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[dinosaur] Dinosaur diversity--research history and estimation methods

Ben Creisler

Two new papers:

Free pdf:

Jonathan P. Tennantâ, Alfio Alessandro Chiarenzaâ & Matthew Baron (2018)
How has our knowledge of dinosaur diversity through geologic time changed through research history?Â
PeerJ 6:e4417
doi:Â https://doi.org/10.7717/peerj.4417

Assessments of dinosaur macroevolution at any given time can be biased by the historical publication record. Recent studies have analysed patterns in dinosaur diversity that are based on secular variations in the numbers of published taxa. Many of these have employed a range of approaches that account for changes in the shape of the taxonomic abundance curve, which are largely dependent on databases compiled from the primary published literature. However, how these âcorrectedâ diversity patterns are influenced by the history of publication remains largely unknown. Here, we investigate the influence of publication history between 1991 and 2015 on our understanding of dinosaur evolution using raw diversity estimates and shareholder quorum subsampling for the three major subgroups: Ornithischia, Sauropodomorpha, and Theropoda. We find that, while sampling generally improves through time, there remain periods and regions in dinosaur evolutionary history where diversity estimates are highly volatile (e.g. the latest Jurassic of Europe, the mid-Cretaceous of North America, and the Late Cretaceous of South America). Our results show that historical changes in database compilation can often substantially influence our interpretations of dinosaur diversity. âGlobalâ estimates of diversity based on the fossil record are often also based on incomplete, and distinct regional signals, each subject to their own sampling history. Changes in the record of taxon abundance distribution, either through discovery of new taxa or addition of existing taxa to improve sampling evenness, are important in improving the reliability of our interpretations of dinosaur diversity. Furthermore, the number of occurrences and newly identified dinosaurs is still rapidly increasing through time, suggesting that it is entirely possible for much of what we know about dinosaurs at the present to change within the next 20 years.


Also for dinosaurs....

Roger A. Close, Serjoscha W. Evers, John Alroy & Richard J. Butler (2018)
How should we estimate diversity in the fossil record? Testing richness estimators using sampling-standardised discovery curves.
Methods in Ecology and Evolution (advance online publication)
DOI: 10.1111/2041-210X.12987


1.To infer genuine patterns of biodiversity change in the fossil record, we must be able to accurately estimate relative differences in numbers of taxa (richness) despite considerable variation in sampling between time intervals. Popular subsampling (=interpolation) methods aim to standardise diversity samples by rarefying the data to equal sample size or equal sample completeness (=coverage). Standardising by sample size is misleading because it compresses richness ratios, thereby flattening diversity curves. However, standardising by coverage reconstructs relative richness ratios with high accuracy. Asymptotic richness extrapolators are widely used in ecology, but rarely applied to fossil data. However, a recently developed parametric extrapolation method, TRiPS (True Richness estimation using Poisson Sampling), specifically aims to estimate the true richness of fossil assemblages.

2.Here, we examine the suitability of a range of richness estimators (both interpolators and extrapolators) for fossil datasets, using simulations and a novel method for comparing the performance of richness estimators with empirical data. We constructed sampling-standardised discovery curves (SSDCs) for two datasets, each spanning 150 years of palaeontological research: Mesozoic dinosaurs at global scale, and Mesozoicâearly Cenozoic tetrapods from North America. These approaches reveal how each richness estimator responds to both simulated best-case and empirical real-world accumulation of fossil occurrences.

3.We find that extrapolators can only truly standardise diversity data once sampling is sufficient for richness estimates to have asymptoted. Below this point, directly comparing extrapolated estimates derived from samples of different sizes may not accurately reconstruct relative richness ratios. When abundance distributions are not perfectly flat and sampling is moderate to good, but not perfect, TRiPS does not extrapolate, because it overestimates binomial sampling probabilities. Coverage-based interpolators, by contrast, generally yield more stable subsampled diversity estimates, even in the face of dramatic increases in face-value counts of species richness. Richness estimators that standardise by coverage are among the best currently-available methods for reconstructing deep-time biodiversity patterns. However, we recommend the use of sampling-standardised discovery curves to understand how biased reporting of fossil occurrences may affect sampling-standardised diversity estimates.

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