AP Biology: Seed Dispersal Kernel Restoration Long-Term Demographic Response Practice Questions
Practice connecting restored seed movement to long-term plant population change with original AP Biology multiple-choice questions. This set follows seeds through stage structure, accounts for recruitment lag and density dependence, interprets population growth rate and source-sink dynamics, and emphasizes multi-year evidence before making 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.
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Answer the questions below — you get instant feedback and a full explanation for each.
1. A corridor restoration doubles the number of seeds arriving in a patch. Which measurement would best show whether that change eventually improves population persistence?
Explanation. A stage structure links dispersal to survival, growth, and reproduction. Following cohorts through multiple life stages and years can reveal whether extra seed arrival produces reproductive adults rather than only a short-lived seed pulse.
2. Seed arrival increases immediately after restoration, but the focal tree requires six years to reach reproductive maturity. Why might adult abundance show little response during the first three years?
Explanation. A recruitment lag is expected when individuals need several years to progress from seed to reproductive adult. Early monitoring may detect seedlings before the same cohort can affect adult abundance or reproduction.
3. A restored patch receives many more seeds, but seedling survival falls as seedling density rises. Which process most directly explains the weaker-than-expected gain in juvenile abundance?
Explanation. Negative density dependence can reduce per-capita survival when crowded seedlings compete for resources or attract pathogens and herbivores. More seeds therefore need not produce a proportional increase in later stages.
4. A stage-structured model estimates a long-run population growth rate of 1.04 for restored patches and 0.96 for matched controls. If the estimates are reliable, what is the clearest interpretation?
Explanation. For the finite population growth rate, values above 1 indicate projected growth and values below 1 indicate projected decline under the modeled conditions. The comparison concerns long-run proportional change, not an exact current difference in adults.
5. Which study design best separates a restoration effect from regional variation in rainfall and seed production?
Explanation. A before-after comparison with concurrent matched controls helps account for background environmental change. Multi-year replication is especially important because annual seed production and establishment can vary strongly.
6. A restored patch has local births below local deaths, yet its abundance remains stable because seeds repeatedly arrive from a neighboring forest. How should the restored patch be classified?
Explanation. In source-sink dynamics, a sink has insufficient local reproduction to balance mortality and persists through immigration. Stable abundance alone does not prove that the patch is self-sustaining.
7. Seedling numbers rise after corridor restoration, but the estimated population growth rate remains below 1. Which finding would best explain the mismatch?
Explanation. An increase in an early stage may not reverse decline if a later, influential transition remains low. Stage-structured demographic analysis identifies whether juvenile survival, adult survival, or fecundity limits long-term growth.
8. After eight years, restored patches have more reproductive adults than controls, but restoration was assigned only to patches that already had better soil. Which conclusion is justified?
Explanation. Preexisting habitat quality is a confounder because it may affect both restoration placement and demographic response. The pattern supports a hypothesis but does not isolate a causal restoration effect without stronger controls or assignment.
9. Which observation most strongly suggests that a restored patch has shifted from a sink toward a source?
Explanation. A source produces a local demographic surplus and can export individuals or propagules. Evidence for both self-sustaining local demography and emigration is stronger than abundance alone.
10. Why should investigators estimate detection probability when monitoring seedlings across restored and control patches?
Explanation. If seedlings are harder to find in dense vegetation, raw counts can differ even when true abundance does not. Accounting for detection probability makes demographic comparisons more credible but does not by itself establish causation.
11. A population model assumes constant seedling survival, but field data show survival declines in high-density years. What is the best model revision?
Explanation. Adding a biologically motivated density dependence term lets survival vary with crowding. Testing predictions against held-out multi-year data checks whether the added mechanism improves forecasts rather than merely fitting past noise.
12. Restoration raises seed arrival in year 1, seedling abundance in years 2-3, and reproductive adult abundance in years 7-9. What is the most defensible synthesis?
Explanation. The temporal sequence matches the expected progression from arrival through establishment to maturity. However, a plausible sequence alone is not conclusive causal evidence; controls and cohort data help rule out shared environmental changes and unrelated recruitment.
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FAQ
Why can seed-dispersal restoration take years to affect adult abundance?
Plants must pass through seed, seedling, juvenile, and reproductive stages. This recruitment lag can delay an adult response even when seed arrival changes immediately.
Does more seed arrival guarantee population growth?
No. Density dependence, low establishment, poor juvenile survival, or low adult survival can keep population growth rate below replacement despite greater seed input.
What evidence supports a long-term demographic restoration effect?
Strong evidence combines multi-year stage-specific monitoring, matched controls, estimates of detection probability, cohort transitions, and population growth models tested against independent observations.