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Exploring how climate will impact plant-insect distributions and interactions using open data and informatics

Author(s): Wendy Clement1, Kathleen Prudic2, Jeffrey Oliver3

1. The College of New Jersey 2. University of Arizona 3. University of Arizona Libraries

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Summary:
This teaching module provides an entry point for students to learn about data science, open data repositories (e.g., citizen science data), and species distribution modeling to study the effects of climate change on butterfly-host plant interactions.
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Description

Open data repositories, including those from citizen science efforts, are rich sources of research grade data that are becoming key to asking and answering questions in ecology. Simultaneously, informatics tools are becoming increasingly accessible to the non-specialist and are more commonly integrated into the college curriculum of biology students. This series of 3 classes (~360 minutes of in class activity time) guides students on how to collect, curate, and analyze citizen science data using common research computing tools: R, RStudio, Git, and GitHub. These are in silica experiments examining (1) the species distributions of butterflies and their host plants based on observations submitted to the web platform iNaturalist and (2) how those distributions may change in the future due to global climate change. Students will download and install software, retrieve and curate citizen science data, model the occurrence data to produce a species distribution of butterfly and host plant, and develop hypotheses on how climate change may or may not affect the future distribution of butterfly and host plant. Students then test these hypotheses using estimates of future climate variables, evaluate the strength of their results, and present a summary of these explorations to their peers using additional class time if desired. This series of experiments will result in 4 group products and 1 individual product for evaluation. This module is versatile and work on any pair of species of interest and analyze data from a variety of open data sources.

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