植物抗逆能力评价方法研究进展

doi:10.17520/biods.2024168

生物多样性 ›› 2025, Vol. 33 ›› Issue (2): 24168. DOI: 10.17520/biods.2024168 cstr: 32101.14.biods.2024168

植物抗逆能力评价方法研究进展

张舒欣¹, 贾紫璇², 方涛¹, 刘一凡¹, 赵微¹, 王荣³, 昌海超¹, 罗芳丽¹^,⁴^,^*()(), 朱耀军⁵^,⁶, 于飞海⁷^,⁸

1.北京林业大学生态与自然保护学院, 北京 100083
2.北京林业大学生物科学与技术学院, 北京 100083
3.北京力科惠泽科技有限公司, 北京 100085
4.黄河流域生态保护国家林业和草原局重点实验室, 北京 100083
5.中国林业科学研究院生态保护与修复研究所(湿地研究所), 北京 100091
6.广东湛江红树林湿地生态系统国家定位观测研究站, 广东湛江 524448
7.台州学院湿地与克隆生态学研究所, 浙江台州 318000
8.台州学院浙江省植物进化生态学与保护重点实验室, 浙江台州 318000

收稿日期:2024-05-07 接受日期:2025-01-06 出版日期:2025-02-20 发布日期:2025-03-17
通讯作者: *E-mail: ecoluofangli@bjfu.edu.cn
基金资助:
第三次新疆综合科学考察项目(2022xjkk1200);国家自然科学基金(32371584);国家自然科学基金(32071525)

Methods to evaluate plant tolerance to environmental stresses

Zhang Shuxin¹, Jia Zixuan², Fang Tao¹, Liu Yifan¹, Zhao Wei¹, Wang Rong³, Chang Haichao¹, Luo Fangli¹^,⁴^,^*()(), Zhu Yaojun⁵^,⁶, Yu Feihai⁷^,⁸

1 School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China
2 School of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
3 Beijing Eco-mind Technology Co., Ltd, Beijing 100085, China
4 Key Laboratory of Ecological Protection in the Yellow River Basin of National Forestry and Grassland Administration, Beijing 100083, China
5 Institute of Ecological Conservation and Restoration (Research Institute of Wetland), Chinese Academy of Forestry, Beijing 100091, China
6 Zhanjiang National Research Station for Mangrove Wetland Ecosystem, Zhanjiang, Guangdong 524448, China
7 Institute of Wetland Ecology & Clone Ecology, Taizhou University, Taizhou, Zhejiang 318000, China
8 Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, Taizhou University, Taizhou, Zhejiang 318000, China

Received:2024-05-07 Accepted:2025-01-06 Online:2025-02-20 Published:2025-03-17
Contact: *E-mail: ecoluofangli@bjfu.edu.cn
Supported by:
Third Xinjiang Scientific Expedition Program(2022xjkk1200);Natural Science Foundation of China(32371584);Natural Science Foundation of China(32071525)

1. 附录.pdf(2050KB)

摘要/Abstract

摘要：

植物在长期适应环境胁迫的过程中, 逐渐形成了与其生境相协调的形态、结构及生理等性状。研究表明可通过植物性状来评价植物的抗逆能力, 且多性状比单一性状评价更为准确。对植物耐受能力评价的方法多样, 各有优缺点, 目前尚没有研究对这些方法进行比较分析。本研究收集了国内外近13年的相关文献, 对目前常用的植物抗逆能力评价方法, 即平均隶属函数值、聚类分析、基于隶属函数和权重的模糊综合评价(简称模糊综合评价)、主成分分析、隶属函数及主成分分析复合评价法(简称复合评价法)、卷积神经网络、灰色关联分析的概念、原理、关键步骤以及优缺点进行了分析和比较。分析发现以上方法均采用多性状来评价植物抗逆能力, 其中, 平均隶属函数值、模糊综合评价和复合评价法基于模糊数学理论, 通过性状模糊化的方式建立模型; 平均隶属函数值、主成分分析、复合评价法、灰色关联分析能筛选出关键抗逆性状。主成分分析和聚类分析可以提供直观、易懂的数据展示方式, 有助于对抗逆能力评价结果的理解。以上方法可以在实践中相互补充, 针对不同的研究目的和数据特征选择合适的评价方法。

关键词: 环境胁迫, 耐受能力评价, 植物性状, 评价方法

Abstract

Aims: Plants are often exposed to environmental stresses. In order to survive, plants must adapt to these hostile environments by developing morphological, structural, and physiological traits that enable the plants to become compatible with their habitats. While there are many methods that would allow researchers can gain insight into the plants’ adaptive strategies, there has not a comparative analysis of these methods. To fill this gap in the literature, we collected articles from the thirteen years on seven methods that are commonly used to evaluate plant stress tolerance: (1) average membership function value, (2) cluster analysis, (3) fuzzy comprehensive evaluation (based on membership function and weights), (4) principal component analysis, (5) composite evaluation (combining membership function and principal component analysis), (6) convolutional neural network, and (7) grey relational analysis. Our objectives are to examine these methods’ main principles, key steps, advantages, and disadvantages. The overall goal is to select appropriate evaluation methods according to different research purposes and data characteristics, and to provide some theoretical basis for the accurate evaluation of plant resilience.

Review results: Our results indicated that fuzzy mathematics is an important theoretical foundation in the three methods(i.e., average membership function value, fuzzy comprehensive evaluation (based on membership function and weights), and composite evaluation (combining membership function and principal component analysis) are based on fuzzy mathematics theory). By using trait fuzzification, these methods enable researchers to establish models demonstrating how plant traits may affect plant resilience. We found that over half the models enable trait selection (i.e., average membership function value, principal component analysis, composite evaluation (combining membership function and principal component analysis), and grey relational analysis).

Conclusion: Average membership function value and gray relational analysis are often used for small sample data sets, and principal component analysis and convolutional neural network are often used for large sample data sets. Principal component analysis and cluster analysis can enable researchers to present their data in easily-interpretable visuals. Currently, the most commonly used method in the domestic biological field is the composite evaluation (combining membership function and principal component analysis). This literature review revealed that these seven methods have strengths that can be used to complement each other during evaluations of plant traits, allowing researchers to select evaluation methods that are tailored to specific research objectives and data characteristics.

Key words: environmental stress, tolerance evaluation, plant trait, evaluation method

张舒欣, 贾紫璇, 方涛, 刘一凡, 赵微, 王荣, 昌海超, 罗芳丽, 朱耀军, 于飞海 (2025) 植物抗逆能力评价方法研究进展. 生物多样性, 33, 24168. DOI: 10.17520/biods.2024168.

Zhang Shuxin, Jia Zixuan, Fang Tao, Liu Yifan, Zhao Wei, Wang Rong, Chang Haichao, Luo Fangli, Zhu Yaojun, Yu Feihai (2025) Methods to evaluate plant tolerance to environmental stresses. Biodiversity Science, 33, 24168. DOI: 10.17520/biods.2024168.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2024168

https://www.biodiversity-science.net/CN/Y2025/V33/I2/24168

图/表 8

图1 使用平均隶属函数值进行植物抗逆能力评价的主要步骤。STI: 耐胁迫指数; EG: 胁迫处理下单个性状的平均值; CG: 对照处理下单个性状的平均值; F(xij): 第i个植物第j个性状的隶属函数值; Ui: 第i个植物的平均隶属函数值; xij: 第i个植物第j个性状的测定值; xjmax: 第j个性状的最大值; xjmin: 第j个性状的最小值。R语言(Version 4.4.2; R Core Team, 2024)中“cluster、factoextra”等包和IBM SPSS Statistics (Version28.0; IBM Corporation, Armonk, NY, USA)中“分析-分类-聚类”均可完聚类分析。R语言中“stats、glmulti”等包和IBM SPPS Statistics中“分析-回归”均可完成回归分析。

Fig. 1 Main process in the evaluation of plant resilience using the average membership function value. STI, Stress tolerance index; EG, Average value of trait under stress; CG, Average value of trait under control; F(xij), Membership function value of the j trait of the i plant; Ui, Average membership function value of the ith plant; xij, Measured value of the j trait of the i plant; xjmax, Maximum value of the j trait; xjmin, Minimum value of the j trait. Packages such as cluster and factoextra in R and Analysis-Category-Cluster in IBM SPSS Statistics can perform cluster analysis. Packages such as stats and glmulti in R and Analysis-regression in IBM SPPS Statistics can perform regression analysis.

图2 使用主成分分析进行植物抗逆能力评价的主要步骤。R语言中“FactoMineR、stats”等包以及IBM SPSS Statistics中“分析-降维-因子分析”均可实现主成分分析。

Fig. 2 Main processes in the evaluation of plant resilience using principal component analysis. Packages such as FactoMineR and stats in R and analysis-dimensionality reduction-factor analysis in IBM SPSS Statistics can perform principal component analysis.

图3 使用基于隶属函数和权重的模糊综合评价进行植物抗逆能力评价的主要步骤。rij: 单一性状i在评价等级j的隶属度; P: 由rij构成的模糊矩阵; B: 结果矩阵; ×: 常见的模糊算子; argmaxj: 求取使得隶属度rij最大的评价等级的索引; Bi: 对于第i个元素, 选择使rij最大的第j个等级。

Fig. 3 Main processes of in the evaluation of plant resilience using fuzzy comprehensive evaluation (based on membership function and weights). rij, Membership degree of single trait i in evaluation grade j; P, Fuzzy matrix composed of rij; B, Result matrix; ×, Common fuzzy operators; argmaxj, The index of evaluation grade that maximized membership degree rij; Bi, For the i element, choose the j rank that maximizes rij.

表1 常见的模糊算子

Table 1 Common fuzzy operators

特点 Features	M(˄, ˅)	M(×, ˅)	M(˄, +)	M(×, +)
体现权重作用 Reflecting the influence of weights	不明显 Not apparent	明显 Apparent	不明显 Not apparent	明显 Apparent
综合程度 Comprehensive degree	弱 Weak	弱 Weak	强 Strong	强 Strong
利用隶属矩阵的信息 Utilizing information from the membership matrix	不充分 Insufficient	不充分 Insufficient	比较充分 Sufficient comparison	充分 Sufficient
类型 Category	主因素突出 Principal factor prominence	主因素突出 Principal factor prominence	加权平均 Weighted average	加权平均 Weighted average

图4 使用聚类分析进行植物抗逆能力评价的主要步骤。R语言中“cluster、stats”等包以及IBM SPSS Statistics可实现聚类分析。

Fig. 4 Main processes in the evaluation of plant resilience using cluster analysis. Packages such as cluster and stats in R and IBM SPSS Statistics can perform cluster analysis.

图5 使用复合评价法进行植物抗逆能力评价的主要步骤。F(xij): 第i个植物第j个性状的隶属函数值; xij: 第i个植物第j个性状的测定值; xjmax: 第j个性状的最大值; xjmin: 第j个性状的最小值; Wa: 第a个主成分的权重, 为第a个主成分的贡献率; Di: 第i个植物的综合得分。

Fig. 5 Main processes in the evaluation of plant resilience using composite evaluation (combining membership function and principal component analysis). F(xij), Membership function value of the j trait of the i plant, xij: Measured value of the j trait of the i plant; xjmax, Maximum value of the j trait; xjmin, Minimum value of the j trait; Wa, Weight of the ath principal component, is the contribution rate of the a principal component; Di, Comprehensive score of the i plant.

图6 典型卷积神经网络模型进行植物抗逆能力评价的结构。输入层: 接收原始图像; 卷积层: 通过卷积核对输入数据进行卷积操作, 提取出局部特征, 形成新的特征图; 池化层: 对特征图进行降采样, 减小特征图的尺寸和参数数量, 并将相似的特征合并为一个; 全连接层: 对池化后的特征图进行线性变换和非线性激活, 展平为一维向量, 执行分类、回归等任务。Keras、Caffe、TensorFlow等深度学习框架可实现卷积神经网络。

Fig. 6 The structure of typical convolutional neural network model for plant stress evaluation. Input layer, Receives the original image; Convolution layer, Performs convolution operation on the input data through the convolution kernel, extracts the local features, and forms a new feature map; Pooling layer, Down samples the feature map, reduces the size of the feature map and the number of parameters, and combines the similar features into a single one; Fully-connected layer, Performs linear transformation and nonlinear activation on the pooled feature map, spreads it into a one-dimensional vector, and performs classification, regression and other tasks. Deep learning frameworks such as Keras, Caffe, and TensorFlow all implement convolutional neural networks.

图7 使用灰色关联分析进行植物抗逆能力评价的主要步骤。Fij: 第i个植物第j个性状的灰色关联系数; Δmin: 为xoo与xij的最小绝对灰色关联系数; β: 分辨系数, 一般取0.5; Δmax: 为xoo与xij的最大绝对灰色关联系数; xoo(k): 参考数列的第k个数据; xij(k): 比较数列的第k个数据; rj: 第j个性状与参考数列的关联度。R语言中greybox等包和Python中Pandas库均可实现灰色关联分析。

Fig. 7 Main processes in the evaluation of plant resilience using grey relational analysis. Fij, Grey correlation degree of j trait of the i plant; Δmin, The minimum absolute grey correlation coefficient between xoj and xij; β, Discrimination coefficient, generally 0.5; Δmax, The maximum absolutegrey correlation coefficient between xoj and xij; xoj(k), K data of reference sequence; xij(k), k data of the comparison sequence; rj, Degree of association between the j trait and the reference sequence. This can be done in packages such as greybox in R and Pandas in Python.

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植物抗逆能力评价方法研究进展

Methods to evaluate plant tolerance to environmental stresses

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