  In marine management, fish stocks are often managed on a stock-by-stock basis
using single-species models. Many of these models are based upon statistical
techniques and are good at assessing the current state and making short-term
predictions; however, as they do not model interactions between stocks, they
lack predictive power on longer timescales. Additionally, there are mechanistic
multi-species models that represent key biological processes and consider
interactions between stocks such as predation and competition for resources.
Due to the complexity of these models, they are difficult to fit to data, and
so many mechanistic multi-species models depend upon single-species models
where they exist, or ad hoc assumptions when they don't, for parameters such as
annual fishing mortality. In this paper we demonstrate that by taking a
state-space approach, many of the uncertain parameters can be treated
dynamically, allowing us to fit, with quantifiable uncertainty, mechanistic
multi-species models directly to data. We demonstrate this by fitting uncertain
parameters, including annual fishing mortality, of a size-based multi-species
model of the Celtic Sea, for species with and without single-species
stock-assessments. Consequently, errors in the single-species models no longer
propagate through the multi-species model and underlying assumptions are more
transparent. Building mechanistic multi-species models that are internally
consistent, with quantifiable uncertainty, will improve their credibility and
utility for management. This may lead to their uptake by being either used to
corroborate single-species models; directly in the advice process to make
predictions into the future; or used to provide a new way of managing
data-limited stocks.
