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131 results for “coastline”
Fig. 3 in Distribution and prey of migratory shorebirds on the northern coastline of Singapore
Fig. 3. Average number of birds per month at different tide levels at the study sites during: A, southward migration; B, northward migration; and C, the wintering period. See Table 1 for full site names.
Fig. 1. South African coastline with the 200 m in Hydromedusae of the Agulhas Current
Fig. 1. South African coastline with the 200 m isobath. The study area along the east coast and the sampling stations are shown with details.
Non-random mating within an island rookery of Hawaiian hawksbill turtles: demographic discontinuity at a small coastline scale
<p>Hawksbill sea turtles (<em>Eretmochelys</em> <em>imbricata</em>) from the Hawaiian archipelago form a small and genetically isolated population, consisting of only a few tens of individuals breeding annually. Most females nest on the island of Hawai'i, but little is known about the demographics of this rookery. This study used genetic relatedness, inferred from 135 microhaplotype markers, to determine breeding sex-ratios, estimate female nesting frequency, and assess relationships between individuals nesting on different beaches. Samples were collected during the 2017 nesting season and final data included 13 nesting females and 1,002 unhatched embryos, salvaged from 41 nests, of which 13 had no observed mother. Results show that most females used a single nesting beach laying 1–5 nests each. From female and offspring alleles the paternal genotypes of 12 breeding males were reconstructed and many showed high relatedness to their mates. Pairwise relatedness of offspring revealed one instance of polygyny but otherwise suggest a 1:1 breeding-sex ratio. Relatedness analysis and spatial-autocorrelation of genotypes indicate non-random mating among complexes of nesting beaches, for both sexes, suggesting strong natal philopatry. Nesting complexes also showed unique patterns of inbreeding across loci, further indicating that Hawaiian hawksbill turtles have demographically discontinuous nesting populations on a fine spatial scale.</p>
Drone orthomosaic and DEM of the coastline near Qaamasoq north of Paamiut on 2 July 2022
<p>This dataset consists of a digital elevation model (DEM) and an orthomosaic of the coastline at Qaamasoq, near the town of Paamiut. The DEM and orthomosaic were produced from 247 aerial images that were acquired by an Autel Evo II drone on 2 July 2022. The images were processed using Agisoft Metashape Professional (version 1.7.1) following protocols established by the United States Geological Survey (USGS) for drone surveys of coastal regions (Over et al., 2021). The drone survey was conducted as part of a sailing science expedition that was funded by the German Scholars Organization and supported by Helmholtz-Zentrum Hereon.</p> <p>Over et al. (2021) Processing Coastal Imagery With Agisoft Metashape Professional Edition, Version 1.6—Structure From Motion Workflow Documentation. USGS Open File Report 2021-1039. doi:10.3133/ofr20211039. <a href="https://pubs.er.usgs.gov/publication/ofr20211039">https://pubs.er.usgs.gov/publication/ofr20211039</a></p>
Great Britain coastline boundary (modified from 2011 Census boundary data) [GeoJSON]
<p><strong>Original purpose</strong></p> <p>This coastline boundary dataset was originally derived for research on population proximity to the UK coast. It required adaptation of boundary files in order to prevent areas close to major rivers from being counted as ‘coastal’. With no single definition of what ‘coastal’ is, we took a decision to cut off the coastline where major estuaries/rivers narrowed to approximately 1km. The original publication that used this approach and informed the development of the dataset is cited below (Wheeler et al, 2012).</p> <p>Please note therefore that this is a somewhat arbitrary definition of what is coastal, and you will need to make sure this definition is appropriate for your application for this to be useful.</p> <p><strong>Method & Data Format</strong></p> <ul> <li>Original source data: UK Census 2011 Lower-layer Super Output Areas / Data Zones – full resolution / Mean High Water version.</li> <li>LSOA/DZ boundaries were dissolved to create outline boundary at Mean High Water.</li> <li>Major estuaries/rivers were manually truncated where they narrowed to approximately 1km width.</li> <li>Data are provided as a GeoJSON file</li> <li>Co-ordinate system is British National Grid (EPSG 27700)</li> </ul> <p><strong>Original data source & copyright</strong></p> <p>This boundary dataset was derived from Ordnance Survey/Office for National Statistics/Scottish Government data, under Open Government Licence. Its use/re-use is dependent on appropriate citation and acknowledgement of the original source data.</p> <p>Licence: Adapted and redistributed under Open Government Licence: <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/</a></p> <p>Copyright statements to appear on any maps/publications containing these data:</p> <p><strong>Contains National Statistics data © Crown copyright and database right 2012</strong></p> <p><strong>Contains Ordnance Survey data © Crown copyright and database right 2012</strong></p> <p><strong>Copyright Scottish Government, contains Ordnance Survey data © Crown copyright and database right (2012).</strong></p> <p> </p> <p><strong>Citation and Attribution</strong></p> <p>The original source of the approach and methodology for this coastal definition should be cited as:</p> <p>Wheeler, B.W., White, M., Stahl-Timmins, W., Depledge, M.H., 2012. Does living by the coast improve health and wellbeing? Health and Place 18: 5, 1198-1201. doi: 10.1016/j.healthplace.2012.06.015</p> <p>The boundary dataset requires the copyright statements as above to be stated on any publication/redistribution.</p> <p>The adapted data are redistributed here under CC-BY Licence - <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Non-random mating within an island rookery of Hawaiian hawksbill turtles: demographic discontinuity at a small coastline scale
Open the record for dataset details and reuse information.
Supplement 5: Comparison of Ptolemy's and Strabo's coastline length data
<p>Supplement 5 to the paper "<strong>The Configuration of the Pontus</strong><strong> Euxinus in Ptolemy’s <em>Geography</em></strong>" submitted to the History of Geo- and Space Sciences (URL: <a href="https://www.history-of-geo-and-space-sciences.net/">https://www.history-of-geo-and-space-sciences.net</a>). The paper is a part of the research project “Development of the Methods for Analysis of the Historical Maps” supported by the Russian Foundation for Basic Research (grant 18-011-00258-a). <em>Correspondence</em>: Dmitry A. Shcheglov (shcheglov@yandex.ru).</p>
Supplement 4: Comparison of Ptolemy's and Pseudo-Arrian's coastline length data
<p>Supplement 4 to the paper "<strong>The Configuration of the Pontus</strong><strong> Euxinus in Ptolemy’s <em>Geography</em></strong>" submitted to the History of Geo- and Space Sciences (URL: <a href="https://www.history-of-geo-and-space-sciences.net/">https://www.history-of-geo-and-space-sciences.net</a>). The paper is a part of the research project “Development of the Methods for Analysis of the Historical Maps” supported by the Russian Foundation for Basic Research (grant 18-011-00258-a). <em>Correspondence</em>: Dmitry A. Shcheglov (shcheglov@yandex.ru).</p>
Dataset Sediment Analysis between Batu Ferringhi Coastline and Persiaran Bayan Indah Reclamation Site, Penang, Malaysia
<p>This dataset provided data analysis in research aimed to differentiate the characteristics between natural and anthropogenic coastal landforms in Penang. Two methods were used here are GPR-Ground Penetrating Radar and sediment sampling analysis. However, the dataset provided in this paper is only for sediment analysis. All GPR radar facies are presented in the research paper.<br> <br> Two sites were selected for this study - the Batu Ferringhi area, representing the natural coastal landform (labelled as BF) and the Persiaran Bayan Indah (parking lot of the Queensbay Mall), later labelled as QB representing the anthropogenic coastal landform. Five sediment samples were collected from each site. Sample BF1, BF2, BF3, BF4 and BF5; and sample QB1, QB2; QB3; QB4; and QB5. These data then calculated for mode, mean, skewness, sorting and kurtosis. All graphic are available in the research paper.</p>
Global Airborne Observatory: Example Rapid Water Quality Maps of Hawaiian Island Coastline
<p>Example maps from the publication:</p> <p>Nicholas R. Vaughn, Marcel König, Kelly L. Hondula, Dominica E. Harrison and Gregory P. Asner. 2024. Rapid Water Quality Mapping from Imaging Spectroscopy with a Superpixel Approach to Bio-Optical Inversion. <em>Remote Sensing</em>. (in press)</p> <h2>Data:</h2> <p>Maps of water quality and auxiliary information produced from Global Airborne Observatory (GAO) VSWIR Spectrometer data for five example sites:</p> <table> <tbody> <tr> <td>Site</td> <td>Island</td> <td>Date Collected</td> <td>Time Collected (24hr)</td> <td>Latitude</td> <td>Longitude</td> </tr> <tr> <td>1. Pelekane Bay</td> <td>Hawaiʻi</td> <td>30 October 2023</td> <td>10:02</td> <td>20.0243</td> <td>−155.8256</td> </tr> <tr> <td>2. East Kaho’olawe</td> <td>Kaho’olawe</td> <td>22 January 2024</td> <td>09:15</td> <td>20.6027</td> <td>−156.5638</td> </tr> <tr> <td>3. Hilo Bay</td> <td>Hawaiʻi</td> <td>15 January 2023</td> <td>11:30</td> <td>19.7353</td> <td>−155.0636</td> </tr> <tr> <td>4. Mā’alaea Bay</td> <td>Maui</td> <td>22 January 2024</td> <td>10:19</td> <td>20.7805</td> <td>−156.4842</td> </tr> <tr> <td>5. South Moloka’i</td> <td>Moloka’i</td> <td>7 January 2024</td> <td>11:31</td> <td>21.0866</td> <td>−157.2159</td> </tr> </tbody> </table> <div> <div> <div> <p>There are 41 maps for each example site:</p> <ol> <li>A single 3-band true-color map of the example site for reference (Site<<em>X_Name></em>_3band.tif)</li> <li>A single full-resolution water quality map produced using a bio-optical inversion model on each pixel (Site<<em>X_Name></em>_PixelbyPixel.tif)</li> <li>For each of 13 different superpixel size settings (50px, 100px, 250px, 500px, 600px, 750px, 1000px, 1250px, 1500px, 2000px, 3000px, 4000px, 5000px), a map of the superpixel ID of each pixel (Site<X_Name>_Size<####>_ClusterID.tif)</li> <li>For each of 13 different superpixel size settings, a superpixel-level map of water quality as estimated at the cluster level. (i.e., each pixel in a superpixel will have the same values, Site<X_Name>_Size<####>_Cluster_Preds.tif)</li> <li>For each of 13 different superpixel size settings, a full-resolution water quality map produced by interpolation of the superpixel level maps in 4. above (Site<X_Name>_Size<####>.tif)</li> </ol> <p>All files are in GeoTiff format using 32-bit floating point values. Most are compressed with the LZW algorithm. Maps are not georeferenced. No data value for map types 2-5 is -9999.</p> </div> </div> </div>
Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines
<p><strong>Code and data for Section 2 of the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines</strong></p> <p><strong>Versions:</strong></p> <p>Version 1.1 This one:</p> <ul> <li>updated region names</li> </ul> <p>Version 1.0 <a href="https://doi.org/10.5281/zenodo.5951626">https://doi.org/10.5281/zenodo.5951626</a></p> <p>This repository contains the code and data needed to produce the trajectories, projections, and observations for the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines.</p> <p>The report can be found on <a href="https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html">https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html</a></p> <p>An interactive tool to study the observations, trajectories, and scenarios can be accessed from <a href="https://sealevel.nasa.gov/task-force-scenario-tool">https://sealevel.nasa.gov/task-force-scenario-tool</a></p> <p>Frequently-asked questions: <a href="https://sealevel.nasa.gov/faq/16/">https://sealevel.nasa.gov/faq/16/</a></p> <p><strong>Authors</strong></p> <ul> <li>William V. Sweet, NOAA National Ocean Service</li> <li>Benjamin D. Hamlington, NASA Jet Propulsion Laboratory</li> <li>Robert E. Kopp, Rutgers University</li> <li>Christopher P. Weaver, U.S. Environmental Protection Agency</li> <li>Patrick L. Barnard, U.S. Geological Survey</li> <li>Michael Craghan, U.S. Environmental Protection Agency</li> <li>Gregory Dusek, NOAA National Ocean Service</li> <li>Thomas Frederikse, NASA Jet Propulsion Laboratory</li> <li>Gregory Garner, Rutgers University</li> <li>Ayesha S. Genz, University of Hawai‘i at Mānoa, Cooperative Institute for Marine and Atmospheric Research</li> <li>John P. Krasting, NOAA Geophysical Fluid Dynamics Laboratory</li> <li>Eric Larour, NASA Jet Propulsion Laboratory</li> <li>Doug Marcy, NOAA National Ocean Service</li> <li>John J. Marra, NOAA National Centers for Environmental Information</li> <li>Jayantha Obeysekera, Florida International University</li> <li>Mark Osler, NOAA National Ocean Service</li> <li>Matthew Pendleton, Lynker</li> <li>Daniel Roman, NOAA National Ocean Service</li> <li>Lauren Schmied, FEMA Risk Management Directorate</li> <li>William C. Veatch, U.S. Army Corps of Engineers</li> <li>Kathleen D. White, U.S. Department of Defense</li> <li>Casey Zuzak, FEMA Risk Management Directorate</li> </ul> <p><strong>Contents</strong></p> <p>This data and code set contains the following directories:</p> <p><em>Results</em></p> <p>The <code>Results</code> folder contains the resulting projections, trajectories and observations from the report.</p> <ul> <li><code>TR_global_projections.nc</code>: GMSL projections, trajectory, and observations</li> <li><code>TR_regional_projections.nc</code>: Regional observations, projections and trajectories</li> <li><code>TR_local_projections.nc</code>: Local observations, projections and trajectories</li> <li><code>TR_gridded_projections.nc</code>: Gridded projections</li> </ul> <p>These files are in the NetCDF forrmat. To read the NetCDF files, many free software packages are available, including <a href="http://meteora.ucsd.edu/~pierce/ncview_home_page.html">ncview</a> and <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a>. Free NetCDF packages are available to directly import the data into <a href="https://github.com/Alexander-Barth/NCDatasets.jl">Julia</a> and <a href="https://unidata.github.io/netcdf4-python/">Python</a> code.</p> <p><em>Code</em></p> <p>The <code>Code</code> folder contains all the computer code used to read and analyze the observations and the projections, and to generate the trajectories.</p> <p>To run this code, you need <a href="https://julialang.org/">Julia</a>. The code requires the Julia packages <code>CSV</code>, <code>Interpolations</code>, <code>JSON</code>, <code>LoopVectorization</code>, <code>MAT</code>, <code>NCDatasets</code>, <code>NetCDF</code>, <code>Plots</code>, <code>XLSX</code>, <code>LinearAlgebra</code>, and <code>Statistics</code>. They can be installed by pressing <code>]</code> at the Julia REPL and typing:</p> <pre><code>add CSV Interpolations JSON LoopVectorization MAT NCDatasets NetCDF Plots XLSX LinearAlgebra Statistics </code></pre> <p>This program also requires <a href="http://segal.ubi.pt/hector/">Hector</a>. Hector needs to be installed or compiled. In the file <code>Hector.jl</code> update the path to the Hector executable on lines 30 and 104.</p> <p>Run <code>Run_TR.jl</code> in the REPL or run <code>julia Run_TR.jl</code> from the command line to run the projections. The projections are then written to the <code>.\Data</code> directory.</p> <p>The folder contains the following files:</p> <ul> <li><code>Run_TR.jl</code>: This is the main routine that (eventually) calls all the functions to compute the projections.</li> <li><code>ConvertNCA5ToGrid.jl</code>: Converts the original NCA5 projections to a set of netCDF files that's used throughout this code</li> <li><code>ProcessObservations.jl</code>: Reads and processes the tide-gauge and altimetry observations</li> <li><code>GlobalProjections.jl</code>: Reads and processes the GMSL observations and projections, and computes the trajectory</li> <li><code>RegionalProjections.jl</code>: Reads and processes the regional projections and computes the trajectories</li> <li><code>LocalProjections.jl</code>: Reads and processes the local projections at the tide-gauge locations and computes the trajectories</li> <li><code>GriddedProjections.jl</code>: Reads the gridded NCA5 projections and add a GMSL baseline correction for the 2005 vs 2000 baseline</li> <li><code>SaveFigureData.jl</code>: Reads the results and writes text files for GMT</li> <li><code>Hector.jl</code>: Wrapper for <a href="http://segal.ubi.pt/hector/">Hector</a>, used to compute trends and uncertainties.</li> <li><code>Masks.jl</code>: Defines the region masks for each region.</li> </ul> <p><em>Data</em></p> <p>The <code>Data</code> directory contains the input data sets used during the computations. Please appropriately cite the input data if you use it. It contains the following:</p> <p>Directories:</p> <ul> <li><code>ClimIdx</code>: Map with climate indices (NAO, PDO, MEI) used to remove internal variability. All the indices come from NOAA <a href="https://psl.noaa.gov/data/climateindices/">Physical Sciences Laboratory (PSL)</a> and <a href="https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml">NOAA Climate Prediction Centre (CPC)</a></li> <li><code>NCA5_projections</code> Contains the NCA5 projections for each scenario (Low, IntLow, Int, IntHigh, and High). For each scenario, the GMSL projections, projections at tide-gauge locations and on a 1-degree grid are provided.</li> </ul> <p>Files:</p> <ul> <li><code>basin_codes.nc</code>: Map with basin codes. from Eric Leuliette/NOAA. Data provided by the NOAA Laboratory for Satellite Altimetry.</li> <li><code>CDS_monthly_1993_2020.nc</code>: Monthly-mean sea level (1993-2020) from gridded altimetry. Obtained from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global">Copernicus Climate Data Store</a>. This dataset contains modified Copernicus Climate Change Service information [2020]</li> <li><code>enso_correction.mat</code>: GMSL correction for ENSO/PDO from Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., & Adhikari, S. (2020). Investigating the Acceleration of Regional Sea‐level Rise During the Satellite Altimeter Era. Geophysical Research Letters. <a href="https://doi.org/10.1029/2019GL086528">https://doi.org/10.1029/2019GL086528</a></li> <li><code>filelist_psmsl.txt</code>: List with PSMSL file names and PSMSL IDs. Obtained from the Permanent Service for Mean Sea Level (<a href="http://www.psmsl.org/">PSMSL</a>), 2021, Retrieved 29 Nov 2021. Simon J. Holgate, Andrew Matthews, Philip L. Woodworth, Lesley J. Rickards, Mark E. Tamisiea, Elizabeth Bradshaw, Peter R. Foden, Kathleen M. Gordon, Svetlana Jevrejeva, and Jeff Pugh (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research: Volume 29, Issue 3: pp. 493 – 504. <a href="https://doi.org/:10.2112/JCOASTRES-D-12-00175.1">https://doi.org/:10.2112/JCOASTRES-D-12-00175.1</a>.</li> <li><code>GEBCO_bathymetry_05.nc</code>: Bathymetry map of the global oceans from the General Bathymetric Chart of the Oceans (<a href="https://www.gebco.net/">GEBCO</a>). Source: GEBCO Compilation Group (2021) GEBCO 2021 Grid (<code>doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f</code>) The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>GIA_Caron_stats_05.nc</code>: Glacial Isostatic Adjustment estimates from Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. <a href="https://doi.org/10.1002/2017GL076644">https://doi.org/10.1002/2017GL076644</a>. The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>global_timeseries_measures.nc</code>: Time series of estimated 20th-century GMSL and its components, based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_ensembles.nc</code>: Ensemble GMSL reconstruction from tide-gauges based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_TPJAOS_5.0_199209_202106.txt</code>: Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1 [Data set]. NASA Physical Oceanography DAAC. <a href="https://doi.org/10.5067/GMSLM-TJ151">https://doi.org/10.5067/GMSLM-TJ151</a>. This altimetry dataset uses the methods as described in Beckley, B. D., Callahan, P. S., Hancock, D. W., Mitchum, G. T., & Ray, R. D. (2017). On the “Cal-Mode” Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series. Journal of Geophysical Research: Oceans, 122(11), 8371–8384. <a href="https://doi.org/10.1002/2017JC013090">https://doi.org/10.1002/2017JC013090</a></li> <li><code>grd_1992_2020.nc</code>: Seafloor deformation due to contemporary GRD effects based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>region_mask.nc</code>: Mask with the definition of all regions.</li> <li><code>US_tg_monthly.xlsx</code>: Tide gauge observations from the NOAA tide gauge network</li> </ul> <p><em>GMT</em></p> <p>This directory contains the <a href="https://www.generic-mapping-tools.org/">GMT</a> scripts to make Figures 1.2, 2.1, 2.2, 2.6, and A.1.2 from the report. To generate the figures, make sure GMT is installed and run the Shell script in each directory.</p>
Fig. 2 in Inventory Of Annelida Polychaeta In Gulf Of Oran (Western Algerian Coastline)
Fig. 2. Specific richness of polychaetes by station.
Fig. 3 in Inventory Of Annelida Polychaeta In Gulf Of Oran (Western Algerian Coastline)
Fig. 3. The frequency of the most abundant species.
Fig. 1 in Inventory Of Annelida Polychaeta In Gulf Of Oran (Western Algerian Coastline)
Fig. 1. Abundance of polychaetes by station.
Drone aerial imagery of a macroalgal-covered coastline in Kobbefjord (SW Greenland) in 2018 and 2019
<p>Aerial images of inner Kobbefjord (SW Greenland) were acquired on two occasions in 2018 (2018-09-07 15:06; 2018-09-07 16:00) representing different tidal levels and on one occasion in 2019 (2019-08-26). Images were acquired using a DJI Mavic Air drone. The imagery was acquired in connection with the “Greenland Ecosystem Monitoring Program” “Nuuk Basis” monitoring of marine flora (http://<a href="http://g-e-m.dk/">g-e-m.dk</a>/). This drone survey was carried out with the primary goal of documenting and quantifying the area distribution of the tidal vegetation (dominated by the brown macroalga <em>Ascophyllum nodosum</em>) and shallow subtidal vegetation at the monitoring site. Images were processed using Agisoft Metashape Professional to produce high resolution orthomosaics from each UAV survey.</p>
Data Accompanying "Leveraging successional facilitation to improve restoration of foundational dune grasses along a frequently disturbed coastline"
<div> <h1>Experimental Data accompanying "Leveraging successional facilitation to improve restoration of foundational dune grasses along a frequently disturbed coastline"</h1> Authors: Hallie Fischman, Copeland Cromwell, Joe Morton, Ralph Temmink, Tjisse van der Heide, Pete Adams, Christine Angelini <h1>Manuscript Abstract</h1> <a href="https://github.com/halliefischman1/Dune-Succession/blob/main/README.md#manuscript-abstract"></a></div> <p>Coastal ecosystems provide critical storm and flood protection services, but are rapidly degrading worldwide, making their restoration urgent. Here, we evaluated whether successional facilitation, where pioneers facilitate climax species, could be leveraged to accelerate coastal dune revegetation. A survey spanning 270km of Southeast US coastline revealed that Panicum amarum (bitter panicum) supported higher plant richness than Uniola paniculata (sea oats), and that sea oat cover was 230% greater on mature dunes than disturbed dunes, suggesting bitter panicum functions as a pioneer and sea oats is a climax species. A reciprocal transplant experiment confirmed this interpretation: bitter panicum stem production and height fell by 37% and >20 cm, respectively, when planted proximate to sea oats versus in isolation, whereas sea oats produced 38% more and >12 cm taller stems when planted proximate to bitter panicum versus in isolation. A second experiment evaluating the density-dependence of this facilitative interaction revealed that sea oats transplanted into low densities of bitter panicum grew >15% taller than isolated and high-density treatments. However, within seven months, wave inundation eliminated >60% propagules in both experiments. To explore foredune inundation frequency and its implications for dune revegetation, we applied empirical wave runup models at 101 locations throughout Volusia County, Florida. While disturbance frequency varied seasonally and annually, sites with low dune crests and steep beach slopes experienced frequent inundation (>50 events/year). Given the interactions between geomorphology and vegetation success, we present decision matrix to guide managers in determining optimal revegetation methods tailored to project goals and site conditions.</p> <div> <h1>Methods and Data Description</h1> <a href="https://github.com/halliefischman1/Dune-Succession/blob/main/README.md#methods-and-data-description"></a></div> <p>Experiment 1: Uniola paniculata (sea oats) and panicum amarum (bitter panicum) were planted in one of three treatments. Controls are in bare areas, Adjacent treatments (adj) are planted 50cm away from naturally occuring patches of the other species (ie, SO-adj-NPA is sea oats planted adjacent to natural bitter panicum), and Within treatments are planted in a naturally occuring patch of the other species (ie, SO-in-NPA is sea oats planted into a patch of natural bitter panicum). Each replicate consisted of 5 transplant propagules in an X configuration. At each monitoring, all stems were counted and the maximum height of each plot was measured. Plot treatments were reassigned during monitoring, if needed, such that any plot with the heterospecific growing within the 0.25 m2 area was considered a “within plot” and any plot with the heterospecific <50 cm away was considered an “adjacent plot”. See maunscript for additional details.</p> <p>Experiment 2: Sea oats were planted in the same configuration (5 propagules in an X) surrounded by varrying densities of natural bitter panicum. At each monitoring, we counted all bitter panicum stems within a 1m radius of the transplant, all propogule stems, and measured the height of the tallest transplant in each plot. Treatments were binned based on the number of surrounding bitter panicum stems at the final monitoring point: “Zero”, containing no stems within the 1 m radius (n=20 plots); “Low”, 1-9 stems in the surrounding 1 m radius (n=46 plots); “Mid” 10-34 stems in the 1 m radius (n=29 plots); and “High”, 35-70 stems in the 1m radius (n=25 plots). See manuscript for additional details.</p> <p>Datasets contains stem counts and heights of planted grasses over time. Stem counts are provided for each of the 5 propagules that comprise a single experimental unit, and a single maximum height is provided per plot. Plots were monitored approximately bimonthly until grasses were eroded by waves (see manuscript). Additional information on treatments are provided in the manuscript and data methods sections.</p>
Fig. 4 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 4 — Extent of damages documented off the Nagapattinam coast, Tamil Nadu
Fig. 3 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 3 — Cyclone shelters in the villages showing distance to access
Fig. 6 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 6 — Periodicity and usage frequency of the weather forecast
Fig. 5 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 5 — Percentage variations in respondents using different source of forecast
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