Bioenergetics modeling of the annual consumption of zooplankton by pelagic fish feeding in the Northeast Atlantic.

The present study uses bioenergetics modeling to estimate the annual consumption of the main zooplankton groups by some of the most commercially important planktivorous fish stocks in the Northeast Atlantic, namely Norwegian spring-spawning (NSS) herring (Clupea harengus), blue whiting (Micromesistius poutassou) and NEA mackerel (Scomber scombrus). The data was obtained from scientific surveys in the main feeding area (Norwegian Sea) in the period 2005-2010. By incorporating novel information about ambient temperature, seasonal growth and changes in the diet from stomach content analyses, annual consumption of the different zooplankton groups by pelagic fish is estimated. The present study estimates higher consumption estimates than previous studies for the three species and suggests that fish might have a greater impact on the zooplankton community as foragers. This way, NEA mackerel, showing the highest daily consumption rates, and NSS herring, annually consume around 10 times their total biomass, whereas blue whiting consume about 6 times their biomass in zooplankton. The three species were estimated to consume an average of 135 million (M) tonnes of zooplankton each year, consisting of 53-85 M tonnes of copepods, 20-32 M tonnes of krill, 8-42 M tonnes of appendicularians and 0.2-1.2 M tonnes of fish, depending on the year. For NSS herring and NEA mackerel the main prey groups are calanoids and appendicularians, showing a peak in consumption during June and June-July, respectively, and suggesting high potential for inter-specific feeding competition between these species. In contrast, blue whiting maintain a low consumption rate from April to September, consuming mainly larger euphausiids. Our results suggest that the three species can coexist regardless of their high abundance, zooplankton consumption rates and overlapping diet. Accordingly, the species might have niche segregation, as they are species specific, showing annual and inter-annual variability in total consumption of the different prey species. These estimates and their inter-annual and inter-specific variation are fundamental for understanding fundamental pelagic predator-prey interactions as well as to inform advanced multispecies ecosystem models.

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 1(st) 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.

Comparative analysis of marine ecosystems: workshop on predator-prey interactions.

Climate and human influences on marine ecosystems are largely manifested by changes in predator-prey interactions. It follows that ecosystem-based management of the world's oceans requires a better understanding of food web relationships. An international workshop on predator-prey interactions in marine ecosystems was held at the Oregon State University, Corvallis, OR, USA on 16-18 March 2010. The meeting brought together scientists from diverse fields of expertise including theoretical ecology, animal behaviour, fish and seabird ecology, statistics, fisheries science and ecosystem modelling. The goals of the workshop were to critically examine the methods of scaling-up predator-prey interactions from local observations to systems, the role of shifting ecological processes with scale changes, and the complexity and organizational structure in trophic interactions.

Artificial evolution of life history and behavior.

We present an individual-based model that uses artificial evolution to predict fit behavior and life-history traits on the basis of environmental data and organism physiology. Our main purpose is to investigate whether artificial evolution is a suitable tool for studying life history and behavior of real biological organisms. The evolutionary adaptation is founded on a genetic algorithm that searches for improved solutions to the traits under scrutiny. From the genetic algorithm's "genetic code," behavior is determined using an artificial neural network. The marine planktivorous fish Müller's pearlside (Maurolicus muelleri) is used as the model organism because of the broad knowledge of its behavior and life history, by which the model's performance is evaluated. The model adapts three traits: habitat choice, energy allocation, and spawning strategy. We present one simulation with, and one without, stochastic juvenile survival. Spawning pattern, longevity, and energy allocation are the life-history traits most affected by stochastic juvenile survival. Predicted behavior is in good agreement with field observations and with previous modeling results, validating the usefulness of the presented model in particular and artificial evolution in ecological modeling in general. The advantages, possibilities, and limitations of this modeling approach are further discussed.