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

Abstract

Listen

Translate article

The pairwise statistical comparison of ring-width series is the basic analysis of dendro-provenancing studies. It is assumed that statistical proximity indicates similar provenance, but this assumption often remains untested. Especially for small areas with high topographic complexity, it is unknown to what extent statistical proximity and geographical provenance are correlated.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-frequency signal. Mean classification success for the relevant stages of the k-NN (CSR¯open)11CSR¯open is a figure for the classification success rate of a k-NN and is described in detail in the Methods section. ranged from 71.8% to 79.2% for the best performing measures. Classification errors occurred mainly between sites at elevations of 1000–1198 m 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.