Abstract

Crown width (CW) is an important indicator for assessing tree health and stand dynamics. Competition and climate are the main factors influencing CW not only through independent effects but also through interactive interactions. In addition, there is limited understanding of whether such influences vary by tree species and tree size. Forest management and policy making under climate change urgently need these clear messages, which have been ignored so far. According to this, we conducted a pioneering study to assess the impacts of climate, competition, and their interactions on CW using data from larch (Larix gmelinii Rupr.), birch (Betula platyphylla Suk.), and poplar (Populus davidiana Dode) in Chinese boreal naturalsecondary forests. Hierarchical partitioning analysis and plot-level nonlinear mixed-effects models were used to accomplish this study. The results proved that the impacts of competition and climate on CW differed depending on tree species and tree size. Larch and poplar were mainly affected by intraspecific symmetric and interspecific asymmetric competition, respectively, while birch was weakly affected by competition. The main types of climatic variables affecting CW were extreme temperature, wind speed, and solar radiation. The climate sensitivity of CW for broadleaf species was greater than conifers, in the order of birch, poplar, and larch. The CW of large-sized trees was more sensitive to climate, while the CW of medium-sized trees showed the greatest response to competition. There were significant interactions between climate and intraspecific competition, which could moderate the negative effects of both on CW. Based on these promising results, we proposed two forest management recommendations: 1) the relationship between CW and climate could be analyzed by sampling only large-sized trees; and 2) the negative effects of climate pressures on CW could be offset by an appropriate increase in intraspecific competition. Our study will certainly provide important insights and valuable ideas for the understanding of CW-climate linkages and forest management under climate change.

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