A species may live in relative equilibrium with its environment, hardly changing from generation to generation, for millions of years. Turtles, for example, have barely changed since the Cretaceous, when dinosaurs still roamed the Earth.
But if the environment changes–critically, if selective pressures change–then the species will change, too. This was most famously demonstrated with English moths, which changed color from white-and-black speckled to pure black when pollution darkened the trunks of the trees they lived on. To survive, these moths need to avoid being eaten by birds, so any moth that stands out against the tree trunks tends to get turned into an avian snack. Against light-colored trees, dark-colored moths stood out and were eaten. Against dark-colored trees, light-colored moths stand out.
This change did not require millions of years. Dark-colored moths were virtually unknown in 1810, but by 1895, 98% of the moths were black.
The time it takes for evolution to occur depends simply on A. The frequency of a trait in the population and B. How strongly you are selecting for (or against) it.
Let’s break this down a little bit. Within a species, there exists a great deal of genetic variation. Some of this variation happens because two parents with different genes get together and produce offspring with a combination of their genes. Some of this variation happens because of random errors–mutations–that occur during copying of the genetic code. Much of the “natural variation” we see today started as some kind of error that proved to be useful, or at least not harmful. For example, all humans originally had dark skin similar to modern Africans’, but random mutations in some of the folks who no longer lived in Africa gave them lighter skin, eventually producing “white” and “Asian” skin tones.
(These random mutations also happen in Africa, but there they are harmful and so don’t stick around.)
Natural selection can only act on the traits that are actually present in the population. If we tried to select for “ability to shoot x-ray lasers from our eyes,” we wouldn’t get very far, because no one actually has that mutation. By contrast, albinism is rare, but it definitely exists, and if for some reason we wanted to select for it, we certainly could. (The incidence of albinism among the Hopi Indians is high enough–1 in 200 Hopis vs. 1 in 20,000 Europeans generally and 1 in 30,000 Southern Europeans–for scientists to discuss whether the Hopi have been actively selecting for albinism. This still isn’t a lot of albinism, but since the American Southwest is not a good environment for pale skin, it’s something.)
You will have a much easier time selecting for traits that crop up more frequently in your population than traits that crop up rarely (or never).
Second, we have intensity–and variety–of selective pressure. What % of your population is getting removed by natural selection each year? If 50% of your moths get eaten by birds because they’re too light, you’ll get a much faster change than if only 10% of moths get eaten.
Selection doesn’t have to involve getting eaten, though. Perhaps some of your moths are moth Lotharios, seducing all of the moth ladies with their fuzzy antennae. Over time, the moth population will develop fuzzier antennae as these handsome males out-reproduce their less hirsute cousins.
No matter what kind of selection you have, nor what part of your curve it’s working on, all that ultimately matters is how many offspring each individual has. If white moths have more children than black moths, then you end up with more white moths. If black moths have more babies, then you get more black moths.
So what happens when you completely remove selective pressures from a population?
Back in 1968, ethologist John B. Calhoun set up an experiment popularly called “Mouse Utopia.” Four pairs of mice were given a large, comfortable habitat with no predators and plenty of food and water.
Predictably, the mouse population increased rapidly–once the mice were established in their new homes, their population doubled every 55 days. But after 211 days of explosive growth, reproduction began–mysteriously–to slow. For the next 245 days, the mouse population doubled only once every 145 days.
The birth rate continued to decline. As births and death reached parity, the mouse population stopped growing. Finally the last breeding female died, and the whole colony went extinct.
As I’ve mentioned before Israel is (AFAIK) the only developed country in the world with a TFR above replacement.
It has long been known that overcrowding leads to population stress and reduced reproduction, but overcrowding can only explain why the mouse population began to shrink–not why it died out. Surely by the time there were only a few breeding pairs left, things had become comfortable enough for the remaining mice to resume reproducing. Why did the population not stabilize at some comfortable level?
Professor Bruce Charlton suggests an alternative explanation: the removal of selective pressures on the mouse population resulted in increasing mutational load, until the entire population became too mutated to reproduce.
What is genetic load?
As I mentioned before, every time a cell replicates, a certain number of errors–mutations–occur. Occasionally these mutations are useful, but the vast majority of them are not. About 30-50% of pregnancies end in miscarriage (the percent of miscarriages people recognize is lower because embryos often miscarry before causing any overt signs of pregnancy,) and the majority of those miscarriages are caused by genetic errors.
Unfortunately, randomly changing part of your genetic code is more likely to give you no skin than skintanium armor.
But only the worst genetic problems that never see the light of day. Plenty of mutations merely reduce fitness without actually killing you. Down Syndrome, famously, is caused by an extra copy of chromosome 21.
While a few traits–such as sex or eye color–can be simply modeled as influenced by only one or two genes, many traits–such as height or IQ–appear to be influenced by hundreds or thousands of genes:
Differences in human height is 60–80% heritable, according to several twin studies and has been considered polygenic since the Mendelian-biometrician debate a hundred years ago. A genome-wide association (GWA) study of more than 180,000 individuals has identified hundreds of genetic variants in at least 180 loci associated with adult human height. The number of individuals has since been expanded to 253,288 individuals and the number of genetic variants identified is 697 in 423 genetic loci.
Obviously most of these genes each plays only a small role in determining overall height (and this is of course holding environmental factors constant.) There are a few extreme conditions–gigantism and dwarfism–that are caused by single mutations, but the vast majority of height variation is caused by which particular mix of those 700 or so variants you happen to have.
The situation with IQ is similar:
The general figure for the heritability of IQ, according to an authoritative American Psychological Association report, is 0.45 for children, and rises to around 0.75 for late teens and adults. In simpler terms, IQ goes from being weakly correlated with genetics, for children, to being strongly correlated with genetics for late teens and adults. … Recent studies suggest that family and parenting characteristics are not significant contributors to variation in IQ scores; however, poor prenatal environment, malnutrition and disease can have deleterious effects.…
And from a recent article published in Nature Genetics, Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence:
Despite intelligence having substantial heritability2 (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered3, 4, 5. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10−8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10−6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10−6). Despite the well-known difference in twin-based heritability2 for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression P = 5.4 × 10−29). These findings provide new insight into the genetic architecture of intelligence.
The greater number of genes influence a trait, the harder they are to identify without extremely large studies, because any small group of people might not even have the same set of relevant genes.
High IQ correlates positively with a number of life outcomes, like health and longevity, while low IQ correlates with negative outcomes like disease, mental illness, and early death. Obviously this is in part because dumb people are more likely to make dumb choices which lead to death or disease, but IQ also correlates with choice-free matters like height and your ability to quickly press a button. Our brains are not some mysterious entities floating in a void, but physical parts of our bodies, and anything that affects our overall health and physical functioning is likely to also have an effect on our brains.
Like height, most of the genetic variation in IQ is the combined result of many genes. We’ve definitely found some mutations that result in abnormally low IQ, but so far we have yet (AFAIK) to find any genes that produce the IQ gigantism. In other words, low (genetic) IQ is caused by genetic load–Small Yet Important Genetic Differences Between Highly Intelligent People and General Population:
The study focused, for the first time, on rare, functional SNPs – rare because previous research had only considered common SNPs and functional because these are SNPs that are likely to cause differences in the creation of proteins.
The researchers did not find any individual protein-altering SNPs that met strict criteria for differences between the high-intelligence group and the control group. However, for SNPs that showed some difference between the groups, the rare allele was less frequently observed in the high intelligence group. This observation is consistent with research indicating that rare functional alleles are more often detrimental than beneficial to intelligence.
Greg Cochran has some interesting Thoughts on Genetic Load. (Currently, the most interesting candidate genes for potentially increasing IQ also have terrible side effects, like autism, Tay Sachs and Torsion Dystonia. The idea is that–perhaps–if you have only a few genes related to the condition, you get an IQ boost, but if you have too many, you get screwed.) Of course, even conventional high-IQ has a cost: increased maternal mortality (larger heads).
Wikipedia defines genetic load as:
the difference between the fitness of an average genotype in a population and the fitness of some reference genotype, which may be either the best present in a population, or may be the theoretically optimal genotype. … Deleterious mutation load is the main contributing factor to genetic load overall. Most mutations are deleterious, and occur at a high rate.
There’s math, if you want it.
Normally, genetic mutations are removed from the population at a rate determined by how bad they are. Really bad mutations kill you instantly, and so are never born. Slightly less bad mutations might survive, but never reproduce. Mutations that are only a little bit deleterious might have no obvious effect, but result in having slightly fewer children than your neighbors. Over many generations, this mutation will eventually disappear.
(Some mutations are more complicated–sickle cell, for example, is protective against malaria if you have only one copy of the mutation, but gives you sickle cell anemia if you have two.)
Throughout history, infant mortality was our single biggest killer. For example, here is some data from Jakubany, a town in the Carpathian Mountains:
We can see that, prior to the 1900s, the town’s infant mortality rate stayed consistently above 20%, and often peaked near 80%.
When I first ran a calculation of the infant mortality rate, I could not believe certain of the intermediate results. I recompiled all of the data and recalculated … with the same astounding result – 50.4% of the children born in Jakubany between the years 1772 and 1890 would diebefore reaching ten years of age! …one out of every two! Further, over the same 118 year period, of the 13306 children who were born, 2958 died (~22 %) before reaching the age of one.
Historical infant mortality rates can be difficult to calculate in part because they were so high, people didn’t always bother to record infant deaths. And since infants are small and their bones delicate, their burials are not as easy to find as adults’. Nevertheless, Wikipedia estimates that Paleolithic man had an average life expectancy of 33 years:
Based on the data from recent hunter-gatherer populations, it is estimated that at 15, life expectancy was an additional 39 years (total 54), with a 0.60 probability of reaching 15.
In other words, a 40% chance of dying in childhood. (Not exactly the same as infant mortality, but close.)
Wikipedia gives similarly dismal stats for life expectancy in the Neolithic (20-33), Bronze and Iron ages (26), Classical Greece(28 or 25), Classical Rome (20-30), Pre-Columbian Southwest US (25-30), Medieval Islamic Caliphate (35), Late Medieval English Peerage (30), early modern England (33-40), and the whole world in 1900 (31).
Over at ThoughtCo: Surviving Infancy in the Middle Ages, the author reports estimates for between 30 and 50% infant mortality rates. I recall a study on Anasazi nutrition which I sadly can’t locate right now, which found 100% malnutrition rates among adults (based on enamel hypoplasias,) and 50% infant mortality.
As Priceonomics notes, the main driver of increasing global life expectancy–48 years in 1950 and 71.5 years in 2014 (according to Wikipedia)–has been a massive decrease in infant mortality. The average life expectancy of an American newborn back in 1900 was only 47 and a half years, whereas a 60 year old could expect to live to be 75. In 1998, the average infant could expect to live to about 75, and the average 60 year old could expect to live to about 80.
Back in his post on Mousetopia, Charlton writes:
Michael A Woodley suggests that what was going on [in the Mouse experiment] was much more likely to be mutation accumulation; with deleterious (but non-fatal) genes incrementally accumulating with each generation and generating a wide range of increasingly maladaptive behavioural pathologies; this process rapidly overwhelming and destroying the population before any beneficial mutations could emerge to ‘save; the colony from extinction. …
The reason why mouse utopia might produce so rapid and extreme a mutation accumulation is that wild mice naturally suffer very high mortality rates from predation. …
Thus mutation selection balance is in operation among wild mice, with very high mortality rates continually weeding-out the high rate of spontaneously-occurring new mutations (especially among males) – with typically only a small and relatively mutation-free proportion of the (large numbers of) offspring surviving to reproduce; and a minority of the most active and healthy (mutation free) males siring the bulk of each generation.
However, in Mouse Utopia, there is no predation and all the other causes of mortality (eg. Starvation, violence from other mice) are reduced to a minimum – so the frequent mutations just accumulate, generation upon generation – randomly producing all sorts of pathological (maladaptive) behaviours.
Historically speaking, another selective factor operated on humans: while about 67% of women reproduced, only 33% of men did. By contrast, according to Psychology Today, a majority of today’s men have or will have children.
Today, almost everyone in the developed world has plenty of food, a comfortable home, and doesn’t have to worry about dying of bubonic plague. We live in humantopia, where the biggest factor influencing how many kids you have is how many you want to have.
Back in 1930, infant mortality rates were highest among the children of unskilled manual laborers, and lowest among the children of professionals (IIRC, this is Brittish data.) Today, infant mortality is almost non-existent, but voluntary childlessness has now inverted this phenomena:
Yes, the percent of childless women appears to have declined since 1994, but the overall pattern of who is having children still holds. Further, while only 8% of women with post graduate degrees have 4 or more children, 26% of those who never graduated from highschool have 4+ kids. Meanwhile, the age of first-time moms has continued to climb.
In other words, the strongest remover of genetic load–infant mortality–has all but disappeared; populations with higher load (lower IQ) are having more children than populations with lower load; and everyone is having children later, which also increases genetic load.
Take a moment to consider the high-infant mortality situation: an average couple has a dozen children. Four of them, by random good luck, inherit a good combination of the couple’s genes and turn out healthy and smart. Four, by random bad luck, get a less lucky combination of genes and turn out not particularly healthy or smart. And four, by very bad luck, get some unpleasant mutations that render them quite unhealthy and rather dull.
Infant mortality claims half their children, taking the least healthy. They are left with 4 bright children and 2 moderately intelligent children. The three brightest children succeed at life, marry well, and end up with several healthy, surviving children of their own, while the moderately intelligent do okay and end up with a couple of children.
On average, society’s overall health and IQ should hold steady or even increase over time, depending on how strong the selective pressures actually are.
Or consider a consanguineous couple with a high risk of genetic birth defects: perhaps a full 80% of their children die, but 20% turn out healthy and survive.
Today, by contrast, your average couple has two children. One of them is lucky, healthy, and smart. The other is unlucky, unhealthy, and dumb. Both survive. The lucky kid goes to college, majors in underwater intersectionist basket-weaving, and has one kid at age 40. That kid has Down Syndrome and never reproduces. The unlucky kid can’t keep a job, has chronic health problems, and 3 children by three different partners.
Your consanguineous couple migrates from war-torn Somalia to Minnesota. They still have 12 kids, but three of them are autistic with IQs below the official retardation threshold. “We never had this back in Somalia,” they cry. “We don’t even have a word for it.”
People normally think of dysgenics as merely “the dumb outbreed the smart,” but genetic load applies to everyone–men and women, smart and dull, black and white, young and especially old–because we all make random transcription errors when copying our DNA.
I could offer a list of signs of increasing genetic load, but there’s no way to avoid cherry-picking trends I already know are happening, like falling sperm counts or rising (diagnosed) autism rates, so I’ll skip that. You may substitute your own list of “obvious signs society is falling apart at the genes” if you so desire.
Nevertheless, the transition from 30% (or greater) infant mortality to almost 0% is amazing, both on a technical level and because it heralds an unprecedented era in human evolution. The selective pressures on today’s people are massively different from those our ancestors faced, simply because our ancestors’ biggest filter was infant mortality. Unless infant mortality acted completely at random–taking the genetically loaded and unloaded alike–or on factors completely irrelevant to load, the elimination of infant mortality must continuously increase the genetic load in the human population. Over time, if that load is not selected out–say, through more people being too unhealthy to reproduce–then we will end up with an increasing population of physically sick, maladjusted, mentally ill, and low-IQ people.
(Remember, all mental traits are heritable–so genetic load influences everything, not just controversial ones like IQ.)
If all of the above is correct, then I see only 4 ways out:
- Do nothing: Genetic load increases until the population is non-functional and collapses, resulting in a return of Malthusian conditions, invasion by stronger neighbors, or extinction.
- Sterilization or other weeding out of high-load people, coupled with higher fertility by low-load people
- Abortion of high load fetuses
- Genetic engineering
#1 sounds unpleasant, and #2 would result in masses of unhappy people. We don’t have the technology for #4, yet. I don’t think the technology is quite there for #2, either, but it’s much closer–we can certainly test for many of the deleterious mutations that we do know of.