Peacock Emily E.

No Thumbnail Available
Last Name
Peacock
First Name
Emily E.
ORCID
0000-0003-0194-7282

Filters

2021 - 2023 4
3 1
4
Reset filters

Settings
Author: Batchelder, Sidney × Start date: 2006 ×

Search Results

Now showing 1 - 4 of 4
  • Dataset
    Martha's Vineyard Coastal Observatory 2021
    (Woods Hole Oceanographic Institution, 2022-06-24) Cinquino, Eve ; Batchelder, Sidney ; Fredericks, Janet J. ; Sisson, John D. ; Faluotico, Stephen M. ; Popenoe, Hugh ; Sandwith, Zoe O. ; Crockford, E. Taylor ; Peacock, Emily E. ; Shalapyonok, Alexi ; Sosik, Heidi M. ; Kirincich, Anthony R. ; Edson, James B. ; Trowbridge, John H.
    Martha's Vineyard Coastal Observatory (MVCO) is a leading research and engineering facility operated by Woods Hole Oceanographic Institution. MVCO has been collecting ocean and atmospheric data at 3 sites on and near Martha's Vineyard since 2001. A meteorological mast (met mast) on South Beach in Edgartown, MA has collected atmospheric data since May 31 2001. An Air Sea Interaction Tower (ASIT) has been collecting atmospheric and subsurface oceanic data since August 5, 2004. A seafloor node (12m node) has been collecting oceanic data from the seafloor since June 14, 2001. This dataset encompasses the core data (wind speed and direction, air pressure, temperature and relative humidity, water temperature and salinity, and wave data) that has been collected during this period. To learn more about the facility and see additional data collected during short term deployments, visit the MVCO Website (https://mvco.whoi.edu/).
  • Dataset
    Martha’s Vineyard Coastal Observatory
    (Woods Hole Oceanographic Institution, 2021-10-15) Cinquino, Eve ; Batchelder, Sidney ; Fredericks, Janet J. ; Sisson, John D. ; Faluotico, Stephen M. ; Popenoe, Hugh ; Sandwith, Zoe O. ; Crockford, E. Taylor ; Peacock, Emily E. ; Shalapyonok, Alexi ; Sosik, Heidi M. ; Kirincich, Anthony R. ; Edson, James B. ; Trowbridge, John H.
    Martha's Vineyard Coastal Observatory (MVCO) is a leading research and engineering facility operated by Woods Hole Oceanographic Institution. MVCO has been collecting ocean and atmospheric data at 3 sites on and near Martha's Vineyard since 2001. A meteorological mast (met mast) on South Beach in Edgartown, MA has collected atmospheric data since May 31 2001. An Air Sea Interaction Tower (ASIT) has been collecting atmospheric and subsurface oceanic data since August 5, 2004. A seafloor node (12m node) has been collecting oceanic data from the seafloor since June 14, 2001. This dataset encompasses the core data (wind speed and direction, air pressure, temperature and relative humidity, water temperature and salinity, and wave data) that has been collected during this period. To learn more about the facility and see additional data collected during short term deployments, visit the MVCO Website (https://mvco.whoi.edu/).
  • Dataset
    Martha's Vineyard Coastal Observatory 2022
    (Woods Hole Oceanographic Institution, 2023-01-31) Cinquino, Eve ; Batchelder, Sidney ; Fredericks, Janet J. ; Sisson, John D. ; Faluotico, Stephen M. ; Popenoe, Hugh ; Sandwith, Zoe O. ; Crockford, E. Taylor ; Peacock, Emily E. ; Shalapyonok, Alexi ; Sosik, Heidi M. ; Kirincich, Anthony R. ; Edson, James B. ; Trowbridge, John H.
    Martha's Vineyard Coastal Observatory (MVCO) is a leading research and engineering facility operated by Woods Hole Oceanographic Institution. MVCO has been collecting ocean and atmospheric data at 3 sites on and near Martha's Vineyard since 2001. A meteorological mast (met mast) on South Beach in Edgartown, MA has collected atmospheric data since May 31 2001. An Air Sea Interaction Tower (ASIT) has been collecting atmospheric and subsurface oceanic data since August 5, 2004. A seafloor node (12m node) has been collecting oceanic data from the seafloor since June 14, 2001. This dataset encompasses the core data (wind speed and direction, air pressure, temperature and relative humidity, water temperature and salinity, and wave data) that has been collected during this period. To learn more about the facility and see additional data collected during short term deployments, visit the MVCO Website (https://mvco.whoi.edu/).
  • Article
    Temperature dependence of parasitoid infection and abundance of a diatom revealed by automated imaging and classification
    (National Academy of Sciences, 2023-07-03) Catlett, Dylan ; Peacock, Emily E. ; Crockford, E. Taylor ; Futrelle, Joe ; Batchelder, Sidney ; Stevens, Bethany L. F. ; Gast, Rebecca J. ; Zhang, Weifeng Gordon ; Sosik, Heidi M.
    Diatoms are a group of phytoplankton that contribute disproportionately to global primary production. Traditional paradigms that suggest diatoms are consumed primarily by larger zooplankton are challenged by sporadic parasitic “epidemics” within diatom populations. However, our understanding of diatom parasitism is limited by difficulties in quantifying these interactions. Here, we observe the dynamics of Cryothecomonas aestivalis (a protist) infection of an important diatom on the Northeast U.S. Shelf (NES), Guinardia delicatula, with a combination of automated imaging-in-flow cytometry and a convolutional neural network image classifier. Application of the classifier to >1 billion images from a nearshore time series and >20 survey cruises across the broader NES reveals the spatiotemporal gradients and temperature dependence of G. delicatula abundance and infection dynamics. Suppression of parasitoid infection at temperatures <4 °C drives annual cycles in both G. delicatula infection and abundance, with an annual maximum in infection observed in the fall-winter preceding an annual maximum in host abundance in the winter-spring. This annual cycle likely varies spatially across the NES in response to variable annual cycles in water temperature. We show that infection remains suppressed for ~2 mo following cold periods, possibly due to temperature-induced local extinctions of the C. aestivalis strain(s) that infect G. delicatula. These findings have implications for predicting impacts of a warming NES surface ocean on G. delicatula abundance and infection dynamics and demonstrate the potential of automated plankton imaging and classification to quantify phytoplankton parasitism in nature across unprecedented spatiotemporal scales.