DataSheet_1_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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ftfrontimediafig:oai:figshare.com:article/22724513 2023-06-11T04:15:01+02:00 DataSheet_1_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.s001 https://figshare.com/articles/dataset/DataSheet_1_Using_ecological_partitions_to_assess_zooplankton_biogeography_and_seasonality_pdf/22724513 unknown doi:10.3389/fmars.2023.989770.s001 https://figshare.com/articles/dataset/DataSheet_1_Using_ecological_partitions_to_assess_zooplankton_biogeography_and_seasonality_pdf/22724513 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.s001 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 |
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Open Polar |
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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_1_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_1_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf |
title_short |
DataSheet_1_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf |
title_full |
DataSheet_1_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf |
title_fullStr |
DataSheet_1_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf |
title_full_unstemmed |
DataSheet_1_Using ecological partitions to assess zooplankton biogeography and seasonality.pdf |
title_sort |
datasheet_1_using ecological partitions to assess zooplankton biogeography and seasonality.pdf |
publishDate |
2023 |
url |
https://doi.org/10.3389/fmars.2023.989770.s001 https://figshare.com/articles/dataset/DataSheet_1_Using_ecological_partitions_to_assess_zooplankton_biogeography_and_seasonality_pdf/22724513 |
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.s001 https://figshare.com/articles/dataset/DataSheet_1_Using_ecological_partitions_to_assess_zooplankton_biogeography_and_seasonality_pdf/22724513 |
op_rights |
CC BY 4.0 |
op_doi |
https://doi.org/10.3389/fmars.2023.989770.s001 |
_version_ |
1768371503056814080 |