The molecular networks are based on matching spectral patterns between paired mass spectra, accomplished by calculating cosine similarity. This metric measures the cosine of the angle between the vectors representing non-zero spectral data points in a multidimensional hyperspace, where the dimensions correspond to the considered mass-to-charge ratio (m/z) variables. The intensity of each data point corresponds to the coordinate value along the respective mass axis within this hyperspace. The molecular networks were created using the online workflow at Global Natural Products Social molecular networking (). An Ion Identity Molecular Network (IIMN) was created (job: 25b1448341ab454c9002c1767fba98e1) where edges were filtered to have a cosine score above 0.4 and at least 4 matching peaks. Further edges between two nodes were kept in the network if and only if each of the nodes appeared in each other's respective top 10 most similar nodes. All matches kept between the network spectra and the library spectra were required to have a score above 0.7 and at least 4 matched peaks. The default speclibs from the GNPS platform were used for the spectral library search. A Feature Based Molecular Network was realised (job: 0f64e360d2ba4227beb6a2e0a03a5335) to decrease the library search cosine-score threshold from 0.7 to 0.4, with at least 4 matching peaks. The default speclibs from the GNPS platform were used for the spectral library search. The FBMN results were used for Network Annotation Propagation (NAP) step (job: 35984a76a5794491acd88cb694e30843). The parameters used to proceed with analysis were the following: N first candidates for consensus score 10, Accuracy for exact mass candidate search (ppm) 20, acquisition mode Positive, Multiple adduct types [M+H] + , [M+Na] + , [M-H2O+H] + , user-provided database: NAP formatted in house Pinaceae structural database. The maximum number of candidate structures in the graph was set to 3.