What can exploratory modelling tell us about the ecobiology of European sea bass ( Dicentrarchus labrax ): a comprehensive overview

European sea bass, Dicentrarchus labrax, is a highly valuable species in Europe, both for aquaculture in the Mediterranean Sea and for commercial and recreational fisheries in the North East Atlantic Ocean. Subjected to increasing fishing pressure, the wild population has recently experienced significant recruitment fluctuation as well as a northward extension of its distribution area in the North Sea. While the nature of the ecological and/or physiological processes involved remains unresolved, ontogenetic habitat shifts and adult site fidelity could increase the species’ vulnerability to climate change and overfishing. As managers look for expert information to propose management scenarios leading to sustainable exploitation, exploratory modelling appears to be a cost-efficient approach to enhance the understanding of recruitment dynamics and the spatio-temporal scales over which fish populations function. A conceptual modelling framework and its specific data requirements are discussed to tackle some sound ecological questions regarding this species. We consequently provide an updated review of current knowledge on bass population structure, biology and ecology. This paper will hence be particularly valuable to develop spatially-explicit models of European sea bass dynamics under environmental and anthropogenic forcing. Knowledge gaps requiring further research efforts are also reported.

The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MAT-LAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/(Mentaschi et al., 2016).