Performance of nonparametric species richness estimators in a high diversity plant community

Abstract

The efficiency of four nonparametric species richness estimators first-order Jackknife, second-order Jackknife, Chao2 and Bootstrap was tested using simulated quadrat sampling of two field data sets (a sandy 'Dune' and adjacent 'Swale') in high diversity shrublands (kwongan) in south-western Australia. The data sets each comprised > 100 perennial plant species and > 10 000 individuals, and the explicit (x-y co-ordinate) location of every individual. We applied two simulated sampling strategies to these data sets based on sampling quadrats of unit sizes 1/400th and 1/100th of total plot area. For each site and sampling strategy we obtained 250 independent sample curves, of 250 quadrats each, and compared the estimators' performances by using three indices of bias and precision: MRE (mean relative error), MSRE (mean squared relative error) and OVER (percentage overestimation). The analysis presented here is unique in providing sample estimates derived from a complete, field-based population census for a high diversity plant community. In general the true reference value was approached faster for a comparable area sampled for the smaller quadrat size and for the swale field data set, which was characterized by smaller plant size and higher plant density. Nevertheless, at least 15-30% of the total area needed to be sampled before reasonable estimates of St (total species richness) were obtained. In most field surveys, typically less than 1% of the total study domain is likely to be sampled, and at this sampling intensity underestimation is a problem. Results showed that the second-order Jackknife approached the actual value of St more quickly than the other estimators. All four estimators were better than Sobs (observed number of species). However, the behaviour of the tested estimators was not as good as expected, and even with large sample size (number of quadrats sampled) all of them failed to provide reliable estimates. First- and second-order Jackknives were positively biased whereas Chao2 and Bootstrap were negatively biased. The observed limitations in the estimators' performance suggests that there is still scope for new tools to be developed by statisticians to assist in the estimation of species richness from sample data, especially in communities with high species richness

Dinâmica da comunidade arbórea em uma floresta estacional semidecidual sob queimadas recorrentes

“…A riqueza de espécies esperada para Q1 e Q2 e para T1 e T2, foi calculada usando o estimador de riqueza Jackknife de primeira ordem, que prevê o número de espécies em uma área com base na frequência das espécies encontradas nela, ou seja, projeta o número total de espécies a partir da heterogeneidade da amostra (Chiarucci et al 2003).…”

Assessment of richness estimation methods on macroinvertebrate communities of mountain ponds in Castilla y León (Spain)

“…The real debate is about choosing the best estimator for a particular study, taxonomical group or data set. This is a controversial issue, still under study, because different authors reach different conclusions on which is the best estimator, as shown by contrasting results obtained in their studies by, for example, Colwell and Coddington (1994); Walther and Morand (1998); Chiarucci et al (2003); Foggo et al (2003) and Hortal et al (2006). Therefore, until more conclusive information is available, checking the suitability of several estimators seems a convenient starting point when studying little known communities.…”

“…Nine of them, ACE, ICE, Chao 1, Chao 2, Jackknife 1, Jackknife 2, Bootstrap, MMRuns and MMMeans were calculated with the software EstimateS version 7.0 (Colwell, 1997). They have all been widely used and studied (Chazdon et al, 1998;Brose et al, 2003;Chiarucci et al, 2003). Chao 1 and Jackknife 1 are designed to estimate richness from single samples while the rest require several samples.…”

“…The performance of several of these estimators have been carried out and compared in a number of studies (Chazdon et al, 1998;Keating et al, 1998;Peterson and Slade, 1998;Walther and Morand, 1998;Chiarucci et al, 2001;Walther and Martin, 2001;Brose, 2002;Longino et al, 2002;Borges and Brown, 2003;Chiarucci et al, 2003;Melo et al, 2003;Brose and Martinez, 2004; see review in Walther and Moore, 2005). However, in freshwaters their use has mostly been restricted to stream invertebrates (Melo and Froehlich, 2001) and lake zooplankton (Dumont and Segers, 1996;Arnott et al, 1998).…”

Estimating species richness of arthropods in azorean pastures: the adequacy of suction sampling and pitfall trapping

“…Looking for the four richness estimators they performed differently, Jackknife 1 and Chao 1 generating large estimates of species richness, Bootstrap giving a lower estimate of species richness and Michaelis-Menten mean (MMMean) clearly underestimating species richness. Since the available estimators are probably underestimating true species richness in most studies (Chiarucci et al, 2003), all but Michaelis-Menten mean estimator gave probably realistic estimates of species richness. This contrasts with the study of Toti et al (2000), in which Chao 1 gave unrealistic large estimates and MMMean performed better.…”