A modeling framework for quantifying spatial recruitment dynamics using abundance estimation and sibship analysis: code and simulation study output

Quantifying recruitment at the sibling group offers a powerful methodology for understanding density-dependent and environmental drivers of recruitment. We propose a modeling framework that combines sibship and abundance estimation datasets to estimate mean sibling group size, sibling group size process error, environmental and density-dependent effects on sibling group size, dispersal, and mortality rate. Geographic states in the model consist of discrete habitat patches connected via dispersal. Simulations were used to investigate the influence of sampling processes and sibling group size on parameter estimation within our modeling framework. Mean sibling-group size, environmental effects on recruitment, and dispersal rate among habitat patches were estimated with high accuracy under a wide range of sampling conditions, including imprecise out-of-model estimates of capture probability and subsampling both within and among habitat patches. Density-dependent effects on recruitment and p..., The simulation results were obtained using the code provided in the linked software related work (R code and Stan code provided)., , # A Modeling Framework for Quantifying Spatial Recruitment Dynamics Using Abundance Estimation and Sibship Analysis: Code and Simulation Study Output [https://doi.org/10.5061/dryad.2fqz612zd](https://doi.org/10.5061/dryad.2fqz612zd) ## Description of the data and file structure The simulation results provided summarizes simulation output obtained using the associated software code provided (R scripts and Stan model code). The raw simulation output (stan model for each model run) was summarized by 1) extracting the parameter values and model diagnostics of interest from each stan model fit to a simulated dataset and 2) calculating the relative error and precision for each simulation. ### Files and variables #### File: SimResults\_MultiTimeStep.csv **Description:**Â ##### Variables * mean: mean of the posterior distribution * 10%: 10th quantile of the posterior distribution * 90%: 90th quantile of the posterior distribution * TrueSimValue: value used in the data-generating simulati...量化同胞群（sibling group）的种群补充量，为理解种群补充的密度依赖效应与环境驱动因子提供了强有力的研究方法。本研究提出一种整合同胞鉴定（sibship）与丰度估计数据集的建模框架，用于估计平均同胞群规模、同胞群规模的过程误差、环境与密度依赖效应对同胞群规模的影响、扩散速率与死亡率。模型中的地理状态由通过扩散连接的离散生境斑块构成。本研究通过模拟实验，探究了采样过程与同胞群规模对所提建模框架中参数估计的影响。在多种采样条件下——包括捕获概率的非模型估计存在偏差、生境斑块内部及斑块间的二次采样——平均同胞群规模、种群补充的环境效应以及生境斑块间的扩散速率均能被高精度地估计。种群补充的密度依赖效应及[原文内容截断]。本研究的模拟结果源自关联软件相关工作中提供的代码（已提供R代码与Stan代码）。 # 基于丰度估计与同胞群分析的空间种群补充动态量化建模框架：代码与模拟研究结果 [https://doi.org/10.5061/dryad.2fqz612zd](https://doi.org/10.5061/dryad.2fqz612zd) ## 数据与文件结构说明 本次提供的模拟结果汇总了通过配套软件代码（已提供R脚本与Stan模型代码）生成的模拟输出。原始模拟输出（对应每次模型运行的Stan模型）通过以下两步进行汇总：1）从适配模拟数据集的各Stan模型中提取目标参数值与模型诊断结果；2）计算每次模拟的相对误差与估计精度。 ### 文件与变量 #### 文件：SimResults_MultiTimeStep.csv **描述：** ##### 变量 * mean：后验分布的均值 * 10%：后验分布的10%分位数 * 90%：后验分布的90%分位数 * TrueSimValue：数据生成模拟中使用的真实值[原文截断]