The photosynthetic rate is considered to be affected by individual biomass and limited by nutrients. Metabolic scaling models are often utilized to predict photosynthetic rates based on plant size and other factors, such as temperature and plant nutrient composition. However, the intrinsic regulatory mechanisms of the combined factors that affect the photosynthetic rates of living organisms are subject to debate. Here, we present a model developed from the metabolic scaling model, the Michaelis‐Menten equation and elemental stoichiometric models to precisely predict the relationship between plant photosynthetic rate and biomass. The developed model was verified against data for small woody and nonwoody plants, and in comparison with the typical metabolic scaling model, this model was shown to be more capable of explaining the photosynthesis‐biomass relationship. Moreover, the results showed that the combined factors affected photosynthesis via the regulatory effect of nutrients on photosynthesis‐biomass allometry. We highlight that nutrients have direct effects on the allocation of plant biomass and photosynthetic investment under stable and balanced growth states.
With the rapid development of wind power, there are increasing concerns about the negative ecological effects of its construction and operation. However, previous studies have mainly focused on the effects of wind farms on flying fauna (i.e., birds and bats) or climate change separately from communities or ecosystems, and little attention has been paid to vegetation during wind farm operation. Furthermore, few studies have referred to vulnerable ecosystems with low biomass and biodiversity. In this research, a field study was conducted to investigate the effects of wind farms on the individual traits, community structures and ecosystem functions of Gobi Desert ecosystems. The effects were measured by comparing interfering areas (IAs, located between 40 m and 90 m in the downstream direction of the wind turbine) with non-interfering areas (NIAs, located over 200 m from the wind turbine matrixes). The results showed that (1) plant individuals in IAs were less stressed and in better physiological states than those in NIAs; (2) for community structures, IA plants tended to be shorter and denser and had a higher coverage condition than that of NIA plants; and (3) ecosystem functions in IAs were significantly improved due to the existence of shrubs and higher biomass. Meanwhile, significant correlations were identified between the wind wake caused by the large spinning blades and the community structures. Constructing wind turbines in the Gobi Desert is a win-win strategy that both contributes to the growth of desert vegetation with a favourable microclimate and sufficiently utilizes wind power to produce clean energy.
The limited availability of nitrogen (N) is a fundamental challenge for many crop plants. We have hypothesized that the relative crop photosynthetic rate (P) is exponentially constrained by certain plant-specific enzyme activities, such as ribulose-1,5bisphosphate carboxylase/oxygenase (Rubisco), NADP-glyceraldehyde-3-phosphate dehydrogenase (NADP-G3PDH), 3-phosphoglyceric acid (PGA) kinase, and chloroplast fructose-1,6-bisphosphatase (cpFBPase), in Triticum aestivum and Oryza sativa. We conducted a literature search to compile information from previous studies on C 3 and C 4 crop plants, to examine the photosynthetic rate responses to limited leaf [N] levels. We found that in Zea mays, NADP-malic enzyme (NADP-ME), PEP carboxykinase (PCK), and Rubisco activities were positively correlated with P. A positive correlation was also observed between both phosphoenolpyruvate carboxylase (PEPC) and Rubisco activity with leaf [N] in Sorghum bicolor. Key enzyme activities responded differently to P in C 3 and C 4 plants, suggesting that other factors, such as leaf [N] and the stage of leaf growth, also limited specific enzyme activities. The relationships followed the best fitting exponential relationships between key enzymes and the P rate in both C 3 and C 4 plants. It was found that C 4 species absorbed less leaf [N] but had higher [N] assimilation rates (A rate) and higher maximum photosynthesis rates (P max), i.e., they were able to utilize and invest more [N] to sustain higher carbon gains. All C 3 species studied herein had higher [N] storage (N store) and higher absorption of [N], when compared with the C 4 species. N store was the main [N] source used for maintaining photosynthetic capacity and leaf expansion. Of the nine C 3 species assessed, rice had the greatest P max , thereby absorbing more leaf [N]. Elevated CO 2 (eCO 2) was also found to reduce the leaf [N] and P max in rice but enhanced the leaf [N] and N use efficiency of photosynthesis in maize. We concluded that eCO 2 affects [N] allocation, which directly or indirectly affects P max. These results highlight the need to further study these physiological and biochemical processes, to better predict how crops will respond to eCO 2 concentrations and limited [N].
The loglinear pattern of respiratory scaling has been studied for over a century, while an increasing number of non-loglinear patterns have been found in the plant kingdom. Several previous studies had attempted to reconcile conflicting patterns from the aspects of statistical approaches and developmental stages of the organisms. However, the underlying enzymatic mechanism was largely ignored. Here, we propose an enzyme-driven law of photosynthetic scaling and test it in typical crop seedlings under different water conditions. The results showed that the key enzyme activity, the relative photosynthetic assimilation and the relative growth rate were all constrained by the available water, and the relationship between these biological traits and the available water supported our predictions. The enzyme-driven law appears to be more suitable to explain the curvature of photosynthetic scaling than the well-established power law, since it provides insight into the biochemical origin of photosynthetic assimilation.
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