untitled
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<datestamp>2018-01-11</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Practical Estimation of Diversity from Abundance Data</title>
<creator>Marcon, Eric</creator>
<contributor>Ecologie des forêts de Guyane (ECOFOG) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - Institut National de la Recherche Agronomique (INRA) - Université des Antilles et de la Guyane (UAG) - AgroParisTech - Université de Guyane (UG) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<identifier>hal-01212435</identifier>
<identifier>https://hal-agroparistech.archives-ouvertes.fr/hal-01212435</identifier>
<identifier>https://hal-agroparistech.archives-ouvertes.fr/hal-01212435v2/document</identifier>
<identifier>https://hal-agroparistech.archives-ouvertes.fr/hal-01212435/file/Estimation.pdf</identifier>
<source>https://hal-agroparistech.archives-ouvertes.fr/hal-01212435</source>
<source>2015</source>
<language>en</language>
<subject lang=en>Biodiversity</subject>
<subject lang=en>HCDT entropy</subject>
<subject lang=en>Phylodiversity</subject>
<subject>[SDE.BE] Environmental Sciences/Biodiversity and Ecology</subject>
<type>info:eu-repo/semantics/preprint</type>
<type>Preprints, Working Papers, ...</type>
<description lang=en>Measuring biodiversity requires empirical techniques to effectively estimate it from real data. The well-known underestimation of the number of species applies to low orders of diversity in general. I test nine estimators including three new ones on geometric and lognormal distributions that represent realistic, hyper-diverse communities. The best two estimators allow a good estimation of diversity of orders over 0.5, even when the sampling effort is low. I provide criteria to choose the estimator and the necessary code in the R package entropart.</description>
<date>2015-10-06</date>
</dc>
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