Key stress indicators from chlorophyll fluorescence in five desert plant species. (December 2022)

• Record Type: Journal Article
• Authors: Jin, Chuan; Zha, Tianshan; Bourque, Charles P.-A.; Liu, Peng; Jia, Xin; Tian, Yun; Li, Xinhao; Liu, Xinyue; Guo, Xiaonan; Xu, Mingze; Kang, Xiaoyu; Guo, Zifan; Wang, Ning
• Is Part Of: Ecological indicators. Volume 145(2023)
• Publication Date: 2022-12
• Subjects: Chlorophyll fluorescence -- Desert species -- Dryland -- Environmental stress -- Random forest -- Photosystem II photochemistry; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2022.109679
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: We studied the prospect of creating new daytime indicators in desert plant species. Parametric and non-parametric methods were used to test the feasibility. The y-intercept and slope of Ф PSII -to- PAR -based regressions provided improvement. The new method provided greater discrimination at the interspecific level. Abstract: Pulse-amplitude modulated chlorophyll fluorescence (ChlF) is widely used to measure environmental stress in plants. Yet, its continuous, long-term usage in situ is challenged due to the lack of appropriate daytime indicators. We investigated the prospect of creating new daytime indicators based on an application of linear regression on daily measurements of photochemical efficiency ( Ф PSII ) and photosynthetically active radiation acquired in situ at 30-minute intervals. Daily regression parameters, thus generated, were subsequently used in a broader context of desert-plant response to environmental change. Here, we compared parametric and non-parametric methods to test the feasibility of daytime-based regression parameters, i.e., maximal quantum yield of photosystem II photochemistry ( F v / F m ) and daily mean Ф PSII, in gauging physiological response in three shrub and two herb species as influenced by eight environmental variables. Results demonstrated that: (i) Random Forest (RF; a non-parametric method) provided the best assessment of interaction between ChlF-based parameters and environmental variables, compared with multiple linear regression (MLR; a conventional parametric method); (ii) variable importance and partial dependence plots indicated that the daily regression parameters of the Ф PSII -to- photosynthetic photon flux density relationship (y-intercept and slope) were superior to those of Ф PSII and F v / F m ; and (iii) compared with F v / F m, the y-intercept and slope improved discrimination of interspecific differences ( p < 0.05). Our research showed that the regression y-intercept and slope can serve as practical daytime indicators of desert-plant photosynthetic physiology in field applications of continuous ChlF measurements. RF is extremely skilled at capturing nonlinearities in eco-environmental interactions compared with standard parametric methods. This new method provides a basis for the collection of key ecological information in assessing desert-plant physiological response to environmental variability.