Comparative performance of species‐richness estimators using data from a subtropical forest tree community

Abstract

We used survey data collected from a large plot (20 ha) of sub-tropical forest in the Dinghushan Nature Reserve, Guangdong Province, southern China, in 2005 to test the comparative performance of nine speciesrichness estimators (number of observed species, three species-individual curve models, five nonparametric estimators). As the true species richness, we used the 210 free-standing shrub and tree species of >1 cm diameter at breast height recorded during the survey. This true species richness was then used to calculate performance measures of bias, accuracy, and precision for each estimator, whereby we distinguished performance for low, medium, and high sampling intensity. Unsurprisingly, all estimators performed better than the number of observed species in terms of bias and accuracy. Surprisingly, however, two curve models (logistic and logarithm) outperformed all other estimators in terms of bias, accuracy, and precision, which is in contrast to most other previous studies, in which nonparametric methods usually outperform curve models. Intriguingly, relative estimator performance changed between low, medium, and high sampling intensity, sometimes dramatically, reinforcing the assertion that the influence of sampling intensity on estimator performance is an important aspect to investigate and to consider when choosing estimators for ecological surveys. Because these results are based on only one dataset, the results should be treated with caution, both because (1) the generality of these results needs to be confirmed with simulated datasets and (2) more work is needed to establish what ''true'' species richness is extrapolated by each of the tested estimators in both the statistical and the practical sense. Nevertheless, the two curve estimators, namely Logistic and Logarithm, should be considered in future studies of comparative performance of species-richness estimators because of their outstanding performance in this study.

Exposing malaria in-host diversity and estimating population diversity by capture-recapture using massively parallel pyrosequencing

“…Rarefaction curves were calculated using EstimateS (http://viceroy.eeb.uconn.edu/estimates) using individual-based curves (sampling without replacement) (24) based on the Michaelis-Menten models (49). We note that the impact on the shape of species-accumulation curves of different community structures is an area of active research (50); with the data at hand (six samples), we are unable to satisfactorily explore this issue.…”

Plant diversity patterns at different spatial scales in a semi-arid savanna ecosystem in central Namibia

“…As a special feature, well developed biological soil crusts (BSCs) built up by cyanobacteria, green algae, and lichens can be found on both Observatories and give the soil surface a dark colour which increases the soil temperature due to the absorption of sun radiation In many ecological studies, species richness is taken as a measure for biological diversity of the studied site and ecosystem (COLWELL & CODDINGTON, 1994;CHIARRUCCI et al, 2003;WEI et al, 2010). However, it is extremely challenging to achieve reliable richness values for larger areas by sampling, especially of small or mobile taxa like bacteria, fungi, or animals.…”

“…Six types of estimators have been proposed: (1) number of actually sampled species, (2) extrapolation of the species-area relationship (SAR) (PALMER, 1990;DENGLER, 2009;, (3) extrapolation of the species-sampling relationship (SSR) (STOUT & VANDERMEER, 1975;, (4) so called 'non-parametric estimators' (CHAO, 1984;MAGGURAN, 2004), (5) the integration of log-normal distribution (PRESTON, 1948;COHEN, 1959), and (6) the new 11 approach of upscaling with an universal species-area curve (HARTE et al, 2009). Several studies evaluated the output of a subset of these estimators to simulated (CHIARUCCI et al, 2003; or to empirical data (PALMER, 1990;STILES & SCHEINER, 2007;WEI et al, 2010) but the results of these studies are hardly comparable since divergent estimators, data sampling methodologies, and typologies were used, all of which strongly influence the performance of the estimators (DENGLER, 2009b).…”

“…In this study, the LEE operated as an useful indicator to select the best extrapolating models. Thus, the quadratic power function which got the first LEE rank presumably gave a realistic species richness estimate at least for the Duruchaus Observatory, the normal power function (LEE rank 2) realistic ones for both Observatories (see In order to compare the resulting estimates regarding bias and accuracy (WALTHER & MOORE, 2005) and to determine the reliability of the tested estimators definitively (CHIARUCCI et al, 2001), the 'true' total species richness data of the target area (1 km²) as complete as possible is needed (PALMER, 1990;WEI, 2010). However, with reasonable time and personnel effort, it is nearly impossible to sample such a large area (PALMER, 1995;GASTON, 1996).…”

“…Thus, it should be avoided in ecological studies to use only the observed species richness as a value for the total richness of a distinct site. Every estimator would give more reliable results (PALMER, 1991;CHIARRUCCI et al, 2003;WALTHER & MOORE, 2005;WEI et al, 2010). Furthermore, I can assume that estimators resulting in lower species richness than the accumulated records are negatively biased.…”