Bayesian methods in conservation biology

Bayesian statistical inference provides an alternate way to analyze data th at is likely to be more appropriate to conservation biology problems than t raditional statistical methods. I contrast Bayesian techniques with traditi onal hypothesis-testing techniques using examples applicable to conservatio n. I use a trend analysis of two hypothetical populations to illustrate how easy it is to understand Bayesian results, which are given in terms of pro bability Bayesian trend analysis indicated that the two populations had ver y different chances of declining at biologically important rates. For examp le, the probability that the first population was declining faster than 5% per year was 0.00, compared to a probability of 0.86 for the second populat ion. The Bayesian results appropriately identified which population was of greater conservation concern. The Bayesian results contrast with those obta ined with traditional hypothesis testing Hypothesis testing indicated that the first population, which the Bayesian analysis indicated had no chance o f declining at >5% per year, was declining significantly because it was dec lining at a slow rate and the abundance estimates were precise. Despite the high probability that the second population was experiencing a serious dec line, hypothesis testing failed to reject the null hypothesis of no decline because the abundance estimates were imprecise. Finally, I extended the tr end analysis to illustrate Bayesian decision theory, which allows for choic e between more than two decisions and allows explicit specification of the consequences of various errors. The Bayesian results again differed from th e traditional results: the decision analysis led to the conclusion that the first population was declining slowly and the second population was declin ing rapidly.