Real-Time Ichthyoplankton Drift in Northeast Arctic Cod and Norwegian Spring-Spawning Herring
Background: Individual-based biophysical larval models, initialized and parameterized by observations, enable numerical investigations of various factors regulating survival of young fish until they recruit into the adult population. Exponentially decreasing numbers in Northeast Arctic cod and Norwe...
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Online Access: | http://hdl.handle.net/11250/108118 https://doi.org/10.1371/journal.pone.0027367 |
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ftimr:oai:imr.brage.unit.no:11250/108118 2023-05-15T14:30:23+02:00 Real-Time Ichthyoplankton Drift in Northeast Arctic Cod and Norwegian Spring-Spawning Herring Vikebø, Frode Ådlandsvik, Bjørn Albretsen, Jon Sundby, Svein Stenevik, Erling Kåre Huse, Geir Svendsen, Einar Kristiansen, Trond Eriksen, Elena 2011-11-16 application/pdf http://hdl.handle.net/11250/108118 https://doi.org/10.1371/journal.pone.0027367 eng eng Public Library of Science urn:issn:1932-6203 http://hdl.handle.net/11250/108118 http://dx.doi.org/10.1371/journal.pone.0027367 6 PLoS ONE 11 numerical simulations numerisk havmodellering VDP::Mathematics and natural science: 400::Zoology and botany: 480::Marine biology: 497 Journal article Peer reviewed 2011 ftimr https://doi.org/10.1371/journal.pone.0027367 2021-09-23T20:15:37Z Background: Individual-based biophysical larval models, initialized and parameterized by observations, enable numerical investigations of various factors regulating survival of young fish until they recruit into the adult population. Exponentially decreasing numbers in Northeast Arctic cod and Norwegian Spring Spawning herring early changes emphasizes the importance of early life history, when ichthyoplankton exhibit pelagic free drift. However, while most studies are concerned with past recruitment variability it is also important to establish real-time predictions of ichthyoplankton distributions due to the increasing human activity in fish habitats and the need for distribution predictions that could potentially improve field coverage of ichthyoplankton. Methodology/Principal Findings: A system has been developed for operational simulation of ichthyoplankton distributions. We have coupled a two-day ocean forecasts from the Norwegian Meteorological Institute with an individual-based ichthyoplankton model for Northeast Arctic cod and Norwegian Spring Spawning herring producing daily updated maps of ichthyoplankton distributions. Recent years observed spawning distribution and intensity have been used as input to the model system. The system has been running in an operational mode since 2008. Surveys are expensive and distributions of early stages are therefore only covered once or twice a year. Comparison between model and observations are therefore limited in time. However, the observed and simulated distributions of juvenile fish tend to agree well during early fall. Area-overlap between modeled and observed juveniles September 1st range from 61 to 73%, and 61 to 71% when weighted by concentrations. Conclusions/Significance: The model system may be used to evaluate the design of ongoing surveys, to quantify the overlap with harmful substances in the ocean after accidental spills, as well as management planning of particular risky operations at sea. The modeled distributions are already utilized during research surveys to estimate coverage success of sampled biota and immediately after spills from ships at sea. Article in Journal/Newspaper Arctic cod Arctic Northeast Arctic cod Institute for Marine Research: Brage IMR Arctic PLoS ONE 6 11 e27367 |
institution |
Open Polar |
collection |
Institute for Marine Research: Brage IMR |
op_collection_id |
ftimr |
language |
English |
topic |
numerical simulations numerisk havmodellering VDP::Mathematics and natural science: 400::Zoology and botany: 480::Marine biology: 497 |
spellingShingle |
numerical simulations numerisk havmodellering VDP::Mathematics and natural science: 400::Zoology and botany: 480::Marine biology: 497 Vikebø, Frode Ådlandsvik, Bjørn Albretsen, Jon Sundby, Svein Stenevik, Erling Kåre Huse, Geir Svendsen, Einar Kristiansen, Trond Eriksen, Elena Real-Time Ichthyoplankton Drift in Northeast Arctic Cod and Norwegian Spring-Spawning Herring |
topic_facet |
numerical simulations numerisk havmodellering VDP::Mathematics and natural science: 400::Zoology and botany: 480::Marine biology: 497 |
description |
Background: Individual-based biophysical larval models, initialized and parameterized by observations, enable numerical investigations of various factors regulating survival of young fish until they recruit into the adult population. Exponentially decreasing numbers in Northeast Arctic cod and Norwegian Spring Spawning herring early changes emphasizes the importance of early life history, when ichthyoplankton exhibit pelagic free drift. However, while most studies are concerned with past recruitment variability it is also important to establish real-time predictions of ichthyoplankton distributions due to the increasing human activity in fish habitats and the need for distribution predictions that could potentially improve field coverage of ichthyoplankton. Methodology/Principal Findings: A system has been developed for operational simulation of ichthyoplankton distributions. We have coupled a two-day ocean forecasts from the Norwegian Meteorological Institute with an individual-based ichthyoplankton model for Northeast Arctic cod and Norwegian Spring Spawning herring producing daily updated maps of ichthyoplankton distributions. Recent years observed spawning distribution and intensity have been used as input to the model system. The system has been running in an operational mode since 2008. Surveys are expensive and distributions of early stages are therefore only covered once or twice a year. Comparison between model and observations are therefore limited in time. However, the observed and simulated distributions of juvenile fish tend to agree well during early fall. Area-overlap between modeled and observed juveniles September 1st range from 61 to 73%, and 61 to 71% when weighted by concentrations. Conclusions/Significance: The model system may be used to evaluate the design of ongoing surveys, to quantify the overlap with harmful substances in the ocean after accidental spills, as well as management planning of particular risky operations at sea. The modeled distributions are already utilized during research surveys to estimate coverage success of sampled biota and immediately after spills from ships at sea. |
format |
Article in Journal/Newspaper |
author |
Vikebø, Frode Ådlandsvik, Bjørn Albretsen, Jon Sundby, Svein Stenevik, Erling Kåre Huse, Geir Svendsen, Einar Kristiansen, Trond Eriksen, Elena |
author_facet |
Vikebø, Frode Ådlandsvik, Bjørn Albretsen, Jon Sundby, Svein Stenevik, Erling Kåre Huse, Geir Svendsen, Einar Kristiansen, Trond Eriksen, Elena |
author_sort |
Vikebø, Frode |
title |
Real-Time Ichthyoplankton Drift in Northeast Arctic Cod and Norwegian Spring-Spawning Herring |
title_short |
Real-Time Ichthyoplankton Drift in Northeast Arctic Cod and Norwegian Spring-Spawning Herring |
title_full |
Real-Time Ichthyoplankton Drift in Northeast Arctic Cod and Norwegian Spring-Spawning Herring |
title_fullStr |
Real-Time Ichthyoplankton Drift in Northeast Arctic Cod and Norwegian Spring-Spawning Herring |
title_full_unstemmed |
Real-Time Ichthyoplankton Drift in Northeast Arctic Cod and Norwegian Spring-Spawning Herring |
title_sort |
real-time ichthyoplankton drift in northeast arctic cod and norwegian spring-spawning herring |
publisher |
Public Library of Science |
publishDate |
2011 |
url |
http://hdl.handle.net/11250/108118 https://doi.org/10.1371/journal.pone.0027367 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic cod Arctic Northeast Arctic cod |
genre_facet |
Arctic cod Arctic Northeast Arctic cod |
op_source |
6 PLoS ONE 11 |
op_relation |
urn:issn:1932-6203 http://hdl.handle.net/11250/108118 http://dx.doi.org/10.1371/journal.pone.0027367 |
op_doi |
https://doi.org/10.1371/journal.pone.0027367 |
container_title |
PLoS ONE |
container_volume |
6 |
container_issue |
11 |
container_start_page |
e27367 |
_version_ |
1766304236115591168 |