Teacher Notes
Anchoring Phenomenon: A specific crop of plants shows a normal distribution (bell curve) for expressed height. When the environmental factor changes (e.g., from Optimal to Severe Drought), the entire distribution curve shifts, altering the expressed traits of the population without changing the underlying genetic variation.
NGSS Alignment (HS-LS3-3):
- Science and Engineering Practice (SEP): Analyzing and Interpreting Data. Students use the simulation to generate and analyze statistical data, applying concepts of probability to determine relationships between trait occurrence and environmental factors.
- Disciplinary Core Idea (DCI): LS3.B: Variation of Traits. Students discover that environmental factors affect the expression of traits and the probability of their occurrence, showing that trait distribution depends on both genetics and environment.
- Crosscutting Concept (CCC): Scale, Proportion, and Quantity. Students use algebraic thinking (mean, variance, shift) to examine scientific data and predict the effect of changing the environmental variable.
Evidence Statements Addressed:
- “Students organize the given data by the frequency, distribution, and variation of expressed traits in the population.” (Students record Mean, Standard Deviation, and Probability in their data tables).
- “Students perform and use appropriate statistical analyses of data, including probability measures, to determine the relationship between a trait’s occurrence within a population and environmental factors.” (Students calculate and observe the probability of a plant falling within 1 SD across different environmental conditions).
- “Students analyze and interpret data to explain the distribution of expressed traits, including: i. Recognition and use of patterns in the statistical analysis to predict changes in trait distribution within a population if environmental variables change; and ii. Description of the expression of a chosen trait and its variations as causative or correlational to some environmental factor based on reliable evidence.” (Students answer reflection questions identifying the correlational/causative relationship between the environmental slider and the shift in expressed mean height).
Estimated Time: 45-60 minutes Materials: Computer with internet access, Trait Distribution Model simulation, Student Handout.
Student Handout: Trait Distribution and Environmental Impact
Engage
Look at a population of tall sunflowers or tall corn stalks. Even though they might come from the exact same batch of seeds, not every plant is the exact same height.
- Why do you think some plants are taller or shorter than others if they share similar genetics? _____
- If a severe drought occurred during their growing season, how would you expect the overall height of the population to change? Would the “average” plant be shorter, taller, or the same? _____
Explore
Open the Trait Distribution Model simulation. This model represents a population of 10,000 plants and tracks their height based on both their DNA and their environment.
Part 1: The Genetic Baseline
- Set the Base Genetic Potential (Mean Height) to 100 cm.
- Set the Genetic Diversity (Variance) to 15.
- Set the Environmental Factor (Rainfall/Soil) to 0 (Optimal).
- Observe the “Trait Distribution (Histogram)”. What is the shape of the graph? _____
Part 2: Changing the Environment Keep your Genetic Potential at 100 cm and Genetic Diversity at 15. You will now change the environmental factor to simulate different rainfall levels and record the statistical outputs.
| Environmental Factor | Expressed Mean Height (μ) | Standard Deviation (σ) | Probability of a plant falling within 1 SD of the mean |
|---|---|---|---|
| Optimal (0) | _____ | _____ | _____ |
| Moderate Drought (-20) | _____ | _____ | _____ |
| Severe Drought (-40) | _____ | _____ | _____ |
| Abundant Rain (20) | _____ | _____ | _____ |
Explain
- As the environment shifted from Optimal to Severe Drought (-40), what happened to the Expressed Mean Height of the population? _____
- Did changing the Environmental Factor change the Standard Deviation (σ) (how spread out the data is)? Why or why not based on what the standard deviation represents? _____
- Look at the “Probability of a plant falling within 1 SD of the mean”. Did this percentage change significantly as the mean shifted? What does this tell you about the shape of a normal distribution curve? _____
Elaborate
Now, let’s look at Genetic Diversity.
- Reset the environment to 0 (Optimal).
- Change the Genetic Diversity (Variance) slider from 15 down to 5, and then up to 30.
- What happens to the shape of the curve when diversity is low (5) vs high (30)? _____
- If a farmer wants to ensure that almost all of their crops are exactly the same height for easier mechanical harvesting, would they prefer a crop with low genetic diversity or high genetic diversity? Explain your reasoning. _____
Evaluate (Deliverable)
Using the data you collected and your observations from the simulation, write a short paragraph answering the following prompt: Prompt: Explain how the variation and distribution of expressed plant heights depend on both genetic factors and environmental factors. In your explanation, use specific data from your table (mean, standard deviation, and probability) to prove that an environmental change can alter the expressed traits of a population even if their underlying genetics remain the same.
Extension Options
- Mathematical Modeling: If the Mean is 100 cm and the SD is 15 cm, calculate the exact range of heights (in cm) that fall within 1 Standard Deviation.
- Real-World Application: Research human height. Is human height determined solely by genetics, or do environmental factors (like childhood nutrition) play a role? How does this align with the model you just explored?