Appendix C. A figure showing the distribution of AUC for model fits by the two different methods of simulation.

A figure showing the distribution of AUC for model fits by the two different methods of simulation

Appendix A. A figure showing colony areas over time and relative colony size relationships.

A figure showing colony areas over time and relative colony size relationships

Appendix B. A figure showing results of sensitivity analyses for initial conditions of the simulations.

A figure showing results of sensitivity analyses for initial conditions of the simulations

Appendix D. A figure summarizing extinction patterns over time from simulations.

A figure summarizing extinction patterns over time from simulations

Supplement 1. Code for parameter estimation and simulation with the stochastic patch-occupancy model.

<h2>File List</h2><div> <p><a href="SPOMcode.txt">SPOMcode.txt</a> (MD5: e9aca88ace4c6c59761eb5cffe061f70) </p> </div><h2>Description</h2><div> <p>The MATLAB script SPOMcode.txt generates the likelihood equation to be maximized for parameter estimation. The script also contains a function (pdogoptTMC) for conducting the estimation and script for conducting simulations with the estimated parameters. </p> </div

The state level observed Interstate Certificate of Veterinary Inspection data and the <i>N_i</i>/10 sample of interstate movements from 1000 kernel generated networks were highly correlated and consistently more correlated to the observed data than randomized networks.

<p>The heavy line in the boxplots represents the median value, the box area represents the 25^th and 75^th percentile of the data and the whiskers represent the maximum and minimum values.</p

Conceptual diagram of data, model, and validation.

<p>Conceptual diagram of data, model, and validation.</p

Visual comparison for three example states shows that cattle movement networks generated from the Bayesian distance kernel model (right panels) and cattle movements observed from 2009 Interstate Certificates of Veterinary Inspection (ICVI; left panels) are similar.

<p>The observed movements are from a systematic 10% sample of ICVIs from each state and the generated movements are 10% of interstate movements sampled from a single realization out of 1000 kernel generated networks. Darker shading represents the number of cattle premises per county.</p

Comparison of the betweenness centrality scores of the cattle shipment networks generated from the 1000 realizations of the Bayesian distance kernel model (black lines), 1000 realizations of randomized networks (gray lines), and cattle shipments observed from 2009 Interstate Certificate of Veterinary Inspection records (red line).

<p>The betweenness score is a count of the number of shortest paths between any two nodes in a network (<i>i,j</i>), that pass through a node (<i>k</i>).</p

Comparison of the in-degree (A) and out-degree (B) distributions of the cattle shipment networks generated from the 1000 realizations of the Bayesian distance kernel model (black lines), 1000 realizations of randomized networks (gray lines), and cattle shipments observed from 2009 Interstate Certificate of Veterinary Inspection records (red line).

<p>Comparison of the in-degree (A) and out-degree (B) distributions of the cattle shipment networks generated from the 1000 realizations of the Bayesian distance kernel model (black lines), 1000 realizations of randomized networks (gray lines), and cattle shipments observed from 2009 Interstate Certificate of Veterinary Inspection records (red line).</p