Incorporating food-web parameter uncertainty into Ecopath-derived ecological network indicators

Ecological network analysis (ENA) provides numerous ecosystem level indices offering a valuableapproach to compare and categorize the ecological structure and function of ecosystems. The inclusionof ENA methods in Ecopath with Ecosim (EwE) has insured their continued contribution to ecosystem-based management. In EwE, ENA-derived ecological conclusions are currently based on single values ofENA indices calculated from a unique input flow matrix. Here, we document an easy-to-use routine thatallows EwE users to incorporate uncertainty in EwE input data into the calculation of ENA indices. Thisroutine, named ENAtool, is a suite of Matlab functions that performs three main steps: (1) import of anexisting Ecopath model and its associated parameter uncertainty values in the form of uncertainty inter-vals into Matlab; (2) generation of an ensemble of Ecopath models with the same structure as the original,and with parameter values varying based on the prescribed uncertainty limits; and (3) calculation of aset of 13 ENA indices for each ensemble member (one set of flow values) and of summary statisticsacross the whole ensemble. This novel routine offers the opportunity to calculate ENA indices rangesand confidence intervals, and thus to perform quantitative data analyses. An application of ENAtool on apre-existing Ecopath model of the Bay of Biscay continental shelf is presented, with a focus on the robust-ness of previously published ENA-based ecological traits of this ecosystem when the newly introduceduncertainty values are added. We also describe the sensitivity of the ENAtool results to both the numberof ensemble members used and to the uncertainty interval set around each input parameter. Ecologicalconclusions derived from EwE, particularly those regarding the comparison of structural and functionalelements for a range of ecosystem types or the assessment of ecosystem properties along gradients ofenvironmental conditions or anthropogenic disturbances, will gain in statistical interpretability.