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Observations and Models Module

Module Activities

Grade level

High School



There are 8 activities in this module. Each activity will require 1-2 class periods (assuming 50-minute class periods) to complete.


This module introduces two types of data sources: data derived from observations, and data produced by models. Students will explore simulated marine environments using a computer-based virtual reality (VR) data visualization tool called VES-V (The 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.

Some data used in VES-V are the output of scientific computer models, while other data comes from actual observations made in each specific environment. Students will learn about differences and similarities between these two data sources, how to recognize each, and how each type of data is used by scientists to make predictions.

Students will first learn how models are used to study weather and climate, then investigate model-based datasets of marine species populations using the VES-V software. Students will create a pair of extremely simple models to estimate temperature values in places or times where/when observational data is not available. They will then use these simple temperature models to learn why and how scientists validate and improve models by comparing data from models with observational data.

Students will also explore the methods biologists, ecologists and other scientists use to estimate the sizes and compositions of populations of living creatures by observing limited samples from those populations. Students will investigate these methods in potentially familiar environments, including a strawberry patch in a garden and birds at a feeder, before applying the concepts to marine habitats.

Finally, students will apply the concepts of observations, models, model validation, and population sampling to marine environment datasets presented in VES-V. Students will gather data from VES-V about two organisms that people eat, tuna and lobsters, and estimate future population sizes for each. Students will explore the question: “Can the data from models and observations help us predict future population sizes of fish and other marine organisms?”.


Ecosystem Management

Modern techniques and technologies make fishing so efficient that it is possible for humans to severely deplete populations of marine organisms. People must therefore monitor and manage populations of marine creatures to ensure that they are sustainable. This usually involves placing upper limits on the number of fish or other marine organisms allowed to be caught each year. Government agencies, such as NOAA Fisheries, strive to set catch limits that balance peoples’ desire for fish as a food source 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 impossible to count all the fish in the sea, so fishery scientists use different methods to estimate populations of marine organisms. Two major sources of data about fish populations are data derived from observations, and data generated by scientific models.

Observational Data

Observational data is gathered by directly observing marine organisms at specific times and places. Data collection might involve towing a net behind a ship, SCUBA diving, 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, 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 organisms 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 type of 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, to minimize sampling biases and errors. Sometimes observations of the types and numbers 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 populations. If we want to know the abundance of a specific species - like fish, or another marine species, we can look at factors such as water temperature, availability of food, and the prevalence of predators. All of these might affect a species’ population size. By comparing these factors with past observations, scientists can create models that may predict current and future populations. Data from models can fill in the gaps from 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 always use caution when interpreting data derived from models.

Observational data is the starting point for generating scientific models. Observations can also be used to test the quality of a model. Scientists validate, or test the accuracy, of a model by comparing it with observational data. For example, imagine we had observational data for tuna populations over the past thirty years. If we also had a model for tuna populations, we could use the conditions from 30 years ago as the starting input for the model, and then run the model for 30 years of simulated time. If the model’s prediction of tuna populations today was very close to the observational data, we would have high confidence in using that model to predict the future of tuna populations. If, however, the model’s prediction of current tuna populations was quite different from observations, our model would need to be refined and improved before we could trust its predictions. Scientists use this validation process to test the quality of models, and to make changes and improvements to those models.

If we want to better understand the populations of organisms, we need a somewhat accurate estimate of their sizes and what affects them. Both observations and models have limitations in their ability to produce accurate counts of fish and other marine organisms which are needed if we want to sustainably manage their populations.

Using observational data and data generated by models, scientists can get the best possible estimate of actual populations, which helps fishery managers decide how large of a catch they should allow to maintain a sustainable population.

Predictions about weather and climate also rely heavily on a combination of observations and models. This lesson includes weather-related examples, which may be more intuitively familiar to students, as a comparison to the approaches used with fisheries data.

Learning Objectives

  • Students will construct two simple models to estimate the temperatures at locations between two known values. The first model will use simple interpolation, the second model will also incorporate geographic influences.
  • Students will construct another simple temperature model, then validate it by comparing its predictions to observations.
  • Students will analyze a series of temperature data graphs to learn distinctions between data from observations and data generated by models.
  • Students will estimate the number of berries in a garden to learn how scientists use sampling and extrapolation to estimate the number of organisms in a large area.
  • Students will discuss the effectiveness of counting birds at a bird feeder as a technique for sampling and estimating a neighborhood bird population.
  • Students will use a virtual reality software environment (VES-V) to visualize marine habitats and gather data about organisms in those habitats.
  • Students will collect data and observe graphs of lobster biomass in the virtual marine habitats to compare observational data with model-generated data.
  • Students will discuss the extent to which data generated by models can help improve predictions about population levels of marine organisms.

Key Words - Vocabulary

  • 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.
  • Data Visualization Tool - computer software that represents data in a visual manner. Software used to make graphs is a simple example of a data visualization tool.
  • Extrapolation - estimating a value for some data, based on known values in that dataset, that is outside of (higher or lower than) the known values.
  • Interpolation - estimating a value for some data, based on known values in that dataset, that is between known values.
  • 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.
  • Prediction or Forecast - the estimated value for some data in the future. For example, the weather forecast tells us what weather conditions to expect tomorrow.
  • Sample/sampling - making observations to collect a limited amount of data from a larger dataset. The sample should be representative of data values in other parts of the larger dataset. Samples are used to estimate the value of some data for the entire dataset.
  • Validation (model validation) - comparing values generated by a model to observations of the same dataset to see how well the model matches “reality”.


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

  1. Ask students whether they eat any seafood. Since some students may not like or care about seafood, also ask them whether they work in a restaurant that serves seafood, or hope to get a job in food service. What might happen to the seafood they eat, or to jobs in those restaurants, if seafood prices rose because of limited supplies?
  2. Discussion: Engage students in a discussion with the following suggested prompts:
    • Ask students what happens if we notice that some marine species become 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?
    • Can scientific models help us make predictions? How reliable and accurate are those models?


  • Activity I: Introduce VES-V with a “Virtual Dive”
    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 for specific activities later in this module.

  • Activity II: A Simple Temperature Model for Three Towns
    Students will construct a simple model to estimate the temperature in a fictional town (Middleton) that is located halfway between two other fictional towns (Warmville and Coolville) where the temperature is known. Students should realize that due to geographic influences, the temperature in Middleton should be more akin to the temperature in Coolville.

  • Activity III: Hawai’i Temperature Observations and Model
    Students will construct a simple model for the temperature at a specific place, then use observational data at that location to test their model for accuracy.

  • Activity IV: Temperature Graphs of Observations and Models
    In this activity students will explore temperature datasets. Some of the datasets are derived from observations, others use data generated by models, and some include data from models and observations.

  • Activity V: Sampling to Estimate the Number of Strawberries
    Students will discover some of the challenges and explore some of the techniques used to collect observational data about living organisms. They will explore these ideas in two contexts: gathering berries in a garden and observing birds at a bird feeder.

  • Activity VI: Data from Observations vs. Models in VES-V
    Students will conduct a “virtual dive” in VES-V to explore a habitat in the Atlantic Ocean off the northeast coast of the United States. They will use two different datasets to populate the organisms in the simulation. The first dataset has data generated by a model. The second dataset has data from observations. Students will try to distinguish between the two datasets, identifying which is model-based and which is based on observations.

  • Activity VII: Model Validation in VES-V
    Students will compare observational data about the biomass of a marine organism with model-based data for the same creature over a similar time span. Then they will check the validity of the model and how well it matches with actual observations. If the model’s validity is high, it can be used with confidence to predict future biomass levels.

  • Activity VIII: Predicting Future Lobster and Tuna Populations
    Students will revisit the Big Question posed at the start of the module: “How do Individuals, communities or governments ensure that there is enough seafood for people to eat in the future? They will look at projections from a model of future lobster biomass and determine their level of confidence in those projections based on what they’ve learned about model validation. They will also look at biomass projections from models for another common food source, tuna. Finally, students will observe the large impacts that human activities, in this case fishing, have on populations of marine organisms.