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48 results for “seagrass meadows”
Data for Ferretto et al, 2021 "Naturally-detached fragments of the endangered seagrass Posidonia australis collected by citizen scientists can be used to successfully restore fragmented meadows"
<p>Please find attached the data for the manuscript "Ferretto et al, 2021" and a brief description of each file.</p>
Microsatellite genotypes for adult and seedlings of the temperate seagrass (ribbon weed), Posidonia australis, from four meadows at Rottnest Island, Western Australia
<p>Adult shoots and seedings of the ribbon weed (<em>Posidonia australis</em> Hook.f.), a widespread temperate seagrass, were sampled from four meadows around Rottnest Island, Western Australia. The data set contains multilocus genotypes for adult shoots from four meadows and seedlings from three meadows over two consecutive years. The metadata file contains: Pop number (1 – 10), Sample site, Latitude (S), Longitude (E), individual sample code, year of sampling, life stage (adult shoot or seedling), genotypes (2 columns per locus). The seven polymorphic microsatellite loci are: <em>Pa</em>A1, <em>Pa</em>A105, <em>Pa</em>A120, <em>Pa</em>B6, <em>Pa</em>B8, <em>Pa</em>B112, <em>Pa</em>D113. Most genotypes are diploid, however, 3N genotypes are included.</p>
Data from: An unintended ecological benefit from human intervention: The enhancement of carbon storage in seagrass meadows
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Bulk sediment geochemistry for seagrass meadows in three California estuaries
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Single nucleotide polymorphisms, environmental data and R scripts used in the work: A donor registry: Genomic analyses of Posidonia australis seagrass meadows identifies adaptive genotypes for future-proofing
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Data from: Estimation of metabolic dynamics of restored seagrass meadows in a Southeast Asia Islet: Insights from ex situ benthic incubation
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Microsatellite genotypes for adult and seedlings of the temperate seagrass (ribbon weed), Posidonia australis, from four meadows at Rottnest Island, Western Australia
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Global patterns and drivers of seagrass biomass, net primary production and meadow structure
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Recovery of a cultivation grazer: A mechanism for compensatory growth of Thalassia testudinum in a Caribbean seagrass meadow grazed by green turtles
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Data from: Contrasting CO2 dynamics in seagrass meadows between organic carbon (OC)-rich reef and OC-poor terrestrial sediments: Implications for enhanced alkalinity production
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High seasonal variability in sediment carbon stocks of cold-temperate seagrass meadows
<p>Dataset for "High seasonal variability in sediment carbon stocks of cold-temperate seagrass meadows" containing sedimentary carbon and nitrogen data used in the study. </p>
Data from: The role of flow velocity combined with habitat complexity as a top–down regulator in seagrass meadows
Large‐scale losses of seagrass areas have been associated with eutrophication events, which have led to an overproduction of photosynthetic organisms including epiphytes. Grazers that feed on epiphytes can exert a significant top–down control in the system, but the effects of physical factors on grazing activity and feeding behaviour have been rarely examined. We addressed the combination of hydrodynamic regime and seagrass shoot density can alter the feeding and foraging behaviours of mesograzers. A full factorial experiment, with flow velocity (high, medium and low) and shoot density (high versus low) as main factors, was conducted in a racetrack flume using artificial seagrass plots. The results showed that when high flow velocity conditions were combined with low shoot density, consumption of epiphytes by mesograzers was strongly reduced. In contrast, when flow velocity was low or shoot density was high, mesograzers exhibited high feeding rates and vigorous swimming behaviour. These results clearly indicate that hydrodynamic stress reduces the time that mesograzers can spend feeding, since it inhibits their swimming behaviour, and thus indirectly affecting to the density of epiphytes. Therefore, the triggering of trophic cascade effects in seagrass communities under these experimental conditions depended on the interrelationship and feedbacks among shoot density, abiotic (flow velocity) and biotic (epiphytes and mesograzers) compartments, with flow velocity exerting a top–down control on seagrass ecosystems.
FIGURE 9 in Two new species of sea cucumbers (Echinodermata: Holothuroidea) from the seagrass meadow of Penang, Malaysia
FIGURE 9. Ossicles of Acaudina spinifera sp. nov. USMCRC-Echi 029. A–B. Spinose doughnut-shaped bodies from dorsal body wall; C. Spinose perforated plates from dorsal body wall; D. Spinose sub-spherical bodies from dorsal body wall; E. Spinose perforated plates from caudal region body wall; F. Spinose sub-spherical bodies from caudal region body wall; G. Rosettes from caudal region body wall; H. Rosette-like rods from caudal region body wall; I. Thick rounded rods from caudal region body wall; J. Dumbbell-shaped rod from caudal region body wall; K. Rosette in tentacle.
FIGURE 5 in Two new species of sea cucumbers (Echinodermata: Holothuroidea) from the seagrass meadow of Penang, Malaysia
FIGURE 5. Ossicles of Euthyonidiella zulfigaris sp. nov. USMCRC-Echi 010. A. Tables in dorsal body wall; B. Tables with three-pillared spires in dorsal body wall; C. Endplate in tube feet; D. Large rods in tentacle; E. Small rod in tentacle; F. Spinose rod in tentacle; G. Rosettes in tentacle; H. Tables in body wall near anus; I. Table in dorsal body wall.
FIGURE 4 in Two new species of sea cucumbers (Echinodermata: Holothuroidea) from the seagrass meadow of Penang, Malaysia
FIGURE 4. Calcareous ring of Euthyonidiella zulfigaris sp. nov. viewed from body cavity. USMCRC-Echi 010. R=Radial plate; IR=Interradial plate; A=Anterior; P=Posterior.
FIGURE 3 in Two new species of sea cucumbers (Echinodermata: Holothuroidea) from the seagrass meadow of Penang, Malaysia
FIGURE 3. Euthyonidiella zulfigaris sp. nov. USMCRC-Echi 010; ventral view. A=Anterior; P=Posterior.
Methane emissions in seagrass meadows as a small offset to carbon sequestration
<p>Station P: Masterfile includes environmental data and CO2, CH4 and radon concentration at Station P </p> <p>Station S: Masterfile includes the environmental data and CO2, CH4 and radon concentration at Station S </p> <p>Sediment Core: Master file includes the porewater DIC and CH4 concentration</p> <p>Survey: Master file of spatial survey includes GPS location, CH4 concentration </p>
Data from: The role of flow velocity combined with habitat complexity as a top–down regulator in seagrass meadows
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Data from: Spatiotemporal variability in the structure of seagrass meadows and associated macrofaunal assemblages in southwest England (UK): using citizen science to benchmark ecological pattern
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Potential distribution of seagrass meadows based on MaxEnt model in Chinese coastal waters
<p><span>Seagrass meadows are generally diverse in China and have the same essential ecosystem services as elsewhere. However, an evaluation of seagrass distribution across China is still lacking, and the magnitude and direction of changes in seagrass meadows remains unclear. Our primary objective was to provide a nationwide seagrass distribution map, and to explore the dynamic changes of seagrass population under global climate change. We use simulation studies within the modelling software MaxEnt with 58961 occurrence records and 27 marine environmental variables, to simulate the potential distribution of seagrasses and calculate the area. 7 environmental variables were deleted before the modelling processes based on a correlation analysis to ensure predicted suitability. The predicted area was 790.09 km<sup>2</sup>, which is much larger than the known seagrass distribution in China, and would be increased to 923.62 km<sup>2</sup> by the year 2100. However, the suitable habitat of almost all seagrass will shift northwest in the future. The sum of individual family will under-predict the national distribution of seagrass, showed a downward trend consistently in the future. Out of all environmental variables, the physical ones (e.g. depth, land distance and sea surface temperature) had the greatest contribution in predicting seagrass distributions, and nutrients (e.g. nitrate, phosphate) ranked among the key influential predictors for habitat suitability in our focal area. As this is a first effort to fill a gap in our understanding of the distribution of seagrass in China, further studies are necessary using both modeling and biological/ecological approaches. </span></p>
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