Methods to evaluate plant tolerance to environmental stresses

doi:10.17520/biods.2024168

Abstract

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

Zhang Shuxin, Jia Zixuan, Fang Tao, Liu Yifan, Zhao Wei, Wang Rong, Chang Haichao, Luo Fangli, Zhu Yaojun, Yu Feihai. Methods to evaluate plant tolerance to environmental stresses[J]. Biodiv Sci, 2025, 33(2): 24168.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2024168

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Figures/Tables 8

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.

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.

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.

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

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.

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.

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.

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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