Ecosystems represent archetypal complex dynamical systems, often modelled by coupled differential equations of the form dxi dt = xifi(x1 , . . . , xN), where N represents the number of species and xi, the abundance of species i. Among these families of coupled differential equations, Lotka–Volterra (LV) equations, corresponding to fi(x1 , . . . , xN) = r i - xi + (G x)i, play a privileged role, as the LV model represents an acceptable trade-off between complexity and tractability. Here, r i is the intrinsic growth of species i and G stands for the interaction matrix: Gij represents the effect of species j over species i. For large N, estimating matrix G is often an overwhelming task and an alternative is to draw G at random, parameterizing its statistical distribution by a limited number of model features. Dealing with large random matrices, we naturally rely on random matrix theory (RMT). The aim of this review article is to present an overview of the work at the junction of theoretical ecology and large RMT. It is intended to an interdisciplinary audience spanning theoretical ecology, complex systems, statistical physics and mathematical biology.