Modeling Ecosystems Computationally - Preview

Modeling Ecosystems Computationally

Subject: Biology
Time: 5-7 classes, 45-50 min each

Introductory High School Biology

Unit Overview

Students will develop an understanding of how to develop a computational model starting from basic intuitions about individual behavior, then gradually and iteratively making improvements and adding complexity.  They will begin by using block-based programming tools, but will eventually transition to simple text-based programming.  Finally, they will learn how to critically evaluate models for their realism and usefulness using data from the Isle Royale ecosystem, and use the model they built over the unit to make predictions about the future of that ecosystem.

Specifically, this unit uses the NetTango block-based programming interface and the NetLogo programming language for many of the lessons.  These tools will help facilitate discovery of important concepts in ecology and modeling including basic predator-prey population dynamics, evaluating models, and predicting ecological impacts.

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Next Generation Science Standards
  • Life Science
    • [HS-LS4] Biological Evolution: Unity and Diversity
    • [HS-LS2] Ecosystems: Interactions, Energy, and Dynamics
  • NGSS Crosscutting Concept
    • Patterns
    • Systems
    • Stability and Change
  • NGSS Practice
    • Using Models
    • Conducting Investigations
    • Analyzing Data

Computational Thinking in STEM
  • Modeling and Simulation Practices
    • Using Computational Models to Find and Test Solutions
    • Using Computational Models to Understand a Concept
  • Computational Problem Solving Practices
    • Troubleshooting and Debugging
  • Data Practices
    • Analyzing Data
    • Manipulating Data
    • Visualizing Data
  • Systems Thinking Practices
    • Investigating a Complex System as a Whole
    • Thinking in Levels
    • Understanding the Relationships within a System


This work is supported by the National Science Foundation (grants CNS-1138461, CNS-1441041 and DRL-1020101) and the Spencer Foundation (grant 201600069). Any opinions, findings, conclusions, and/or recommendations are those of the investigators and do not necessarily reflect the views of the funding organizations.

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