Index Files

Index File

Appendix B. The development of a general method for correcting for age-specific inputs into the censused population.

The development of a general method for correcting for age-specific inputs into the censused population

Supplement 2. The Matlab code for running a stochastic matrix model and generating diagnostic plots.

<h2>File List</h2><blockquote> <p> Matlab code: </p> <p> <a href="gensimests.m">gensimests.m</a> <br> <a href="marshmat.m">marshmat.m</a> <br> <a href="paramperf.m">paramperf.m</a> <br> <a href="SimpleRun.m">SimpleRun.m</a> </p> <p> All files are in ASCII text. </p> </blockquote><h2>Description</h2>The following are matlab files (in ascii format) to run 1000 simulations of a stochastic matrix model and then graph diagnostic plots of the parameter and risk metric estimates using ML, running sum, Heyde-Cohen, Kalman and slope parameterization methods. To run, copy the files to a directory and run the "SimpleRun.m" script. This calls functions to run simulations and make plots described in the other files. <p> The file SimpleRun.m calls marshmat.m to specify the stochastic model. It then calls gensimests.m to make 1000 simulated time series, estimate the diffusion approximation parameters using the ML, running sum, Heyde-Cohen, Kalman and slope methods, and saves the results to a file. Finally, SimpleRun.m calls paramperf.m which makes diagnostic plots of the different parameterization methods. </p> <p> The file marshmat.m specifies the matrix model, the level of stochasticity for each matrix element, and what segment of the population is censused. </p> <p> The file gensimests.m runs the model specified in marshmat.m to create simulated time series. To each time series, it adds either none or one of the three levels of sampling error. From this time series, it then estimates diffusion approximation parameters via ML, running sum, Heyde-Cohen, Kalman or slope methods. It saves the results in a data file. </p> <p> The file paramperf.m takes the data file created by gensimests.m and makes diagnostic plots of the percentage error in estimation of m, s², l, and the probability of 90% decline. </p> <p> </p

Supplement 1. R code for simulation implementation and parameter estimation.

<h2>File List</h2><div> <p><a href="EstimationComparison.R">EstimationComparison.R</a> (MD5: 5055e25ebc61fe8dbf012b48198eadda)</p> <p><a href="Simulations.R">Simulations.R</a> (MD5: 56eef6b34cb59a85d5072b0b20e645ec)</p> <p><a href="CreateFigures.R">CreateFigures.R</a> (MD5: 2c19a14d1ac5887ba544c2937d8639ca)</p> </div><h2>Description</h2><div> <p>EstimationComparison.R - This code was used to simulate time-series of length 30 time-step and either 1, 2, or 3 replicated observations at each time-step. Parameters were then estimated using three different methodologies: restricted maximum likelihood (REML) based on first differences, REML based on second differences and maximum likelihood using the Kalman filter, as implemented in the MARSS R package.</p> <p>Simulations.R - This code was used to simulate time-series of varying length, with varying numbers of replicated observations, under two different rates of decline and several different process and non-process error variances. Estimates of trend, process and non-process error variances were then made with the MARSS R package.</p> <p>CreateFigures.R - This code was used to create the figures that appear in the manuscript and appendices.</p> </div

Appendix B. Full results from all simulations.

Full results from all simulations

Appendix A. A comparison of estimation methods.

A comparison of estimation methods

Appendix A. Salmon time series used in the analyses.

Salmon time series used in the analyses

Appendix C. Derivation of the distributions for cross-validating parameter estimates.

Derivation of the distributions for cross-validating parameter estimates

Supplement 2. Raw data for the salmon time series used in the cross-validations.

<h2>File List</h2><blockquote> <p> <a href="rawdata.txt">rawdata.txt</a><br> </p> <p> File is in ASCII text. </p> </blockquote><h2>Description</h2><p> The file rawdata.txt contains the salmon time series used in the cross-validations. The data are described in <a href="appendix-A.htm">Appendix A</a>. Each time series entry has the stream name, ESU name, species, data source, and type of data. Types include total live spawner estimates (tlc), dam or weir counts (dc), peak live or carcass counts (peak), redd count (redd), or redds per mile (rpm). Missing data are indicated by -99. </p

Supplement 1. S-PLUS code for estimating parameters and calculating risk metrics from a time series.

<h2>File List</h2><blockquote> <p> S-Plus code: </p> <p> <a href="DHMethod.txt">DHMethod.txt</a><br> <a href="riskmetrics.txt">riskmetrics.txt</a><br> </p> <p> All files are in ASCII text. </p> </blockquote><h2>Description</h2>Following are S-Plus files (in ascii format) to estimate parameters and risk metrics from a time series using the Dennis-Holmes method. <p> The file DHMethod.txt contains code to estimate -- IMAGE: Please see in attached file. -- using running sums and -- IMAGE: Please see in attached file. --² using the slope method from a time series of counts.</p> <p> The file riskmetrics.txt contains code to estimate risk metrics given estimates of -- IMAGE: Please see in attached file. -- and -- IMAGE: Please see in attached file. --². The following metrics are calculated: confidence intervals on -- IMAGE: Please see in attached file. --, probability of extinction with 95% confidence intervals, probability of decline with 95% confidence intervals, probability of observing extinction, and probability of observing a given decline. </p