Editorial Type: Research Articles
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Online Publication Date: 01 Jul 2015

Competitive Strength Effect In the Climate Response of Scots Pine Radial Growth In South-Central Siberia Forest-Steppe

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Article Category: Research Article
Page Range: 106 – 117
DOI: 10.3959/1536-1098-71.2.106
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ABSTRACT

This paper presents a method for classification of trees in groups depending on parameters of the age trend in tree-ring width. The method is tested on a sample containing 194 trees of Scots pine (Pinus sylvestris L.) growing in the forest-steppe zone of the South of Central Siberia. The climatic response of tree-ring width in such climatic conditions is complex. The influence of temperature in May-September is negative (moisture reducing). Warm-season precipitation serving as a source of moisture is a positive factor. Another positive factor is cold-season precipitation as frost protection. We determined the dependence of this response on the local conditions (soil, landscape and anthropogenic factors). The competitive strength of the trees influences both the sensitivity of individual trees to extreme climatic factors and the timing of growth processes. The latter implies the duration of the period of significant response to climate. It appears promising to take this influence into account in dendroclimatic reconstructions by using separate clusters of trees based on the competitive strength and having the maximum response to the reconstructed factor.

Keywords: tree-ring width; climate response; forest-steppe zone; Scots pine; competitive strength; age trend; cluster analysis
Copyright: Copyright © 2015 by The Tree-Ring Society 2015
Figure 1.
Figure 1.

Location of the study area with insert showing sample collection sites (▴) and meteorological station “Minusinsk” (•).

Figure 2.
Figure 2.

Averaged monthly data of temperature and precipitation for years 1915–2012 from the meteorological station “Minusinsk”.

Figure 3.
Figure 3.

Classification of the chronologies by the parameters of age curves: (a) a hierarchical classification; (b) quality indicators (F-test and the Wilks Lambda) of individual age curves classification using the K-Means method as a function of the clusters number.

Figure 4.
Figure 4.

Classification results: individual annual age curves and average age trend functions for each cluster.

Figure 5.
Figure 5.

Distribution of individual series by clusters.

Figure 6.
Figure 6.

The age structure of the regional, local sample sets (a) and clusters (b) of trees.

Figure 7.
Figure 7.

The correlation coefficients of local and regional chronologies with climatic variables for the period 1915–2012.

Figure 8.
Figure 8.

The correlation coefficients of cluster chronologies with climatic variables for the period 1915–2012.

Contributor Notes

* Corresponding author: babushkina70@mail.ru
Received: 01 Apr 2014
Accepted: 02 Mar 2015
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