Pyrolysis-compound-specific isotope analysis (Py-CSIA) precisely predicts the geographic location of a xerophytic species in a volcanic valley from Tenerife Island.

Abstract

Compound-specific isotope analysis (CSIA) of plant lipids, due to the high chemical stability and sensibility to climate variations, is used for tracing environmental and geographic variables [1]. Analytical pyrolysis (Py-GC) when combined with isotope ratio mass spectrometry (IRMS) (Py-CSIA) can be performed in a direct manner, using untreated samples, minimizing sample manipulation and avoiding pre-treatments (e.g. extraction, derivatization, purification). Furthermore, the simultaneous analysis of chemical compounds with different biogenic origin is also possible i.e. lipids and waxes, lignin, polysaccharides, polypeptides, ¿ [2]. Here, Py-CSIA is used as tool for tracing the influence of geographic variables in the isotope composition of plant biomass, and to identify the extent to which the isotopic ratios of different compound families correlate with these variables. Specimens of the crasulaceae Aeonium canariense [3] were collected in a volcanic valley ¿Barranco de Ruíz¿ (Tenerife, Spain) at different altitude (m a.s.l.), longitude and oceanic influence (latitude). Bulk isotope compositions (¿13C, ¿15N, ¿18O and ¿2H) were obtained by EA-IRMS. Also compound-specific isotope ratios were recorded for C and H using Py-CSIA and the results grouped into 4 main biogenic families; fatty acids (FA), n-alkanes (ALK), carbohydrates (S) and aromatic compounds (A), i.e., ¿13CFA, ¿13CALK, ¿13CS, ¿13CA, ¿13CFA, ¿2HALK, ¿2HS and ¿2HA. Linear correlation showed significant models between oceanic influence and ¿13C and ¿2H values (bulk and specific compounds). Multiple linear regression (MLR) and partial least squares regression (PLS) indicated that ¿2HALK values can be explained by geographic variables mainly longitude and altitude (m a.s.l.). This was confirmed by multidimensional scaling (MDS), that indicated that ¿2H and ¿13CALK were the ratios most responsive to spatial information. Multiple regression and discriminant analysis confirmed that the pattern of compound-specific isotope ratios (12 variables) includes sufficient information to predict sample origin amongst discrete areas (>99% significance and up to 100% correctly classified), in terms of the progressive marine influence or of the slope in the valley.