Yi Zou, Peng Zhao, Naicheng Wu, Jiangshan Lai, Pedro R. Peres-Neto, Jan C. Axmacher
{"title":"rarestR:一个用稀疏度量估计不完全样本α-和β-多样性的R包","authors":"Yi Zou, Peng Zhao, Naicheng Wu, Jiangshan Lai, Pedro R. Peres-Neto, Jan C. Axmacher","doi":"10.1111/ddi.13954","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Aim</h3>\n \n <p>Species abundance data is commonly used to study biodiversity patterns. In this context, comparing α- and β-diversity across incomplete samples can lead to biases. Therefore, it is essential to employ methods that enable standardised and accurate comparisons of α- and β-diversity across varying sample sizes. In addition, biodiversity studies also often require robust estimates of the total number of species within a community and the number of species shared by two communities.</p>\n </section>\n \n <section>\n \n <h3> Innovation</h3>\n \n <p>Rarefaction methods are commonly used to calculate α-diversity for standardised sample sizes, and they can also serve as the basis for calculating β-diversity. In this application note, we present <span>rarestR</span>, a new R package designed for calculating abundance-based α- and β-diversity measures for inconsistent samples using rarefaction-based metrics. The package also includes parametric extrapolation techniques to estimate the total expected number of species within a community, as well as the total number of species shared between two communities. Additionally, <span>rarestR</span> provides visualisation tools for curve-fitting associated with these estimators.</p>\n </section>\n \n <section>\n \n <h3> Main Conclusions</h3>\n \n <p>Overall, the <span>rarestR</span> package is a valuable tool for comparing α- and β-diversity values among incomplete samples, such as those involving highly mobile or species-rich taxa. In addition, our species estimators offer a complementary approach to non-parametric methods, including the Chao series of estimators.</p>\n </section>\n </div>","PeriodicalId":51018,"journal":{"name":"Diversity and Distributions","volume":"31 1","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ddi.13954","citationCount":"0","resultStr":"{\"title\":\"rarestR: An R Package Using Rarefaction Metrics to Estimate α- and β-Diversity for Incomplete Samples\",\"authors\":\"Yi Zou, Peng Zhao, Naicheng Wu, Jiangshan Lai, Pedro R. Peres-Neto, Jan C. Axmacher\",\"doi\":\"10.1111/ddi.13954\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Aim</h3>\\n \\n <p>Species abundance data is commonly used to study biodiversity patterns. In this context, comparing α- and β-diversity across incomplete samples can lead to biases. Therefore, it is essential to employ methods that enable standardised and accurate comparisons of α- and β-diversity across varying sample sizes. In addition, biodiversity studies also often require robust estimates of the total number of species within a community and the number of species shared by two communities.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Innovation</h3>\\n \\n <p>Rarefaction methods are commonly used to calculate α-diversity for standardised sample sizes, and they can also serve as the basis for calculating β-diversity. In this application note, we present <span>rarestR</span>, a new R package designed for calculating abundance-based α- and β-diversity measures for inconsistent samples using rarefaction-based metrics. The package also includes parametric extrapolation techniques to estimate the total expected number of species within a community, as well as the total number of species shared between two communities. Additionally, <span>rarestR</span> provides visualisation tools for curve-fitting associated with these estimators.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Main Conclusions</h3>\\n \\n <p>Overall, the <span>rarestR</span> package is a valuable tool for comparing α- and β-diversity values among incomplete samples, such as those involving highly mobile or species-rich taxa. 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rarestR: An R Package Using Rarefaction Metrics to Estimate α- and β-Diversity for Incomplete Samples
Aim
Species abundance data is commonly used to study biodiversity patterns. In this context, comparing α- and β-diversity across incomplete samples can lead to biases. Therefore, it is essential to employ methods that enable standardised and accurate comparisons of α- and β-diversity across varying sample sizes. In addition, biodiversity studies also often require robust estimates of the total number of species within a community and the number of species shared by two communities.
Innovation
Rarefaction methods are commonly used to calculate α-diversity for standardised sample sizes, and they can also serve as the basis for calculating β-diversity. In this application note, we present rarestR, a new R package designed for calculating abundance-based α- and β-diversity measures for inconsistent samples using rarefaction-based metrics. The package also includes parametric extrapolation techniques to estimate the total expected number of species within a community, as well as the total number of species shared between two communities. Additionally, rarestR provides visualisation tools for curve-fitting associated with these estimators.
Main Conclusions
Overall, the rarestR package is a valuable tool for comparing α- and β-diversity values among incomplete samples, such as those involving highly mobile or species-rich taxa. In addition, our species estimators offer a complementary approach to non-parametric methods, including the Chao series of estimators.
期刊介绍:
Diversity and Distributions is a journal of conservation biogeography. We publish papers that deal with the application of biogeographical principles, theories, and analyses (being those concerned with the distributional dynamics of taxa and assemblages) to problems concerning the conservation of biodiversity. We no longer consider papers the sole aim of which is to describe or analyze patterns of biodiversity or to elucidate processes that generate biodiversity.