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