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387 results for “Oysters”
Spatial Data Recovery and Resilience of Oyster Reefs in the Big Bend of Florida
<p><strong>Project:</strong> Recovery and Resilience of Oyster Reefs in the Big Bend of Florida</p> <p><a href="https://wec.ifas.ufl.edu/oysterproject/">https://wec.ifas.ufl.edu/oysterproject/</a></p> <p><strong>Lone Cabbage Reef Restoration Spatial Data (2017-2023) Repository:</strong></p> <p><a href="../communities/lonecabbagereef">https://zenodo.org/communities/lonecabbagereef</a></p> <p><strong>Contact:</strong> Joe Aufmuth, University of Florida, George A. Smathers Libraries, Academic Research and Consulting Services Department, <a href="mailto:mapper@ufl.edu">mapper@ufl.edu</a>, (352) 273-0371.</p> <p><strong>Clarifying Publication: </strong>Aufmuth, Moore, Pine, and Ennis (2024 in progress), An Oyster’s Pearl: Restoring the Elevation of Lone Cabbage Reef, Florida.</p> <p>The repository contains ArcGIS Map Packages (v3.2.0) that are listed in the repository file Descriptions_Lone_Cabbage_Reef_map_package_list_xls. </p> <p><strong>Purpose:</strong> Data collected varies in scale as well as positional and attribute accuracy. It is the responsibility of the user to verify that the data are appropriate for their project. No warranties or guarantees are made that the data are appropriate for uses other than the Recovery and Resilience of Oyster Reefs in the Big Bend of Florida project.</p> <p><strong>Data Collection:</strong> Elevation data was collected through professional certified surveyors (Lone Cabbage Reef 2017, 2018, and 2021) as well as through field data collection efforts using Trimble survey grade GPS equipment (University of Florida 2019). Oyster count data locations were collected through field efforts and mapped to field transects using Juniper GPS survey equipment (2018, 2019, 2020, 2021, 2022, 2023). Other spatial data layers included in this data set are credited in the layouts that produce the individual maps in the map packages.</p>
Methylation and gene expression data from: Differential DNA methylation across environments has no effect on gene expression in the eastern oyster
<p>1. It has been hypothesized that environmentally induced changes to gene body methylation could facilitate adaptive transgenerational responses to changing environments.</p> <p>2. We compared patterns of global gene expression (Tag-seq) and gene body methylation (reduced representation bisulfite sequencing) in 80 eastern oysters (<i>Crassostrea virginica</i>) from six full-sib families, common gardened for 14 months at two sites in the northern Gulf of Mexico that differed in mean salinity.</p> <p>3. At the time of sampling, oysters from the two sites differed in mass by 60% and in parasite loads by nearly two orders of magnitude. They also differentially expressed 35% of measured transcripts. However, we observed differential methylation at only 1.4% of potentially methylated loci in comparisons between individuals from these different environments, and little correspondence between differential methylation and differential gene expression.</p> <p>4. Instead, methylation patterns were largely driven by genetic differences among families, with a PERMANOVA analysis indicating nearly a two orders of magnitude greater number of genes differentially methylated between families than between environments.</p> <p>5. An analysis of CpG observed/expected values (CpG O/E ) across the <i>C. virginica</i> genome showed a distinct bimodal distribution, with genes from the first cluster showing the lower CpG O/E values, greater methylation, and higher, and more stable gene expression, while genes from the second cluster showed lower methylation, and lower and more variable gene expression.</p> <p>6. Taken together, the differential methylation results suggest that only a small portion of the <i>C. virginica</i> genome is affected by environmentally induced changes in methylation. At this point, there is little evidence to suggest that environmentally induced methylation states would play a leading role in regulating gene expression responses to new environments.</p>
Coastal squeeze on temperate reefs: long-term shifts in salinity, water quality, and oyster-associated communities
<p>Foundation species such as mangroves, saltmarshes, kelps, seagrasses, and oysters thrive within suitable environmental envelopes as narrow ribbons along the land-sea margin. Therefore, these habitat-forming species and resident fauna are sensitive to modified environmental gradients. For oysters, many estuaries impacted by sea-level rise, channelization, and municipal infrastructure are experiencing saltwater intrusion and water-quality degradation that may alter reef distributions, functions, and services. To explore decadal-scale oyster-reef community patterns across a temperate estuary in response to environmental change, we resampled reefs in the Newport River Estuary (NRE) during 2013-2015 that were previously studied during 1955-1956. We also coalesced historical NRE reef distribution (1880s-2015), salinity (1913-2015), and water-quality driven shellfish closure boundary (1970s-2015) data to document environmental trends that could influence reef ecology and service delivery. Over the last 60-120 years, the entire NRE has shifted toward higher salinities. Consequently, oyster-reef communities have become less distinct across the estuary, manifest by 20-27% lower species turnover and decreased faunal richness among NRE reefs in the 2010s relative to the 1950s. During the 2010s, NRE oyster-reef communities tended to cluster around a euhaline, intertidal-reef type more so than during the 1950s. This followed from faunal expansions farther up-estuary and biological degradation of subtidal reefs as NRE conditions became more marine and favorable for aggressive, reef-destroying taxa. In addition to these biological shifts, the area of suitable bottom on which subtidal-reefs persist (ultimately regulated by up-estuary intrusion of marine waters) and support human harvest (driven by water quality, eroding from up-estuary) has decreased by >75% since the natural history of NRE reefs was first explored. This "coastal squeeze" on harvestable subtidal oysters (reduced from a 4.5-km to a 0.75-km envelope along the NRE's main axis) will likely have consequences regarding the economic incentives for future oyster conservation, as well as the suite of services delivered by remaining shellfish reefs (e.g., biodiversity maintenance, seafood supply). More broadly, these findings exemplify how "squeeze" may be a pervasive concern for biogenic habitats along terrestrial or marine ecotones during an era of intense global change.</p>
Data from: A 26-year time series of mortality and growth of the Pacific oyster C. gigas recorded along French coasts
<p><strong>Contents:</strong> database of oyster growth (<em>i.e., </em>the changes in mass over time) and mortality along French coasts since 1993. To build this database, we took advantage of the Pacific oyster production monitoring network coordinated by IFREMER (the French Research Institute for the Exploitation of the Sea). This network monitors the growth and mortality of spat (less than one-year-old individuals) and half-grown (between one and two-year-old individuals) <em>Crassostrea gigas</em> oysters since 1993. As the number of sites monitored over the years varied, we focused on 13 sites that were almost continuously monitored during this period. For these locations, we modeled growth and cumulative mortality for spat and half-grown oysters as a function of time, to cope with changes in data acquisition frequency, and produced standardized growth and cumulative mortality indicators to improve data usability. Code to reproduce these analyses are archived here, as well as figures included in the companion data paper: "A 26-year time series of mortality and growth of the Pacific oyster <em>C. gigas</em> recorded along French coasts".</p> <p><strong>Sampling protocol: </strong>in the oyster production monitoring network, oysters were mainly reared in plastic meshed bags fixed on iron tables, mimicking the oyster farmers practices. After their deployment at the beginning of the campaign (seeding dates from February to April depending on the year), growth and mortality were longitudinally monitored yearly. At each sampling date, local operators carefully emptied each bag in separate baskets, counted the dead individuals and alive ones, and removed the dead individuals. Then local operators weighed all alive individuals in each basket (mass taken at the bag level, protocol mainly used between 1993 and 1998 and since 2004) and/or collected 30 individuals to individually weigh them in the laboratory (mass taken at the individual level, protocol used between 1995 and 2010 for spat and since 1996 for half-grown oysters).</p> <p><strong>Data:</strong></p> <ul> <li>AllDataresco. csv is a csv file containing the raw observations of oyster growth and mortality recorded within the REMORA, RESCO and ECOSCOPA programs. This data set is a modified extraction (carried out on 2021-07-20) of the RESCO REMORA Database (https://doi.org/10.17882/53007) available in SEANOE, an academic publisher or marine research data. The table contains 571101 rows and 18 columns. Description of columns: <ul> <li>program: the name of the program. Blank cells indicate that this information was not available.</li> <li>mnemonic_site: the mnemonic is a unique identifier of the site and is constructed as follows: code of the marine area - P (for monitoring point) - order number of the monitoring location in the marine area. For example, 014-P-055.</li> <li>site: the name of the site.</li> <li>class_age: the age class of the oyster: N0 (spat), J1 (half-grown) or A2 (commercial size). Blank cells indicate that this information was not available.</li> <li>ploidy: the ploidy of the oysters: diploïdes or triploïdes (in English: diploid or triploid). Blank cells indicate that this information was not available.</li> <li>date: the date of data collection (format DD/MM/YYYY).</li> <li>mnemonic_date: mnemonic of the visit. The name of the quarterly operation (P0, P1, P2, P3 or RF: last data collection). For intermediate operations, we use the previous name of the operation followed by an underscore and the number of the week. For example, data collection on 2019-05-06 corresponds to P1_S19. Biométrie initiale (in English: initial biometrics) is equivalent to P0 (first data collected during the campaign).</li> <li>param: the name of the measured parameter: Nombre d'individus morts, Nombre d'individus vivants, Poids de l'individu or Poids total des individus vivants (in English: number of dead oysters, number of alive oysters, mass of the individual and total mass of alive individuals).</li> <li>code_param: code of the measured parameter. INDVVIVNB = number of alive oysters, INDVMORNB = number of dead oysters, INDVPOID = mass of the individual, TOTVIVPOI = total mass of alive individuals (i.e., the mass of the bag).</li> <li>unit_measure: the unit of measurement: Gramme or Unité de dénombrement (d'individus, de cellules, ...)</li> <li>fraction: either the measure was made at the bag level on which case the fraction is "Sans objet" = Not applicable or the measure was made at the individual level (code_param = INDVPOID), in which case the fraction indicates the part of the oyster that was measured: Chair totale égouttée or coquille (in English: total flesh drained or shell).</li> <li>method: the method used to obtain the data. For the number of alive and dead oysters (code_param = INDVVIVNB and INDMORNB), the method is comptage macroscopique (in English: macroscopic count). For mass taken at the individual level (code_param = INDVPOID), the method is Pesée après lyophilisation or Pesée simple sans préparation (in English: weighing without preparation or weighing after lyophilization).</li> <li>id_ind: the id of the individual oyster when code_param is INDVPOI or the id of the bag when code_param is INDVVIVNB, INDVPOID and TOTVIVPOI.</li> <li>value: numeric value of the measurement.</li> <li>mnemonic_sampling: This is a concatenated field. Its coding is not consistent throughout the dataset. Indeed, it is sometimes composed of the first letter of the program name attached to 2 numbers indicating the year of data collection and the age class (gj: spat, ga: half-grown or commercial size oysters) - 2 letters indicating the region attached to a 4-character site identifier- mnemonic passage. For example, R05gj-NOBV02-P0 corresponds to data collected in the program REMORA in 2005 on gigas spat (gj) in Normandy (NO) in the site Géfosse 02 (BV02) in the 1<sup>st</sup> quarter (P0). Other times the mnemonic_prelevement is composed of the first two letters of the program name attached to 2 numbers indicating the year of data collection _ the age class (GJ: spat, GA18: half-grown, GA30: commercial size oysters) attached to the origin of the initial spat group (this information is not always indicated) (CN + number: identifier of wild-caught site, ET + character: identifier of the hatchery, NSI: Argenton hatchery via a standardized protocol) _ a 4-character identifier for the site. For example, RE12_GJET2_BV02 corresponds to data collected in the program REMORA in 2012 (RE12) on gigas spat born in hatchery 2 (GJET2) in the site Géfosse 02 (BV02). Finally, mnemonic_prelevement is sometimes: Biométrie initiale (initial biometrics), Biométrie initiale 6 mois (initial biometrics of spat), Biométrie initiale 18 mois and Biométrie initiale adulte (both correspond to initial biometrics of half-grown oysters), Biométrie initiale 30 mois (initial biometrics of commercial size oysters), Biométrie initiale NSI (initial biometrics of spat batch produced in Argenton Ifremer hatchery via a standardized protocol).</li> <li>long: The longitudinal coordinate of the site given in decimal in the WGS 84 system.</li> <li>lat: The latitudinal coordinate of the site given in decimal in the WGS 84 system.</li> <li>pop_init_batch: this is a concatenated field. It is composed of the two first letters of the name of the program name attached to 2 numbers indicating the year of data collection _ the age class code (GJ: gigas spat, GA: gigas half-grown, GA30: gigas commercial size) _ the origin of the initial spat group (CN: wild-caught, ET: hatchery, NSI: Argenton hatchery) attached to two numbers indicating the year of birth of the initial spat group _ the birth place of the initial spat group (this one is optional). For example, RE00_GJ_CN99_AR corresponds to data collected in the program REMORA in 2000 (RE00) on spat oysters (GJ) born in 1999 and wild-caught (CN99) in the Bay of Arcachon (AR). Blank cells indicate that this information was not available.</li> </ul> </li> </ul> <p> </p> <ul> <li>sites.csv is a csv file of 7 columns and 13 rows containing information about the 13 sites. Description of the columns found in the data set: <ul> <li>num: a unique identifier for each site. Ranges between 1 and 13.</li> <li>site: the abbreviated name of the site.</li> <li>Name: the full name of the site.</li> <li>zone_fr: the French name of the zone where data collection took place.</li> <li>zone_en: the English name of the zone where data collection took place.</li> <li>lat: the latitudinal coordinate of the site given in decimal in the WGS 84 system.</li> <li>long: the longitudinal coordinate of the site given in decimal in the WGS 84 system.</li> </ul> </li> </ul> <p> </p> <ul> <li>DataResco_clean.csv is the curated data set of oyster growth and mortality (csv file). The table contains 5178 rows and 13 columns. Each row corresponds to the mean cumulative mortality and mean mass of oysters for a specific date x site x age class combination. This is the data set we used to fit logistic and Gompertz models to describe mean mass and cumulative mortality at time <em>t</em>. Description of the columns found in the data set: <ul> <li>num, site, name, zone_en, lat, long: see the description above for the data set sites.csv.</li> <li>campaign: the year of data collection. Ranges between 1993 and 2018.</li> <li>class_age: the age class of the oyster (i.e. spat: N0 or half-grown: J1).</li> <li>batch: the identifier of the batch (group of oysters born from the same reproductive event, having experienced strictly the same zootechnical route). It is a field that concatenates the campaign, the age class of oysters (spat: N0 or half-grown: J1), the origin of the initial spatgroup (wild-caught: CAPT or Ifremer hatchery: ECLO), ploidy (diploid: 2n) and birthplace of the original spatgroup (AR: Bay of Arcachon or E4: Ifremer hatchery of Argenton).</li> <li>date: the day of data collection (format YYYY-MM-DD).</li> <li>DOY: the day of the year (count of days since the beginning of the year). It ranges between 46 and 354.</li> <li>mean_CM: the mean cumulative mortality of oysters. It ranges between 0 and 0.956 (<em>i.e.</em>, between 0–95.6%). We first calculated the cumulative mortality for each bag x date x site x age class combination according to the following formula: CM<em><sub>t</sub></em> = 1 – ((1 – CM<em><sub>t-1</sub></em>) × (1 – IM<em><sub>t</sub></em>)). CM<em><sub>t</sub> </em>= Cumulative mortality at time <em>t</em>; CM<em><sub>t-1 </sub></em>= Cumulative mortality at time <em>t</em>-1; IM<em><sub>t</sub></em> = Mortality rate at time <em>t</em>. IM<em><sub>t</sub></em> was obtained by dividing the number of dead oysters by the sum of alive and dead oysters at time <em>t</em>. When several bags were followed, we then averaged the cumulative mortality per date x site x age class combinations. IM<sub>t</sub> was obtained by dividing the number of dead oysters by the sum of alive and dead oysters at time <em>t</em>. When several bags were followed, we then averaged the cumulative mortality by date x site x age class combination. NA values indicate that this information was not available.</li> <li>mean_mass: The mean mass of oysters in grams. It ranges between 0.28 and 122.51 g. For mass data collected until 2008, we calculated the mean of the individual mass per date x site x age class combination by averaging the mass of the individuals. In other cases (mass data collected since 2009), we calculated the mean mass of individuals for each bag x date x site x class age combination by dividing the total mass of living oysters by the number of living individuals and then averaged data by date x site x age class combination. The mean mass is thus the mean of the individual mass until 2008 and the mean mass of individuals since 2009. NA values indicate that this information was not available.</li> </ul> </li> </ul> <p> </p> <ul> <li>data/clean/DataResco_predicted.csv is a csv file containing the cumulative mortality and mass of oysters predicted by the best sigmoid model. The table contains 148239 rows and 11 columns. Each row corresponds to the cumulative mortality and mass predicted for a specific day x campaign x site x age class combination. <ul> <li>num, site, name, zone_en, lat, long: see the description above for the data set sites.csv.</li> <li>campaign: the year of the data prediction. Ranges between 1993 and 2018.</li> <li>classe_age: the age class of the oyster (<em>i.e.,</em> spat: N0 or half-grown: J1).</li> <li>DOY: the day of the year (count of days since the beginning of the year). It ranges from 65 (median day of seeding date) to 337 (median day of the end of the monitoring).</li> <li>CM_pred: cumulative mortality predicted by the best model (<em>i.e.,</em> Gompertz model).</li> <li>mass_pred: mass predicted (in grams) by the best model (<em>i.e.,</em> logistic model).</li> </ul> </li> </ul> <p><strong>Code: </strong></p> <ul> <li>0_map_sampling_location.R contains the code to recreate the map of the sampling sites</li> <li>1_cleaning_data.R contains the code for data cleaning. This script also computes the mean cumulative mortality and mean mass of oysters per date x site x class age combination (DataResco_clean.csv)</li> <li>2_analysis_sigmoide.Rmd does the logistic and Gompertz models of the mean cumulative mortality and mean mass for spat and half-grown oysters. This script computes the cumulative mortality and mass of oysters predicted by the best sigmoid model (DataResco_predicted.csv).</li> </ul> <p>Contacts: for questions, please contact: elodie.fleury@ifremer.fr or Julien.normand@ifremer.fr</p>
CPineMarineEcology/Blue-Groper-Oyster-Reef-Data: Blue Groper Oyster Reef Data
<p>Data for the paper titled "Observation of Juvenile Eastern Blue Groper (Achoerodus viridis) on Remnant Oyster Reefs in New South Wales, Australia." by Pine et al. (2022) published in <em>Ecology</em> - in <em>The Scientific Naturalist</em> section.</p>
Genetic parentage reconstruction as a practical tool applied to aquaculture and restoration programs for the European flat oyster, Ostrea edulis
<p>Preserving and maximizing genetic diversity in conservation programs, including for restocking, are of high importance. The threatened European flat oyster (<em>Ostrea edulis</em>) is currently the subject of several applied conservation and restocking programs, but concerns have been raised over potential negative side effects of these programs, for example due to our limited knowledge about the genetic effects in natural populations of releasing offspring of hatchery origin. Here, we developed a cost-effective, easily applicable and highly reliable method to assess the genetic diversity and parental contributions in flat oyster hatchery production based on analyses of 17 microsatellite loci. We analyzed broodstock and hatchery-produced spat (total n = 289) and compared diversity to that in wild samples of adults and spat from the broodstock source in the Limfjorden (total n = 190). Based on four hatchery tank experiments, we found that five swarming events (larval releases) were characterized by a single maternal and multiple paternal contributions, and that the number of contributing parents varied greatly both among individual tanks, and between swarming events within tank. On average, the effective number of breeders was only one third of the actual broodstock size. Although the broodstock exhibited high genetic variation, the high reproductive skew resulted in produced seed representing only a relatively small subset of this variation. The work demonstrates potential impact of hatchery reared offspring on decreasing genetic diversity in wild populations, but also that genetic monitoring can be integrated in conservation programs to minimize negative effects on restoration and supplementary restocking programs that utilize hatchery reared spats to support natural populations.</p>
A single genomic region involving a putative chromosome rearrangement in flat oyster (Ostrea edulis) is associated with differential host resilience to the parasite Bonamia ostreae
<p>European flat oyster (<em>Ostrea edulis</em>) is an ecologically and economically important marine bivalve, that has been severely affected by the intracellular parasite <em>Bonamia ostreae</em>. In this study, a flat oyster SNP array (~14,000 SNPs) was used to validate previously reported outlier loci for divergent selection associated with <em>B. ostreae</em> exposure in the Northeast Atlantic Area. A total of 134 wild and hatchery individuals from the North Sea, collected in naïve (NV) and long-term affected (LTA) areas, were analysed. Genetic diversity and differentiation were related to the sampling origin (wild vs hatchery) when using neutral markers, and to bonamiosis status (NV vs LTA) when using outlier loci for divergent selection. Two genetic clusters appeared intermingled in all sampling locations when using outlier loci and their frequency was associated with their bonamiosis status. When both clusters were compared, outlier datasets showed high genetic divergence (FST > 0.25) unlike neutral loci (FST not ≠ 0). Moreover, the cluster associated with LTA samples showed much higher genetic diversity and significant heterozygote excess with outlier loci, but not with neutral data. Most outliers mapped on chromosome 8 (OE-C8) of the flat oyster genome, supporting a main genomic region underlying resilience to bonamiosis. Furthermore, differentially expressed genes previously reported between NV and LTA strains showed higher mapping density on OE-C8. A range of relevant immune functions were specifically enriched among genes annotated on OE-C8, providing hypotheses for resilience mechanisms to an intracellular parasite. The results suggest that marker-assisted selection could be applied to breed resilient strains of <em>O. edulis</em> to bonamiosis, if lower parasite load and/or higher viability of the LTA genetic cluster following <em>B. ostreae</em> infection is demonstrated.</p>
Data from: Characterizing fish habitat use of fringing oyster reefs using acoustic imaging
<p>Data and scripts associated with the paper "Characterizing fish habitat use of fringing oyster reefs using acoustic imaging"</p>
Simultaneous determination method of 10 organophosphate esters in oyster, an environmental indicator organism
<p><span>Oyster is a common indicator organism of environment pollution. A method was built to simultaneously determine ten common commercial organophosphate ester flame retardants in oysters. After the careful optimization of conditions, the organophosphate esters (OPEs) in oyster sample were extracted with hexane assisted by ultrasound, purified with NH2 solid-phase extraction column, eluted with the mixture solution of dichloromethane and ethyl acetate at the ratio of 1:1 (v/v), and finally detected by gas chromatography coupled with tandem triple quadrupole mass spectrometry (GC–MS/MS). All the ten compounds in the concentrations ranging from 0.5 ng·mL-1 to 100 ng·mL-1 showed good linearity with their R2 higher than 0.9991. The limits of detection and limits of quantification for the organophosphate esters were from 0.03 ng·g-1 to 0.47 ng·g-1 (dry weight) and from 0.1 ng·g-1 to 1.56 ng·g-1 (dry weight), respectively. At the three spiking levels with five replicates, recoveries of organophosphate esters ranged from 82% to 108% with the relative standard deviation ranging from 0.9% to 6.4%. In the samples collected from mariculture farms, nine OPE compounds were detected in oysters with the concentration ranging from 6.52 ng·g-1 to 22 ng·g-1 (dry weight). It indicated the method was suitable for OPEs determination in oysters.</span></p>
FIG. 2 in Systematics, palaeoecology and taphonomy of Turonian oysters from the northern Gabon Coastal Basin
FIG. 2. — Lithostratigraphy of the PK 12 section plotted against occurrence of oysters, gastropods and bivalve.
Morphometrics and nutrient concentration of eastern oysters (Crassostrea virginica) sampled from Chesapeake Bay, USA
<p><span><strong>Acknowledgements</strong></span> Ward Slacum and Olivia Caretti from the Oyster Recovery Partnership for providing the support and resources to compile the initial oyster BMP dataset and maintenance. </p> <p><strong><u>Background Information</u></strong></p> <p>The Chesapeake Bay Program (CBP) approved the use of eastern oyster (<em>Crassostrea virginica</em>) aquaculture as a nitrogen and phosphorus reduction best management practice in December 2016. The CBP decision was based on the recommendations of the Oyster Best Management Practice Expert Panel. The approved BMP report, “Panel Recommendations on the Oyster BMP Nutrient and Suspended Sediment Reduction Effectiveness Determination Decision Framework and Nitrogen and Phosphorus Assimilation in Oyster Tissue Reduction Effectiveness for Oyster Aquaculture Practices” is available here: <a href="https://d18lev1ok5leia.cloudfront.net/chesapeakebay/documents/Oyster_BMP_1st_Report_Final_Approved_2016-12-19.pdf">https://d18lev1ok5leia.cloudfront.net/chesapeakebay/documents/Oyster_BMP_1st_Report_Final_Approved_2016-12-19.pdf</a></p> <p>And a summary fact sheet about the report and the panel’s findings is available here: <a href="https://oysterrecovery.org/wp-content/uploads/June_2018-FINAL-bmp-fact-sheet-1.pdf">https://oysterrecovery.org/wp-content/uploads/June_2018-FINAL-bmp-fact-sheet-1.pdf</a></p> <p>The panel’s recommendations were based on an analysis of two datasets: 1) available literature for eastern oyster tissue nitrogen and phosphorus concentrations (%) across the Northeast region of the United States, and 2) available literature for the relationship between shell height (mm) vs. tissue dry weight (g) for eastern oysters sampled within Chesapeake Bay, USA. The nitrogen and phosphorus concentration datasets were relatively small, and the data were published in the report. The dataset describing the relationship between oyster shell height vs. tissue dry weight was much larger (n = 6,816). The report contains summary statistics and visual representations of the data, but the data themselves were not publicly released.</p> <p>A second report was released by the Oyster Best Management Practice Expert Panel in 2023, with additional analysis of an expanded dataset for the relationship between oyster shell height vs. tissue dry weight (n = 10,786) and a new dataset for the relationship between oyster shell height vs. shell dry weight to support the development of an oyster restoration BMP. The second BMP report, “Nitrogen and Phosphorus Reduction Associated with Harvest of Hatchery-Produced Oysters and Reef Restoration: Assimilation and Enhanced Denitrification” is available here:</p> <p><a href="https://d18lev1ok5leia.cloudfront.net/chesapeakebay/documents/Oyster-BMP-Second-Report_Approved_with_Minutes.pdf">https://d18lev1ok5leia.cloudfront.net/chesapeakebay/documents/Oyster-BMP-Second-Report_Approved_with_Minutes.pdf</a></p> <p>The second report contains summary statistics and visual representations of the data, but the data themselves were not publicly released.</p> <p><strong><em><u>Data Description</u></em></strong></p> <p>This repository contains 77% of the data (n = 8,395) used by the Chesapeake Bay Program’s Oyster Best Management Practice Expert Panel to develop its recommendations for both the 2016 first report and the 2023 second report. Some of the data in this repository were previously published in the peer-reviewed literature as summary statistics, but the raw data were not archived. The remaining data are either not currently available or are published in the gray literature. Two datasets that were used in the Expert Panel analysis are not included in this repository, as the data are unpublished with the data owners intending to publish these data in the future.</p> <p>The repository is organized with individual oyster samples as rows and a combination of numerical and categorical information as columns. Each row/sample in the repository contains data on oyster shell height (mm) and oyster dry weight (g), data source, and sample collection location information. Location information is presented across columns with different levels of spatial resolution. This repository also contains additional information provided by scientists when available, such as nitrogen, carbon, or phosphorus concentration, details about the oyster source and growth conditions, and sampling time (e.g., season/date/year).</p>
Morphometrics and nutrient concentration of farmed eastern oysters (Crassostrea virginica) from the US Northeast Region
<p><strong><u>Acknowledgements</u></strong></p> <p>This work was supported by the NOAA Fisheries Northeast Fisheries Science Center and the NOAA Fisheries Office of Aquaculture. Thanks to Marta Gomez-Chiarri, PG Harris, Mark Luckenbach, and Christine Thompson for sharing data, although these data were not included in the final repository.</p> <p><strong><u>Background Information</u></strong></p> <p>The removal of excess nitrogen from eutrophic environments is an ecosystem service provided by shellfish aquaculture that is well described in the literature (Lindahl et al., 2005; Rose et al., 2014; Petersen et al., 2014; Clements and Comeau, 2019). Nitrogen removal associated with shellfish farms can occur via three mechanisms: the assimilation of nitrogen into tissue and shell, which is removed from the waterbody when animals are harvested; the enhancement of sediment denitrification through biodeposit production on farms; and the long-term burial of biodeposits.</p> <p>A robust calculation of the nitrogen removed at shellfish harvest has been previously published using relatively simple metrics: the nitrogen concentration of tissue/shell, the number and mean size of animals harvested, and a conversion of animal size to tissue and shell dry weight (Reichert-Nguyen et al., 2016; Clements and Comeau, 2019). The quantity of nitrogen removed at shellfish harvest has been previously predicted with high confidence, which has led to the integration of oyster and clam aquaculture into nutrient management programs at the local and estuary scale in the United States (Town of Mashpee, 2015; Reichert-Nguyen et al., 2016; Reitsma et al., 2017).</p> <p>The data in this repository were compiled with the intention of expanding the geographic scope of calculation of nitrogen removal associated with harvested eastern oysters (<em>Crassostrea virginica</em>), and to evaluate variation in this ecosystem service across common cultivation practices and ploidy. Data were obtained from sampling locations across the US from the state of North Carolina north to the state of Maine. Data are included for both diploid and triploid oysters, and for three common styles of cultivation: oysters grown on bottom without the use of aquaculture gear, oysters grown in bottom cages, and oysters grown in floating gear at the sea surface. Data curation was undertaken to obtain a dataset that most closely reflects on-farm conditions as possible. The highest priority was to find data collected from working oyster farms, and a second priority was to identify data collected by scientists who employed common cultivation practices in their research studies. In one state within the region (Rhode Island) we were unable to locate data that fit the previous description, and instead have included data from wild oysters from waterbodies that have oyster farms.</p> <p>This data set was used to support the development of the Aquaculture Nutrient Removal Calculator (ANRC,<a href="https://connect.fisheries.noaa.gov/ANRC/">https://connect.fisheries.noaa.gov/ANRC/</a>), a tool designed for use by both shellfish farmers and managers within the aquaculture permit review process. The ANRC is a publicly available, simple online tool that was developed in direct response to feedback from aquaculture resource managers. The ANRC accurately predicts harvest-based nitrogen removal from an eastern oyster farm located within the geographic range of North Carolina to Maine, USA. We have taken an adaptive management approach to tool development, basing our tool on current best available scientific information, with the intention of maintaining and updating this tool when new information and data become available in the future.</p> <p><strong><u>Data Description</u></strong></p> <p>This repository contains information on morphometrics and nitrogen concentration of tissue and shell for eastern oysters sampled within the US geographic region spanning the states of North Carolina to Maine. Some of the data in this repository (6 of 10 sources) were previously published as summary statistics in the peer-reviewed literature, but the raw data included here were not archived. Three datasets were not previously published in raw or summary form. One dataset is publicly available in a technical report.</p> <p>The repository is organized with individual oysters as rows and numerical/categorical information associated with those oyster samples as columns. Each sample in the repository contains data on oyster shell height (mm), tissue dry weight (g), data source, ploidy, cultivation practice, and location of sample collection. The repository also contains additional information provided by data sources as available, such as shell dry weight, nitrogen, carbon, sampling date, oyster stock, and other morphometric measurements.</p> <p><strong><u>References</u></strong></p> <p>Clements, J.C., Comeau, L.A., 2019. Nitrogen removal potential of shellfish aquaculture harvests in eastern Canada: A comparison of culture methods. Aquaculture Reports 13, 100183.</p> <p>Lindahl, O., Hart, R., Hernroth, B., Kollberg, S., Loo, L.-O., Olrog, L., Rehnstam-Holm, A.-S., Svensson, J., Svensson, S., Syversen, U., 2005. Improving marine water quality by mussel farming - a profitable solution for Swedish society. Ambio 34, 129-136.</p> <p>Petersen, J.K., Hasler, B., Timmermann, K., Nielsen, P., Tørring, D.B., Larsen, M.M., Holmer, M., 2014. Mussels as a tool for mitigation of nutrients in the marine environment. Marine Pollution Bulletin 82, 137-143.</p> <p>Reichert-Nguyen, J., Cornwell, J., Rose, J., Kellogg, L., Luckenbach, M., Bricker, S., Paynter, K., Moore, C., Parker, M., Sanford, L., Wolinski, B., Lacatell, A., Fegley, L., Hudson, K., French, E., Slacum, W., 2016. Panel recommendations on the oyster BMP nutrient and suspended sediment reduction effectiveness determination decision framework and nitrogen and phosphorus assimilation in oyster tissue reduction effectiveness for oyster aquaculture practices, Report to the Chesapeake Bay Program. Available online at <a href="https://www.oysterrecovery.org/wp-content/uploads/2017/01/Oyster-BMP-1st-Report_Final_Approved_2016-12-19.pdf">https://www.oysterrecovery.org/wp-content/uploads/2017/01/Oyster-BMP-1st-Report_Final_Approved_2016-12-19.pdf.</a></p> <p>Reitsma, J., Murphy, D.C., Archer, A.F., York, R.H., 2017. Nitrogen extraction potential of wild and cultured bivalves harvested from nearshore waters of Cape Cod, USA. Marine Pollution Bulletin 116, 175-181.</p> <p>Rose, J.M., Bricker, S.B., Tedesco, M.A., Wikfors, G.H., 2014. A Role for Shellfish Aquaculture in Coastal Nitrogen Management. Environmental Science & Technology 48, 2519-2525.</p> <p>Rose J.M.,, Morse, R., and Schillaci, C. 2024. Development and application of an online tool to quantify nitrogen removal associated with harvest of cultivated eastern oysters. PLoS ONE 19(9): e0310062. https://doi.org/10.1371/journal.pone.0310062</p> <p>Town of Mashpee Sewer Commission, 2015. Comprehensive watershed nitrogen management plan, Town of Mashpee, Available online at http://www.mashpeewaters.com/documents.html.</p>
Figure 1 in Distribution of intertidal rock oysters in the Pilbara, Western Australia
Figure 1. Map of the Pilbara region of Western Australia showing ports mentioned in the text.
An in-silico analysis of information sharing systems for adaptable resources management: a case study of oyster farmers
<p>Model and data outcomes --> We developed an agent-based models involving oyster farmers sharing information to adapt to an ill-understood virus. Various scenarios of heterogeneity and information sharing (through social networks and centralized information system) are simulated.</p>
Figure 2 in Interpopulation differences in shell forms of the pearl oyster, Pinctada imbricata radiata (Bivalvia: Pterioida), in the northern Persian Gulf inferred from principal component analysis and elliptic Fourier analysis
Figure 2. Morphological measurements of the shell in P. imbricata radiata that were used in PCA.
Figure 2 in New record of an oyster species from Chilika Lagoon, Odisha, north-western Bay of Bengal
Figure 2. External feature of Isognomon ephippium.
Figure 1 in New record of an oyster species from Chilika Lagoon, Odisha, north-western Bay of Bengal
Figure 1. Chilika lagoon, showing four sampling points at outer channel area.
Figure 3 in Estimation of individual growth of the violet oyster Chama coralloides Reeve, 1846 (Bivalvia: Venerida) using Schnute model cases
Figure 3. Modal progression of individual cohorts for C. coralloides in Acapulco, Guerrero, Mexico.
Figure 1 in Estimation of individual growth of the violet oyster Chama coralloides Reeve, 1846 (Bivalvia: Venerida) using Schnute model cases
Figure 1. Monthly frequency of length of C. coralloides in Acapulco, Guerrero, Mexico.
Coopetition as an ecological dynamic: interactions between dense seasonal macroalgal mats and oysters on temperate shellfish reefs
<p>We document the proliferation of dense algal mats on intertidal oyster reefs in the Southeastern USA. We also use data from a small field experiment to understand the effects that oysters have on macroalgae. The data and code included here can be used to replicate all analyses and figures in the manuscript.</p>
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