The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD, Australia.
Department of Agriculture and Fisheries, Toowoomba, QLD, Australia.
Ann Bot. 2018 Apr 18;121(5):941-959. doi: 10.1093/aob/mcx187.
Functional-structural plant (FSP) models have been widely used to understand the complex interactions between plant architecture and underlying developmental mechanisms. However, to obtain evidence that a model captures these mechanisms correctly, a clear distinction must be made between model outputs used for calibration and thus verification, and outputs used for validation. In pattern-oriented modelling (POM), multiple verification patterns are used as filters for rejecting unrealistic model structures and parameter combinations, while a second, independent set of patterns is used for validation.
To test the potential of POM for FSP modelling, a model of avocado (Persea americana 'Hass') was developed. The model of shoot growth is based on a conceptual model, the annual growth module (AGM), and simulates photosynthesis and adaptive carbon allocation at the organ level. The model was first calibrated using a set of observed patterns from a published article. Then, for validation, model predictions were compared with a different set of empirical patterns from various field studies that were not used for calibration.
After calibration, our model simultaneously reproduced multiple observed architectural patterns. The model then successfully predicted, without further calibration, the validation patterns. The model supports the hypothesis that carbon allocation can be modelled as being dependent on current organ biomass and sink strength of each organ type, and also predicted the observed developmental timing of the leaf sink-source transition stage.
These findings suggest that POM can help to improve the 'structural realism' of FSP models, i.e. the likelihood that a model reproduces observed patterns for the right reasons. Structural realism increases predictive power so that the response of an AGM to changing environmental conditions can be predicted. Accordingly, our FSP model provides a better but still parsimonious understanding of the mechanisms underlying known patterns of AGM growth.
功能结构植物（FSP）模型已被广泛用于理解植物结构与潜在发育机制之间的复杂相互作用。然而，为了获得模型准确捕捉这些机制的证据，必须明确区分用于校准（即验证）和用于验证的模型输出。在面向模式的建模（POM）中，使用多个验证模式作为筛选器来拒绝不现实的模型结构和参数组合，而第二个独立的模式集用于验证。
为了测试 POM 用于 FSP 建模的潜力，开发了鳄梨（Persea americana 'Hass'）的模型。该模型的枝条生长基于概念模型——年生长模块（AGM），模拟器官水平的光合作用和适应性碳分配。该模型首先使用来自已发表文章的一组观测模式进行校准。然后，为了验证，将模型预测与来自各种田间研究的不同模式集进行比较，这些模式集未用于校准。
在经过校准后，我们的模型同时再现了多个观测到的结构模式。然后，该模型无需进一步校准即可成功预测验证模式。该模型支持以下假设：可以将碳分配建模为依赖于当前器官生物量和每个器官类型的汇强度，还预测了观察到的叶片源库转换阶段的发育时间。
这些发现表明，POM 可以帮助提高 FSP 模型的“结构现实主义”，即模型再现观测模式的可能性，是因为模型以正确的理由再现了观测模式。结构现实主义提高了预测能力，因此可以预测 AGM 对环境变化的响应。因此，我们的 FSP 模型提供了对 AGM 生长已知模式背后机制的更好但仍然简约的理解。
