How do animals maintain stable populations? Why is ensuring stability important for evolution?
Populations with uncontrolled growth or insignificant growth are not stable populations. Certain behavioral strategies help promote stability through reproduction and choosing when to compete with others.
Read more about stable populations and the behavioral strategies that can help protect stability.
Ensuring Stable Populations
How animals decide when to reproduce is a complicated question. Some scientists, most notably zoologist Vero Copner Wynne-Edwards, believe that animals control their reproduction rates to avoid overpopulating an area. They’re aiming for stable populations. However, this would be a group-selection based theory, which doesn’t seem to mesh with the idea of the selfish gene. It’s worth noting that Wynne-Edwards was a prominent champion of the group-selection theory of evolution, and his ideas are influential enough to be worth considering—even if only to refute them.
Whatever the reason, it’s clear that animal populations don’t grow at the incredible rates they’d be theoretically capable of. Many populations remain fairly stable, with birth and death rates roughly matching each other. Others, like lemmings, grow rapidly and then decline sharply, sometimes to the point of total extinction in a particular area.
Starvation is one major factor in keeping animal populations under control. If animals reproduce too quickly for an area to support, naturally many of them will starve. However, starvation cannot fully explain how animal populations stay under control. If starvation were the only control set on population size, scientists would expect all creatures to work like lemmings do: Their numbers would increase exponentially until the region couldn’t support them, then suddenly crash as most of them starved.
Therefore, it’s clear that there are methods limiting birth rates as well as death rates—animals don’t have infinite numbers of offspring. The question is not whether birth rates are controlled, but why they are controlled.
The group-selection theory would say the reason is altruistic: Animals regulate their birth rates for the good of the entire population. The selfish gene theory would say that it’s selfish: Animals regulate their birth rates because it gives them and their offspring the best chance of survival.
Game Theory and Behavioral Strategies
Game theory can be used to explain behaviors, especially at the population level. By using game theory, scientists can determine effective behavioral strategies. They can also find—or at least approximate—an ESS for a population made up of such behaviors. These behavioral strategies support stable populations.
The Prisoner’s Dilemma is a logistical riddle closely tied to game theory. In the Prisoner’s Dilemma there are two players, each with two options: Cooperate and Betray. Neither player knows which option the other has chosen, and they are not allowed to influence the other’s choice in any way.
If both players choose Cooperate, they each get a significant payout—but a smaller one than in the next situation. If one player Cooperates and the other Betrays, the betrayer gets a large payout while the cooperative player suffers a large penalty. If both players Betray, they each suffer a small penalty. Cooperation and betrayal are altruistic and selfish actions, respectively. Therefore, everything we know about the Dilemma could be compared to nature.
In any single instance of the game, the logical choice is to simply pick Betray. If your opponent chose Cooperate, you’ll get a larger payout than if you’d also chosen Cooperate. If your opponent chose Betray, you’ll suffer a smaller penalty than if you’d chosen Cooperate.
However, the strategy becomes much more complex if you play over and over again. Now one Always-Betray strategy can get stuck in a penalizing loop with another one, while more cooperative strategies have the chance to reap mutual benefits. Of course, an Always-Cooperate strategy will still lose every time to an Always-Betray strategy.
Which Strategies Are the Most Effective?
Professor Robert Axelrod once invited programmers to create programs that use Prisoner’s Dilemma strategies, which he then entered into a virtual “tournament.” In this tournament, each program played 200 rounds of Prisoner’s Dilemma with each of the others, including a copy of itself. Whichever program had the highest total score at the end was the winner.
There were a number of complex strategies submitted for this tournament, some of which were quite cutthroat. However, the surprising winner of the tournament was a simple program called Tit for Tat. This program always played Cooperate on the first round, then for each round after that it simply copied what its opponent had done last. This made it so cooperative strategies were rewarded, while aggressive strategies were punished. Afterward, Axelrod calculated that a so-called “Tit for Two Tats” program would have done even better; such a program wouldn’t Betray until having been betrayed twice in a row itself, and would therefore have avoided some penalizing loops that Tit for Tat got caught in.
However, Tit for Tat alone—or any program that never Betrays first—can’t be considered a true ESS. Such a population could be infiltrated by a mutant program that never picks Betray. As long as there are no aggressive programs in the field, that new program can spread. That would leave the entire population vulnerable if an aggressive program were later introduced. Axelrod coined the term “Collectively Stable Strategy” to describe this situation (remember that an ESS cannot be invaded by another strategy).
Attaining Stable Populations
While it will be difficult for another strategy to invade a population of Always-Betray individuals, it’s almost inevitable that it will happen eventually. A population of creatures with the Always-Betray strategy will continually weaken itself, while a population of more cooperative individuals will prosper and spread. Sooner or later the balance will tip back toward the cooperative population.
Therefore, while Always-Betray is technically an Evolutionarily Stable Strategy (in the sense that no other program could do better in a population of Always-Betray), and Tit for Tat is technically not an ESS (because it could be invaded by a mutant program and eventually overthrown), it could be said that Tit for Tat has a long-term stability that Always-Betray lacks.
———End of Preview———
Like what you just read? Read the rest of the world's best book summary and analysis of Richard Dawkins's "The Selfish Gene" at Shortform.
Here's what you'll find in our full The Selfish Gene summary:
- Why organisms don't matter, only genes do
- How all life forms begin with a replicating molecule
- How species need to balance aggression and pacifism to survive