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Abstract Detail

Bryology and Lichenology

Scharnagl, Klara [1], Prather, Alan [1].

Using collections data to explore patterns of lichen diversity across the North American landscape.

Lichens are small but terrestrially ubiquitous organisms that serve as biological indicators of ecosystem health and global change. Mapping lichen distributions can help inform the role of lichens from the ecosystem to the regional scale, and collections data from lichen herbaria are a critical resource for the creation of robust distribution maps. As a component of my dissertation on the latitudinal gradient of lichen diversity, I am querying data from the Consortium of North American Lichen Herbaria (CNALH) to determine whether collections data support a North American latitudinal gradient of lichen diversity, and to investigate potential drivers thereof. Previous research of both opportunistic field sampling, and collections data in the western region of the United States provided a baseline for assessing latitudinal patterns of North American lichen diversity. In this study, I built upon this baseline by determining patterns of diversity for multiple fungal families using geo-referenced herbarium data from CNALH. I then compared these data to geo-referenced sequence data available on MycoBank and GenBank, and used a niche modeling approach in R to map lichen diversity across the North American landscape, determine effects of land use change, and predict lichen ranges based upon a combination of records data and niche modeling. I made corrections based upon taxonomic revisions, removed implausible locations data, and scanned for potential sampling bias in determining patterns of diversity across the landscape. This study supports the use of collections data for large-scale ecological and evolutionary studies in lichens.

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1 - Michigan State University, Plant Biology, 612 Wilson Rd, East Lansing, MI, 48824, USA


Presentation Type: Oral Paper
Session: 5, Bryology and Lichenology (ABLS) I
Location: Sundance 2/Omni Hotel
Date: Monday, June 26th, 2017
Time: 11:15 AM
Number: 5009
Abstract ID:38
Candidate for Awards:A. J. Sharp Award

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