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48 results for “seagrass meadows”
Algae, and Seagrass Coverage Relative to Presence of Sargassum in a Seagrass Meadow within Crandon Park, Florida, USA, March 2024 – March 2025
This package contains data on percent coverage of sargassum, seagrass, and algae recorded along 3 transects extending from the shoreline of Crandon Park, Florida from 2024-03-07 to 2025-03-26 as part of the Coastal Ecosystem-Research Experience for Teachers (CE-BIORETS) program. The data also include epiphyte-cover scores and water visibility, temperature, and salinity values. We placed a m^2 quadrat on the seafloor at 5-meter intervals along three 50-meter transects extending from the shoreline. We then recorded percent coverage of seagrass, algae, and sargassum taxa (to genus or species) and the epiphyte load within the quadrat. We also recorded water visibility, temperature, and salinity at the water surface along the transect. We collected these data to investigate the impact sargassum has on native seagrass meadows. Data collection for this project is complete
Thalassia leaf morphology and productivity measurements from arbitrary plots located in a Thalassia seagrass meadow in Rabbit Key Basin, Florida Bay (FCE) from March 2000 to April 2001
Thalassia leaf morphology and productivity were measured from six arbitrary 200 cm2 plots within a Thalassia seagrass meadow in Rabbit Key Basin, Florida Bay.
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.
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>
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>
Oxygen Flux in a Restored Seagrass Meadow on the Coast of Virginia, 2011-2012
Oxygen flux was measured seasonally using the eddy correlation technique in a restored eelgrass (Zostera marina L.) meadow in the Virginia coastal bays (USA). In 5 intensive field campaigns, we covered seasonal variation in oxygen metabolism and biomass with overlap in late summer to observe interannual variability.
Dataset from: Tolerance to aerial exposure influences distributional patterns in multi-species intertidal seagrass meadows
<p>This is the dataset for an article published in Marine Environmental Research titled, 'Tolerance to aerial exposure influences distributional patterns in multi-species intertidal seagrass meadows', in October 2023. Following is the abstract for the paper for which this was the primary data:</p><p>Multi-specific seagrass meadow assemblages dominate most tropical intertidal regions but the relative role of environmental stress in determining distribution patterns is still uncertain. Here we combine observational and experimental approaches to examine aerial exposure as a factor driving species occurrence patterns in intertidal meadows of the Andaman archipelago, where up to 6 seagrass species co-occur. In the studied meadow, patterns of exposure did not map onto distance from the coast, instead creating a patchy matrix of exposure, based on fine-scale bathymetric differences. Distributional surveys showed that seagrass species were similarly patchy, often tracking the degree of aerial exposure during low tide. While some species (<i>Halophila ovalis, Halophila minor,</i> and <i>Thalassia hemprichii</i>) frequently occurred in submerged or subtidal areas and were rarely found in completely exposed areas, other species (<i>Cymodocea rotundata</i>, <i>Halophila beccarii,</i> and <i>Halodule uninervis</i>) also occupied areas that were subject to partial or complete aerial exposure during low tide. To confirm this pattern, we used field-based transplant experiments, employing a natural gradient of tidal exposure to subject six seagrass species to different desiccation exposure times. After a month, <i>H. beccarii</i> and <i>H. uninervis</i> transplants survived in areas that sustained more than 3 h of aerial tidal exposure without significant mortality, compared with other species (<i>H. ovalis, H. minor, T. hemprichii, C. rotundata</i>) that showed dramatic shoot mortality at the same exposure regimes. For all species, 4 h represented the upper limit of exposure, in both experimental and distributional studies. However, despite their wider tolerance of exposure to air, <i>H. beccarii</i> and <i>H. uninervis</i> did not dominate the entire meadow. This could be a result either of their poor tolerance to other environmental factors or their lower competitive abilities among other mechanisms. This suggests that in tropical multi-specific meadows, strong environmental filters could override clear intertidal zonation to create patchy matrices based on species tolerances.</p>
Dataset for "Structural complexity and benthic metabolism: resolving the links between carbon cycling and biodiversity in restored seagrass meadows"
<p>This dataset accompanies the article "Structural complexity and benthic metabolism: resolving the links between carbon cycling and biodiversity in restored seagrass meadows" accepted for publication in Biogeosciences (https://doi.org/10.5194/bg-2023-173). The dataset includes benthic fluxes and biodiversity data in from bare sediments, restored <em>Zostera marina</em> and a natural <em>Z. marina</em> meadow collected in Gåsö, Sweden (58.2325, 11.3984) between July 05 - July 20, 2022. </p>
Fig. 2 in Composition and abundance of decapod crustaceans in mixed seagrass meadows in the Paraguaná Peninsula, Venezuela
Fig. 2. Non-metric multidimensional scaling ordination (NMDS) for the abundance of decapods associated with two mixed seagrass meadows seagrass meadows at the Paraguaná Peninsula, FalcÓn State, Venezuela.
Fig. 1 in Composition and abundance of decapod crustaceans in mixed seagrass meadows in the Paraguaná Peninsula, Venezuela
Fig. 1. Location of the sampling sites at the Paraguaná Peninsula, FalcÓn State, Venezuela (, El SupÍ; ▲, AdÍcora).
Data from: Sedimentary organic carbon and nitrogen sequestration across a vertical gradient on a temperate wetland seascape including salt marshes, seagrass meadows and rhizophytic macroalgae beds
<p>Dataset </p> <p> </p> <p>Coastal wetlands are key in regulating coastal carbon and nitrogen dynamics and contribute significantly to climate change mitigation and anthropogenic nutrient reduction. We investigated organic carbon (OC) and total nitrogen (TN) stocks and burial rates at four adjacent vegetated coastal habitats across the seascape elevation gradient of Cádiz Bay (South Spain), including one species of salt marsh, two of seagrasses, and a macroalgae. OC and TN stocks in the upper 1 m sediment layer were higher at the subtidal seagrass <em>Cymodocea nodosa</em> (72.3 Mg OC ha<sup>-1</sup>, 8.6 Mg TN ha<sup>-1</sup>) followed by the upper intertidal salt marsh <em>Sporobolus maritimus</em> (66.5 Mg OC ha<sup>-1</sup>, 5.9 Mg TN ha<sup>-1</sup>), the subtidal rhizophytic macroalgae <em>Caulerpa prolifera</em> (62.2 Mg OC ha<sup>-1</sup>, 7.2 Mg TN ha<sup>-1</sup>), and the lower intertidal seagrass <em>Zostera noltei</em> (52.8 Mg OC ha<sup>-1</sup>, 5.2 Mg TN ha<sup>-1</sup>). The sedimentation rates increased from lower to higher elevation, from the intertidal salt marsh (0.24 g cm<sup>-2</sup> yr<sup>-1</sup>) to the subtidal macroalgae (0.12 g cm<sup>-2</sup> yr<sup>-1</sup>). The organic carbon burial rate was highest at the intertidal salt marsh<em> </em>(91 ± 31 g OC m<sup>-2</sup> yr<sup>-1</sup>), followed by the intertidal seagrass, (44 ± 15 g OC m<sup>-2</sup> yr<sup>-1</sup>), the subtidal seagrass (39 ± 6 g OC m<sup>-2</sup> yr<sup>-1</sup>), and the subtidal macroalgae (28 ± 4 g OC m<sup>-2</sup> yr<sup>-1</sup>). Total nitrogen burial rates were similar among the three lower vegetation types, ranging from 5 ± 2 to 3 ± 1 g TN m<sup>-2</sup> yr<sup>-1</sup>, and peaked at <em>S. maritimus </em>salt marsh with 7 ± 1 g TN m<sup>-2</sup> yr<sup>-1</sup>. The contribution of allochthonous sources to the sedimentary organic matter also decreased with elevation, from 72% in <em>C. prolifera</em> to 33% at <em>S. maritimus</em>. Our results highlight the need of using habitat-specific OC and TN stocks and burial rates to improve our ability to predict OC and TN sequestration capacity of vegetated coastal habitats at the seascape level. We also demonstrated that the stocks and burial rates in <em>C. prolifera </em>habitats were within the range of well-accepted blue carbon ecosystems such as seagrass meadows and salt marshes.</p>
Analysis - Threatened North African seagrass meadows have supported green turtle populations for millennia
<p>Scripts and data to run bagplots and discriminant analysis associated with the paper:</p> <p>Threatened North African seagrass meadows have supported green turtle populations for millennia, de Kock et al.</p>
Integration of environmental DNA metabarcoding technique to reinforce fish biodiversity assessments in seagrass ecosystems: A case study of Gazi Bay Seagrass meadows
<p><span>Assessing biodiversity in marine nearshore ecosystems is crucial for effective management, especially in the context of climate change and overexploitation of marine resources. Conventional methods often fall short in providing comprehensive information for managing seagrass ecosystems. However, the emergence of environmental DNA (eDNA) techniques has transformed the field by enabling non-invasive surveys that are cost-effective and provide detailed information with high resolution. In this study, we utilized eDNA to assess fish diversity and compared its effectiveness to conventional techniques such as catch assessment surveys and underwater surveys. </span>We sampled three habitats (A: mangrove-seagrass, B: seagrass only, and C: coral-seagrass) with 4 replicates. Site A recorded 8 fish species, site B had 16 species, and site C, characterized by coral and seagrass habitats, exhibited the highest fish diversity with 45 species (mean H' index = 2.455), underscoring its ecological importance. To ensure accurate taxonomic identification, we utilized an updated MiFish reference database containing a larger number of fish species compared to the initial library. This expanded reference database with 9,569 fish species, facilitated more precise identification and enhanced the reliability of our findings. Notably, the eDNA technique outperformed conventional methods by detecting 23 additional fish species that went undetected using traditional surveys. Moreover, our study documented five fish species previously unknown to occur within the study region, further emphasizing the value of eDNA analysis in uncovering hidden biodiversity. These findings strongly advocate for integrating eDNA techniques into the monitoring and assessment of biodiversity in shallow tropical habitats of the Western Indian Ocean. By leveraging eDNA surveys, we can gain valuable insights into fish diversity, discover hidden species, and make informed decisions for the conservation and management of these ecologically significant areas.</p>
Integration of environmental DNA metabarcoding technique to reinforce fish biodiversity assessments in seagrass ecosystems: A case study of Gazi Bay Seagrass meadows
Open the record for dataset details and reuse information.
Seagrass ecosystem metabolic carbon capture in response to green turtle grazing across Caribbean meadows, 2016 - 2018
This dataset contains ecosystem metabolism and seagrass meadow data from five locations in the Greater Caribbean and Gulf of Mexico regions at which green turtle populations had established foraging areas. Ecosystem metabolic rates were compared between grazed and adjacent ungrazed areas of seagrass (Thalassia testudinum) to investigate the effects of green turtle grazing on metabolic carbon capture rates in seagrass meadows across a wide geographic area. Seagrass data are provided for site descriptions and drivers of variation in metabolic rates. Ecosystem metabolic rates are also included for meadows of the invasive seagrass Halophila stipulacea from two locations for comparison to rates in the native seagrass meadows where this invasive seagrass is encroaching upon green turtle foraging areas. Data were collected from one location (Little Cayman) in 2016, and from the remaining four locations (Bonaire; St. Croix; Eleuthera, Bahamas; west coast of Florida) in 2018.
Sediment characteristics in experimental seagrass meadows in Virginia, 2012
Using a large-scale restoration (>1700 ha) in the Virginia coastal bays as a model system, we evaluated the role of seagrass, Zostera  marina, restoration in carbon storage in sediments of shallow coastal ecosystems. Sediments of replicate seagrass meadows representing different age treatments (as time since seeding: 0, 4, and 10 years), were analyzed for % carbon, % nitrogen, bulk density, and organic matter content at 1-cm increments to a depth of 10 cm.
Sediment characteristics in experimental seagrass meadows in Virginia, 2011
Using a large-scale restoration (>1700 ha) in the Virginia coastal bays as a model system, we evaluated the role of seagrass, Zostera  marina, restoration in carbon storage in sediments of shallow coastal ecosystems. Sediments of replicate seagrass meadows representing different age treatments (as time since seeding: 0, 4, and 10 years), were analyzed for % carbon, % nitrogen, bulk density, organic matter content, and ^210Pb for dating at 1-cm increments to a depth of 10 cm.
Dataset: Experimental carbon emissions from degraded Mediterranean seagrass (Posidonia oceanica) meadows under current and future summer temperatures.
<p> Experimental carbon emissions from degraded Mediterranean seagrass (<em>Posidonia oceanica</em>) meadows.</p> <p> </p> <p>Guillem Roca, Javier Palacios, Sergio Ruíz-Halpern, Núria Marbà</p> <p>Contact details: Guillem Roca, guillemrocac@gmail.com</p> <p>Issue date:</p> <p>Identifier:</p> <p> </p> <p>Citation: Roca, Guillem; Palacios, Javier; Ruíz-Halpern, Marbà, Núria;</p> <p>Experimental carbon emissions from degraded Mediterranean seagrass (<em>Posidonia oceanica</em>) meadows. [Dataset]</p> <p> </p> <p>Abstract: The dataset provides data on sediment C0<sub>2 </sub>efflux rates (μmol CO<sub>2 </sub>m<sup>-2 </sup>s<sup>-1</sup>), carbon emissions during the experiment (gm<sup>-2</sup>), % Organic Carbon, Organic Matter content (g m<sup>-2</sup>) of the <em>Posidonia oceanica</em> seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions.</p> <p> </p> <p>Keywords: C0<sub>2 </sub>efflux rates, C0<sub>2</sub> emissions, Sediment, Seagrass, <em>Posidonia Oceanica</em>, experiment, temperature treatment, Sediment suspension Blue carbon, Organic Carbon.</p> <p> </p> <p>Description: The dataset contains data on sediment C0<sub>2 </sub>efflux rates, carbon emissions during the experiment (gm<sup>-2</sup>), % Organic Carbon, Organic Matter content of the <em>Posidonia oceanica</em> seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions. Sediments used in the experiment were extracted in October 2017 from the <em>P. Oceanic</em>a meadow of Pollença in Mallorca Island at six-meter depth Figure (1). Sediments were sampled in October 2017 using sediment cores (9 cm ID and 30cm long) and directly transported to the laboratory. Only the top 10 cm of the sediment cores were used since this fraction is the most susceptible to erosion. Living seagrass tissues (roots, rhizomes, and leaves) were removed and sediment was mixed and homogenized. 40ml of sediments were poured into glass containers of 750ml with 500ml of seawater. Finally, each recipient contained a sediment layer of approximately 1.1cm in each container. Containers were placed at five different temperature baths (26,27.5, 29, 30.5, 32 ºC) simulating summer temperatures in the bay (Garcias-Bonet et al., 2019) at different agitation regimes (agitation/repose) to simulate exposed and sheltered conditions.10 containers were sampled right after the experiment started to provide initial sediment conditions. Five containers per temperature and agitation treatment were removed 7, 21, 43, 67, and 98 days from the experiment start, to analyse sediment organic matter and CaCO<sub>3</sub> content. CO<sub>2</sub> incubations were run 5, 14, 56, and 91 days from the experiment start. Sampling times were distributed considering that organic matter remineralisation was likely to follow an exponential trend, including a rapid phase of loss of the more labile material followed by a slower loss of more recalcitrant substrates (Arndt et al., 2013). The experiment was run in the dark to avoid photosynthesis in an isothermal chamber at 21ºC.</p> <p> </p> <p><strong>Organic Carbon analysis</strong></p> <p>In each sampling time, organic matter content in sediments (OM %DW) was estimated as the percentage weight loss of dry sediment sample after combustion at 550ºC for 4 hours. Organic carbon (Corg) was calculated from OM content using the relation described in (Mazarrasa et al., 2017b)</p> <p> </p> <p>y = 0.29x – 0.64; (R2=0.98, p< 0.0001, n=60)</p> <p> </p> <p>OM and POC stocks along the experiment (mg OM ml-1 and mg POC ml-1) were estimated by multiplying the OM and POC (%DW) by the sediment dry weight (mg) remaining in each experimental unit and standardized to the initial volume of sediment (40 ml) introduced in every glass container. Inorganic carbon was estimated as the percentage weight loss of already combusted sediment (550ºC) after combustion at 1000ºC.</p> <p> </p> <p><strong>Sediment CO<sub>2</sub> production</strong></p> <p>Container headspace CO<sub>2</sub> gas concentration was measured during 20 minutes continuum incubations (4 replicates) in each temperature and agitation treatment in all sampling times. CO<sub>2</sub> air concentration measures were carried out using an Infra Red Gas Analyser EGM4 from PPSystems. Concentration of dissolved CO<sub>2</sub> in seawater (in μmol CO<sub>2</sub> L<sup>−1</sup>) was calculated from the concentration of CO<sub>2</sub> (in ppm) measured in headspace air samples after equilibration as described in (Garcias-Bonet and Duarte, 2017; Wilson et al., 2012). Briefly, we calculate the dissolved CO<sub>2</sub> remaining in seawater after equilibration with the air phase ([CO<sub>2</sub>]SW−eq) by,</p> <p> </p> <p>[CO<sub>2</sub>]SW−eq = 10−6 β [C CO<sub>2</sub>]Air P</p> <p> </p> <p>where β is the Bunsen solubility coefficient of CO<sub>2</sub>, calculated according to Wiesenburg and Guinasso (1979), as a function of seawater temperature and salinity; [CO<sub>2</sub>]Air is the CO<sub>2</sub> concentration measured in containers headspace air (in ppm) and P is the atmospheric pressure (in atm) of dry air that was corrected by the effect of multiple sampling applying Boyle’s Law. Then, the initial CO<sub>2</sub> concentration in seawater before the equilibrium ([CO<sub>2]SW</sub>−before eq) was calculated (in ml CO<sub>2</sub> /ml H<sub>2</sub>O) by,</p> <p> </p> <p>[CO<sub>2</sub>]<sub>SW−before eq</sub> = ([CH<sub>4</sub>]<sub>SW−eq</sub> V<sub>Sw</sub> + 10−6 ([CO<sub>2</sub>]Air −[CO<sub>2</sub>]<sub>Air background</sub>) V<sub>Air</sub>)/V<sub>SW</sub></p> <p> </p> <p>Where V<sub>Sw</sub> is the volume of seawater in the core or in the seawater closed circuit, [CO<sub>2</sub>]<sub>Air background</sub> is the atmospheric CO<sub>2</sub> background level and V<sub>Air</sub> is the volume of the headspace or the closed air circuit. Finally, the initial CO<sub>2</sub> concentration was transformed to µmol CH<sub>4</sub> L<sup>−1</sup> by applying the ideal gas law.</p> <p>CO<sub>2</sub> efflux values were calculated from CO<sub>2</sub> variation per time unit. Then, we converted the rates to aerial (taking in account container surface) base, and thickness (in μmol m<sup>-2 </sup>s<sup>-1</sup>).</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: An unintended ecological benefit from human intervention: The enhancement of carbon storage in seagrass meadows
<p>Human interventions have had unintended consequences for the diverse functions of various ecosystems. Hydrological interventions have the potential to alter vegetated habitats in coastal nearshore ecosystems, but little is known about the impacts on organic carbon (C<sub>org</sub> ) sequestration, which plays an important role in climate change mitigation.</p> <p>We examined the effects of past human interventions through artificial inlet opening in a lagoonal seagrass meadow. The sediment profiles of geological and biogeochemical characteristics were measured to obtain the historical changes in the lagoon environment and C<sub>org</sub> accumulation rates.</p> <p>Isotopic and elemental signatures and diatom assemblages showed that the interventions increased the duration of seawater exchanges and the extent of the seagrass meadow. C<sub>org</sub> accumulation rates increased more than 1.9-fold after the intervention, resulting in additional C<sub>org</sub> storage in the seagrass meadow during last 64 years.</p> <p><i>Synthesis and applications</i>. Artificial opening of tidal inlet can potentially provide unintended ecological benefits, such as increased carbon sequestration in seagrass meadows. Hydrological interventions could be undertaken intentionally as a potential active intervention to sequester carbon, as well as control flood risk and provide other benefits to people in estuarine systems. Our results illustrate that physical factors such as improving salinity conditions and mineral particle supply have an impact on the feasibility of hydrological interventions for enhancing carbon sequestration. We propose that biogeochemical-physical coupled approach will help stakeholders to evaluate the ecological and social risks and synergies of implementing hydrological interventions.</p>
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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.