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:** 

##### 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...