Several lessons in this curriculum use computational models designed using a piece of software called NetLogo. In this lesson, we will try to understand what these models are and how to use them.

This lesson specifically focuses on learning science with computational models of emergent natural phenomena. Emergent phenomena are ones in which simple interactions between agents and their environment result in complex patterns. Ffor example, a flock of birds (see below). 

In a flock of birds, most people assume that the "head" bird is a leader of the flock. However, flocks actually emerge from each bird following a simple set of rules regarding alignment, coherence and separation with neighboring birds. This means that the shape of a flock is emergent and not directed by any particular leader bird.

Learning Goals - 

• In this lesson, we will use a NetLogo model about Forest Fires to learn about how to computationally study a scientific phenomenon.
• We will learn how to engage in the scientific inquiry practices of constructing knowledge.
• We will learn how to engage computational thinking practices. We will focus on four computational thinking practices: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices.

Let's get started!

Scientists use scientific modeling approaches to construct knowledge about the world. In this section, we explore the ideas behind scientific models.

Here's a version of a fire model that a team of researchers tried to modify, but it does not run as they expected. In fact, it's totally broken and does not run at all.

Can we help them fix it?

Let's investigate how the density of the trees affects the spread of a forest fire. 

We will first generate some data using the model and then visualize it using another computational tool called CODAP.

Let's follow an experimental design that is described below:

Research Question: How does density of trees in a forest affect spread of a forest fire?

Hypothesis: As the density of trees in the forest increases, the percentage of forest burned will increase linearly. (That means, if density of trees doubles, the percentage of forest burned will also double)

Let's test our hypothesis using the model.

Change the values of density systematically. Record the value of 'percentage forest burned' in the data table. Make sure that you press 'setup' button every time you do a trial. Make sure to run each different value of density twice and finally, make sure you record values for each experimental trial. 

CODAP will automatically plot the average of the two values that you will record. 

In this lesson, you will study a phenomenon about a population of rock pocket mice. You will explore a computational model of a population of rock pocket mice and observe changes in the population over time. You will be able to explain how the color of fur coat of mice changed because of a change in the environment where they lived. 

The American Southwest is a fantastic place to study rock pocket mice with different fur coat colors. Ancestral pocket mice had light-colored fur coats that blended in with the rocks and sandy soil that was prevalent in the region. This kept the mice hidden from their predators (mainly owls).  Then, a series of volcanic eruptions spewed a river of black lava more than 40 miles long that wove right through the middle of pocket-mouse territory. Lava flows created huge patches of dark rock among the surrounding light-colored sand.

Today there are now two forms of pocket mice:

• light-colored mice that live on sandy soil
• dark-colored mice that live on black lava rock

Researchers noticed that rock pocket mice with a dark fur coat were more common on the dark lava flows, whereas the mice with light colored fur coat were more common on the light-colored sand. How might this have happened? 

In the next few lessons we will investigate this case of pocket mice evolution using computational models.

Let's start by watching a video developed by HHMI Biointeractive about this phenomenon.

Here is a computational model of a population of pocket mice. 

The rules of interactions between the pocket mice are similar to the Hardy-Weinberg activity that you must have already done in class. 

Each clock tick in the model is a mouse-generation. In each generation, male and female mice move around randomly, search for a partner, and reproduce if they find a partner. The heritable trait that is modeled here is fur-coat-color of the mice. 

Explore the model and answer the questions below.

Let's investigate how genotype frequencies change over time in this population.

The five conditions for Hardy-Weinberg law of genetic equilibrium are:

1. The breeding population is large.

2. Mating is random.

3. There is no mutation of the alleles. 

4. No differential migration occurs.

5. There is no selection.

In this lesson you will learn the basic idea of a Hardy Weinberg Equilibrium. You will also calculate phenotype frequencies both mathematically and computationally (using a computational model).

This model is same as the one that you used in the previous lesson. Just play with the model for a few minutes. Change the parameters and make observations.

In this lesson, you will investigate how natural selection affects the genetic constitution of a population over time. You will design and perform an experiment about natural selection in the case of rock pocket mice using a computational model to test your hypothesis.

This model is similar to the previous model. Just play with the model for a few minutes. Change the parameters and make observations.

In this lesson, you will investigate how genetic drift affects the genetic constitution of a population over time. You will design and perform an experiment about genetic drift using a computational model to test your hypothesis.

You have already used this model. Answer the questions below to refresh your memory.

The purpose of this lesson is to learn how the combination of mutations, natural selection, and environmental change generate progressively better-suited adaptations.

Purpose  
So far we have studied how environmental changes affect population of species. In this lesson, we will explore how new species emerge.

Brainstorm
You know that many species that were alive in the past have gone extinct.  Many of the species that are alive today did not exist at one point in the past.  

There are 13 species of finch on the islands, but they are at once both so similar and so diverse that they have provided a fertile ground for exploring evolution since Darwin’s 1835 visit.  Darwin himself did not realize their role in explaining evolution until after ornithologists revealed the abundance of speciation to him.

Let's watch a video developed by Howard Hughes Medical Institute based on data collected by Peter and Rosemary Grant, Princeton University.

The finches are proposed to have arrived on the volcanic islands from the South American mainland and are now considered part of the tanager family rather than the finch family.  There are four genera recognized in the group, and the species occupy overlapping but distinct ecological niches. In the genus Geospiza, there are six species.  In good times, they often eat the same foods, but in times of scarcity, each species has a specialized niche – large seeds, cactus fruits, etc. – on which they rely.  Their mating behaviors, such as times and songs, differ greatly, maintaining the distinct species.  The ecology of the different islands influences which species live on each island, and especially which species co-exist on an island.  Gene flow between islands occurs with occasional immigrants depending on storms and the distance between islands.

For several decades, scientists have gone to the Galapagos islands to study the physical characteristics of the finches there.  They recorded data on many traits including beak dimensions and weight.  In this lesson you will explore some of that data to understand the processes underlying speciation and adaptive radiation.

Below is a data analysis tool called CODAP created by educators at the Concord Consortium. Using this computational tool, you will be able to delve deeply into the finch data mentioned on the previous page. When the page loads you will see the basic finch dataset with columns for sex, weight (g), beak length (mm), beak depth (mm). In CODAP we are able to interact dynamically with the data, allowing us to make connections and draw conclusions. We will use this set to answer several questions about these Galapagos finches.

The scientist that have collected this data have done so for over 40 years now, the first bar graph that you saw in section one contains finch data from 1973 -1981. Lets see what we can find if we look deeper into the data.

In this data set, "Last Year" is the record of the last year an individual finch was seen by the researchers.  This typically means that the individual finch died during that year.

Use the methods you learned in the last activity to compare the finches that died during 1977 with the finches that survived 1977 and answer the questions below.

This CODAP frame has a much larger data set than those you have explored in the previous activities.  To help you gain a better understanding of the finches in the Galapagos, there are many more physical traits to explore.  Use the skills you developed in the previous activities to use CODAP to look at several traits and compare them across species and locations.

Not all of the traits were measured on each individual, so some traits will have more complete information than others.  We will focus on a trait that has a lot of data points, beak height.

The purpose of this activity is to understand how new species can form from old species through the mechanisms of evolution covered so far in the unit (mutation, genetic drift, changes in environmental conditions, and natural selection).

Purpose  
What are other ways that new species can form?

Brainstorm
Last lesson you looked at species formation over time based on geographic separation. Is this the only way that new species can form?

Your teacher will play this video for the class. It is provided here just for your reference.

• Press SETUP. and Press GO/STOP to run the model. 

• You can switch the VISUALIZE-TIME-STEPS to “years” or “days”. Years runs faster, but days lets you see the actual difference (if any) in flowering times between plants. 

• Run, the model for at least a hundred years.

• Analyze the FLOWER-TIMES graph.

• Record your data in your Observation section.

• Press SETUP. and Press GO/STOP to run the model. 

• You can switch the VISUALIZE-TIME-STEPS to “years” or “days”. Years runs faster, but days lets you see the actual difference (if any) in flowering times between plants. 

• Run, the model for at least a hundred years.

• Analyze the FLOWER-TIMES graph.

• Record your data in your Observation section.

Purpose

Where do new species come from?

Predict

In the next model run you will allow both flower time mutations and metal tolerance mutations to occur in the offspring.

1. Press SETUP. and Press GO/STOP to run the model.Keep the model running until the SIMULTANEOUS FLOWERING graph shows that the number of simultaneously flowering plants in the contaminated soil and in the regular soil, is very close to zero.

2. Now make sure you have switched back VISUALIZE-TIME-STEPS to “days” mode and keep the model running.Below, record what you notice about when the flowers on the left side of the ecosystem are blooming versus the flowers on the right side of the ecosystem.

In the next set of questions you will try to explain why the flowers on the right have a different flower time than those on the left.  To do this you may want to rerun this previous exploration in “days” mode, change labels, and study the model graphs.  Feel free to conduct new experiments in this exploration to help you understand and explain why the initial plant population has “speciated”.

Where do new species come from?