RT Journal Article
SR Electronic
T1 The shape of density dependence and the relationship between population growth, intraspecific competition and equilibrium population density
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 485946
DO 10.1101/485946
A1 Emanuel A. Fronhofer
A1 Lynn Govaert
A1 Mary I. O’Connor
A1 Sebastian J. Schreiber
A1 Florian Altermatt
YR 2018
UL http://biorxiv.org/content/early/2018/12/04/485946.abstract
AB Many ecologists and evolutionary biologists use the logistic growth model to capture density dependence. However, assumptions and limitations of this popular model are not well appreciated. Here, we derive population growth models from underlying consumer-resource dynamics and show that the logistic is likely not applicable to many biological systems.We first validate that filter feeders (type I functional response) using abiotic resources generally follow a convex density-regulation function, fully described by the continuous-time Beverton-Holt model. Furthermore, we show that saturating consumers (type II functional response) exhibit density-regulation functions that can switch from concave to convex. We derive a density-regulation function for saturating feeders on abiotic resources and show that more complex consumer dynamics can be well approximated with a continuous-time Maynard Smith-Slatkin model.Importantly, we show how population level parameters, such as intrinsic rates of increase and equilibrium population densities are not independent, but are functions of the same underlying parameters. Our work highlights that the commonly assumed positive relationship between equilibrium population density and competitive ability is typically invalid. Alternatively, we propose simple and general relationships between intrinsic rates of increase and equilibrium population densities that capture the essence of different consumer-resource systems. Finally, we expand our considerations to include multiple consumer and resource species.Relating population level models to underlying mechanisms allows us to discuss applications to evolutionary outcomes and how these models depend on environmental conditions, like temperature via metabolic scaling. Finally, we use time-series from microbial food chains to fit population growth models and validate theoretical predictions.Density-regulation functions need to be chosen carefully as their shapes will depend on the study system’s biology. Importantly, we provide a mechanistic understanding of correlations between model parameters, which has implications for theory and for formulating biologically sound and empirically testable predictions.