Nine things that make things happen Part 1:
We live in a world in which everything seems to affect everything else.  My position has long been that it’s all demographic.  That’s obviously an oversimplification so let me play with a few factors.

I shall consider population size, inbreeding depression, outbreeding depression, conflict, cooperation, wealth, disease, famine and natural disaster and take a look at each and how it interacts with all the others.  Fear not; there will be no massive diagram, although the points could be represented by the eight vertices of a cube connected by the twelve edges, the twelve diagonals of the faces and the four diagonals through the center.  That’s thirty six topics, which is why I’m breaking this up.

A population size can be figured in a number of ways.  First you decide who is in it and who is not.  A geographical area works fine, but there are any number of alternatives, such as all members of a social pool or all ambidextrous people.  I will go with the social pool, although obviously the lines will always be blurry, particularly when extended over time. 

Then there are three definitions to choose among.  The obvious one is the census; just count the folks and keep counting over a period of time.  The next most obvious one is the genetic “effective population size.”  That is a technical term for a formalized way of getting a number. 

What the experts do is to look at a genome and find some mutation that is of no consequence.  You know, of course, that genes are made of DNA in long strands called chromosomes and that they contain base pairs that encode for RNA sequences that in turn encode for the polypeptides that make up proteins, each polypeptide being a series of amino acids.  There are four possible bases in each location with three in a row encoding what will be one amino acid; as it turns out this makes more possible combinations than there are amino acids and in fact in a few cases there is more than one code for an amino acid.  The logic then is that if an amino acid is encoded by one sequence and there is a mutation – pretty much a random change in a base pair – that produces a sequence encoding for the same amino acid, it’s all the same in the end, and the mutation has no ultimate effect.  This breezes past the possibility that one code of an amino acid is read off as quickly and easily as another; I am not sure that has been proven, but the experts all pretty much agree that it’s true.

So having chosen a particular mutation, they go on to figure out how frequently that mutation happens.  Next they figure that from time to time a mutation will be lost not because it was selected against but simply because it didn’t turn up in the next generation by chance.  To a first approximation they say (if I follow this properly) that the number of generations before a mutation vanishes is about the same as the population size.  So if you know the rate at which the mutation occurs and know how frequently you see it in the population then you know how big the population is.  Or at least you have a number.  You don’t have to look at an enormous number of genomes to get a good handle on the number because there are an enormous number of sites you can look at on any one genome. 

Some years ago I read, if memory serves, “Population geneticists were very interested in looking at the population genetics of Iceland because they expected that the effective population size would be rather small; they were disappointed to find that it was about 20,000 just like everybody else.”  That number is obviously far too high to be a random mating gene pool size, which appears to be limited well below 1,000.  And nobody really has a good explanation of just what the genetic population size is.  So I generally avoid it, and if I say, “Effective population size,” it’s probably a mistake.

The third population size is the mating pool.  If we knew just a little more we might be able to call it the “epigenetic effective population size.”  But that is the population of which I speak.  So if I say a population will collapse before it reaches 20,000, don’t think, “That’s silly.  The population of New York is in the millions.”  Most of them haven’t even met each other, much less married.

And a population can do things.  That’s obvious.  We’re talking about people.

The next driver I want to consider is inbreeding depression.  If a couple are too closely kin, the number of children they have (if you count only children that are actually born) and the number of grandchildren will be reduced.  So obviously population size affects inbreeding depression.  And inbreeding depression must, of necessity affect the population size.  Moreover, inbreeding depression has a destabilizing effect.  If the depression further reduces the population size the population goes into a death spiral as inbreeding depression increases.  On the other hand if the  population goes just above that which produces inbreeding depression, reproductive rate is maximal and the population explodes.  Although one might imagine a population balanced nicely on the edge of moderate inbreeding depression being stable, that would be a precarious position.  Any displacement would accelerate away from equilibrium. 

Outbreeding depression occurs at a larger population size, but this time the population can be stable.  If the population size is optimal then when the size is displaced downward fertility increases and if displaced upward fertility falls.  Either way, the population seeks equilibrium. 


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