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199 results for “Legacy Data”
MCR LTER: Coral Reef: Material legacy disturbance type model; data for Kopecky et al., 2023 Ecology
This data package contains the code necessary to create a mathematical model of coral reef recovery dynamics following different types and intensities of disturbances that either remove dead coral skeletons (e.g., tropical storms) or leave standing dead skeletons (e.g., coral bleaching) and run associated analyses. We explored the sensitivity of the model to variation in key parameters, such as the strength of herbivory, and the degree to which dead skeletons protect algae from herbivory. Further, we assessed disturbance intensities and values of these parameters that lead to shifts between coral and macroalgae-dominated reefs. This code was published in Ecology and were a part of the thesis of K. Kopecky (2023). Analyses and full methods descriptions of this model can be found in the manuscript “Material legacies can degrade resilience: Structure-retaining disturbances promote regime shifts on coral reefs” (DOI: https://doi.org/10.1002/ecy.4006). No novel data were used or generated in this study. 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).
Data from: The genetic legacy of extreme exploitation in a polar vertebrate
<p>Microsatellite data (39 loci) from Antarctic fur seals and Subantarctic fur seals, used in the paper: "The genetic legacy of extreme exploitation in a polar vertebrate"</p> <p> </p> <p><strong>Abstract</strong></p> <p>Understanding the effects of human exploitation on the genetic composition of wild populations is important for predicting species persistence and adaptive potential. We therefore investigated the genetic legacy of large-scale commercial harvesting by reconstructing on a global scale the recent demographic history of the Antarctic fur seal (<em>Arctocephalus gazella</em>), a species that was hunted to the brink of extinction by 18<sup>th</sup> and 19<sup>th</sup> century sealers. Molecular genetic data from over 2,000 individuals, sampled from all eight major breeding colonies across the species᾿ circumpolar geographic distribution, show that at least four relict populations around Antarctica survived commercial hunting. Coalescent simulations suggest that all of these populations experienced severe bottlenecks down to effective population sizes of around 150–200. Nevertheless, comparably high levels of neutral genetic variability were retained as these declines are unlikely to have been strong enough to deplete allelic richness by more than around 15%. These findings suggest that even dramatic short-term declines need not necessarily result in major losses of diversity, and explain the apparent contradiction between the high genetic diversity of this species and its extreme exploitation history.</p> <p> </p> <p><strong>Funding</strong></p> <p>This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in<br> the framework of a Sonderforschungsbereich (project numbers 316099922 and 396774617–TRR 212) and the<br> priority programme "Antarctic Research with Comparative Investigations in Arctic Ice Areas" SPP 1158 (project<br> number 424119118). It was also funded by Norwegian Antarctic Research Expeditions (NARE) programme.<br> This work contributes to the Ecosystems project of the British Antarctic Survey, Natural Environmental Research<br> Council, and is part of the Polar Science for Planet Earth Programme. The Department of Environmental Affairs<br> provided logistical support for research at Marion Island and the Department of Science and Technology of<br> South Africa provided funding through the National Research Foundation (NRF). We are grateful to Caroline<br> Bonin, Debbie Baird-Bower and Iain Staniland together with the seal biologists working within the Marion<br> Island Marine Mammal Programme for sample collection and logistics. We acknowledge support for the Article<br> Processing Charge by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld<br> University.</p>
Supplementary material 3: World Spider Catalog Bibliographic Data: Treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
List of journal/publisher by ranked by treatment count exported from the World Spider Catalog 14 October 2014 with total treatments by source, cumulative treatments, and cumulative proportion of treatments.
Supplementary material 2: World Spider Catalog Bibliographic Data: Publications from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Ranked list of journal/publisher exported from the World Spider Catalog 14 October 2014 with total articles by source, cumulative articles, and cucmulative proportion of articles.
Data from: The legacy of the extinct Neotropical megafauna on plants and biomes
<p>The main dataset consists of ecoregion-level data on five plant functional traits (wood density, leaf size, stem spines, leaf spines and latex production), as well as ecoregion-level data on extinct megafauna historical patterns, fire, climate, soil, hurricanes and geografical variables (first spreadsheet) for the Neotropical biogeographic realm (Table 1). It also includes species-level plant functional trait data, and the abundance (presence-absence for leaf size) of these species, and the occurrences extinct megafauna and extant mammal herbivore species per Neotropical ecoregion, as well as diet data compiled for megafauna species. The species-level functional trait data was compiled from the literature and the names of the species in these data was used to search for occurrence data for these species in the Global Biodiversity Information Facility (Data available from GBIF using the following doi: WD: 10.15468/dl.3vua3x; Stem spines: 10.15468/dl.ar5ddj; Latex: 10.15468/dl.m8dzjd; Leaf spines: 10.15468/dl.vv8gw4; Leaf size: 10.15468/dl.k98nxc). During the process, species level were corrected and updated using tools from the "rgbif" package for R. We then croped only the Neotropical region, and calculate ecoregion level trait means for continuous traits (Wood Density and Leaf Size) and maximum por binary traits (Stem and Leaf Spines, Latex), using the ecoregion shapefile provided in https://storage.googleapis.com/teow2016/Ecoregions2017.zip. We obtained data on historical distribution of megafauna species and extant mammal species from the MegaPast2Future/PHYLACINE_1.2 dataset, and obtained diet information from literature sources. Climate data was obtained from WorldClim 2.1 (10 minute spatial resolution) and was based on climate data from 1970 and 2000. Soil data were obtained from SoilGrids (5 km of spatial resolution), and consisted of mean values for two depths, 0.05 and 2 m. We obtained the number (a proxy for frequency) and intensity of wildfires per ecoregion area using the MODIS active fire location product (MCD14ML). We only considered fires with detection confidence of 95% or higher occurring from November 2000 to December 2019 (both included). To ensure that only wildfires were considered, we associated each fire pixel with a land cover type (300 m of spatial resolution) from for a buffer area of 1000 m surrounding the fire pixel centroid. We excluded all of the fires occurring in areas in which more than 10% of the surrounding land cover pixels corresponded to agricultural, urban and water classes. We calculated the number of wildfires per ecoregion area by dividing the fire count of each Ecoregion by the ecoregion area, and multiplying the resulting value by the proportion of vegetated land cover pixels (same classes used to exclude fires in anthropogenic areas and water bodies above). Fire intensity was estimated as the average fire radiative power across all detected wildfires in the ecoregion. We also classified ecoregions into insular (1), when most of the ecoregion area was located in islands, vs. continental (0), otherwise. We also compiled data on hurricane activity, as woody density was suggested to confer resistance against this disturbance. We used data from 1990 to 2019 from the HURDAT2 dataset, containing six-hourly information about the location of all of the known tropical and subtropical cyclones (0.1° latitude/longitude). We used the sum of hurricane occurrences per ecoregions divided by ecoregion area as an indicator of hurricane activity.</p> <p>Three .txt files containing the custom codes developed for building the Ecoregion-level dataset (predictors and traits) and for data analyses used in the article are also included.</p>
Legacy seismic data fo the 1928 Parral, Mexico earthquake (M6.3)
<p>This data set is part of the 01/11/1928 Parral, Mexico earthquake (M6.3)</p> <p>Includes records from the 1928 National Seismological Service SSN) network, recorded on Wiechert seismographs smoked paper, as well as records from the California network Caltech archive.</p> <p> </p>
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
Decomissioned Site: Arthur Brook's (D01 ARTH) Legacy and Prototype Aquatic invertebrates (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains legacy and prototype NEON aquatic invertebrates from the decommissioned Arthur Brook's site in Worcester County, Massachusets. These samples and specimens were collected using NEON protocol NEON.DP1.20120 and are summarized in NEON Prototype dataset e7d152a1-1181-4c7e-ac75-ae707ffc7299. This data is available here.
Decomissioned Site: Ichawanochaway Creek (D03 ICHA) Legacy and Prototype Aquatic invertebrates (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains legacy and prototype NEON aquatic invertebrates from the decommissioned Ichawanochaway Creek site in Baker County, Georgia. These samples and specimens were collected using NEON protocol NEON.DP1.20120 and are summarized in NEON Prototype dataset 1599af29-fad4-4721-9b09-f8324b672d50. This data is available here.
Supplementary material 12: Collector dashboard: specimens collected by Y. M. Marusik from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing only specimens collected by Y. M. Marusik. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 9: Integrated legacy literature and prospective publishing dashboard: species-rank treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 42 articles published either as open access articles published in Zootaxa or in Biodiversity Data Journal, containing treatments on spiders. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 8: Integrated legacy literature and prospective publishing dashboard: all treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 42 articles published either as open access articles published in Zootaxa or in Biodiversity Data Journal, containing treatments on spiders. This page shows data from all treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 5: Legacy literature dashboard: species-rank treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 37 open access articles published in Zootaxa containing treatments on spiders. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 dashboard charts.
Supplementary material 16: Author dashboard: Jeremy A. Miller from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing content from articles by Jeremy A. Miller (lead author). When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 4: Legacy literature dashboard: all treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 37 open access articles published in Zootaxa containing treatments on spiders. This page shows data from all treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 dashboard charts.
Supplementary material 1: Global Biodiversity Information Facility: Taxa and Records from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
All records in GBIF with taxonomic ranks (kingdom, phylum, class, order, and species), basis of record (e.g., preserved specimen), and count of records, exported from GBIF on 7 December 2014.
Figure 12. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 12. - Excerpt from a taxonomic treatment with ambiguously structured materialsCitations data in the source document. The top frame shows the published PDF, the lower frame shows the same content in GoldenGATE with the treatment and materialsCitation tags revealed. The source document is ambiguous about how many paratype specimens are deposited in which natural history collection. This is represented in XML by associating the collection event data (place, time, collector) with each of the listed institutional collections but no quantity of specimens is assigned to any collection. The 110 male and 44 female specimens are also associated with the collection event data, but with no institutional collection.
Supplementary material 11: Collecting country dashboard: specimens collected in Russia from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing only specimens collected in Russia. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Figure 11. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 11. - Dashboard charts summarizing data published in open access articles in Zootaxa and Biodiversity Data Journal containing treatments on spiders, filtered to show only one lead author: Jeremy A. Miller (Suppl. material 16). Miller was lead author on two publications in Biodiversity Data Journal and one in Zootaxa, and was the only lead author on open access publications featuring spider treatments in both journals. Note that in addition to the three articles on which he is lead author, he is also a contributing author to a fourth article (Wang et al. 2010), but content from this article is not included here (see Discussion: Tracking individuals).
Figure 10. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 10. - Dashboard charts summarizing data published in open access articles in Zootaxa and Biodiversity Data Journal containing treatments on spiders, filtered to show only one species: Tenuiphantestenuis (Suppl. material 15). Data on Tenuiphantestenuis was included in three treatments, all published in Biodiversity Data Journal.
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