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Mathematical and statistical models underlie many of the world's most important fisheries management decisions. Since the 19th century, difficulty calibrating and fitting such models has been used to justify the selection of simple, stationary, single-species models to aid tactical fisheries management decisions. Whereas these justifications are reasonable, it is imperative that we quantify the value of different levels of model complexity for supporting fisheries management, especially given a changing climate, where old methodologies may no longer perform as well as in the past. Here we argue that cost-benefit analysis is an ideal lens to assess the value of model complexity in fisheries management. While some studies have reported the benefits of model complexity in fisheries, modeling costs are rarely considered. In the absence of cost data in the literature, we report, as a starting point, relative costs of single-species stock assessment and marine ecosystem models from two Australian organizations. We found that costs varied by two orders of magnitude, and that ecosystem model costs increased with model complexity. Using these costs, we walk through a hypothetical example of cost-benefit analysis. The demonstration is intended to catalyze the reporting of modeling costs and benefits.

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., Comment: 23 pages (main text + supplementary material) 9 figures (main text + supplementary material)