Detection of anomalies in hyperspectral clutter is an important task in military surveillance. Most algorithms for unsupervised anomaly detection make either explicit or implicit assumptions about hyperspectral clutter statistics: for instance that the abundance is either normally distributed or elliptically contoured. In this paper we investigate the validity of such claims. We show that while non-elliptical contouring is not necessarily a barrier to anomaly detection, it may be possible to do better. In this paper we show how various generative models which replicate the competitive behaviour of vegetation at a mathematically tractable level lead to hyperspectral clutter statistics which do not have Elliptically Contoured (EC) distributions. We develop a statistical test and a method for visualizing the degree of elliptical contouring of real data. Having observed that in common with the generative models much real data fails to be elliptically contoured, we develop a new method for anomaly detection that has good performance on non-EC data.