ArticleAnnals of botany2014
An approach to multiscale modelling with graph grammars.
Article in Annals of botany, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Functional-Structural Plant Models Mission in Advancing Crop Science: Opportunities and Prospects.Frontiers in plant science · 2021Review
- Reshaping Plant Biology: Qualitative and Quantitative Descriptors for Plant Morphology.Frontiers in plant science · 2017Review
- Virtual Plants Need Water Too: Functional-Structural Root System Models in the Context of Drought Tolerance Breeding.Frontiers in plant science · 2017Review
- Simulation of carbon allocation and organ growth variability in apple tree by connecting architectural and source-sink models.Annals of botany · 2016Article
- Functional-structural plant models: a growing paradigm for plant studies.Annals of botany · 2014Article
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4 authors.
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Abstract
BACKGROUND AND
aimsFunctional-structural plant models (FSPMs) simulate biological processes at different spatial scales. Methods exist for multiscale data representation and modification, but the advantages of using multiple scales in the dynamic aspects of FSPMs remain unclear. Results from multiscale models in various other areas of science that share fundamental modelling issues with FSPMs suggest that potential advantages do exist, and this study therefore aims to introduce an approach to multiscale modelling in FSPMs.
methodsA three-part graph data structure and grammar is revisited, and presented with a conceptual framework for multiscale modelling. The framework is used for identifying roles, categorizing and describing scale-to-scale interactions, thus allowing alternative approaches to model development as opposed to correlation-based modelling at a single scale. Reverse information flow (from macro- to micro-scale) is catered for in the framework. The methods are implemented within the programming language XL. KEY
resultsThree example models are implemented using the proposed multiscale graph model and framework. The first illustrates the fundamental usage of the graph data structure and grammar, the second uses probabilistic modelling for organs at the fine scale in order to derive crown growth, and the third combines multiscale plant topology with ozone trends and metabolic network simulations in order to model juvenile beech stands under exposure to a toxic trace gas.
conclusionsThe graph data structure supports data representation and grammar operations at multiple scales. The results demonstrate that multiscale modelling is a viable method in FSPM and an alternative to correlation-based modelling. Advantages and disadvantages of multiscale modelling are illustrated by comparisons with single-scale implementations, leading to motivations for further research in sensitivity analysis and run-time efficiency for these models.
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