3. Niche Overlap
4. Size Overlap
5. Species Diversity
6. Standard Tests
7. Guild Structure
EcoSim allows you to test for community patterns with non-experimental data. EcoSim performs Monte Carlo randomizations to create pseudo-communities (Pianka 1986), then statistically compares the patterns in these randomized communities with those in the real data matrix. These null model tests have wide applicability in both applied and basic ecology. Kinds of questions you can ask with EcoSim are:
1) Is the species richness and evenness of unpolluted streams significantly different from that of polluted streams?
2) Is there evidence for unusual segregation in diet or perching microhabitats of co-occurring lizard species?
3) Does the taxonomic diversity of an avian island community differ from that of the adjacent mainland source pool?
EcoSim's Help file introduces you to null model analysis and shows you how to use EcoSim for null model tests to analyze your own data. Tutorials, default settings, and sample data sets are included with each model. EcoSim's input and output screens are friendly and easy to use.
| Citing EcoSim
Gotelli, N.J. and G.L. Entsminger. 2012. EcoSim 7.72. Acquired Intelligence, Inc.
This version of EcoSim is the most current and represents the original programming effort (funded by NSF for 7 years). It still works fine, although it is beginning to show its age, as the modules have not been substantively updated in 10 years. I will keep it available here at this website as long as it remains compatable with current versions of Windows. As always, I will continue to answer questions and provide advice about analyses with EcoSim.
Unfortunately, a "new" commercial version of EcoSim has recently appeared elsewhere on the web. The commercialization of EcoSim was done without my consent or approval, and I strongly oppose it. I had no involvement with the commercial product, and I cannot confirm the validity of any of the algorithms or modules contained in it.
To combat the commercialization of EcoSim, Aaron Ellison and I are initiating a new project. We are beginning to recode the routines in EcoSim to the R language and will release EcoSimR ("EcoSimmer") at this site. EcoSimR will consist of fully annotated R-script files, along with help files, tutorials, and sample data sets for you to work with. For modules that are computationally intensive (such as co-occurrence), we will code some functions in C for faster execution.
EcoSimR will offer four distinct advantages over the original EcoSim:
We hope that EcoSimR will serve as a gateway drug and inspire you to do your own R-programming and modify the code to suit your specific needs!
So what is the solution? Instead of calculating a statistical test for the entire matrix, the pattern can be tested for each individual pair of species in the matrix. But this solution generates a new problem: because there are S(S-1)/2 species pairs, the analysis would generate thousands of individual p-values (one for each unique species pair), even for a modest-sized matrix. This problem has also arisen in genomics and proteomics, where it is now possible to rapidly screen expression levels of thousands of genes. The empirical Bayes approach allows for a realistic adjustment and screening of the individual pairs to limit the rate of false positives (Effron 2005). Ulrich and Gotelli (2010) adapt these methods for co-occurrence analysis and apply them to a large set of empirical matrices from the ecological literature. Ulrich's Pairs software implements these procedures and represents an important step forward for the analysis of species co-occurrence. Be sure to check out Werner's many useful FORTRAN programs for ecological data analysis.
Castro-Arellano, I., T.E. Lacher, Jr., M.R. Willig, and T.F. Rangel. 2010. Assessment of assemblage-wide temporal niche segregation using null models. Methods in Ecology & Evolution 1: 311-318.
Colwell, R.K., A. Chao, N.J. Gotelli, S-Y. Lin, C.X. Mao, R.L. Chazdon, and J.T. Longino. Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages. Journal of Plant Ecology, in press
Gotelli, N.J. and W. Ulrich. Statistical challenges in null model analysis. Oikos, in press.
Gotelli, N.J. and W. Ulrich. 2010. The empirical Bayes approach as a tool to identify non-random species associations. Oecologia 162:463-477.
Stone, L., and A. Roberts. 1990. The checkerboard score and species distributions. Oecologia 85:74–79.
Ulrich, W., M. Almeida-Neto, and N.J. Gotelli. 2009. A consumer's guide to nestedness analysis. Oikos 118: 3-17.
Ulrich, W. and N.J. Gotelli. 2007a. Disentangling community patterns of nestedness and species co-occurrence. Oikos 116:2053-2061.
Ulrich, W. and N.J. Gotelli. 2007b. Null model analysis of species nestedness patterns. Ecology 88:1824-1831.