Biology subjects

Beerli, P.

Publications and source records attributed to Beerli, P..

6 recordsLinked to original sources

bioRxiv2024

Population genetics, phylogeography and gene flow of mainland and island vampire bat (Desmodus rotundus) populations: an investigation into mainland-island bat movement

Movement of animals and plants from mainland populations contributes to the genetic diversity and viability of geographically isolated island biota, but also carries risks of pathogen introductions. The bat fauna of the island of Trinidad reflects species diversity on the neighbouring South American mainland and includes the common vampire bat (Desmodus rotundus). We determined relationships between Trinidad and mainland vampire bat populations and the extent of mainland-island movement by comparing the genetic structure (nuclear and mitochondrial) and morphology of the Trinidadian D. rotundus population to populations in neighbouring regions of the South American mainland and estimating evolutionary histories and patterns of gene flow. Results indicate that Trinidadian D. rotundus are genetically and morphologically distinct from mainland populations, although limited unidirectional male-biased mainland to island gene flow occurs at an estimated rate of 3.3 migrants per year. Two geographically-defined Desmodus cytochrome b clades were identified within Trinidad (i.e., one restricted to the South-Western peninsula which grouped with Venezuelan sequences and the other found throughout the rest of the island which grouped with sequences from Suriname and Guyana) suggesting long-standing female philopatry. The geographic distribution of these clades mirrors that of two previously identified geographically defined rabies virus (RABV) lineages introduced to Trinidad from the mainland. This finding suggests that bat dispersals and RABV introductions occur via both the northern and south-western island peninsulas, with subsequent male-driven intra-island viral spread enabled by bat nuclear homogeneity of these populations. These study findings will contribute to the development of contemporary evidence-based vampire bat population control and rabies prevention programs within island populations.

evolutionary biology↗

bioRxiv2023

Multilocus Phylogeny Estimation Using Probabilistic Topic Modeling

AO_SCPLOWBSTRACTC_SCPLOWMethods for rapidly inferring the evolutionary history of species or populations with genome-wide data are progressing, but computational constraints still limit our abilities in this area. We developed an alignment-free method to infer genome-wide phylogenies and implemented it in the Python package TO_SCPLOWOPICC_SCPLOWCO_SCPLOWONTMLC_SCPLOW. The method uses probabilistic topic modeling (specifically, Latent Dirichlet Allocation or LDA) to extract topic frequencies from k-mers, which are derived from multilocus DNA sequences. These extracted frequencies then serve as an input for the program CO_SCPLOWONTMLC_SCPLOW in the PHYLIP package, which is used to generate a species tree. We evaluated the performance of TO_SCPLOWOPICC_SCPLOWCO_SCPLOWONTMLC_SCPLOW on simulated datasets with gaps and three biological datasets: (1) 14 DNA sequence loci from two Australian bird species distributed across nine populations, (2) 5162 loci from 80 mammal species, and (3) raw, unaligned, non-orthologous PO_SCPLOWACC_SCPLOWBO_SCPLOWIOC_SCPLOW sequences from 12 bird species. Our empirical results and simulated data suggest that our method is efficient and statistically robust. We also assessed the uncertainty of the estimated relationships among clades using a bootstrap procedure.

evolutionary biology↗

bioRxiv2022

Geodesics to characterize the phylogenetic landscape

Phylogenetic trees are fundamental for understanding evolutionary history. However, finding maximum likelihood trees is challenging due to the complexity of the likelihood landscape and the size of tree space. Based on the Billera-Holmes-Vogtmann (BHV) distance between trees, we describe a method to generate intermediate trees on the shortest path between two trees, called pathtrees. These pathtrees give a structured way to generate and visualize treespace in an area of interest. They allow investigating intermediate regions between trees of interest, exploring locally optimal trees in topological clusters of treespace, and potentially finding trees of high likelihood unexplored by tree search algorithms. We compared our approach against other tree search tools (PO_SCPLOWAUPC_SCPLOW*, RAxML, and RO_SCPLOWEVC_SCPLOWBO_SCPLOWAYESC_SCPLOW) in terms of generated highest likelihood trees, new topology proportions, and consistency of generated treespace. We assess our method using two datasets. The first consists of 23 primate species (CytB, 1141 bp), leading to well-resolved relationships. The second is a dataset of 182 milksnakes (CytB, 1117 bp), containing many similar sequences and complex relationships among individuals. Our method visualizes the treespace using log likelihood as a fitness function. It finds similarly optimal trees as heuristic methods and presents the likelihood landscape at different scales. It revealed that we could find trees that were not found with MCMC methods. The validation measures indicated that our method performed well mapping treespace into lower dimensions. Our method complements heuristic search analyses, and the visualization allows the inspection of likelihood terraces and exploration of treespace areas not visited by heuristic searches.

evolutionary biology↗

bioRxiv2022

Spread of new mutations through space

The terms population size and population density are often used interchangeably, when in fact they are quite different. When viewed in a spatial landscape, density is defined as the number of individuals within a square unit of distance, while population size is simply the total count of a population. In discrete population genetics models, the effective population size is known to influence the interaction between selection and random drift with selection playing a larger role in large populations while random drift has more influence in smaller populations. Using a spatially explicit simulation software we investigate how population density affects the flow of new mutations through a geographical space. Using population density, selectional advantage, and dispersal distributions, a model is developed to predict the speed at which the new allele will travel, obtaining more accurate results than current diffusion approximations provide. We note that the rate at which a neutral mutation spreads begins to decay over time while the rate of spread of an advantageous allele remains constant. We also show that new advantageous mutations spread faster in dense populations.

evolutionary biology↗

bioRxiv2019

Population divergence time estimation using individual lineage label switching

Divergence time estimation from multilocus genetic data has become common in population genetics and phylogenetics. We present a new Bayes inference method that treats the divergence time as a random variable. The divergence time is calculated from an assembly of splitting events on individual lineages in a genealogy. The waiting time for such a splitting event is drawn from a hazard function of the truncated normal distribution. This allows easy integration into the standard coalescence framework used in programs such as MIGRATE. We explore the accuracy of the new inference method with simulated population splittings over a wide range of divergence time values and with a dataset of the Zika virus; the geographic analyses of the expansion of the pathogen follows a trajectory from Africa to Asia to America, corroborating analyses based only on the dates of incidences. Evaluations of simple divergence models show high accuracy, whereas the accuracy of the results of isolation with migration (IM) models depend on the magnitude of the immigration rate and potentially on the number of samples. High immigration rates lead to a time of the most recent common ancestor of the sample that predates the divergence time, thus loses any potential signal of the divergence event in the sample data. This reduced accuracy with high immigration rates is problematic for all IM methods, including ours.

evolutionary biology↗

bioRxiv2017

The fractional coalescent

A new approach to the coalescent, the fractional coalescent (f-coalescent), is introduced. Two derivations are presented: first, the f-coalescent is based on an extension of the discrete-time Wright-Fisher model. In this extension, for the population of size N, the probability that two randomly selected individuals have the same parent in the previous generation depends on the variable . Second, the f-coalescent is based on an extension of the discrete-time Canning population model that the variance of the number of offspring is assumed as a random variable which depends on the variable . In the second derivation, the f-coalescent emerges also as a continuous-time semi-Markov process. The additional parameter affects the variability in the patterns of the waiting times; values of < 1 lead to an increase of short time intervals, but allows occasionally for very long time intervals. When = 1, the f-coalescent and Kingmans n-coalescent are equivalent. The mode of the distribution of the time of the most recent common ancestor in the f-coalescent is lower than n-coalescent when the number of sample size increases, and the time which modes happen on that is smaller compare to n-coalescent. Also, this distribution showed that the f-coalescent leads to distributions with heavier tails than the n-coalescent. Also, the probability that n genes descend from m ancestral genes for f-coalescent is derived. The f-coalescent has been implemented in the population genetic model inference software MO_SCPCAPIGRATEC_SCPCAP. Simulation studies suggest that it is possible to infer the correct values from data that was generated with known values. When data is simulated using models with < 1 or for three example datasets (H1N1 influenza, Malaria parasites, Humpback whales), Bayes factor comparisons show an improved model fit of the f-coalescent over the n-coalescent.

evolutionary biology↗