DataSheet_2_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf

Zooplankton play a crucial role in marine ecosystems as the link between the primary producers and higher trophic levels, and as such they are key components of global biogeochemical and ecosystem models. While phytoplankton spatial-temporal dynamics can be tracked using satellite remote sensing, no...

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Main Authors: Niall McGinty, Andrew J. Irwin, Zoe V. Finkel, Stephanie Dutkiewicz
Format: Dataset
Language:unknown
Published: 2023
Subjects:
Online Access:https://doi.org/10.3389/fmars.2023.989770.s002
https://figshare.com/articles/dataset/DataSheet_2_Using_ecological_partitions_to_assess_zooplankton_biogeography_and_seasonality_pdf/22724516
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spelling ftfrontimediafig:oai:figshare.com:article/22724516 2023-06-11T04:15:01+02:00 DataSheet_2_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf Niall McGinty Andrew J. Irwin Zoe V. Finkel Stephanie Dutkiewicz 2023-05-01T04:07:21Z https://doi.org/10.3389/fmars.2023.989770.s002 https://figshare.com/articles/dataset/DataSheet_2_Using_ecological_partitions_to_assess_zooplankton_biogeography_and_seasonality_pdf/22724516 unknown doi:10.3389/fmars.2023.989770.s002 https://figshare.com/articles/dataset/DataSheet_2_Using_ecological_partitions_to_assess_zooplankton_biogeography_and_seasonality_pdf/22724516 CC BY 4.0 Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering zooplankton biogeochemical model seasonality correlation biomass Dataset 2023 ftfrontimediafig https://doi.org/10.3389/fmars.2023.989770.s002 2023-05-03T23:11:57Z Zooplankton play a crucial role in marine ecosystems as the link between the primary producers and higher trophic levels, and as such they are key components of global biogeochemical and ecosystem models. While phytoplankton spatial-temporal dynamics can be tracked using satellite remote sensing, no analogous data product is available to validate zooplankton model output. We develop a procedure for linking irregular and sparse observations of mesozooplankton biomass with model output to assess regional seasonality of mesozooplankton. We use output from a global biogeochemical/ecosystem model to partition the ocean according to seasonal patterns of modeled mesozooplankton biomass. We compare the magnitude and temporal dynamics of the model biomass with in situ observations averaged within each partition. Our analysis shows strong correlations and little bias between model and data in temperate, strongly seasonally variable regions. Substantial discrepancies exist between model and observations within the tropical partitions. Correlations between model and data in the tropical partitions were not significant and in some cases negative. Seasonal changes in tropical mesozooplankton biomass were weak, driven primarily by local perturbations in the velocity and extent of currents. Microzooplankton composed a larger fraction of total zooplankton biomass in these regionsWe also examined the ability of the model to represent several dominant taxonomic groups. We identified several Calanus species in the North Atlantic partitions and Euphausiacea in the Southern Ocean partitions that were well represented by the model. This partition-scale comparison captures biogeochemically important matches and mismatches between data and models, suggesting that elaborating models by adding trait differences in larger zooplankton and mixotrophy may improve model-data comparisons. We propose that where model and data compare well, sparse observations can be averaged within partitions defined from model output to quantify zooplankton ... Dataset North Atlantic Southern Ocean Frontiers: Figshare Southern Ocean
institution Open Polar
collection Frontiers: Figshare
op_collection_id ftfrontimediafig
language unknown
topic Oceanography
Marine Biology
Marine Geoscience
Biological Oceanography
Chemical Oceanography
Physical Oceanography
Marine Engineering
zooplankton
biogeochemical model
seasonality
correlation
biomass
spellingShingle Oceanography
Marine Biology
Marine Geoscience
Biological Oceanography
Chemical Oceanography
Physical Oceanography
Marine Engineering
zooplankton
biogeochemical model
seasonality
correlation
biomass
Niall McGinty
Andrew J. Irwin
Zoe V. Finkel
Stephanie Dutkiewicz
DataSheet_2_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf
topic_facet Oceanography
Marine Biology
Marine Geoscience
Biological Oceanography
Chemical Oceanography
Physical Oceanography
Marine Engineering
zooplankton
biogeochemical model
seasonality
correlation
biomass
description Zooplankton play a crucial role in marine ecosystems as the link between the primary producers and higher trophic levels, and as such they are key components of global biogeochemical and ecosystem models. While phytoplankton spatial-temporal dynamics can be tracked using satellite remote sensing, no analogous data product is available to validate zooplankton model output. We develop a procedure for linking irregular and sparse observations of mesozooplankton biomass with model output to assess regional seasonality of mesozooplankton. We use output from a global biogeochemical/ecosystem model to partition the ocean according to seasonal patterns of modeled mesozooplankton biomass. We compare the magnitude and temporal dynamics of the model biomass with in situ observations averaged within each partition. Our analysis shows strong correlations and little bias between model and data in temperate, strongly seasonally variable regions. Substantial discrepancies exist between model and observations within the tropical partitions. Correlations between model and data in the tropical partitions were not significant and in some cases negative. Seasonal changes in tropical mesozooplankton biomass were weak, driven primarily by local perturbations in the velocity and extent of currents. Microzooplankton composed a larger fraction of total zooplankton biomass in these regionsWe also examined the ability of the model to represent several dominant taxonomic groups. We identified several Calanus species in the North Atlantic partitions and Euphausiacea in the Southern Ocean partitions that were well represented by the model. This partition-scale comparison captures biogeochemically important matches and mismatches between data and models, suggesting that elaborating models by adding trait differences in larger zooplankton and mixotrophy may improve model-data comparisons. We propose that where model and data compare well, sparse observations can be averaged within partitions defined from model output to quantify zooplankton ...
format Dataset
author Niall McGinty
Andrew J. Irwin
Zoe V. Finkel
Stephanie Dutkiewicz
author_facet Niall McGinty
Andrew J. Irwin
Zoe V. Finkel
Stephanie Dutkiewicz
author_sort Niall McGinty
title DataSheet_2_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf
title_short DataSheet_2_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf
title_full DataSheet_2_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf
title_fullStr DataSheet_2_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf
title_full_unstemmed DataSheet_2_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf
title_sort datasheet_2_using ecological partitions to assess zooplankton biogeography and seasonality.pdf
publishDate 2023
url https://doi.org/10.3389/fmars.2023.989770.s002
https://figshare.com/articles/dataset/DataSheet_2_Using_ecological_partitions_to_assess_zooplankton_biogeography_and_seasonality_pdf/22724516
geographic Southern Ocean
geographic_facet Southern Ocean
genre North Atlantic
Southern Ocean
genre_facet North Atlantic
Southern Ocean
op_relation doi:10.3389/fmars.2023.989770.s002
https://figshare.com/articles/dataset/DataSheet_2_Using_ecological_partitions_to_assess_zooplankton_biogeography_and_seasonality_pdf/22724516
op_rights CC BY 4.0
op_doi https://doi.org/10.3389/fmars.2023.989770.s002
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