AP Biology: Seed Dispersal Kernel Model Comparison Practice Questions
Practice data-based ecological reasoning with original AP Biology multiple-choice questions about comparing seed dispersal kernel models. These questions emphasize model fit across distance classes, tail behavior, sampling limits, independent validation, and the distinction between useful predictions and causal explanations.
Free original practice questions for study purposes. Open Exam Prep is an independent study resource and is not affiliated with, endorsed by, or sponsored by the makers of AP Exams.
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Answer the questions below — you get instant feedback and a full explanation for each.
1. Researchers fit two models to seed-deposition data. Model X matches both the high counts near parent plants and the lower counts across distant sampling bands, while model Y consistently underpredicts distant seeds. Which conclusion is best supported?
Explanation. A model that captures both nearby and distant observations better represents the measured dispersal pattern. Good fit does not by itself identify the dispersal vector or prove a mechanism.
2. Why should researchers compare residuals across distance classes when evaluating dispersal-kernel models?
Explanation. Residuals are differences between observations and predictions. A pattern in those differences, such as repeated underprediction far from parents, can reveal a model's weak region.
3. Two models fit the sampled data similarly, but one has many more adjustable parameters. What is the main reason to evaluate that added complexity?
Explanation. A highly flexible model can overfit idiosyncrasies in the calibration data. Model comparison should ask whether added complexity produces meaningful and repeatable predictive improvement.
4. Which procedure provides the strongest test of how well two fitted dispersal kernels predict new observations?
Explanation. Independent validation tests predictions on data not used for fitting. Comparable detection and sampling methods help ensure that performance differences reflect the models rather than a changed protocol.
5. A model comparison uses traps only within 15 meters of parent plants. What is the clearest limitation?
Explanation. Short-range sampling can compare the central portions of kernels but supplies little evidence about their tails. It cannot justify strong claims about rare events outside the detection range.
6. Model A predicts the observed seed counts well, but its predictions change drastically when one distant observation is removed. What does this sensitivity suggest?
Explanation. Strong dependence on a single point indicates that tail parameters are weakly supported by the available sample. Researchers should report this uncertainty and gather more far-distance observations.
7. A fitted kernel accurately predicts seed arrival but not the distribution of established seedlings. Which explanation is most appropriate?
Explanation. A deposition kernel describes arrival. Recruitment also depends on post-dispersal survival and habitat conditions, so the seedling pattern can differ even when arrival predictions are accurate.
8. Researchers find that a mechanistic wind model predicts deposition better during calm periods but worse during storms than a flexible statistical model. What is the best next step?
Explanation. The performance difference suggests that relevant storm processes may be missing. Testing measured conditions on new data evaluates that explanation without treating statistical fit as proof of cause.
9. Why is reporting uncertainty around predicted long-distance dispersal probabilities important?
Explanation. Rare events provide relatively little information, making tail estimates imprecise. Reporting uncertainty communicates the range of predictions compatible with the data.
10. Two kernels have similar overall error, but one repeatedly underpredicts the farthest distance class. Which evaluation is most informative for a study focused on range expansion?
Explanation. An overall score can hide poor performance in a biologically important region. If range expansion is the focus, predictive accuracy in the tail deserves explicit evaluation.
11. A dispersal model fits data from one forest but performs poorly in a forest with a more open canopy. What is the most defensible interpretation?
Explanation. Model performance can change when wind, animal movement, or vegetation structure differs. The result limits generalization and motivates validation across environments.
12. Model B predicts distant seed deposition after a storm more accurately than Model A. Which claim goes beyond the evidence?
Explanation. Predictive performance supports a model's usefulness for the measured pattern but does not prove that its proposed mechanism caused each event. Causal claims require targeted measurements or experiments.
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FAQ
Is the model with the best fit always the best dispersal model?
No. Researchers also consider complexity, uncertainty, biological plausibility, performance in important distance classes, and predictions on independent data.
Why can two dispersal kernels fit nearby seeds but disagree about long-distance movement?
The dense center of the distribution may constrain both models similarly, while sparse tail observations leave their far-distance shapes less certain.
Does accurate prediction prove a dispersal mechanism?
No. Prediction shows that a model represents observed patterns. Establishing mechanism requires additional evidence such as controlled releases, vector tracking, weather measurements, or source assignment.