect of salinity and hydroperiod on seedling growth rates of 2 mangrove species( Laguncularia racemosa and Rizhophora mangle). Data analyses are currently being performed.
Biomass and abiotic variable data in the study of the ecosystem engeneering effect of oysters on Suaeda linearis distribution in Georgia salt marshes (2008-2009)
Oysters are ecosystem engineers in marine ecosystems, but the functions of oyster shell deposits in intertidal salt marshes are not well understood. The annual plant Suaeda linearis is associated with oyster shell deposits in Georgia salt marshes. We hypothesized that oyster shell deposits promoted the distribution of Suaeda linearis by engineering soil conditions unfavorable to dominant salt marsh plants of the region (the shrub Borrichia frutescens, the rush Juncus roemerianus and the grass Spartina alterniflora). We tested this hypothesis using common garden pot experiments and field transplant experiments. Suaeda linearis thrived in Borrichia frutescens stands in the absence of neighbors, but was suppressed by Borrichia frutescens in the with-neighbor treatment, suggesting that Suaeda linearis was excluded from Borrichia frutescens stands by interspecific competition. Suaeda linearis plants all died in Juncus roemerianus and Spartina alterniflora stands, indicating that Suaeda linearis is excluded from these habitats by physical stress (likely water-logging). In contrast, Borrichia frutescens, Juncus roemerianus and Spartina alterniflora all performed poorly in Suaeda linearis stands regardless of neighbor treatments, probably due to physical stresses such as low soil water content and low organic matter content. Thus, oyster shell deposits play an important ecosystem engineering role in influencing salt marsh plant communities by providing a unique niche for Suaeda linearis, which otherwise would be rare or absent in salt marshes in the southeastern US. Since the success of Suaeda linearis is linked to the success of oysters, efforts to protect and restore oyster reefs may also benefit salt marsh plant communities.
Effects of Small-scale Armoring and Residential Development on the Salt Marsh/Upland Ecotone in Coastal Georgia, USA
Use of small-scale armoring placed near the marsh-upland interface to protect single-family homes from flooding is a widespread coastal development practice, but its effect on the environment is under studied. We compared the biota and environmental characteristics of 60 marshes on the coast of Georgia, USA, that were adjacent to either a bulkhead, a residential backyard with no armoring, or an intact forest during June-July 2013 using a nested, spatially blocked sampling design. For each plot in each sampling site, we used real-time kinematic (RTK) GPS to measure the location and elevation of the upper marsh. We collected cores to determine porewater salinity and nutrient concentrations as well as grain size distribution of marsh sediments. We quantified flora (vegetation composition) and fauna (snail, bivalve, and crab abundance) in the upper marsh ecotone. For sites with bulkheads, we recorded the height, thickness and condition of the bulkhead, as well as surveyed the animals living on and around the bulkheads.
Effect of salt water intrusion on the distribution of invertebrates in a GA tidal freshwater marshes from the GCE Seawater Addition Long-Term Experiment (SALTEx) project.
To characterize the effect of persistent and episodic salt water intrusion on the distribution of common freshwater marsh invertebrates, we monitored the density of adult and juvenile fiddler crabs and snails. Prior to the start of salt water addition treatments, we collected data on the distribution of crabs and snails in all 30 experimental plots (6 replicates of 5 treatments: pressed salt water addition, pulsed salt water addition, fresh water addition, procedural control structure, and control no structure). In each experimental plot, we counted the number of adult and juvenile fiddler crab burrows and snails visible on the marshs surface in a 50cm x 75cm plot (juvenile fiddler crabs were counted in only half of this area) that was positioned in the Northeastern corner of each experimental plot. Initial data was collected in March 2014. A Bentho Torch was used to measure the concentrations of cyanobacteria, diatoms, and green algae on the marsh surface in 2015 and 2016.
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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.