## Materials and Methods
### Data Analysis  
Differences between lakes in terms of animal community composition (considering both fish and benthic invertebrates) were tested using contingency tables based on chi-square (\(\chi^{2}\)) tests, Monte Carlo permutation tests and the associated Cramer's V index (a measure of the strength of association among communities; Past 3.0 software package). Specimens collected in each sampling site (and replicates) were grouped by type or taxon (respectively for basal resources and animals) for each lake. The Shannon diversity index (Hs) of invertebrate fauna for each lake was calculated at family level considering a total abundance of the taxa collected in each lake. Given that assessing Shannon diversity is only possible at the level of equal identification of all taxa, the few individuals belonging to the Gastropoda, Oligochaeta, Nematoda, and Nemereta classes (together accounting for less than 1.5% of total fauna) were excluded from the Shannon diversity index computation. [PERSON]'s diversity \(t\)-test and the associated bootstrap procedure (9999 replicates), both available in the Past 3.0 software package, were applied to Hs values to test for significant differences [51]. [PERSON]'s diversity \(t\)-test is a modified version of the classic \(t\)-test and is based on comparison of Hs variances. The \(t\) statistics of [PERSON] are defined as:  
\[t=\frac{\left|Hs_{i}-Hs_{j}\right|}{\sqrt{var(Hs_{i})+var(Hs_{j})}} \tag{1}\]  
which follows Student's \(t\) distribution. In the equation, \(i\) and \(j\) referred to the invertebrate communities of the lakes in paired comparisons, \(Hs\) represents the Shannon diversity index and \(var(Hs)\) its variance. The isotopic values of collected organisms were used to reconstruct the diets of the eel _Anguilla anguilla_ and the annular seabream _Diplodus annularis_ in each lake. The diets were estimated on thebasis of the Isotopic Trophic Unit (ITU) method [31]. The isotopic signatures of single basal resources, invertebrates, and fish were represented in the bi-dimensional isotopic space (Figure S1). This was subdivided into squares (ITUs) corresponding to \(1\times 1\permpermo\delta^{15}\)N and \(\delta^{13}\)C values, starting from the lowest \(\delta^{13}\)C value in the dataset and a \(\delta^{15}\)N value of zero. The ITUs were thus identified and labelled (Figure S1). The diets of each ITU containing individuals of the two fish species were calculated by means of Bayesian Mixing Models (R software ver. 3.5.3, SIMMr package) [52] considering a Trophic Enrichment factor (TEF) of \(3.4\pm 1.0\permo\) for \(\delta^{15}\)N and \(1.0\pm 0.5\permo\) for \(\delta^{13}\)C [18; 37; 49; 53; 54; 55; 56] and uninformative priors. These TEF values (expressed as mean \(\pm\) standard deviation) are considered a robust and widely applicable assumption in the presence of multiple trophic pathways and different types of food sources [37; 56]. For all SIMMr models, we ran three Markov Chain Monte Carlo chains of 300,000 iterations each with a burn-in of 200,000 and a thinning rate of 100 iterations. We assumed that all incoming food items had the same probability of being included in the consumer's diet. The model considers both variance in the isotopic signatures of the resources and uncertainty regarding the trophic enrichment of the consumer (TEF). The model results were expressed in the form of a probability distribution of plausible contribution values. The central tendency values of the distribution (mode, mean, median) allowed us to identify the most important food sources, while the upper and lower limits of the credibility ranges (CI: 50%, 75%, 95%) revealed the range of feasible contributions. The pool of food sources was selected based on the mixing model outputs in accordance with [PERSON] et al. [31]. Since the _A. anguilla_ and _D.