  When making predictions about ecosystems, we often have available a number of
different ecosystem models that attempt to represent their dynamics in a
detailed mechanistic way. Each of these can be used as simulators of
large-scale experiments and make forecasts about the fate of ecosystems under
different scenarios in order to support the development of appropriate
management strategies. However, structural differences, systematic
discrepancies and uncertainties lead to different models giving different
predictions under these scenarios. This is further complicated by the fact that
the models may not be run with the same species or functional groups, spatial
structure or time scale. Rather than simply trying to select a 'best' model, or
taking some weighted average, it is important to exploit the strengths of each
of the available models, while learning from the differences between them. To
achieve this, we construct a flexible statistical model of the relationships
between a collection or 'ensemble' of mechanistic models and their biases,
allowing for structural and parameter uncertainty and for different ways of
representing reality. Using this statistical meta-model, we can combine prior
beliefs, model estimates and direct observations using Bayesian methods, and
make coherent predictions of future outcomes under different scenarios with
robust measures of uncertainty. In this paper we present the modelling
framework and discuss results obtained using a diverse ensemble of models in
scenarios involving future changes in fishing levels. These examples illustrate
the value of our approach in predicting outcomes for possible strategies
pertaining to climate and fisheries policy aimed at improving food security and
maintaining ecosystem integrity.
