The Development and Use of Isoscapes to Determine the Geographical Origin of Quercus spp. in the United States.

The stable isotope ratios of oxygen, hydrogen, carbon and sulfur from extracted wood of 87 samples of oaks from the United States were analysed. Relationships with climate variables and the stable isotope ratios of the 69 training dataset samples were investigated to a monthly resolution using long-term monthly mean climate data from NASA and the University of East Anglia's Climate Research Unit, in conjunction with forecast data for hydrogen and oxygen isotope ratios in precipitation. These relationships were used to construct model isoscapes for oxygen, hydrogen, carbon and sulfur for US oak with the aim of using them to forecast isotopic patterns in areas that were not sampled and predict values in samples not used to construct the models. The leading predictors for isoscape generation were oxygen isotope ratios in January precipitation for oak oxygen isotope ratios, hydrogen isotope ratios in July precipitation for oak hydrogen isotope ratios, water vapour in April for carbon isotope ratios, and reflected shortwave radiation in March in combination with sulfate concentration in May for oak sulfur isotopes. The generated isoscapes can be used to show regions an unknown sample may have originated from with a resolution dependent on the rarity of the stable isotope signature within the United States. The models were assessed using the data of 18 samples of georeferenced oak. The assessment found that 100% of oxygen, 94% of hydrogen, 78% of carbon, and 94% of sulfur isotope ratios in the 18 test dataset samples fell within two standard deviations of the isoscape models. Using the results of the isoscapes in combination found that there were 4/18 test samples which did not fall within two standard deviations of the four models, this is largely attributed to the lower predictive power of the carbon isoscape model in conjunction with high local variability in carbon isotope ratios in both the test and training data. The method by which this geographic origin method has been developed will be useful to combat illegal logging and to validate legal supply chains for the purpose of good practice due diligence. [ABSTRACT FROM AUTHOR]