Two populations of bacteria start with different genotypes on a rugged fitness landscape with two peaks — one low, one high — separated by a fitness valley. Both populations evolve by natural selection alone for many generations. What is the most likely outcome?
ABoth populations converge on the highest peak, because natural selection always maximizes fitness
BEach population climbs to whichever local peak is nearest to its starting genotype, regardless of which peak is higher
CThe populations merge genotypes through recombination and jointly reach the global optimum
DBoth populations stagnate because rugged landscapes prevent any evolutionary change
Natural selection is a hill-climbing algorithm: it moves populations toward higher fitness but cannot move them through fitness valleys. Each population will reach the nearest local peak to its starting position — and stop. Crossing the valley would require passing through intermediate genotypes of lower fitness, which selection systematically opposes. The population at the lower local peak is 'stuck' even though a better solution exists elsewhere. This is the central lesson of fitness landscapes: natural selection does not guarantee globally optimal outcomes, only locally optimal ones.
Question 2 Multiple Choice
Which mechanism is most capable of allowing a small population to escape a local fitness peak and potentially reach a higher one?
ADirectional selection, which consistently pushes allele frequencies in one direction
BGenetic drift, which causes random fluctuations in allele frequencies that can move a population off a local peak
CStabilizing selection, which reduces variation and keeps the population near the existing peak
DGene flow, which introduces alleles from another population already at the global peak
Genetic drift — random fluctuation in allele frequencies due to finite population size — can push a small population off a local fitness peak and into the basin of attraction of a higher one, even if the path passes through genotypes of lower fitness. This is precisely why Sewall Wright emphasized the importance of population subdivision and drift in his shifting balance theory. Directional selection (A) cannot cross valleys; stabilizing selection (C) actively resists movement away from the peak. Gene flow (D) could in principle introduce alleles from a better-adapted population, but requires that other population to already be at the higher peak.
Question 3 True / False
Natural selection is expected to drive a population to the genotype with the highest possible fitness, given a stable environment and sufficient generations.
TTrue
FFalse
Answer: False
This is the key misconception fitness landscape thinking dismantles. Natural selection climbs fitness peaks locally — it moves toward higher fitness from wherever the population currently is, but cannot cross fitness valleys. On a rugged landscape with multiple peaks, the population reaches the nearest local optimum and cannot escape it through selection alone. The 'best' solution (global optimum) may be separated from the current local peak by a valley of lower-fitness intermediates that selection would eliminate. Evolution does not produce optimal outcomes; it produces locally adapted ones.
Question 4 True / False
Epistasis — where the fitness effect of a mutation at one gene depends on the alleles present at other genes — is the primary reason real fitness landscapes are rugged rather than smooth.
TTrue
FFalse
Answer: True
On a smooth landscape with no epistasis, each mutation would have a fixed fitness effect regardless of genetic background, producing a single peak that selection reliably climbs. Epistasis creates interactions: mutation A is beneficial in combination with allele B but harmful in combination with allele C. These interactions create alternating peaks and valleys as you move through genotype space, making the landscape 'rugged.' Nearby genotypes in sequence space can have very different fitnesses when gene interactions are complex, which is exactly what produces the multiple-peak structure that traps populations at local optima.
Question 5 Short Answer
Why can natural selection get stuck at a local optimum, and what mechanisms can allow a population to escape to a higher peak?
Think about your answer, then reveal below.
Model answer: Natural selection consistently favors higher-fitness genotypes, so it moves populations uphill on the fitness landscape. When a population reaches a local peak — a genotype fitter than all its immediate neighbors — selection has nothing left to climb toward, even if a higher peak exists elsewhere. Reaching the higher peak would require passing through lower-fitness intermediate genotypes, which selection eliminates. Escape mechanisms include: genetic drift (random allele frequency changes in small populations that can push the population off a local peak), mutation (continually exploring neighboring genotypes), recombination (generating novel combinations that jump across the landscape), and environmental change (which reshapes the landscape so that the current peak becomes a valley, forcing movement).
The fitness landscape framework reveals that evolution is path-dependent: where a population ends up depends not just on what's optimal but on where it started and what routes were passable. This insight explains why different populations facing the same selective pressure often arrive at different adaptive solutions — they started from different positions on the landscape and climbed different local peaks.