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    基于气候敏感因子的黑龙江省红松人工林松塔产量模型构建

    Development of cone yield model for Pinus koraiensis plantations based on climate-sensitive factors in Heilongjiang Province

    • 摘要:
      目的 构建气候敏感型红松人工林松塔产量预测模型,阐明黑龙江地区松塔产量的空间分布规律及其气候响应机制,为红松果材兼用林的精准经营提供科学依据。
      方法 基于林口林业局(2004—2012年)和孟家岗林场(2008、2013—2023年)24块红松人工林结实量数据及区域气候数据,采用相关性分析识别红松结实的气候敏感阶段与关键指标,利用零膨胀负二项模型建立以年份为哑变量的单木松塔产量混合效应模型,并通过图解法验证因子影响与模型的可靠性。
      结果 (1)红松果实的生殖发育可划分为花原基形成及越冬、幼果形成及越冬、幼果发育与成熟3个连续阶段;相关分析表明,单木松塔产量受立地指数、林分断面积、胸径、前年生长季平均最高气温及8月降水量的显著影响。(2)零膨胀负二项分布模型有效解决了数据零膨胀与过度离散问题,以年份为哑变量可有效反映结实的丰歉年规律;引入气候因子和样木混合效应后,模型的AIC值下降94,BIC值下降109,结果更稳健,且丰年、歉年与平年产塔概率差异显著。
      结论 以年份为哑变量的零膨胀负二项分布模型可有效预测红松人工林单木松塔产量,立地条件、林分密度、胸径及结实前年生长季平均最高气温与8月降水量是影响红松松塔产量的主导因子。该模型可与单木直径生长模型耦合,实现区域尺度松塔产量的预估。

       

      Abstract:
      Objective This study aimed to develop a climate-sensitive cone yield model for Pinus koraiensis plantations, clarify the spatial variation patterns of cone yield across Heilongjiang Province, and elucidate the climatic response mechanisms governing cone production, thereby providing a scientific basis for precision management of timber-nut multifunctional Korean pine forests.
      Method Based on cone yield data from 24 permanent plots of Korean pine plantations in the Linkou Forestry Bureau (2004−2012) and Mengjiagang Forest Farm (2008, 2013−2023), along with corresponding regional climate data, correlation analysis was conducted to identify climate-sensitive periods and key climatic indicators influencing cone production of Korean pine. A zero-inflated negative binomial mixed-effects model was subsequently developed to estimate individual-tree cone yield, with year included as a dummy variable, and graphical analysis was employed to validate the effects of explanatory variables and the reliability of the model.
      Result (1) The reproductive development of Korean pine cones can be divided into three consecutive stages: differentiation and overwintering of floral primordia, initiation and overwintering of young cones, and subsequent cone enlargement leading to physiological maturation. Correlation analysis indicated that site index, stand basal area, diameter at breast height, mean maximum temperature during the growing season two years prior, and precipitation in August of the same growing season were the principal factors influencing individual-tree cone yield. (2) The zero-inflated negative binomial model effectively resolved the issues of excessive zeros and overdispersion in the cone production data. By including year as a dummy variable, the model successfully captured interannual variation. After incorporating climatic factors and random effects at the individual-tree level, the model's Akaike Information Criterion (AIC) decreased by 94 and the Bayesian Information Criterion (BIC) decreased by 109, indicating enhanced model performance. Moreover, significant differences were observed in cone production probabilities among mast, poor, and normal years.
      Conclusion The zero-inflated negative binomial model incorporating year as a dummy variable provides reliable estimates of individual-tree cone yield in Korean pine plantations. Site index, stand density, diameter at breast height, mean maximum temperature during the growing season two years prior, and precipitation in August of the same growing season were identified as the dominant factors influencing cone production. The proposed model can be integrated with individual-tree diameter growth models to dynamically predict regional-scale cone yield.

       

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