AP Biology: Seed Dispersal Kernel Resistance Surface Validation Practice Questions
Practice ecological model evaluation with original AP Biology multiple-choice questions about validating landscape resistance surfaces for seed dispersal. These questions emphasize competing hypotheses, independent observations, spatial scale, sensitivity analysis, detection probability, source variation, model transfer, and limits on causal claims.
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.
advertisement
Answer the questions below — you get instant feedback and a full explanation for each.
1. Researchers create three candidate resistance surfaces based on canopy cover, road density, and a combination of both. Which analysis best evaluates the competing hypotheses?
Explanation. Candidate surfaces represent competing hypotheses about movement costs. Comparing their predictions against independent movement or seed-assignment observations tests which hypothesis generalizes beyond the data used to build it.
2. A resistance surface is fitted with bird-tracking records and then evaluated on the same records. What is the main concern?
Explanation. Testing a model on its calibration data does not measure its ability to predict new observations. Holding out independent tracks, individuals, sites, or time periods gives a stronger validation.
3. Which dataset would most directly validate a resistance surface intended to represent movement by seed-carrying mammals?
Explanation. Independent GPS tracks provide direct observations of the movement process represented by the surface. Linking tracks to seed carrying makes the validation more relevant to dispersal than landscape variables alone.
4. A surface predicts movement well at a 100-meter grid resolution but poorly at a 1-kilometer resolution. What is the best interpretation?
Explanation. Averaging land-cover features into larger cells can erase barriers or corridors relevant to movement. Validation at multiple resolutions reveals whether conclusions are robust to spatial scale.
5. Why should researchers vary assigned resistance values during a sensitivity analysis?
Explanation. Resistance values are often uncertain. Sensitivity analysis shows whether the predicted routes and connectivity rankings are stable or depend on a narrow set of assumed costs.
6. Tagged seeds are harder to find in dense vegetation than in open fields. How can this affect surface validation?
Explanation. Observed recoveries combine movement and detection. If detection probability differs among land-cover types, failing to estimate it can produce biased resistance estimates.
7. Two resistance surfaces have similar average prediction error, but one consistently misses rare long-distance seed movements. Which comparison is most relevant to range expansion?
Explanation. Rare long-distance movements can strongly affect colonization and range expansion. A tail-specific validation metric exposes performance that an overall average may conceal.
8. An observational study finds that seed arrival is higher along low-resistance routes. Which statement exceeds the evidence?
Explanation. Association and predictive fit do not prove a causal mechanism. Source abundance, disperser behavior, detection, and correlated habitat features can provide alternative explanations.
9. A model calibrated in one forest is tested in a second forest with the same sampling protocol. What does strong performance in the second forest provide?
Explanation. Successful external validation supports transferability under the conditions represented by the second forest. It does not establish universality or replace tests across other species and environments.
10. Low-resistance routes begin near parent trees that produce far more seeds than trees near high-resistance routes. What should researchers do?
Explanation. Seed arrival depends on both available seed supply and transport probability. Accounting for source strength helps isolate evidence about movement through the landscape.
11. A candidate surface predicts movement between nearby patches but not between distant patches. Which next step is most informative?
Explanation. Movement cost can accumulate with distance or operate differently across scales. Sampling both near and far movements allows the model to test rather than hide that interaction.
12. A road-avoidance surface fits movement data better after many resistance parameters are adjusted. Which evidence would best reduce concern about overfitting?
Explanation. Out-of-sample performance tests whether the fitted complexity captures a repeatable pattern instead of noise. Holding out individuals or seasons also makes the independence of validation explicit.
📘 Want a full structured course and official-style practice tests? Browse top-rated AP Exams prep books and courses. Some links are affiliate links; we may earn a commission at no cost to you.
advertisement
FAQ
What is a resistance surface in seed-dispersal research?
It is a spatial model that assigns movement costs to landscape features. Lower or higher costs represent hypotheses about how readily a particular disperser moves through each feature.
Why use independent data to validate a resistance surface?
Independent observations test whether the surface predicts movements it was not fitted to. This reduces the risk of mistaking calibration fit for general predictive ability.
Does a validated resistance surface prove a causal mechanism?
No. Validation supports predictive usefulness under tested conditions. Experiments, interventions, or additional mechanistic measurements are needed to distinguish causal effects from correlated landscape features.