Contact: Dr. Danielle Garneau (SUNY-Plattsburgh), Dr. Matthew Heard (Belmont University), Dr. Mary Beth Kolozsvary (Siena College): eren.lichen@gmail.com
Initiated: 2020
Project Status: Currently accepting participants
Description
Welcome to Lichens in Diverse Landscapes (LDL)! In this 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 entire project and all activities can be viewed in this Adobe Spark webpage.
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.
Training Webinar, Recorded on August 13, 2020 (Closed Captioning Available)
Hypotheses/Objectives: To examine how abiotic and biotic factors impact the presence, abundance, and distribution of lichens in diverse landscapes.
Summary of Methods: 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.
Expanded Project Info: LDL Modules, NEON & Environmental Datasets
Curriculum: LDL curricular materials (Assignment ideas: templates, examples, rubrics) are designed to achieve these Student Learning Outcomes:
- Gain experience in species identification, morpho-species classification, and how to make abiotic and biotic measurements in field settings.
- Develop technical skills using modern technology (smartphone apps, spreadsheet programs, data analysis packages) and everyday materials (ruler, freezer baggies, Sharpie permanent marker) to make scientific measurements and analyze data.
- Examine 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.
- Understand how data gathered from multiple projects (e.g., National Ecological Observatory Network – NEON, National Land Cover Data – NLCD, air quality – US Environmental Protection Agency – US EPA, iNaturalist) can be combined to investigate ecological questions at multiple spatial scales.
Other Project Materials:
Publications will be linked here when available.