Using new tools to reveal secrets of mega diverse chewing lice genus Myrsidea

Wednesday, November 16, 2022

4:54 PM – 5:06 PM PT

Location: Vancouver Convention Centre, Meeting Room 118

Presenting Author(s)

Stanislav Kolencik (he/him/his)

Postdoctoral Researcher
University of Nevada, Reno
Reno, Nevada

Co-Author(s)

RL

Raphael LaFrance

University of Nevada
Reno, Nevada
ES

Edward L. Stanley

Florida Museum of Natural History
Gainesville, Florida
IB

Isabella Burgos

University of Nevada
Reno, Nevada
JM

Julie M. Allen

University of Nevada
Reno, Nevada

Chewing lice in the genus Myrsidea represent one of the most diverse genera of lice. Myrsidea has a global distribution and almost 400 known species, ~80% of which only parasitize a single avian host species. Over the last two decades there has been increased effort in understanding the diversity of this genus. For example, it is expected that in Brazil alone, there are more than 900 undescribed species. While most of the known species have been described using only morphological traits, over the last two decades several phylogenetic studies have helped to understand the diversity and speciation of this genus. However, many of these phylogenetic studies lack in-depth analyses of morphological traits resulting in almost 300 specimens described only to genus level. Here we present new tools for understanding the biodiversity of this genus. First, a machine learning model to extract a comprehensive list of morphological characters from species descriptions, and thus, increase the effectiveness of species descriptions and use of all characters. Second, we present microCT scanning as a new emerging tool for studying traits which were not possible using traditional slide microscopy, including 3D measurements such as volume of body, head, muscles, and mandibles, or using mesh data for three dimensional geometric morphometric analyses. We highlight these tools as methods for innovating how taxonomy and systematics are conducted.