Send to

Choose Destination
PeerJ. 2019 Aug 9;7:e7422. doi: 10.7717/peerj.7422. eCollection 2019.

Functional group based marine ecosystem assessment for the Bay of Biscay via elasticity analysis.

Author information

Commonwealth Scientific and Industrial Research Organisation, Data61, Hobart, Tasmania, Australia.
Ifremer, Nantes, France.


The transitory and long-term elasticities of the Bay of Biscay ecosystem to density-independent and density-dependent influences were estimated within a state space model that accounted for both process and observation uncertainties. A functional group based model for the Bay of Biscay fish ecosystem was fit to time series obtained from scientific survey and commercial catch and effort data. The observation model parameters correspond to the unknown catchabilities and observation error variances that vary across the commercial fisheries and fishery-independent scientific surveys. The process model used a Gompertz form of density dependence, which is commonly used for the analysis of multivariate ecological time series, with unknown time-varying fishing mortalities. Elasticity analysis showed that the process model parameters are directly interpretable in terms of one-year look-ahead prediction elasticities, which measure the proportional response of a functional group in the next year given a proportional change to a variable or parameter in the current year. The density dependent parameters were also shown to define the elasticities of the long term means or quantiles of the functional groups to changes in fishing pressure. Evidence for the importance of indirect effects, mediated by density dependence, in determining the ecosystem response of the Bay of Biscay to changes in fishing pressure is presented. The state space model performed favourably in an assessment of model adequacy that compared observations of catch per unit effort against cross-validation predictive densities blocked by year.


Compensatory dynamics; Cumulative impacts; Elasticity; Fox model; Gompertz model; Predictive cross-validation

Conflict of interest statement

The authors declare there are no competing interests.

Supplemental Content

Full text links

Icon for PeerJ, Inc. Icon for PubMed Central
Loading ...
Support Center