Dataset Information

Using museum specimens to estimate broad-scale species richness: Exploring the performance of individual-based and spatially explicit rarefaction.

ABSTRACT: Estimates of spatial patterns of broad-scale species richness are central to major questions in ecology, evolution and conservation. Yet, they are scarce due to incomplete information on species distributions. Often the only germane data derives from museum specimens collected during non-standardized sampling. Rarefaction, a promising approach to estimate broad-scale richness with these data, estimates the expected number of species represented in subsets of n specimens drawn from N specimens collected in a sampling unit. One version of rarefaction, known as individual-based rarefaction, assumes that the N specimens collected in a sampling unit constitute a random sample of individuals in that sampling unit. Another version, known as spatially explicit rarefaction, assumes that the N specimens collected in a sampling unit are spatially aggregated. We examined the working hypothesis that, when applied to museum specimen data, spatially explicit rarefaction is less biased than individual-based rarefaction because it reduces overestimation due to spatially aggregated sampling. We derived five predictions from this working hypothesis and tested them using computer simulation experiments based on a database of 129,782 plant specimens from Nicaragua, and sampling units of 5 x 5, 50 x 50, and 100 x 100 km. One experiment was a negative control, whereby we simulated collection of randomly chosen individuals from each sampling unit. In contrast, three other experiments included spatially aggregated sampling. In all experiments we applied individual-based and spatially explicit rarefaction to estimate richness, with n = 200 and n = 500 specimens. As expected, the experiment designed as a negative control did not support the working hypothesis. The other three experiments supported the working hypothesis in analyses of larger sampling units, but not in 5 x 5 km sampling units. The predictions we derived from the working hypothesis can be used to assess which rarefaction version is best in particular systems.

SUBMITTER: Osazuwa-Peters OL

PROVIDER: S-EPMC6209151 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Using museum specimens to estimate broad-scale species richness: Exploring the performance of individual-based and spatially explicit rarefaction.

Osazuwa-Peters Oyomoare L OL Stevens W D WD Jiménez Iván I

PloS one 20181031 10

Estimates of spatial patterns of broad-scale species richness are central to major questions in ecology, evolution and conservation. Yet, they are scarce due to incomplete information on species distributions. Often the only germane data derives from museum specimens collected during non-standardized sampling. Rarefaction, a promising approach to estimate broad-scale richness with these data, estimates the expected number of species represented in subsets of n specimens drawn from N specimens co ...[more]

PMID: 30379842

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Project description:Fisher's logseries is widely used to characterize species abundance pattern, and some previous studies used it to predict species richness. However, this model, derived from the negative binomial model, degenerates at the zero-abundance point (i.e., its probability mass fully concentrates at zero abundance, leading to an odd situation that no species can occur in the studied sample). Moreover, it is not directly related to the sampling area size. In this sense, the original Fisher's alpha (correspondingly, species richness) is incomparable among ecological communities with varying area sizes. To overcome these limitations, we developed a novel area-based logseries model that can account for the compounding effect of the sampling area. The new model can be used to conduct area-based rarefaction and extrapolation of species richness, with the advantage of accurately predicting species richness in a large region that has an area size being hundreds or thousands of times larger than that of a locally observed sample, provided that data follow the proposed model. The power of our proposed model has been validated by extensive numerical simulations and empirically tested through tree species richness extrapolation and interpolation in Brazilian Atlantic forests. Our parametric model is data parsimonious as it is still applicable when only the information on species number, community size, or the numbers of singleton and doubleton species in the local sample is available. Notably, in comparison with the original Fisher's method, our area-based model can provide asymptotically unbiased variance estimation (therefore correct 95% confidence interval) for species richness. In conclusion, the proposed area-based Fisher's logseries model can be of broad applications with clear and proper statistical background. Particularly, it is very suitable for being applied to hyperdiverse ecological assemblages in which nonparametric richness estimators were found to greatly underestimate species richness.

Dataset Information

Using museum specimens to estimate broad-scale species richness: Exploring the performance of individual-based and spatially explicit rarefaction.

Publications

Using museum specimens to estimate broad-scale species richness: Exploring the performance of individual-based and spatially explicit rarefaction.

Similar Datasets

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