基于零膨胀模型及混合效应模型相结合的蒙古栎林林木进界模拟研究

【目的】林木的进界是确保森林长期维持的基本条件,而进界模型能够预测森林的发展,是量化森林生态系统未来健康和生产力的基础。【方法】以吉林省1995年设立的295块蒙古栎固定样地数据为例,构建基于林分因子、立地因子及气象因子的蒙古栎林林木进界模型。模型的基本形式包括泊松分布和负二项分布两种离散形式。考虑到样地中存在大量零值的问题,在这些基础模型上考虑加入零膨胀模型。为了解决模型存在的嵌套和纵向数据问题,在构建模型时把样地的随机效应考虑进去。最后利用验证数据来验证。【结果】林分算数平均直径和林分公顷株数是影响林木进界概率和数量最重要的影响因子,并且均与林木进界概率和数量呈反比。立地和气象因子中的各项因子对进界均没有产生明显影响。负二项分布模型由于考虑了数据过度离散问题,模拟精度要高于泊松分布;在考虑样地的随机效应后,除了标准负二项分布模型外所有模型都明显提高了模型的模拟精度;同时考虑随机效应和零膨胀的负二项分布模型,其模型的模拟效果最好,验证结果也支持此结论。【结论】为了确保进界的发生,在进行森林经营时,确定合理的初植和经营密度至关重要。

Simulating study on tree recruitment of Quercus mongolica based on zero-inflated model and mixed effect model methods

ObjectiveTree recruitment is the basis to ensure forest long-term maintenance, and the recruitment model can predict the development of forest and quantify the future health and productivity of forest ecosystem.MethodAbout 295 permanent sample plots were established across the natural range of Quercus mongolica in the Jilin Province of northeastern China in 1995. Stand factor, site factor, and climate factor were selected to construct recruitment model of Quercus mongolica. The basic forms of model include Poisson distribution and negative binomial distribution. The zero-inflated model was added to these basic models because of the existence of a large number of zero values in the sample plots. The sample plot’s random effect was taken into account in order to solve the problem of nested and longitudinal data in the model. Finally, the validation data were used to verify the fitness of model.ResultStand arithmetic mean diameter and the number of trees per hectare were the most important factors, and both were negatively correlated with the probability and quantity of tree recruitment. Both site and climate factors had no significant effect on tree recruitment. The accuracy of the negative binomial distribution model was higher than that of the Poisson distribution due to the over-dispersion of the data. After considering sample plot’s random effect, all the models obviously improved the simulation accuracy of the model except for the standard negative binomial distribution model. The simulation effect of the negative binomial distribution model was the best when considering random effect and zero-inflated model.ConclusionIn order to ensure the occurrence of tree recruitment, it is very important to determine science management and initial planting density in forest management.