Biology subjects

McKenzie, P. F.

Publications and source records attributed to McKenzie, P. F..

8 recordsLinked to original sources

bioRxiv2026

Estimating self-fertilization rates from the diploid genotype spectrum

The distribution of self-fertilization across the tree of life remains poorly characterized. Here we present the diploid genotype spectrum (DGS) as a useful genomic data summary for characterizing populations undergoing partial self-fertilization (selfing). This statistic provides a straightforward baseline for estimating selfing rates from population genomic data. At every segregating site across a genome, the diploid genotype configuration records the numbers of homozygous ancestral, heterozygous, and homozygous derived individuals, and the DGS compiles the counts of these configurations across sites. We show that the expected relative frequencies of DGS configurations among segregating sites can be computed under the standard coalescent approximation for partial selfing. We present two methods for doing this, one that is an intuitive recursion through possible states of the model, and one that is a more efficient combinatorial version. We give a worked example, validate the analytical expectations using forward simulations, and introduce a software package that estimates population selfing rates from diploid SNP data. The computations here mechanistically link selfing to observed patterns of diploid genotype configurations.

genomics↗

bioRxiv2026

Unmasking the large and highly repetitive genome of Phlox reveals a complex evolutionary history of speciation with gene flow

The large size and high repetitive content of many plant genomes have hindered elucidation of how the fundamental processes of evolution give rise to speciation. Here we present chromosome-level assemblies of 6 gigabase genomes for four closely related Phlox wildflower species. We generate extensive population genetic data for the three well-studied annual species P. drummondii, P. cuspidata, and P. roemeriana, including structural variation from whole-genome long-read resequencing data. The whole-genome assemblies reveal extensive differences in amount and distribution of genetic variation within and between species, reflective of differences in life-history strategies, mating systems, and edaphic specialization. The population genetic data exposes a history of widespread and consistent gene flow between all three annual Phlox species throughout their divergence and speciation. Our unmasking of structural variants and repetitive elements exposes rich and dynamic forms of genetic variation that show strong phylogenetic signal and pervasive patterns of gene flow. Our results lay a foundation for untangling the highly complex genomes of plants to make advancements in our understanding of the processes of speciation and divergence.

evolutionary biology↗

bioRxiv2025

High-throughput iNaturalist image analysis reveals flower color divergence in Monarda fistulosa

Characterizing patterns of trait variation across widespread species is a fundamental goal of natural history. Here we create a pipeline to analyze a large community science dataset and test hypothesized flower color divergence across the range of a widespread wildflower. Monarda fistulosa is a North American perennial that produces showy lavender inflorescences. Although previous literature suggests that the flowers of western M. fistulosa might display a deeper purple color than the eastern varieties, this divergence has not been assessed at scale. We process over 40,000 community science photographs of M. fistulosa to identify flowers and extract color. We demonstrate that the flowers of the montane western variety have lower lightness and higher chroma, corresponding to a deeper violet color, than those of eastern M. fistulosa. Our approach and validation provides a scalable framework for phenotyping community science images and enables analysis of geographic color variation in other widespread species.

evolutionary biology↗

bioRxiv2024

A massive community-science flower color dataset reveals convergent evolution of delayed flowering phenology in North American red-flowering plants

The radiation of angiosperms is marked by a phenomenal diversity of floral size, shape, color, scent, and reward. Through hundreds of years of documentation and quantification, scientists have sought to make sense of this variation by defining pollination syndromes. These syndromes are the convergent evolution of common suits of floral traits across distantly related species that have evolved by selection to optimize pollination strategies. The availability of community-science datasets provides an opportunity to develop new tools and to examine new traits that may help further characterize broad patterns of flowering plant diversity. Here we test the hypothesis that flowering phenology can also be a pollination syndrome trait. We generate a novel flower color dataset by using GPT-4 with Vision (GPT-4V) to assign flower color to 11,729 North American species. We map these colors to 1,674,908 community-scientist observations of flowering plants to investigate patterns of phenology. We demonstrate constrained flowering time in the eastern United States for plants with red or orange flowers relative to plants with flowers of other colors. Red-and orange-colored flowers are often characteristic of the "hummingbird" pollination syndrome; importantly, the onset of red and orange flowers corresponds to the arrival of migratory hummingbirds. Our results suggest that the hummingbird pollination syndrome can include flowering phenology and reveal an opportunity to expand the suite of traits included in pollination syndromes. Our methods demonstrate an effective pipeline for leveraging enormous amounts of community science data by using artificial intelligence to extract information about patterns of trait variation.

evolutionary biology↗

bioRxiv2022

Estimating Waiting Distances Between Genealogy Changes under a Multi-Species Extension of the Sequentially Markov Coalescent

Genomes are composed of a mosaic of segments inherited from different ancestors, each separated by past recombination events. Consequently, genealogical relationships among multiple genomes vary spatially across different genomic regions. Expectations for the amount of genealogical variation among unlinked (uncorrelated) genomic regions is well described for either a single population (coalescent) or multiple structured populations (multispecies coalescent). However, the expected similarity among genealogies at linked regions of a genome is less well characterized. Recently, an analytical solution was derived for the expected distribution of waiting distances between changes in genealogical trees spatially across a genome for a single population with constant effective population size. Here we describe a generalization of this result, in terms of the expected distribution of waiting distances between changes in genealogical trees and topologies, for multiple structured populations with branch-specific effective population sizes (i.e., under the multispecies coalescent). Our solutions establish an expectation for genetic linkage in multispecies datasets and provide a new likelihood framework for linking demographic models with local ancestry inference across genomes.

evolutionary biology↗

bioRxiv2020

Novel insights from point-dendrometers in an urban setting: linking environmental variation to fluctuations in stem radius.

BackgroundReal-time monitoring of tree growth can provide novel information about trees in urban/suburban areas and the myriad ecosystem services they provide. By monitoring irrigated specimen trees we test the hypothesis that in trees with sufficient water, growth is governed by environmental factors regulating energy gain rather than by factors related to water use. MethodsInternet enabled, high-resolution dendrometers were installed on three trees in Southampton, NY. The instruments, along with a weather station, streamed data to a project web page that was updated once an hour. (https://ecosensornetwork.com). Growing periods were determined using a Hidden Markov Model based on Zweifel et al.s (2016) zero-growth model. Linear models and conditional inference trees correlated environmental variables to growth magnitude and rate of growth. ResultsGrowth was governed by the interacting environmental variables of air temperature, soil moisture, VPD and took place primarily at night. Radial growth of spruce began April 14 after the accumulation of 69.7 {degrees}C growing degrees days and ended September 7th. Cedar growth began later (April 26th), after the accumulation of 160.6 {degrees}C and ended later (November 3rd). During the observation period, these three modest suburban trees sequestered 108.3 kg of CO2. ConclusionsThough irrigated, residential tree growth in our experiment was affected by environmental factors relating to both water use and energy gain through photosynthesis. Linking tree growth to fluctuations in environmental conditions facilitates the development of a predictive understanding useful for ecosystem management and growth forecasting across future altering climates.

ecology↗

bioRxiv2020

The Multispecies Coalescent in Space and Time

A key distinction between species tree inference under the multi-species coalescent model (MSC), and the inference of gene trees in sliding windows along a genome, is in the effect of genetic linkage. Whereas the MSC explicitly assumes genealogies to be unlinked, i.e., statistically independent, genealogies located close together on genomes are spatially auto-correlated. Here we use tree sequence simulations with recombination to explore the effects of species tree parameters on spatial patterns of linkage among genealogies. We decompose coalescent time units to demonstrate differential effects of generation time and effective population size on spatial coalescent patterns, and we define a new metric, "phylogenetic linkage," for measuring the rate of decay of phylogenetic similarity by comparison to distances among unlinked genealogies. Finally, we provide a simple example where accounting for phylogenetic linkage in sliding window analyses improves local gene tree inference.

evolutionary biology↗

bioRxiv2020

ipcoal: An interactive Python package for simulating and analyzing genealogies and sequences on a species tree or network

Summaryipcoal is a free and open source Python package for simulating and analyzing genealogies and sequences. It automates the task of describing complex demographic models (e.g., with divergence times, effective population sizes, migration events) to the msprime coalescent simulator by parsing a user-supplied species tree or network. Genealogies, sequences, and metadata are returned in tabular format allowing for easy downstream analyses. ipcoal includes phylogenetic inference tools to automate gene tree inference from simulated sequence data, and visualization tools for analyzing results and verifying model accuracy. The ipcoal package is a powerful tool for posterior predictive data analysis, for methods validation, and for teaching coalescent methods in an interactive and visual environment. Availability and implementationSource code is available from the GitHub repository (https://github.com/pmckenz1/ipcoal/) and is distributed for packaged installation with conda. Complete documentation and interactive notebooks prepared for teaching purposes are available at https://ipcoal.readthedocs.io/.

bioinformatics↗