Fitting state-space integral projection models to size-structured time series data to estimate unknown parameters
Fitting state-space integral projection models to size-structured time series data to estimate unknown parameters
复制标题
将状态空间积分投影模型拟合到大小结构时间序列数据以估计未知参数
DOI：
10.1002/eap.1398
复制
发表时间：
2016
影响因子：
5
通讯作者：
Botsford, Louis W.
中科院分区：
文献类型：
--
作者：
White, J. Wilson;Nickols, Kerry J.;Malone, Daniel;Carr, Mark H.;Starr, Richard M.;Cordoleani, Flora;Baskett, Marissa L.;Hastings, Alan;Botsford, Louis W.
积分投影模型（IPM）在分析规模结构种群动态方面比矩阵模型方法具有许多优势，因为后者需要对每个年龄或阶段过渡进行参数估计。然而，IPM仍然需要适当的数据。通常情况下，它们是使用个体尺度的身体大小和人口统计率之间的关系进行参数化，但这些并不总是可用的。我们提出了一种使用贝叶斯状态空间IPM（SSIPM）从规模结构调查数据的时间序列估计人口统计参数的替代方法。通过在状态空间框架中拟合IPM，我们估计未知参数并明确说明数据集中的过程和测量误差，以估计底层过程模型动态。我们通过将SSIPM拟合到模拟数据来测试我们的方法;该模型很好地拟合了模拟的大小分布，并准确地估计了未知的人口统计参数。然后，我们说明了我们的方法，使用九年的年度调查的密度和大小分布的两种鱼类（蓝岩鱼，鱼，和地鼠岩鱼，S. carnatus）在七个海带森林网站在加州。SSIPM对数据进行了合理的拟合，并估计了这两个物种的捕捞率，这些捕捞率高于我们基于海岸范围的捕捞量评估估计的贝叶斯先验估计。这一改进加强了能够从当地规模的监测数据中估计人口参数的价值。我们强调了SSIPM开发中的一些关键决策点（例如，开放与封闭的人口统计学，状态空间滤波器中的粒子数），以便用户可以将该方法应用于他们自己的数据集。
Integral projection models (IPMs) have a number of advantages over matrix‐model approaches for analyzing size‐structured population dynamics, because the latter require parameter estimates for each age or stage transition. However, IPMs still require appropriate data. Typically they are parameterized using individual‐scale relationships between body size and demographic rates, but these are not always available. We present an alternative approach for estimating demographic parameters from time series of size‐structured survey data using a Bayesian state‐space IPM (SSIPM). By fitting an IPM in a state‐space framework, we estimate unknown parameters and explicitly account for process and measurement error in a dataset to estimate the underlying process model dynamics. We tested our method by fitting SSIPMs to simulated data; the model fit the simulated size distributions well and estimated unknown demographic parameters accurately. We then illustrated our method using nine years of annual surveys of the density and size distribution of two fish species (blue rockfish,Sebastes mystinus, and gopher rockfish,S. carnatus) at seven kelp forest sites in California. The SSIPM produced reasonable fits to the data, and estimated fishing rates for both species that were higher than our Bayesian prior estimates based on coast‐wide stock assessment estimates of harvest. That improvement reinforces the value of being able to estimate demographic parameters from local‐scale monitoring data. We highlight a number of key decision points in SSIPM development (e.g., open vs. closed demography, number of particles in the state‐space filter) so that users can apply the method to their own datasets.