  Ecosystem models are often used to predict the consequences of management
decisions in applied ecology, including fisheries management and threatened
species conservation. These models are high-dimensional, parameter-rich, and
nonlinear, yet limited data is available to calibrate them, and they are rarely
tested or validated. Consequently, the accuracy of their forecasts, and their
utility as decision-support tools is a matter of debate. In this paper, we
calibrate ecosystem models to time-series data from 110 different experimental
microcosm ecosystems, each containing between three and five interacting
species. We then assess how often these calibrated models offer accurate and
useful predictions about how the ecosystem will respond to a set of standard
management interventions. Our results show that for each timeseries dataset, a
large number of very different parameter sets offer equivalent, good fits.
However, these calibrated ecosystem models have poor predictive accuracy when
forecasting future dynamics and offer ambiguous predictions about how species
in the ecosystem will respond to management interventions. Closer inspection
reveals that the ecosystem models fail because calibration cannot determine the
types of interactions that occur within the ecosystem. Our findings call into
question claims that ecosystem modelling can support applied ecological
decision-making when they are calibrated against real-world datasets.
