| Abstract Detail
Education and Outreach Klein, Laura [1]. Digital leaf morphology in the classroom. Morphology is an important biological feature used by scientists to describe and categorize species, diagnose biotic or abiotic environmental effects on phenotype, or infer developmental processes, among other uses. Digital leaf morphometrics, which uses scanned images of leaves, is one of many new ways technology is being integrated to perform powerful analyses on morphological data. After using this technique for primary research, we then expanded digital leaf morphometrics as a lab activity in an undergraduate plants and fungi course. The major objectives of this lab were to help students: a) understand the function and value of morphometric analyses; b) become familiar with data collection and analysis programs; c) describe morphological variation using digitally-collected data; and d) interpret results from data collection. We used images of grape leaves from a common garden plot of Vitis riparia and V. rupestris at the Missouri Botanical Garden, used for research purposes to determine if leaf morphology can be used to distinguish between species, within species, and among individuals. Students used free, open source computer programs and an existing protocol to collect homologous points (i.e., landmarks) on grape leaf images of V. riparia and V. rupestris to describe and quantify differences in morphology using landmark analysis. Digital morphometrics is an inexpensive, accessible method of describing morphological variation that can easily be developed for course-based undergraduate research experiences (CUREs) in many different systems.
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1 - Saint Louis University, Biology Department, 3507 Laclede Ave, Saint Louis, MO, 63103, United States
Keywords:
morphology
geometric morphometrics
digital imaging
Research experiences
undergraduate mentoring.
Presentation Type: Oral Paper
Session: EO1, Education and Outreach I
Location: Tucson C/Starr Pass
Date: Monday, July 29th, 2019
Time: 11:15 AM
Number: EO1013
Abstract ID:1024
Candidate for Awards:None |
