As species biology and ecology is profoundly influenced by climate, any climatic alteration may have severe consequences on marine pelagic ecosystems and their food webs. It remains challenging to estimate the influence of climate on both structural and functional properties of food webs. In this study, we proposed an innovative approach to assess the propagating effects of climate change on ecosystem food web. The approach is based on a sensitivity analysis of a food-web model, a linear inverse model using a Monte Carlo method coupled with a Markov Chain, in which changes in the values of parameters are driven by external Ecological Niche Model outputs. Our sensitivity analysis was restricted to parameters regarding a keystone functional group in marine ecosystems, i.e. small pelagic fish. At the ecosystem level, the consequences were evaluated using both structural and functional ecological network indices. The approach is innovative as it is the first time that these three methods were combined to assess ecological network indices sensitivity to future climatic pressure. This coupling method was applied on the French continental shelf of the Bay of Biscay for which a food-web model already exists and where future changes in the distribution of small pelagic fish have already been examined through model building and projections. In response to the sensitivity analysis corresponding to an increase in small pelagics production only, our results suggested a more active system with an intense plankton-small pelagics-seabirds chain and an efficient recycling to maximize detritus use in the system in relation with detritus export. All results combined together seemed to be in favor of a system adapting to sustain the tested increase in production of small pelagic planktivores. Finally, regarding the innovative combination of numerical tools presented, even if further investigations are still necessary to get a more realistic view of cumulative effects resulting from one given pressure (or more) on a food web (e.g. altering different biological compartments at the same time), the Ecological Network Analysis indices values showed a higher variability under the scenarios of change. Our study thus pointed out a promising methodology to assess propagating changes in structural and functional ecosystem properties.