松栎林天然更新模拟与不确定性分析

森林天然更新的复杂性和不确定性是森林生态系统动态预测中的关键问题。本研究引入贝叶斯技术和全局敏感性分析,构建基于竞争、气候和地形3类因子的秦岭松栎林天然更新模型。备选模型形式以泊松(Poisson)模型、负二项(negative binomial,NB)模型、零膨胀泊松(zero-inflated Poisson,ZIP)模型和零膨胀负二项(zero-inflated negative binomial,ZINB)模型为基础。同时,根据模型参数传递的不确定性量化分析结果,阐释影响森林更新小概率事件的主导因子。结果表明: ZINB模型在油松和锐齿栎更新模拟中均优于其他模型。林分总断面积、光截留、坡位和生长季最低温是影响松栎林中油松天然更新的最关键因子;而林分总断面积、坡向与海拔的组合、年均温和最热季节降水量则是影响松栎林中锐齿栎天然更新的关键因子。油松更新模拟中,各类因子对模型输出的不确定性贡献率从小到大依次为: 竞争因子(25%)＜气候因子(29%)＜地形因子(46%);锐齿栎更新模拟中为: 气候因子(12%)＜竞争因子(24%)＜地形因子(64%)。油松天然更新数量对生长季最低温和最干季节降水量为正响应,对最干季节均温为负响应;锐齿栎天然更新数量对年均温、生长季最低温和最热季节降水量为正响应,对最干季节均温为负响应。基于贝叶斯技术的ZINB模型可以量化森林更新的影响因子,并解释参数传递的不确定性,是预测森林天然更新的有力工具。

Simulation and uncertainty analysis of natural regeneration for pine-oak forests.

The complexity and uncertainty of forest regeneration is crucial for predicting forest ecosystem dynamics. A natural regeneration model of pine-oak forests in Qinling Mountains was constructed with competition, climate and topography factors using Bayesian statistics and global sensitivity analysis (GSA). The alternative models were based on Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models. According to the uncertainty of model parameter transfer, the analysis results were quantified, and the dominant factors of small probability events affecting forest regeneration were explained. The results showed that the ZINB model was the best one in the simulation of Pinus tabuliformis and Quercus aliena var. acuteserrata. Stand basal area, light interception, slope location and minimum temperature during growing season were the most critical factors affecting natural regeneration of P. tabuliformis, while stand basal area, cosine of aspect interacted with the natural logarithm of elevation, annual mean temperature, and precipitation of the warmest quarter were the most critical factors for Q. aliena var. acuteserrata. The contributions of various factors to the predictive uncertainty were: competition factor (25%) < climate factor (29%) < topography factor (46%) for the simulation of P. tabuliformis regeneration, and climate factor (12%) < competition factor (24%) < topography factor (64%) for the simulation of Q. aliena var. acuteserrata regeneration. The natural regeneration quantity of P. tabuliformis was positively correlated with mean annual temperature and minimum precipitation during growing season, and negatively correlated with the mean temperature in the driest quarter. The natural regeneration quantity of Q. aliena var. acuteserrata was positively correlated with mean annual temperature, minimum precipitation during growing season, precipitation of the warmest quarter, and negatively correlated with mean temperature of the driest quarter. The ZINB model based on Bayesian methods could effectively quantify the major factors driving forest regeneration and interpret the uncertainty propagated from parameters, which was useful for predicting forest regeneration.