Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages

By Robert K. Colwell, A. Chao, Nicholas Gotelli, Shang-Yi Lin, Chang Xuan Mao, R. Chazdon, J. Longino. 2012

1,941 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/a06450a0fea55490d4bba203a3f5b6b6ad402b54

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In Ecological Modeling

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Citations in the corpus

Effects of library size variance, sparsity, and compositionality on the analysis of microbiome data (2015, methodology): “We modified the 256 normalization methods to those in Table S1 (none, proportion, rarefying without replacement as 257 in the hypergeometric model (Colwell et al. 2012), CSS (Paulson et al. 2013), logUQ (Bullard et 258 al.”
Dissecting a biodiversity hotspot: The importance of environmentally marginal habitats in the Atlantic Forest Domain of South America (2017, methodology): “…how well the floristic diversity is captured in our dataset by calculating the expected species accumulation curves for rain forest and marginal habitats, using sample- based rarefaction (Colwell et al., 2012) with the “specaccum” function in the statistical package vegan (Oksanen et al., 2016).”
Measuring temporal change in alpha diversity: A framework integrating taxonomic, phylogenetic and functional diversity and the iNEXT.3D standardization (2021, background): “Colwell et al. (2012) and Chao et al. (2014) demonstrated that replicated incidence data support statistical approaches to the biological inference that are as powerful as the corresponding abundance- data- based approaches.”
iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers) (2016, methodology): “Chao et al. (2014) extended Colwell et al. (2012) and Chao & Jost (2012) to Hill numbers and proposed the sample-size-and coverage-based R/E of Hill numbers as a uniﬁed framework for estimating species diversity, and for making statistical comparisons based on these estimates.”
VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires (2015, methodology): “We have used common diversity measures: the observed diversity (number of unique clonotypes), Chao [47] and Efron [48] estimates for lower bound on total species diversity, Shannon [49] and Simpson [50] indices, as well as extrapolated Chao estimate [51].”
Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. (2012, methodology): “Recently, Colwell et al. (2012) linked the interpolation (rarefaction) curve given in Eq.”
KnowBR: An application to map the geographical variation of survey effort and identify well-surveyed areas from biodiversity databases (2018, methodology): “…KnowBR we aim to provide a tool to assess the levels of survey completeness across a territory, rather than an application for comparing species richness between sites by the use of the analytic rarefaction and extrapolation techniques developed by Chao and Jost (2012) and Colwell et al. (2012).”
Measuring and Estimating Species Richness, Species Diversity, and Biotic Similarity from Sampling Data (2013, methodology): “This unconditional variance for abundance data is calculated as follows (Colwell et al., 2012): s2indðmÞ ¼ Xn k ¼ 1 ð1 akmÞ2fk ½~SindðmÞ 2=Sest ½5 where Sest denotes an estimated species richness (such as Chao1, described in the section Species Richness Estimation) and akm ¼ n k m , n m for krn m,…”
Does pollen-assemblage richness reflect floristic richness? A review of recent developments and future challenges (2016, neutral): “Given a 632 reference assemblage, rarefaction and extrapolation formulae (Colwell et al., 2012) for taxon 633 richness provide estimates of Hurlbert’s PIE measure.”
Normalization and microbial differential abundance strategies depend upon data characteristics (2017, methodology): “We amended the rarefying method to the hypergeometric model [75], which is much more common in microbiome studies [23, 24].”
Rarefaction and extrapolation of phylogenetic diversity (2015, methodology): “We then generalize the sample-sizebased approach of Colwell et al. (2012) and the coveragebased approach of Chao & Jost (2012) to phylogenetic versions, guided by an estimator of the undetectedPD.”
Entropy and the species accumulation curve: a novel entropy estimator via discovery rates of new species (2013, methodology): “For species richness, the integrated samplesize- and coverage-based rarefaction and extrapolation were developed respectively by Colwell et al. (2012) and Chao & Jost (2012).”
Molecular tracing of confiscated pangolin scales for conservation and illegal trade monitoring in Southeast Asia (2015, methodology): “Sample coverage methods (Huang andWeir, 2001)were adapted from techniques that have been used in ecological investigations to estimate the total number of species based on a single sample or a set of samples (Chao et al., 2000; Colwell et al., 2012).”
Geographical, temporal and taxonomic biases in insect GBIF data on biodiversity and extinction (2021, background): “While species accumulation curves estimate expected species richness with different sample numbers, ICE accumulation curve is a robust measure for indicating completeness of sampling (Colwell et al., 2012).”
Diversity and abundance of solitary and primitively eusocial bees in an urban centre: a case study from Northampton (England) (2015, methodology): “Rarefaction curves were calculated (Colwell et al. 2004, 2012) to estimate true species richness in urban and reference sites using the statistical software EstimateS 9.1 (Colwell 2013), with the data extrapolated up to 1000 samples to check the total expected number of species that had not been…”
Coextinction and persistence of dependent species in a changing world (2012, methodology): “Using sampling-theoretic, nonparametric predictors, Colwell et al. (2012) developed estimators (with unconditional confidence intervals) for the expected number of additional species to be found in h∗ additional samples.”
Biodiversity consequences of land-use change and forest disturbance in the Amazon:a multi-scale assessment using ant communities (2016, background): “We then compared richness from a rarefied n = 9 transects, as well as extrapolated values, acknowledging that extrapolation beyond threetimes the sample size can be unreliable (Colwell et al. 2012).”
Ecological specialization matters: long‐term trends in butterfly species richness and assemblage composition depend on multiple functional traits (2015, methodology): “We estimated change based on the extrapolation of the species richness of the least sampled period up to the larger sample size, which when dealing with randomly sampled data provides reliable results up to about triple the original number of the records (Colwell et al., 2012).”