Formulating variable carrying capacity by exploring a resource dynamics-based feedback mechanism underlying the population growth models

Abstract

Most of the population growth models comprise the concept of carrying capacity presume that a stable population would have a saturation level characteristic. This indicates that the population growth models have a common implicit feature of resource-limited growth, which contributes at a later stage of population growth by forming a numerical upper bound on the population size. However, a general underlying resource dynamics of the models has not been previously explored, which is the focus of present study. In this paper, we found that there exists a conservation of energy relationship comprising the terms of available resource and population density, jointly interpreted here as total available vital energy in a confined environment. We showed that this relationship determines a density-dependent functional form of relative population growth rate and consequently the parametric equations are in the form depending upon the population density, resource concentration, and time. Thus, the derived form of relative population growth rate is essentially a feedback type, i.e., updating parametric values for the corresponding population density. This resource dynamics-based feedback approach has been implemented for formulating variable carrying capacity in a confined environment. Particularly, at a constant resource replenishment rate, a density-dependent population growth equation similar to the classic logistic equation is derived, while one of the regulating factors of the underlying resource dynamics is that the resource consumption rate is directly proportional to the resource concentration. Likewise two other population growth equations similar to two known popular growth equations are derived based on this resource dynamics-based feedback approach. Using microcosm-derived data of fungus T. virens, we fitted one derived population growth model against the datasets, and concluded that this approach is practically implementable for studying a single population growth regulation in a confined environment.