Monitoring and Forecasting Diversity: Entropy Unifies Molecules and Ecosystems

At all scales from molecules to ecosystems, we measure biodiversity to indicate outcomes of natural changes or threatening processes, so that we can compare these with forecasts under various management schemes. Every biodiversity level has four basic processes – dispersal, adaptation, random change, and generation of novel ecosystems, species, or genetic variants. How can we exploit this similarity? Entropy is an obvious choice, being a general forecasting and measurement tool throughout science. It is also a simple transform of the biodiversity-measure ‘profile’: Richness; Gini-Simpson; and Shannon. Conservation managers mostly use Richness and Shannon for biodiversity measurement, and have some forecasts for MaxEnt (Shannon) and Simpson - so there is a mismatch between what is forecast and measured. In contrast, measures and forecasts in molecular ecology are now well developed for the entire profile of biodiversity-measures, within and between areas (Trends Ecol.Evol. 32:948). Shannon approaches outperform others in some important tasks, such as tracing rangeshift or invasion, and genetic estimates of dispersal for input to metapopulation models. Thus the stage is set to unify our monitoring and forecasting of these four processes that are common across all biodiversity levels, using a complete diversity profile that encompasses Richness, Shannon and Simpson. This will integrate well with the many entropic methods in studies of the physical environment.