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The history of DEMO: An experiment in regeneration harvest of northwestern forest ecosystems. Northwest Science 73:3-11.


Coping with Change Through Innovation: New Approaches, Tools, & Collaborations

The Evergreen State College, Olympia, Washington - March 27-30, 2018

MAPS: Evergreen State College Area-Campus (2 pages),  Campus Trail-Parking

presented by Northwest Lichenologists

Mini-Foray: A campus forest hike to Evergreen Beach (2 miles round trip).  

Tuesday March 27, 2:30 - 4:30pm: Meet in F Lot near Beach Trail sign at back of parking lot.

Workshop: Crustose Lichens on Coniferous Bark in the Pacific Northwest 

Thursday March 29, 1:00 - 5:00pm:  Lab 1, Rooms 1040 & 1050

New Tools for Science in the 21st Century Northwest

Come see demonstrations of new field science technology, including demonstrations of LiDAR technology, drones, plant science, root cameras, and high-throughput phenotyping technology. A mix of presenters representing vendors, non-government organizations (NGOs), and academic labs will demonstrate their specialized, new cutting-edge technology. This is an interactive session so come prepared to ask questions about these tools and view activities.

Organizer: Dylan Fischer,

Applications in R programming for the Natural Resources

note: Class size is full as of March 9th registration!

Organizer: Matt Brousil,

R statistical software is a free tool that is widely-used for research in ecology, natural resources, and other scientific fields. Through guided introductions we’ll explore topics in data presentation and tools for research and publication using R.  This workshop is for participants that have previously used R for analyses and can import data and install packages, but are looking to expand the use of R in their work. 

  • A brief introduction to package creation in RStudio including GitHub archiving. If time allows, we can also explore document production (MS Word, PDF, and HTML) using RMarkdown.
  • R packages and techniques to make manuscript quality maps and associated graphs that highlight important statistical and spatial relationships in your data.
  • An applied approach to resolve common error messages and misuses of GLMMs (Generalized Linear Mixed Models) that can plague researchers attempting to get statistical results. GLMMs have their own sets of assumptions, pitfalls, and quirks that can create headaches if not accounted for.    
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