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Lichen Niches and Distributions Lab Activity

Author(s): Jessica Coyle

Saint Mary's College of California

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Summary:
This is a 2-part lab activity based on the EREN-NEON Flexible Learning Project: Lichens in Diverse Landscapes. Students conduct field surveys of lichen abundance near their homes and explore large-scale geospatial data on lichens and the environment.

Licensed under CC Attribution-ShareAlike 4.0 International according to these terms

Version 1.0 - published on 14 May 2021 doi:10.25334/SBG4-RM36 - cite this

Adapted from: Lichens in Diverse Landscapes: EREN-NEON Flexible Learning Project v 1.0

Keywords

  1. linear regression
  2. GIS
  3. NEON
  4. air quality
  5. National Ecological Observatory Network
  6. Lichen
  7. field based methods
  8. EREN lichen
  9. airpollution

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Description

This lab activity implements Modules 1 and 2 of the EREN-NEON Flexible Learning Project: Lichens in Diversity Landscapes. Please see the project webpage for links to their materials: http://erenweb.org/eren-neon-flexible-learning-projects/lichens-in-diverse-landscapes/

This lab activity adaptation includes teaching notes and all necessary instructional materials for students. It includes three data sets based on publicly-available data that have been prepared for student use, along with instructions and R scripts for downloading preparing these data.

Learning Objectives:

  • Explain how a species’ niche affects its distribution.
  • Gain experience in species identification, morpho-species classification, and how to make abiotic and biotic measurements in field settings.
  • Explore how bioindicator organisms can be used to assess land-use change, air pollution, and other environmental impacts.
  • Explore how to use multi-site collaborative data to conduct regional and continental scale analyses to address ecological and macroecological questions.
  • Conduct an observational study to distinguish among multiple hypotheses.
  • Display relationships among categorical or continuous variables using figures that are appropriate for the data types.
  • Evaluate whether a relationship exists between two variables using a statistical analysis that is appropriate for the data types.

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