  Prior distributions elicited for modelling the natural fluctuations or the
uncertainty on parameters of Bayesian fishery population models, can be chosen
among a vast range of statistical laws. Since the statistical framework is
defined by observational processes, observational parameters enter into the
estimation and must be considered random, similarly to parameters or states of
interest like population levels or real catches. The former are thus perceived
as nuisance parameters whose values are intrinsically linked to the considered
experiment, which also require noninformative priors. In fishery research
Jeffreys methodology has been presented by Millar (2002) as a practical way to
elicit such priors. However they can present wrong properties in multiparameter
contexts. Therefore we suggest to use the elicitation method proposed by Berger
and Bernardo to avoid paradoxical results raised by Jeffreys priors. These
benchmark priors are derived here in the framework of sequential population
analysis.
