Department of Biology and Marine Biology, University of North Carolina Wilmington, Wilmington, North Carolina, 28043, USA.
Division of Science and Environmental Policy, California State University Monterey Bay, Seaside, California, 93955, USA.
Ecol Appl. 2016 Dec;26(8):2675-2692. doi: 10.1002/eap.1398. Epub 2016 Sep 30.
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.
积分投影模型 (IPM) 在分析大小结构种群动态方面具有许多优于矩阵模型方法的优势，因为后者需要对每个年龄或阶段的过渡进行参数估计。然而，IPM 仍然需要适当的数据。它们通常使用个体尺度上的体型和人口统计率之间的关系进行参数化，但这些关系并不总是可用的。我们提出了一种使用贝叶斯状态空间 IPM (SSIPM) 从大小结构调查数据的时间序列中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，并明确考虑数据集在过程和测量误差方面，以估计潜在过程模型动态。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，我们使用加利福尼亚州七个巨藻林地点的两年鱼物种（蓝石斑鱼，Sebastes mystinus 和岩鱼，S. carnatus）的密度和大小分布的九年年度调查数据说明了我们的方法。SSIPM 对数据产生了合理的拟合，并估计了两种物种的捕捞率，这些捕捞率高于我们基于沿海范围的捕捞评估对收获量的贝叶斯先验估计。这种改进增强了能够从本地规模监测数据中估计人口统计参数的价值。我们强调了 SSIPM 开发中的一些关键决策点（例如，开放与封闭人口统计学，状态空间滤波器中的粒子数量），以便用户可以将该方法应用于自己的数据集。
