http://lod.bco-dmo.org/id/dataset/1007572
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2026-09-10
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Environmental data from sites (source populations, and experimental sites) from common garden experiments with Eastern oyster (Crassostrea virginica) run as part of CviMVP project from 2023-2025 in the Chesapeake Bay
2026-10-06
publication
2026-10-06
revision
BCO-DMO Linked Data URI
2026-10-06
creation
http://lod.bco-dmo.org/id/dataset/1007572
Katie Lotterhos
Northeastern University
principalInvestigator
Jessica Small
The College of William & Mary
principalInvestigator
Ryan Carnegie
Virginia Institute of Marine Science
principalInvestigator
Madeline Eppley
Northeastern University
principalInvestigator
Camille Rumberger
Northeastern University
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Rumberger, C., Lotterhos, K., Small, J., Eppley, M., Carnegie, R., Mongillo, N., Bajaj, K., Segnitz, Z. (2026) Environmental data from sites (source populations, and experimental sites) from common garden experiments with Eastern oyster (Crassostrea virginica) run as part of CviMVP project from 2023-2025 in the Chesapeake Bay. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2026-10-06 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/1007572 [access date]
Dataset Description: <p>Eastern oyster, Crassostrea virginica, urn:lsid:marinespecies.org:taxname:140657</p> Methods and Sampling: <p>The experimental source population dataset consisted of historic data for our six wild populations and two selection lines, which was input as the starting environment in the offset model (i.e., the environment being predicted from). We developed abiotic and biotic (e.g., disease caused by parasites) environmental datasets for genomic offset modeling, and explored how the inclusion of biotic variables affected the predictive performance of offset models.</p>
<p>For abiotic variables, we included extreme quantiles (10th and 90th quantiles) for temperature (ºC) and salinity (ppt). Extreme quantiles were chosen to represent environmental conditions as they presumably capture the extreme conditions likely to impose selective pressures, without suffering from the statistical issues associated with averaging maximums or minimums. Environmental data for the seascape training dataset and experimental source population site-of-origin data included the 10th and 90th quantiles for salinity (ppt) and temperature (ºC) across as many years as data were available (Eppley et al. 2026, Bajaj &amp; Mongillo et al. 2026). Environmental data for the common garden environment at Lewisetta and York River included the 10th and 90th quantiles for salinity (ppt) and temperature (ºC) &nbsp;for only the experimental period.&nbsp;</p>
<p>For biotic (disease) variables, we included population-level prevalence of the two parasites (disease prevalence). In the experimental source population dataset, which was collected during months with historic precedent of peak disease, the prevalence of MSX and Dermo for each population was calculated according to the proportion of individual oysters with each disease as determined by multiplex qPCR assay (supplemental methods) (Piesz et al. 2022; Ewart and Ford 1993). For the common garden sites, the prevalence of MSX and Dermo in the surrounding environment was determined by using Ray's Fluid Thioglycollate Medium (RFTM) assay on a set of 20 naive “sentinel” oysters deployed on both locations and then assessed monthly during peak disease season (July through November) (Ray 1952; Dungan and Bushek 2015).</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-2043905 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2043905
onGoing
Katie Lotterhos
Northeastern University
781-581-7370 X304
Northeastern University Marine Sciences Center 430 Nahant Rd.
Nahant
MA
01908
USA
k.lotterhos@northeastern.edu
pointOfContact
Jessica Small
The College of William & Mary
jamoss@vims.edu
pointOfContact
Ryan Carnegie
Virginia Institute of Marine Science
carnegie@vims.edu
pointOfContact
Madeline Eppley
Northeastern University
eppley.m@northeastern.edu
pointOfContact
Camille Rumberger
Northeastern University
rumberger.c@northeastern.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
site_name
Latitude_manual
Longitude_manual
salinity_quantile_10
salinity_quantile_90
temp_quantile_10
temp_quantile_90
Dermo_Prevalence
MSX_Prevalence
theme
None, User defined
site
latitude
longitude
salinity
water temperature at measurement depth
No BCO-DMO term
featureType
BCO-DMO Standard Parameters
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP)
https://osprey.bco-dmo.org/project/876610
CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP)
<p><strong>NSF Award Abstract:</strong><br />
Environmental change can be rapid and involve multiple aspects of the environment changing at the same time, such as warming and increased disease pressure. Rapid environmental change threatens the productivity of aquaculture and crops on which humans depend. Predicting organisms' vulnerabilities to rapid and multifactor environmental change, however, is a major scientific challenge. A hurdle to addressing this challenge arises from the complex and non-intuitive ways that organisms adapt, through changes at the level of the DNA sequence, to many environmental stresses at the same time. Thus, there is a need for new approaches to understand and predict adaptation in multivariate environments. To address this need, this project integrates research and education with a Model Validation Program (MVP). The research is developing and evaluating Machine Learning Algorithms (MLAs) for understanding and predicting adaptation of organisms to multivariate environments from their DNA sequences. To evaluate MLAs, this research combines both data simulation and an empirical test in the field with the Eastern Oyster, which provide important ecosystem services and support a multi-million dollar industry. For oysters, this research is studying how temperature, disease pressure, and salinity interact with evolutionary history to determine fitness in the field. This research advances efforts toward addressing the major scientific challenge of predicting adaptation in complex environments by integrating concepts across the frontiers of marine, evolutionary, and statistical sciences in a new way. Machine learning and model validation are not traditionally taught in the marine and environmental sciences, but are becoming increasingly relevant to these fields. As part of a broader education program, this research is developing MVP Learning Modules for high school students and undergraduates, which help students build the foundational knowledge they need to critically evaluate and apply models. Modules are being disseminated to hundreds of students in the greater Boston area and are being made available online for widespread use. The MVP mentoring program is training graduate students, undergraduates, and high school students in marine evolutionary ecology, statistical genomics, and machine learning. This research addresses a pressing societal need to more informatively match genotypes to environments for restoration, farming, and assisted gene flow efforts. Results are being disseminated to stakeholders in the oyster industry.</p>
<p>The goal of this research is to evaluate if MLAs, which can model non-linearities, can be used to understand and predict adaptation to multivariate environments under a wide range of scenarios. In Objective 1, the Principal Investigator (PI) is creating simulated datasets with different aspects of realism, and using them to evaluate and refine the MLAs. This novel set of simulations is studying genome evolution under high gene flow in complex, multivariate environments. In Objective 2, the PI is building on their expertise with the Eastern oyster to evaluate the MLAs in a field setting. The PI is first developing a comprehensive seascape genomic dataset and using it to train MLAs to predict an individual's multivariate environment based on a single nucleotide polymorphism genotype. Then, the PI is testing if the MLA prediction can predict the fitness of different genotypes from across the species range when raised in common garden field conditions. In Objective 3, the PI is integrating research and education by using the data obtained from Objs. 1 and 2 to develop a series of original "MVP Learning Modules" with interactive web apps for persons at different levels of understanding, using the relatable example of an oyster restoration project. This research lays the foundation for future studies by producing datasets that could become classical examples for developing and benchmarking innovative modeling approaches.</p>
<p>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.</p>
Model Validation Program
largerWorkCitation
project
eng; USA
oceans
-97.174
-69.532
28.096
44.0245
2022-09-01
2025-05-01
East coast of North America
0
BCO-DMO catalogue of parameters from Environmental data from sites (source populations, and experimental sites) from common garden experiments with Eastern oyster (Crassostrea virginica) run as part of CviMVP project from 2023-2025 in the Chesapeake Bay
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/1009145.rdf
Name: site_name
Units: unitless
Description: <p>Common garden or experimental source population site name</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009146.rdf
Name: Latitude_manual
Units: Decimal degrees
Description: <p>Experimental site latitude</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009148.rdf
Name: Longitude_manual
Units: Decimal degrees
Description: <p>Experimental site longitude</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009150.rdf
Name: salinity_quantile_10
Units: parts per thousand (PPT)
Description: <p>10% quantile of salinity for a given site</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009151.rdf
Name: salinity_quantile_90
Units: parts per thousand (PPT)
Description: <p>90% quantile of salinity for a given site</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009152.rdf
Name: temp_quantile_10
Units: Celsius (deg C)
Description: <p>10% quantile of temperature for a given site</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009153.rdf
Name: temp_quantile_90
Units: Celsius (deg C)
Description: <p>90% quantile of temperature for a given site</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009154.rdf
Name: Dermo_Prevalence
Units: unitless
Description: <p>Prevalence of the oyster disease Dermo during the experimental period. Proportion Infected</p>
http://lod.bco-dmo.org/id/dataset-parameter/1009155.rdf
Name: MSX_Prevalence
Units: unitless
Description: <p>Prevalence of the oyster disease MSX during the experimental period. Proportion Infected</p>
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
https://osprey.bco-dmo.org/dataset/1007572/data/download
download
onLine
dataset
<p>The experimental source population dataset consisted of historic data for our six wild populations and two selection lines, which was input as the starting environment in the offset model (i.e., the environment being predicted from). We developed abiotic and biotic (e.g., disease caused by parasites) environmental datasets for genomic offset modeling, and explored how the inclusion of biotic variables affected the predictive performance of offset models.</p>
<p>For abiotic variables, we included extreme quantiles (10th and 90th quantiles) for temperature (ºC) and salinity (ppt). Extreme quantiles were chosen to represent environmental conditions as they presumably capture the extreme conditions likely to impose selective pressures, without suffering from the statistical issues associated with averaging maximums or minimums. Environmental data for the seascape training dataset and experimental source population site-of-origin data included the 10th and 90th quantiles for salinity (ppt) and temperature (ºC) across as many years as data were available (Eppley et al. 2026, Bajaj &amp; Mongillo et al. 2026). Environmental data for the common garden environment at Lewisetta and York River included the 10th and 90th quantiles for salinity (ppt) and temperature (ºC) &nbsp;for only the experimental period.&nbsp;</p>
<p>For biotic (disease) variables, we included population-level prevalence of the two parasites (disease prevalence). In the experimental source population dataset, which was collected during months with historic precedent of peak disease, the prevalence of MSX and Dermo for each population was calculated according to the proportion of individual oysters with each disease as determined by multiplex qPCR assay (supplemental methods) (Piesz et al. 2022; Ewart and Ford 1993). For the common garden sites, the prevalence of MSX and Dermo in the surrounding environment was determined by using Ray's Fluid Thioglycollate Medium (RFTM) assay on a set of 20 naive “sentinel” oysters deployed on both locations and then assessed monthly during peak disease season (July through November) (Ray 1952; Dungan and Bushek 2015).</p>
Specified by the Principal Investigator(s)
<p>For abiotic variables, we included extreme quantiles (10th and 90th quantiles) for temperature (ºC) and salinity (ppt). Extreme quantiles were chosen to represent environmental conditions as they presumably capture the extreme conditions likely to impose selective pressures, without suffering from the statistical issues associated with averaging maximums or minimums. Environmental data for the seascape training dataset and experimental source population site-of-origin data included the 10th and 90th quantiles for salinity (ppt) and temperature (ºC) across as many years as data were available (Eppley et al. 2026, Bajaj &amp; Mongillo et al. 2026). Environmental data for the common garden environment at Lewisetta and York River included the 10th and 90th quantiles for salinity (ppt) and temperature (ºC) &nbsp;for only the experimental period.&nbsp;</p>
<p>For biotic (disease) variables, we included population-level prevalence of the two parasites (disease prevalence). In the experimental source population dataset, which was collected during months with historic precedent of peak disease, the prevalence of MSX and Dermo for each population was calculated according to the proportion of individual oysters with each disease as determined by multiplex qPCR assay (supplemental methods) (Piesz et al. 2022; Ewart and Ford 1993). For the common garden sites, the prevalence of MSX and Dermo in the surrounding environment was determined by using Ray's Fluid Thioglycollate Medium (RFTM) assay on a set of 20 naive “sentinel” oysters deployed on both locations and then assessed monthly during peak disease season (July through November) (Ray 1952; Dungan and Bushek 2015).</p>
Specified by the Principal Investigator(s)
Curation Actions Performed on Data
- Loaded CviMVP_expdat_env.csv, naming resource cvimvp_expdat_env, with missing values "", "NA" set
- Set column types on cvimvp_expdat_env: Dermo_Prevalence, Latitude_manual, Longitude_manual, MSX_Prevalence, salinity_quantile_10, salinity_quantile_90, temp_quantile_10, temp_quantile_90 as number; site_name as string
- Updated column metadata on cvimvp_expdat_env: added descriptions, standard name IDs, and supplied units for Dermo_Prevalence, Latitude_manual, Longitude_manual, MSX_Prevalence, salinity_quantile_10, salinity_quantile_90, site_name, temp_quantile_10, temp_quantile_90; marked Latitude_manual and Longitude_manual as primary parameters
- Renamed resource cvimvp_expdat_env to 1007572_cvimvp-env-data
- Loaded CviMVP_disease_averages_common_gardens.csv, naming resource disease-averages-common-gardens, with missing values "", "nd", "NA" set; Pea_crab_se values of 0 retained as genuine numeric zero rather than treated as missing
- Set column types on disease-averages-common-gardens: Dermo_Prevalence, Dermo_Prevalence_se, MSX_Prevalence, MSX_Prevalence_se, Pea_crab, Pea_crab_se as number; site_name as string
- Updated column metadata on disease-averages-common-gardens: added descriptions, standard name IDs, and supplied units for Dermo_Prevalence, Dermo_Prevalence_se, MSX_Prevalence, MSX_Prevalence_se, Pea_crab, Pea_crab_se, site_name
- Saved as final tables: 1007572_cvimvp-env-data.csv, disease-averages-common-gardens.csv
Curation Actions Performed on Metadata
* References for in text citations were not provided. Most were found in the metadata of the Related Datsets about the same experiment, or the results publication. However these two were not found "Piesz et al. 2022, Dungan and Bushek 2015"
* Time range of the dataset was not supplied. The data manager entered it as the time of:
From: 2022-09 (start of broodstock collection) since this dataset reports the source population environmental data from those broodstock sources.
To: 2025-05 (final tissue sampling date 2025-05-27, marking the end of the ~2-year monitoring period)
* Pending - the data submitter is revising the submission with metadata changes.
Known Issues [Potentially Impacting Reuse]
* TBD (submitter is revising submission)
Specified by BCO-DMO Data Managers
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact