吉林蛟河针阔混交林群落邻体竞争效应

Su Jialin,Wang Juan,Fan Chunyu,Zhang Chunyu,Zhao Xiuhai

Scientia Silvae Sinicae（2022）

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Abstract

[目的]点格局分析中完全空间随机零模型要求生境背景必须同质,但复层异龄天然林中生境因子多呈异质性分布,不可避免地会对点格局研究带来影响.本研究尝试通过生境划分的方法,将异质性样地划分为2个亚区(每个亚区内部生境相对同质),并通过标记点格局分析探讨不同生境亚区中林木个体间相互作用规律,旨在为森林经营作业提供理论支撑.[方法]以吉林蛟河21.12 hm2 天然针阔混交林监测样地为基础,根据地形变量将样地划分成A、B 2个生境亚区,利用标记相关函数结合完全空间随机模拟过程检验相邻个体间的作用效应.[结果]单个树种的标记相关分析显示,A区和B区中绝大多数树种表现出个体胸径间的空间负相关;并且在r＜6 m尺度上A区中呈负相关的树种数量明显高于B区,在r＜9 m尺度上未检测到个体胸径间呈正相关的树种.如果不考虑生境条件的异质性,在整个样地中进行标记相关分析,林冠层林木个体胸径在0～8 m尺度上显著负相关,亚林层及林下层的林木胸径间没有显著的空间关联性.在相对同质的生境中检验各林层中林木胸径属性的空间自相关时,亚林层及林下层个体胸径间没有显著的空间关系,而林冠层个体胸径在小尺度上具有显著的负相关,A区中林冠层个体间作用尺度为0～6.9 m,B区为0～5.6 m.[结论]在不同生境条件下(即A,B亚区),林冠层内相邻个体胸径空间负关联的尺度不同,在森林经营过程中,确定最终保留密度时要充分考虑生境差异带来的影响.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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