A gradsect or gradient-directed transect is a low-input, high-return sampling method where the aim is to maximise information about the distribution of biota in any area of study. Most living things are rarely distributed at random, their placement being largely determined by a hierarchy of environmental factors. For this reason, standard statistical designs based on purely random sampling or systematic (e.g. grid-based) systems tend to be less efficient in recovering information about the distribution of taxa than sample designs that are purposively directed instead along deterministic environmental gradients.
Ecologists have long been aware of the significance of environmental gradient based approaches to better understand community dynamics and this is reflected especially in the work of Robert Whittaker (1967)[1] and others. Although in practice, life-scientists intuitively sample gradients, until the early 1980s there was little formal theoretical or empirical support for such an approach, sample design being driven largely by traditional statistical methods based on probability theory incorporating random sampling.