Sigrunn Eliassen

Bio: Sigrunn Eliassen is an academic researcher from University of Bergen. The author has contributed to research in topics: Population & Foraging. The author has an hindex of 16, co-authored 31 publications receiving 3318 citations.

Fishing-induced evolution of growth: concepts, mechanisms and the empirical evidence

Abstract: The interest in fishing-induced life-history evolution has been growing in the last decade, in part because of the increasing number of studies suggesting evolutionary changes in life-history traits, and the potential ecological and economic consequences these changes may have. Among the traits that could evolve in response to fishing, growth has lately received attention. However, critical reading of the literature on growth evolution in fish reveals conceptual confusion about the nature of ‘growth’ itself as an evolving trait, and about the different ways fishing can affect growth and size-at-age of fish, both on ecological and on evolutionary time-scales. It is important to separate the advantages of being big and the costs of growing to a large size, particularly when studying life-history evolution. In this review, we explore the selection pressures on growth and the resultant evolution of growth from a mechanistic viewpoint. We define important concepts and outline the processes that must be accounted for before observed phenotypic changes can be ascribed to growth evolution. When listing traits that could be traded-off with growth rate, we group the mechanisms into those affecting resource acquisition and those governing resource allocation. We summarize potential effects of fishing on traits related to growth and discuss methods for detecting evolution of growth. We also challenge the prevailing expectation that fishing-induced evolution should always lead to slower growth.

Exploration or exploitation: life expectancy changes the value of learning in foraging strategies

Abstract: The acquisition of information is a fundamental part of individual foraging behaviour in heterogeneous and changing environments. We examine how foragers may benefit from utilizing a simple learning rule to update estimates of temporal changes in resource levels. In the model, initial expectation of resource conditions and rate of replacing past information by new experiences are genetically inherited traits. Patch-time allocation differs between learners and foragers that use a fixed patch-leaving threshold throughout the foraging season. It also deviates from foragers that obtain information about the environment at no cost. At the start of a foraging season, learners sample the environment by frequent movements between patches, sacrificing current resource intake for information acquisition. This is done to obtain more precise and accurate estimates of resource levels, resulting in increased intake rates later in season. Risk of mortality may alter the tradeoff between exploration and exploitation and thus change patch sampling effort. As lifetime expectancy decreases, learners invest less in information acquisition and show lower foraging performance when resource level changes through time.

Testing the novelty effect of an m-learning tool on internalization and achievement: A Self-Determination Theory approach

Abstract: Perceived novelty in mobile applications is an inevitable aspect of today's technologies. Studies suggest that this perceived novelty effect increases motivation but wanes once the user becomes accustomed to the product. Using a Self-Determination Theory approach, the present study investigates how different tools relate to students' motivation, basic psychological needs, and achievement, over and above the effect of perceived novelty. The results from a randomized controlled experiment show that a mobile-learning tool and a digital version of a textbook are perceived as more novel than a traditional textbook. However, only the mobile-learning tool enhances the students' basic psychological needs. Additionally, using path-analysis, we find that the mobile-learning tool, need-satisfaction within the mobile-learning tool, and autonomous motivation account for achievement and internalization, over and above the effect of novelty. We argue that this finding is due to the inherent need-supportive elements within the mobile-learning tool that satisfy the basic psychological needs.

Quantifying the adaptive value of learning in foraging behavior.

Abstract: The value of acquiring environmental information depends on the costs of collecting it and its utility. Foragers that search for patchily distributed resources may use experiences in previous patches to learn the habitat quality and adjust their behavior. We map the ecological landscape for the evolution of learning under a range of conditions, including both spatial and temporal heterogeneity. We compare the learning strategy with genetically fixed patch-leaving rules and with strategies of foragers that have free and perfect information about their environment. The model reveals that the efficiency of learning is highest when low encounter stochasticity results in reliable estimates of patch quality, when there is no or little temporal change, and when there is little spatial variability. This partially contrasts with the value of learning, which is highest when there is temporal change, because flexible strategies may track the environmental trend, and when there is spatial variability, because there is a need to distinguish between good and bad patches. Learning rules with short-term memory are beneficial when patch information is accurate and when there is temporal change, whereas learning rules that update slowly are generally more robust to spatial variability.

Citation classic - optimal foraging - a selective review of theory and tests

Abstract: Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.

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.