Estimating True Species Richness from Braun Blanquet Scale
Abstract
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 (S_est ) for 9 vegetation plots under three scenarios (Rare species are singletons: with 1/1 probability 〖(Sc〗_1), with 1/2 probability (〖Sc〗_2), with 1/3 probability 〖(Sc〗_3)).
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 (〖Sc〗_3 ) 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 S_est. For employing S_est, all the data consists of the number of singletons (f_1 ) and super doubletons (f_(2+) ). f_1 and f_(2+) can be obtained from BB scale because its "r" code usually corresponds to f_1. However, some scientists prefer to use "r" in description of a few species. That causes an uncertainty about f_1. Using S_est, this study offers an approach and a spreadsheet program to estimate true species richness even though the number of singletons is uncertain.
Keywords:
Keywords
Cover-abundance Scale, Prediction, Rare Species, Species Diversity, Enigmatic Statistic, Negative Bias
References
- Camiz, S., Torres, P. & Pillar, V. D. (2017). Recoding and multidimensional analyses of vegetation data: a comparison. Community Ecology, 18(3), 260-279.
- Campbell, B. M. (1974). Phytosociological study of some forest patches on Table Mountain. University of Cape Town Honours project.
- Çekim, H. O. & Kadılar, C. (2020). In-type estimators for the population variance in stratified random sampling. Communications in Statistics-Simulation and Computation, 49(7), 1665-1677.
- Chao, A. (1987). Estimating the population size for capture-recapture data with unequal catchability. Biometrics, 783-791.
- Chao, A. & Jost, L. (2012). Coverage‐based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology, 93(12), 2533-2547.
- Chao, A. & Chiu, C. (2016). Nonparametric Estimation and Comparison of Species Richness, In: eLS. Wiley Online Referance in the Life Sciences. John Wiley and Sons, DOI: 10.1002/9780470015902.a0026329.
- Chao, A., Colwell, R. K., Chiu, C. H. & Townsend, D. (2017). Seen once or more than once: Applying Good–Turing theory to estimate species richness using only unique observations and a species list. Methods in Ecology and Evolution, 8(10), 1221-1232.
- Chao, A., Hsieh, T. C., Chazdon, R. L., Colwell, R. K. & Gotelli, N. J. (2015). Unveiling the species‐rank abundance distribution by generalizing the Good‐Turing sample coverage theory. Ecology, 96(5), 1189-1201.
- Chmura, D. & Salachna, A. (2016). The errors in visual estimation of plants cover in the context of education of phytosociology. Chemistry-Didactics-Ecology-Metrology, 21(1-2), 75-82.
- Eren, M. I., Chao, A., Hwang, W. H. & Colwell, R. K. (2012). Estimating the richness of a population when the maximum number of classes is fixed: a nonparametric solution to an archaeological problem. PLoS One, 7(5), e34179.