Building weighted networks for plankton communities from semi-quantitative data

Quantitative weighted network models are approaches widely used for understanding properties of the system. Although plankton community data in terms of composition and abundance are widely available, additional semi-quantitative ecological information are needed to build weighted networks. The goal of this study is the definition of an approach for developing quantitative networks from semi-quantitative data of plankton communities. The approach is based on a controlled iterative process that is used to develop synthetic networks that are then tested for realism using simple and general emerging properties by node and by the whole network as validating tests. We used realistic ranges for metabolic parameters of plankton community and we have defined semiquantitative indications of the strength of interactions between each taxa. An iterative MCMC approach was used by extracting random values for parameters from their range, through a uniform distribution, and assigning random quantitative values of diet proportions. A large group of valid models (ensemble of 1000 networks) were obtained for two different years for the Venice lagoon as a case study. Whole network indicators were applied to valid models. Preliminary results indicate a stressed system, dominated by detritus fluxes, so with a low mean trophic level and a high efficiency.