Marine ecosystems are changing rapidly due to ocean warming, overfishing and a raft of other anthropogenic impacts. Such changes are expected to disrupt productivity dynamics and alter marine food webs, with likely negative consequences for ecosystem services. It is, therefore, essential to devise and implement methods that can rapidly and inexpensively monitor changes in the marine food web structure. Unfortunately, conventional methods for surveying marine food webs are typically laborious, expensive and often destructive, resulting in only a small fraction of marine ecosystems being well studied, and an even smaller subset of them being studied through time. Here, we pilot a low‐cost approach to reconstructing trophic networks of marine tropical, temperate and polar regions, using taxonomical inventories arising from published environmental DNA (eDNA) metabarcoding studies, and building trophic links based on primary literature information. Although the trophic webs obtained are a simplified approximation of those constructed with traditional methods, they generate realistic networks that fit with expectations, and allow ecological inference over time scales and costs that are orders of magnitude smaller than that traditionally achieved. We show the potential of a new application of environmental DNA analysis that promises to offer a rapid and scalable approach to gather vital information on ecosystem structure, hence boosting marine monitoring at a time of increasingly rapid environmental changes.
The present work is devoted to investigation of numerical issues related to combustion instability simulation through a quasi-1D Eulerian solver. The main aspects addressed are the choice of a suitable multispecies model and heat release response function formulation. Experimental data and high fidelity simulation results, available in literature, are reproduced with acceptable approximation. Main features of the flow field at limit cycle are shown. Moreover, a parametric study has been performed on time-lag response function characteristic parameters, leading to important conclusions on the pertinence of each assumption in the frame of a nonlinear tool
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