Detection and characterization of hydraulically active fractures in a carbonate aquifer: Results from self-potential, temperature and fluid conductivity logging along a 260-m-deep borehole in the Combioula hydrothermal system in the southwestern Swiss Alps

A geophysical and geochemical study has been conducted in a fractured carbonate aquifer located at Combioula in the southwestern Swiss Alps with the objective to detect and characterize hydraulically active fractures along a 260-m-deep borehole. Hydrochemical analyses, borehole diameter, temperature and fluid electrical conductivity logging data were integrated in order to relate electrokinetic self-potential signals to groundwater flow inside the fracture network. The results show a generally good, albeit locally variable correlation of variations of the self-potential signals with variations in temperature, fluid electrical conductivity and borehole diameter. Together with the hydrochemical evidence, which was found to be critical for the interpretation of the self-potential data, these measurements not only made it possible to detect the hydraulically active fractures but also to characterize them as zones of fluid gain or fluid loss. The results complement the available information from the corresponding litholog and illustrate the potential of electrokinetic self-potential signals in conjunction with temperature, fluid electrical conductivity and hydrochemical analyses for the characterization of fractured aquifers, and thus may offer a perspective for an effective quantitative characterization of this increasingly important class of aquifers and geothermal reservoirs.

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

Self-rule Index for Local Authorities (Release 1.0)

Local autonomy is a highly valued feature of good governance. The continuous attempts of many European countries to strengthen the autonomy of local government show the importance given to decentralisation and far-reaching competences at the lowest units of a state. Measuring and comparing local autonomy, however, has proven to be a difficult task. Not only are there diverging ideas about the core elements of local autonomy, there are also considerable difficulties to apply specific concepts to different countries. This project suggests a comprehensive methodology to measure local autonomy. It analyses 39 European countries and reports changes between 1990 and 2014. A network of experts on local government assessed the autonomy of local government of their respective countries on the basis of a common code book. The eleven variables measured are located on seven imensions and can be combined to a "Local Autonomy Index" (LAI). The data show an increase of local autonomy between 1990 and 2005, especially in the new Central and Eastern European countries. Countries with a particularly high degree of local autonomy are Switzerland, the Nordic countries, Germany and Poland.