T.-J. Shen

OA Journals ≠Predatory Journals

沈宗荏

tjshen@nchu.edu.tw Oct 22, 2019

@ NCHU Library

Institute of Statistics & Department of Applied Mathematics

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NCHU

f(amath) dSt

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OA VS. Predatory journals

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OA VS. Predatory journals

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OA VS. Predatory journals

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Take Good Advantage of Library Resources

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Some Myths as to OA Journals Submitted papers will always not be

rejected

Reviewing processes could be very quick

As long as you pay APC (article processing charges) to OA journals, which are happy to publish your papers

APC is a huge amount

Reviewing reports from OA journals are not important and usually not demanding

My experiences

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PLOS Computational Biology

Frontiers Series OA Journals

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From: Hui Li bjfulihui@gmail.comSubject: from Forest Ecosystems

Date: May 8, 2019 at 2:27 PMTo: tjshen@nchu.edu.tw

Dear Dr. Shen,About your manuscript, we have a great deal of thought this week. According to "Regulations on Publication in China", your institute "Institute of Statistics &Department of Applied Mathematics, National Chung Hsing University,250 Kuo KuangRoad, Taichung 40227, Taiwan" should be written " Institute of Statistics & Department ofApplied Mathematics, National Chung Hsing University,250 Kuo Kuang Road, Taichung40227,Taiwan, China".I don't know whether you agree to the change. If you agree, your manuscript will becontinued.Hope to hear from you as soon as possible.

best regardsLI Hui (磷眻)------------------------Editorial office of Forest EcosystemsBox 148 Beijing Forestry University35 Qinghua Donglu, Haidian DistrictBeijing 100083, P. R. China Tel/fax: 86-10-62337915

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BMC Ecology: APC and Reviewing Time

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Order detailsPayment detailsCustomer number: 1600317661Order number: 6877526Payment method: VisaBilling AddressTsung-Jen Shen250 Guoguang Rd., South Dist.40227 Taichung, TW

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BMC EcologyEstimating species pools for a very local ecological assemblageBack to BioMed CentralTotal£1,370.00

Page 10 of 11Shen et al. BMC Ecol (2017) 17:45

Author details1 Institute of Statistics & Department of Applied Mathematics, National Chung Hsing University, 250 Kuo Kuang Road, Taichung 40227, Taiwan. 2 Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China. 3 Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2H1, Canada. 4 School of Software, Harbin Normal University, Harbin, China.

AcknowledgementsThe authors thank the editor Prof. Cang Hui and two reviewers for their con-structive comments on helping improve the paper greatly. We also thank the Center for Tropical Forest Science for generously providing the BCI plot data. The BCI forest dynamics research project was founded by S.P. Hubbell and R.B. Foster and is now managed by R. Condit, S. Lao, and R. Perez under the Center for Tropical Forest Science and the Smithsonian Tropical Research in Panama. Numerous organizations have provided funding, principally the U.S. National Science Foundation, and hundreds of field workers have contributed. Y.C. is supported by the Hundred Talents Program of CAS. T.J.S. is supported by Taiwan Ministry of Science and Technology under Contract 105-2918-I-005-002.

Competing interestsThe authors declare that they have no competing interests.

Availability of data and materialsThe BCI forest plot dataset analyzed in the current study is available by submitting a data request form to the website of the Center for Tropical Forest Science (http://www.forestgeo.si.edu/). The computational code by R software for the present study with the hypothetical example illustrated in our paper is provided in Additional file 2.

Consent to publishNot applicable.

Ethics approval and consent to participateNot applicable.

FundingThe project and the publication of this paper was funded by the Taiwan Minis-try of Science and Technology.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in pub-lished maps and institutional affiliations.

Received: 28 July 2017 Accepted: 13 December 2017

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20. Lessard J, Weinstein B, Borregaard M, Marske K, Martin D, McGuire J, et al. Process-based species pools reveal the hidden signature of biotic interac-tions amid the influence of temperature filtering. Am Nat. 2016;187:75–88.

21. Lewis R, Szava-Kovats R, Partel M. Estimating dark diversity and species pools: an empirical assessment of two methods. Methods Ecol Evol. 2015. https://doi.org/10.1111/2041-210X.12443.

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23. Zobel M. The relative role of species pools in determining plant richness: an alternative explanation of species coexistence? Trends Ecol Evol. 1997;12:266–9.

24. Partel M, Szava-Kovats R, Zobel M. Dark diversity: shedding light on absent species. Trends Ecol Evol. 2011;26:124–8.

25. Ronk A, de Bello F, Fibich P, Partel M. Large-scale dark diversity estimates: new perspectives with combined methods. Ecol. Evol. 2016;6:6266–81.

26. Chao A. Non-parametric estimation of the number of classes in a popula-tion. Scand J Stat. 1984;11:265–70.

27. Chao A, Shen T. Nonparametric estimation of Shannon’s index of diversity when there are unseen species in sample. Environ Ecol Stat. 2003;10:429–43.

28. Hui C, Veldtman R, McGeoch M. Measures, perceptions and scaling pat-terns of aggregated species distributions. Ecography. 2010;33:95–102.

29. Zillio T, He F. Modeling spatial aggregation of finite populations. Ecology. 2010;91:3698–706.

30. Pielou E. Mathematical ecology. New York: Wiley; 1977. 31. Chao A, Bunge J. Estimating the number of species in a stochastic abun-

dance model. Biometrics. 2002;58:531–9. 32. Shen T, He F. An incidence-based richness estimator for quadrats sam-

pled without replacement. Ecology. 2008;87:2052–60. 33. Condit R, Pitman N, Leigh EG, Chave J, Terborgh J, Foster RB, et al. Beta-

diversity in tropical forest trees. Science. 2002;295:666–9. 34. Condit R, Hubbell S, Foster R. Changes in a tropical forest with a shifting

climate: results from a 50-ha permanent census plot in Panama. J Trop Ecol. 1996;12:231–56.

35. Condit R, Chisholm R, Hubbell S. Thirty years of forest census at Barro Colorado and the importance of immigration in maintaining diversity. PLoS ONE. 2012;7:e40926.

36. Volkov I, Banavar J, Hubbell S, Maritan A. Neutral theory and relative spe-cies abundance in ecology. Nature. 2003;424:1035–7.

37. Burnham K, Overton W. Estimation of the size of a closed popula-tion when capture probabilities vary among animals. Biometrika. 1978;65:625–33.

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Ecology and Evolution: APC and Reviewing Time

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1Scientific RepoRtsȁͽǣ 998 ȁǣͷͶǤͷͶ;ȀͺͷͻͿ;ǦͶͷͽǦͶͷͶͽͶǦ

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A general framework for predicting delayed responses of ecological communities to habitat lossYouhua Chenͷǡ & Tsung-Jen Shen

ơǡǦǡǡǤǡȋȌȋȌǡȋͶͷͻȌǦǤǡǦơǤƤǡǯǣȋͷȌǫȋȌǫȋȌơǫȋǦȌǡǡǡǡǤơǤ

Habitat destruction, one of the principal factors driving global biodiversity crisis, causes time-lagged, if not instantaneous, loss of species. Such a delayed consequence, described as extinction debt1–5, has been increasingly documented in empirical and !eld studies6–8. In addition to debt, ecological processes such as immigration and speciation, also part of the responses that require time to ful!l, can positively contribute to species richness of ecological communities, which are usually termed as immigration credit3, 4, 9. However, other than simply count-ing the delayed loss or gain of species, how will the community structure of species assemblages be altered due to habitat loss3? Under what conditions will extinction debt or immigration credit occur in local samples? "erefore, it is necessary to compare the community patterns at equilibriums before and a#er habitat destruction in order to adequately address these important, but yet unsolved, issues.

Recently, Kitzes and Harte (2015) (hereina#er referred as KH for brevity) developed a novel method to esti-mate the magnitude of extinction debt or immigration credit from two ecological community patterns, namely regional species abundance distribution (SAD) and species-speci!c spatial abundance distribution (SAAD). "eir method was based on several important assumptions: !rstly, local communities in the region are always open to speciation or immigration; secondly, the number of species in a local community is determined jointly by SAD and SAAD; thirdly, regional SAD before habitat loss and a#er a long run since habitat loss is assumed to be in steady equilibrium; and lastly but most importantly, the whole community or region a#er reaching new steady state will follow the same parametric SAD curve as the original regional SAD before habitat loss. "is assumption states that the underlying regional SAD model will be kept invariant (some speci!c parameters may be changed).

"ere are indeed other ways to estimate extinction debts10–12. However, the elegancy of KH’s method rests with the fact that it is parsimonious and requires only available information as inputs, including the total number of individuals of all species, the area size of the whole region, and the percent of habitat loss. Moreover, by using this simple information, the !tting of unknown parameters in SAD is very straightforward. In contrast, in many previous methods for modelling extinction debts11, 12, some parameters are very di$cult to estimate and the

ͷǡǡǡͼͷǡǤInstitute of Statistics ƬǡǡͻͶǡǡͺͶͽǡǡǤǡǡǡǡͺͶͶͽǡǤǤǤǤǤȋǣͷͼǤȌǤǦ ǤǤȋǣǤǤ)

Received: 31 October 2016

Accepted: 21 March 2017

Published: xx xx xxxx

OPEN

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delayed responses of ecological communities to habitat loss

Author name: Youhua Chen

Scientific Reports: APC

17

PeerJ: APC and Reviewing Time

Evaluation of the estimate bias magnitudeof the Rao’s quadratic diversity index

Youhua Chen1,*, Yongbin Wu2,* and Tsung-Jen Shen3

1 CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization &Ecological Restoration and Biodiversity Conservation Key Laboratory of Sichuan Province,Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China

2 College of Forestry and Landscape Architecture, South China Agricultural University,Guangzhou, China

3 Institute of Statistics & Department of Applied Mathematics, National Chung Hsing University,Taichung, Taiwan

* These authors contributed equally to this work.

ABSTRACTRao’s quadratic diversity index is one of the most widely applied diversity indices

in functional and phylogenetic ecology. The standard way of computing Rao’s

quadratic diversity index for an ecological assemblage with a group of species with

varying abundances is to sum the functional or phylogenetic distances between a pair

of species in the assemblage, weighted by their relative abundances. Here, using both

theoretically derived and observed empirical datasets, we show that this standard

calculation routine in practical applications will statistically underestimate the true

value, and the bias magnitude is derived accordingly. The underestimationwill become

worse when the studied ecological community contains more species or the pairwise

species distance is large. For species abundance data measured using the number of

individuals, we suggest calculating the unbiased Rao’s quadratic diversity index.

Subjects Biodiversity, Ecology, Mathematical Biology, Plant Science

Keywords Biometrics, Forest ecology, Biodiversity measure, Estimation accuracy, Phylogeneticecology, Functional traits

INTRODUCTIONBiodiversity is constituted by multifaceted components. Measures of biodiversity thus

should take into account species richness and abundance as well as other characteristics

(like abundance evenness) quantified by information metrics, which are also valuable and

should be incorporated. Rao’s quadratic diversity index is one of the most important

biodiversity metrics that is widely applied to studies of functional and phylogenetic

ecology (Rao, 1982, 2010;Mouchet et al., 2010). Its standard computation is to sum up the

species’ distance between a pair of species i and j (dij) that is weighted by the product of

the relative abundances of both species (pi and pij), given by QðpÞ ¼P

i 6¼j dijpipj(Botta-Dukat, 2005; Ricotta, 2005a; Gusmao et al., 2016), where p ¼ ðp1; :::; pSÞ representsthe relative abundance distribution of the assemblage with S species. Here, species’

distance can be very flexible, ranging from phylogenetic to functional (or trait) distances

(Ricotta, 2005b).

However, ecologists do not normally consider the statistical bias of Rao’s quadratic

diversity index when applying it to practical research questions. Herein, statistical bias was

How to cite this article Chen et al. (2018), Evaluation of the estimate bias magnitude of the Rao’s quadratic diversity index.

PeerJ 6:e5211; DOI 10.7717/peerj.5211

Submitted 19 March 2018Accepted 20 June 2018Published 6 July 2018

Corresponding authorTsung-Jen Shen,tjshen@nchu.edu.tw

Academic editorPaolo Giordani

Additional Information andDeclarations can be found onpage 10

DOI 10.7717/peerj.5211

Copyright2018 Chen et al.

Distributed underCreative Commons CC-BY 4.0

18

Traditional Journals:Reviewing Time and OA Charges

Methods Ecol Evol. 2019;10:1015–1023. bѴ;omѴbm;Ѵb0u-uĺ1olņfoum-Ѵņl;;ƒՊ|Պ1015

!;1;b;7ĹƐƏ-m-uƑƏƐƖՊ |Պ 11;r|;7ĹƐƑrubѴƑƏƐƖDOI: 10.1111/2041-210X.13197

R E S E A R C H A R T I C L E

Inferring multispecies distributional aggregation level from limited line transect-derived biodiversity data

Youhua Chen1Պ| Tsung-Jen Shen2 Պ| Hoang Van Chung1Պ| Shengchao Shi1Պ| Jianping Jiang1Պ| Richard Condit3,4Պ| Stephen P. Hubbell5,6

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K E Y W O R D S

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19

Traditional Journals:Reviewing Time

Contents lists available at ScienceDirect

Ecological Informatics

journal homepage: www.elsevier.com/locate/ecolinf

Comparing Allee effect-based and dispersal-based neutral models for speciesabundance distribution patterns

Yongbin Wua, Youhua Chenb,c,⁎, Tsung-Jen Shend,⁎⁎

a College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, Chinab College of Economics and Management, Nanjing Forestry University, Nanjing 210037, Chinac Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, Chinad Institute of Statistics & Department of Applied Mathematics, National Chung Hsing University, 250 Kuo Kuang Road, Taichung 40227, Taiwan

A R T I C L E I N F O

Keywords:Species extinctionBiodiversity crisisStochasticityTropical biodiversityStatistical ecology

A B S T R A C T

Dispersal is important for biodiversity maintenance in both neutral and niche theories. However, little is knownabout the potential role of Allee effect at the community level. In the present study, we developed neutral modelsfor quantifying the separate and joint influences of the Allee effect and dispersal process, respectively, on speciesabundance distribution (SAD) patterns. Tree census data from Barro Colorado Island (BCI), Panama were used asthe case to compare different neutral SAD models. Results showed that Allee effects were not detected in the BCItree SAD curve. By contrast, the neutral models with the incorporation of dispersal process (including bothimmigration and emigration) can remarkably improve the fitting power of neutral models on the BCI tree SADcurve. Finally, even though the influence is not detectable, the Allee effect-based SAD models still might bealternative SAD models for model comparison and null hypothesis testing.

1. Introduction

The neutral theory of biodiversity and biogeography has gainedmuch attention over the last two decades since its establishment(Alonso et al., 2006; Hubbell, 2001). Many theoretical and empiricalstudies on testing and developing the ecological neutral theory havebeen published since that time (Etienne and Haegeman, 2011; He et al.,2012; McGill, 2003; Rosindell et al., 2010; Volkov et al., 2003, 2007;Zhou and Zhang, 2008a, 2008b). The neutral model was found to quiteadequately predict species diversity patterns in tropical areas (Rosindellet al., 2012) where species diversity is very high (Chisholm and Pacala,2010; Zhou and Zhang, 2008a, 2008b).The Allee effect predicts a po-sitive correlation between species per-capita growth rates and the po-pulation size (Allee, 1931; Allee and Bowen, 1932). The existence of theAllee effect accelerates the extinction risk of species (Allen, 2003;Dennis, 2002; Geol and Richter-Dyn, 1974), because a species will ul-timately go extinct after some time passes when its population size isless than the Allee-effect threshold. Although previous studies madegreat progress on the neutral theory of biodiversity (Hubbell, 2001) andused neutral models to predict species abundance distribution (SAD)patterns (Etienne, 2007; Etienne and Alonso, 2005; Etienne and Olff,2005; He, 2005; He and Hu, 2005; Volkov et al., 2003, 2007), only a

few studies so far have focused on evaluating the impacts of Allee ef-fects on SAD patterns. So, are there any new insights when the Alleeeffect is incorporated into the neutral model? A natural expectationmight be that when the Allee effect is incorporated, the resultant fitwith empirical SADs should be much better given the fact that newbiological information is added to the neutral models.

Indeed, there are some recent theoretical studies which provideinsights into the role of the Allee effect on neutral theories of biodi-versity. For example, in a previous study (Zhou and Zhang, 2006), itwas found that the Allee effect-based theoretical SAD curve was de-pressed compared to the theoretical SAD curve predicted by a modelthat did not consider the Allee effect. This result was expected given thefact that the Allee effect tends (as explained by Zhou and Zhang, 2006)(i) to increase the risk of local extinctions of the set of originally rarespecies (hence the resulting reduction in species richness) and (ii) todecrease relative abundances of those species which originally had in-termediate levels of abundance, with those species progressively be-coming “new” contributors to the set of rare species (i.e., thereby re-placing the originally rare, now extinct, species). However, the previousstudy (Zhou and Zhang, 2006) presented only simulation-based results,and those still need to be tested against empirical datasets. Can neutralSAD models be deduced quantitatively by explicitly incorporating the

https://doi.org/10.1016/j.ecoinf.2019.05.002Received 6 March 2019; Received in revised form 1 May 2019; Accepted 2 May 2019

⁎ Corresponding author at: Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China⁎⁎ Corresponding author.E-mail addresses: haydi@126.com (Y. Chen), tjshen@nchu.edu.tw (T.-J. Shen).

(FRORJLFDO,QIRUPDWLFV²

$YDLODEOHRQOLQH0D\(OVHYLHU%9$OOULJKWVUHVHUYHG

7

Contents lists available at ScienceDirect

Ecological Informatics

journal homepage: www.elsevier.com/locate/ecolinf

Comparing Allee effect-based and dispersal-based neutral models for speciesabundance distribution patterns

Yongbin Wua, Youhua Chenb,c,⁎, Tsung-Jen Shend,⁎⁎

a College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, Chinab College of Economics and Management, Nanjing Forestry University, Nanjing 210037, Chinac Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, Chinad Institute of Statistics & Department of Applied Mathematics, National Chung Hsing University, 250 Kuo Kuang Road, Taichung 40227, Taiwan

A R T I C L E I N F O

Keywords:Species extinctionBiodiversity crisisStochasticityTropical biodiversityStatistical ecology

A B S T R A C T

Dispersal is important for biodiversity maintenance in both neutral and niche theories. However, little is knownabout the potential role of Allee effect at the community level. In the present study, we developed neutral modelsfor quantifying the separate and joint influences of the Allee effect and dispersal process, respectively, on speciesabundance distribution (SAD) patterns. Tree census data from Barro Colorado Island (BCI), Panama were used asthe case to compare different neutral SAD models. Results showed that Allee effects were not detected in the BCItree SAD curve. By contrast, the neutral models with the incorporation of dispersal process (including bothimmigration and emigration) can remarkably improve the fitting power of neutral models on the BCI tree SADcurve. Finally, even though the influence is not detectable, the Allee effect-based SAD models still might bealternative SAD models for model comparison and null hypothesis testing.

1. Introduction

The neutral theory of biodiversity and biogeography has gainedmuch attention over the last two decades since its establishment(Alonso et al., 2006; Hubbell, 2001). Many theoretical and empiricalstudies on testing and developing the ecological neutral theory havebeen published since that time (Etienne and Haegeman, 2011; He et al.,2012; McGill, 2003; Rosindell et al., 2010; Volkov et al., 2003, 2007;Zhou and Zhang, 2008a, 2008b). The neutral model was found to quiteadequately predict species diversity patterns in tropical areas (Rosindellet al., 2012) where species diversity is very high (Chisholm and Pacala,2010; Zhou and Zhang, 2008a, 2008b).The Allee effect predicts a po-sitive correlation between species per-capita growth rates and the po-pulation size (Allee, 1931; Allee and Bowen, 1932). The existence of theAllee effect accelerates the extinction risk of species (Allen, 2003;Dennis, 2002; Geol and Richter-Dyn, 1974), because a species will ul-timately go extinct after some time passes when its population size isless than the Allee-effect threshold. Although previous studies madegreat progress on the neutral theory of biodiversity (Hubbell, 2001) andused neutral models to predict species abundance distribution (SAD)patterns (Etienne, 2007; Etienne and Alonso, 2005; Etienne and Olff,2005; He, 2005; He and Hu, 2005; Volkov et al., 2003, 2007), only a

few studies so far have focused on evaluating the impacts of Allee ef-fects on SAD patterns. So, are there any new insights when the Alleeeffect is incorporated into the neutral model? A natural expectationmight be that when the Allee effect is incorporated, the resultant fitwith empirical SADs should be much better given the fact that newbiological information is added to the neutral models.

Indeed, there are some recent theoretical studies which provideinsights into the role of the Allee effect on neutral theories of biodi-versity. For example, in a previous study (Zhou and Zhang, 2006), itwas found that the Allee effect-based theoretical SAD curve was de-pressed compared to the theoretical SAD curve predicted by a modelthat did not consider the Allee effect. This result was expected given thefact that the Allee effect tends (as explained by Zhou and Zhang, 2006)(i) to increase the risk of local extinctions of the set of originally rarespecies (hence the resulting reduction in species richness) and (ii) todecrease relative abundances of those species which originally had in-termediate levels of abundance, with those species progressively be-coming “new” contributors to the set of rare species (i.e., thereby re-placing the originally rare, now extinct, species). However, the previousstudy (Zhou and Zhang, 2006) presented only simulation-based results,and those still need to be tested against empirical datasets. Can neutralSAD models be deduced quantitatively by explicitly incorporating the

https://doi.org/10.1016/j.ecoinf.2019.05.002Received 6 March 2019; Received in revised form 1 May 2019; Accepted 2 May 2019

⁎ Corresponding author at: Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China⁎⁎ Corresponding author.E-mail addresses: haydi@126.com (Y. Chen), tjshen@nchu.edu.tw (T.-J. Shen).

(FRORJLFDO,QIRUPDWLFV²

$YDLODEOHRQOLQH0D\(OVHYLHU%9$OOULJKWVUHVHUYHG

7

20

Traditional Journals:Reviewing Time

Conservation Methods

A Bayesian-weighted approach to predicting thenumber of newly discovered rare speciesTsung-Jen Shen 1 and Youhua Chen 2 ∗

1Institute of Statistics & Department of Applied Mathematics, National Chung Hsing University, 250 Kuo Kuang Road, Taichung40227, Taiwan2CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration and BiodiversityConservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041,China

Abstract: In natural ecological communities, most species are rare and thus susceptible to extinction. Con-sequently, the prediction and identification of rare species are of enormous value for conservation purposes.How many newly found species will be rare in the next field survey? We took a Bayesian viewpoint and usedobserved species abundance information in an ecological sample to develop an accurate way to estimate thenumber of new rare species (e.g., singletons, doubletons, and tripletons) in an additional unknown sample.A similar method has been developed for incidence-based data sets. Five seminumerical tests (3 abundancecases and 2 incidence cases) showed that our proposed Bayesian-weight estimator accurately predicted thenumber of new rare species with low relative bias and low relative root mean squared error and, accordingly,high accuracy. Finally, we applied the proposed estimator to 6 conservation-directed empirical data sets(3 abundance cases and 3 incidence cases) and found the prediction of new rare species was quite accurate;the 95% CI covered the true observed value very well in most cases. Our estimator performed similarly to orbetter than an unweighted estimator derived from Chao et al. and performed consistently better than the naıveunweighted estimator. We recommend our Bayesian-weight estimator for conservation applications, althoughthe unweighted estimator of Chao et al. may be better under some circumstances. We provide an R packageRSE (rare species estimation) at https://github.com/ecomol/RSE for implementation of the estimators.

Keywords: Bayesian statistics, biodiversity survey, diversity estimation, sampling theory, species rarity

Un Metodo con Ponderacion Bayesiana para Predecir el Numero de Especies Raras Recientemente Descubiertas

Resumen: En las comunidades ecologicas naturales, la mayorıa de las especies son raras y por lo tantosusceptibles a la extincion. Como consecuencia, la prediccion e identificacion de las especies raras son deenorme valor para los propositos de la conservacion. ¿Cuantas especies recientemente descubiertas seranclasificadas como raras en el siguiente censo de campo? Tomamos un punto de vista bayesiano y utilizamosinformacion de la abundancia observada de especies en una muestra ecologica para desarrollas una maneracertera para estimar el numero de nuevas especies raras (p. ej.: singleton, doubleton, y tripleton) en unamuestra adicional desconocida. Un metodo similar se ha desarrollado para conjuntos de datos basados en laincidencia. Cinco pruebas semi-numericas (tres casos de abundancia y dos casos de incidencia) mostraronque nuestra propuesta de estimador con ponderacion bayesiana predijo con certeza el numero de nuevasespecies raras con un bajo sesgo relativo y un bajo error de la raız cuadrada media relativa y, de maneraacorde, una alta certeza. Finalmente, aplicamos el estimador propuesto a seis conjuntos de datos empıricosdirigidos hacia la conservacion (tres casos de abundancia y tres casos de incidencia) y encontramos quela prediccion de nuevas especies raras fue certera; el 95% del CI cubrio muy bien al verdadero valorobservado en la mayorıa de los casos. Nuestro estimador funciono de manera similar o incluso mejorque un estimador sin ponderacion derivado de Chao et al. (2015) y funciono constantemente mejor que

∗email haydi@126.comArticle impact statement: A novel Bayesian-weighted estimator can accurately predict number of newly found rare species in additionalecological samples.Paper submitted October 15, 2017; revised manuscript accepted July 30, 2018.

444Conservation Biology, Volume 33, No. 2, 444–455C© 2018 Society for Conservation BiologyDOI: 10.1111/cobi.13253

Conservation Methods

A Bayesian-weighted approach to predicting thenumber of newly discovered rare speciesTsung-Jen Shen 1 and Youhua Chen 2 ∗

1Institute of Statistics & Department of Applied Mathematics, National Chung Hsing University, 250 Kuo Kuang Road, Taichung40227, Taiwan2CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration and BiodiversityConservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041,China

Abstract: In natural ecological communities, most species are rare and thus susceptible to extinction. Con-sequently, the prediction and identification of rare species are of enormous value for conservation purposes.How many newly found species will be rare in the next field survey? We took a Bayesian viewpoint and usedobserved species abundance information in an ecological sample to develop an accurate way to estimate thenumber of new rare species (e.g., singletons, doubletons, and tripletons) in an additional unknown sample.A similar method has been developed for incidence-based data sets. Five seminumerical tests (3 abundancecases and 2 incidence cases) showed that our proposed Bayesian-weight estimator accurately predicted thenumber of new rare species with low relative bias and low relative root mean squared error and, accordingly,high accuracy. Finally, we applied the proposed estimator to 6 conservation-directed empirical data sets(3 abundance cases and 3 incidence cases) and found the prediction of new rare species was quite accurate;the 95% CI covered the true observed value very well in most cases. Our estimator performed similarly to orbetter than an unweighted estimator derived from Chao et al. and performed consistently better than the naıveunweighted estimator. We recommend our Bayesian-weight estimator for conservation applications, althoughthe unweighted estimator of Chao et al. may be better under some circumstances. We provide an R packageRSE (rare species estimation) at https://github.com/ecomol/RSE for implementation of the estimators.

Keywords: Bayesian statistics, biodiversity survey, diversity estimation, sampling theory, species rarity

Un Metodo con Ponderacion Bayesiana para Predecir el Numero de Especies Raras Recientemente Descubiertas

Resumen: En las comunidades ecologicas naturales, la mayorıa de las especies son raras y por lo tantosusceptibles a la extincion. Como consecuencia, la prediccion e identificacion de las especies raras son deenorme valor para los propositos de la conservacion. ¿Cuantas especies recientemente descubiertas seranclasificadas como raras en el siguiente censo de campo? Tomamos un punto de vista bayesiano y utilizamosinformacion de la abundancia observada de especies en una muestra ecologica para desarrollas una maneracertera para estimar el numero de nuevas especies raras (p. ej.: singleton, doubleton, y tripleton) en unamuestra adicional desconocida. Un metodo similar se ha desarrollado para conjuntos de datos basados en laincidencia. Cinco pruebas semi-numericas (tres casos de abundancia y dos casos de incidencia) mostraronque nuestra propuesta de estimador con ponderacion bayesiana predijo con certeza el numero de nuevasespecies raras con un bajo sesgo relativo y un bajo error de la raız cuadrada media relativa y, de maneraacorde, una alta certeza. Finalmente, aplicamos el estimador propuesto a seis conjuntos de datos empıricosdirigidos hacia la conservacion (tres casos de abundancia y tres casos de incidencia) y encontramos quela prediccion de nuevas especies raras fue certera; el 95% del CI cubrio muy bien al verdadero valorobservado en la mayorıa de los casos. Nuestro estimador funciono de manera similar o incluso mejorque un estimador sin ponderacion derivado de Chao et al. (2015) y funciono constantemente mejor que

∗email haydi@126.comArticle impact statement: A novel Bayesian-weighted estimator can accurately predict number of newly found rare species in additionalecological samples.Paper submitted October 15, 2017; revised manuscript accepted July 30, 2018.

444Conservation Biology, Volume 33, No. 2, 444–455C© 2018 Society for Conservation BiologyDOI: 10.1111/cobi.13253

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