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Linking population dynamics models with empirically derived models through phytoplankton primary production.

Motomi Genkai-Kato1

  • 1Graduate School of Kuroshio Science Kochi University Kochi Japan.

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|December 23, 2021
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Comparing phytoplankton primary production (PP) models reveals sensitivity to sinking, grazing, and light. Including parasitism in cyanobacterial bloom models resolves discrepancies between process-oriented and population dynamics methods.

Keywords:
cyanobacteriaphytoplanktonpopulation dynamicsprimary production

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Area of Science:

  • Aquatic Ecology
  • Limnology
  • Biogeochemistry

Background:

  • Phytoplankton primary production (PP) is crucial for aquatic ecosystems.
  • Existing models for estimating PP differ in their calculation approaches.
  • Process-oriented models use photosynthesis-light relationships, while population dynamics models use population data.

Purpose of the Study:

  • To compare process-oriented and population dynamics-based PP estimation methods.
  • To identify factors influencing PP estimates in various lake types.
  • To enhance population dynamics models using insights from process-oriented models.

Main Methods:

  • Comparison of two fundamentally different PP estimation methods: process-oriented and population dynamics-based.
  • Analysis of sensitivity to phytoplankton sinking, zooplankton grazing, and light parameters (e.g., day length).
  • Inclusion of parasite-related loss terms for cyanobacterial blooms to resolve model discrepancies.

Main Results:

  • Both PP estimation methods are sensitive to phytoplankton sinking and zooplankton grazing rates.
  • Process-oriented models are sensitive to light parameters like day length.
  • Discrepancies between methods, especially during cyanobacterial blooms, were resolved by incorporating parasite-related mortality (19%-33% loss).

Conclusions:

  • Parasitism plays a critical role in aquatic ecosystem metabolism, particularly in regulating cyanobacterial populations.
  • Accounting for parasite-induced mortality is essential for accurate PP estimation, especially during blooms.
  • Integrating insights from different PP models enhances our understanding of aquatic ecosystem functioning.