AP Biology: Seed Dispersal Kernel Circuit Theory Connectivity Practice Questions
Practice landscape-connectivity reasoning with original AP Biology multiple-choice questions about using circuit theory to study seed dispersal. These questions emphasize how multiple paths contribute to connectivity, how bottlenecks concentrate modeled current, how candidate maps are validated with independent observations, and why spatial scale, detection probability, source strength, and causal limits matter.
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. Why can a circuit-theory model differ from a single least-cost path when predicting movement between two forest patches?
Explanation. A least-cost analysis identifies one minimum-cost route, whereas circuit theory represents movement potential across multiple paths. Parallel routes can therefore increase modeled connectivity even when none is the unique cheapest route.
2. A current-density map shows a narrow strip with much higher modeled current than nearby cells. What is the most appropriate interpretation?
Explanation. When alternative routes are limited, modeled flow becomes concentrated in a narrow area. This pattern identifies a potential bottleneck, but it remains a prediction that should be tested with movement or seed-arrival data.
3. Two landscapes have the same straight-line distance between source and destination patches. Which result indicates greater modeled connectivity in a circuit framework?
Explanation. Effective resistance summarizes the combined influence of resistance and the availability of parallel routes. Lower effective resistance indicates greater modeled connectivity between the specified source and destination.
4. Researchers map high current density through a corridor and recover many tagged seeds there. Which statement is best supported?
Explanation. Agreement with independent observations supports the model's predictive usefulness. Modeled current is not a literal seed count, and observational agreement alone does not establish that the mapped corridor caused every movement.
5. A proposed crossing adds a second low-resistance route around a narrow bottleneck. What does circuit theory generally predict, all else equal?
Explanation. Adding a parallel low-resistance route provides another pathway for movement and can reduce effective resistance. The amount of improvement depends on the resistance and arrangement of both routes.
6. Which study best validates a circuit-theory connectivity map for seeds dispersed by fruit-eating birds?
Explanation. Independent bird movements or assigned seed locations directly test predictions not used during model construction. Reusing the resistance inputs would measure internal consistency rather than out-of-sample predictive performance.
7. A bottleneck appears at a 30-meter grid resolution but disappears when cells are aggregated to 1 kilometer. What should researchers conclude?
Explanation. Coarser cells can merge narrow corridors and barriers, changing network structure. Comparing predictions with observations at biologically relevant scales helps determine which resolution represents the movement process.
8. Tagged seeds are detected less often inside dense vegetation located along a predicted high-current corridor. What bias could result?
Explanation. Observed recoveries reflect both seed movement and the chance of finding a moved seed. Habitat-specific detection should be estimated or standardized so low recovery is not mistaken for weak connectivity.
9. A model predicts strong connectivity from a patch containing many fruiting trees and weak connectivity from a patch with few fruiting trees. Which variable must be considered before attributing the difference to landscape routes?
Explanation. More seed arrivals can result from greater seed supply even if per-seed movement probabilities are equal. Measuring source strength separates availability from the effects of route connectivity.
10. Two candidate resistance maps produce different bottleneck locations. What is the strongest way to choose between them?
Explanation. The maps encode competing hypotheses about how the landscape affects dispersal. Out-of-sample comparisons show which candidate better predicts new evidence and reduce the risk of selecting an overfit map.
11. A high-current area overlaps a road verge where seed arrivals are common. Which additional evidence would most strengthen a causal claim that modifying the verge increases connectivity?
Explanation. A controlled comparison helps separate the intervention from time trends and other differences among sites. Circuit predictions and observational overlap alone support association, not a causal effect of modifying the verge.
12. A circuit model predicts a broad zone of moderate current rather than one narrow high-current route. What is the best biological interpretation?
Explanation. Distributed modeled current is expected when the landscape offers several comparable routes. Independent movement data are still needed because circuit current represents relative movement potential under specified assumptions.
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FAQ
What does circuit theory represent in landscape connectivity?
It treats landscape cells as parts of a resistance network and estimates relative connectivity between defined sources and destinations across multiple possible routes. The electrical quantities are mathematical analogies, not literal electricity in organisms.
What is a connectivity bottleneck?
It is an area where modeled movement becomes concentrated because few comparable alternatives connect the selected locations. Field observations are needed to determine whether dispersers and seeds actually rely on that area.
Can a circuit-theory map prove what causes seed dispersal?
No. It predicts patterns from assigned landscape resistances. Independent validation supports prediction, while experiments or controlled interventions are needed to strengthen causal conclusions.