Effect of environmental factors on solar-induced chlorophyll fluorescence across typical tree species in the Giant Panda National Park

The giant panda (Ailuropoda melanoleuca) is a unique species for biodiversity conservation in China. The establishment of the Giant Panda National Park (GPNP) holds significant ecological value. Monitoring the photosynthetic performance of dominant vegetation and their responses to environmental factors in GPNP is crucial for assessing habitat changes and developing conservation strategies. Solar-induced chlorophyll fluorescence (SIF) has emerged as a novel and noninvasive probe for photosynthesis. However, SIF variability among dominant vegetation and its sensitivity to environmental factors remain understudied in GPNP. This study analyzed the effects of environmental factors on SIF across typical vegetation types in the GPNP during 2023–2024 based on tower-based monitoring systems. Using the 3FLD algorithm, a comparative analysis was conducted of SIF responses to six environmental factors and vegetation growth strategies under environmental stress across seasons. Then, the generalized additive models (GAM) were developed to generate daily continuous SIF for trend analysis and photosynthetic capacity assessment. The results indicated that: (1) coniferous forests exhibited low seasonal SIF variability (non-growing season: 49.12%; growing season: 51.59%), while broadleaf species showed high and fluctuating SIF during growing season (99.97% and 84.21%) and diminished signals during non-growing season (62.88% and 104.34%); (2) relationships between environmental factors and SIF showed evident seasonal differences in subalpine coniferous forests, with atmospheric moisture dominating SIF in non-growing season, while photosynthetically active radiation (r = 0.78) and vapor pressure deficit (r = 0.73) dominated growing season; (3) warmer conditions consistently enhanced SIF in subalpine forests; (4) from the estimated SIF variation for 2019–2024 by GAM, shrub species displayed a statistically significant increasing trend in SIF compared to the other two species. Our findings highlight the seasonal dynamics of SIF and potential climate adaptation strategies in subalpine forests, offering insights into evaluating habitat alterations in GPNP and supporting the scientific formulation of conservation strategies.

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Journal
Geo-spatial Information Science
Published
2026-02-24
DOI
https://doi.org/10.1080/10095020.2026.2626667
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
Field-Weighted Citation Impact
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article

Effect of environmental factors on solar-induced chlorophyll fluorescence across typical tree species in the Giant Panda National Park

Limin Chen, Yi Bai, Xiaohan Lin, Ainong Li et al.
Geo-spatial Information Science
Plant Water Relations and Carbon Dynamics
article

Effect of environmental factors on solar-induced chlorophyll fluorescence across typical tree species in the Giant Panda National Park

Limin Chen, Yi Bai, Xiaohan Lin, Ainong Li, Guangbin Lei, Tao Xu, Lianjun Zhao, Xi Nan, Yi Deng, Zhengjian Zhang, Jinhu Bian, Siyuan Li
article en

Abstract

The giant panda (Ailuropoda melanoleuca) is a unique species for biodiversity conservation in China. The establishment of the Giant Panda National Park (GPNP) holds significant ecological value. Monitoring the photosynthetic performance of dominant vegetation and their responses to environmental factors in GPNP is crucial for assessing habitat changes and developing conservation strategies. Solar-induced chlorophyll fluorescence (SIF) has emerged as a novel and noninvasive probe for photosynthesis. However, SIF variability among dominant vegetation and its sensitivity to environmental factors remain understudied in GPNP. This study analyzed the effects of environmental factors on SIF across typical vegetation types in the GPNP during 2023–2024 based on tower-based monitoring systems. Using the 3FLD algorithm, a comparative analysis was conducted of SIF responses to six environmental factors and vegetation growth strategies under environmental stress across seasons. Then, the generalized additive models (GAM) were developed to generate daily continuous SIF for trend analysis and photosynthetic capacity assessment. The results indicated that: (1) coniferous forests exhibited low seasonal SIF variability (non-growing season: 49.12%; growing season: 51.59%), while broadleaf species showed high and fluctuating SIF during growing season (99.97% and 84.21%) and diminished signals during non-growing season (62.88% and 104.34%); (2) relationships between environmental factors and SIF showed evident seasonal differences in subalpine coniferous forests, with atmospheric moisture dominating SIF in non-growing season, while photosynthetically active radiation (r = 0.78) and vapor pressure deficit (r = 0.73) dominated growing season; (3) warmer conditions consistently enhanced SIF in subalpine forests; (4) from the estimated SIF variation for 2019–2024 by GAM, shrub species displayed a statistically significant increasing trend in SIF compared to the other two species. Our findings highlight the seasonal dynamics of SIF and potential climate adaptation strategies in subalpine forests, offering insights into evaluating habitat alterations in GPNP and supporting the scientific formulation of conservation strategies.

Geo-spatial Information Science
Chinese Academy of Sciences (CN), Chengdu University of Information Technology (CN), Chengdu University of Technology (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 52%
Plant Water Relations and Carbon Dynamics
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