Stochastic Population Models

The chapter “Stochastic Population Models” introduces the concept of stochasticity, why it is sometimes incorporated into models, the consequences of stochasticity for population models, and how these types of models are used to evaluate extinction risk. Ecological systems are (seemingly) governed by randomness, or “stochasticity.” A stochastic model is one that explicitly includes randomness in the prediction of state variable dynamics. Because these models have a random component, each model run will be unique and will rarely look like a deterministic simulation. In this chapter, simple unstructured and density-dependent models are presented to show core concepts, and extensions to structured and density-dependent models are given.

Compartor: a toolbox for the automatic generation of moment equations for dynamic compartment populations

Summary Many biochemical processes in living organisms take place inside compartments that can interact with each other and remodel over time. In a recent work, we have shown how the stochastic dynamics of a compartmentalized biochemical system can be effectively studied using moment equations. With this technique, the time evolution of a compartment population is summarized using a finite number of ordinary differential equations, which can be analyzed very efficiently. However, the derivation of moment equations by hand can become time-consuming for systems comprising multiple reactants and interactions. Here we present Compartor, a toolbox that automatically generates the moment equations associated with a user-defined compartmentalized system. Through the moment equation method, Compartor renders the analysis of stochastic population models accessible to a broader scientific community. Availability and implementation Compartor is provided as a Python package and is available at https://pypi.org/project/compartor/. Source code and usage tutorials for Compartor are available at https://github.com/zechnerlab/Compartor.

Note on the permanence of stochastic population models

The concept of permanence of any system is an important technical issue. This concept is very significant to all kind of systems, e.g., social, medical, biological, population, mechanical, or electrical. It is desirable by scientists and investigators that any system under consideration must be long time survival. For example, if we consider any ecosystem, it is always pre-requisite that this system is permanent. In general language, permanence is just the persistent and bounded system in a particular surface time frame. But the meaning may vary with the type of systems. For example, deterministic and stochastic biological systems have different concepts of permanence in an abstract mathematical platform. The reason is simple: it is due to the mathematical nature of parameters, methods of derivations of the model, biological assumptions, details of the study, etc. In this short note, we consider the stochastic models for their permanence. To address stochastic permanence of biological systems, many different approaches have been proposed in the literature. In this note, we propose a new definition of permanence for stochastic population models (SPM). The proposed definition is applied to the well-known Lotka–Volterra two species stochastic population model. The note is closed with the open ended discussion on the topic.