9 May 2019
IMPORTANT: Operating system limitations of EstimateS 9.1.0 (2019)
- MacOS. This version does not operate fully under MacOS 10.12 (Sierra), nor under subsequent versions (High Sierra, Mojave....).
- Windows. This version does not operate fully under Windows 10, nor probably under any subsequent Windows operating systems.
- "What shall I do?" Anne Chao's website offers excellent R-based tools to compute most of what EstimateS computes (and much more!). You can download the R code, but you need not know any R to use most of Anne's tools, as they are also implemented in easy-to-use, on-screen input/output tools. There is a comprehensive downloadable User's Guide.
- "Why can't you fix it?" I first builit EstimateS more than twenty years ago, long before open source software (like R) was available. So it has always run under 4th Dimension, a proprietary development environment. To make it run under current Mac and Windows operating systems would require purchasing about USD$5000 worth of 4D development tools and licenses. I am now retired from teaching and living on a fixed income, so that is not practical (unless some generous benefactor would like to fund it). Anne Chao's approach is the way to go, for now and the future, and Anne has always been my key statistical advisor for EstimateS. ~Robert Colwell
Major new features of EstimateS 9 (2016)
- A comprehensively revised User's Guide covering all the new features in EstimtateS 9 and all the traditional ones of previous versions.
- An entirely new capability for handing individual-based rarefaction with true (unconditional) confidence intervals (and of course, sample-based rarefaction, the core of all previous versions of EstimateS).
- Rarefaction of richness estimators and diversity indices for individual-based data (as well as sample-based data, as in previous versions).
- Non-parametric extrapolation of rarefaction curves for both sample-based and individual-based data (Colwell et al. 2012).
- Batch input and export option for both sample-based and individual-based datasets.
- Options for computing, displaying, and exporting subsets of results for evenly-spaced intervals for rarefaction and extrapolation of samples or individuals (interval-sampling or knots).
- Automatic support for International and US number formats.
- EstimateS 9 for Windows runs under Windows 8, Windows 7, Vista, and XP.
- EstimateS 9 for Mac OS runs under OS 10.5 (Leopard) through 10.8 (Mountain Lion).
- EstimateS 9 is blazing fast, compared to earlier versions.
With these classic features of previous versions
- Computes Colwell and Mao's smooth species accumulation curves (sample-based rarefaction curves) with true confidence intervals, based on analytical formulas (Colwell et al. 2004).
- Computes a wide range of species richness estimators for sample-based abundance and incidence (presence/absence) data (Chao, Jackknife, ICE, ACE and others).
- Computes asymmetric (log-transformed) confidence intervals for Chao1 andChao2.
- Computes diversity indices (Shannon, Simpson, Fisher's alpha, Hill numbers).
- All richness estimators and diversity indices are computed for every level of sample accumulation, averaged over resamplings.
- Choice of resampling with or without replacement.
- Computes Chao's shared species estimator for sample pairs.
- Computes Chao's Sørensen and Jaccard similarity estimators (Chao et al. 2005).
- Computes classic Jaccard, Sørensen, Bray-Curtis, and Morista-Horn similarity indices for sample pairs.
Homepage references (many more in the User's Guide)
Colwell, R. K., A. Chao, N. J. Gotelli, S.-Y. Lin, C. X. Mao, R. L. Chazdon, and J. T. Longino. 2012. Models and estimators linking individual-based and sample-based rarefaction, extrapolation, and comparison of assemblages. Journal of Plant Ecology 5:3-21. Read it online or download pdf.
Colwell, R. K., C. X. Mao, & J. Chang. 2004. Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology 85, 2717-2727. Download pdf. Spanish Version: Download pdf.
Chao, A., R. L. Chazdon, R. K. Colwell, and T.-J. Shen. 2005. A new statistical approach for assessing compositional similarity based on incidence and abundance data. Ecology Letters 8:148-159. Download pdf. Spanish Version: Download pdf.