Pub Date : 2021-07-01DOI: 10.1093/insilicoplants/diab025
M. van der Meer, P. D. de Visser, E. Heuvelink, L. Marcelis
Light distribution within canopies is important for plant growth. We aimed to quantify the influence of row orientation on inter- and within-row variation of light absorption and photosynthesis in a hedgerow crop. An experiment with two row orientations of a tomato crop was conducted which was then used to calibrate a functional–structural plant model (FSPM). The FSPM was used to analyse light absorption and photosynthesis for each of the row facing directions in the double-row trellis system (e.g. north- and south-facing rows for the east–west row orientation). The measured leaf area decreased by 18 % and specific leaf area by 10 %, while fruit dry weight increased by 7 % for south-facing compared to north-facing rows, but total plant dry weight did not significantly differ. Model simulations showed a 7 % higher light absorption for the south-facing rows than north-facing rows, while net photosynthesis was surprisingly −4 % lower, due to local light saturation. When in the model leaf area was kept equal between the rows, light absorption for the south-facing rows was 19 % and net photosynthesis 8 % higher than for north-facing rows. We conclude that although south-facing rows would be expected to have a higher photosynthesis than north-facing rows, plants can adapt their morphology such that differences in light absorption and photosynthesis between north- and south-facing rows are minimal. Rows oriented north–south were more uniform in light absorption and photosynthesis than east–west rows, but the overall crop light absorption and photosynthesis were minimally affected (both 3 % lower compared to east–west orientation).
{"title":"Row orientation affects the uniformity of light absorption, but hardly affects crop photosynthesis in hedgerow tomato crops","authors":"M. van der Meer, P. D. de Visser, E. Heuvelink, L. Marcelis","doi":"10.1093/insilicoplants/diab025","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab025","url":null,"abstract":"\u0000 Light distribution within canopies is important for plant growth. We aimed to quantify the influence of row orientation on inter- and within-row variation of light absorption and photosynthesis in a hedgerow crop. An experiment with two row orientations of a tomato crop was conducted which was then used to calibrate a functional–structural plant model (FSPM). The FSPM was used to analyse light absorption and photosynthesis for each of the row facing directions in the double-row trellis system (e.g. north- and south-facing rows for the east–west row orientation). The measured leaf area decreased by 18 % and specific leaf area by 10 %, while fruit dry weight increased by 7 % for south-facing compared to north-facing rows, but total plant dry weight did not significantly differ. Model simulations showed a 7 % higher light absorption for the south-facing rows than north-facing rows, while net photosynthesis was surprisingly −4 % lower, due to local light saturation. When in the model leaf area was kept equal between the rows, light absorption for the south-facing rows was 19 % and net photosynthesis 8 % higher than for north-facing rows. We conclude that although south-facing rows would be expected to have a higher photosynthesis than north-facing rows, plants can adapt their morphology such that differences in light absorption and photosynthesis between north- and south-facing rows are minimal. Rows oriented north–south were more uniform in light absorption and photosynthesis than east–west rows, but the overall crop light absorption and photosynthesis were minimally affected (both 3 % lower compared to east–west orientation).","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41574001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1093/insilicoplants/diab027
C. Bahr, Dominik Schmidt, M. Friedel, K. Kahlen
Leaf removal is a standard vineyard management technique to influence grape composition or to reduce disease pressure; however, the timing and intensity of leaf removal is a widely discussed issue. The interplay of different goals and effects over time does not make experimental studies any easier. To gain insight into positive and negative consequences of leaf removal on grapevine development, a first step can be to study how leaf removal affects the canopy’s light absorption using a dynamic model approach. Functional–structural plant models combine canopy architecture with physiological processes and allow analysing canopy interaction with the environment with great topological detail. The functional–structural plant model Virtual Riesling simulates Riesling vines in a vineyard set-up depending on temperature and plant management. We implemented leaf removal and applied this method in or above the bunch zone to compare the light absorption in canopies. Leaf removal in the bunch zone led to greater loss of absorbed light, but canopies of both scenarios could compensate for most of the loss during the simulation time frame. Compensation was mainly driven by lateral leaves closing the gaps induced by leaf removal and by leaves in the proximity of the leaf removal zones, re-exposed to light. Results showed similar effects as observed in in vivo studies; hence, we suggest extending these simulations to investigate other effects linked to light distribution such as berry sunburn. Simple modifications of implemented leaf removal techniques also allow for testing different application scopes and their impact on canopy light absorption.
{"title":"Leaf removal effects on light absorption in virtual Riesling canopies (Vitis vinifera)","authors":"C. Bahr, Dominik Schmidt, M. Friedel, K. Kahlen","doi":"10.1093/insilicoplants/diab027","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab027","url":null,"abstract":"\u0000 Leaf removal is a standard vineyard management technique to influence grape composition or to reduce disease pressure; however, the timing and intensity of leaf removal is a widely discussed issue. The interplay of different goals and effects over time does not make experimental studies any easier. To gain insight into positive and negative consequences of leaf removal on grapevine development, a first step can be to study how leaf removal affects the canopy’s light absorption using a dynamic model approach. Functional–structural plant models combine canopy architecture with physiological processes and allow analysing canopy interaction with the environment with great topological detail. The functional–structural plant model Virtual Riesling simulates Riesling vines in a vineyard set-up depending on temperature and plant management. We implemented leaf removal and applied this method in or above the bunch zone to compare the light absorption in canopies. Leaf removal in the bunch zone led to greater loss of absorbed light, but canopies of both scenarios could compensate for most of the loss during the simulation time frame. Compensation was mainly driven by lateral leaves closing the gaps induced by leaf removal and by leaves in the proximity of the leaf removal zones, re-exposed to light. Results showed similar effects as observed in in vivo studies; hence, we suggest extending these simulations to investigate other effects linked to light distribution such as berry sunburn. Simple modifications of implemented leaf removal techniques also allow for testing different application scopes and their impact on canopy light absorption.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47250713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1093/insilicoplants/diab023
B. Bailey, E. Kent
While functional–structural plant models (FSPMs) have been proposed as a tool for better analysing and predicting interactions between plant structure and function, it is still unclear as to what spatial resolution is required to adequately resolve such interactions. Shadows cast by neighbouring leaves in a plant canopy create extremely large spatial gradients in absorbed radiation at the sub-leaf scale, which are usually not fully resolved in ‘leaf-resolving’ plant models. This failure to resolve sharp radiative gradients can propagate to other dependent biophysical models, and result in dramatic overprediction of whole-plant and -canopy fluxes with errors significantly higher than that of a statistical ‘big leaf’ or turbid medium model. Under-resolving radiative gradients creates a diffusive effect in the probability distribution of absorbed radiation, and smears out the effect of canopy structure, effectively undermining the original goal of a leaf-resolving model. Errors in whole-canopy fluxes of photosynthesis increased approximately linearly with increasing LAI, projected area fraction G, and decreased logarithmically as the fraction of incoming diffuse radiation was increased. When only one discrete element per leaf was used, errors in whole-canopy net CO2 flux could be in excess of 100 %. Errors due to sub-leaf resolution decreased exponentially as the number of elements per leaf was increased. These results prompt closer consideration of the impact of sub-leaf resolution on model errors, which is likely to prompt an increase in resolution relative to current common practice.
{"title":"On the resolution requirements for accurately representing interactions between plant canopy structure and function in three-dimensional leaf-resolving models","authors":"B. Bailey, E. Kent","doi":"10.1093/insilicoplants/diab023","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab023","url":null,"abstract":"\u0000 While functional–structural plant models (FSPMs) have been proposed as a tool for better analysing and predicting interactions between plant structure and function, it is still unclear as to what spatial resolution is required to adequately resolve such interactions. Shadows cast by neighbouring leaves in a plant canopy create extremely large spatial gradients in absorbed radiation at the sub-leaf scale, which are usually not fully resolved in ‘leaf-resolving’ plant models. This failure to resolve sharp radiative gradients can propagate to other dependent biophysical models, and result in dramatic overprediction of whole-plant and -canopy fluxes with errors significantly higher than that of a statistical ‘big leaf’ or turbid medium model. Under-resolving radiative gradients creates a diffusive effect in the probability distribution of absorbed radiation, and smears out the effect of canopy structure, effectively undermining the original goal of a leaf-resolving model. Errors in whole-canopy fluxes of photosynthesis increased approximately linearly with increasing LAI, projected area fraction G, and decreased logarithmically as the fraction of incoming diffuse radiation was increased. When only one discrete element per leaf was used, errors in whole-canopy net CO2 flux could be in excess of 100 %. Errors due to sub-leaf resolution decreased exponentially as the number of elements per leaf was increased. These results prompt closer consideration of the impact of sub-leaf resolution on model errors, which is likely to prompt an increase in resolution relative to current common practice.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49105460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1093/insilicoplants/diab035
L. Pagès
Root system scientists strive to understand how a single root, emerging from a plant’s seed, can form a complex, dynamic and plastic network of thousands of individual roots. They investigate how such a network is ideally suited to perform a number of functions required for the harmonious development of the whole plant. Everyone in the community also knows how complicated it can be to study root systems, with tasks ranging from digging plants out of the soil, creating experimental setups that allow the observation of the roots, to quantifying the root network itself or the processes underlying its formation. Within the community, there is one person, Dr Loïc Pagès, who has been working on all these tasks for many years, and who has moved the field forward numerous times. On the occasion of his soon-to-be retirement, we would like to express our appreciation to him via this editorial. Loïc Pagès started studying the development of root systems almost 40 years ago and has not stopped ever since. Providing an exhaustive summary of Loïc’s achievements would be a daunting task (according to Scopus, Loïc has published over 130 papers, with more than 249 collaborators; Fig. 1). Here we would like to highlight some of his key contributions to the field. Loïc has been working on many facets of root research. Most importantly, Loïc spent a lot of time observing roots. He dug out and quantified thousands of root systems of more than 60 different plant species, sometimes from his own garden (Pagès and Kervella 2018). One root system at a time, this rich experimental work was Loïc’s foundation for the discovery and conceptualization of a parsimonious set of developmental rules that he was able to apply to a wide range of plant species (Lecompte et al. 2001; Pagès 2016; Pagès and Kervella 2018). Briefly, these rules highlight the importance of the range—and not the average—of root diameters that can be found within a root system and the allometric relationship between roots of different orders. The unique approach of Loïc was to rely on these rules for designing and implementing computational root models. Loïc Pagès is one of the founding fathers of root system modelling. When he published his first computational model, SARAH, in 1988 (Pagès and Ariès 1988), there were only a handful of scientists working in this emerging research area: him, D. Lungley (Lungley 1973), A. Fitter (Fitter 1987) and A. Diggle (Diggle 1988). SARAH was a simple root growth model that included all the available knowledge about root system development. This was so new at the time that it is easy to imagine the scepticism of some contemporary agronomists (Loïc’s personal communication). But this did not stop him from continuing on this path. Since then, Loïc has published more than 15 different root models (Fig. 2). His modelling work spanned from purely structural models of single species (maize (Pagès et al. 1989), peach tree (Pagès et al. 1992), rubber tree (Thaler and Pagès 1998),
根系科学家努力了解从植物种子中长出的单根是如何形成由数千个单根组成的复杂、动态和可塑的网络的。他们研究了这种网络如何理想地适用于执行整个工厂和谐发展所需的许多功能。社区中的每个人都知道研究根系有多复杂，任务包括从土壤中挖出植物，创建可以观察根系的实验装置，以及量化根系网络本身或其形成过程。在社区内，有一个人，Loïc Pagès博士，多年来一直致力于所有这些任务，并多次推动这一领域向前发展。值此他即将退休之际，我们谨通过这篇社论向他表示感谢。Loïc Pagès大约40年前就开始研究根系的发育，此后一直没有停止。对洛伊奇的成就进行详尽的总结将是一项艰巨的任务（根据Scopus的说法，洛伊奇已经发表了130多篇论文，有249多名合作者；图1）。在这里，我们要强调他对该领域的一些关键贡献。Loïc一直致力于根系研究的许多方面。最重要的是，洛伊奇花了很多时间观察根系。他挖掘并量化了60多种不同植物的数千个根系，有时来自自己的花园（Pagès和Kervella，2018）。一次一个根系，这项丰富的实验工作是Loïc发现和概念化一套简约的发育规则的基础，他能够将这些规则应用于广泛的植物物种（Lecompte et al.2001；Pagès 2016；Pagés和Kervella 2018）。简言之，这些规则强调了根系中根系直径范围（而不是平均值）的重要性，以及不同阶根系之间的异速关系。Loïc的独特方法是依靠这些规则来设计和实现计算根模型。Loïc Pagès是根系建模的创始人之一。当他在1988年发表他的第一个计算模型SARAH（Pagès和Ariès 1988）时，只有少数科学家在这个新兴的研究领域工作：他、D.Lungley（Lungley 1973）、a.Fitter（Fitter 1987）和a.Diggle（Diggle 1988）。SARAH是一个简单的根系生长模型，包含了有关根系发育的所有可用知识。这在当时是如此新鲜，以至于很容易想象一些当代农学家的怀疑（洛伊奇的个人交流）。但这并没有阻止他继续走这条路。从那时起，Loïc已经发表了超过15个不同的根模型（图2）。他的建模工作从单一物种的纯结构模型（玉米（Pagès等人，1989年）、桃树（Pagés等人，1992年）、橡胶树（Thaler和Pagès，1998年）、拟南芥（Brun等人，2010年）），到能够代表从草到树的广泛根系的通用结构模型（RootTyp（Pagás等人2004年）或RSCone（Pagàs等人2020b））。Loïc还开发了功能-结构模型，包括各种功能，如水流（Doussan等人，1998年）、碳分配（GRAAL（Drouet和Pagès，2003年）、MassFlowDyn（Bidel等人，2000年））、养分分配（GRAAL-CN（Droute和Pagés，2007年））或与周围土壤的相互作用（Gérard等人，2017；Cast等人，2019）。然而，最能概括Loïc工作的模型可能是ArchiSimple（Pagès等人，2014）。顾名思义，ArchiSimple（英语中的SuperSimple）需要不到10个参数来模拟复杂的根系统，但仍然能够代表各种复杂的根架构（Pagès和Picon‐Cochard 2014；Lobet等人2017）。因此，ArchiSimple是一个强大的工具，可以通过一小组数据点来综合复杂多样的体系结构。Loïc从未停止过对他的建模方法的质疑：从元建模方法的使用（Pagès等人，2020）到代表根尖生长、根直径和局部碳有效性之间关系的新方法的建议（Pagés et al.，2020）。除了建模工作外，Loïc还参与了该领域采样技术的思考和开发（Pellerin等人，1994；Pagès等人，2012），并在受控条件下通过设计根管（Drouet等人，2005）、根图像分析工具（DART（Le Bot等人，2010）、SmartRoot（Lobet等人，2011））和根数据分析管道（archiDART（Delory等人，2016），根系统标记语言（Lobet等人，2015））。最近，Loïc还参与编写了一本详尽的根系生态学手册，该手册为根系采样和分类以及测量根系性状提供了详细的指南和标准化协议（Freschet等人，2021）。
{"title":"Loïc Pagès, founding scientist in root ecology and modelling","authors":"L. Pagès","doi":"10.1093/insilicoplants/diab035","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab035","url":null,"abstract":"Root system scientists strive to understand how a single root, emerging from a plant’s seed, can form a complex, dynamic and plastic network of thousands of individual roots. They investigate how such a network is ideally suited to perform a number of functions required for the harmonious development of the whole plant. Everyone in the community also knows how complicated it can be to study root systems, with tasks ranging from digging plants out of the soil, creating experimental setups that allow the observation of the roots, to quantifying the root network itself or the processes underlying its formation. Within the community, there is one person, Dr Loïc Pagès, who has been working on all these tasks for many years, and who has moved the field forward numerous times. On the occasion of his soon-to-be retirement, we would like to express our appreciation to him via this editorial. Loïc Pagès started studying the development of root systems almost 40 years ago and has not stopped ever since. Providing an exhaustive summary of Loïc’s achievements would be a daunting task (according to Scopus, Loïc has published over 130 papers, with more than 249 collaborators; Fig. 1). Here we would like to highlight some of his key contributions to the field. Loïc has been working on many facets of root research. Most importantly, Loïc spent a lot of time observing roots. He dug out and quantified thousands of root systems of more than 60 different plant species, sometimes from his own garden (Pagès and Kervella 2018). One root system at a time, this rich experimental work was Loïc’s foundation for the discovery and conceptualization of a parsimonious set of developmental rules that he was able to apply to a wide range of plant species (Lecompte et al. 2001; Pagès 2016; Pagès and Kervella 2018). Briefly, these rules highlight the importance of the range—and not the average—of root diameters that can be found within a root system and the allometric relationship between roots of different orders. The unique approach of Loïc was to rely on these rules for designing and implementing computational root models. Loïc Pagès is one of the founding fathers of root system modelling. When he published his first computational model, SARAH, in 1988 (Pagès and Ariès 1988), there were only a handful of scientists working in this emerging research area: him, D. Lungley (Lungley 1973), A. Fitter (Fitter 1987) and A. Diggle (Diggle 1988). SARAH was a simple root growth model that included all the available knowledge about root system development. This was so new at the time that it is easy to imagine the scepticism of some contemporary agronomists (Loïc’s personal communication). But this did not stop him from continuing on this path. Since then, Loïc has published more than 15 different root models (Fig. 2). His modelling work spanned from purely structural models of single species (maize (Pagès et al. 1989), peach tree (Pagès et al. 1992), rubber tree (Thaler and Pagès 1998), ","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46594448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1093/INSILICOPLANTS/DIAB018
Daniela Bustos-Korts, M. Boer, K. Chenu, B. Zheng, S. Chapman, F. V. van Eeuwijk
Yield is a function of environmental quality and the sensitivity with which genotypes react to that. Environmental quality is characterized by meteorological data, soil and agronomic management, whereas genotypic sensitivity is embodied by combinations of physiological traits that determine the crop capture and partitioning of environmental resources over time. This paper illustrates how environmental quality and genotype responses can be studied by a combination of crop simulation and statistical modelling. We characterized the genotype by environment interaction for grain yield of a wheat population segregating for flowering time by simulating it using the the Agricultural Production Systems sIMulator (APSIM) cropping systems model. For sites in the NE Australian wheat-belt, we used meteorological information as integrated by APSIM to classify years according to water, heat and frost stress. Results highlight that the frequency of years with more severe water and temperature stress has largely increased in recent years. Consequently, it is likely that future varieties will need to cope with more stressful conditions than in the past, making it important to select for flowering habits contributing to temperature and water-stress adaptation. Conditional on year types, we fitted yield response surfaces as functions of genotype, latitude and longitude to virtual multi-environment trials. Response surfaces were fitted by two-dimensional P-splines in a mixed-model framework to predict yield at high spatial resolution. Predicted yields demonstrated how relative genotype performance changed with location and year type and how genotype by environment interactions can be dissected. Predicted response surfaces for yield can be used for performance recommendations, quantification of yield stability and environmental characterization.
{"title":"Genotype-specific P-spline response surfaces assist interpretation of regional wheat adaptation to climate change","authors":"Daniela Bustos-Korts, M. Boer, K. Chenu, B. Zheng, S. Chapman, F. V. van Eeuwijk","doi":"10.1093/INSILICOPLANTS/DIAB018","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIAB018","url":null,"abstract":"\u0000 Yield is a function of environmental quality and the sensitivity with which genotypes react to that. Environmental quality is characterized by meteorological data, soil and agronomic management, whereas genotypic sensitivity is embodied by combinations of physiological traits that determine the crop capture and partitioning of environmental resources over time. This paper illustrates how environmental quality and genotype responses can be studied by a combination of crop simulation and statistical modelling. We characterized the genotype by environment interaction for grain yield of a wheat population segregating for flowering time by simulating it using the the Agricultural Production Systems sIMulator (APSIM) cropping systems model. For sites in the NE Australian wheat-belt, we used meteorological information as integrated by APSIM to classify years according to water, heat and frost stress. Results highlight that the frequency of years with more severe water and temperature stress has largely increased in recent years. Consequently, it is likely that future varieties will need to cope with more stressful conditions than in the past, making it important to select for flowering habits contributing to temperature and water-stress adaptation. Conditional on year types, we fitted yield response surfaces as functions of genotype, latitude and longitude to virtual multi-environment trials. Response surfaces were fitted by two-dimensional P-splines in a mixed-model framework to predict yield at high spatial resolution. Predicted yields demonstrated how relative genotype performance changed with location and year type and how genotype by environment interactions can be dissected. Predicted response surfaces for yield can be used for performance recommendations, quantification of yield stability and environmental characterization.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49246771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The “holy grail” of trait-based ecology is to predict the fitness of a species in a particular environment based on its functional traits, which has become all the more relevant in the light of global change. However, current ecological models are ill-equipped to predict ecological responses to novel conditions due to their reliance on statistical methods and current observations rather than the mechanisms underlying how functional traits interact with the environment to determine plant fitness. Here, I will advocate the use of functional-structural plant (FSP) modelling in combination with evolutionary modelling to explore climate change responses in natural plant communities. Gaining a mechanistic understanding of how trait-environment interactions drive natural selection in novel environments requires consideration of individual plants with multidimensional phenotypes in dynamic environments that include abiotic gradients and biotic interactions, and their effect on the different vital rates that determine plant fitness. Evolutionary FSP modelling explicitly represents the trait-environment interactions that drive eco-evolutionary dynamics from individual to population scales and allows for efficient navigation of the large, complex and dynamic fitness landscapes that emerge from considering multidimensional plants in multidimensional environments. Using evolutionary FSP modelling as a tool to study climate change responses of plant communities can further our understanding of the mechanistic basis of these responses, and in particular, the role of local adaptation, phenotypic plasticity, and gene flow.
{"title":"Using evolutionary functional-structural plant modelling to understand the effect of climate change on plant populations","authors":"Jorad de Vries","doi":"10.32942/osf.io/6be84","DOIUrl":"https://doi.org/10.32942/osf.io/6be84","url":null,"abstract":"The “holy grail” of trait-based ecology is to predict the fitness of a species in a particular environment based on its functional traits, which has become all the more relevant in the light of global change. However, current ecological models are ill-equipped to predict ecological responses to novel conditions due to their reliance on statistical methods and current observations rather than the mechanisms underlying how functional traits interact with the environment to determine plant fitness. Here, I will advocate the use of functional-structural plant (FSP) modelling in combination with evolutionary modelling to explore climate change responses in natural plant communities. Gaining a mechanistic understanding of how trait-environment interactions drive natural selection in novel environments requires consideration of individual plants with multidimensional phenotypes in dynamic environments that include abiotic gradients and biotic interactions, and their effect on the different vital rates that determine plant fitness. Evolutionary FSP modelling explicitly represents the trait-environment interactions that drive eco-evolutionary dynamics from individual to population scales and allows for efficient navigation of the large, complex and dynamic fitness landscapes that emerge from considering multidimensional plants in multidimensional environments. Using evolutionary FSP modelling as a tool to study climate change responses of plant communities can further our understanding of the mechanistic basis of these responses, and in particular, the role of local adaptation, phenotypic plasticity, and gene flow.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43963341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1093/INSILICOPLANTS/DIAB015
R. Hunt, R. Colasanti
To illuminate mechanisms supporting diversity in plant communities, we construct 2D cellular automata and ‘grow’ virtual plants in real experiments. The plants are 19 different, fully validated functional types drawn from universal adaptive strategy theory. The scale of approach is far beyond that of even the most ambitious investigations in the physical world. By simulating 496 billion plant–environment interactions, we succeed in creating conditions that sustain high diversity realistically and indefinitely. Our simulations manipulate the levels of, and degree of heterogeneity in the supply of, resources, external disturbances and invading propagules. We fail to reproduce this outcome when we adopt the assumptions of unified neutral theory. The 19 functional types in our experiments respond in complete accordance with universal adaptive strategy theory. We find that spatial heterogeneity is a strong contributor to long-term diversity, but temporal heterogeneity is less so. The strongest support of all comes when an incursion of propagules is simulated. We enter caveats and suggest further directions for working with cellular automata in plant science. We conclude that although (i) the differentiation of plant life into distinct functional types, (ii) the presence of environmental heterogeneity and (iii) the opportunity for invasion by propagules can all individually promote plant biodiversity, all three appear to be necessary simultaneously for its long-term maintenance. Though further, and possibly more complex, sets of processes could additionally be involved, we consider it unlikely that any set of conditions more minimal than those described here would be sufficient to deliver the same outcome.
{"title":"Real communities of virtual plants explain biodiversity on just three assumptions","authors":"R. Hunt, R. Colasanti","doi":"10.1093/INSILICOPLANTS/DIAB015","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIAB015","url":null,"abstract":"\u0000 To illuminate mechanisms supporting diversity in plant communities, we construct 2D cellular automata and ‘grow’ virtual plants in real experiments. The plants are 19 different, fully validated functional types drawn from universal adaptive strategy theory. The scale of approach is far beyond that of even the most ambitious investigations in the physical world. By simulating 496 billion plant–environment interactions, we succeed in creating conditions that sustain high diversity realistically and indefinitely. Our simulations manipulate the levels of, and degree of heterogeneity in the supply of, resources, external disturbances and invading propagules. We fail to reproduce this outcome when we adopt the assumptions of unified neutral theory. The 19 functional types in our experiments respond in complete accordance with universal adaptive strategy theory. We find that spatial heterogeneity is a strong contributor to long-term diversity, but temporal heterogeneity is less so. The strongest support of all comes when an incursion of propagules is simulated. We enter caveats and suggest further directions for working with cellular automata in plant science. We conclude that although (i) the differentiation of plant life into distinct functional types, (ii) the presence of environmental heterogeneity and (iii) the opportunity for invasion by propagules can all individually promote plant biodiversity, all three appear to be necessary simultaneously for its long-term maintenance. Though further, and possibly more complex, sets of processes could additionally be involved, we consider it unlikely that any set of conditions more minimal than those described here would be sufficient to deliver the same outcome.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIAB015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45193438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1093/INSILICOPLANTS/DIAB016
E. Lochocki, J. McGrath
Circadian rhythms play critical roles in plant physiology, growth, development and survival, and their inclusion in crop growth models is essential for high-fidelity results, especially when considering climate change. Commonly used circadian clock models are often inflexible or result in complex outputs, limiting their use in general simulations. Here we present a new circadian clock model based on mathematical oscillators that easily adapts to different environmental conditions and produces intuitive outputs. We then demonstrate its utility as an input to Glycine max development models. This oscillator clock model has the power to simplify the inclusion of circadian cycles and photoperiodic effects in crop growth models and to unify experimental data from field and controlled environment observations.
{"title":"Integrating oscillator-based circadian clocks with crop growth simulations","authors":"E. Lochocki, J. McGrath","doi":"10.1093/INSILICOPLANTS/DIAB016","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIAB016","url":null,"abstract":"\u0000 Circadian rhythms play critical roles in plant physiology, growth, development and survival, and their inclusion in crop growth models is essential for high-fidelity results, especially when considering climate change. Commonly used circadian clock models are often inflexible or result in complex outputs, limiting their use in general simulations. Here we present a new circadian clock model based on mathematical oscillators that easily adapts to different environmental conditions and produces intuitive outputs. We then demonstrate its utility as an input to Glycine max development models. This oscillator clock model has the power to simplify the inclusion of circadian cycles and photoperiodic effects in crop growth models and to unify experimental data from field and controlled environment observations.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIAB016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47547236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1093/INSILICOPLANTS/DIAB002
K. Boote, J. Jones, G. Hoogenboom
In silico plant modelling is the use of dynamic crop simulation models to evaluate hypothetical plant traits (phenology, processes and plant architecture) that will enhance crop growth and yield for a defined target environment and crop management (weather, soils, limited resource). To be useful for genetic improvement, crop models must realistically simulate the principles of crop physiology responses to the environment and the principles by which genetic variation affects the dynamic crop carbon, water and nutrient processes. Ideally, crop models should have sufficient physiological detail of processes to incorporate the genetic effects on these processes to allow for robust simulations of response outcomes in different environments. Yield, biomass, harvest index, flowering date and maturity are emergent outcomes of many interacting genes and processes rather than being primary traits directly driven by singular genetics. Examples will be given for several grain legumes, using the CSM-CROPGRO model, to illustrate emergent outcomes simulated as a result of single and multiple combinations of genotype-specific parameters and to illustrate genotype by environment interactions that may occur in different target environments. Specific genetically influenced traits can result in G × E interactions on crop growth and yield outcomes as affected by available water, CO2 concentration, temperature, and other factors. An emergent outcome from a given genetic trait may increase yield in one environment but have little or negative effect in another environment. Much work is needed to link genetic effects to the physiological processes for in silico modelling applications, especially for plant breeding under future climate change.
{"title":"Incorporating realistic trait physiology into crop growth models to support genetic improvement","authors":"K. Boote, J. Jones, G. Hoogenboom","doi":"10.1093/INSILICOPLANTS/DIAB002","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIAB002","url":null,"abstract":"\u0000 In silico plant modelling is the use of dynamic crop simulation models to evaluate hypothetical plant traits (phenology, processes and plant architecture) that will enhance crop growth and yield for a defined target environment and crop management (weather, soils, limited resource). To be useful for genetic improvement, crop models must realistically simulate the principles of crop physiology responses to the environment and the principles by which genetic variation affects the dynamic crop carbon, water and nutrient processes. Ideally, crop models should have sufficient physiological detail of processes to incorporate the genetic effects on these processes to allow for robust simulations of response outcomes in different environments. Yield, biomass, harvest index, flowering date and maturity are emergent outcomes of many interacting genes and processes rather than being primary traits directly driven by singular genetics. Examples will be given for several grain legumes, using the CSM-CROPGRO model, to illustrate emergent outcomes simulated as a result of single and multiple combinations of genotype-specific parameters and to illustrate genotype by environment interactions that may occur in different target environments. Specific genetically influenced traits can result in G × E interactions on crop growth and yield outcomes as affected by available water, CO2 concentration, temperature, and other factors. An emergent outcome from a given genetic trait may increase yield in one environment but have little or negative effect in another environment. Much work is needed to link genetic effects to the physiological processes for in silico modelling applications, especially for plant breeding under future climate change.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIAB002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43506239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1093/INSILICOPLANTS/DIAB012
Yutaka Tsutsumi-Morita, E. Heuvelink, S. Khaleghi, Daniela Bustos-Korts, L. Marcelis, K. Vermeer, Hannelore van Dijk, F. Millenaar, G. van Voorn, F. V. van Eeuwijk
Yield as a complex trait may either be genetically improved directly, by identifying QTLs contributing to yield, or indirectly via improvement of underlying components, where parents contribute complementary alleles to different components. We investigated the utility of two yield dissection models in tomato for identifying promising yield components and corresponding QTLs. In a harvest dissection, marketable yield was the product of number of fruits and individual fruit fresh weight. In a biomass dissection, total yield was the product of fruit fresh-dry weight ratio and total fruit dry weight. Data came from a greenhouse experiment with a population of hybrids formed from four-way RILs. Trade-offs were observed between the component traits in both dissections. Genetic improvements were possible by increasing the number of fruits and the total fruit dry weight to offset losses in fruit fresh weight and fruit fresh-dry weight ratio. Most yield QTLs colocalized with component QTLs, offering options for the construction of high-yielding genotypes. An analysis of QTL allelic effects in relation to parental origin emphasized the complementary role of the parents in the construction of desired genotypes. Multi-QTL models were used for the comparison of yield predictions from yield QTLs and predictions from the products of components following multi-QTL models for those components. Component QTLs underlying dissection models were able to predict yield with the same accuracy as yield QTLs in direct predictions. Harvest and biomass yield dissection models may serve as useful tools for yield improvement in tomato by either or both of combining individual component QTLs and multi-QTL component predictions.
{"title":"Yield dissection models to improve yield: a case study in tomato","authors":"Yutaka Tsutsumi-Morita, E. Heuvelink, S. Khaleghi, Daniela Bustos-Korts, L. Marcelis, K. Vermeer, Hannelore van Dijk, F. Millenaar, G. van Voorn, F. V. van Eeuwijk","doi":"10.1093/INSILICOPLANTS/DIAB012","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIAB012","url":null,"abstract":"\u0000 Yield as a complex trait may either be genetically improved directly, by identifying QTLs contributing to yield, or indirectly via improvement of underlying components, where parents contribute complementary alleles to different components. We investigated the utility of two yield dissection models in tomato for identifying promising yield components and corresponding QTLs. In a harvest dissection, marketable yield was the product of number of fruits and individual fruit fresh weight. In a biomass dissection, total yield was the product of fruit fresh-dry weight ratio and total fruit dry weight. Data came from a greenhouse experiment with a population of hybrids formed from four-way RILs. Trade-offs were observed between the component traits in both dissections. Genetic improvements were possible by increasing the number of fruits and the total fruit dry weight to offset losses in fruit fresh weight and fruit fresh-dry weight ratio. Most yield QTLs colocalized with component QTLs, offering options for the construction of high-yielding genotypes. An analysis of QTL allelic effects in relation to parental origin emphasized the complementary role of the parents in the construction of desired genotypes. Multi-QTL models were used for the comparison of yield predictions from yield QTLs and predictions from the products of components following multi-QTL models for those components. Component QTLs underlying dissection models were able to predict yield with the same accuracy as yield QTLs in direct predictions. Harvest and biomass yield dissection models may serve as useful tools for yield improvement in tomato by either or both of combining individual component QTLs and multi-QTL component predictions.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIAB012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47626241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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