What do we know to evaluate the health of brown trout (Salmo trutta) populations?

peer reviewedThe renewed emphasis on the concept of the health of ecosystems highlights society’s interest in taking measures to protect environments transformed by human activity. The criteria used for evaluating the health of fish population are rarely discussed within the scientific community. The exercise proposed here aimed to discuss these for the brown trout (Salmo trutta), a flagship species from the freshwater fish community typical from headwaters of watercourses which represent most of the French hydrographic network. This initiative aimed to gather the ideas of a limited number of experts on the function of these populations and on the criteria for evaluating their function. The main key parameters were identified and organised into a hierarchical framework for each development stage. A consensus emerged on the fact that in the current stage of knowledge, the diagnosis can be established based on the analysis of abiotic parameters crucial for the biology and, with more difficulty, on the analysis of biotic parameters. For all the development stages, the identified parameters are linked to habitat (substrate, stream flow, temperature and water quality), hydrology and connectivity. Further knowledge must be acquired in order to be able to measure the biological criteria. That implies to reinforce longterm biological monitoring and research to understand the variability in biological parameters, the relevant spatiotemporal scales and the functional processes

Role of some environmental variables in trout abundance models using neural networks

Neural networks provide a "black box" model for explaining and predicting trout abundance with 8 environmental variables. This work investigates the specific effect of each variable, by inputting fictitious configurations of explanatory variables and by checking the responses of the model. The comparison between this response of the model to environmental variables on one hand, and results from field observations on the other hand, shows similarities and indicates neural network modelling can be trusted. The elevation appears to be the major explanatory factor. The influence of shelters, bottom velocity and Froude number also play an important role. When considered separately, depth does not have a notable influence on the density of trout. Such confirmations of field observations suggest that these models can be used to obtain a clear identification and hierarchization of the factors influencing the abundance of trout and the mode of action of the factors. This approach can be extended to other applications in quantitative ecology in which non-linear relationships are usually observed