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Real-Time Ichthyoplankton Drift in Northeast Arctic Cod and Norwegian Spring-Spawning Herring


, : Real-Time Ichthyoplankton Drift in Northeast Arctic Cod and Norwegian Spring-Spawning Herring. PLoS ONE 6(11): e27367, 1-7

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.

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