Influence of sampling and disturbance history on climatic sensitivity of temperature-limited conifers

Abstract

Accurately capturing medium- to low-frequency trends in tree-ring data is vital to assessing climatic response and developing robust reconstructions of past climate. Non-climatic disturbance can affect growth trends in tree-ring-width (RW) series and bias climate information obtained from such records. It is important to develop suitable strategies to ensure the development of chronologies that minimize these medium- to low-frequency biases. By performing high density sampling (760 trees) over a ~40-ha natural high-elevation Norway spruce (Picea abies) stand in the Romanian Carpathians, this study assessed the suitability of several sampling strategies for developing chronologies with an optimal climate signal for dendroclimatic purposes. There was a roughly equal probability for chronologies (40 samples each) to express a reasonable (r = 0.3?0.5) to non-existent climate signal. While showing a strong high-frequency response, older/larger trees expressed the weakest overall temperature signal. Although random sampling yielded the most consistent climate signal in all sub-chronologies, the outcome was still sub-optimal. Alternative strategies to optimize the climate signal, including very high replication and principal components analysis, were also unable to minimize this disturbance bias and produce chronologies adequately representing climatic trends, indicating that larger scale disturbances can produce synchronous pervasive disturbance trends that affect a large part of a sampled population. The Curve Intervention Detection (CID) method, used to identify and reduce the influence of disturbance trends in the RW chronologies, considerably improved climate signal representation (from r = 0.28 before correction to r = 0.41 after correction for the full 760 sample chronology over 1909?2009) and represents a potentially important new approach for assessing disturbance impacts on RW chronologies. Blue intensity (BI) also shows promise as a climatically more sensitive variable which, unlike RW, does not appear significantly affected by disturbance. We recommend that studies utilizing RW chronologies to investigate medium- to long-term climatic trends also assess disturbance impact on those series.