AP Biology: Seed Dispersal Kernel Restoration Population Viability Analysis Practice Questions
Practice evaluating whether seed-dispersal restoration can improve long-term population persistence. These original AP Biology questions connect demographic data to population viability analysis, distinguish environmental and demographic stochasticity, compare management scenarios, interpret quasi-extinction thresholds, and test model predictions with out-of-sample evidence.
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. Managers want to know whether restoring a dispersal corridor will help a rare tree persist for the next 50 years. Which population viability analysis output most directly addresses their question?
Explanation. Population viability analysis projects extinction risk or the probability of crossing a conservation threshold over a stated time horizon. Comparing restored and unrestored scenarios links the management action to the persistence question.
2. A model reports the probability that a population will fall below 20 reproductive adults within 40 years. What does 20 adults represent?
Explanation. A quasi-extinction threshold is a specified low abundance below which persistence or recovery is considered severely threatened. It is not necessarily literal extinction, and its biological justification should be stated.
3. A drought reduces seedling survival in every patch during the same year. Which source of variation should a viability model use to represent this event?
Explanation. Environmental stochasticity describes temporal variation in survival or reproduction driven by conditions such as drought. Because the drought affects many individuals and patches together, it is not simply random variation among individual birth and death events.
4. Why does demographic stochasticity usually contribute more to extinction risk in a population of 12 plants than in a population of 12,000 plants?
Explanation. Chance outcomes for individuals occur in populations of all sizes, but each event represents a larger fraction of a small population. That proportional impact can push a small population toward a quasi-extinction threshold.
5. A sensitivity analysis shows that projected extinction risk changes most when adult annual survival is varied within its plausible range. What is the most useful interpretation?
Explanation. Sensitivity analysis identifies assumptions or vital rates that strongly affect projections. It can guide data collection and management priorities, but influence in a model does not prove that a rate can be changed in the field.
6. Which comparison best evaluates two management scenarios in a population viability analysis?
Explanation. A fair management scenario comparison holds shared conditions constant and compares distributions across many simulations. Different horizons or cherry-picked outcomes would confound the action with the modeling setup.
7. A viability model was fitted with demographic data from 2010-2020. Which result provides the strongest out-of-sample validation?
Explanation. Out-of-sample validation evaluates predictions against independent times or locations. Agreement with the fitting data alone may reflect overfitting and does not show that the model generalizes.
8. Under current conditions, 30% of simulations cross the quasi-extinction threshold within 50 years; under corridor restoration, 10% do. Which statement is most accurate?
Explanation. The simulated risk is lower in the restoration scenario, but a probability is not a guarantee. Managers should consider confidence in the inputs, model structure, threshold choice, and how well predictions validate.
9. Restoration creates two additional habitat patches. Which pattern would most reduce regional extinction risk from environmental stochasticity?
Explanation. Spatially separated populations can buffer regional risk when their environmental fluctuations are not perfectly synchronized, while some connectivity permits rescue or recolonization. Strongly correlated catastrophes reduce this portfolio effect.
10. The predicted benefit of restoration disappears when seedling survival is set near the lower end of its confidence interval. What should managers do with this result?
Explanation. A decision that changes across plausible parameter values is sensitive to that uncertainty. Additional data and scenario analysis can show whether the restoration choice is robust rather than hiding the unfavorable result.
11. A model projects unlimited exponential growth after corridor restoration even when adult density becomes very high. Which revision is most biologically defensible?
Explanation. Resources and establishment sites are finite, so density dependence can limit survival or recruitment as abundance rises. Including and validating that mechanism avoids an unrealistic projection of unlimited growth.
12. Restoration was applied only to the largest, highest-quality patches, and those patches later had lower projected extinction risk than untreated patches. What is the strongest conclusion?
Explanation. Initial habitat quality may influence both treatment selection and population performance, creating confounding. Matched controls, before-after data, or randomized assignment would strengthen causal inference beyond the model association.
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FAQ
What does population viability analysis estimate?
It uses demographic rates and stated assumptions to estimate outcomes such as extinction risk or the probability of crossing a quasi-extinction threshold over a defined time horizon.
Why include stochasticity in a viability model?
Environmental conditions and individual births and deaths vary. Environmental and demographic stochasticity represent this variation so projections show a distribution of possible outcomes instead of one fixed trajectory.
How can managers judge whether a viability model is trustworthy?
They can examine sensitivity to uncertain inputs, compare alternative model structures, test predictions with out-of-sample observations, and state the limits of causal inference and transfer to new sites.