﻿ Modeling Marine Ecosystems with Virtual Reality: NOAA's National Ocean Service Error processing SSI file

# Module Activities

High School

#### Time

There are 7 activities in this module. Each activity will require 1-2 class periods (assuming 50-minute class periods) to complete.

#### Summary

This module introduces students to models used to predict populations of organisms within an ecosystem. Students will explore a simulated marine environment using a computer-based virtual reality (VR) data visualization tool called VES-V (Virtual Ecosystem Scenario Viewer). VES-V displays marine environments as though one was SCUBA diving in those habitats. The numbers and types of fish and other aquatic creatures displayed in different locations in the VES-V simulations are based on actual abundances of marine creatures.

Students will first learn about interactions between populations of creatures by studying a simple system with just two organisms. That simple system includes a single species of predator, and a single species of prey. Students will discuss factors that might influence the birth rate, death rate, and population size for each of the two species. Students will learn about a few simple equations that can be used to model these concepts about predator and prey populations. Students will then use these equations to build a simple mathematical model of a predator-prey system.

Students will construct the predator-prey model using a spreadsheet program. Students will enter the simple equations that govern a predator-prey system into the spreadsheet. Students will “run” this model for several simulated years by filling in successive spreadsheet rows with each monthly population value for the predator and its prey, using the model’s equations. Students will graph the two population curves (one for the predator, one for the prey) over time and observe that both populations rise and fall in a cycle.

Next, students will analyze the patterns in the cyclic graphs of predator and prey populations to see how the two curves are related. Students will observe that when prey populations are high, the predator population increases because food is plentiful. When prey are few, the predator population decreases. Also, when the predator population is high, the prey population decreases because more prey get eaten. Likewise, when there are few predators, the prey population increases.

After exploring this population model, students will examine observational data from a real system that is similar to the simple system used in the model. The Canada lynx is a predator that preys almost exclusively on a single species of prey, the snowshoe hare. Students will analyze a graph of historical lynx and hare fur counts, and observe that the patterns of rising and falling populations in the historical data are similar to the patterns in the simple spreadsheet model. Students will observe that the observational data is somewhat erratic and “messy” compared to the relatively smooth curves of the graphs of data from the model.

Next, students will apply their new understanding of predator-prey systems to a marine environment, conducting virtual dives using the VES-V software to collect data about a predator and its prey. Students will virtually visit the Gulf of Maine and collect biomass data about a predator (cod) and a species it preys upon (mackerel), observing trends in the cod population and forming a hypothesis about the corresponding change in the prey (mackerel) population. They will test their hypothesis by comparing it with data about the mackerel population. Students will conduct this process twice, first using data from a model, then using observational data. Students will compare the cod and mackerel biomass trends with their earlier observations of the hare and lynx populations. Students will learn that cod and mackerel are part of a complex food web, which is quite different from a simple system with a single predator and a single prey. Students will observe that some of the features of the simple system are still evident in the more complex food web.

The module concludes with a return to a Big Question: “How do Individuals, communities or governments ensure that there is enough seafood for people to eat in the future? ” Students will consider whether knowledge about predator-prey systems could help with predictions of future fish populations. Students will also consider how useful computer models might be in helping to make such predictions.

#### Background

Fishing techniques and technologies make fishing so efficient that it is possible for humans to severely deplete the populations of marine organisms. People must therefore monitor and manage populations of marine creatures to ensure that those populations are sustainable. This usually involves placing upper limits on the number of fish or other marine organisms that are allowed to be caught each year. NOAA strives to set catch limits that balance peoples’ desire for fish as a food source and for other uses with the need to keep fish stocks above critical thresholds that ensure sustainable populations.

Fishery management experts need data about fish populations to assess the size and health of those populations, so they can set appropriately balanced catch limits. It is obviously impossible to count all the fish in the sea, so fishery scientists use various methods to estimate populations of marine organisms. There are two major sources of data about fish populations: data derived from observations, and data generated by models.

Observational data is gathered by directly observing marine organisms at specific times and places. Data collection might involve dragging a net behind a ship, SCUBA diving, or using underwater cameras or robotic submarines. In each case, scientists note the numbers and types of organisms captured in a net or otherwise observed. The number of observations are limited; it isn’t possible to monitor every place in the ocean 24 hours a day and 365 days a year. Scientists must make assumptions, based on a limited number of observations, about populations in places or at times where direct observations are not available. Also, scientists must be careful to make sure their sampling techniques don’t introduce biases. If a net with a large mesh size is used to capture samples, smaller fish might slip through it and fail to be counted. Observations made near the surface could fail to detect a large population of bottom-dwelling species. Schools of fish can be highly localized; observations near a large school might detect thousands of fish, but the same observations made less than a mile away might not detect any. Scientists must use clever sampling techniques and be cautious when extrapolating observations to other places and times, in order to minimize sampling biases and errors. Sometimes observations of the range of fish caught by fishing boats can supplement other data. However, such fishing data generally has a strong bias driven by the types of nets used and where in the water column those nets are placed.

Observational data helps scientists create computer models of expected fish populations. If we want to know the abundance of a specific species, we can look at factors such as water temperature, availability of food, and prevalence of predators, that might affect the population of that specific species. By comparing these factors with past observations, scientists can generate models that predict future populations. Data from models can fill in the gaps in observations, providing “educated guesses” about population sizes in places where and at times when no observations are available. As is the case with observations, the models are not perfect - at best, they provide reasonable estimates, but scientists must also use caution when interpreting data derived from models.

Sometimes it is difficult to make observations about a specific species that humans are interested in. In some of those cases, it may be easier to observe a different species than the one we are interested in eating. Or, it may be easier to observe a predator that eats the species of interest. In either case, we would like to know whether such observations can help provide a better estimate of the population size of the species of interest. Can data about the availability or scarcity of food sources help us predict the population size for an organism? Can data about the number of predators help us predict the population size for an organism? This module investigates those questions.

#### Learning Objectives

• Students will construct and run a simple model of a predator-prey relationship.
• Students will analyze graphs of populations of predators, their prey, observe patterns and relationships between the two populations, and compare historical data with model-generated data and observational data.
• Students will use a virtual reality software environment (VES-V) to visualize marine habitats and gather data about organisms in those habitats.
• Students will compare the data trends for marine predator-prey systems and terrestrial predator-prey systems.
• Students will discuss the extent to which knowledge of predator-prey systems can help improve predictions about population levels of marine organisms that are difficult to directly observe.

#### Key Words - Vocabulary

• Biomass - the total mass of a group of organisms combined. The biomass is the sum of the masses of all the individuals in the group.
• Dataset - a collection of related data values. The data might come from observations, or might be generated by a model. Examples include daily high temperatures for a month for a specific city, or the number of each different species of fish in a certain region of the ocean.
• Food Web - a complex diagram showing the organisms in an ecosystem and the relationships between them in terms of what-eats-what.
• Model (scientific model, computer model) - contrast with scale model, fashion model, etc.
• Observations - data collected by directly observing some system or phenomenon. For example, reading the temperature value from a thermometer.
• Predator - an organism that kills and eats another creature.
• Prey - an organism that is eaten by another creature.
• Validation (model validation) - comparing values generated by a model to observations of the same dataset to see how well the model matches “reality”.

#### Procedure

1. Introduction: Introduce the Predators and Prey Module with a Big Question: “How do Individuals, communities or governments ensure that there is enough seafood for people to eat in the future?”

2. Discussion: Engage students in a discussion with the following suggested prompts:
• Ask students what happens if we notice that some marine species becomes severely depleted? What might that mean for the health of the rest of the ecosystem?
• How can we make sure that there are adequate supplies of seafood (or any other limited resource) for people to eat/use in the future?
• How can we predict the population sizes for various kinds of marine species, or the amount of other potentially limited resources people consume, or use in the future?
• If we are interested in a certain type of fish (such as tuna), and we notice large changes in the populations of other fish that tuna eat, does that tell us anything about the likely future tuna population?

## Activities

• Activity I: Introduce VES-V with a “Virtual Dive”
In this activity students will conduct a “virtual dive” using the VES-V simulation software to become familiar with its features and to generate student interest in using it. VES-V will be used to support activities throughout this module.

• Activity II: Setting up a Predator-Prey Model
Students will discuss the factors that define a predator-prey model and investigate a simple relationship between a single predator and a single prey species. Throughout this activity students learn how birth rate, death rate, and rate of predation control the behavior of this simple population model. Students will also set up a simple predator-prey model in a spreadsheet program, run this model and analyze the results from it in the upcoming activities in this lesson.

• Activity III: Running a Predator-Prey Model
Students will “run” the predator-prey model created in the previous activity. Students will use the model to predict hare and lynx populations over a multi-year timespan. Students will create a graph of hare and lynx populations to better visualize the changing populations over time. Students will adjust some input values in the model to see how that affects the outcome.

• Activity IV: Analyzing Results of a Predator-Prey Model
Students will analyze graphs of hare and lynx populations produced by the predator-prey spreadsheet model. This analysis will provide students with the opportunity to observe the relationships between the two populations, and how each population changes in relation to the other. Ultimately, students will formulate and state a principle about how the two populations change in response to each other. Students will make connections between the graphs and mathematical model and the basic behaviors of lynx and hares that generate the patterns observed in the graphs.

• Activity V: Model Validation with a Predator-Prey Historical Example
Students will analyze population data from a “real world” predator-prey system. Students will compare data from this real world system with the simple predator-prey model they investigated in the previous activities. Students will observe similarities and differences between the model and the actual system.

• Activity VI: Marine Predator-Prey Case Study using VES-V
Students will explore a predator-prey relationship in the ocean using the VES-V software. Students will compare the population trends of a predator (cod) and one of its prey (mackerel). Students will observe the trend over time of cod biomass in the Gulf of Maine. Based on the trend in cod biomass, students will predict the trend in mackerel (prey) biomass. Students will then look at the graph of mackerel biomass in VES-V to test their hypothesis. Students will follow these steps twice, first using data generated by a model, then using data from observations.

• Activity VII: Revisit Initial Question - Enough Seafood?
Students will revisit the Big Question posed at the start of the lesson: “How do Individuals, communities or governments ensure that there is enough seafood for people to eat in the future?”. Students will use their knowledge of predator-prey relationships to address the Big Question.

## Module References

• Predator-Prey Interaction. This page from Northern Arizona University includes the equations for a predator-prey system as well as the data from and a graph of the historical Hudson Bay Company record of lynx and hare pelts.

• Predator Prey Oscillation Simulation Using Excel. A YouTube video by Charles Marzzacco describing the setup of a predator-prey model in a Microsoft Excel spreadsheet.

• CLE Curriculum: Oscillating Systems - Predator and Prey Interactions. This predator-prey simulation of wolves and moose was developed by The Creative Learning exchange. It includes more factors, and thus is a bit more complex, than the simple spreadsheet model used in this lesson.

• Population Dynamics. This simulation by Jolene Pappas also includes more factors than the simple spreadsheet model used in this lesson. The web page also provides links to population dynamics simulations from several other sources.

• Predator-Prey Model. This model by Jon Darkow also includes more factors than the simple spreadsheet model.

• Rabbits and Wolves. This simulation from Shodor shows the locations of predators and prey within the simulated environment.

• Population Dynamics. This simulation from HHMI BioInteractive illustrates exponential growth and carrying capacity for a single species.

• Predator-Prey Relationship Dynamics. This activity from HHMI BioInteractive guides students through interpretation of a graph from a scientific paper about fox and lemming populations.

• Population Dynamics: Predator/Prey. This simple predator-prey activity from Stanford uses physical manipulatives (slips of paper) to illustrate a predator-prey system.

• Deer Me: A Predator/Prey Simulation (www.wolfquest.org/pdfs/Deer%20Me%20Lesson.pdf). This hands-on activity from the WolfQuest project models deer and wolves in a forest using cards and sheets of paper.

• ​​Deer Me: A Predator/Prey Simulation. This hands-on activity from the Minnesota Zoo also models deer and wolves in a forest using cards and sheets of paper.

• Predation. This short article from the CK-12 Foundation describes predator-prey interactions and includes a graph of oscillating populations of predators and prey.

• Trophic Links: Predation and Parasitism. This article from the University of Michigan includes a graph of the Hudson Bay Company’s lynx and hare data.

Author: NOAA