Mesopelagic Species and Their Potential Contribution to Food and Feed Security-A Case Study from Norway.

The projected increase in global population will demand a major increase in global food production. There is a need for more biomass from the ocean as future food and feed, preferentially from lower trophic levels. In this study, we estimated the mesopelagic biomass in three Norwegian fjords. We analyzed the nutrient composition in six of the most abundant mesopelagic species and evaluated their potential contribution to food and feed security. The six species make up a large part of the mesopelagic biomass in deep Norwegian fjords. Several of the analyzed mesopelagic species, especially the fish species Benthosema glaciale and Maurolicus muelleri, were nutrient dense, containing a high level of vitamin A1, calcium, selenium, iodine, eicopentaenoic acid (EPA), docosahexaenoic acid (DHA) and cetoleic acid. We were able to show that mesopelagic species, whose genus or family are found to be widespread and numerous around the globe, are nutrient dense sources of micronutrients and marine-based ingredients and may contribute significantly to global food and feed security.

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.