Students will develop an understanding of how populations interact with each  other within a community, discussing ideas concerning carrying capacity, competition, and interdependence. From there students will use models to explain the connection between genetic drift, natural selection, and speciation.

Modeling Interactions in Ecosystems

Purpose

What are the types of interactions that affect populations of organisms in an ecosystem?

Procedure

Brainstorm a list of organisms you might find in each of the forests that are pictured below.

Populations of organisms are often heavily influenced by their environments.  However, other populations within an environment can also have a large influence.  In the questions below, you will examine some of the ways in which populations can influence each other.

Background Information

Ecosystems are often difficult to understand because they usually include interactions between a large number of species. Isle Royale is different. It is a relatively simple island ecosystem, located 24 km from the shore of Canada in Lake Superior.

While there are many types of small animals on the island, and almost 20 types of mammals, only two species of the mammals that live on the island are relatively large. These are the wolves and the moose. On this island, wolves are the only predator of moose, and moose are essentially the only food for wolves.

To understand nature, it helps to observe an ecosystem where human impact is limited. On Isle Royale, there are no towns and people do not hunt wolves or moose or cut the forest. It is a very rare place on the planet where wolves, their prey, and the plants that support the prey are all left unharvested by humans. Isle Royale is remarkable, because nature runs wild there.

Moreover, because the wolves and moose on Isle Royale are isolated from the mainland by the surrounding water, they are unable to leave and new individuals are unable to come to the island except in very rare cases.  Since scientists began observing the island in 1959, only one migration has occurred, which was a single wolf joining the island.  Therefore, any population changes we might observe therefore are not the mere wanderings of wolves and moose to or from the island.

You're going to make some predictions about how the size of the wolf and moose population might change over time.

Scientists counted the number of wolves and moose on Island Royale every year starting in 1959. Here is the data for 1959 and for 2010.

In order to compare data from two populations, scientists plot the data using two different y-axes on the same graph. The y-axis on the left side of the graph below is the scale for the size of the wolf population. The y-axis on the right side of the graph below is the scale for the size of the moose population.

Based on the information you have been provided and the questions you have thought about, brainstorm a possible set of behavior rules for the wolves and moose and record them using the questions below.  Don't worry about trying to come up with the 'right answer'.  There are many possible answers which are valid, so just try to come up with a plausible, reasonable set of rules.

Discuss your rules with your classmates.  Have some students behave as wolves and others behave as moose according to the rules established by the class, and think about what observations you can make about the ecosystem.

The model that we will end up using gives the same simple instructions to each type of organism, in this case moose, wolf, and plant.

What simple rules could you give to each of these types that would lead to a natural ecosystem? Think of simple commands like move, direction etc,,

Remember all organisms of the same type will follow the same rules.

Based on your observations during the class simulation, do you have new thoughts about the population trends on Isle Royale?

Sketch the population trend you expect to see below.

Now that you have worked on developing a model of the Isle Royale ecosystem, you may have gained some insight into how and why models are useful in a variety of contexts.

The original purpose of this activity was to answer: "What type of interactions affect populations of organisms in ecosystems?"

Students will develop an understanding of how populations interact with each  other within a community, discussing ideas concerning carrying capacity, competition, and interdependence. From there students will use models to explain the connection between genetic drift, natural selection, and speciation.

Think back to the activity in Lesson 1 where you modeled a simple ecosystem consisting of predators (wolves) and prey (moose).  As you may have observed during that activity, predator and prey populations oscillate  over time.  A graph of population data from Isle Royale is shown below as an example of these oscillations.  Two scientists (Lotka and Volterra) modeled these oscillations using differential equations.  A graph of these equations is provided for comparison.

The equations used in this model state that these population oscillations are based on the birth and death rates of the predators and prey.  The model also claims that the prey death rate and the predator birth rate are proportional to the number of prey caught by the predators.

It is often helpful to understand how a model works before using it to investigate a phenomenon.  In this activity, you will look at and interpret some of the underlying code that governs how the model runs.  If you want to investigate beyond the questions in this activity, you can look at the complete code of any NetLogo model by clicking on the tab labeled 'NetLogo Code'.

All of the models you will see throughout this unit will have some things in common.  For example, every model has individuals called agents which interact with each other in a variety of ways based on rules that govern their behaviors.  Also, in every model, time moves forward in short steps called ticks.  At the end of each tick, the model uses the agents' rules to calculate what the state of the model will be in the next tick.  This means that to understand how the model works, it will be important to understand how the agents behave.

This model has two main types of agents, which are wolves and moose.  The questions below will address how each of them behaves. 

There are two main variations to this model that you will be working with.

In the first variation, wolves and moose wander randomly around the landscape, while the wolves look for moose to prey on. Each step costs the wolves energy, and they must eat moose in order to renew their energy. If wolves are unable to catch enough moose , they will die. At each time interval both wolves and moose have a fixed probability of reproducing, depending on the corresponding sliders.  This form of the model follows the same assumptions as the Lotka-Volterra equation-based model described earlier.

Basics of the model

1. Make sure to select the "moose-wolves" option in the model version dropdown menu.

2. Click the SETUP button.  You can click this button at any time to reset the simulation to its initial settings.

3. Press the GO button to begin the simulation.  If you press the GO button while the model is running, this will pause the simulation.

4. Look at the monitors to see the current population sizes.

5. On the top of the world view you will see the word “ticks” with a number next to it. Each tick represents a unit of time passing by.

6. Look at the POPULATIONS plot to watch the populations fluctuate over time.

The behaviors of these organisms are influenced by changing the values of the green sliders. Go ahead and mess around with the model and sliders to get used to interacting with it.

Before answering the questions, let the simulation run for at least 300 ticks and observe the interactions of the wolf and moose over that time.

As stated earlier, a stable system will tend to have a relatively steady population over the course of time, and an unstable system will eventually result in the extinction of one or more of the populations.

Your challenge at this point is to turn the "moose-wolves" ecosystem (which is based on the Lotka-Volterra equation-based model) into a stable system that allows for continuous generations of both wolves and moose for at least 300 ticks.

The variable "moose-wolves" must be selected in the model version dropdown menu.

You should only spend about 5-10 minutes on this activity.  Don't worry if you aren't able to find a way to stabilize the system.  Whether you stabilize it or not, describe your thought process by answering the questions below.

In the previous activity, you probably weren't able to find a way to stabilize the ecosystem for 300 ticks.  That is perfectly fine!  In fact, it is not possible to make that ecosystem stable over long periods of time.  In order to do that, there need to be some changes to the model itself, which you will explore next.

The second variation includes plants in addition to wolves and moose. The behavior of the wolves is identical to the first variation, however this time the moose must also eat plants in order to maintain their energy, and if they don’t they will die. Plants that are eaten by moose will regrow after a fixed amount of time depending on the plant regrowth time slider.

Select "moose-wolves-plant" in the model version dropdown menu. Keep all other settings the same.

Hit "setup" and "go" to start the simulation. Let the simulation run for 300 ticks before pausing the simulation by hitting "go" again and answering the questions.

Find three different ways that you can manipulate the simulation so that both populations die off without changing the initial population of either wolves or moose. When you figure this out, describe the variable(s) that you changed for each situation, and then explain why you think both populations were not able to survive.

Make sure to select "moose-wolves-plant" in the model version dropdown menu.

At the beginning of the lesson, you were introduced to an equation-based model of population dynamics.  In that model, population oscillations are based on the birth and death rates of the predators and prey, and the prey death rate and the predator birth rate are proportional to the number of prey caught by the predators.

Here you will evaluate and compare this equation-based model and the NetLogo model.

Students will develop an understanding of how populations interact with each  other within a community, discussing ideas concerning carrying capacity, competition, and interdependence. From there students will use models to explain the connection between genetic drift, natural selection, and speciation.

Purpose

What causes competition between individuals in an ecosystem?

Overview

In class today, you will participate in a competition against your classmates.  Understanding ways in which competition occurs between individuals in a population is necessary to understand how complex interactions lead to changes in populations over time.

First, open up the HubNet Client 6.0.2 application. A window will pop up asking for you to enter a username (you can just use your name) and a "Server" which which is a string of numbers and decimal points (called an IP address) that your teacher will give you. Once you've entered both of those, go ahead and click enter.

Model Rules

Let's make sure you're familiar with how the model works. In this model, each student controls a single bug. By clicking on the view, you can change the direction your bug travels. The goal is to get your bug to eat as much grass as possible. In addition to student-controlled bugs, there can be "bot" bugs that just randomly wander and eat grass around the world.

This model uses a software package in NetLogo called HubNet. This allows many different students on many different computers to control a single NetLogo model. Your view focuses only on your bug. Only the teacher can see the whole model.  After everyone has answered the questions below, your teacher will start the simulation.

Record the energy of your bug at the end of the competition. Write this value on a post-it note.  Then add your post-it note to the correct spot on the class histogram of all the bugs' energy levels.

Question

If the bugs move randomly and blindly through the ecosystem (instead of being controlled by you), how do you predict the outcome of the competition will compare to the previous exploration? 

Make a prediction

Now the teacher will repeat the previous activity, but instead of the bugs being controlled by you and your fellow students, they will move randomly.

Choose one or two bugs to follow, and pay attention to how they affect and are affected by the bugs around them.

The original purpose of this activity was to answer: "What causes competition between individuals in an ecosystem?"

Students will develop an understanding of how populations interact with each  other within a community, discussing ideas concerning carrying capacity, competition, and interdependence. From there students will use models to explain the connection between genetic drift, natural selection, and speciation.

Competition Between Populations

Purpose

How do populations affect each other in ecosystems?

Model Rules

In this model, a population of bugs are able to wander the world, eating grass as they go.  As the bugs eat grass, they gain energy, and as they move they lose energy.  If they run out of energy, the bugs will die, and if they gain enough energy, they will reproduce.

In this scenario, you will investigate how the bug population behaves with no outside influences.

Set the region % grassland to 100 in and the initial-birds to zero in both ecosystems before you begin.

Model Rules

In the next model birds will be introduced into the ecosystem.  If a bird catches a bug it gains energy.  As it moves it loses energy.   A bird can die if it loses all of its energy. A bird can have offspring if it collects enough energy.

Make a Prediction

In the ecosystem you looked at in the previous activity, there were no predators.

Now, you will run an investigation about how introducing birds to the ecosystem affects carrying capacity and fluctuation size for the bug population. The model allows you to setup two ecosystems.

• On the left, setup an ecosystem that has no birds. On the right create an ecosystem that has birds.
• Determine values of other variables that you will keep constant. Ensure that all variable values are the same on the right and left side, except for the number of birds.
• Setup and re-run the model a few times until you feel like you have generated enough evidence to reliably claim that the carrying capacity for the bugs and size of fluctuation for the bugs are the same, slightly different or noticeably different in the two ecosystems.

Now, you will run an investigation about how different levels of resources in the ecosystem affects carrying capacity for the bug population. The model allows you to setup two ecosystems.

• Make sure that both ecosystems (left and right) have no birds.
• On the left, set up an ecosystem with 50% grassland.  On the right, set up an ecosystem with 100% grassland.
• Determine values of other variables that you will keep constant. Ensure that all variable values are the same on the right and left side, except for the amount of grassland.
• Setup and re-run the model a few times until you feel like you have generated enough evidence to reliably claim that the carrying capacity for the bugs and for the bugs are the same, slightly different or noticeably different in the two ecosystems.

In this activity you will examine the effects of a competitor species on a population.  The competitor species in this case is called invaders.  The invaders eat grass, just like the bugs.  The invaders do not eat bugs, and the bugs do not eat invaders.

1. Set the initial number of birds to zero and the grassland region to 100%.

2. Press SETUP and then press GO/STOP. As the model is running, press the LAUNCH AN INVASION button on one side, sometime between a time of 100 and 500 .

3. Run the model until it pauses on its own.

4. Record your observations below.

You have separately examined the effects of predators and competing species on a bug population.  However, in real ecosystems it is typical that both predators and competing species will be present at the same time.  In the next scenario you will examine the behavior of the bug population when both of these are present.

Before moving on to that exploration, answer the questions below and think about your observations from the earlier activities.

1. Choose an initial number of bugs, invaders, and birds to include in each environment.  Make each at least 5.

2. Set the initial values for the amount of food bugs eat, the amount of food invaders eat, and the region amount of grassland to different combinations you decide.  If you want to test the effect of changing one variable between the ecosystems, remember to make the other variables the same in both ecosystems.

3. Press SETUP and then press GO/STOP. As the model is running, press the LAUNCH AN INVASION button, sometime between a time of 100 and 500 .

4. Run the model until it pauses on its own.

5. Record your observations below. Try getting the two outcomes you selected by trying different combinations of values. If you can't find at least one set of values for each of the two outcomes you selected, then test some new possible combinations of values if time permits.

Design Your Investigation

Pick two of the following outcomes to generate in the model:

Outcome A: All three populations (bugs, invaders, and birds) die off.

Outcome B: The predator population dies off, but bugs or invaders survive.

Outcome C: The average predator population is above 100.

Try to find solutions that work consistently.  If you run the model several times with your parameters, your chosen outcome should happen most of the time.

The original purpose of this activity was to answer: "How can we describe population size changes In ecosystem?"

In this lesson, you will experiment with a population of bacteria. In this model the environment has sugar that bacteria use as an energy source. There are different phenotypes of bacteria in this population. Different types of bacteria have different number of flagella. Flagella are the appendages that allow bacteria move in specific direction. It's important to note that in this model there is no advantage for a bacterium to have more number of flagella.

Let's explore this model to investigate the phenomenon of genetic drift. Genetic drift is a mechanism of evolution. You will observe and learn about how the population evolves over time because of genetic drift. When you finish working through this lesson, we expect that you learn about genetic drift works as a mechanism of evolution. Let's get started!

The model on this page is of a population of bacteria in an environment where sugar is an energy source. Before we explore the phenomenon of genetic drift, let's get to know this model first. 

[* If the model is runs very slowly in your browser, use this GENETIC DRIFT MODEL for completing this lesson. You MUST have NetLogo installed on your computer to use the downloaded version of the model.]

Click 'SETUP' to initialize the model. Do NOT change any other parameters. 

Make sure that the parameter values are as following:

#-phenotypes = 5

initial-#-bacteria-per-phenotype = 6

Run the model for 500 ticks for three times and answer the following questions.

Do you remember what 'carrying capacity' means from the previous lesson?

Let's investigate how resource distribution influences the carrying capacity in this model.

Imagine a bacterial population where there are only TWO TYPES to begin with. Let's make some predictions about how we expect such population will evolve.

Answer the following questions and then, on the next page, you'll test your predictions using the model.

Set number of phenotypes to 2. 

Set %-resource-distribution to 20%. 

Run the model for 5000 ticks, using the speed slider at the top of the model to make it run faster if necessary. Note your observations in the space provided below the model. Then run the model for really long time (more than 100,000 ticks). Note your observations again.

Explain your observations by answering the questions below. Let's focus on the left region while you answer these questions.

Genetic drift is the process of one type/color surviving without having any selective advantage. In this model, having higher number of flagella do not confer any selective advantage. We've observed that if we run the model long enough, we see only one type survive in the population.

So far we have explored the phenomenon of genetic drift, when there are only two types. Let's explore the phenomenon further when there are multiple types.

Let's focus on the left region while you answer these questions.

Answer question 14 below before you run the model to note down your predictions. Then run the model to test those predictions.

Genetic drift is sometimes referred to as statistical selection, because it is probabilistic or random. Let's understand the randomness in the phenomenon of genetic drift. 

Let's investigate effect of carrying capacity on the process of genetic drift.

Set very low carrying capacity for the left region (10 % resource distribution) and very high carrying capacity for the right region ( 80 % resource distribution). In which region will you expect genetic drift to happen faster?

The original idea of this lesson was to explore the phenomenon of genetic drift.

In this activity students engage in simulated natural selection to discover how natural selection emerges from mechanisms: a) variation in heritable traits in a population and b) interactions in the environment give individuals with some variations a competitive advantage over other individuals. Another purpose  of this lesson is to describe the outcome of natural selection as an increase in the proportion of individuals with advantageous heritable trait variations in a population over multiple generations.

Let's look now at a different version of the bacteria flagella model that we used in the last lesson. 

In this model, you are a predator. You can kill bacteria by moving the mouse cursor over and clicking.

Make sure to choose "none" from the VISUALIZE-PHENOTYPE menu, then set up the model and click on the button "run one minute experiment". The model will run for one minute. Try to kill as many bacteria as you can in that one minute.  After the time is up, choose "flagella and color" from the VISUALIZE-PHENOTYPE menu. And click on "visualize" button. 

Note your observations in the space provided below after you finish the experiment.

Purpose

Find different environmental conditions that generate different trends in which number of flagella become more common over time due to natural selection.  For example, what conditions favor lots of flagella?  What conditions favor few flagella?

With a partner, discuss possible experimental conditions (in terms of %-resource-distribution or resource-location) that might generate these outcomes in the model. 

Use the model to test your ideas with at least three environmental conditions. 

[* If the model is runs very slowly in your browser, use this NATURAL SELECTION MODEL for completing this lesson. You MUST have NetLogo installed on your computer to use the downloaded version of the model.]

Flagella help a bacterium to move and thus increase its chances of being near food before its eaten by other other bacteria. However, a bacterium has to incur some cost in terms of energy that it needs to spend to make and maintain a flagellum.

Make a prediction about which phenotype will survive after you run the model with limited resource conditions.

Set the given values for the following parameters: 

Click setup and run the model until only one phenotype survives. Repeat the experiment 5 times and note your results in the table below.   Make sure you uncheck the "view updates" box at the top of the model.  This will still allow you to see the plots update, and will make the model run much more quickly.

In the previous experiment you must have observed that the phenotype with maximum number of flagella is not always successful in terms of its survival. This is because of the energy cost per flagellum. 

Let's investigate the effect of the energy cost per flagellum on the process of natural selection. 

Download this Bacteria Food Hunt - Natural Selection NetLogo Model. You MUST have NetLogo installed on your computer to use the downloaded version of the model.

The software that you are using to explore the phenomenon of natural selection, NetLogo, has a feature called BehaviorSpace that can be used to conduct such experiments. 

In this part you will use BehaviorSpace to conduct an experiment for investigating the effect of ENERGY-COST-PER-FLAGELLUM on 'avg. # of flagella' in the population after a certain time period. 

• Go to "Tools" at the top of the screen and click "BehaviorSpace"
• Create a new experiment.

• The independent variable in this experiment will be the energy-cost-per-flagellum. In order to vary the energy-cost-per-flagellum, you will use the the BehaviorSpace tool. The format below is for varying this parameter systematically. 

["energy-cost-per-flagellum" [energy-cost-in-the-first-run step-size energy-cost-in-the-final-run] ]

• Press OK and run the experiment.
• Select "Table output".
• Uncheck "Update view" and "Update plots and monitors" to run the experiment faster. 
• The output will be in the form of a "csv" file. You will open it in Excel to analyze your data.

[* In case if you have problems using the BehaviorSpace experiment, use this data for your analysis.]

How does natural selection change populations over time?

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

Purpose  
How do 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.

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, but it is provided here for 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?