PiscesLogoSmallerStill Species richness estimators

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Species Diversity and Richness offers a variety of methods for species richness estimation. Generally reliable methods are the Chao and Jackknife techniques. Interestingly, with the Chao method the results obtained with presence/ absence data are almost as good as those obtained with quantitative data. If the data set comprises presence/absence data then the only method suitable for you is the Chao Presence/absence method labeled as Chao Pr. Ab.

Species inventories for particular habitats or localities are frequently required for purposes such as conservation management. Because a complete census is rarely feasible the community must be sampled. An important problem that then arises is to estimate via sampling the total species (or other taxon) number, Smax , for the locality. This will give both a measure of the completeness of the inventory and also allow comparison with the species richness of other localities. An estimate of the maximum species number is also useful when assessing if the further information to be gained from continued sampling justifies the cost. At present no clear consensus as to the best approach is available.


The program also offers 4 rarefaction procedures (3 individual-based methods, plus a sample-based method called here Sample Interpolation) to make allowance for differences in sampling effort when comparing the species complement of different samples.






Heterogeneity test

Chao Quant.

Chao Pr. Ab.

Chao & Lee 1

Chao & Lee 2

1st Order Jackknife

2nd Order Jackknife



Pooled rarefaction

Single sample rarefaction

Across sample rarefaction


Sample interpolation



Using the Michaelis-Menten method as a stopping rule

Using parametric models based on relative abundance


The output from all of the species richness estimators are presented in similar fashion.


On the top bar there is a drop-down menu that allows the selection of the number of random selections of sample order (R). To let you see how the estimators improve with the addition of further samples estimators are calculated for 1,2, 3, 4 .....,n samples, where n is the total number of samples in the data set. These calculations will be performed R times and the estimators presented are averages over the R runs. The samples selected for each run are chosen at random. If you would like to see the calculations performed on your actual data in the sample order of the original data set then set R to 1.


The change in an estimator with sample number can also be shown graphically, just click on the Graph button in the upper left-hand corner of the window holding the data.


To export or print the data see Printing and exporting your results.