Evaluating estimators of species richness: the importance of considering statistical error rates

Gwinn, Daniel C.; Allen, Michael S.; Bonvechio, Kimberly I.; Hoyer, Mark V.; Beesley, Leah

doi:10.1111/2041-210x.12462

Cited by 38 publications

(39 citation statements)

References 56 publications

(109 reference statements)

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“…Although the method described by Chao et al (2014) is intended to estimate true richness based on coverage regardless of sequencing depth, both observed ( p < 0.001, r 2 = 0.4) and estimated richness ( p < 0.001, r 2 = 0.35) significantly correlated with library size (Supplementary Figure S5). This may indicate that the species abundance distributions vary widely among our datasets (Gwinn et al, 2016). Likewise, NMDS based on un-rarefied sequence libraries showed the influence of initial library size even after relative abundance, RLE, and TMM normalizations (Supplementary Figure S6).…”

Section: Resultsmentioning

confidence: 99%

Seasonal Succession of Free-Living Bacterial Communities in Coastal Waters of the Western Antarctic Peninsula

et al. 2016

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The marine ecosystem along the Western Antarctic Peninsula undergoes a dramatic seasonal transition every spring, from almost total darkness to almost continuous sunlight, resulting in a cascade of environmental changes, including phytoplankton blooms that support a highly productive food web. Despite having important implications for the movement of energy and materials through this ecosystem, little is known about how these changes impact bacterial succession in this region. Using 16S rRNA gene amplicon sequencing, we measured changes in free-living bacterial community composition and richness during a 9-month period that spanned winter to the end of summer. Chlorophyll a concentrations were relatively low until summer when a major phytoplankton bloom occurred, followed 3 weeks later by a high peak in bacterial production. Richness in bacterial communities varied between ~1,200 and 1,800 observed operational taxonomic units (OTUs) before the major phytoplankton bloom (out of ~43,000 sequences per sample). During peak bacterial production, OTU richness decreased to ~700 OTUs. The significant decrease in OTU richness only lasted a few weeks, after which time OTU richness increased again as bacterial production declined toward pre-bloom levels. OTU richness was negatively correlated with bacterial production and chlorophyll a concentrations. Unlike the temporal pattern in OTU richness, community composition changed from winter to spring, prior to onset of the summer phytoplankton bloom. Community composition continued to change during the phytoplankton bloom, with increased relative abundance of several taxa associated with phytoplankton blooms, particularly Polaribacter. Bacterial community composition began to revert toward pre-bloom conditions as bacterial production declined. Overall, our findings clearly demonstrate the temporal relationship between phytoplankton blooms and seasonal succession in bacterial growth and community composition. Our study highlights the importance of high-resolution time series sampling, especially during the relatively under-sampled Antarctic winter and spring, which enabled us to discover seasonal changes in bacterial community composition that preceded the summertime phytoplankton bloom.

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Section: Resultsmentioning

confidence: 99%

Seasonal Succession of Free-Living Bacterial Communities in Coastal Waters of the Western Antarctic Peninsula

et al. 2016

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“…Gwinn, Allen, Bonvechio, Hoyer, and Beesley () found high sensitivity of bias and precision of change estimates to the relative abundances of species in a dataset. Gonzalez et al () argued that the risk of biased estimates of change needs to be considered in short time series of species richness.…”

Section: Methodsmentioning

confidence: 99%

“…Gaussian errors were assumed (Colwell et al, ) with variances specific to each year. In simple tests for a change in species richness, Gwinn et al () found inflated type I errors when using estimates of species richness as data. To limit this, the actual species counts are used as data, and their variances are modeled and kept above a minimum.…”

Section: Methodsmentioning

confidence: 99%

“…In a comparison of two assemblages, species accumulation curves can cross (Chao & Jost, 2012;Lande, DeVries, & Walla, 2000;Thompson & Withers, 2003;Van Dooren, 2016), such that patterns in the relative numbers of species found at low sampling efforts or at rarefied sample sizes (e.g., Biesmeijer et al, 2006) can be independent of actual species richness differences. Gwinn, Allen, Bonvechio, Hoyer, and Beesley (2016) found high sensitivity of bias and precision of change estimates to the relative abundances of species in a dataset. Gonzalez et al (2016) argued that the risk of biased estimates of change needs to be considered in short time series of species richness.…”

Section: Materials and Me Thodsmentioning

confidence: 97%

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Assessing species richness trends: Declines of bees and bumblebees in the Netherlands since 1945

Dooren

2019

Ecology and Evolution

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Estimating and predicting temporal trends in species richness is of general importance, but notably difficult because detection probabilities of species are imperfect and many datasets were collected in an opportunistic manner. We need to improve our capabilities to assess richness trends using datasets collected in unstandardized procedures with potential collection bias. Two methods are proposed and applied to estimate richness change, which both incorporate models for sampling effects and detection probability: (a) nonlinear species accumulation curves with an error variance model and (b) Pradel capture–recapture models. The methods are used to assess nationwide temporal trends (1945–2018) in the species richness of wild bees in the Netherlands. Previously, a decelerating decline in wild bee species richness was inferred for part of this dataset. Among the species accumulation curves, those with nonconstant changes in species richness are preferred. However, when analyzing data subsets, constant changes became selected for non‐Bombus bees (for samples in collections) and bumblebees (for spatial grid cells sampled in three periods). Smaller richness declines are predicted for non‐Bombus bees than bumblebees. However, when relative losses are calculated from confidence intervals limits, they overlap and touch zero loss. Capture–recapture analysis applied to species encounter histories infers a constant colonization rate per year and constant local species survival for bumblebees and other bees. This approach predicts a 6% reduction in non‐Bombus species richness from 1945 to 2018 and a significant 19% reduction for bumblebees. Statistical modeling to detect species richness time trends should be systematically complemented with model checking and simulations to interpret the results. Data inspection, assessing model selection bias, and comparisons of trends in data subsets were essential model checking strategies in this analysis. Opportunistic data will not satisfy the assumptions of most models and this should be kept in mind throughout.

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“…Despite nonparametric estimators of species richness (e.g., Chao and Jackknife estimators; see Gotelli & Chao, 2013 for a review) allow taking into account spatial heterogeneity, they are sensitive to shifts in species-abundance distribution (Gwinn, Allen, Bonvechio, Hoyer, & Beesley, 2016) and mainly structured to provide lower bound estimates of species richness at local scale (Gotelli & Colwell, 2001;Shen, Chao, & Lin, 2003). Same considerations apply when estimates are obtained by fitting asymptotic models (e.g., negative exponential or Michaelis-Menten functions; reviewed by Tjørve, 2003) to the smoothed sample-based accumulation curve, because large areas likely accumulate species at a constant or even an increasing rate due to environmental changes supporting distinctive species assemblages (Gotelli & Colwell, 2011).…”

Section: Introductionmentioning

confidence: 99%

An approach based on the total‐species accumulation curve and higher taxon richness to estimate realistic upper limits in regional species richness

Bevilacqua

Ugland

Plicanti

et al. 2017

Ecology and Evolution

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Most of accumulation curves tend to underestimate species richness, as they do not consider spatial heterogeneity in species distribution, or are structured to provide lower bound estimates and limited extrapolations. The total‐species (T–S) curve allows extrapolations over large areas while taking into account spatial heterogeneity, making this estimator more prone to attempt upper bound estimates of regional species richness. However, the T–S curve may overestimate species richness due to (1) the mismatch among the spatial units used in the accumulation model and the actual units of variation in β‐diversity across the region, (2) small‐scale patchiness, and/or (3) patterns of rarity of species. We propose a new framework allowing the T–S curve to limit overestimation and give an application to a large dataset of marine mollusks spanning over 11 km2 of subtidal bottom (W Mediterranean). As accumulation patterns are closely related across the taxonomic hierarchy up to family level, improvements of the T–S curve leading to more realistic estimates of family richness, that is, not exceeding the maximum number of known families potentially present in the area, can be considered as conducive to more realistic estimates of species richness. Results on real data showed that improvements of the T–S curve to accounts for true variations in β‐diversity within the sampled areas, small‐scale patchiness, and rarity of families led to the most plausible richness when all aspects were considered in the model. Data on simulated communities indicated that in the presence of high heterogeneity, and when the proportion of rare species was not excessive (>2/3), the procedure led to almost unbiased estimates. Our findings highlighted the central role of variations in β‐diversity within the region when attempting to estimate species richness, providing a general framework exploiting the properties of the T–S curve and known family richness to estimate plausible upper bounds in γ‐diversity.

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Evaluating estimators of species richness: the importance of considering statistical error rates

Cited by 38 publications

References 56 publications

Seasonal Succession of Free-Living Bacterial Communities in Coastal Waters of the Western Antarctic Peninsula

Seasonal Succession of Free-Living Bacterial Communities in Coastal Waters of the Western Antarctic Peninsula

Assessing species richness trends: Declines of bees and bumblebees in the Netherlands since 1945

An approach based on the total‐species accumulation curve and higher taxon richness to estimate realistic upper limits in regional species richness

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