The Ecospace model applied to the North Sea: Evaluating spatial predictions with fish biomass and fishing effort data

Giovanni Romagnoni, Steven Mackinson, Jiang Hong, Anne Maria Eikeset

Abstract

The Ecospace model has been developed from the Ecopath with Ecosim food web model to add a spatial dimension for investigating marine ecosystems. In this study, we evaluated the sensitivity of an Ecospace model developed for the North Sea ecosystem to some of its key parameters, and we examined this model's capability to reproduce trends in spatial time-series of fish biomass and fishing effort. We measured the fit between the spatiotemporal model predictions and the corresponding data of biomass for 12 species and effort for three fishing fleets. Our results suggest that the Ecospace model for the North Sea can predict quite successfully the species distribution, but not the distribution of fishing effort. We hypothesise that the reason might be that Ecospace assumes spatial effort distribution to be driven mainly by profit, while other factors might be more important in our system at the spatiotemporal scale explored. The model might thus fail to capture fisher's behaviour accurately for this system. Despite the limitations of our ad hoc approach for sensitivity analysis, these results hint that some problems exist in our model, which might extend to other Ecospace models and perhaps to the framework in general. This study highlights the importance of validating Ecospace models with data if their results are used for management advice. We suggest that, in order to make of Ecospace a more robust tool for management advice, some critical improvements are needed: the development of an algorithm for parameter optimisation through fitting the model predictions to data, and advancement of the effort distribution model.

 

In Ecological Modelling, Volume 300, 24 March 2015, Pages 50–60

doi:10.1016/j.ecolmodel.2014.12.016

Read more: http://www.sciencedirect.com/science/article/pii/S0304380014006292

Published Oct. 27, 2015 2:37 PM