DOI: 10.5061/dryad.g9k46
title: Data from: Evaluating the accuracy of biodiversity changes through geological times: from simulation to solution

description: Estimating biodiversity and its variations through geologic time is a
 notoriously difficult task, due to several taphonomic and methodological
 effects that make the reconstructed signal potentially distinct from the
 unknown, original one. Through a simulation approach, we examine the
 effect of a major, surprisingly still understudied, source of potential
 disturbance: the effect of time discretization through biochronological
 construction, which generates spurious coexistences of taxa within
 discrete time intervals (i.e., biozones), and thus potentially makes
 continuous- and discrete-time biodiversity curves very different. Focusing
 on the taxonomic-richness dimension of biodiversity (including estimates
 of origination and extinction rates), our approach relies on generation of
 random continuous-time richness curves, which are then time-discretized to
 estimate the noise generated by this manipulation. A broad spectrum of
 data-set parameters (including average taxon longevity and biozone
 duration, total number of taxa, and simulated time interval) is evaluated
 through sensitivity analysis. We show that the deteriorating effect of
 time discretization on the richness signal depends highly on such
 parameters, most particularly on average biozone duration and taxonomic
 longevity because of their direct relationship with the number of false
 coexistences generated by time discretization. With several worst-case but
 realistic parameter combinations (e.g., when relatively short-lived taxa
 are analyzed in a long-ranging biozone framework), the original and
 time-discretized richness curves can ultimately show a very weak to zero
 correlation, making these two time series independent. Based on these
 simulation results, we propose a simple algorithm allowing the
 back-transformation of a discrete-time taxonomic-richness data set, as
 customarily constructed by paleontologists, into a continuous-time data
 set. We show that the reconstructed richness curve obtained this way fits
 the original signal much more closely, even when the parameter combination
 of the original data set is particularly adverse to an effective
 time-discretized reconstruction.
description: Supplementary results
 S1-S21Supplementary results
 and figures S1-S21 for Evaluating the accuracy of biodiversity changes
 through geological times: from simulation to solutionSupplementary Tables
 S1-S12Pearson correlation
 coefficient between original, discretized and reconstructed richness and
 rates time series : Supplementary Tables S1-S12R function. Evaluating
 the accuracy of biodiversity changes through geological
 timesBased on a
 presence/absence matrix, the function allows the back-transformation of
 this discretized dataset into a reconstructed continuous-time dataset. The
 function generates plots of taxonomic richness and Foote’s taxonomic rates
 variation through time and a list of the 3 different (richness,
 origination p rate and extinction q rate) datasets and the associated 95 %
 confidence interval.Reconstruction algorithm
 : the R functiondivContinuousTime.random.R