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.
Highlights
Growing concern about the status and future of the world’s biodiversity in the face of human-induced climate and land-use change has focussed attention on the need to mitigate these negative effects (Botkin et al 2007)
Georeferenced plant species specimens for Ecuador were downloaded from the Botanical Information and Ecology Network (BIEN) (Enquist et al 2009; http://bien.nceas.ucsb.edu/bien/)
The spatial pattern of raw and estimated species richness mirrored the spatial patterns of number of specimens (Figs. 1, 2), with the exception of rarefied species richness (Fig. 3)
Summary
Growing concern about the status and future of the world’s biodiversity in the face of human-induced climate and land-use change has focussed attention on the need to mitigate these negative effects (Botkin et al 2007). A primary goal of large-scale conservation efforts is to conserve as much biodiversity as possible with minimum investment (Myers et al 2000). This requires comparable and reliable estimates of species richness across large geographic scales (Iban~ez et al 2006). Species distributions are often poorly understood (Wallacean short-fall) and many species remain undescribed (Linnaean shortfall) (Whittaker et al 2005; Sheth et al 2012; Ter Steege et al 2013) This is true with respect to the tropics (Ferrier 2002). Sampling methods and sampling intensity have been inconsistent across space and time, making the calculation of accurate and comparable species richness estimates problematic (Colwell et al 2012)
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