Diversity Estimation
To account for potential sampling issues in both datasets, diversity was
estimated using three methods. Richness was estimated using the
S2/m equation of Alroy (2020). This gives similar
results (Fig. S1) to that of Chao 1 (Chao, 1984) in our analyses but is
more aggressive and a consistent lower bound (Alroy, 2020), so we
emphasise it in the main text.
To supplement this, we used the analytical version (Chao and Jost, 2012)
of shareholder quorum subsampling (SQS - Alroy [2010a, 2010b,
2010c]). We note that SQS is routinely referred to as coverage-based
rarefaction (CBR) in the ecological literature and that the distinction
between SQS and CBR is not conceptual but operational, as with the
distinction between the original formulation of rarefaction (Sanders,
1968) and the analytical formulation (Hurlbert, 1971) that is now widely
used.
As these methods are influenced by sample size in our data (Fig. S2), we
use Simpson’s D (Simpson, 1949) as a diversity metric. When computed
using the modern formula (Hurlbert, 1971), Simpson’s D is almost
independent of sample size, and it has long been used in the analysis of
ecological field data (Magurran, 2013, Morris et al. , 2014).
Additionally, we use Fisher’s alpha, which is often used in the study of
high diversity systems (Hubbell, 2015).
All analyses were done in R (version 4.0.2; R Core Team [2020]), and
the functions for generating diversity and richness estimates have been
included in the Supplementary Information.