## 3 Results  
### Spatial Variability  
The 14 plants analyzed to assess the spatial variability of floral scent showed the presence of 70 compounds with a high variability between samples (min 14; max 28; mean 19.4). Remarkably, no compound was found to be present in all the samples. This variability has been observed considering both the composition and abundance of VOCs. Ethyl dodecanoate was the most frequent compound; it was determined in 11 samples. Hexadecane and \(\beta\)-bisabolene (Figure 4a) were detected in 10 samples, \(\delta\)-selinene and \(\beta\)-sesquiphellandrene (Figure 4e) were detected in 9 samples, whereas caryophyllene (Figure 4b), _cis-\(\alpha\)_-bergamonte (Figure 4f), and heptadecane were found in 8 samples. Considering the most abundant compounds, each sample afforded a different result. Verbeno was the main component in Pisticci 1 and \(\beta\)-sesquiphellandrene in Pisticci 2; Calciano gave pristane as prevalent; in Sant'Arcangelo, the main components were alternatively \(\alpha\)-zingiberene, verbenone, and pristane. Caryophyllene was the principal component in Tolve 1, \(i\)-propyl 14-methyl-pentadecanoate was the main component in Tolve 2, whereas farnesol has the same role in Tolve 3. The Pomarico sample gave \(p\)-ment-8-en-1-ol as a main component (Figure 4c); in the two samples of Vietri, we found \(\beta\)-sesquiphellandrene and citronellol (Figure 4d) as dominant, whereas the Potenza plant gave pristane as the most abundant compound. \(\beta\)-sesquiphellandrene was also the main component in the sample of Savoia. Only two compounds were found as dominant in more than two samples: pristane in S. Arcangelo 3, Calciano and Potenza and \(\beta\)-sesquiphellandrene in Pisticci 2, Vietri 1 and Savoia. Notably, except for Vietri 1 and Savoia, these groups comprise samples located rather distantly geographically. Coherently, the overall similarities between samples were rather low, with a mean value of 18.97 \(\pm\) 1.6 SE (min 0.96, max 60.94). The full VOC compositions for each sample are reported in Table 1.  
The NMDS ordination resulted in a two-dimensional solution with a final stress of 0.14 (Figure 5a). The VOCs most strongly correlated with the first axis were \(\beta\)-sesquiphellandrene (positively) and longipinene (negatively), whereas \(p\)-ment-8-en-1-ol (positively) and \(i\)-propyl 14-methyl-pentadecanoate (negatively) were the most correlated compounds with the second axis.  
We did not identify a clear geographical structure in the dataset, except for the samples from Savoia and Vietri, which seemed to group together; these are also very close to each other geographically (Figure 1), and there was a weak correlation between axis 1 and altitude. However, in the NMDS, most of the samples coming from nearby locations (e.g., Tolve 1 vs. Tolve 3 and Pisticci 1 vs. Pisticci 2) were strongly separated along the two axes, and the position of the Potenza sample, located at the higher altitude, is not coherent.  
Furthermore, in the hierarchical clustering (Figure 5b) within the subcluster including samples from Vietri and Savoia a sample from Pisticci was also unexpectedly included, the locality placed at the maximum geographical distance from Vietri and Savoia (Figure 1). The lack of a significative geographical driver underlying our dataset was also confirmed by the results of the Mantel test, which showed that there is no correlation between composition in VOCs and geographical distances between samples (Mantel test: r = 0.17; \(p\) = 0.19).  
The same result was obtained from analyzing the correlation between VOC composition and the environmental distances (i.e., ecological niche) of sample sites (Mantel test: r = 0.10; \(p\) = 0.28).