Ecological paradigms, change detection and prediction

Abstract: In an environment of massive environmental degradation it is very important that we get our basic assumptions and tools correct. Whether or not we are able to perceive the environmental problems being created, and whether we are able to respond effectively will depend on what evidence we can muster for environmental degradation, how we explain the phenomena we observe and how we plan, predict the outcomes of our actions and set policy. Hitherto we have tended to let theory drive observation rather than the reverse. There has been much emphasis on Gaussian statistics, sampling power, analysis of variance and various forms of dynamical simulation models. Effort has been placed on controlling “noise ” in data rather than trying to understand and model it. Recent analyses of water quality data have revealed that what was hitherto thought to be noise in the data is actually small-scale information. The data reveal multi-fractal behaviour, and provide evidence for self-organised criticality and strong non-linear coupling at small scales. Different nutrient pools and biological components exhibit differing turnover times and contingent histories. Ignoring this small scale information means that many (if not most) ecological data are probably collected at inappropriate scales and are seriously aliased. Small scale interactions can have far reaching consequences in non equilibrium systems. The realisation that all water bodies contain much contingent small-scale information raises a seriou