Environmental Response of Tree Species Distribution in Northeast China with the Joint Species Distribution Model

IF 2.4 2区 农林科学 Q1 FORESTRY Forests Pub Date : 2024-06-13 DOI:10.3390/f15061026
Juan Yong, Guangshuang Duan, Shaozhi Chen, Xiangdong Lei
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Abstract

The composition, distribution, and growth of native natural forests are important references for the restoration, structural adjustment, and close-to-nature transformation of artificial forests. The joint species distribution model is a powerful tool for analyzing community structure and interspecific relationships. It has been widely used in biogeography, community ecology, and animal ecology, but it has not been extended to natural forest conservation and restoration in China. Therefore, based on the 9th National Forest Inventory data in Jilin Province, combined with environmental factors and functional traits of tree species, this study adopted the joint species distribution model—including a model with all variables (model FULL), a model with environmental factors (model ENV), and a model with spatial factors (model SPACE)—to examine the distribution of multiple tree species. The results show that, in models FULL and ENV, the environmental factors explaining the model variation were ranked as follows, climate > site > soil. The explanatory power was as follows: model FULL (AUC = 0.8325, Tjur R2 = 0.2326) > model ENV (AUC = 0.7664, Tjur R2 = 0.1454) > model SPACE (AUC = 0.7297, Tjur R2 = 0.1346). Tree species niches in model ENV were similar to those in model FULL. Compared to predictive power, we found that the information transmitted by environmental and spatial predictors overlaps, so the choice between model FULL and ENV should be based on the purpose of the model, rather than the difference in predictive ability. Both models can be used to study the adaptive distribution of multiple tree species in northeast China.
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物种联合分布模型对中国东北地区树种分布的环境响应
原生天然林的组成、分布和生长情况是人工林恢复、结构调整和近自然改造的重要参考依据。物种联合分布模型是分析群落结构和种间关系的有力工具。该模型已广泛应用于生物地理学、群落生态学和动物生态学等领域,但在中国尚未推广到天然林保护与恢复领域。因此,本研究以吉林省第九次全国森林资源清查数据为基础,结合环境因子和树种功能性状,采用树种联合分布模型--包括所有变量模型(FULL模型)、环境因子模型(ENV模型)和空间因子模型(SPACE模型)--研究多种树种的分布。结果表明,在 FULL 模型和 ENV 模型中,解释模型变化的环境因素排序如下:气候 > 地点 > 土壤。解释力如下:模型 FULL(AUC = 0.8325,Tjur R2 = 0.2326)> 模型 ENV(AUC = 0.7664,Tjur R2 = 0.1454)> 模型 SPACE(AUC = 0.7297,Tjur R2 = 0.1346)。模型 ENV 中的树种生态位与模型 FULL 中的相似。与预测能力相比,我们发现环境预测因子和空间预测因子所传递的信息是重叠的,因此在选择 FULL 模型还是 ENV 模型时应基于模型的目的,而不是预测能力的差异。两种模型均可用于研究中国东北地区多种树种的适应性分布。
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来源期刊
Forests
Forests FORESTRY-
CiteScore
4.40
自引率
17.20%
发文量
1823
审稿时长
19.02 days
期刊介绍: Forests (ISSN 1999-4907) is an international and cross-disciplinary scholarly journal of forestry and forest ecology. It publishes research papers, short communications and review papers. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodical details must be provided for research articles.
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