基于广义混合效应模型的云冷杉林天然更新计数方法研究

The Natural Regeneration Count Model of Spruce-fir Mixed Stand Based on Generalized Mixed Effect Models

  • 摘要: 天然更新是森林恢复较好的办法,对未来林分结构和生物多样性具有深远的影响。更新模型能够模拟天然更新的现实和未来状况,为森林经营者提供准确的森林计划。以吉林省汪清林业局2013年设置的12块云冷杉针阔混交林为例,选择泊松和负二项分布形式,考虑样地间的随机效应,构建基于林分因子的云冷杉针阔混交林天然更新模型。结果表明:各个树种对林分因子的反应不一,白桦更新株数与林分每公顷株数和平均直径均呈负相关;红松和水曲柳更新株数不受各个林分因子的影响,更新株数是随机的;冷杉更新株数与林分每公顷断面积呈负相关;色木槭和云杉更新株数均与林分平均直径呈负相关。在考虑样地的随机截距效应后,模型的模拟效果显著提高。在构建天然更新模型时,林分密度是十分重要的因子,如果要人工促进天然更新,就要科学合理的采取经营措施,以确保合理的林分密度。另外样地间的差异是不容忽略的因素,需要利用混合效应模型方法来降低预测误差。

     

    Abstract: As the best method of forest restoration, natural regeneration has a profound impact on the structure and biodiversity of future forest. The model of natural regeneration can simulate reality and future situation of natural regeneration and provide accurate forest plan for forest managers. The 12 spruce-fir coniferous-broad-leaved mixed forest plots in Wangqing Forestry Bureau of Jilin Province were selected an example, the natural regeneration model was constructed. The Poisson and negative binomial distribution were selected, and the plot’s random effect was taken into account. The findings indicated that different stand factors lead to differences in various tree species. The regeneration number of Betula platyphylla was negatively correlated with stand basal area per hectare and stand mean square diameter; the regeneration number of Pinus koraiensis and Fraxinus mandschurica were not affected by stand factor, and the regeneration number was stochastic; the regeneration number of Abies nephrolepis was negatively correlated with stand basal area per hectare; the regeneration number of Acermono and Picea jezoensis var. micrisperma were negatively correlated with stand mean square diameter. After considering the plot’s random intercept effect, the simulation effect of the model is significantly improved. In construction of natural regeneration model, stand density is a very important factor. If we want to artificially promote natural regeneration, we should take scientific and reasonable management measures to ensure reasonable stand density. In addition, the difference among plots can’t be ignored, so it is necessary to use the mixed effect models method to reduce the prediction error.

     

/

返回文章
返回