Erling Kåre Stenevik

Identification of Norwegian spring spawning herring (Clupea harengus L.) larvae from spawning grounds off Western Norway applying otolith microstructure analysis

Abstract After the stock collapse in the 1960s the main spawning grounds of Norwegian spring spawning herring have been located off the Mare coast (northern grounds) and after 1989 spawning has also occurred at previous important grounds off the south-western coast of Norway (southern grounds). An overall objective of this study is to identify offspring from the different main spawning grounds in their nursery areas several months later. This requires criteria for identification of larval components from the main hatching areas. Peak hatching time at the two main spawning areas differ by approximately one month, being earlier at the northern spawning grounds. Passive drift from the southernmost areas adds another 2-3 months to this time difference until the larvae from the southern spawning grounds reach the spawning grounds off More. Otolith microstructure analysis of larvae collected along the drift path of the separate larval populations are used to trace past growth patterns. The results show a signif...

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 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.

Vertical migration of Norwegian spring-spawning herring larvae in relation to predator and prey distribution

Abstract A diel vertical migration (DVM) pattern of Norwegian spring spawning herring (Clupea harengus) larvae was investigated during 19 and 20 April 2009. Factors influencing DVM included physical and biological properties of the water column. Data on larvae, prey and predators were collected with a depth-stratified multisampling device, inshore of Sklinna bank, close to the Norwegian coast, while light conditions were calculated using a Matlab® algorithm. A type I DVM pattern (i.e. deep during daytime, shallow at night) was observed for herring larvae, mainly occurring above the thermocline. No size-dependent differences were observed for larval vertical positioning. The highest overlap in depth distribution with their main prey was observed during daytime, when larvae were distributed deeper in the water column. From acoustics and macroplankton trawl data, a type I DVM was also observed for krill, although their concentrations were relatively low in the area. Cumulative predator–prey overlap plots suggest that krill most likely forage on copepods and smaller organisms. During the day, larvae concentrate near the thermocline to feed, while they move towards the surface at dusk, possibly to use the remaining light to continuing feeding. During the night, when light levels were too low to feed, larvae spread out in the water column above the thermocline. This migration pattern reduces the overlap between larvae and potential predators such as krill, which also move higher up during nighttime. It is suggested that the pattern of herring larvae DVM is a behavioural response to active pursuit of prey.

Characteristics of the Norwegian Coastal Current during years with high recruitment of Norwegian spring spawning herring (Clupea harengus L.)

Norwegian Spring Spawning herring (NSSH) Clupea harengus L. spawn on coastal banks along the west coast of Norway. The larvae are generally transported northward in the Norwegian Coastal Current (NCC) with many individuals utilizing nursery grounds in the Barents Sea. The recruitment to this stock is highly variable with a few years having exceptionally good recruitment. The principal causes of recruitment variability of this herring population have been elusive. Here we undertake an event analysis using data between 1948 and 2010 to gain insight into the physical conditions in the NCC that coincide with years of high recruitment. In contrast to a typical year when northerly upwelling winds are prominent during spring, the years with high recruitment coincide with predominantly southwesterly winds and weak upwelling in spring and summer, which lead to an enhanced northward coastal current during the larval drift period. Also in most peak recruitment years, low-salinity anomalies are observed to propagate northward during the spring and summer. It is suggested that consistent southwesterly (downwelling) winds and propagating low-salinity anomalies, both leading to an enhanced northward transport of larvae, are important factors for elevated recruitment. At the same time, these conditions stabilize the coastal waters, possibly leading to enhanced production and improved feeding potential along the drift route to Barents Sea. Further studies on the drivers of early life history mortality can now be undertaken with a better understanding of the physical conditions that prevail during years when elevated recruitment occurs in this herring stock.

Precision in estimates of density and biomass of Norwegian spring-spawning herring based on acoustic surveys

Abstract The abundance and biomass of the Norwegian spring-spawning herring (NSSH) stock are assessed annually using a virtual population analysis (VPA) applied to catch-at-age data from the fishery and fishery-independent abundance indices derived from research vessel surveys for calibration (‘tuning’). The most important of these surveys is the International Ecosystem Survey in the Nordic Seas (IESNS) and this is highly influential for the outcome of the assessment. Until now, the abundance indices from the IESNS have been reported without any measure of uncertainty. In this study, the sampling errors associated with the density estimates of NSSH from the IESNS for the years 2009–2012 are estimated using design-based survey sampling theory. The annual survey estimates of number and biomass of herring per square nautical mile (nm2) were relatively precise for all age groups combined, with relative standard error (RSE) ranging from 8% to 15%, while for each individual age group (3–12) the RSE was less than 30% for most years. For age groups 1 and 2, and all age groups older than 12 years, the density estimates are highly imprecise, with RSE ranging from 30% to 100%. The precision in estimated density provided here indicates that the time series from the survey can be used to study trends in overall abundance and biomass of the stock. It is recommended that statistical assessment models that can account for sampling errors in input data from survey indices by age group and catch-at-age be tested in the assessments of the NSSH stock.