  Ecosystem approach to fisheries requires a thorough understanding of fishing
impacts on ecosystem status and processes as well as predictive tools such as
ecosystem models to provide useful information for management. The credibility
of such models is essential when used as decision making tools, and model
fitting to observed data is one major criterion to assess such credibility.
However, more attention has been given to the exploration of model behavior
than to a rigorous confrontation to observations, as the calibration of
ecosystem models is challenging in many ways. First, ecosystem models can only
be simulated numerically and are generally too complex for mathematical
analysis and explicit parameter estimation; secondly, the complex dynamics
represented in ecosystem models allow species-specific parameters to impact
other species parameters through ecological interactions; thirdly, critical
data about non-commercial species are often poor; lastly, technical aspects can
be impediments to the calibration with regard to the high computational cost
potentially involved and the scarce documentation published on fitting complex
ecosystem models to data. This work highlights some issues related to the
confrontation of complex ecosystem models to data and proposes a methodology
for a sequential multi-phases calibration of ecosystem models. We propose
criteria to classify the parameters of a model: model dependency and time
variability of the parameters. These criteria and the availability of
approximate initial estimates are used as decision rules to determine which
parameters need to be estimated, and their precedence order in the sequential
calibration process. The end-to-end ecosystem model ROMS-PISCES-OSMOSE applied
to the Northern Humboldt Current Ecosystem is used as an illustrative case
study.
