Bayesian statistical inference in ecology: an example of its use in a Chilean marine preserve

Bayesian inference allows setting hypotheses under uncertainty, for which requires explicit assignment of prior probabilities for a parameter, quantities or situation. This a priori probabilities, together with observed data described by a likelihood function and using Bayes' theorem, allows to obtain an a posteriori distribution which updates the initial uncertainty given the new evidence. In this study, the potential of bayesian inference in ecology is illustrated using available information about the effect of spatial heterogeneity and human exclusion upon two intertidal key-hole limpet populations of a single non replicated Marine Preserve and a harvested site at central Chile. Bayesian inference has a direct interpretation about the parameter or uncertain value, is operative in non replicated studies; and furthermore, its formal structure provides a potential tool allowing the improvement of ecological theory. This can then be applied to decisionmaking processes and the management of natural systems.