Limited sampling hampers “big data” estimation of species richness in a tropical biodiversity hotspot

Ecology and Evolution

Kristine Engemann⁴  Brian J. Enquist¹  Brody Sandel⁵  Brad Boyle¹  Peter M. Jørgensen⁶  Naia Morueta-Holme²  Robert K. Peet⁷  Cyrille Violle³ 

[1] Ecology & Evolutionary Biology, University of Arizona, Tuscon, Arizona;Integrative Biology, University of California, Berkeley, California;CEFE UMR 5175, CNRS – Université de Montpellier – Université Paul-Valéry Montpellier, Montpellier, CEDEX 5, France;ORCID:orcid.org/0000-0003-1431-1726;Ecoinformatics & Biodiversity, Department of Bioscience, Aarhus University, Aarhus C, Denmark;Missouri Botanical Garden, St. Louis, Missouri;Department of Biology, University of North Carolina, Chapel Hill, North Carolina

关键词: Ecuador;    rarefaction;    resampling;    richness estimation;    sampling effort;   

DOI  :  10.1002/ece3.1405

来源: Wiley

【 摘 要 】

Abstract

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.

【 授权许可】

CC BY   
© 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.