Why is Asari (= Manila) clam Ruditapes philippinarum fitness poor in Arcachon Bay: a meta-analysis to answer?

Asari (= Manila) clam, Ruditapes philippinarum, is the second bivalve mollusc in terms of production in the world and, in many coastal areas, can beget important socio-economic issues. In Europe, this species was introduced after 1973. In Arcachon Bay, after a decade of aquaculture attempt, Asari clam rapidly constituted neo-naturalized population which is now fished. However, recent studies emphasized the decline of population and individual performances. In the framework of a national project (REPAMEP), some elements of fitness, stressors and responses in Arcachon bay were measured and compared to international data (41 publications, 9 countries). The condition index (CI=flesh weight/shell weight) was the lowest among all compared sites. Variation in average Chla concentration explained 30% of variation of CI among different areas. Among potential diseases, perkinsosis was particularly prevalent in Arcachon Bay, with high abundance, and Asari clams underwent Brown Muscle Disease, a pathology strictly restricted to this lagoon. Overall element contamination was relatively low, although arsenic, cobalt, nickel and chromium displayed higher values than in other ecosystems where Asari clam is exploited. Finally, total hemocyte count (THC) of Asari clam in Arcachon Bay, related to the immune system activity, exhibited values that were also under what is generally observed elsewhere. In conclusion, this study, with all reserves due to heterogeneity of available data, suggest that the particularly low fitness of Asari clam in Arcachon Bay is due to poor trophic condition, high prevalence and intensity of a disease (perkinsosis), moderate inorganic contamination, and poor efficiency of the immune system.

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).