Contrasting adaptation and optimization of stomatal traits across communities at continental scale

Liu, Congcong; Sack, Lawren; Li, Ying; He, Nianpeng

doi:10.1093/jxb/erac266

Cited by 9 publications

(6 citation statements)

References 73 publications

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“…We also found that the P mass of tree leaves was significantly higher than that of shrub leaves. Plants of different life forms have unique niches and different resource utilization strategies for light, temperature and water under environmental pressure [ 2 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. However, there was no significant difference in N mass between tree and shrub leaves.…”

Section: Discussionmentioning

confidence: 99%

“…As the main substrate for plant growth, soil contains organic matter, nitrate nitrogen and ammonium nitrogen, which are decomposed to continuously provide essential nutrients for the normal physiological activities of plants. This enables the soil and plant to achieve and maintain a balanced element ratio through the dynamic exchange of nutrient supply and demand [ 28 , 29 , 30 , 31 , 32 ]. We found that only soil N mass was significantly correlated with leaf N mass , offering evidence that the growth and development of Pinus tabuliformis forests were mainly limited by the supply of soil N mass , and P mass was not the key element limiting factor.…”

Section: Discussionmentioning

confidence: 99%

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Effects of Soil Nutrients on Plant Nutrient Traits in Natural Pinus tabuliformis Forests

Gao

2023

Plants

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In light of global warming, the interaction between plant nutrient traits and soil nutrients is still unclear. Plant nutrient traits (e.g., N and P) and their stoichiometric relationships (N/P ratio) are essential for plant growth and reproduction. However, the specific role of soil nutrients in driving variation in plant nutrient traits remains poorly understood. Fifty natural Pinus tabuliformis forests were used as the research object to clarify the interaction between plant nutrient traits and soil nutrients. We show that: (1) The Nmass, Pmass and N/P ratios of leaves were significantly higher than those of roots. The N/P ratio of both leaves and roots was less than 14. (2) Leaf nutrient traits showed diverse relationship patterns with root nutrient traits throughout the growing period. Significant changes were found in root nutrient PC2 (the second principal component of root nutrient traits) and leaf nutrient PC1 (the first principal component of leaf traits), and non-significant changes were found in other relationships between leaf and root traits (p > 0.05). Root nutrient traits explained 36.4% of the variance in leaf nutrient traits. (3) With the increase in soil nutrient PC2 (related to N), leaf PC2 (related to N) showed a significant trend of first decreasing and then increasing (p < 0.05). Only the soil Nmass was significantly correlated with the leaf Nmass (p < 0.05), which demonstrated that the growth and survival of Pinus tabuliformis forests were mainly affected by N-limitation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Effects of Soil Nutrients on Plant Nutrient Traits in Natural Pinus tabuliformis Forests

Gao

2023

Plants

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“…Second, we used a null model of complete spatial randomness (CSR) to identify the deviations of observed spatial aggregation (Ms com ) from the null model expectations, and thus to determine whether the observed spatial pattern of Ms com is dominated by deterministic or stochastic processes (Figure 3C; Figure S1), followed by assessment of the non‐random influence of spatial aggregation on ANPP based the standardized effect sizes (SES; Figure S2). SES was calculated as follows(Liu et al, 2022; Mason et al, 2012):

SES goodbreak= \frac{{Effect size}_{obs} - mean ()(, {Effect size}_{null})}{SD ()(, {Effect size}_{null})},

where Effect size obs is the observed influence of Ms com on ANPP, Effect size null is the effect of spatial aggregation generated by the complete spatial randomness model, and SD is the standard deviation. Effect size is the standardized coefficients when only Ms com is considered for the fixed effect variable in the linear mixed effects model.…”

Section: Methodsmentioning

confidence: 99%

“…from the null model expectations, and thus to determine whether the observed spatial pattern of Ms com is dominated by deterministic or stochastic processes (Figure 3C; Figure S1), followed by assessment of the non-random influence of spatial aggregation on ANPP based the standardized effect sizes (SES; Figure S2). SES was calculated as follows (Liu et al, 2022;Mason et al, 2012):…”

Section: Statistical Analysesmentioning

confidence: 99%

Spatial assembly of grassland communities and interrelationships with productivity

Cheng

et al. 2023

Functional Ecology

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Knowledge on the fine‐scale spatial aggregation is pertinent for deciphering plant community dynamics, as interactions among sessile plants mainly occur in the neighbourhood. However, the spatial assemblage mechanism and interrelationships with productivity remain poorly understood. Here, species segregation index (Mscom) was used to quantify the interspecific segregation (or intraspecific aggregation) of the neighbourhood in plant communities from three contrasting plateaus. We employed the coordinates for 125,726 individual plants (within 1 m2 plots) sampled in natural grassland communities across two temperate grasslands (Loess Plateau and Mongolian Plateau) and one alpine grassland (Tibetan Plateau) to elucidate the assembly mechanisms of Mscom and interrelationships with above‐ground net primary productivity (ANPP). We found that these grassland communities tended to assemble into intraspecific aggregations; the phenomenon was more apparent in regions with low annual precipitation. In grassland communities on Loess Plateau, the strong intraspecific aggregation was mainly due to low sexual reproduction allocation; on Mongolian Plateau, reproductive allocation was also the dominant factor for Mscom variation, followed by the interactive effect of annual precipitation and species richness, whereas on Tibetan Plateau, low species richness was the main reason for increased intraspecific aggregation, followed by the interactive effect of annual precipitation and species richness, and reproductive allocation. More importantly, on all three plateaus, the independent influence of Mscom on ANPP is stronger than that of species and phylogenetic diversity. We elucidated the potential mechanisms shaping spatial aggregation in grassland communities at large scale, and emphasize the critical role of spatial aggregation in predicting the variation of ANPP. Read the free Plain Language Summary for this article on the Journal blog.

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“…Plants show a range of stomatal sizes and shapes on the leaf epidermis depending on the plant species and variety (7). Diversity in stomatal features allowed plants to adapt to a wide range of environments (8, 9). Considering a changing climate, the flexible and dynamic nature of stomatal traits makes them primary targets for improving crop productivity and stability.…”

Section: Introductionmentioning

confidence: 99%

Rapid non-destructive method to phenotype stomatal traits

Pathoumthong

Zhang

Roy

et al. 2022

Preprint

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Background: Stomata are tiny pores located on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and any variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious, which impedes research on stomatal physiology and hinders efforts to develop resilient crops with optimised stomatal patterning. We developed a rapid non-destructive method to phenotype stomatal traits in four species: wheat, rice, tomato, and Arabidopsis. Results: The method consists of two steps. The first step is to capture images of a leaf surface directly and non-destructively using a handheld microscope, which only takes a few seconds compared to minutes using other methods. This rapid method also provides higher quality images for automated data analysis. The second step is to analyse stomatal features using a machine-learning model that automatically detects, counts stomata and measures size. The accuracy of the machine-learning model in detecting stomata ranged from 89% to 96%, depending on the species. Conclusions: We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.

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Contrasting adaptation and optimization of stomatal traits across communities at continental scale

Cited by 9 publications

References 73 publications

Effects of Soil Nutrients on Plant Nutrient Traits in Natural Pinus tabuliformis Forests

Effects of Soil Nutrients on Plant Nutrient Traits in Natural Pinus tabuliformis Forests

Spatial assembly of grassland communities and interrelationships with productivity

Rapid non-destructive method to phenotype stomatal traits

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