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348 results for “seagrass”
Long-Term Trends in Seagrass Metabolism Data from Figures Covering 2007-2018
This dataset contains the data used to create figures 2, 4 and 5 in: Berger, A.C., Berg, P., McGlathery, K.J. and Delgard, M.L. (2020), Long-term trends and resilience of seagrass metabolism: A decadal aquatic eddy covariance study. Limnol Oceanogr, 65: 1423-1438. https://doi.org/10.1002/lno.11397
Mean Seagrass Meadow Metabolism by Time of Day in Virginia, USA
The study examined benthic O2 fluxes over a seagrass meadow using the aquatic eddy covariance technique, while measuring variables that have been shown to affect metabolism (PAR, temperature, O2 concentration, current velocity). Accurate daily metabolic estimates of respiration, gross primary production, and net ecosystem metabolism are widely used to assess ecosystem health and blue carbon sequestration and storage in vegetated coastal ecosystems. Aquatic eddy covariance allows direct measurements under naturally varying in situ conditions of oxygen (O2) fluxes between a benthic substrate and the water above. Here, we used hourly O2 fluxes measured with this approach to examine how respiration for a Zostera marina seagrass meadow varies through night and day, and how this affects commonly performed metabolic estimates. Fitting our database of 2,115 hourly benthic O2 fluxes for a seagrass meadow revealed that respiration decreased linearly by 29% through the night. We primarily attribute this to consumption of highly labile compounds that are formed by photosynthesis and accumulate during daytime. Furthermore, a corresponding linear increase in respiration through the day coupled with photosynthetic production described by a standard photosynthesis irradiance curve provided an accurate prediction of measured O2 fluxes (R2 = 0.993). These results document that night- and daytime respiration vary significantly in a seagrass meadow, and that both can be described accurately by a piecewise linear relationship. Many studies have questioned the widely used assumption in metabolic estimates that night- and daytime respiration are constant and equal. However, if night- and daytime respiration can be approximated as we found here by piecewise linear relationships, these standard means for calculating daily metabolic numbers remain valid. It is, however, important that such estimates are based on full 24-h records of benthic flux data.
Hydrodynamic, sediment, and bivalve data from seagrass edges in South Bay, VA, 2021 to 2022
The northern edge of the South Bay seagrass meadow was studied for two years to quantify flow characteristics, sediment movement, and bivalve abundance. ADCPs (Aquadopp, Vector, Vectrino) and wave gauges were used to measure hydrodynamic conditions, sediment sensors and sediment traps were used to measure sediment movement, and sediment cores were used to measure bivalve abundance. Data were collected across seagrass edges in vegetated and unvegetated locations, or along transects spanning the natural edge of meadow vegetation. Manmade bare patches were also created in the study area to collect data along patch edges. Study sites 1 and 2 were approximately 100m apart along the northern edge of the seagrass meadow. A PDF figure describing the locations is included as Site_Figure.pdf along with the data tables.
Literature survey of seagrass disturbance-recovery studies up to May 2022
To provide context for the seagrass recovery experiment (SRE) a literature synthesis was conducted on prior studies of seagrass disturbance and recovery. The synthesis provided evidence that 1) less than half (47%) of all seagrass species have been included, 2) were primarily on monospecific meadows of Zostera, 3) where experimental, mostly done on small spatial scales (median = 0.25 m2), and 4) that there is a positive, non-linear relationship between recovery time and disturbance area. Most meadows had some, if not full recovery during the study period suggesting that seagrass meadows are stable across a broad set of conditions.
Sediment elevation transects for the Seagrass Recovery Experiment, South Bay, VA 2022
To understand intra-meadow stability, the Seagrass Recovery Experiment was designed to ask 1) is recovery faster at sites with less thermal stress owing to greater exchange with cooler oceanic water at the meadow edge? 2) what is the shape of recovery? and 3) what are the recovery mechanisms? To conduct this experiment, aboveground seagrass biomass was removed from 28.3 m2 plots within the interior and along an edge of a restored seagrass meadow in South Bay, VA. Sites 1-3 correspond to the meadow interior while sites 4-6 correspond to the northern meadow edge. Each site was comprised of a control (i.e., C) where no seagrass was disturbed and a treatment (i.e., T) where seagrass was removed (n = 12 sites total, e.g., 1C, 1T, 2C...). A nor'easter storm moved through the area in early May 2022 producing 59% and 48% of the year's total Gale and Near Gale force winds. After the storm passed, depressions were noticed within the edge treatment sites. To quantify the depression depths a survey of the sediment surface depth was coordinated among all sites in October 2022. Results provide evidence that the edge treatment sites were depressed by 9.4-10.4 cm relative to outside of the treatment plots.
BWILD: Beach seagrass Wrack Identification Labelled Dataset
<h1>Training dataset</h1> <p>BWILD is a dataset tailored to train Artificial Intelligence applications to automate beach seagrass wrack detection in RGB images. It includes oblique RGB images captured by SIRENA beach video-monitoring systems, along with corresponding annotations, auxiliary data and a README file. BWILD encompasses data from two microtidal sandy beaches in the Balearic Islands, Spain. The dataset consists of images with varying fields of view (9 cameras), beach wrack abundance, degrees of occupation, and diverse meteoceanic and lighting conditions. The annotations categorise image pixels into five classes: i) Landwards, ii) Seawards, iii) Diffuse wrack, iv) Intermediate wrack, and v) Dense wrack.</p> <h1>Technical details</h1> <p>The BWILD version 1.1.0 is packaged in a compressed file (BWILD_v1.1.0.zip). A total of 3286 RGB images are shared in PNG format, corresponding annotations and masks in various formats (PNG, XML, JSON,TXT), and the README file in PDF format.</p> <h2>Data preprocessing</h2> <p>The BWILD dataset utilizes snapshot images from two SIRENA beach video-monitoring systems. To facilitate annotation while maintaining a diverse range of scenarios, the original 1280x960 pixel images were cropped to smaller regions, with a uniform resolution of 640x480 pixels. A subset of images was carefully curated to minimize annotation workload while ensuring representation of various time periods, distances to camera, and environmental conditions. Image selection involved filtering for quality, clustering for diversity, and prioritizing scenes containing beach seagrass wracks. Further details are available in the README file. </p> <h2>Data splitting</h2> <p>Data splitting requirements may vary depending on the chosen Artificial Intelligence approach (e.g., splitting by entire images or by image patches). Researchers should use a consistent method and document the approach and splits used in publications, enabling reproducible results and facilitating comparisons between studies. </p> <h2>Classes, labels and annotations</h2> <p>The BWILD dataset has been labelled manually using the 'Computer Vision Annotation Tool' (CVAT), categorising pixels into five labels of interest using polygon annotations.</p> <table> <tbody> <tr> <td><strong> Label</strong></td> <td><strong> Description</strong></td> </tr> <tr> <td>landwards</td> <td>Pixels that are towards the landside with respect to the shoreline</td> </tr> <tr> <td>seawards</td> <td>Pixels that are towards the seaside with respect to the shoreline</td> </tr> <tr> <td>diffuse wrack</td> <td>Pixels that potentially resembled beach wracks based on colour and shape, yet the annotator could not confirm this with certainty, were denoted as ‘diffuse wrack’</td> </tr> <tr> <td>Intermediate wrack</td> <td>Pixels with low-density beach wracks or mixed beach wracks and sand surfaces</td> </tr> <tr> <td>Dense wrack</td> <td>Pixels with high-density beach wracks</td> </tr> </tbody> </table> <p>Annotations were exported from CVAT in four different formats: (i) CVAT for images (XML); (ii) Segmentation Mask 1.0 (PNG); (iii) COCO (JSON); (iv) Ultralytics YOLO Segmentation 1.0 (TXT). These diverse annotation formats can be used for various applications including object detection and segmentation, and simplify the interaction with the dataset, making it more user-friendly. Further details are available in the README file. </p> <h2>Parameters</h2> <p>RGB values or any transformation in the colour space can be used as parameters.</p> <h2>Data sources</h2> <p>A SIRENA system consists of a set of RGB cameras mounted at the top of buildings on the beachfront. These cameras take oblique pictures of the beach, with overlapping sights, at 7.5 FPS during the first 10 minutes of each hour in daylight hours. From these pictures, different products are generated, including snapshots, which correspond to the frame of the video at the 5th minute. In the Balearic Islands, SIRENA stations are managed by the Balearic Islands Coastal Observing and Forecasting System (SOCIB), and are mounted at the top of hotels located in front of the coastline. The present dataset includes snapshots from the SIRENA systems operating since 2011 at Cala Millor (5 cameras) and Son Bou (4 cameras) beaches, located in Mallorca and Menorca islands (Balearic Islands, Spain), respectively. All latest and historical SIRENA images are available at the Beamon app viewer (https://apps.socib.es/beamon). </p> <h2>Data quality</h2> <p>All images included in BWILD have been supervised by the authors of the dataset. However, variable presence of beach segrass wracks across different beach segments and seasons impose a variable distribution of images across different SIRENA stations and cameras. Users of BWILD dataset must be aware of this variance. Further details are available in the README file. </p> <h2>Image resolution</h2> <p>The resolution of the images in BWILD is of 640x480 pixels.</p> <h2>Spatial coverage</h2> <p>The BWILD version 1.1.0 contains data from two SIRENA beach video-monitoring stations, encompassing two microtidal sandy beaches in the Balearic Islands, Spain. These are: Cala Millor (<em>clm</em>) and Son Bou (<em>snb</em>). </p> <table> <tbody> <tr> <td><strong>SIRENA station</strong></td> <td><strong> Longitude</strong></td> <td><strong> Latitude</strong></td> </tr> <tr> <td><em>clm</em></td> <td>3.383</td> <td>39.596</td> </tr> <tr> <td><em>snb</em></td> <td>4.077</td> <td>39.898</td> </tr> </tbody> </table> <h2>Contact information</h2> <p>For further technical inquiries or additional information about the annotated dataset, please contact jsoriano@socib.es.</p>
Indicative distribution map for Ecosystem Functional Group M1.1 Seagrass meadows
<p>This archive contains indicative distribution maps and profiles for <strong>M1.1 Seagrass meadows</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Seagrass morphometry and chlorophyll content at nine stations along a eutrophication gradient in West Falmouth Harbor, 2019
West Falmouth Harbor (West Falmouth, MA, USA) has been experiencing a dramatic increase in nitrogen loading from an upgradient municipal wastewater treatment facility since the early 2000’s. As part of a long-term study into the effects of this nitrogen enrichment, in 2019 we sampled seagrass (Zostera marina) to assess the relationship between sediment biogeochemistry and seagrass ecosystem parameters. This dataset contains information on aboveground and belowground biomass ratios, seagrass density, epiphyte biomass, and chlorophyll content from sites across the eelgrass bed in West Falmouth Harbor that receive varying inputs of nitrogen. The data supports findings reported in Haviland et al., 2022 (https://doi.org/10.1002/lno.12025).
Consumption rates of tethered live and dead pinfish and dried squid in mudflat and seagrass habitats in the Upper Laguna Madre in 2018.
These data were recorded during surveys of a tethering experiment conducted on June 7, 2018, during which 180 tethered prey were deployed in two habitat types (seagrass and mudflat) in the Upper Laguna Madre, Texas, USA (27.544006, -97.285912). In each habitat, the following prey types were deployed: squidpops (1 cm2 discs of dried squid (Duffy et al. 2015) attached to 5 cm tethers, n=50), live (n=20) and dead (n=20) pinfish (Lagodon rhomboides, 4 cm fork length, attached to 20 cm tethers) at midday in each habitat. The presence/absence of tethered prey on each stake was observed and recorded after 1 hour and 24 hours. The rate of decay (i.e., disappearance or consumption rate of tethered prey over time) was calculated as the slope of an exponential model fit to discrete observations of the presence/absence of tethered prey over time. Additional environmental variables (temperature, salinity) were also recorded at the site.
Percent cover, species richness, and canopy height data of seagrass communities in Shark Bay, Western Australia, with accompanying abiotic data, from October 2012 to July 2013
This dataset provides cover, canopy height, and species richness estimates for seagrass communities throughout Shark Bay, as well as ancilliary physical data. These data will be used to determine spatial patterns of seagrass loss and recovery, as well as recovery rates and succisional changes in community composition, if present.
Organic and inorganic data for soil cores from Brazil and Florida Bay seagrasses to support Howard et al 2018, CO2 released by carbonate sediment production in some coastal areas may offset the benefits of seagrass “Blue Carbon” storage, Limnology and Oceanography, DOI: 10.1002/lno.10621
Using piston corers, soils from Florida Bay and Brazilian seagrass meadows were collected to complete organic and inorganic carbon inventories for the top 1 m of soil. Instrumental analyses and loss on ignition at 500C were used to measure C content of downcore slices.
Data set for the article "Tides, topography, and seagrass cover controls on the spatial distribution of Pinna nobilis on a coastal lagoon tidal flat"
<p>Data set includes: coordinates of the GNSS points (reference system WGS84 UTM33N); density of P. nobilis and cover of C.nodosa detected in the orthophoto in the 25m<sup>2</sup> cells; tidal levels measured (and, for comparison, simulated with the hydrodynamic model) corrected with respect to the IGM datum; number of emersions and flood duration for different levels of the tidal flat; statistics. The first Excel sheet includes a detailed description of the data.</p>
Data for: Seasonal dynamics of faunal diversity and population ecology in an estuarine seagrass bed
<p>These are the data used in the analyses described in the paper titled "Seasonal dynamics of faunal diversity and population ecology in an estuarine seagrass bed", accepted at Estuaries and Coasts. We acknowledge the tangata whenua for the rohe in which these data were collected, Ngāi Tārewa and Ngāti Īrakehu. We thank the Akaroa Taiāpure for their support of this research.</p> <p>The data included are:</p> <p>Raw count data of taxa for each tow, associated with additional metadata including the date of collection, tow coordinates, and estimated seagrass cover (MonthlyRawSampling_Duvauchelle_2020.csv). This data was put through cleaning steps outlined in the file docs/dataCleaning.Rmd prior to being used in any analyses.</p> <p>The cleaned community composition data (cleanedCommunity.csv), output from <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/dataCleaning.Rmd">docs/dataCleaning.Rmd</a> and used in the downstream community and population analyses.</p> <p>The GPS coordinates for the tows (gpsdat.csv). These were extracted from the raw data in the data cleaning process.</p> <p>NZsyngnathids_measurements.csv contains the measurements of the pipefish from images. These data also underwent a cleaning process documented in <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/dataCleaning.Rmd">docs/dataCleaning.Rmd</a>.</p> <p>The cleaned pipefish trait data (pipefishTraits.csv), output from docs/dataCleaning.Rmd and used in the downstream population analysis documented in <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/populationAnalyses.Rmd">docs/populationAnalyses.Rmd</a>.<br> </p>
Grand Bay National Estuarine Research Reserve Seagrass Survey Data (2005 - 2010)
<p>Seagrass beds at the Grand Bay National Estuarine Research Reserve were surveyed using a transect method, twice a year at five sites.</p> <p>Details of the transect location and methods are described in the following:</p> <p>https://www.jstor.org/stable/26367667</p>
Effect of Low Irradiance on Seagrass from Rødsand lagoon (DK)
<p>This dataset provides the raw and postprocessed data from laboratory experiments investigating the effect of reduced light irradiance on the health status and biomechanical properties of seagrass species Zostera marina collected in Rødsand lagoon (DK). The experiments were conducted as part of Hydralab+ and were completed at Loughborough University. Plants used in experiments were supplied by DHI.</p> <p>During experiments seagrass health status was monitored with a chlorophyll fluorometer. The morphological properties of seagrass blades were measured using rulers, Vernier scales and a scale, and their flexural rigidity was measured via cantilever tests.</p>
ARTICLE DATASET - ANTHROPOGENIC MICROPARTICLES ACCUMULATION IN SMALL-BODIED SEAGRASS MEADOWS: THE CASE OF TROPICAL ESTUARINE SPECIES IN BRAZIL
<p>This dataset with the data analysis script refers to the publication <a href="https://www.sciencedirect.com/science/article/abs/pii/S0025326X24007768?via%3Dihub"><strong>https://doi.org/10.1016/j.marpolbul.2024.116799</strong></a>.</p> <p>The dataset consists of a spreadsheet containing 9 tabs with survey data described below and their respective captions, found in the first line of each tab.</p> <p>The script for statistical analysis in the R language contains descriptive and statistical analyses, in addition to the functions for creating the graphs displayed in the article.</p> <p><strong>Description of the spreadsheet dataset tabs --------------------------------------------------------------------------------</strong></p> <ol> <li>description - Contains general information about the article</li> <li>meadows - Contains properties related to the characteristics of the multispecific grassland.</li> <li>ap_samples - Contains properties related to the abundances of anthropogenic microparticles, as to classifications by shape, size (mm) and color in units, kg and frequency of occurrence.</li> <li>ap_categories - Contains raw data related to the classifications of anthropogenic microparticles, in terms of shape, size (mm) and color.</li> <li>sediment - Contains raw data related to the classifications of sediment particles.</li> <li>granulometry - Contains properties related to sediment particles in µm, and their classifications by predominance, frequency and kg.</li> <li>shape - Contains raw data related to the classification of the shapes of anthropogenic microparticles in units and kg.</li> <li>size - Contains raw data related to the classification of the sizes of anthropogenic microparticles in units and kg.</li> <li>color - Contains raw data related to the classification of the colors of anthropogenic microparticles in units and kg.</li> </ol>
Data from: Microplastic and organic carbon storage in sediments of intertidal and subtidal seagrass meadows
<p>This datasets support the scientific article (submitted) " Microplastic and organic carbon storage in sediments of intertidal and subtidal seagrass meadows." It contains detailed data on sedimentary organic carbon content and the abundance of microplastics in both intertidal and subtidal seagrass meadows within the Ria Formosa lagoon (Southern Portugal). The datasets are accompanied by analysis code, available at GitHub repository, allowing for reproducibility and further exploration of the data.</p> <p>The data is composed by 4 datasets with the following variables:</p> <p><strong>data_cores.csv. </strong>Contains properties related to the sampling of the sediment cores.</p> <ul> <li>core_id [character] - unique core identification code used in the field.</li> <li>core_id_new [character] - unique core identification code used in the article.</li> <li>species [character] - species of the seagrass meadow.</li> <li>replicate [character] - replicate number of the core in each seagrass meadow.</li> <li>core_depth [numeric] - depth sampled with the core (in centimeters).</li> <li>sample_length [numeric] - length of the sampled core measured in the laboratory (in centimeters).</li> <li>compaction_factor [numeric] - fraction of the sample depth interval reduced due to compaction. It is calculated by dividing the core length by the core depth.</li> <li>compaction_perc [numeric] - core compaction in percentage (%). It is calculated as 100*(1 - compaction_factor).</li> </ul> <p><br><strong>data_samples.csv. </strong>Contains properties of the sediment samples.</p> <ul> <li>core_id [character] - unique core identification code used in the field.</li> <li>sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample).</li> <li>depth_middle [numeric] - middle depth of a sampling increment, calculating as the average of depth_min and depth_max (in centimeters).</li> <li>depth_min [numeric] - minimum depth of a sampling increment, corrected for compaction (in centimeters).</li> <li>depth_max [numeric] - maximum depth of a sampling increment, corrected for compaction (in centimeters).</li> <li>sample_volume [numeric] - volume of the sediment sample, corrected for compaction (in cubic centimeters).</li> <li>sample_dw [numeric] - dry mass of the sample (in grams of dry weight).</li> <li>percentage_organic_matter [numeric] - mass of organic matter relative to sample dry mass, obtained by loss-on-ignition (in percentage of dry weight).</li> <li>percentage_organic_carbon [numeric] - mass of organic carbon relative to sample dry mass, obtained by a local organic carbon to organic carbon ratio (as a percentage of dry weight).</li> <li>bag_id [character] - identification code for the aluminium envelope containing the sample.</li> <li>weight_sample_mp [numeric] - dry mass of the sample used for the microplastic extraction (in grams of dry weight).</li> <li>dry_bulk_density [numeric] - dry mass per unit volume of the sample. This is calculated as the sample_dw divided by the sample_dw (in grams of dry weight per cubic centimeter). </li> </ul> <p><br><strong>data_particles_visual.csv. </strong>Contains properties of the suspected microplastic particles found in the sediment samples and the negative controls, based on visual inspection.</p> <ul> <li>core_id [character] - unique core identification code used in the field.</li> <li>core_id_new [character] - unique core identification code used in the article.</li> <li>species [character] - species of the seagrass meadow.</li> <li>habitat_label [character] - text for labelling purposes regarding the habitat.</li> <li>type [factor] - whether the particle comes from a sediment sample ("sediment") or a control sample ("control"). </li> <li>cycle [character] - cycle in which samples were analysed.</li> <li>sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample).</li> <li>bag_id [character] - identification code for the aluminium envelope containing the sample.</li> <li>filter_id [character] - identification code of the filter used for the particle extraction.</li> <li>filter_area [numeric] - area of the filter that was screened for microplastics (in fraction of total).</li> <li>visual_id [character] - unique particle identification code based on visual identification.</li> <li>colour [factor] - particle colour category: black_grey, blue_green, brown_tan, opaque, orange_pink_red, transparent, white_cream, yellow.</li> <li>shape [factor] - particle shape category: film, foam, fragment, line, pellet.</li> <li>major [numeric] - longest dimension of the particle, analysed in ImageJ (in micrometers).</li> <li>minor [numeric] - Longest dimension perpendicular to major, analysed in ImageJ (in micrometers).</li> </ul> <p><br><strong>data_particles_ftir.csv. </strong>Contains properties of the suspected microplastic particles found in the sediment samples and the negative controls, based on the FTIR analysis.</p> <ul> <li>species [character] - species of the seagrass meadow.</li> <li>habitat_label [character] - text for labelling purposes regarding the habitat.</li> <li>core_id [character] - unique core identification code used in the field.</li> <li>core_id_new [character] - unique core identification code used in the article.</li> <li>filter_id [character] - identification code of the filter used for the particle extraction.</li> <li>filter_area [numeric] - area of the filter that was screened for microplastics (in fraction of total).</li> <li>cycle [character] - cycle in which samples were analysed.</li> <li>type [factor] - whether the particle comes from a sediment sample ("sediment") or a control sample ("control"). </li> <li>sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample).</li> <li>num_ftir [numeric] - numerical order in which particles were identified within a filter.</li> <li>colour [factor] - particle colour category: black_grey, blue_green, brown_tan, opaque, orange_pink_red, transparent, white_cream, yellow.</li> <li>shape [factor] - particle shape category: film, foam, fragment, line, pellet.</li> <li>ref_analysis [boolean] - whether the reflection analysis was preformed or not.</li> <li>atr_analysis [boolean] - whether the ATR analysis was preformed or not.</li> <li>ftir_match_ref [character] - name of the polymer with the highest match found using µFTIR for reflection analysis.</li> <li>match_ref [numeric] - percentage of match corresponding to highest match for reflection analysis.</li> <li>ftir_match_atr [character] - name of the polymer with the highest match found using µFTIR for ATR analysis.</li> <li>match_atr [numeric] - percentage of match corresponding to highest match for ATR analysis.</li> <li>plastic_ref [boolean] - whether the particle is classified as having a plastic composition or not, based on the reflection analysis.</li> <li>polymer_group_ref [factor] - polymer group based on the reflection analysis: Non-plastic, Nylon-polyamides, Poluacrylamides, Polyacrylates, Polyesters, Polyethylene, Polyglycols, Polyhaloolefins, Polymethylmethacrylate, Polypropylene, Polystyrene, Polyurethane, Polyvinylalcohol, Silicones, Others.</li> <li>plastic_atr [boolean] - whether the particle is classified as having a plastic composition or not, based on the ATR analysis.</li> <li>polymer_group_atr [factor] - polymer group based on the ATR analysis: Non-plastic, Nylon-polyamides, Poluacrylamides, Polyacrylates, Polyesters, Polyethylene, Polyglycols, Polyhaloolefins, Polymethylmethacrylate, Polypropylene, Polystyrene, Polyurethane, Polyvinylalcohol, Silicones, Others.</li> <li>ftir_match_final [character] - final decision on the polymer composition, including the option "unclear".</li> <li>plastic_final [factor] - whether the particle is classified as having a plastic composition or not, based on final decision "ftir_match_final", includes categories: yes, no, unclear.</li> <li>final_analysis [character] - the analysis performed and used for the final decision, includes categories: ref (reflection analysis), atr (ATR analysis), both-but-atr-more-conclusive, both-but-ref-more-conclusive, both-unclear.</li> <li>polymer_group_final [character] - polymer group based final decision: Non-plastic, Nylon-polyamides, Poluacrylamides, Polyacrylates, Polyesters, Polyethylene, Polyglycols, Polyhaloolefins, Polymethylmethacrylate, Polypropylene, Polystyrene, Polyurethane, Polyvinylalcohol, Silicones, Others.</li> </ul>
Data from: Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and extent
<p>These datasets support the scientific article "Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and area extent" published in 2025 (Journal of Sea Research, 205, 102580; <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.seares.2025.102580" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.seares.2025.102580</a>). It contains detailed data on the distribution and area extent of intertidal and subtidal seagrass meadows in the four main wetlands of the Algarve region (Southern Portugal): Ria de Alvor, Arade Estuary, Ria Formosa, Guadiana Estuary.</p> <p>The data is composed by 5 datasets with the following variables:</p> <p><strong>1) data_field_points.csv</strong></p> <p>Contains data points based on field surveys.</p> <ul> <li>dataset_id [character] - Unique identifier for the data set.</li> <li>data_id [character] - Unique identifier for the data point.</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>quadrat_id [character] - Name of the quadrat as recorded in the field.</li> <li>photo_id [character] - Name of the pictured associated to the observation.</li> <li>sampling_id - Name of the observation as recorded in the field.</li> <li>date [date] - Date of observation (YYYY-MM-DD).</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>habitat_class [factor] - Type of habitat: unvegetated, seagrass intertidal, seagrass subtidal, seagrass unknown, salt marsh low, caulerpa.</li> <li>species [factor] - Vegetation species: No vegetation, <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified species, <em>Caulerpa prolifera</em>, <em>Sporobolus maritimus</em>.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> <li>method [factor] - Method used for the observation: boat and camera, boat and snorkelling, kayak and camera, on foot.</li> <li>survey_area [character] - Number of the survey area.</li> <li>site [character] - Name of the site.</li> <li>observers [character] - Name of the researcher(s) who collected the data.</li> </ul> <p> </p> <p><strong>2) data_compilation_records.csv</strong></p> <p>Contains information on the records (i.e. sources) screened during the systematic review for the compilation od seagrass occurrence data.</p> <ul> <li>record_id [character] - unique id for the compiled records.</li> <li>short_citation [character] - short citation of the record, with author and publication year.</li> <li>included [boolean] - whereas the record was used to extract data or informacion.</li> <li>record_type [factor] - type of record: journal article, book or book chapter, PhD or MSc thesis, report, others.</li> <li>publication_year [integer] - year of the publication of the record, YYYY.</li> <li>title_record [character] - title of the record.</li> <li>link [character] - link to access the record, if available (DOI, handle, others URLs).</li> <li>full_citation [character] - full citation of the record, with authors, publication year, title, etc.</li> </ul> <p> </p> <p><strong>3) data_compilation_points_raw.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the original raw file with all the compiled points.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_clean.csv).</li> <li>included [boolean] - whether the data point was kept in the clean dataset or not: 1, the point has been validated and it is included in the final dataset; 0, the point is excluded due to unprecise location (on land, open ocean, etc.).</li> <li>reason_exclusion [character] - Reason to exclude the data point from the clean dataset.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> <li>duplicated [boolean] - whether the data point was flagged as duplicated or not.</li> </ul> <p> </p> <p><strong>4) data_compilation_points_clean.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the clean file after elimitating duplicates and points with unprobable or unprecise location.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_raw.csv).</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> </ul> <p> </p> <p><strong>5) data_compilation_extent.csv</strong></p> <p>Contains area extent data of seagrass meadows based on the systematic review.</p> <ul> <li>data_id [character] - Unique identifier for the data.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted.</li> <li>short_citation [character] - Short reference (author(s) and year).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>value [boolean] - whether the data extracted from the record is a extent value (i.e., a value of area covered by seagrasses).</li> <li>polygon [boolean] - whether the data extracted from the record is a polygon.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>area_source [numeric] - The area extent given in the record.</li> <li>area_source_cover [factor] - The cover of the wetland for the compiled extent from the record means: total, partial or unknown.</li> <li>area_gis [numeric] - The area extent obtained using GIS.</li> <li>area_gis_cover [factor] - The cover of the wetland for the obtained extent from GIS means: total, partial or unknown.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> </ul>
Combined exposure to CO2 and H2S significantly reduces the performance of the Mediterranean seagrass Posidonia oceanica: evidence from a volcanic CO2 vent
<p>The dataset is an excel file consisting of six sheets. The associated metadata file contains the description and other information about the dataset</p>
Measurements of ethylene production (using the acetylene reduction assay) as a proxy for nitrogen fixation of epiphytes on seagrass in West Falmouth Harbor during July from 2005 through 2019.
West Falmouth Harbor (West Falmouth, MA, USA) has been experiencing a dramatic increase in nitrogen loading from an upgradient municipal wastewater treatment facility since the early 2000’s. As part of a long-term study into the effects of this nitrogen enrichment, we have measured nitrogen fixation rates of seagrass-associated epiphytes using the acetylene reduction technique. Samples were taken annually in July at two sites, one in the well-flushed outer basin (OH) and one in the inner basin closer to the dominant groundwater N source (Snug Harbor, SH). Additional data are presented in 2019 at 18 sites spatially distributed through the seagrass bed to assess spatial heterogeneity. Individual replicate data are presented. These data are in support of a manuscript submitted to the journal Biogeochemistry by Marino et al, submitted for publication (12/2022).
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