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Painting turtles: an introduction to species distribution modeling in R

Cooperative learning: Students can work through the exercises in groups, using either the painted turtle data or group-specific taxa (i.e., each group selects a taxon to model). This module addresses multiple, complex concepts – online data availability & access, spatial query & analysis, global climate models, theoretical concepts of SDMs, model fitting – any of which can be expanded depending on the instructor’s preference, group work can be especially beneficial for facilitating comprehension and may be more efficient.

Jigsaw: The module can be easily extended to allow each student/group to examine the distribution of a different species and explore data availability among taxa as well as different aspects of model fitting.
Format
Primary or BEN resource type
Secondary resource type
General Biology Core Concepts
Discipline Specific Core Concepts
Life science discipline (subject)
Keywords Geospatial/mapping skills, R/command-line skills, data manipulation and plotting of large spatial datasets, introductory distribution modeling, searching public databases
Audience
Intended End User Role
Language
Educational Language
Pedagogical Use Description Students use R to search, download, and plot the
distribution of painted turtles from the Global
Biodiversity Information Facility (GBIF), then implement the BioClim modeling algorithm to build an
occurrence-based species distribution model for painted turtles using the BioClim variables.
Primary Author Controlled Name
Primary Author Affiliation Department of Ecology, Evolution & Organismal Biology, Iowa State University
Primary Author email (acarter1@iastate.edu)
Rights TIEE, Volume 13 © 2017 - Anna L. Carter and the Ecological Society of America - Copyright Statement
Teaching Issues and Experiments in Ecology (TIEE) is a project of the Committee on Diversity and Education of the Ecological Society of America.
This page was originally published on 1 November 2017 and was last revised 1 November 2017.
Date Of Record Submission 2017-11-27

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