Machine learning predictions of trophic status indicators and plankton dynamic in coastal lagoons

Multivariate trophic indices provide an efficient way to assess and classify the eutrophication level and ecological status of a given water body, but their computation requires the availability of experimental information on many parameters, including biological data, that might not always be available. Here we show that machine learning techniques - once trained against a full data set - can be used to infer plankton biomass information from chemical and physical parameter only, so that trophic index can then be computed without using additional biological data. More specifically, we reconstruct plankton information from chemical and physical data, and this information together with chemical data was used to compute the TRIX, which was eventually used to assess the eutrophication status of the water body. The RF was also used to evaluate the prevailing mechanism (bottom up versus top-down) controlling plankton dynamic. The case study was a Mediterranean lagoon, the Ghar El Melh Lagoon, which has been used as a natural laboratory to test the effectiveness of the proposed approach. Based on the resulting TRIX values (4.2 in April - 5.7 in December) the Ghar El Melh Lagoon can be classified an eutrophic ecosystem. This modeling process suggests that phytoplankton growth in Ghar El Melh Lagoon is mainly bottom-up control by nutrients availability, whereas the top-down control exerted by the zooplankton is relatively weak. Results highlight that in bottom up controlled lagoon machine learning technique can efficiently be used to compute ecological indicators even with low availability of biological data.