Darlim, G. | Search bioRxiv

bioRxiv2025

The effects of cryptic diversity on diversification dynamics analyses in Crocodylia

Incomplete taxon sampling due to underestimation of present-day biodiversity biases diversification analysis by favouring slowdowns in speciation rates towards recent time. For instance, in diversification dynamics studies in Crocodylia, long-term low net-diversification rates and slowdowns in speciation rates have been suggested to characterise crocodylian evolution. However, crocodylian cryptic diversity has never been considered. Here, we explore the effects of incorporating cryptic diversity into a diversification dynamics analysis of extant crocodylians. We inferred a time-calibrated cryptic-species-level phylogeny using cytochrome b sequences of 45 lineages compared with the formally recognized 26 crocodylian species. Diversification rate estimates using the cryptic-species-level phylogeny show increasing speciation and net-diversification rates towards the present time, which is contrasting to previous findings. Cryptic diversity should be considered in future macroevolutionary analyses, however representation of cryptic extinct taxa represents a major challenge. Additionally, further investigation of crocodylian diversification dynamics under different underlying genomic data is encouraged upon advances in population genetics. Our case study adds to diversification dynamics knowledge of extant taxa and demonstrates that cryptic species and robust taxonomic assessment are essential to study recent biodiversity dynamics with broad implications for evolutionary biology and ecology.

evolutionary biology↗

bioRxiv2024

Mixture Models for Dating with Confidence

Robust estimation of divergence times is commonly performed using Bayesian inference with relaxed clock models. The specific choice of relaxed clock model and tree prior model can impact divergence time estimates, thus necessitating model selection among alternative models. The common approach is to select a model based on Bayes factors estimated via computational demanding approaches such as stepping stone sampling. Here we explore an alternative approach: mixture models that analytically integrate over all candidate models. Our mixture model approach only requires one Markov chain Monte Carlo analysis to both estimate the parameters of interest (e.g., the time-calibrated phylogeny) and to compute model posterior probabilities. We demonstrate the application of our mixture model approach using three relaxed clock models (uncorrelated exponential, uncorrelated lognormal and independent gamma rates) combined with three tree prior models (constant-rates pure birth process, constant-rate birth-death process and piecewise-constant birth-death process) and mitochondrial genome dataset of Crocodylia. We calibrate the phylogeny using well-defined fossil node calibrations. Our results show that Bayes factors estimated using stepping stone sampling are unreliable due to noise in repeated analyses while our analytical mixture model approach shows higher precision and robustness. Thus, divergence time estimates under our mixture model are comparably robust as previous relaxed clock approaches but model selection is significantly faster and avoids marginal likelihood estimation. Finally, our time-calibrated phylogeny of Crocodylia presents a robust benchmark for further studies in the group.

evolutionary biology↗