Predicting Population Dynamics from the Properties of Individuals: A Cross-Level Test of Dynamic Energy Budget Theory

Abstract

Individual-based models (IBMs) are increasingly used to link the dynamics of individuals to higher levels of biological organization. Still, many IBMs are data hungry, species specific, and time-consuming to develop and analyze. Many of these issues would be resolved by using general theories of individual dynamics as the basis for IBMs. While such theories have frequently been examined at the individual level, few cross-level tests exist that also try to predict population dynamics. Here we performed a cross-level test of dynamic energy budget (DEB) theory by parameterizing an individual-based model using individual-level data of the water flea, Daphnia magna, and comparing the emerging population dynamics to independent data from population experiments. We found that DEB theory successfully predicted population growth rates and peak densities but failed to capture the decline phase. Further assumptions on food-dependent mortality of juveniles were needed to capture the population dynamics after the initial population peak. The resulting model then predicted, without further calibration, characteristic switches between small- and large-amplitude cycles, which have been observed for Daphnia. We conclude that cross-level tests help detect gaps in current individual-level theories and ultimately will lead to theory development and the establishment of a generic basis for individual-based models and ecology.

Individual-based models in ecology after four decades

“…This theory has been widely used to describe individual organisms but has recently been shown to correctly predict population dynamics (e.g. cycles) and response to toxicant for laboratory populations of the waterflea, Daphnia magna [62,63]. …”

Per Aspera ad Astra: Through Complex Population Modeling to Predictive Theory

“…ABMs based on a more balanced consideration of parsimony will still focus on avoiding false inclusions to represent mechanisms operating at lower hierarchical levels (e.g., individuals) and local spatial scales. Examples include energy budgets of individuals (Topping et al 2010;Martin et al 2013); adaptive behavior, such as optimal foraging (Stillman and Goss-Custard 2008); habitat selection (Railsback and Harvey 2013); and generic representations of interactions among individuals (Berger and Hildenbrand 2000;Weiner et al 2001).…”

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). 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).…”

“…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).…”

“…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). Extremization principles could be used to facilitate the application of these models, with individual transitions and updates based on an extremization principle.…”