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Nitrogen inputs in aquatic ecosystems are increasing and climate change is likely to exacerbate cultural eutrophication. The recovery of aquatic ecosystem functionality requires strenuous efforts and entails considerable costs. Therefore, the development of early warning ecological indicators that can help arrest the phenomenon in its early stages is highly desirable. Stable isotope analysis of Nitrogen in algal primary producers has proved useful in determining the origins of Nitrogen inputs in several marine and freshwater ecosystems. Nitrogen signatures are often assigned to impact or non-impact classes by comparing the Nitrogen signature of samples with the Nitrogen signature ranges of potential sources, which can hinder objective ecological evaluation when sample signatures are close to the upper/lower boundaries of source ranges. To overcome this problem, we obtained the Nitrogen signatures of the epilithic associations collected in the littoral zone of Lake Bracciano (Central Italy), covering a pre-drought (2015–2016) and ongoing drought (2017–2019) period. The Bayesian Gaussian Mixture Model determined four probability distributions, each associated with a Nitrogen impact class, and assigned the observed epilithic signatures to the most appropriate classes. Application of the approach at various spatial and temporal scales allowed us to compare the pre-drought and ongoing drought Nitrogen input dynamics. At each spatial and temporal scale, we observed differences in the input dynamics arising from the side effects of the drought on human activities, which were reflected in changes in the probability of Nitrogen signatures belonging to one or the other impact class. Based on this probability, the proposed analytical protocol provided a useful tool for prioritizing specific management measures in areas affected by specific Nitrogen inputs. Moreover, with a few recalibrations, the model proposed for Lake Bracciano can be extended to other contexts.

The River Continuum Concept (RCC) and the Riverine Ecosystem Synthesis (RES) are two different theories proposed by river ecologists to describe the response of biotic communities to environmental variability. River network directional patterns are conveniently described by asymmetric eigenvector maps, an eigenfunction-based spatial filtering method specifically proposed for situations where a hypothesized directional spatial process influences the species distribution. In this work asymmetric eigenvector maps are used in conjunction with canonical redundancy analysis and variation partitioning analysis to describe the distribution of macroinvertebrate communities of a river system in Northern Italy and to test the link between the river theories and the available data. Benthic macrofauna data were collected during the summer of 2009–2013 in 16 rivers, for a total of 283 replicates. We investigate the effects of some measured environmental factors on the benthic macrofauna community, accounting for directional effects intrinsic to the river network structure. The proposed protocol allows to highlight and discuss some of the features relevant to the two river theories. According to the RCC theory, altitude and temperature were relevant factors affecting the macrozoobenthic community, together with the distance from the spring and water depth. Environmental factors representing local and lateral dimensions were less relevant for explaining the variability of the community composition. Nonetheless a role of the surrounding land use was also found, suggesting the presence of lateral effects due to human activities. Overall, the results demonstrated that RCC is a reliable model to describe the distribution of macrobenthic communities in river networks. In socio-ecological systems, the local and lateral dimensions postulated by the RES theory could be mainly related to surrounding land use and naturalness degree.

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