Yuan Yu, Huixing Kang, Han Wang, Yuheng Wang, Yanhong Tang
{"title":"基于叶片质量的光合作用优化模型比基于面积的光合作用优化模型能更好地预测光合作用适应性","authors":"Yuan Yu, Huixing Kang, Han Wang, Yuheng Wang, Yanhong Tang","doi":"10.1093/aobpla/plae044","DOIUrl":null,"url":null,"abstract":"Background and Aims Leaf-scale photosynthetic optimization models can quantitatively predict photosynthetic acclimation and have become important means of improving vegetation and land surface models. Previous models have generally been based on the optimality assumption of maximizing the net photosynthetic assimilation per unit leaf area (i.e., the area-based optimality), while overlooking other optimality assumption such as maximizing the net photosynthetic assimilation per unit leaf dry mass (i.e., the mass-based optimality). Methods This paper compares the predicted results of photosynthetic acclimation to different environmental conditions between the area-based optimality and the mass-based optimality models. The predictions are then verified using the observational data from the literatures. Key Results The mass-based optimality model better predicted photosynthetic acclimation to growth light intensity, air temperature and CO2 concentration, and captured more variability in photosynthetic traits than the area-based optimality models. Conclusions The findings suggest that the mass-based optimality approach may be a promising strategy for improving the predictive power and accuracy of optimization models, which have been widely used in various studies related to plant carbon issues.","PeriodicalId":48955,"journal":{"name":"AoB Plants","volume":"15 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The leaf-scale mass-based photosynthetic optimization model better predicts photosynthetic acclimation than the area-based\",\"authors\":\"Yuan Yu, Huixing Kang, Han Wang, Yuheng Wang, Yanhong Tang\",\"doi\":\"10.1093/aobpla/plae044\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background and Aims Leaf-scale photosynthetic optimization models can quantitatively predict photosynthetic acclimation and have become important means of improving vegetation and land surface models. Previous models have generally been based on the optimality assumption of maximizing the net photosynthetic assimilation per unit leaf area (i.e., the area-based optimality), while overlooking other optimality assumption such as maximizing the net photosynthetic assimilation per unit leaf dry mass (i.e., the mass-based optimality). Methods This paper compares the predicted results of photosynthetic acclimation to different environmental conditions between the area-based optimality and the mass-based optimality models. The predictions are then verified using the observational data from the literatures. Key Results The mass-based optimality model better predicted photosynthetic acclimation to growth light intensity, air temperature and CO2 concentration, and captured more variability in photosynthetic traits than the area-based optimality models. Conclusions The findings suggest that the mass-based optimality approach may be a promising strategy for improving the predictive power and accuracy of optimization models, which have been widely used in various studies related to plant carbon issues.\",\"PeriodicalId\":48955,\"journal\":{\"name\":\"AoB Plants\",\"volume\":\"15 1\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AoB Plants\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/aobpla/plae044\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AoB Plants","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/aobpla/plae044","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECOLOGY","Score":null,"Total":0}
The leaf-scale mass-based photosynthetic optimization model better predicts photosynthetic acclimation than the area-based
Background and Aims Leaf-scale photosynthetic optimization models can quantitatively predict photosynthetic acclimation and have become important means of improving vegetation and land surface models. Previous models have generally been based on the optimality assumption of maximizing the net photosynthetic assimilation per unit leaf area (i.e., the area-based optimality), while overlooking other optimality assumption such as maximizing the net photosynthetic assimilation per unit leaf dry mass (i.e., the mass-based optimality). Methods This paper compares the predicted results of photosynthetic acclimation to different environmental conditions between the area-based optimality and the mass-based optimality models. The predictions are then verified using the observational data from the literatures. Key Results The mass-based optimality model better predicted photosynthetic acclimation to growth light intensity, air temperature and CO2 concentration, and captured more variability in photosynthetic traits than the area-based optimality models. Conclusions The findings suggest that the mass-based optimality approach may be a promising strategy for improving the predictive power and accuracy of optimization models, which have been widely used in various studies related to plant carbon issues.
期刊介绍:
AoB PLANTS is an open-access, online journal that has been publishing peer-reviewed articles since 2010, with an emphasis on all aspects of environmental and evolutionary plant biology. Published by Oxford University Press, this journal is dedicated to rapid publication of research articles, reviews, commentaries and short communications. The taxonomic scope of the journal spans the full gamut of vascular and non-vascular plants, as well as other taxa that impact these organisms. AoB PLANTS provides a fast-track pathway for publishing high-quality research in an open-access environment, where papers are available online to anyone, anywhere free of charge.