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MODULE 3 (LIFE SCIENCE) INTRODUCTION
Module Name: Ecosystems as Complex Systems
Content of this Introduction:
1. Overview of the Module
2. Prerequisite knowledge and assumptions encompassed by the Module
3. Standards covered by the Module
4. Materials needed for the Module
5. Pacing Guides for 5 Lessons, including Learning Objectives and Assessment
Questions
1. Overview of the Module
This Life Science module begins with an exploration of a simple predator-prey model to
consider who eats whom—and what happens when one population grows faster than
another. After learning more about ecosystem dynamics, producers and consumers,
and interdependent relationships within an ecosystem, students develop their own
model of a local ecosystem.
The primary goal of this unit is to engage students in simple interactive activities to
explore ecosystems concepts, and in the use, modification, and creation of an agent-
based model of a simple virtual ecosystem.
2. Prerequisite knowledge and assumptions encompassed by the Module
This lesson assumes that the teacher has already introduced ecosystems concepts
such as a) the definition of an ecosystem, b) indirect interactions within ecosystems, c)
direct interactions between organisms in ecosystems, d) food chains and food webs, e)
energy flows in ecosystems, f) trophic levels and g) biomass in ecosystems. [See the
document “Ecosystems as Complex Systems” for background information.]
It is necessary to have completed Module 1 prior to commencing this module, in order
to have the necessary skills to perform the modeling required in this module.
3. Standards covered by the Module
Please see the Standards Document for a detailed description of Standards covered by
this Module, Lesson by Lesson.
4. Materials needed for this Module
You will need the following materials to teach this module:
- Computer and projector
- Ecosystems as Complex Systems background document [for reference]
- Rabbits and Grass base StarLogo Nova model
- Guided Introduction to StarLogo Nova document [for reference]
- CS Concepts guide document [for reference & student handout]
- StarLogo Nova Blocks Reference Guide [for reference & student handout]
- StarLogo Nova Blocks Reference Guide Module 3 [for reference & student
handout]
- Scientific Practices with Computer Modeling & Simulation document [student
handout]
- Experimental Design form document [student handout]
- Model observation form [student handout]
- Project design form [student handout]
- Model design form [student handout]
- Lesson plans for 5 lessons
- Slide presentation presentations with instructions
- New commands and concepts sheets for each lesson [student handout]
5. Pacing Guides for 5 Lessons, including Learning Objectives and Assessment
Questions (See following pages.)
DAY 2: Rabbits and Grass Model
Pacing Guide
Getting Started
(Review)
Review of the previous day’s lesson and concepts.
Connection to today’s lesson.
5 min
Activity 1
(Discovery)
Under the Hood: inspecting the Rabbits and Grass
model, variables, looping and execution order.
20 min
Activity 2
(Guided Practice)
Designing and running experiments: specify your
question, write up your experimental design and run
your experiments. (Review how to change a
parameter, add a slider, a graph, etc).
20 min
Wrap-Up
(Reflection)
How does experimental design with computer models
differ from experimental design without computers?
What does the computer model enable us to do that
would be difficult to do in the real world?
5 min
Learning Objectives: Students will…
Complex Adaptive
Systems
Make observations of ecosystems dynamics and change in population
sizes over time.
Disciplinary Core
Ideas
Growth of organisms and population increases are limited by access
to resources. Ecosystems are dynamic in nature; their characteristics
can vary over time. Disruptions to any physical or biological
component of an ecosystem can lead to shifts in all its populations.
Modeling and
Simulation
Ask a question and design an experiment. Conduct an experiment.
Make observations (drawing simple correlations).
Computer Science Decode a simple model. Trace a program’s execution.
Assessments of understanding:
Complex Adaptive
Systems
Is the Rabbits and Grass ecosystem a complex adaptive system?
Why or why not?
Disciplinary Core
Ideas
What are the three different outcomes seen in the rabbits and grass
model?
Modeling and
Simulation
What variables were we able to manipulate in Rabbits and Grass?
Give a good explanation of what happens when a simulation is run.
What does it mean if a model produces different outcomes each time I
run it?
Computer Science Diagram an execution loop showing what calls what in the Rabbits
and Grass model.
DAY 3: Adding a Predator
Pacing Guide
Getting Started
(Review)
Review of the previous day’s lessons and concepts;
connection to today’s lesson.
5 min
Activity 1
(Guided Practice)
Adding a predator, and running an experiment. What is
the impact of adding a top predator on the ecosystem?
20 min
Activity 2
(Guided Practice)
Running an experiment. What is the impact of adding a
top predator on the ecosystem?
20 min
Wrap-Up
(Reflection)
In the real world, what might impact how animals use
and gain energy? How can computer models be useful
in understanding ecosystems?
5 min
Learning Objectives: Students will…
Disciplinary Core
Ideas
Organisms, and populations of organisms, are dependent on their
environmental interactions both with other living things and with
nonliving factors. Ecosystems are dynamic in nature; their
characteristics can vary over time. Disruptions to any physical or
biological component of an ecosystem can lead to shifts in all its
populations.
Modeling and
Simulation
Design and conduct and experiment. Collect and analyze data to look
for patterns.
Computer Science Modify a simple computer model. Practice Pair Programming and
Iterative design, implement and test cycle. Learn CS concepts of user-
defined variables and subclasses or breeds.
Assessments of understanding:
Disciplinary Core
Ideas
How would you compare the health of the ecosystem with and without
a predator?
Modeling and
Simulation
What was the impact of adding a predator? How would you describe
the distribution of different outcomes?
Computer Science What is an example of how an IF/THEN was used in this model?
DAY 5: Designing and Running an Experiment and Sharing Your Findings
Pacing Guide
Getting Started Review of the previous day’s lessons and concepts;
connection to today’s lesson
5 mins
Activity 1 Finish implementing your model 20 mins
Activity 2 Running experiments with your model 20 mins
Wrap-Up Analyze the results of your experiments and discuss
your conclusions. Relate the results back to the bigger
issue of Ecosystems as Complex Systems. Prepare
your model and results for presentation.
5 mins
Learning Objectives: Students will…
Complex Adaptive
Systems
Revisit the concept of population growth and feedback loops and come
up with a possible feedback loop related to ecosystems. [The more fish
there are, the more baby fish they will produce.]
Disciplinary Core
Ideas
Gain a deeper understanding of ecosystem dynamics. They will learn
that organisms, and populations of organisms, are dependent on their
environmental interactions both with other living things and with
nonliving factors. Growth of organisms and population increases are
limited by access to resources. Ecosystems are dynamic in nature;
their characteristics can vary over time.
Modeling and
Simulation
Use their new model as a test bed to run experiments. Learn that the
results of their experiments can inform them of ways to further improve
their model.
Computer Science Follow the correct execution of their models and apply debugging
techniques to fix their code.
Assessments of understanding:
Complex Adaptive
Systems
Describe a feedback loop in ecosystems.
Disciplinary Core
Ideas
Describe how adding a predator can impact an ecosystem.
Modeling and
Simulation
What experiments did you run in the model and why? What real world
information could help you to improve the model? Complete the
“Scientific Practices with Computer Modeling & Simulation” document.
Computer Science Define debugging and give an example of some debugging you had to
do in your code [LO6].
Lesson Objectives
The student will: Learn characteristics of complex systems that relate to ecosystems [LO1] Experience population growth and limits to growth through a simulation [LO2] Graph different patterns of growth and learn to distinguish them [LO3] Learn the concept of a carrying capacity [LO4] Make observations of the behavior of a system using a computer model [LO5] Speculate as to why computer models can be valuable scientific tools [LO6]
Teaching Guide
Materials, Resources and Preparation
For the Students ● Computers ● Rabbits and Grass StarLogo Nova model ● Recycled printing paper
For the Teacher ● Large open space ● Computer and projector ● Rabbits and Grass StarLogo Nova model ● Large chart paper for collecting and graphing data and markers ● Piece of newspaper
Getting Started - 10 min
1. Ecosystems as Complex Adaptive Systems (excerpt from the document “Ecosystems as Complex Adaptive Systems”)
Start with a 10-minute review of ecosystems concepts using direct instruction
- An ecosystem consists of a specific area and all of the organisms in that area.
- All organisms take up some space, take in nourishment from the environment, and excrete waste, these are indirect interactions between organisms.
- Direct interactions between organisms include predation, competition, symbiosis,
- Whether direct or indirect, these interactions allow changes to ripple through the organisms and environment of an ecosystem, to affect many others types of organisms.
- The difference between food chains and food webs.
- Energy flows in an ecosystem from producers to consumers
- Trophic levels and biomass. At each step along the chain of producers and consumers, less energy is available – and the biomass that is available gets smaller and smaller.
Then move on characteristics of complex adaptive systems seen in ecosystems: (CCC: Systems and Systems models)
- One of the characteristics of a complex system is that the behavior of some aspect of the system, seen as a whole, doesn't necessarily follow directly from an understanding of how the individual “parts” of the system work. In other words, “the sum of the parts is greater than the whole.” (We saw this in the Walk & Turn activity in Module 1.)
- Another characteristic of most complex adaptive systems is feedback. Feedback is a circular process in which a system's output is returned or “fed back” into the system as input. For example, if we look at the ecosystem of fish in a pond, where the fish are not being consumed by predators we see that as the population approaches the carrying capacity of the pond, the rate of population growth decreases. This happens via limits in required resources (e.g. oxygen in the water). So the increase in the fish population leads to a reduction in the necessary resources available to each member of the population, which in turn leads to moderation in the rate of increase in the population. We will see this type of pattern in the participatory simulation and models that accompany this module. (This type of feedback is called negative, or damping feedback.)
- Possibly most important, ecosystems often demonstrate emergent behavior. This is related to the first point, where the overall behavior turns out not to be obvious from the component behavior. In a high desert ecosystem, simply knowing that rabbits eat grass, coyotes eat rabbits, and mountain lions eat rabbits and coyotes, doesn't tell us much (beyond giving us a general sense) about the patterns in the respective populations over time – we really need to study the ecosystem as a whole. From the above, we can see that ecosystems are usually complex adaptive systems, as well.
Activity #1: Papercatchers - 25 min
In this activity, students will learn about population growth and limits to growth (DCI: LS2.A). Students will play the part of members of a growing population and experience limits to the growth of populations when resources are limited. Students will analyze different patterns of population growth (Practice: Analyzing and Interpreting Data) including exponential and logistic growth (CCC: Patterns) and will learn the ecosystem concept of carrying capacity (DCI: LS2.A).
2. Participatory Simulation ● Gather materials and set up a table and graph on a whiteboard or chalkboard. Label the x-axis with generations #1-10 and the y-axis with population size 0-50. Tell students we are going to participate in a participatory simulation called “Papercatchers.” ● In round 1 begin by asking all students to crumple up a piece of scrap paper then pick one person to represent the initial member of the population. In one color mark the table and graph with generation 0, population 1. When the instructor gives the next generation command, have the initial population member throw the piece of paper 2 feet overhead and attempt to catch it. If he/she succeeds, then he/she survives into the next generation and reproduces by selecting a student from the audience to join in the population. If he/she does not catch the paper ball, he/she does not survive and must sit down. Mark the table and graph with the new population at next generation. Repeat in this manner for several more generations while recording the population size and generation number after each throw. If the population crashes or becomes extinct, begin again, noting that sometimes populations will crash by chance when numbers are small. Once all members of the audience are standing, take a look at the graph and have student reflect on the pattern. ASK: What type of pattern do you see? ASK: What do you predict would happen if we could play with an unlimited number of people? (The result would be exponential growth / sometimes also called a “population explosion.”) (Practice: Analyzing and Interpreting Data) (Practice: Use mathematics and Computational Thinking) (CCC: Patterns) ● In round 2, place a large piece of newspaper on the floor. Tell students they will follow the same rules (if they catch their paper ball, they stay in the population and reproduce; if they drop their paper ball, they die and must sit down) but this time, there is an added constraint. To
Teaching Tip Encourage students to think about what is missing from the model or what is inaccurate in the model. [example: Rabbits giving birth to just one rabbit at a time.]
Wrap-Up - 5 min
6. Patterns of Growth in ecosystems. ● What growth patterns did you see in the rabbits and grass populations? ● Were there limits to growth? If so, what were they? ● If you were to study a real-world ecosystem, what kind of data would you want to collect?
Assessment Questions
● Name a characteristic of complex systems that can be seen in ecosystems [LO1] ● Describe and draw two growth patterns you saw in Papercatchers. [LO2, LO3] ● Describe what limited growth in the Papercatchers activity [LO4] ● Describe two outcomes you witnessed in the demonstration of the rabbits and grass model [LO5] ● Discuss why a computer model might be helpful in studying ecosystems [LO6]
Standards Addressed
NGSS Performance Expectations Ecosystems: Interactions, Energy, and Dynamics MS-LS2-1. Analyze and interpret data to provide evidence for the effects of resource availability on organisms and populations of organisms in an ecosystem.
NRC Disciplinary Core Ideas
Interdependent Relationships in Ecosystems DCI-LS2.A: Organisms, and populations of organisms, are dependent on their environmental interactions both with other living things and with nonliving factors. In any ecosystem, organisms and populations with similar requirements for food, water, oxygen, or other resources may compete with each other for limited resources, access to which consequently constrains their growth and reproduction. Growth of organisms and population increases are limited by access to resources.
Ecosystem Dynamics, Functioning, and Resilience DCI-LS2.C: Ecosystems are dynamic in nature; their characteristics can vary over time. Disruptions to any physical or biological component of an ecosystem can lead to shifts in all its populations.
NRC Scientific and Engineering Practice Standards
Practice 3: Planning and carrying out investigations 3E: Collect data about the performance of a proposed object, tool, process or system under a range of conditions.
Practice 4: Analyzing and interpreting data 4A: Construct, analyze, and/or interpret graphical displays of data and/or large data sets to identify linear and nonlinear relationships. 4B: Use graphical displays (e.g., maps, charts, graphs, and/or tables) of large data sets to identify temporal and spatial relationships. 4G: Analyze and interpret data to determine similarities and differences in findings.
Practice 5: Using mathematics and computational thinking 5B: Use mathematical representations to describe and/or support scientific conclusions and design solutions.
Practice 6: Constructing explanations and designing solutions 6A: Construct an explanation that includes qualitative or quantitative relationships between variables that predict(s) and/or describe(s) phenomena. 6D: Apply scientific ideas, principles, and/or evidence to construct, revise and/or use an explanation for real-world phenomena, examples, or events.
Practice 7: Engaging in argument from evidence
7C: Construct, use, and/or present an oral and written argument supported by empirical evidence and scientific reasoning to support or refute an explanation or a model for a phenomenon or a solution to a problem.
NRC Scientific and Engineering Practice Standards Practice 2: Developing and using models 2C: Use and/or develop a model of simple systems with uncertain and less predictable factors. 2E: Develop and/or use a model to predict and/or describe phenomena. 2G: Develop and/or use a model to generate data to test ideas about phenomena in natural or designed systems, including those representing inputs and outputs, and those at unobservable scales.
Practice 3: Planning and carrying out investigations 3B: Conduct an investigation and/or evaluate and/or revise the experimental design to produce data to serve as the basis for evidence that meet the goals of the investigation. 3D: Collect data or produce data to serve as the basis for evidence to answer scientific questions or test design solutions under a range of conditions.
Practice 4: Analyzing and interpreting data 4A: Construct, analyze, and/or interpret graphical displays of data and/or large data sets to identify linear and nonlinear relationships. 4B: Use graphical displays (e.g., maps, charts, graphs, and/or tables) of large data sets to identify temporal and spatial relationships. 4D: Analyze and interpret data to provide evidence for phenomena.
Practice 5: Using mathematics and computational thinking 5B: Use mathematical representations to describe and/or support scientific conclusions and design solutions. 5D: Apply mathematical concepts and/or processes (e.g., ratio, rate, percent, basic operations, simple algebra) to scientific and engineering questions and problems.
Practice 6: Constructing explanations and designing solutions 6A: Construct an explanation that includes qualitative or quantitative relationships between variables that predict(s) and/or describe(s) phenomena. 6B: Construct an explanation using models or representations.
NRC Crosscutting Concepts
1. Patterns: 1B: Patterns in rates of change and other numerical relationships can provide information about natural and human designed systems. 1C: Patterns can be used to identify cause and effect relationships. 1D: Graphs, charts, and images can be used to identify patterns in data. 3. Scale, Proportion, and Quantity 3A: Time, space, and energy phenomena can be observed at various scales using models to study systems that are too large or too small.
Lesson 1 - Student Activity #1 Guide
Papercatchers
Name: Date:
Instructions:
1) Copy the data collected during the Papercatchers activity into the table below. Note that
you may not be able to complete all 10 generations in class.
2) In this simulation of an ecosystem, what were the independent and dependent variables?
Generation Round 1 – no
paper
Round 2 -
newspaper
Round 3 – sheet
paper
Population size Population size Population size
3) Using different colored pens or markers for each Round, create line graphs of the
populations over time in generations. Make note of any carrying capacity or limits on
growth in each round.
Remember to label your axes.
Lesson Overview (New Learning and Exploration)
In this lesson students will participate in two activities that USE the Rabbits and Grass model. The first activity is a look under the hood at the model to understand what was included and left out of the model (abstraction). In the second activity, students will learn to design and conduct systematic experiments using the model as an experimental test bed. They will instrument their model to collect data, then analyze data and report out on their findings.
Teaching Summary
Getting Started – 5 minutes
- Review of the previous day’s lesson and concepts and connection to today’s lesson.
Activity #1: Looking under the Hood – 20 minutes (New Learning / Discovery)
- Familiar and New Command Blocks
- Decoding a model – looking for the parts and interactions between them
- What calls what? – execution of the program loop
Activity #2: Designing and Running Experiments – 20 minutes (Guided Practice)
- Experimental design
- Running experiments
- Collecting and analyzing data
Wrap-Up – 5 minutes
- What does computer modeling and simulation allow us to do that would be difficult to do in the real world?
Lesson Objectives
The student will: Decode a simple model of a complex adaptive system [LO7] Trace a program’s execution [LO8]
Lesson 2
Rabbits and Grass Model
50 minutes (1 day)
Ask a question and design an experiment [LO9)] Conduct an experiment using a computer model [LO10] Make observations (drawing simple correlations) [LO11]
Teaching Guide
Materials, Resources and Preparation
For the Students ● Computers
- Rabbits and Grass base model
- Model Observation Form
- Scientific Practices with Computer Modeling & Simulation sheet
- Experimental Design Form
- New Commands and Concepts sheet
For the Teacher ● Computer and projector ● PPT with simple commands ● Rabbits and Grass base model
Getting Started - 5 min
1. Review of previous day’s lesson and link to where we are going today ● What observations did you make while experimenting with the Rabbits and Grass model? ● What do you think is going on in the model? (before looking under the hood)
Activity #1: Looking under the Hood - 20 min
There are three major abstractions in any agent-based model: agents with rules that they follow, the environment in which they coexist, and time. In StarLogo Nova, the first two are easy to see – the agents are the different turtles and the environment is Spaceland.
