Appendix A. Additional analyses and species-specific detectability and occupancy estimates.

Additional analyses and species-specific detectability and occupancy estimates

Black rail microsatellite genotypes

Microsatellite genotypes for California black rails captured in the San Francisco Bay Area (population 1) and Sierra Nevada Foothills (population 2). Data are formatted for Genepop and can be used as an input file for GeneClass2

Appendix B. Survival estimates derived from mark–recapture analyses and life history traits of alcids used in comparative analysis.

Survival estimates derived from mark–recapture analyses and life history traits of alcids used in comparative analysis

Stable isotopes for black rails

Stable C, N, and S values measured from feathers of California black rails captured in the San Francisco Bay Area and Sierra Nevada Foothills

Stable isotopes for wetland soils

Stable C, N, and S values measured from wetland soils collected in the home ranges of rails in the Sierra Nevada Foothills

Appendix C. Literature cited for Appendices A and B.

Literature cited for Appendices A and B

Index Files

Index File

Appendix A. Ratio of hatch-year to after-hatch-year individuals (R) and life history traits for 29 bird species used in the comparative analysis.

Ratio of hatch-year to after-hatch-year individuals (R) and life history traits for 29 bird species used in the comparative analysis

R code for constructing and analyzing matrices with nonbreeders

R code to construct and analyze matrices in systems with pre- or post-breeding census, different types of nonbreeders, and with or without negative frequency dependence in breeder survival or fecundity. Analyses include growth rate, stable stage structure, reproductive value, demographic variance, comparison to equivalent Leslie matrix model, sensitivity analysis, effect of only observing breeders, and more

Supplement 1. R-code used to simulate occupancy data and to fit the IFM naïve, IFM missing, and IFM robust models.

<h2>File List</h2><blockquote> <a href="1_MCMC_FUNCTIONS.R">1_MCMC_FUNCTIONS.R</a><br> <a href="2_IFM_NO_MISSING_MCMC_FUNCTION.R">2_IFM_NO_MISSING_MCMC_FUNCTION.R</a><br> <a href="3_IFM_MISSING_MCMC_FUNCTION.R">3_IFM_MISSING_MCMC_FUNCTION.R</a><br> <a href="4_IFM_ROBUST_MCMC_FUNCTION.R">4_IFM_ROBUST_MCMC_FUNCTION.R</a><br> <a href="5_CHAIN_DIAGNOSTICS_FUNCTIONS.R">5_CHAIN_DIAGNOSTICS_FUNCTIONS.R</a>.<br> <a href="6_IFM_SIM_DATA_PREP.R">6_IFM_SIM_DATA_PREP.R</a><br> <a href="7_AUDIT_INM.R">7_AUDIT_INM.R</a><br> <a href="8_AUDIT_IFM_MISSING.R">8_AUDIT_IFM_MISSING.R</a><br> <a href="9_AUDIT_IFM_ROBUST.R">9_AUDIT_IFM_ROBUST.R</a> </blockquote><h2>Description</h2><blockquote> <p>The files in this Supplement are used to fit the three models described in this study (IFM Naïve, IFM Missing, and IFM Robust) and include a program that simulates occupancy data following the Incidence Function Model.</p> <p>The file 1_MCMC_FUNCTIONS.R contains four auxiliary functions used in 2_IFM_NO_MISSING_MCMC_FUNCTION.R, 3_IFM_MISSING_MCMC_FUNCTION.R, and 4_IFM_ROBUST_MCMC_FUNCTION.R. It includes a function that accepts or rejects proposed values in the Metropolis-Hastings algorithm, a function that counts the acceptance rates, a function that counts the number of missing values, and a function that is used when proposing values from a bivariate normal distribution for the parameters gamma and beta.</p> <p>The file 2_IFM_NO_MISSING_MCMC_FUNCTION.R contains the function that uses MCMC to estimate the IFM Naïve. The methods are described in <a href="appendix-A.htm">Appendix A</a>.</p> <p>The file 3_IFM_MISSING_MCMC_FUNCTION.R contains the function that uses MCMC to estimate the IFM Missing. The methods are described in <a href="appendix-A.htm">Appendix A</a>.</p> <p>The file 4_IFM_ROBUST_MCMC_FUNCTION.R contains the function that uses MCMC to estimate the IFM Robust. It implements the IFM Robust, and the methods are described in <a href="appendix-A.htm">Appendix A</a>.</p> <p>The file 5_CHAIN_DIAGNOSTICS_FUNCTIONS.R contains a number of functions used to diagnose the convergence of MCMC chains. In particular, the function coda.create converts the files created by the MCMC functions to a format that can be read by the “coda” R-package.</p> <p>The file 6_IFM_SIM_DATA_PREP.R simulates occupancy data from the Incidence Function Model. These data sets are then used in the subsequent programs.</p> <p>The file 7_AUDIT_INM.R estimates the parameters of the IFM Naïve using data created in 6_IFM_SIM_DATA_PREP.R.</p> <p>The file 8_AUDIT_IFM_MISSING.R estimates the parameters of the IFM Missing using data created in 6_IFM_SIM_DATA_PREP.R.</p> <p>The file 9_AUDIT_IFM_ROBUST.R estimates the parameters of the IFM Robust using data created in 6_IFM_SIM_DATA_PREP.R.</p> </blockquote