Espen Strand

Bio: Espen Strand is an academic researcher from University of Bergen. The author has contributed to research in topics: Calanus finmarchicus & Mesopelagic zone. The author has an hindex of 12, co-authored 26 publications receiving 2984 citations.

Artificial evolution of life history and behavior.

Abstract: 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 Muller’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‐hist...

Implementing behaviour in individual-based models using neural networks and genetic algorithms

Abstract: Even though individual-based models (IBMs) have become very popular in ecology during the last decade, there have been few attempts to implement behavioural aspects in IBMs. This is partly due to lack of appropriate techniques. Behavioural and life history aspects can be implemented in IBMs through adaptive models based on genetic algorithms and neural networks (individual-based-neural network-genetic algorithm, ING). To investigate the precision of the adaptation process, we present three cases where solutions can be found by optimisation. These cases include a state-dependent patch selection problem, a simple game between predators and prey, and a more complex vertical migration scenario for a planktivorous fish. In all cases, the optimal solution is calculated and compared with the solution achieved using ING. The results show that the ING method finds optimal or close to optimal solutions for the problems presented. In addition it has a wider range of potential application areas than conventional techniques in behavioural modelling. Especially the method is well suited for complex problems where other methods fail to provide answers.

Adaptive Patch Searching Strategies in Fragmented Landscapes

Abstract: The search strategies dispersers employ to search for new habitat patches affect individuals’ search success and subsequently landscape connectivity and metapopulation viability. Some evidence indicates that individuals within the same species may display a variety of behavioural patch searching strategies rather than one species-specific strategy. This may result from landscape heterogeneity. We modelled the evolution of individual patch searching strategies in different landscapes. Specifically, we analysed whether evolution can favour different, co-existing, behavioural search strategies within one population and to what extent this coexistence of multiple strategies was dependent on landscape configuration. Using an individual-based simulation model, we studied the evolution of patch searching strategies in three different landscape configurations: uniform, random and clumped. We found that landscape configuration strongly influenced the evolved search strategy. In uniform landscapes, one fixed search strategy evolved for the entire spatially structured population, while in random and clumped landscapes, a set of different search strategies emerged. The coexistence of several search strategies also strongly depended on the dispersal mortality. We show that our result can affect landscape connectivity and metapopulation dynamics.

Tutorial on agent-based modelling and simulation

Abstract: Agent-based modelling and simulation (ABMS) is a relatively new approach to modelling systems composed of autonomous, interacting agents. Agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions. The development of agent modelling tools, the availability of micro-data, and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines. This article provides a brief introduction to ABMS, illustrates the main concepts and foundations, discusses some recent applications across a variety of disciplines, and identifies methods and toolkits for developing agent models.

Individual-based modeling and ecology

Abstract: Individual-based modeling is a new, exciting discipline that allows ecologists to explore, using computer simulations, how properties of populations and ecosystems might evolve from the characteristics and behaviors of individual organisms. Individual-based Modeling and Ecology, written by Volker Grimm and Steven F. Railsback, gives an excellent introduction and overview of this field. It should be read by everyone interested in individual-based modeling, and especially by anyone contemplating developing, or being involved with a group developing, an individualbased model.

Position paper: Characterising performance of environmental models

Abstract: In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate level of confidence in their performance. This paper reviews techniques available across various fields for characterising the performance of environmental models with focus on numerical, graphical and qualitative methods. General classes of direct value comparison, coupling real and modelled values, preserving data patterns, indirect metrics based on parameter values, and data transformations are discussed. In practice environmental modelling requires the use and implementation of workflows that combine several methods, tailored to the model purpose and dependent upon the data and information available. A five-step procedure for performance evaluation of models is suggested, with the key elements including: (i) (re)assessment of the model's aim, scale and scope; (ii) characterisation of the data for calibration and testing; (iii) visual and other analysis to detect under- or non-modelled behaviour and to gain an overview of overall performance; (iv) selection of basic performance criteria; and (v) consideration of more advanced methods to handle problems such as systematic divergence between modelled and observed values.