Benefits of the Bayesian choice
The prior distribution points out a unique advantage of Bayesian methods: an explicit framework for incorporating prior knowledge into an analysis. Bayesian theory takes into account differences between observers, thus allowing the investigator to examine the differences in assessments and decisions due to varying amounts of information, and hence to measure the value of additional information. Further more, loss functions also give greater flexibility than the hypothesis-testing framework because they allow consideration of a range of outcomes rather than only 2 (the null and alternative hypotheses).Human errors in judgment, especially in assessing the importance of the data relative to existing info, can be reduced.
Bayesian Contras
However, if the Bayesian formalization of the scientific process is not done well, it can easily make matters worse. Moreover, in the science of ecology, with the current state of data-analytic technology, it often cannot be done even by the scientists who have access to the best prior information. Communication problems also constitute a threat for this method which relies heavily on the sharing of information: Can a knowledge that required an ecologist years or decades of study to acquire be passed totally intact to the statistician? And of course, subjectivity is nothing but a pretext for all kinds of subterfuge, including the often tempting choice of the most advantageous procedures.
It can actually be argued the opposite way, namely, that the Bayesian approach is essentially more objective than other inferential methods, because, first, it separates the different subjective inputs of the inferential process (sample distribution, prior, loss function), thus leaving ground for possible modifications, and then it develops in addition objective tools to assess the influence of the prior distribution (noninformative distributions, sensitivity analysis, etc...)
More technically, although all posterior quantities are automatically defined as integrals with respect to the posterior distribution, it may be quite difficult to provide a numerical value in practice, and, in particular, an explicit form of the posterior distribution cannot always be derived.
Frequentist contras
First, any null hypothesis can be rejected (and similarly any significant test made significant) by choosing an appropriately large sample size. And finding a significant difference does not make any statement about the magnitude of the difference, something that is usually of great importance to ecologists. Second, the frequentist approach does not give us the answer to what we want to ask (what are the relative probabilities of the competing hypotheses?) and its paradigm is not reductive enough to lead to a single optimal estimator.
95% intervals: with the classical procedure, whether determining a 95% interval or a P-value, it is not the parameter q which belongs to an interval with probability 95% conditionally on x, but the interval derived from x which contains the fixed value q with probability 0.95. The nonrepeatability of most practical experiments comes to question this frequentist point of view.