Abstract

Tree radial growth is influenced by climatic and various non-climatic factors, which can complicate the extraction of climate signals from tree rings. We investigated the influence of disturbance on tree-ring width (RW) and latewood blue intensity (BI) chronologies of Norway spruce from the Carpathian Mountains to explore the extent to which disturbance can affect temperature signals in tree rings. Overall, ∼15000 high-elevation Norway spruce tree cores from 34 sites grouped into four regions (Slovakia, Ukraine, North and South Romania) were analyzed. The curve intervention detection (CID) method was applied to detect and correct identified disturbance trends. RW chronology structural comparisons were performed among disturbance-affected and disturbance-corrected chronologies for various spatial (regional / site) scales and sampling subsets. Structural comparisons were also performed for RW and BI chronologies developed from separate groups of series (i.e., disturbed, and undisturbed) for five sites exhibiting clear disturbance trends. Temperature sensitivity was assessed for all chronology variants of both parameters. We found that disturbance trends only affected RW chronologies at the site/subset scale with relatively small series replication and were not detected at the regional scale. Unlike RW, BI chronologies were generally unaffected by disturbance. BI data also contained much stronger growing season temperature signals, which appeared to be both spatially and temporally more coherent. Whereas highly replicated and spatially extensive datasets can help minimize or eliminate disturbance trends in RW chronologies, this potential influence should be considered when interpreting climatic signals in tree rings and reconstructing historical climate in weakly replicated periods. On the other hand, BI is a promising alternative tree ring parameter with stronger and more stable growing season temperature signals, whose seemingly disturbance-free chronology structure does not appear to suffer from this ecological bias, and therefore represents a more suitable parameter for dendroclimatological research.

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