Appendix A. A Gibbs sampler for the hierarchical model.

A Gibbs sampler for the hierarchical model

Model results.

<p>Model results.</p

SGS simulation results.

<p>Average coefficient of relatedness (cr) at distances up to 100 m for u_s = 20 (blue), 100 (red), 800 (purple), 3500 (green), 7000 (black). Left: several out-of-plot seed sources, Duke Forest. Right: randomly distributed seed sources, Coweeta.</p

Observed within-stand dispersal distances (dark) and minimum dispersal distances for immigrant seed and pollen (light).

<p>Top – Distances between in-plot mother-seedling pairs (dark bars) and between seedlings with an out-of-plot mother and the nearest plot edge (light bars). Bottom – Distances between in-plot mother-father pairs (dark bars) and between mother trees paired with an out-of-plot father and the nearest plot edge (light bars). The mean dispersal distance for both seed and pollen increases when out-of-plot parentage is considered.</p

DISTANCE results.

<p>DISTANCE results.</p

Within-stand mother-offspring distances as estimated by Bayesian model vs. nearest-parent distances as estimated by CERVUS.

<p>The Bayesian model rejects many of the parent matches identified by ML approach, due to low seed production, the necessity of assuming high genotyping error, and/or the proximity of a seedling to the stand edge (and potential sources of immigrant seed). However, both models suggest higher seed movement at Duke Forest relative to Coweeta.</p

Appendix A. The Gibbs sampler for Bayesian state-space models.

The Gibbs sampler for Bayesian state-space models

Appendix A. Detailed description of stand selection, plot layout, and experimental gap creation.

Detailed description of stand selection, plot layout, and experimental gap creation

Spatial Genetic Structure results.

<p>Correlation in genotype at different distance classes for large adults, small adults, and seedlings. Bars indicate bootstrapped 95% CI. If bars do not overlap zero, this indicates a significant correlation at that scale (indicated with asterixes).</p

Appendix A. Markov-chain Monte Carlo algorithms, together with prior parameter values, marginal posteriors, a discussion of convergence, and examples using Acer rubrum.

Markov-chain Monte Carlo algorithms, together with prior parameter values, marginal posteriors, a discussion of convergence, and examples using Acer rubrum