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Seasonal Variability of Phytoplankton Population Dynamics in Coastal New Hampshire.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Seasonal Variability of Phytoplankton Population Dynamics in Coastal New Hampshire./
Author:
Erickson, Molly.
Published:
Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:
52 p.
Notes:
Source: Masters Abstracts International, Volume: 83-12.
Contained By:
Masters Abstracts International83-12.
Subject:
Biological oceanography. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29169439
ISBN:
9798819384015
Seasonal Variability of Phytoplankton Population Dynamics in Coastal New Hampshire.
Erickson, Molly.
Seasonal Variability of Phytoplankton Population Dynamics in Coastal New Hampshire.
- Ann Arbor : ProQuest Dissertations & Theses, 2022 - 52 p.
Source: Masters Abstracts International, Volume: 83-12.
Thesis (M.S.)--University of New Hampshire, 2022.
This item must not be sold to any third party vendors.
Phytoplankton population dynamics are significant indicators of ecosystem health and marine food web structure. Phytoplankton population dynamics are also highly variable, especially within complex coastal ocean systems. To capture temporal phytoplankton population dynamics, a weekly time series measuring temperature, salinity, tide height, chlorophyll a concentration, nutrients, and phytoplankton biodiversity was performed at two sites in New Hampshire over an annual cycle. Despite being geographically close in proximity, the coastal and estuarine locations were very different in terms of blooming periods, community composition, and ecological factors. Phytoplankton blooms occurred at least once each season on the coast, consisting of Rhizosolenia sp., Skeletonema sp., and Coscinodiscus sp., while the estuary displayed only a spring bloom of Coscinodiscus sp., Detonula sp., and Thalassiosira sp. Diatom species richness was higher on the coast (13 species, 11,925 mean diatoms L-1) even though the estuary had double the diatom abundance (10 species, 22,641 mean diatoms L-1). PO4, NH4, NO2, and SiO2 differed in seasonality between locations, with nutrients being higher in winter on the coast, and higher in summer in the estuary. Seasonal variability of phytoplankton population dynamics and environmental factors portray the importance of frequent, long-term measurements of these parameters in diverse habitats. Ultimately, time series can provide essential information for ecosystem modeling, the fisheries and aquaculture industries, and future trends in climate change.
ISBN: 9798819384015Subjects--Topical Terms:
2122748
Biological oceanography.
Subjects--Index Terms:
Blooms
Seasonal Variability of Phytoplankton Population Dynamics in Coastal New Hampshire.
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Phytoplankton population dynamics are significant indicators of ecosystem health and marine food web structure. Phytoplankton population dynamics are also highly variable, especially within complex coastal ocean systems. To capture temporal phytoplankton population dynamics, a weekly time series measuring temperature, salinity, tide height, chlorophyll a concentration, nutrients, and phytoplankton biodiversity was performed at two sites in New Hampshire over an annual cycle. Despite being geographically close in proximity, the coastal and estuarine locations were very different in terms of blooming periods, community composition, and ecological factors. Phytoplankton blooms occurred at least once each season on the coast, consisting of Rhizosolenia sp., Skeletonema sp., and Coscinodiscus sp., while the estuary displayed only a spring bloom of Coscinodiscus sp., Detonula sp., and Thalassiosira sp. Diatom species richness was higher on the coast (13 species, 11,925 mean diatoms L-1) even though the estuary had double the diatom abundance (10 species, 22,641 mean diatoms L-1). PO4, NH4, NO2, and SiO2 differed in seasonality between locations, with nutrients being higher in winter on the coast, and higher in summer in the estuary. Seasonal variability of phytoplankton population dynamics and environmental factors portray the importance of frequent, long-term measurements of these parameters in diverse habitats. Ultimately, time series can provide essential information for ecosystem modeling, the fisheries and aquaculture industries, and future trends in climate change.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29169439
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