Based on a cross-validation test, CEGS outperforms all other key distributions when fitted to the new paper's data set. Support is stronger in richer communities. When CEGS holds, all other richness estimators are inaccurate. Another completed manuscript soon to be submitted.

CEGS arises from a two-parameter population dynamics model assuming that recruitment rates are per-species and death rates are per-individual. CEGS fit to the Barro Colorado Island tree inventory is near-perfect, so nothing more needs to be assumed. Completed manuscript soon to be submitted.

Back that up. What exactly is wrong, how is it not addressed in the 16 section-long discussion, and how is my main argument affected? Take another look at Fig. 5A, for example. "A better alternative is the Poisson log-normal distribution" (O'Hara 2005), but assuming it invalidates H and D.

Um, they're a long way from sucking this year. The Mets might not have swept that series last week but they did beat the Cubs solidly (18 runs to 10).

The article conflates fitting and description with "training" and "prediction". The figure shows a saturated descriptive model, not a predictive one. Cross-validation is barely mentioned and saturation not at all. Parsimonious models work: they make robust predictions about new data.

No problem. It's CEGS. Here's a "subsample at random and estimate richness" analysis of the classic BCI data set of Condit et al. (1996). CEGS asymptotes almost immediately. OPR is much the same. ACE and Chao 1 are way off, consistent with your results. Great paper, I totally agree with it.

Australians suffer from the illusion that there's more to the news than Trump. Kind of.

Probably not. Among other things, they have to play the Mets six times.

Seriously, it's a sign of the ongoing apocalypse. Hill numbers are everywhere!