Nine things to keep in mind about mathematical modelling in ecology and evolution.

Modelling in ecology and evolution, especially in India, is often done by researchers trained in physics or engineering without much experience of studying living systems. This is partly driven by a fallacious conviction that modelling is largely about mathematical skills and that, consequently, modellers can equally effectively apply their skills to problems in fields as diverse as physics/engineering and ecology/evolution. I discuss why this fallacy arises, and the many ways in which modelling in ecology or evolution is actually a very different endeavour from that in much of physics, even though the form of the equations deployed across disciplines is typically quite similar. Since modelling is not primarily about the mathematics but about the system being studied, I believe that a reasonable degree of comfort with models and modelling is important for those researchers in ecology and evolution who primarily undertake empirical studies, whether in the laboratory or the field. Equally, I suggest that researchers doing modelling in ecology and evolution, who were trained in the mathematical or physical sciences, need to understand the systems they attempt to model and also appreciate how modelling ecological and evolutionary processes differs from much of the modelling done in classical physics and allied fields. I also discuss what models are, whether modelling is subjective or objective, and what modelling entails if it is to meaningfully add to scientific understanding. This article is aimed primarily at young researchers interested in ecological and evolutionary questions, whether coming from a background in the biological or physical/mathematical sciences.