Department of Biology

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Kristine Engemann

Limited sampling hampers “big data” estimation of species richness in a tropical biodiversity hotspot: Sampling Hampers Species Richness Estimation

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  • ece31405

    Submitted manuscript, 1.93 MB, PDF document

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  • Kristine Engemann
  • Brian J. Enquist, Department of Ecology and Evolutionary Biology, University of Arizona / The Santa Fe Institute, United States
  • Brody Steven Sandel
  • Brad Boyle, Department of Ecology and Evolutionary Biology, University of Arizona / The iPlant Collaborative, United States
  • Peter M. Jørgensen, Missouri Botanical Garden, United States
  • Naia Morueta-Holme, Department of Integrative Biology, University of California, Berkeley, Denmark
  • Robert K. Peet, Department of Biology, University of North Carolina, United States
  • Cyrille Violle, CNRS, UMR5175, Centre d’Ecologie Fonctionnelle et Evolutive, France
  • J.-C. Svenning
Macro-scale species richness studies often use museum specimens as their main source of information. However, such datasets are often strongly biased due to variation in sampling effort in space and time. These biases may strongly affect diversity estimates and may, thereby, obstruct solid inference on the underlying diversity drivers, as well as mislead conservation prioritization. In recent years, this has resulted in an increased focus on developing methods to correct for sampling bias. In this study, we use sample-size-correcting methods to examine patterns of tropical plant diversity in Ecuador, one of the most species-rich and climatically heterogeneous biodiversity hotspots. Species richness estimates were calculated based on 205,735 georeferenced specimens of 15,788 species using the Margalef diversity index, the Chao estimator, the second-order Jackknife and Bootstrapping resampling methods, and Hill numbers and rarefaction. Species richness was heavily correlated with sampling effort, and only rarefaction was able to remove this effect, and we recommend this method for estimation of species richness with “big data” collections.
Original languageEnglish
JournalEcology and Evolution
Volume5
Issue3
Pages (from-to)807-820
Number of pages14
ISSN2045-7758
DOIs
Publication statusPublished - 2015

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