How to fit nonlinear plant growth models and calculate growth rates: an update for ecologists
How to fit nonlinear plant growth models and calculate growth rates: an update for ecologists
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DOI：
10.1111/j.2041-210x.2011.00155.x
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发表时间：
2012-04-01
影响因子：
6.6
通讯作者：
Turnbull, Lindsay A.
中科院分区：
文献类型：
--
作者：
Paine, C. E. Timothy;Marthews, Toby R.;Turnbull, Lindsay A.
1.植物生长是一个基本的生态学过程，从生理到群落动态和生态系统特性都是跨尺度的。植物生长模型的最新改进使人们能够更深入地了解和更准确地预测广泛的生态问题，包括植物之间的竞争，食虫动物的相互作用和生态系统功能。2.模拟植物生长的一个挑战是，由于各种原因，相对生长率（RGR）几乎普遍随着大小的增加而降低，尽管传统的计算假设RGR是恒定的。非线性增长模型具有足够的灵活性，可以考虑不同的增长率。3.我们展示了各种非线性模型，适合模拟植物生长，并为每一个，显示如何计算功能衍生的增长率，允许在一个共同的时间或大小的物种之间的无偏比较。我们展示了如何传播估计参数的不确定性来表达增长率的不确定性。拟合非线性模型可能具有挑战性，因此我们提供了大量的工作示例和实用建议，所有这些都在R中实现。4.使用非线性模型加上功能衍生的增长率，可以促进新的假设在人口和社区生态学的测试。例如，使用这种技术可以更好地了解RGR的组成部分，快速增长的成本以及宿主和寄生虫生长率之间的联系。我们希望这一贡献将揭开非线性建模的神秘面纱，并说服更多的生态学家使用这些技术。
1. Plant growth is a fundamental ecological process, integrating across scales from physiology to community dynamics and ecosystem properties. Recent improvements in plant growth modelling have allowed deeper understanding and more accurate predictions for a wide range of ecological issues, including competition among plants, plantherbivore interactions and ecosystem functioning. 2. One challenge in modelling plant growth is that, for a variety of reasons, relative growth rate (RGR) almost universally decreases with increasing size, although traditional calculations assume that RGR is constant. Nonlinear growth models are flexible enough to account for varying growth rates. 3. We demonstrate a variety of nonlinear models that are appropriate for modelling plant growth and, for each, show how to calculate function-derived growth rates, which allow unbiased comparisons among species at a common time or size. We show how to propagate uncertainty in estimated parameters to express uncertainty in growth rates. Fitting nonlinear models can be challenging, so we present extensive worked examples and practical recommendations, all implemented in R. 4. The use of nonlinear models coupled with function-derived growth rates can facilitate the testing of novel hypotheses in population and community ecology. For example, the use of such techniques has allowed better understanding of the components of RGR, the costs of rapid growth and the linkage between host and parasite growth rates. We hope this contribution will demystify nonlinear modelling and persuade more ecologists to use these techniques.