Knowledge of variations in morphophysiological leaf traits with forest height is essential for quantifying carbon and water fluxes from forest ecosystems. Here, we examined changes in leaf traits with forest height in diverse tree species and their role in environmental acclimation in a tropical rain forest in Borneo that does not experience dry spells. Height-related changes in leaf physiological and morphological traits [e.g., maximum photosynthetic rate (Amax), stomatal conductance (gs), dark respiration rate (Rd), carbon isotope ratio (δ(13)C), nitrogen (N) content, and leaf mass per area (LMA)] from understory to emergent trees were investigated in 104 species in 29 families. We found that many leaf area-based physiological traits (e.g., A(max-area), Rd, gs), N, δ(13)C, and LMA increased linearly with tree height, while leaf mass-based physiological traits (e.g., A(max-mass)) only increased slightly. These patterns differed from other biomes such as temperate and tropical dry forests, where trees usually show decreased photosynthetic capacity (e.g., A(max-area), A(max-mass)) with height. Increases in photosynthetic capacity, LMA, and δ(13)C are favored under bright and dry upper canopy conditions with higher photosynthetic productivity and drought tolerance, whereas lower R d and LMA may improve shade tolerance in lower canopy trees. Rapid recovery of leaf midday water potential to theoretical gravity potential during the night supports the idea that the majority of trees do not suffer from strong drought stress. Overall, leaf area-based photosynthetic traits were associated with tree height and the degree of leaf drought stress, even in diverse tropical rain forest trees.
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http://dx.doi.org/10.1007/s00442-014-3126-0 | DOI Listing |
Environ Microbiol
January 2025
Institute of Microbiology and Dahlem Centre of Plant Sciences, Department of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Berlin, Germany.
The leaf surface, known as the phylloplane, presents an oligotrophic and heterogeneous environment due to its topography and uneven distribution of resources. Although it is a challenging environment, leaves support abundant bacterial communities that are spatially structured. However, the factors influencing these spatial distribution patterns are not well understood.
View Article and Find Full Text PDFPlant Cell
January 2025
State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China.
Tracheary elements (TEs) are vital in the transport of various substances and contribute to plant growth. The differentiation of TEs is complex and regulated by a variety of microRNAs (miRNAs). However, the dynamic changes in miRNAs during each stage of TE differentiation remain unclear, and the miRNA regulatory network is not yet complete.
View Article and Find Full Text PDFTree Physiol
January 2025
Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden.
Although the separate effects of water and nitrogen (N) limitations on forest growth are well known, the question of how to predict their combined effects remains a challenge for modeling of climate change impacts on forests. Here, we address this challenge by developing a new eco-physiological model that accounts for plasticity in stomatal conductance and leaf N concentration. Based on optimality principle, our model determines stomatal conductance and leaf N concentration by balancing carbon uptake maximization, hydraulic risk and cost of maintaining photosynthetic capacity.
View Article and Find Full Text PDFPlant Dis
January 2025
USDA-ARS , Ithaca, United States.
, commonly known as the "Chinese hibiscus", is a widely cultivated shrub with ornamental and medicinal applications (Jadhav et al., 2009). However, it is known to be susceptible to a range of pathogens including bacteria (Chase, 1986).
View Article and Find Full Text PDFPlant Commun
January 2025
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Hubei, China. Electronic address:
In the face of climate change and the growing global population, there is an urgent need to accelerate the development of high-yielding crop varieties. To this end, vast amounts of genotype-to-phenotype data have been collected, and many machine learning (ML) models have been developed to predict phenotype from a given genotype. However, the requirement for high densities of single-nucleotide polymorphisms (SNPs) and the labor-intensive collection of phenotypic data are hampering the use of these models to advance breeding.
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