Estimating the Number of Species: A Review

Bunge, John; Fitzpatrick, Matthew C.

doi:10.1080/01621459.1993.10594330

Cited by 437 publications

(363 citation statements)

References 111 publications

Supporting

Mentioning

352

Contrasting

Unclassified

Order By: Relevance

“…These methods include species-area curve extrapolation, species-abundance distribution estimation, and nonparametric modeling. The advantages and disadvantages of these methods have been thoroughly reviewed by Bunge and Fitzpartrick (1993); Colwell and Coddington (1994); Schwarz and Seber (1999); Chao (2005); Böhning (2008); and Gotelli and Colwell (2009). This article mainly focuses on nonparametric modeling because it is the most flexible of these methodological approaches.…”

Section: Introductionmentioning

confidence: 99%

Small‐Sample Estimation of Species Richness Applied to Forest Communities

Hwang

Shen

2010

Biometrics

View full text Add to dashboard Cite

Many well-known methods are available for estimating the number of species in a forest community. However, most existing methods result in considerable negative bias in applications, where field surveys typically represent only a small fraction of sampled communities. This article develops a new method based on sampling with replacement to estimate species richness via the generalized jackknife procedure. The proposed estimator yields small bias and reasonably accurate interval estimation even with small samples. The performance of the proposed estimator is compared with several typical estimators via simulation study using two complete census datasets from Panama and Malaysia.

show abstract

Section: Introductionmentioning

confidence: 99%

Small‐Sample Estimation of Species Richness Applied to Forest Communities

Hwang

Shen

2010

Biometrics

View full text Add to dashboard Cite

show abstract

“…This problem arises in many fields of applications, even outside of biometry, so that various versions of the problem, including varying sample designs, have been considered. Bunge and Fitzpatrick (1993) provide a comprehensive overview.…”

Section: Introductionmentioning

confidence: 99%

An Estimator of Number of Species from Quadrat Sampling

2005

View full text Add to dashboard Cite

Summary We consider the problem of estimating the number of distinct species S in a study area from the recorded presence or absence of species in each of a sample of quadrats. A generalized jackknife estimator of S is derived, along with an estimate of its variance. It is compared with the jackknife estimator for S proposed by Heltshe and Forrester (1983, Biometrics39, 1–12) and the empirical Bayes estimator of Mingoti and Meeden (1992, Biometrics48, 863–875). We show that the empirical Bayes estimator has the form of a generalized jackknife estimator under a specific model for species distribution. We compare the new estimators of S to the empirical Bayes estimator via simulation. We characterize circumstances under which each is superior.

show abstract

“…Different statistical approaches have been proposed for estimating the species richness (denoted by S) when quadrat sampling is used, including nonparametric approaches (see Smith and Van Belle, 1984), noninformative Bayesian approaches (Mingoti and Meeden, 1992), as well as approaches using closed capture-recapture models (e.g., Boulinier et al, 1998;Williams, Nichols, and Royle, 2001). For a detailed review see Bunge and Fitzpatrick (1993).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Species Richness from Quadrat Sampling Data in the Presence of Prior Information

Dupuis¹,

Joachim²

2006

Biometrics

View full text Add to dashboard Cite

We consider the problem of estimating the number of species of an animal community. It is assumed that it is possible to draw up a list of species liable to be present in this community. Data are collected from quadrat sampling. Models considered in this article separate the assumptions related to the experimental protocol and those related to the spatial distribution of species in the quadrats. Our parameterization enables us to incorporate prior information on the presence, detectability, and spatial density of species. Moreover, we elaborate procedures to build the prior distributions on these parameters from information furnished by external data. A simulation study is carried out to examine the influence of different priors on the performances of our estimator. We illustrate our approach by estimating the number of nesting bird species in a forest.

show abstract

Estimating the Number of Species: A Review

Cited by 437 publications

References 111 publications

Small‐Sample Estimation of Species Richness Applied to Forest Communities

Small‐Sample Estimation of Species Richness Applied to Forest Communities

An Estimator of Number of Species from Quadrat Sampling

Bayesian Estimation of Species Richness from Quadrat Sampling Data in the Presence of Prior Information

Contact Info

Product

Resources

About