## 2 Materials and Methods
### Data Analyses  
Length frequency distributions of individuals from the two sampling areas were compared using the Kolmogorov-Smirnov two-sample test. The cumulative number of prey taxa was plotted against the cumulative number of randomly pooled stomach samples, to measure sample size sufficiency in both sampling areas. To evaluate the rate of feeding activity, the vacuity index %V = (Ne/Ns) \(\times\) 100 was calculated, where Ne is the number of empty stomachs and Ns is the total number of examined stomachs. The feeding diversity of the black scorpionfish was expressed by the Shannon-Wiener diversity index (H\({}^{\prime}\)), using the abundance values of identified prey items [29]. The contribution of each prey taxa to the fish diet was evaluated by calculating the frequency of occurrence (%F), as percentage of stomachs containing a particular prey taxon; abundance (%N), as the number of each prey taxa expressed as a percentage of the total number of identified taxa; and weight (%W), as weight of each prey taxa as a percentage of the total weight of prey ingested. These values were used to calculate: (i) the index of relative importance IRI = (%N + %W) \(\times\) %F [30]; (ii) coefficient of main food item MFI = [(%N + %F)/2] \(\times\) (%W) [31]; and (iii) the feeding coefficient Q = %N \(\times\) %W, for each prey taxa. Prey were classified based on MFI values as follows: MFI \(>\) 75 = preferential prey, 50 \(<\) MFI \(<\) 75 = main prey, 25 \(<\) MFI \(<\) 50 = secondary prey and MFI \(<\) 25 = accessory prey. In addition, prey were also classified based on Q values as follows: Q \(>\) 200 = main prey, 20 \(<\) Q \(<\) 200 = secondary prey and Q \(<\) 20 = accessory prey. Because each individual fish had often consumed only a few of all prey categories recognized in this study, stomachs of the samples from both areas were randomly sorted into groups of four to five, to decrease the number of prey categories with zero values in a sample. Dietary data for groups of individuals were then averaged, to produce a new series of replicates for the Split area and Pag Island area. The number of individuals in each new replicate depended on the total number of fishes in the sample. This approach followed one previously described and used for analyzing large dietary datasets for fishes [32; 33]. Before multivariate analyses of dietary data, numerical composition data (N) for 7 major dietary taxa of each replicate for the black scorpionfish from both areas were square-root transformed. These data were then used to construct a Bray-Curtis resemblance matrix, which was subjected to non-metric multidimensional scaling (nMDS) ordination and one-way analyses of similarities (ANOSIM), to determine whether the diets of this species from two separate areas were significantly different; and to permutational analysis of variance (PERMANOVA), to test whether there were interactions between the diet and the sampling area [34]. The magnitude of the global R-statistic value in the ANOSIM test was used to ascertain the extent to which diets of _S. porcus_ differed between the two areas. In general, R-statistic values range from 1, if the composition of all samples within each group are more similar to each other than to any of the samples from any other group, down to 0 if the average similarities between and within groups are the same [35]. The \(p\) values \(\leq\) 5% from both tests (ANOSIM and PERMANOVA) are referred to as significant. Similarity percentages (SIMPER) were used to determine which dietary categories typified the diets of _S. porcus_ from both areas and which distinguished the diet of fish caught in the Split area from that of fish caught in the Pag Island area [35]. All multivariate analyses were performed using the routines in PRIMER 6, version 6.1.13 [34], in conjunction with PERMANOVA+, version 1.0.3 [36]. The von Bertalanffy growth model was fitted to the estimated age-length dataset using a non-linear least-square procedure of a Gauss-Newton algorithm.