Estimating True Species Richness from Braun Blanquet Scale

Aim of study : The goal of the study was to estimate true species richness from Braun Blanquet (BB) scale data. Area of study : Yazılı Canyon Nature Park (YCNP) located in the Mediterranean Region of Turkey. Material and methods : A bias-corrected approach was adapted based on the Good-Turning frequency formula to estimate true species richness ( ) for 9 vegetation plots under three scenarios (Rare species are singletons: with 1/1 probability ( ) , with 1/2 probability ( ) , with 1/3 probability ( )). Main results : The results indicate that with increasing uncertainty about the number of singletons, the difference between expected species richness and observed species richness decreases. To estimate the species richness of the plots taken from YCNP, scenario III ( ) seems to be the best option due to existing maximum uncertainty concerning the number of singletons. Highlights : All the proposed bias-corrected estimators have been developed by considering the abundance or the incidence-based data except for . For employing , all the data consists of the number of singletons ( ) and super doubletons ( ) . and can be obtained from BB scale because its code usually corresponds to . However, some scientists prefer to use in description of a few species. That causes an uncertainty about . Using , this study offers an approach and a spreadsheet program to estimate true species richness even though the number of singletons is uncertain.

[1]  Cem Kadilar,et al.  In-type estimators for the population variance in stratified random sampling , 2020, Commun. Stat. Simul. Comput..

[2]  V. Pillar,et al.  Recoding and multidimensional analyses of vegetation data: a comparison , 2017 .

[3]  Robert K. Colwell,et al.  Seen once or more than once: applying Good–Turing theory to estimate species richness using only unique observations and a species list , 2017 .

[4]  D. Chmura,et al.  The Errors in Visual Estimation of Plants Cover in the Context of Education of Phytosociology , 2016 .

[5]  Kürsad Özkan,et al.  Application of Information Theory for an Entropic Gradient of Ecological Sites , 2016, Entropy.

[6]  Anne Chao,et al.  Nonparametric Estimation and Comparison of Species Richness , 2016 .

[7]  N. Gotelli,et al.  Unveiling the species-rank abundance distribution by generalizing the Good-Turing sample coverage theory. , 2015, Ecology.

[8]  Hulya Cingi,et al.  Separate Ratio Estimators for the Population Variance in Stratified Random Sampling , 2014 .

[9]  F. Vahdati How plant diversity features change across ecological species groups? A case study of a temperate deciduous forest in northern Iran , 2014 .

[10]  S. Wiser,et al.  Quantifying invasion resistance: the use of recruitment functions to control for propagule pressure. , 2014, Ecology.

[11]  A. Chao,et al.  Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. , 2012, Ecology.

[12]  Wen-Han Hwang,et al.  Estimating the Richness of a Population When the Maximum Number of Classes Is Fixed: A Nonparametric Solution to an Archaeological Problem , 2012, PloS one.

[13]  Martin Zobel,et al.  Dark diversity: shedding light on absent species. , 2011, Trends in ecology & evolution.

[14]  Cem Kadilar,et al.  Improvement in Variance Estimation in Simple Random Sampling , 2007 .

[15]  P. Pyšek,et al.  Effects of abiotic factors on species richness and cover in Central European weed communities , 2005 .

[16]  I. Good,et al.  Turing’s anticipation of empirical bayes in connection with the cryptanalysis of the naval enigma , 2000 .

[17]  P. White,et al.  A Flexible, Multipurpose Method for Recording Vegetation Composition and Structure , 1998 .

[18]  A. Chao Estimating the population size for capture-recapture data with unequal catchability. , 1987, Biometrics.

[19]  G. Belle,et al.  Nonparametric estimation of species richness , 1984 .

[20]  M. Werger On concepts and techniques applied in the Ziirich-Montpellier method of vegetation survey , 1974 .

[21]  I. Good THE POPULATION FREQUENCIES OF SPECIES AND THE ESTIMATION OF POPULATION PARAMETERS , 1953 .

[22]  C. K. Sarmah CHAO, JACKKNIFE AND BOOTSTRAP ESTIMATORS OF SPECIES RICHNESS , 2017 .

[23]  Anne Chao,et al.  Measuring and Estimating Species Richness, Species Diversity, and Biotic Similarity from Sampling Data , 2013 .

[24]  Eddy van der Maarel Transformation of cover-abundance values for appropriate numerical treatment – Alternatives to the proposals by Podani , 2007 .

[25]  A. Solow,et al.  Measuring biological diversity , 2006, Environmental and Ecological Statistics.

[26]  N. Prieditis Vegetation of wetland forests in Latvia : a synopsis , 1997 .

[27]  E. Maarel,et al.  The Braun-Blanquet Approach , 1978 .

[28]  M. E. D. Poore,et al.  The Use of Phytosociological Methods in Ecological Investigations: I. The Braun-Blanquet System , 1955 .