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
process error tended to be estimated with lower accuracy, though accuracy
improved as sibling group size or sampling intensity increased. The main
contribution of this research is a flexible quantitative modeling
framework for parameterizing mechanistic models of recruitment dynamics
with empirical sibship data.