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57 results for “Posidonia oceanica”
Posidonia oceanica meadows (1120) Velopoula
<p>This layer has derived from a habitat classification using satelite imagery and ground truthing data, in the context of REPOSIDONIA project. The project was implemented by iSea in collaboration with terraSolutions mer and funded by Argolic Environment Foundation. More detailed info on the product and appropriate citetation can be found here: https://doi.org/10.5281/zenodo.8127487</p>
Posidonia oceanica meadows (1120) Nisyros,Gyali and surrounding islands
<p>This layer has derived from a habitat classification using satelite imagery and ground truthing data, in the context of REPOSIDONIA project. The project was implemented by iSea in collaboration with MER and terraSolutions mer and funded by Blue Marine Foundation. More detailed info on the product and appropriate citation can be found here: https://doi.org/10.5281/zenodo.8127444</p>
Posidonia oceanica meadows (1120) Formicula island
<p>This layer has derived from a habitat classification using satelite imagery and ground truthing data, in the context of REPOSIDONIA project. The project was implemented by iSea in collaboration with MER and terraSolutions mer and funded by Blue Marine Foundation. More detailed info on the product and appropriate citation can be found here: https://doi.org/10.5281/zenodo.8127444</p>
Figure 1 in Patterns of spatial variability of mobile macro-invertebrate assemblages within a Posidonia oceanica meadow
Figure 1. Map of Pianosa Island. Stars indicate the study areas.
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>
Restoration efforts of the seagrass Posidonia oceanica: a collated evidence review dataset
<p><span>Seagrass meadows are important shallow coastal ecosystems due to their contribution to enhancing biodiversity, nutrient cycling, carbon burial, and sediment stabilisation, but the maintenance of their integrity has been threatened by several anthropogenic disturbances. Active restoration is considered a reliable strategy to enhance recovery of seagrass ecosystems, and decision making for correct seagrass restoration management requires relying on valuable information regarding the effectiveness of past restoration actions and experimental efforts. </span>Previous experimental efforts and human-mediated active restoration actions of the slow growing seagrass <em>Posidonia oceanica</em> have been collated here by combining a literature systematic review and questionnaires consulting seagrass ecology experts. Overall, the poor consistency of the available information on <em>P. oceanica</em> restoration may be due to the wide portfolio of practices and methodologies used in different conditions, that supports the need of further field manipulative experiments in various environmental contexts to fill the identified knowledge gaps. The current situation requires an international, collaborative effort from scientists and stakeholders to jointly design the future strategy forward in identifying the best practices that lead to efficient restorations of <em>P. oceanica</em> habitat and functioning.</p>
Data from: Signs of local adaptation by genetic selection and isolation promoted by extreme temperature and salinity in the Mediterranean seagrass Posidonia oceanica
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Restoration efforts of the seagrass Posidonia oceanica: a collated evidence review dataset
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Data from: A meta-analysis reveals a positive correlation between genetic diversity metrics and environmental status in the long-lived seagrass Posidonia oceanica
The seagrass Posidonia oceanica is a key engineering species structuring coastal marine systems throughout much of the Mediterranean basin. Its decline is of concern, leading to the search for short- and long-term indicators of seagrass health. Using ArcGIS maps from a recent, high-resolution (1–4 km) modelling study of 18 disturbance factors affecting coastal marine systems across the Mediterranean (Micheli et al. 2013, http://globalmarine.nceas.ucsb.edu/mediterranean/), we tested for correlations with genetic diversity metrics (allelic diversity, genotypic/clonal diversity and heterozygosity) in a meta-analysis of 56 meadows. Contrary to initial predictions, weak but significantly positive correlations were found for commercial shipping, organic pollution (pesticides) and cumulative impact. This counterintuitive finding suggests greater resistance and resilience of individuals with higher genetic and genotypic diversity under disturbance (at least for a time) and/or increased sexual reproduction under an intermediate disturbance model. We interpret the absence of low and medium levels of genetic variation at impacted locations as probable local extinctions of individuals that already exceeded their resistance capacity. Alternatively, high diversity at high-impact sites is likely a temporal artefact, reflecting the mismatch with pre-environmental impact conditions, especially because flowering and sexual recruitment are seldom observed. While genetic diversity metrics are a valuable tool for restoration and mitigation, caution must be exercised in the interpretation of correlative patterns as found in this study, because the exceptional longevity of individuals creates a temporal mismatch that may falsely suggest good meadow health status, while gradual deterioration of allelic diversity might go unnoticed.
Posidonia oceanica meadows (1120) Arkoudi island
<p>This layer has derived from a habitat classification using satellite imagery and ground truthing<br>data, in the context of "Protecting the Inner Ionian Archipelago and Formicula island" project.<br>The project was implemented by iSea and funded by Blue Marine Foundation the mapping was<br>produced in collaboration with terraSolutions mer. More detailed info on the product and appropriate citation can be found here:<br>https://zenodo.org/doi/10.5281/zenodo.12672634</p>
Fig. 5 in Specialized compounds across ontogeny in the seagrass Posidonia oceanica
Fig. 5. Map of the sampling area showing where plant material was collected (Cala Xinxell and Cala Comtesa).
Fig. 4 in Specialized compounds across ontogeny in the seagrass Posidonia oceanica
Fig. 4. Principal Component Analysis. Arrows represent the loading of the variables (table S3). Dots represent the score of each replicate on PC1 and PC2. Number of replicates from all tissues analyzed were 6 for young leaves from non-reproductive plants (L1), old leaves from non-reproductive plants (L3), inflorescences (F), and old leaves from reproductive plants (RL3), 10 replicates for leaves from seedlings (SD); and 5 for young leaves from reproductive plants (RL1). Compounds labelled as 1 (chicoric acid), 2 (caffeoylferuloyltartaric acid), 3 (p-coumaroylcaffeoyltartaric acid), 4 (coutaric acid) and 5 (di-p-coumaroyltartaric acid).
Fig. 3 in Specialized compounds across ontogeny in the seagrass Posidonia oceanica
Fig. 3. Mean content of chemical traits in leaves of seedlings (SD), young leaves (L1), old leaves (L3), inflorescences (F), and rhizomes of reproductive (R) and nonreproductive (N) plants. Compounds in mg chicoric acid equivalents/g DW of plant labelled as 1 (chicoric ac.), 2 (caffeoylferuloyltartaric acid), 3 (p-coumaroylcaffeoyltartaric acid), 4 (coutaric acid) and 5 (di-p-coumaroyltartaric acid). Error bars indicate standard error. Dots represent the value of each replicate (for F, L1, L3 n = 6 for SD n = 10). Mean values and standard errors can be found in Table S1.
Fig. 2 in Specialized compounds across ontogeny in the seagrass Posidonia oceanica
Fig. 2. Representative total ion current (TIC) UPLC-MS chromatogram of extracts of Posidonia oceanica samples. Numbers indicate the compound in each peak, IS indicates internal standard.
Data from: Adaptive responses along a depth and a latitudinal gradient in the endemic seagrass Posidonia oceanica
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Data from: A meta-analysis reveals a positive correlation between genetic diversity metrics and environmental status in the long-lived seagrass Posidonia oceanica
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Supplementary material 3 from: Gnisci V, Cognetti de Martiis S, Belmonte A, Micheli C, Piermattei V, Bonamano S, Marcelli M (2020) Assessment of the ecological structure of Posidonia oceanica (L.) Delile on the northern coast of Lazio, Italy (central Tyrrhenian, Mediterranean). Italian Botanist 9: 1-19. https://doi.org/10.3897/italianbotanist.9.46426
: Data type: statistical data
Figure 3 from: Gnisci V, Cognetti de Martiis S, Belmonte A, Micheli C, Piermattei V, Bonamano S, Marcelli M (2020) Assessment of the ecological structure of Posidonia oceanica (L.) Delile on the northern coast of Lazio, Italy (central Tyrrhenian, Mediterranean). Italian Botanist 9: 1-19. https://doi.org/10.3897/italianbotanist.9.46426
Figure 3 Principal Coordinate analyses (PCoA) based on 140 RAPD bands of XX individuals of Posidonia oceanica.
Supplementary material 4 from: Gnisci V, Cognetti de Martiis S, Belmonte A, Micheli C, Piermattei V, Bonamano S, Marcelli M (2020) Assessment of the ecological structure of Posidonia oceanica (L.) Delile on the northern coast of Lazio, Italy (central Tyrrhenian, Mediterranean). Italian Botanist 9: 1-19. https://doi.org/10.3897/italianbotanist.9.46426
: Data type: statistical data
Supplementary material 1 from: Gnisci V, Cognetti de Martiis S, Belmonte A, Micheli C, Piermattei V, Bonamano S, Marcelli M (2020) Assessment of the ecological structure of Posidonia oceanica (L.) Delile on the northern coast of Lazio, Italy (central Tyrrhenian, Mediterranean). Italian Botanist 9: 1-19. https://doi.org/10.3897/italianbotanist.9.46426
: Data type: occurrence
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