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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.
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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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