Simulation Modeling

TL;DR

Simulation models, as algorithmic tools distinct from traditional mathematical models, are increasingly vital in ecology for analyzing complex, nonlinear, stochastic, and discrete systems, advancing understanding despite requiring new skills and philosophical considerations.

Contribution

This paper provides an overview of simulation modeling in ecology, highlighting its advantages, applications, and the methodological and philosophical challenges involved.

Findings

Simulation models are effective for complex ecological systems.

They have advanced understanding of species coexistence and community assembly.

Ecologists need to acquire new skills like coding and sensitivity analysis.

Abstract

With the rise of computers, simulation models have emerged beside the more traditional statistical and mathematical models as a third pillar for ecological analysis. Broadly speaking, a simulation model is an algorithm, typically implemented as a computer program, which propagates the states of a system forward. Unlike in a mathematical model, however, this propagation does not employ the methods of calculus but rather a set of rules or formulae that directly prescribe the next state. Such an algorithmic model specification is particularly suited for describing systems that are difficult to capture or analyze with differential equations such as: (a) systems that are highly nonlinear or chaotic; (b) discrete systems, for example networks or groups of distinct individuals; (c) systems that are stochastic; and (d) systems that are too complex to be successfully treated with classical calculus. As these situations are frequently encountered in ecology, simulation models are now widely applied across the discipline. They have been instrumental in developing new insights into classical questions of species' coexistence, community assembly, population dynamics, biogeography, and many more. The methods for this relatively young field are still being actively developed, and practical work with simulation models requires ecologists to learn new skills such as coding, sensitivity analysis, calibration, validation, and forecasting uncertainties. Moreover, scientific inquiry with complex systems has led to subtle changes to the philosophical and epistemological views regarding simplicity, reductionism, and the relationship between prediction and understanding.