Intraspecific individual variation of temperature tolerance associated with oxygen demand in the European sea bass (Dicentrarchus labrax)

The European sea bass (Dicentrarchus labrax) is an economically important fish native to the Mediterranean and Northern Atlantic. Its complex life cycle involves many migrations through temperature gradients that affect the energetic demands of swimming. Previous studies have shown large intraspecific variation in swimming performance and temperature tolerance, which could include deleterious and advantageous traits under the evolutionary pressure of climate change. However, little is known of the underlying determinants of this individual variation. We investigated individual variation in temperature tolerance in 30 sea bass by exposing them to a warm temperature challenge test. The eight most temperature-tolerant and eight most temperature-sensitive fish were then studied further to determine maximal swimming speed (U-CAT), aerobic scope and post-exercise oxygen consumption. Finally, ventricular contractility in each group was determined using isometric muscle preparations. The temperature-tolerant fish showed lower resting oxygen consumption rates, possessed larger hearts and initially recovered from exhaustive exercise faster than the temperature-sensitive fish. Thus, whole-animal temperature tolerance was associated with important performance traits. However, the temperature-tolerant fish also demonstrated poorer maximal swimming capacity (i.e. lower UCAT) than their temperature-sensitive counterparts, which may indicate a trade-off between temperature tolerance and swimming performance. Interestingly, the larger relative ventricular mass of the temperature-tolerant fish did not equate to greater ventricular contractility, suggesting that larger stroke volumes, rather than greater contractile strength, may be associated with thermal tolerance in this species.

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).