Lichens in Diverse Landscapes (LDL): Modules, Materials, NEON & Environmental Datasets

In the LDL project, students will use a combination of modules on field data collection, geospatial analysis, and statistical analysis to examine how lichen species, which are an important group of bioindicators, are influenced by abiotic and biotic drivers.

An overview of the LDL project and all activities can be viewed in this Adobe Spark webpage. The training webinar presented for this project from August 13, 2020 is also available for viewing that the link below:

Training Webinar, August 13, 2020 (Closed Captioning Available)

Module Overviews – The LDL project has three modules that are filled with individual activities that can be adopted or omitted by instructors. The core modules (Module 1 – Spatial Analysis and Module 2 – Field Data Collection) contain instructions on how to analyze geospatial data e.g. NEON site data and environmental pollution data (including nutrient deposition) and to collect local field data on lichens. Module 3, which focuses on data analysis, is designed to help students learn how to explore and analyze data collected in the first two modules. Instructors can pick and choose individual components of modules that they would like to choose in their classroom or conduct the entire field data analysis module and be a part of a larger EREN citizen science project.

Each of the modules is available to download using the links below. You can download the modules and guides as a PDF or Word format to access when not in contact with the internet or phone service.

NEON & Environmental Datasets – To complete some of the module activities, we have included sample data that we downloaded and cleaned up from NEON and several other sources. This datafile (entitled NEONcomplete.csv) is available in comma separated value format for ease of use in Google Sheets, Microsoft Excel, and R.

Module 1 – Spatial Analysis of Biological and Environmental Data 

 Module 2 – Lichens in YOUR Local Landscape 

 Module 3 – Data Analysis for NEON and Local Field Data

Assignment ideas (templates, examples, rubrics)

Link to Assessment Materials

Alignment with the four dimensions of the 4DEE framework – This project was destined to fit within the four dimensions of the 4DEE framework and that of existing EREN projects. In the first part, students will explore NEON data to examine relationships between lichen presence and percent cover (NEON.DP1.10058) and environmental (NEON DP1.00013.001) and land-use variables. Students will also evaluate other relevant geospatial datasets, such as air quality (EPA) and tree cover (Global Forest Watch), that provide additional explanatory variables. Based on their geospatial queries, students will generate hypotheses for testing with field collected or online data sources. In the second part, students will collect field data on lichen percent cover and other relevant variables (e.g., canopy cover, bark pH). This will be flexible in that it can be done in small urban spaces (e.g., street trees), backyards, campus settings, or local natural areas. Students will use dichotomous keys and iNaturalist as tools to hone species identification skills, use sampling grids to assess lichen percent cover, score air quality using lichens as bioindicators, and gather other field measurements using whatever tools are available to them (e.g., field compass, smartphone compass app). Students will use data collected locally and online to visualize and evaluate their hypotheses within and across sites. This approach combines multiple disciplines including botany, ecology, environmental science, and geospatial science to create a cross-cutting experience that provides an integrative holistic practice for students and faculty. In addition, this project will introduce students to free programs including GoogleSheets, ArcGIS Online, and R to share and analyze data.

Acknowledgements: This project was supported by the National Science Foundation under Grant No. DBI-2037827. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.