Stable isotope-based statistical tools as ecological indicator of pollution sources in Mediterranean transitional water ecosystems

摘要: Abstract N-stable isotope analysis of macroalgae has become a popular method for the monitoring nitrogen pollution in aquatic ecosystems. Basing on changes their δ15N, have been successfully used as biological traps to intercept inputs. As different sources differ isotopic signature, this technique provides useful information origin pollutants and extension water body. However, fractionation potentially resulting from microbial processing, indirect variations due effects physicochemical conditions algal nutrient uptake metabolism, may affect anthropogenic N values during transportation assimilation. This turn can observed signature tissue, inducing not related assimilated nitrogen, representing “background noise” isotope-based studies. In study, we focused three neighbouring coastal lakes (Caprolace, Fogliano Sabaudia lakes) located south Rome (Italy). Lakes were characterized by differences terms pressure (i.e. urbanization, cultivated crops, livestock grazing) potential levels concentration, pH, concentration). Our aim was assess fragments Ulva lactuca specimens after 48 h submersion identify locate origins affecting each lake. δ15N obtained replicated U. spatially distributed cover entire surface lake, previously collected benchmark, unpolluted site. order reduce environmental background noise observations, Bayesian hierarchical model relating variation covariates random spatial describe understand distribution signals procedure (i) allowed remove confounding signals; (ii) detect “hidden” that would be detected when accounting effect noise; (iii) produced maps providing clear representation signal even where high. Maps sources, dissolved quantify extent pollutants, showing localized organic impacting Fogliano, but Caprolace. provided characterization both intra- inter-lake gradients, powerful approach ecological indication management complex systems, transitional waterbodies are.