Sexual Selection

Activities to try

1. 1

   Peacock's tail: when a survival cost pays off

   Why it works: Fitness means reproductive success, not survival or strength: a trait that lowers survival can still spread if it raises mating success enough. The same logic covers other sexually selected traits (bird-of-paradise displays, frog calls) and both routes to mating success — female choice and male-male competition. Ornaments can be honest signals of quality or products of runaway female choice, not mere decoration.

   1. Vary the size of a peacock's ornament and read off two quantities that change with it: a rising survival cost (slower, easier for predators to catch) and a rising mating success (peahens choose showier males).
   2. Combine the two into a net reproductive-success curve and find where it peaks. With the model's settings, notice the peak can sit at a large, 'wasteful'-looking tail rather than a small survival-safe one.
   3. Increase the strength of female preference and observe how the optimum tail size shifts larger. In runaway selection, the preference and the tail evolve together over generations because offspring inherit both, so this pressure can keep strengthening.
   4. Relate the same cost-versus-mating trade-off to other sexually selected traits — bird-of-paradise courtship displays and loud frog mating calls — and note that the mating advantage can arise through female choice OR through male-male competition.
   5. State in your own words why natural selection has NOT removed the costly tail.
2. 2

   Hamilton's rule: why a sterile worker or a sentinel helps

   Why it works: Apparent self-sacrifice toward relatives is a calculable prediction of inclusive fitness: an altruism gene spreads when rB > C. This replaces vague group-benefit reasoning with quantitative individual/gene-level reasoning.

   1. Set a relatedness value r for the helper and the helped (e.g. full sibling r = 0.5), a benefit B to the relative, and a cost C to the helper.
   2. Compute rB and compare it with C; decide whether the altruism gene should spread (rB > C) or die out (rB < C).
   3. Work a model case: treat a meerkat sentinel as paying a cost C to warn relatives (benefit B shared among kin). Vary r, B and C and find the threshold where helping becomes favoured. Note that field studies suggest sentinels may face little extra risk and may also benefit themselves (for example, they often spot danger and reach cover first), so real sentinel behaviour may not be pure kin-selected altruism.
   4. Explain why this is a gene's-eye prediction — helping copies of one's own genes carried in relatives — rather than 'for the good of the species'.
3. 3

   Hawk-dove game: no single best move

   Why it works: The best behaviour can be frequency-dependent: with injury cost exceeding resource value an ESS is a stable mix, not a lone winner. 'Survival of the fittest' does not mean the strongest or most aggressive always wins — the best move depends on what everyone else is playing and on the payoff values (with a low injury cost, pure hawk can instead be favoured).

   1. Read a hawk-dove payoff matrix and work out what a hawk and a dove each gain when they meet a hawk and when they meet a dove.
   2. Seed a population with a chosen starting mix of hawks and doves and let it play repeated contests. With the default payoffs set so injury cost exceeds resource value, watch the proportions settle toward a stable mixed equilibrium (the evolutionarily stable strategy).
   3. Change the payoffs (e.g. the cost of injury in a hawk-hawk fight) and observe how the equilibrium moves — a mixed ESS when injury cost exceeds resource value, shifting toward pure hawk when it does not.
   4. With injury cost set higher than the resource value, predict what happens when a few doves are added to a population that is almost all hawks, then test it: explain why pure aggression stops winning once almost everyone is a hawk.