Biology subjects

DAVIS, C. C.

Publications and source records attributed to DAVIS, C. C..

4 recordsLinked to original sources

bioRxiv2024

The danger zone: the joint trap of incomplete lineage sorting and long-branch attraction in placing Rafflesiaceae

Two key factors have been implicated as major impediments to phylogenomic inference: incomplete lineage sorting (ILS)--especially in cases where clades are in the so-called anomaly zone--and erroneous gene tree estimation--commonly manifested by long- branch attraction in the Felsenstein zone. Rafflesiaceae (Malpighiales) is an iconic parasitic plant clade whose species occur west of Wallaces line in tropical Southeast Asia. This clade has been notoriously difficult to place phylogenetically and is nested within an explosive ancient radiation, rendering the Malpighiales one of the thorniest nodes across the angiosperm Tree of Life. The parasitic family Apodanthaceae has recently been positioned as sister to Rafflesiaceae, offering the hope to stabilize their placements. Here, using a dataset of 2,135 genes and complex species tree inference methods, the monophyletic Rafflesiaceae+Apodanthaceae clade is variously placed with Euphorbiaceae, Peraceae, Putranjivaceae, and Pandaceae. Such unstable placements appear to be the result of excessive levels of rate heterogeneity and ILS, which contribute to a phylogenetic "danger zone" where simulation suggests that current methods and genomic data will never provide a tidy phylogenetic resolution for species trees alike. Despite the topological uncertainty, however, our divergence time estimation identifies a mid-Cretaceous origin of stem group Rafflesiaceae and Apodanthaceae, not only making them the oldest parasitic plant lineage reported to date, but also suggests a likely Gondwana vicariance scenario to explain their disjunct distribution in South America, Africa, Australia, and Northern India.

evolutionary biology↗

bioRxiv2021

PhyloHerb: A phylogenomic pipeline for processing genome skimming data for plants

O_LIPremise of the study: The application of high throughput sequencing, especially to herbarium specimens, is greatly accelerating biodiversity research. Among various techniques, low coverage Illumina sequencing of total genomic DNA (genome skimming) can simultaneously recover the plastid, mitochondrial, and nuclear ribosomal regions across hundreds of species. Here, we introduce PhyloHerb -- a bioinformatic pipeline to efficiently and effectively assemble phylogenomic datasets derived from genome skimming. C_LIO_LIMethods and Results: PhyloHerb uses either a built-in database or user-specified references to extract orthologous sequences using BLAST search. It outputs FASTA files and offers a suite of utility functions to assist with alignment, data partitioning, concatenation, and phylogeny inference. The program is freely available at https://github.com/lmcai/PhyloHerb/. C_LIO_LIConclusions: Using published data from Clusiaceae, we demonstrated that PhyloHerb can accurately identify genes using highly fragmented assemblies derived from sequencing older herbarium specimens. Our approach is effective at all taxonomic depths and is scalable to thousands of species. C_LI

evolutionary biology↗

bioRxiv2020

A new method for counting reproductive structures in digitized herbarium specimens using Mask R-CNN

Phenology-the timing of life-history events-is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current practice of manually harvesting data from individual specimens, however, greatly restricts our ability to scale-up data collection. Recent investigations have demonstrated that machine-learning approaches can facilitate this effort. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., presence/absence of flowers). Here, we use crowd-sourced phenological data of buds, flowers, and fruits from > 3000 specimens of six common wildflower species of the eastern United States (Anemone canadensis L., A. hepatica L., A. quinquefolia L., Trillium erectum L., T. grandiflorum (Michx.) Salisb., and T. undulatum Wild.) to train models using Mask R-CNN to segment and count phenological features. A single global model was able to automate the binary coding of each of the three reproductive stages with > 87% accuracy. We also successfully estimated the relative abundance of each reproductive structure on a specimen with [≥] 90% accuracy. Precise counting of features was also successful, but accuracy varied with phenological stage and taxon. Specifically, counting flowers was significantly less accurate than buds or fruits likely due to their morphological variability on pressed specimens. Moreover, our Mask R-CNN model provided more reliable data than non-expert crowd-sourcers but not botanical experts, highlighting the importance of high-quality human training data. Finally, we also demonstrated the transferability of our model to automated phenophase detection and counting of the three Trillium species, which have large and conspicuously-shaped reproductive organs. These results highlight the promise of our two-phase crowd-sourcing and machine-learning pipeline to segment and count reproductive features of herbarium specimens, thus providing high-quality data with which to investigate plant response to ongoing climatic change.

ecology↗

bioRxiv2020

The Perfect Storm: Gene Tree Estimation Error, Incomplete Lineage Sorting, and Ancient Gene Flow Explain the Most Recalcitrant Ancient Angiosperm Clade, Malpighiales

The genomic revolution offers renewed hope of resolving rapid radiations in the Tree of Life. The development of the multispecies coalescent (MSC) model and improved gene tree estimation methods can better accommodate gene tree heterogeneity caused by incomplete lineage sorting (ILS) and gene tree estimation error stemming from the short internal branches. However, the relative influence of these factors in species tree inference is not well understood. Using anchored hybrid enrichment, we generated a data set including 423 single-copy loci from 64 taxa representing 39 families to infer the species tree of the flowering plant order Malpighiales. This order alone includes nine of the top ten most unstable nodes in angiosperms, and the recalcitrant relationships along the backbone of the order have been hypothesized to arise from the rapid radiation during the Cretaceous. Here, we show that coalescent-based methods do not resolve the backbone of Malpighiales and concatenation methods yield inconsistent estimations, providing evidence that gene tree heterogeneity is high in this clade. Despite high levels of ILS and gene tree estimation error, our simulations demonstrate that these two factors alone are insufficient to explain the lack of resolution in this order. To explore this further, we examined triplet frequencies among empirical gene trees and discovered some of them deviated significantly from those attributed to ILS and estimation error, suggesting gene flow as an additional and previously unappreciated phenomenon promoting gene tree variation in Malpighiales. Finally, we applied a novel method to quantify the relative contribution of these three primary sources of gene tree heterogeneity and demonstrated that ILS, gene tree estimation error, and gene flow contributed to 15%, 52%, and 32% of the variation, respectively. Together, our results suggest that a perfect storm of factors likely influence this lack of resolution, and further indicate that recalcitrant phylogenetic relationships like the backbone of Malpighiales may be better represented as phylogenetic networks. Thus, reducing such groups solely to existing models that adhere strictly to bifurcating trees greatly oversimplifies reality, and obscures our ability to more clearly discern the process of evolution.

evolutionary biology↗