Fitting population dynamic models to time-series data by gradient matching
Fitting population dynamic models to time-series data by gradient matching
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DOI：
10.2307/3072057
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发表时间：
2002-08-01
期刊：
影响因子：
4.8
通讯作者：
Smith, RH
中科院分区：
文献类型：
--
作者：
Ellner, SP;Seifu, Y;Smith, RH
我们描述和测试的方法拟合嘈杂的微分方程模型的时间序列的人口计数，由阶段结构模型的昆虫和浮游动物种群的动机。我们考虑半机械模型，其中的模型结构是来自知识的生命周期，但速率方程估计nonparametrically从时间序列数据。该方法涉及平滑人口时间序列x（t），以估计梯度dx/dt，然后使用惩罚回归样条拟合速率方程。计算机密集型的方法被用来估计和消除的偏差，导致从离散时间样本的数据与采样误差从一个连续的时间过程。半机制建模使得有可能测试假设人口波动背后的机制，而不会被混淆的结果可能任意选择的参数形式的过程速率方程。为了说明这一应用程序，我们分析的时间序列数据的实验室种群的绿蝇和丝光绿蝇。这些模型假设人口受到成年人之间竞争的限制，影响他们目前的出生率和死亡率。结果与实际实验条件不符。李斯特cuprina（其中模型的结构是适当的）可以获得良好的拟合，而对于L.对于绢毛藓（在模型不适当的情况下），拟合模型不能再现观察到的周期的一些主要特征。作为本文的补充，提供了一组用于模型拟合过程中所有步骤的R函数。
We describe and test a method for fitting noisy differential equation models to a time series of Population counts, motivated by stage-structured models of insect and zooplankton populations. We consider semimechanistic models, in which the model structure is derived from knowledge of the life cycle, but the rate equations are estimated nonparametrically from the time-series data. The method involves smoothing the population time series x(t) in order to estimate the gradient dx/dt, and then fitting rate equations using penalized regression splines. Computer-intensive methods are used to estimate and remove the biases that result from the data being discrete time samples with sampling errors from a continuous time process. Semimechanistic modeling makes it possible to test assumptions about the mechanisms behind population fluctuations without the results being confounded by possibly arbitrary choices of parametric forms for process-rate equations. To illustrate this application, we analyze time-series data on laboratory populations of blowflies Lucilia cuprina and Lucilia sericata. The models assume that the populations are limited by competition among adults affecting their current birth and death rates. The results cot-respond to the actual experimental conditions. For L. cuprina (where the model's structure is appropriate) a good fit can be obtained, while for L. sericata (where the model is inappropriate), the fitted model does not reproduce some major features of the observed cycles. A documented set of R functions for all steps in the model-fitting process is provided as a supplement to this article.