Six-Year Nitrogen-Water Interaction Shifts the Frequency Distribution and Size Inequality of the First-Order Roots of Fraxinus mandschurica in a Mixed Mature Pinus koraiensis Forest

六年氮水相互作用改变了混交成熟红松林中白蜡树一级根的频率分布和大小不均等性。

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Abstract

The variation in fine root traits in terms of size inequality at the individual root level can be identified as a strategy for adapting to the drastic changes in soil water and nutrient availabilities. The Gini and Lorenz asymmetry coefficients have been applied to describe the overall degree of size inequality, which, however, are neglected when conventional statistical means are calculated. Here, we used the Gini coefficient, Lorenz asymmetry coefficient and statistical mean in an investigation of Fraxinus mandschurica roots in a mixed mature Pinus koraiensis forest on Changbai Mountain, China. We analyzed 967 individual roots to determine the responses of length, diameter and area of the first-order roots and of branching intensity to 6 years of nitrogen addition (N), rainfall reduction (W) and their combination (NW). We found that first-order roots had a significantly greater average length and area but had smaller Gini coefficients in NW plots compared to in control plots (CK). Furthermore, the relationship between first-order root length and branching intensity was negative in CK, N, and W plots but positive in NW plots. The Lorenz asymmetry coefficient was >1 for the first-order root diameter in NW and W plots as well as for branching intensity in N plots. The bimodal frequency distribution of the first-order root length in NW plots differed clearly from the unimodal one in CK, N, and W plots. These results demonstrate that not only the mean but also the variation and the distribution mode of the first-order roots of F. mandschurica respond to soil nitrogen and water availability. The changes in size inequality of the first-order root traits suggest that Gini and Lorenz asymmetry coefficients can serve as informative parameters in ecological investigations of roots to improve our ability to predict how trees will respond to a changing climate at the individual root level.

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