Abstract
BackgroundTracking timber is necessary in order to prevent illegal logging and protect local timber production, but there is as yet no suitable analytical traceability method. Stable isotope ratios in plants are known to reflect geographical variations. In this study we analysed four stable isotope ratios in order to develop a model able to identify the geographic origin of Norway spruce in the European Alps.Methodology and Principal Findingsδ18O, δ2H, δ13C and δ15N were measured in bulk needles of Picea abies sampled in 20 sites in and around the European Alps. Environmental and spatial variables were found to be related to the measured isotope ratios. An ordinary least squares regression was used to identify the most important factor in stable isotope variability in bulk needles. Spatial autocorrelation was tested for all isotope ratios by means of Moran’s I. δ18O, δ2H and δ15N values differed significantly between sites. Distance from the coast had the greatest influence on δ2H, while latitude and longitude were strongly related to δ18O. δ13C values did not appear to have any relationship with geographical position, while δ15N values were influenced by distance from the motorway. The regression model improved the explanatory power of the spatial and environmental variables. Positive spatial autocorrelations were found for δ18O and δ2H values.ConclusionsThe δ 18O, δ2H and δ15N values in P. abies bulk needles are a suitable proxy to identify geographic origin as they vary according to geographical position. Although the regression model showed the explanatory variables to have significant power and stability, we conclude that our model might be improved by multivariate spatial interpolation of the δ 18O and δ2H values.
Highlights
Illegal logging practices are thought to be the major cause of worldwide deforestation [1], [2], which is responsible for the loss of natural resources and global biodiversity and for up to 30% of human-caused CO2 emissions
The δ 18O, δ2H and δ15N values in P. abies bulk needles are a suitable proxy to identify geographic origin as they vary according to geographical position
The regression model showed the explanatory variables to have significant power and stability, we conclude that our model might be improved by multivariate spatial interpolation of the δ 18O and δ2H values
Summary
Tracking timber is necessary in order to prevent illegal logging and protect local timber production, but there is as yet no suitable analytical traceability method. Stable isotope ratios in plants are known to reflect geographical variations.
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