Espen Strand

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.

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

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

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.

Effects of light intensity on visual prey detection by juvenile Atlantic cod (Gadus morhua L.)

Despite the research on this important fish species for more than a century, surprisingly little is known of some fundamental aspects of the biology of Atlantic cod, such as how light affects foraging behaviour. We measured the reactive distances of juvenile cod (age 1) over light intensities from 0.01 to 64 µmol m−2 s−1 in a controlled laboratory environment, and used these results to estimate the visual range and the parameters for a predictive visual model. The reactive distance at 0.01 µmol m−2 s−1 indicated high sensitivity to low light conditions. The reactive distance was less at intermediate light levels (1.5–6.5 µmol m−2 s−1) and increased thereafter. Only a model with a different set of parameters above and below 5 µmol m−2 s−1 fitted the data, but the validation of the model against another dataset indicated that the generality of the model was poor. We interpret these results as a change in foraging behaviour of juvenile cod at light intensities occurring at twilight in natural habitats, and the results illustrate how behaviour can complicate the relationship between light and reactive distance in a marine teleost.