Dynamic Energy Budget theory meets individual‐based modelling: a generic and accessible implementation

Abstract

Summary1. Dynamic Energy Budget (DEB) theory was designed to understand the dynamics of biological systems from cells to populations and ecosystems via a mass balance approach of individuals. However, most work so far has focused on the level of the individual. To encourage further use of DEB theory in a population context, we developed DEB-IBM, a generic individual-based model (IBM) that is based on DEB theory. 2. The generic IBM is implemented as a computer program using NetLogo, a free software platform that is accessible to biologists with little programming background. The IBM uses DEB to represent assimilation, maintenance, growth and reproduction of individuals. The model description follows the overview, design and details (ODD) protocol, a generic format for describing IBMs, and thereby provides a novel and accessible introduction to DEB theory and how it works in a population context. 3. Dynamic Energy Budget-individual-based model can be used to explore properties of both individual life-history traits and population dynamics, which emerge from the set of DEB parameters of a species, and their interaction with environmental variables such as food density. Furthermore, DEB-IBM can be adapted to address specific research questions, for example by including spatial effects. A user manual explains how this can be done. 4. Dynamic Energy Budget-individual-based model is designed to both facilitate use and testing DEB theory in a population context and to advance individual-based modelling by basing the representation of individuals on well-tested physiological principles.

Physiology in the service of fisheries science: Why thinking mechanistically matters

“…A problem in its application, however, is that many of the underlying processes are intimately interlinked, complicating the study of individual processes or their contributions (Kooijman 2010). Recent DEB extensions link to bioenergetics (Nisbet et al 2012) and individual based models (IBM; Martin et al 2012). Finally, SDMs use spatial ecological data to predict species range and habitat suitability, and can be parameterized with physiological data to offer a mechanistic view of the fundamental niche that can be mapped in a landscape context for robust mechanistic insights (Kearney and Porter 2009).…”

“…This mechanistic model attempts to explain biological dynamics from cells to populations across a wide range of organismal biodiversity via a mass balance energy approach of individuals (Nisbet et al 2012;Kooijman 2001;Martin et al 2012). The DEB model has several advantages including: relying on simple physiological principles common to all species, a limited number of parameters that integrate genetic and environmental effects on the animal, and a structure that allows for the integration of different time scales (including ontogeny and evolutionary time) (Alunno-Bruscia et al 2009;Martin et al 2012). A problem in its application, however, is that many of the underlying processes are intimately interlinked, complicating the study of individual processes or their contributions (Kooijman 2010).…”

Thermodynamic extremization principles and their relevance to ecology

“…However, a key challenge for these models is determining and quantifying relevant parameters, such as metabolic rate (Martin et al 2012). These parameters are central to many applications, but often are difficult to measure directly (Martin et al 2013).…”

“…These parameters are central to many applications, but often are difficult to measure directly (Martin et al 2013). Martin et al (2012) have developed an IBM framework based on DEB, and this framework has been used successfully to link organism-scale measurements and theory to population-scale patterns (Martin et al 2013). However, these models are limited by the need for taxon-specific parameter estimates (Martin et al 2013).…”

Towards a standard model for research in agent-based modeling and simulation

“…It uses a hybrid interpreter and compiler that partially compiles ABM code to JVM bytecode (Sondahl, Tisue & Wilensky, 2006). It comes with powerful built-in procedures and is relatively easy to learn, making ABMs more accessible to researchers without programming experience (Martin et al, 2012). Advantages of having a NetLogo version include real-time visualization of simulation, pseudo-code like model descriptions, simplicity in changing and testing different model aspects and parameters, and command-line access for batch runs and cycling through different parameter sets, even allowing for multithreaded simultaneous execution of multiple runs.…”