Study of morphological variation within Hypericum prolificum - PowerPoint PPT Presentation

Jul 19, 2023 •271 likes •493 views

Study of morphological variation within Hypericum prolificum (Hypericaceae) Anni Poetzl, 64cm Arizona State University Mentor: Dr. George Yatskievych Royal Gorge, Iron County Outline Introduction Classification Hypericum

Study of morphological variation within Hypericum prolificum (Hypericaceae) Anni Poetzl, 64cm Arizona State University Mentor: Dr. George Yatskievych Royal Gorge, Iron County
Outline  Introduction  Classification  Hypericum prolificum  Methods  Measurements  Field work  Analysis  Results  PCA  Mclust: Principal components  Mclust: Raw data  Discussion Analysis   Further studies  Acknowledgments
Hypericaceae  Sect. Myriandra (Robson, 1996)  Hypericum prolificum L. H. perforatum L. H. punctatum Lam. H. lobocarpum Gatt. H. mutilum L.
Distribution
Habitat Big Buffalo Creek, Ripley County
Rock Bridge State Park, Boone County Prairie Garden Trust, Callaway County Near West Plains, Howell County Royal Gorge, Iron County
LINN 943.20 MO 957079
Controversies  Svenson (1940, 1952)  Type specimen: a specimen upon which a taxon is based  Fernald and Schubert (1948)  Adams (1962)
Morphology
Hypothesis  H. prolificum is one species.  Morphometric analysis Near Clearwater Lake, Wayne County
Measurements Characters (mm)  1 measurement of a Leaf length particular character= mean of ten measurements per Leaf width specimen Length to broadest  **5 measurements point on leaf  33 herbarium specimens Internode length Apical notch length Petiole length Pedicel length Bract length Filament length** Sepal length Petal Length
Herbarium specimens and high-resolution images of sheets • Ruler, optical micrometer, ImageJ •
Analysis program  R  prcomp  Mclust
Principal Components in 3D
Mclust: clustering models Model 3-letter acronyms: Spherical Diagonal Elliptical  1st letter:Volume EII EEI EEE  2nd letter: Shape VII VEI VVV EVI EEV  3rd letter: Orientation VVI VEV Letter: E: Equal V: Variable I: NA (spheres); coord. axis (diagonal) (Fraley et al. 2012)
Mclust: Best-fit models  Fits 10 models to the data for up to 9 mixture components (clusters)  Gives Bayesian Information Criterion (BIC) values  Model with absolute value closest to zero is the best-fit cluster model.
Mclust for Principal Components
Mclust for Raw Data
 Null hypothesis was supported.  Highly variable  3-dimensional plot: random dispersion  Mclust: PCA-Best-fit models for one mixture components  Mclust: Raw data-Best-fit models for one mixture of components  More specimens/more characters to make a clear distinction, especially for Mclust with raw data
Hypericum lobocarpum NW of Donaphan, Ripley County
Acknowledgments  David Bogler  Henry Domke and Paul McKenzie  Iván Jiménez  The following herbaria for lending sheets used in the analysis: F, MO, NY, UMO, UARK  The REU interns, especially Chelsea Pretz, Megan Ruffley, & Joel Swift  National Science Foundation  Missouri Botanical Garden
Thank you

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