Date Published: February 16, 2018
Publisher: Public Library of Science
Author(s): Yuri Gori, Ana Stradiotti, Federica Camin, Berthold Heinze.
Local timber is still one of the main sources of work and income for mountain communities. However, illegal logging is a major cause of deforestation in many countries and has significant impacts on local communities and biodiversity. Techniques for tracing timber would provide a useful tool to protect local timber industries and contribute to the fight against illegal logging. Although considerable progress has been made in food traceability, timber provenance is still a somewhat neglected research area. Stable isotope ratios in plants are known to reflect geographical variations. This study reports accurate spatial distribution of δ18O and δ2H in timber from north-eastern Italy (Trentino) in order to trace geographical origin.
We tested the accuracy of four kriging methods using an annual resolution of δ18O and δ2H measured in Picea abies. Pearson’s correlation coefficients revealed altitude to be the most appropriate covariate for the cokriging model, which has ultimately proved to be the best method due to its low estimation error.
We present regional maps of interpolated δ18O and δ2H in Picea abies wood together with the 95% confidence intervals. The strong spatial structure of the data demonstrates the potential of multivariate spatial interpolation, even in a highly heterogeneous area such as the Alps. We believe that this geospatial approach can be successfully applied on a wider scale in order to combat illegal logging.
Exploitation of local timber, especially in Alpine regions, is of central importance to the survival of mountain communities and local economies. As markets have expanded from local to global, many timber species previously in demand on the local market have now been superseded by cheaper, imported timber species. We hold that the use of local timber should be encouraged in order to reduce the environmental impact of transportation. Furthermore, given the global market, consumers are increasingly interested in knowing the geographical origin of timber. Recognising the added value of local timber with its short supply chain and benefits to the local economy, they are often prepared to meet the higher costs of these products for ecological, social-ethical and ideological reasons. This is particularly significant for an Alpine region like Trentino, with 60% of its surface area covered with forest.
We were able to predict geographical provenance of wood on a regional scale through the construction of δ18O and δ2H isoscapes of wood with an annual resolution. To our knowledge, this is the first geospatial model developed for timber provenance.