Dataset Information

Species accumulation curves and incidence-based species richness estimators to appraise the diversity of cultivable yeasts from beech forest soils.

ABSTRACT:

Background

Yeast-like fungi inhabit soils throughout all climatic zones in a great abundance. While recent estimations predicted a plethora of prokaryotic taxa in one gram of soil, similar data are lacking for fungi, especially yeasts.

Methodology/principal findings

We assessed the diversity of soil yeasts in different forests of central Germany using cultivation-based techniques with subsequent identification based on rDNA sequence data. Based on experiments using various pre-cultivation sample treatment and different cultivation media we obtained the highest number of yeasts by analysing mixed soil samples with a single nutrient-rich medium. Additionally, several species richness estimators were applied to incidence-based data of 165 samples. All of them predicted a similar range of yeast diversity, namely 14 to 16 species. Randomized species richness curves reached saturation in all applied estimators, thus indicating that the majority of species is detected after approximately 30 to 50 samples analysed.

Conclusions/significance

In this study we demonstrate that robust species identification as well as mathematical approaches are essential to reliably estimate the sampling effort needed to describe soil yeast communities. This approach has great potential for optimisation of cultivation techniques and allows high throughput analysis in the future.

SUBMITTER: Yurkov AM

PROVIDER: S-EPMC3155558 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Publications

Species accumulation curves and incidence-based species richness estimators to appraise the diversity of cultivable yeasts from beech forest soils.

Yurkov Andrey M AM Kemler Martin M Begerow Dominik D

PloS one 20110812 8

<h4>Background</h4>Yeast-like fungi inhabit soils throughout all climatic zones in a great abundance. While recent estimations predicted a plethora of prokaryotic taxa in one gram of soil, similar data are lacking for fungi, especially yeasts.<h4>Methodology/principal findings</h4>We assessed the diversity of soil yeasts in different forests of central Germany using cultivation-based techniques with subsequent identification based on rDNA sequence data. Based on experiments using various pre-cul ...[more]

PMID: 21858201

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Project description:Understanding the functional relationship between the sample size and the performance of species richness estimators is necessary to optimize limited sampling resources against estimation error. Nonparametric estimators such as Chao and Jackknife demonstrate strong performances, but consensus is lacking as to which estimator performs better under constrained sampling. We explore a method to improve the estimators under such scenario. The method we propose involves randomly splitting species-abundance data from a single sample into two equally sized samples, and using an appropriate incidence-based estimator to estimate richness. To test this method, we assume a lognormal species-abundance distribution (SAD) with varying coefficients of variation (CV), generate samples using MCMC simulations, and use the expected mean-squared error as the performance criterion of the estimators. We test this method for Chao, Jackknife, ICE, and ACE estimators. Between abundance-based estimators with the single sample, and incidence-based estimators with the split-in-two samples, Chao2 performed the best when CV < 0.65, and incidence-based Jackknife performed the best when CV > 0.65, given that the ratio of sample size to observed species richness is greater than a critical value given by a power function of CV with respect to abundance of the sampled population. The proposed method increases the performance of the estimators substantially and is more effective when more rare species are in an assemblage. We also show that the splitting method works qualitatively similarly well when the SADs are log series, geometric series, and negative binomial. We demonstrate an application of the proposed method by estimating richness of zooplankton communities in samples of ballast water. The proposed splitting method is an alternative to sampling a large number of individuals to increase the accuracy of richness estimations; therefore, it is appropriate for a wide range of resource-limited sampling scenarios in ecology.

Dataset Information

Species accumulation curves and incidence-based species richness estimators to appraise the diversity of cultivable yeasts from beech forest soils.

Background

Methodology/principal findings

Conclusions/significance

Publications

Species accumulation curves and incidence-based species richness estimators to appraise the diversity of cultivable yeasts from beech forest soils.

Similar Datasets

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