Appendix A. The jumping distribution for missing data.

The jumping distribution for missing data

Supplement 1. Software for Bayesian analysis of metapopulation data using the (extended) incidence function model.

<h2>File List</h2><blockquote> <p><a href="fmetapop_exe_with_data.zip">fmetapop_exe_with_data.zip</a> -- zip file containing executables and example data<br> <a href="fmetapop_source_code.zip">fmetapop_source_code.zip</a> -- zip file containing c source code.</p> </blockquote><h2>Description</h2><blockquote>The file fmetapop_exe_with_data.zip contains two compiled programs for use under Microsoft Windows to carry out Bayesian analysis of metapopulation data. Both programs implement the (extended) Hanski incidence function model, one with and the other without the rescue effect. There are two examples with simulated data, one of which mimics the tree frog case study of the paper. Readme-files describe the program and examples.</blockquote> <blockquote>The file fmetapop_source_code.zip contains the ANSI C source code for the program. The source has been tested using the Borland C++ compiler version 5.02. </blockquote

Supplement 1. MATLAB software for symmetric and predictive co-correspondence analysis and cross-validatory canonical correspondence analysis.

<h2>File List</h2><blockquote> <p><a href="CoCaMatlab.zip">CoCaMatlab.zip</a> -- zip file containing MATLAB functions and toy examples.</p> <p>see <a href="download.htm">Download page</a> for individual files. </p> <p> </p></blockquote><h2>Description</h2><blockquote> <p>The file CoCaMatlab.zip contains MATLAB functions for symmetric and predictive co-correspondence analysis and crossvalidatory canonical correspondence analysis with toy examples and their output. </p> <p>All functions require MATLAB and some also the PLS_Toolbox. See the readme file in the zip for details. Because MATLAB is a high level matrix algebra language, the source code in the m-files can usually be understood by the mathematically oriented reader. In the source code reference is made to equation numbers in the paper.</p> <p> </p></blockquote

Bivariate scatter plot of environmental variables temperature (in °C), and chlorophyll-a (in log(<i>µg L</i>⁻¹)).

<p>Different climate zones are indicated with different colors.</p

Deviance information criterion (DIC) for individual environmental variables and the best linear combination of them in models with and without traits.

<p>The superscripts, rank of DIC in ascending order.</p><p>! negative <i>p_D</i> value; * No convergence.</p

RLQ biplot of the Phytoplankton data.

<p>The first axis (horizontal) of the RLQ analysis explains 99% the variance in the fourth corner statistics, the second (vertical) 0.5%.</p

Response curves for species along the temperature gradient (in °C), Log(chlorophyll-a) (in log(<i>µg L</i>⁻¹)) and the latent variable.

<p>Response curves for species along the temperature gradient (in °C), Log(chlorophyll-a) (in log(<i>µg L</i>⁻¹)) and the latent variable.</p

Range of environmental variables of lakes included in this study for the different regions: subpolar (49°04′–55°06′S), temperate (35°02′-39°08′S and 41°12′–52°36′N), subtropical (29°09′–34°09′S), and tropical (5°04′–23°05′S).

<p>Abbreviations as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097583#pone-0097583-t002" target="_blank">Table 2</a>. S: southern, and N: northern hemispheres.</p><p>*Sampling in subpolar lakes only included summer season.</p

Appendix A. Results of the simulation study performed in Dray and Legendre (2008) for the two-step procedure of Dray and Legendre (2008) and the new sequential testing procedure.

Results of the simulation study performed in Dray and Legendre (2008) for the two-step procedure of Dray and Legendre (2008) and the new sequential testing procedure

List of environmental variables and trait variables with code and unit of measurement, number of missing values and indicator for the transformation to natural logarithms.

<p>List of environmental variables and trait variables with code and unit of measurement, number of missing values and indicator for the transformation to natural logarithms.</p