No systematic effects of sampling direction on climate-growth relationships in a large-scale, multi-species tree-ring data set
- Journal Article
Rights / licenseCreative Commons Attribution 4.0 International
Ring-width series are important for diverse fields of research such as the study of past climate, forest ecology, forest genetics, and the determination of origin (dendro-provenancing) or dating of archaeological objects. Recent research suggests diverging climate-growth relationships in tree-rings due to the cardinal direction of extracting the tree cores (i.e. direction-specific effect). This presents an understudied source of bias that potentially affects many data sets in tree-ring research. In this study, we investigated possible direction-specific growth variability based on an international (10 countries), multi-species (8 species) tree-ring width network encompassing 22 sites. To estimate the effect of direction-specific growth variability on climate-growth relationships, we applied a combination of three methods: An analysis of signal strength differences, a Principal Component Gradient Analysis and a test on the direction-specific differences in correlations between indexed ring-widths series and climate variables. We found no evidence for systematic direction-specific effects on tree radial growth variability in high-pass filtered ring-width series. In addition, direction-specific growth showed only marginal effects on climate-growth correlations. These findings therefore indicate that there is no consistent bias caused by coring direction in data sets used for diverse dendrochronological applications on relatively mesic sites within forests in flat terrain, as were studied here. However, in extremely dry, warm or cold environments, or on steep slopes, and for different life-forms such as shrubs, further research is advisable. Show more
Journal / seriesDendrochronologia
Pages / Article No.
SubjectTree-rings; Directional growth; Climate signal; Dendro-provenancing; Principal Component Gradient Analysis; Correlation analysis
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