Insights from a general, full-likelihood Bayesian approach to inferring shared evolutionary events from genomic data: Inferring shared demographic events is challenging

Abstract

Factors that influence the distribution, abundance, and diversification of species can simultaneously affect multiple evolutionary lineages within or across communities. These include environmental changes and inter-specific ecological interactions that cause ranges of multiple, co-distributed species to contract, expand, or become fragmented. Such processes predict genetic patterns consistent with temporally clustered evolutionary events across species, such as synchronous population divergences and/or changes in population size. There have been a number of methods developed to infer shared divergences or changes in effective population size, but not both, and the latter has been limited to approximate Bayesian computation (ABC). We introduce a general, full-likelihood Bayesian method that can use genomic data to estimate temporal clustering of an arbitrary mix of population divergences and populationsize changes across taxa. Applying this method to simulated data, we find that estimating the timing and sharing of demographic changes is much more challenging than divergences. Even under favorable simulation conditions, the ability to infer shared demographic events is quite limited and very sensitive to prior assumptions, which is in sharp contrast to accurate, precise, and robust estimates of shared divergence times. Our results also suggest that previous estimates of co-expansion among five Alaskan populations of threespine sticklebacks (Gasterosteus aculeatus) were likely driven by a combination of prior assumptions and the lack of information about the timing of demographic changes when invariant characters are ignored. We conclude by discussing potential avenues to improve the estimation of synchronous demographic changes across populations.

Montane regions shape patterns of diversification in small mammals and reptiles from Madagascar’s moist evergreen forest

“…S4, S5). Thus, we recommend that future studies employ larger, multi‐locus datasets that allow divergence times and demographic changes to be reconstructed with greater power and resolution (Oaks, L’Bahy, L’Bahy, & Cobb, 2019).…”

Identification of hidden population structure in time-scaled phylogenies

“…Thus, we have found this method maintains good performance over a diverse range of situations where phylogenetic structure is produced, including differential transmission rates, epidemiological outbreaks, evolution of beneficial mutations, and differential sampling patterns. Our work is related to the research on species delimitation methods (see for example (Zhang et al 2013)) although targeted at within-species variation, and is also related to recent work on methods for detecting co-diversification of species(Oaks et al 2019). This method appears relatively robust compared to other methods against false-positive identification of phylogenetic structure, but nevertheless has good sensitivity for detecting structure in most situations.…”

Impacts of the Toba eruption and montane forest expansion on diversification in Sumatran parachuting frogs (Rhacophorus)

“…The program thus estimates the timing of demographic events, as well as effective population sizes of ancestral and contemporary populations (which are used to infer shifts in population size). To generate input files for each dataset, we included the full alignments for each locus; Oaks, L’Bahy, and Cobb (2019) showed that using all sites substantially improves estimates of demographic event times. We separated each alignment into northern and southern populations for each species based on clustering analyses (Figure 2) for a total of six populations being compared by ecoevolity.…”