Evaluating the key assumptions underlying dendro-provenancing: How to spruce it up with a scissor plot

Abstract

In this paper, dendro-provenancing is framed as a search for statistical Nearest Neighbors. The ‘k-Nearest Neighbors leave one-out cross-validation’ process (k-NN) is proposed as a method for validating dendro-provenancing approaches. Furthermore, it allows researchers to consistently compare and evaluate different proximity measures with respect to their suitability for dendro-provenancing. The validation process is demonstrated on a data set of 401 ring-width series of Norway spruce (Picea abies (L.) H. Karst.) encompassing 15 sites along elevational gradients in north-eastern Switzerland. Moreover, a new type of plot, the so-called scissor plot, is introduced to visualize the k-NN validation process. Results indicate that dendro-provenancing depends heavily on differences in between sites high-frequencysignal. Mean classification success for the relevant stages of the k-NN (CS¯Ropen)1 ranged from 71.8% to 79.2% for the best performing measures. Classification errors occurred mainly between sites at elevations of 1000–1198m a.s.l. At all other elevations and between different regions of the study area, only moderate differences in classification performance were detected. Thus, the results indicate that dendro-provenancing may be principally feasible even in a small region as studied here.