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Tag archive: Ageing

Peter Godfrey-Smith’s Other Minds, which is mostly a combination of a discussion on the evolution of intelligence/consciousness and a collection of fun cephalopod anecdotes, surprised me by having a very nice presentation of the core ideas of non-adaptive theories of ageing.

On mutation accumulation:

If we are thinking in evolutionary terms, it’s natural to wonder if there is some hidden benefit from aging itself. Because the onset of aging in our lives can seem so “programmed,” this is a tempting idea. Perhaps old individuals die off because this benefits the species as a whole, by saving resources for the young and vigorous? But this idea is question-begging as an explanation of aging; it assumes that the young are more vigorous. So far in the story, there’s no reason why they should be.

In addition, a situation of this kind is not likely to be stable. Suppose we had a population in which the old do graciously “pass the baton” at some appropriate time, but an individual appeared who did not sacrifice himself in this way, and just kept going. This one seems likely to have the chance to have a few extra offspring. If his refusal to sacrifice was also passed on in reproduction, it would spread, and the practice of sacrifice would be undermined. So even if aging did benefit the species as a whole, that would not be enough to keep it around. This argument is not the end of the line for a “hidden benefit” view, but the modern evolutionary theory of aging takes a different approach. […]

Start with an imaginary case. Assume there is a species of animal with no natural decay over time. These animals show no “senescence,” to use the word preferred by biologists. The animals start reproducing early in their life, and reproduction continues until the animal dies from some external cause—being eaten, famine, lightning strike. The risk of death from these events is assumed to be constant. In any given year, there is a (say) 5 percent chance of dying. This rate does not increase or decrease as you get older, but there is some number of years by which time some accident or other has almost certainly caught you. A newborn has less than a 1 percent chance of still being around at ninety years in this scenario, for example. But if that individual does make it to ninety, it will very probably make it to age ninety-one.

Next we need to look at biological mutations. […] Mutations often tend to affect particular stages in life. Some act earlier, others act later. Suppose a harmful mutation arises in our imaginary population which affects its carriers only when they have been around for many years. The individuals carrying this mutation do fine, for a while. They reproduce and pass it on. Most of the individuals carrying the mutation are never affected by it, because some other cause of death gets them before the mutation has any effect. Only someone who lives for an unusually long time will encounter its bad effects.

Because we are assuming that individuals can reproduce through all their long lives, there is some tendency for natural selection to act against this late-acting mutation. Among individuals who live for a very long time, those without the mutation are likely to have more offspring than those who have it. But hardly anyone lives long enough for this fact to make a difference. So the “selection pressure” against a late-acting harmful mutation is very slight. When molecular accidents put mutations into the population, as described above, the late-acting mutations will be cleaned out less efficiently than early-acting ones.

As a result, the gene pool of the population will come to contain a lot of mutations that have harmful effects on long-lived individuals. These mutations will each become more common, or be lost, mostly through sheer chance, and that makes it likely that some will become common. Everyone will carry some of these mutations. Then if some lucky individual evades its predators and other natural dangers and lives for an unusually long time, it will eventually find things starting to go wrong in its body, as the effects of these mutations kick in. It will appear to have been “programmed to decline,” because the effects of those lurking mutations will appear on a schedule. The population has begun to evolve aging.

On antagonistic pleiotropy:

Is it worth saving enough money so that you will live in luxury when you are 120? Perhaps it is, if you have unlimited money coming in. Maybe you will live that long. But if you don’t have unlimited money coming in, then all the money you save for a long retirement is money you can’t do something else with now. Rather than saving the extra amount needed, it might make more sense to spend it, given that you are not likely to make it to 120 anyway.

The same principle applies to mutations. A lot of mutations have more than one effect, and in some cases, a mutation might have one effect that is visible early in life and another effect that is visible later. If both effects are bad, it is easy to see what will happen—the mutation should be weeded out because of the bad effect it has early in life. It is also easy to see what will happen if both effects are good. But what if a mutation has a good effect now and a bad effect later? If “later” is far enough away that you will probably not make it to that stage anyway, due to ordinary day-to-day risks, then the bad effect is unimportant. What matters is the good effect now. So mutations with good effects early in life and bad effects late in life will accumulate; natural selection will favor them. Once many of these have arisen in the population, and all or nearly all individuals carry some of them, a decay late in life will come to seem preprogrammed. Decay will appear in each individual as if on a schedule, though each individual will show the effects a bit differently. This happens not because of some hidden evolutionary benefit of the breakdown itself, but because the breakdown is the cost paid for earlier gains.

[Mutation accumulation and antagonistic pleiotropy] work together. Once each process gets started, it reinforces itself and also magnifies the other. There is “positive feedback,” leading to more and more senescence. Once some mutations get established that lead to age-related decay, they make it even less likely that individuals will live past the age at which those mutations act. This means there is even less selection against mutations which have bad effects only at that advanced age. Once the wheel starts turning, it turns more and more quickly.

The book discusses various wrinkles in the theory as it applies to different kinds of organisms, then goes on to discuss the question of why most cephalopods are so short-lived. Recommended.

Follows from: Why We Age, Part 1; Evolution is Sampling Error; An addendum on effective population size

Last time, I introduced three puzzles in the evolution of ageing:

This, then, is the threefold puzzle of ageing. Why should a process that appears to be so deleterious to the individuals experiencing it have evolved to be so widespread in nature? Given this ubiquity, which implies there is some compelling evolutionary reason for ageing to exist, why do different animals vary so much in their lifespans? And how, when ageing has either evolved or been retained in so many different lineages, have some animals evolved to escape it?

I divided existing theories of the evolution of ageing into two groups, adaptive and nonadaptive, and discussed why one commonly believed nonadaptive theory – namely, simple wear and tear – could not adequately answer these questions.

In this post I’ll discuss other, more sophisticated non-adaptive theories. These theories are characterised by their assertion that ageing provides no fitness benefit to organisms, but rather evolves despite being deleterious to reproductive success. Despite the apparent paradoxicality of this notion, these theories are probably the most widely-believed family of explanations for the evolution of ageing among academics in the field; they’re also the group of theories I personally put the most credence in at present.

How can this be? How can something non-adaptive – even deleterious – have evolved and persisted in so many species across the animal kingdom? To answer this question, we need to understand a few important concepts from evolutionary biology, including genetic drift, relaxed purifying selection, and pleiotropy. First, though, we need to clarify some important terminology.

Mortality, survivorship, and fecundity

For the purposes of this post, a cohort is a group of individuals from the same population who were all born at the same time, i.e. they are of the same age. The survivorship of a cohort at a given age is the percentage of individuals surviving to that age, or equivalently the probability of any given individual surviving at least that long. Conversely, the mortality of a cohort at a given age is the probability of an individual from that cohort dying at that age, and not before or after.

Survivorship and mortality are therefore related, but distinct: survivorship is the result of accumulating mortality at all ages from birth to the age of interest1. As a result, the mortality and survivorship curves of a cohort will almost always look very different; in particular, while mortality can increase, decrease or stay the same as age increases, survivorship must always decrease. As one important example, constant mortality will give rise to an exponential decline in survivorship2.

Four hypothetical mortality curves and their corresponding survivorship curves

Four hypothetical mortality curves and their corresponding survivorship curves.

In evolutionary terms, survival is only important insofar as it leads to reproduction. The age-specific fecundity of a cohort is the average number of offspring produced by an individual of that cohort at that age. Crucially, though, you need to survive to reproduce, so the actual number of offspring you are expected to produce at a given age needs to be downweighted in proportion to your probability of dying beforehand. This survival-weighted fecundity (let’s call it your age-specific reproductive output) can be found by multiplying the age-specific fecundity by the corresponding age-specific survivorship. Since this depends on survivorship, not mortality, it will tend to decline with age: a population with constant mortality and constant fecundity (i.e. no demographic ageing) will show reproductive output that declines exponentially along with survivorship.

Two hypothetical mortality/fecundity curves and their corresponding reproductive outputs

Two hypothetical mortality/fecundity curves and their corresponding reproductive outputs.

The fitness of an individual is determined by their lifetime reproductive output (i.e. the total number of offspring they produce over their entire lifespan)4. Mutations that significantly decrease lifetime reproductive output will therefore be strongly opposed by natural selection. It seems mutations leading to ageing (i.e. an increase in mortality and decrease in fecundity with time) should be in that category. So why does ageing evolve?

What good is immortality?

Imagine a race of beautiful, immortal, ageless beings — let’s call them elves. Unlike we frail humans, elves don’t age: they exhibit constant mortality and constant fecundity. As a result, their age-specific survivorship and reproductive output both fall off exponentially with increasing age — far more slowly, in other words, than occurs in humans.

Survivorship, cumulative fecundity and cumulative reproductive output curves for a population of elves with 1% fecundity and 0.1% mortality per year. Survivorship, cumulative fecundity and cumulative reproductive output curves for a population of elves with 1% fecundity and 0.1% mortality per year.

Under the parameters I’ve used here (1% fecundity, 0.1% mortality), an elf has about a 50% chance of making it to 700 years old and a 10% chance of living to the spry old age of 2,300. An elf that makes it that far will have an average of 23 children over its life; 7 if it only makes it to the median lifespan of 700.

Since fecundity and mortality are constant, an elf that makes it to 3,000 will be just as fit and healthy then as they were as a mere stripling of 500, and will most likely still have a long and bright future ahead of them. Nevertheless, the chance of any given newborn elf making it that far is small (about 5%). This means that, even though an old elf could in principle have as many children as a much younger individual elf, the actual offspring in the population are mainly produced by younger individuals. Just over 50% of the lifetime expected reproductive output of a newborn elf is concentrated into its first 700 years; even though it could in principle live for millennia, producing children at the same rate all the while, its odds of reproducing are best early in life. You can, after all, only breed when you’re living.

This fact — that reproductive output is concentrated in early life even in the absence of ageing — has one very important consequence: natural selection cares much more about you when you’re young.

Natural selection is ageist

No genome is totally stable — mutations always occur. Let’s imagine that three mutations arise in our elven population. Each is fatal to its bearer, but with a time delay, analogous to Huntington’s disease or some other congenital diseases in humans. Each mutation has a different delay, taking effect respectively at 100, 1000, and 10000 years of age. What effect will these mutations have on their bearers’ fitness, and how well will they spread in the population?

Three potential fatal mutations in the elven populations, and their effects on lifetime reproductive output. Three potential fatal mutations in the elven populations, and their effects on lifetime reproductive output.

Although all three mutations have similar impacts on an individual who lives long enough to experience them, from a fitness perspective they are very different. The first mutation is disastrous: almost 90% of wild-type individuals (those without the mutation) live past age 100, and a guaranteed death at that age would eliminate almost 90% of your expected lifetime reproductive output. The second mutation is still pretty bad, but less so: a bit over a third of wild-type individuals live to age 1000, and dying at that age would eliminate a similar proportion of your expected lifetime reproductive output. The third mutation, by contrast, has almost no expected effect: less than 0.005% of individuals make it to that age, and the effect on expected lifetime reproductive output is close to zero. In terms of fitness, the first mutation would be strenuously opposed by natural selection; the second would be at a significant disadvantage; and the third would be virtually neutral.

This extreme example illustrates a general principle:

The impact of a mutation on the fitness of an organism depends on both the magnitude of its effect and the proportion of total reproductive output affected.

— Williams 1957 5

Mutations that take effect later in life affect a smaller proportion of total expected reproductive output and so have a smaller selective impact, even if the size of the effect when they do take effect is just as strong. The same principle applies to mutations with less dramatic effects: those that affect early-life survival and reproduction have a big effect on fitness and will be strongly selected for or against, while those that take effect later will have progressively less effect on fitness and will thus be exposed to correspondingly weaker selection pressure. Put in technical language, the selection coefficient of a mutation depends upon the age at which it takes effect, with mutations affecting later life having coefficients closer to zero.

Evolution is sampling error, and selection is sampling bias. When the selection coefficient is close to zero, this bias is weak, and the mutation’s behaviour isn’t much different from that of a neutral mutation. As such, mutations principally affecting later-life fitness will act more like neutral mutations, and increase and decrease in frequency in the population with little regard for their effects on those individuals that do live long enough to experience them. As a result, while mutations affecting early life will be purged from the population by selection, those affecting late life will be allowed to accumulate through genetic drift. Since the great majority of mutations are negative, this will result in deteriorating functionality at older ages.

So our elves are sadly doomed to lose their immortality, unless something very weird is happening to cause them to keep it. Mutations impairing survival and reproduction early in life will be strenuously removed by natural selection, but those causing impairments later in life will accumulate, leading to a progressive increase in mortality and decline in fecundity. This might seem bad enough, but unfortunately there is more bad news on the horizon — because this isn’t the only way that nonadaptive ageing can evolve.

Perverse trade-offs

Imagine now that instead of a purely negative, Huntingdon-like mutation arising in our ageless elf population, a mutation arose that provided some fitness benefit early in life at the cost of some impairment later; perhaps promoting more investment in rapid growth and less in self-repair, or disposing the bearer more towards risky fights for mates. How would this new mutation behave in the population?

The answer depends on the magnitude of the early-life benefit granted by the mutation, as well as of its later-life cost. However, we already saw that in weighing this trade-off natural selection cares far more about fitness in early life than in later life; as such, even a mutation whose late-life cost far exceeded its early-life benefit in magnitude could be good for overall lifetime fitness, and hence have an increased chance of spreading and becoming fixed in the population. Over time, the accumulation of mutations like this could lead to ever-more-severe ageing in the population, even as the overall fitness of individuals in the population continues to increase.

This second scenario, in which the same mutation provides a benefit at one point in life and a cost at another, is known as antagonistic pleiotropy6. It differs from the mutation accumulation theory of ageing outlined above in that, while in the former case ageing arises primarily through genetic drift acting on late-life-affecting deleterious mutations, the latter proposes that ageing arises as a non-adaptive side effect of a fitness-increasing process. Both theories are “non-adaptive” in that the ageing that results is not in itself good for fitness, and both depend on the same basic insight: due to inevitably declining survivorship with age, the fitness effect of a change in survival or reproduction tends to decline as the age at which it takes effect increases.

Mutation accumulation and antagonistic pleiotropy have historically represented the two big camps of ageing theorists, and the theories have traditionally been regarded as being in opposition to each other. I’ve never really understood why, though: the basic insight required to understand both theories is the same, and conditions that gave rise to ageing via mutation accumulation could easily also give rise to additional ageing via antagonistic pleiotropy7. Importantly, both theories give the same kinds of answers to the other two key questions of ageing I discussed last time: why do lifespans differ between species, and why do some animals escape ageing altogether?

It’s the mortality, stupid

As explanations of ageing, both mutation accumulation and antagonistic pleiotropy depend on extrinsic mortality; that is, probability of death arising from environmental factors like predation or starvation. As long as extrinsic mortality is nonzero, survivorship will decline monotonically with age, resulting (all else equal) in weaker and weaker selection agains deleterious mutations affecting later ages. The higher the extrinsic mortality, the faster the decline in survivorship with age, and the more rapid the corresponding decline in selection strength.

Age-specific survivorship as a function of different levels of constant extrinsic mortality. Higher mortality results in a faster exponential decline in survivorship. Age-specific survivorship as a function of different levels of constant extrinsic mortality.

As a result, lower extrinsic mortality will generally result in slower ageing: your chance of surviving to a given age is higher, so greater functionality at that age is more valuable, resulting in a stronger selection pressure to maintain that functionality.

This is the basic explanation for why bats live so much longer than mice despite being so similar: they can fly, which protects them from predators, which reduces their extrinsic mortality.

The box plot from part 1 of this series, showing that bat species have much longer maximum lifespans than mice species. All data obtained from the AnAge database.

You can see something similar if you compare all birds and all mammals, controlling for body size (being larger also makes it harder to eat you):

Scatterplots of bird and mammal maximum lifespans vs adult body weight from the AnAge database, with central tendencies fit in R using local polynomial regression (LOESS). Bird species tend to have longer lifespans than mammal species of similar body weight. Scatterplots of bird and mammal maximum lifespans vs adult body weight from the AnAge database, with central tendencies fit in R using local polynomial regression (LOESS).

In addition to body size and flight, you are also likely to have a longer lifespan if you are8:

  • Arboreal
  • Burrowing
  • Poisonous
  • Armoured
  • Spiky
  • Social

All of these factors share the property of making it harder to predate you, reducing extrinsic mortality. In many species, females live longer than males even in captivity: males are more likely to (a) be brightly coloured or otherwise ostentatious, increasing predation, and (b) engage in fights and other risky behaviour that increases the risk of injury. I’d predict that other factors that reduce extrinsic mortality in the wild (e.g. better immune systems, better wound healing) would similarly correlate with longer lifespans in safe captivity.

This, then, is the primary explanation non-adaptive ageing theories give for differences in rates of ageing between species: differences in extrinsic mortality. Mortality can’t explain everything, though: in particular, since mortality is always positive, resulting in strictly decreasing survivorship with increasing age, it can’t explain species that don’t age at all, or even age in reverse (with lower intrinsic mortality at higher ages).

It’s difficult to come up with a general theory for non-ageing species, many of which have quite ideosyncratic biology; one might say that all ageing species are alike, but every non-ageing species is non-ageing in its own way. But one way to get some of the way there is to notice that mortality/survivorship isn’t the only thing affecting age-specific reproductive output; age-specific fecundity also plays a crucial role. If fecundity increases in later ages, this can counterbalance, or even occasionally outweigh, the decline in survivorship and maintain the selective value of later life.

Mammals and birds tend to grow, reach maturity, and stop growing. Conversely, many reptile and fish species keep growing throughout their lives. As you get bigger, you can not only defend yourself better (reducing your extrinsic mortality), but also lay more eggs. As a result, fecundity in these species increases over time, resulting – sometimes – in delayed or even nonexistent ageing:

The box plot from part 1 of this series, showing that bat species have much longer maximum lifespans than mice species. All data obtained from the AnAge database. Mortality (red) and fertility (blue) curves from the desert tortoise, showing declining mortality with time. Adapted from Fig. 1 of Jones et al. 2014.

So that’s one way a species could achieve minimal/negative senescence under non-adaptive theories of ageing: ramp up your fecundity to counteract the drop in survivorship. Another way would be to be under some independent selection pressure to develop systems (like really good tissue regeneration) that incidentally also counteract the ageing process. Overall, though, it seems to be hard to luck yourself into a situation that avoids the inexorable decline in selective value imposed by falling survivorship, and non-ageing animal species are correspondingly rare.

Next time in this series, we’ll talk about the other major group of theories of ageing: adaptive ageing theories. This post will probably be quite a long time coming since I don’t know anything about adaptive theories right now and will have to actually do some research. So expect a few other posts on different topics before I get around to talking about the more heterodox side of the theoretical biology of ageing.

  1. In discrete time, the survivorship function of a cohort will be the product of instantaneous survival over all preceding time stages; in continuous time, it is the product integral of instantaneous survival up to the age of interest. Instantaneous survival is the probability of surviving at a given age, and thus is equal to 1 minus the mortality at that age. 

  2. Exponential in continuous time; geometric in discrete time. 

  3. The reproductive output \(r_a\) at some age \(a\) is therefore equal to \(f_a \cdot s_a\), where \(f\) is fecundity and \(s\) is survivorship. Since survivorship is determined by mortality, reproductive output can also be expressed as \(r_a = f_a \cdot \int_0^a m_x \:\mathrm{d}x\) (in continuous time) or \(r_a = f_a \cdot \prod_{k=0}^am_k\) (in discrete time). 

  4. Lifetime reproductive output is equal to \(\int_0^\infty r_a \:\mathrm{d}a\) (in continuous time) or \(\sum_{a=0}^\infty r_a\) (in discrete time), where \(r_a\) is the age-specific reproductive output at age \(a\)

  5. Williams (1957) Evolution 11(4): 398-411. 

  6. Pleiotropy” is the phenomenon whereby a gene or mutation exerts effects of multiple different aspects of biology simultaneously: different genetic pathways, developmental stages, organ systems, et cetera. Antagonistic pleiotropy is pleiotropy that imposes competing fitness effects, increasing fitness in one way while decreasing it in another. 

  7. Which of the two is likely to predominate depends on factors like the relative strength of selection and drift (which is heavily dependent on effective population size) and the commonness of mutations that cause effects of the kind proposed by antagonistic pleiotropy. 

  8. My source for this is personal communication with Linda Partridge, one of the directors at my institute and one of the most eminent ageing scientists in the world. I’m happy to see any of these points contested if people think they have better evidence than an argument from authority. 

This article is cross-posted from the Wild Animal Initiative website. The original is here. It is also available on the EA Forum here.


In order to determine which conditions provide the best overall quality of life for nonhuman animals, it is important to be able to measure their cumulative welfare experience. The ideal measure of cumulative welfare would be comprehensive, objectively measurable, and easy to transfer across species; however, existing approaches fall far short of this ideal. Recent academic work has suggested that measures of biological ageing could provide a highly promising alternative measure of cumulative welfare, which comes much closer to meeting these ideal goals.

Here, I review the existing empirical support for the use of biomarkers of ageing as a measure of cumulative welfare, discuss the prerequisites of applying the method, and explore a number of important caveats that may limit its applicability. Many of these caveats are particularly applicable to the study of wild-animal welfare, though some may also be important in domesticated contexts.

Overall, despite some important potential weaknesses, biomarkers of ageing are likely to represent an important step forward in the assessment of cumulative animal welfare, which could potentially help resolve some important long-running uncertainties and disputes in the animal-welfare movement. Wild Animal Initiative recommends that both researchers and funders take note of these new techniques, and consider how best they can develop them further or apply them in their own domains of expertise.

We need a good way of measuring cumulative animal welfare

While single experiences can be acutely positive or negative, what matters more from a welfare perspective is the lasting cumulative impact of these experiences.

— Bateson & Pourier 2019 1

Over the course of their life, an animal will undergo various positive and negative experiences. Some of these experiences will primarily affect welfare in an acute, short-lived manner, while others will have significant long-lasting welfare effects. The overall welfare state of an animal will be determined by the cumulative effect of all the experiences they have faced in the course of their life to date.

In order to improve animal welfare, we need to know what affects it. In order to improve animal welfare effectively, we need to know which factors have the greatest total effect on animals’ cumulative wellbeing. Actually measuring cumulative wellbeing, however, is highly challenging, and our existing methods for doing so frequently rely on crude proxies or error-prone anthropomorphic judgements. To make matters worse, the welfare effect of a given experience is frequently complex, species-specific, and non-obvious to humans:

  • Species of fish which are naturally solitary exhibit stress indicators upon overcrowding, while schooling fish become stressed if raised at unnaturally low densities 7.
  • Exposure to environmental ultrasound frequencies that are undetectable to humans causes depression-like symptoms in laboratory rats and mice 21.
  • The flickering of some fluorescent lights at frequencies above the human flicker-fusion rate raises stress-hormone levels in starlings 22.
  • The stress-hormone levels induced in captive-bred lizards by different experimental procedures can differ dramatically from how stressful human observers think the procedures are, with one controversial procedure found to generate much lower levels of stress hormone than the “less-stressful” procedure that replaced it 13.

Which specific stimuli are important for an animal’s welfare is therefore very difficult to predict a priori, and the use of anthropomorphism to make these predictions is fraught with danger.

Even if it is possible to determine that a given experience is acutely positive or negative for an animal, it is not obvious how to convert these acute measurements into measurements of lasting impacts on welfare. Experiences with similar acute effects may have dramatically differences on long-run wellbeing: one stressor may involve temporary pain or result in a temporary spike in stress-hormone levels but have no long-term effects, while another might significantly contribute to long-term stress levels. In some cases, the short- and long-term welfare impacts of an exposure may even be of opposite sign: some acute stresses can be beneficial in the long-term, while the short-term pleasure of sugary food might be outweighed by the long-term pain caused by tooth decay and weight gain 1. Worse, the relationship between short-term and long-term welfare impacts is likely to vary significantly based on species, subspecies, chronological age, past experiences, and individual genetic variation, making the long-term welfare effect of an acute experience even more difficult to predict.

A good measure of cumulative affective experience 23 is therefore vital to the study of animal welfare. However, developing a cumulative-welfare metric which is sensitive, easy-to-measure, captures all or almost all of the relevant effects on welfare, and can be transferred with relative ease across species has proven to be highly challenging 1. Existing measures include chronic physiological markers such as resting stress-hormone levels or bodyweight, acute behavioural measures such as the presence of stereotypies, and high-level behavioural measures such as depressive symptoms or cognitive biases 1; other measures combine multiple different sources of evidence, often alongside acute welfare indicators and/or subjective welfare assessments by a trained practitioner. However, all of these methods have serious drawbacks: physiological measures often lack sensitivity and specificity (i.e. they do not always reliably correlate with affective state), stereotypies are highly species-specific and often difficult to interpret, and the behavioural tests required to measure cognitive biases and mood must be developed and validated independently for each species and often require extensive animal training. Combined measures, meanwhile, rely on difficult-to-test assumptions about how different metrics should be combined and weighted to assess overall cumulative wellbeing.

In the ideal case, the many and varied inputs into cumulative animal welfare would be captured by a single, objectively measurable metric, which could then be used as a single readout of cumulative wellbeing for many different species of animals. In the rest of this article, I will review and discuss the evidence supporting a new, surprisingly good candidate for this “objectively measurable common currency” 1 of wellbeing: biomarkers of the ageing process.

Biological ageing and cumulative animal welfare

As we get older, our bodies decay. In various ways, our cells and tissues progressively accumulate increasing levels of damage and dysregulation, leading at the whole-organism level to a decline in functionality, an increase in mortality, and a decrease in reproductive output 8. These deteriorative processes, which occur in broadly similar ways in many different animal species 9, are collectively known as ageing.

Among researchers studying the biology of ageing, it is well-known that simple chronological age is a less-than-perfect measure of the aspects of ageing we tend to care about. In humans, individuals of the same chronological age often differ substantially, both in how old they appear to others (degree of graying, wrinkles, stooping, etc.) and in their age-related health outcomes 10. It is therefore useful to separate the concepts of “chronological age” (time since conception or birth) and “biological age” (degree of age-related change/deterioration in appearance, health, or functionality) 1011. So-called “biological ageing clocks”, which incorporate a variety of different types of biological data, are an active area of research in the study of human ageing, and the best such clocks can predict health and other outcomes much more accurately than chronological age alone 11.

The biological age of an individual depends on their chronological age, genetic background, and environmental history: depending on their genes and experiences, two individuals of the same chronological age can differ substantially in their biological age. A striking example of this is smoking, which produces a variety of ageing-like symptoms and has recently been shown to substantially increase biological age in young humans 12. However, biological age is affected by a wide variety of chemical, psychological and social stimuli, many of which are also known to have important effects on an individual’s wellbeing. To take just one example, a wide variety of negative experiences (including anxiety, depression, childhood trauma, chronic pain, and various forms of stress) are associated with reduced telomere length in humans, while positive lifestyle factors are associated with longer telomeres 412526. Despite important differences in telomere biology between species, a variety of stress manipulations (including social isolation, sleep disruption, injection of stress hormones, and crowding) have also been found to accelerate telomere attrition in various nonhuman animals, including wild and laboratory mice, chickens, starlings, and various other bird species 4.

In addition to this and other empirical data, there are good theoretical reasons to expect the rate of biological ageing to correlate with the cumulative affective experience of an individual 1. Evolutionarily speaking, the affective state of an experience serves to motivate an animal to seek or avoid similar experiences; hence, affectively negative experiences tend to be those that reduce animal’s fitness, while affectively positive experiences tend to be those that increase it. An important way in which an experience can decrease fitness is by causing or contributing to some sort of damage or dysregulation in the body; hence, experiences which contribute to damage or dysregulation will typically be perceived as aversive to the organism, while those that prevent or reverse damage (or have no effect on damage but are positive for some other reason) will be perceived as attractive. In general, therefore, there is good reason to expect physiologically-damaging experiences to be affectively negative, and vice versa 3, with a similar connection between physiologically-protective experiences and positive affect. Since ageing is characterised by the progressive accumulation of various forms of physiological damage, this suggests that there is good reason to expect the affective valence (i.e. the positivity or negativity) of an experience to also generally correlate with its effect on biological age.

There are, therefore, both empirical data and theoretical arguments suggesting a relationship between cumulative affective experience and ageing. If this relationship exists, the biological age of an individual relative to their chronological age could be used to assess that individual’s cumulative welfare experience up to that point. If further testing bears this out, there are several reasons to expect biological age to be a particularly valuable tool for assessing wellbeing:

  • It is highly general, including all (or almost all) causes of stress and wellbeing experienced by the animal, including those not obvious or perceptible to humans
  • It is cumulative, giving a readout of the total affective history of an individual
  • It is objectively measurable, with well-established biomarkers already known for a number of species
  • Finally, it is plausibly relatively phylogenetically neutral: as ageing is a general phenomenon shared by very many species, whose measurement does not generally rely on anthropomorphic judgements, it can potentially be used to investigate welfare in many different animal groups.

Given these potential advantages, how might we go about actually measuring biological age?

Measuring biological age in non-human animals

In any given species, the ageing process will manifest itself in a plethora of different phenotypes, many of which can be used to try to quantify biological ageing. In humans, a wide variety of biological readouts have been used as biomarkers of ageing, including telomere length and attrition rate, DNA methylation patterns, gene expression profiles, changes in neuroanatomy, proteomic and metabolomic changes, and various composites of clinically relevant symptoms 156. While many of these biomarkers predict health outcomes better than chronological age, they often reflect different aspects of the ageing process and do not always correlate well with one another 15. Combining different measures (e.g. with machine-learning-based prediction tools) can overcome these problems and improve the ability of a biological ageing measure to predict health outcomes 612.

As different biomarkers of ageing track different aspects of the ageing process, they may differ in how well they measure cumulative subjective wellbeing. A good biomarker of cumulative experience should provide a single, continuous, easy-to-measure readout that responds in opposite directions to positive and negative affective experiences in a cumulative and dose-dependent manner 41. Different biomarkers will also differ in the money, expertise and time required to obtain good measurements, and in the ease with which they can be validated in a new context. As nonhuman animals, unlike humans, cannot explicitly self-report their subjective experience, validation of a new potential biomarker’s relationship with wellbeing is much more challenging in these species; as a result, the ease and reliability with which a biomarker can be transferred between species is an additional important consideration when choosing how to measure biological age in an animal-welfare context.

Overall, I would expect more complex and multi-modal measures to provide a more accurate, precise and thorough measure of biological age and so give a better idea of an animal’s cumulative experience. On the other hand, I would expect these complex measures to be more expensive and time-consuming to obtain for each individual, and less transferable between species.

For some widely-used farmed species and experimental model organisms, it may well be worth developing sophisticated species-specific methods of measuring biological age and hence cumulative welfare; however, in contexts where resources are highly limited and/or the number of species of concern is large, cost and transferability concerns are likely to mitigate in favour of simpler, faster, cruder measures 28. Bateson & Pourier 41 suggest telomere length and hippocampal volume as two metrics that are simple, well-defined, and likely to retain validity across a wide variety of vertebrate species; further work may reveal other promising candidates. However, when using such very simple readouts of biological age, it is important to remember that they may give a significantly more partial and inaccurate reading than more sophisticated measures, and to seek to develop such improved measures where feasible.

A concrete example: the welfare effect of crowding on farmed fishes

Many farmed fish are kept at very high densities, in a manner which often appears to be detrimental to their welfare 7. The potential welfare effects of crowding are many and varied, including social stresses, reduced water quality, and increased disease transmission. However, the actual welfare effect of a given level of crowding will vary between fish populations, depending on the level of crowding and water quality they are adapted to cope with, the robustness of their immune systems, and whether they have been vaccinated, among other factors. It would be useful to measure the total cumulative welfare effect of different crowding regimes on different species, and to know the degree to which other interventions such as vaccination mitigate any crowding-induced welfare reduction. Biological age provides an ideal means of addressing these questions.

To apply this method, we would need some sort of biological ageing clock for each of the fish species of interest, as well as some way of keeping track of the chronological age of each individual. Once these two methods are in place for each species, the experiment is simple: simply raise populations of fish at different levels of crowding, sample the biological ages of individuals in each population, then compare the biological ages of chronological-age-matched individuals from different conditions. Those populations exhibiting the highest biological age relative to their chronological age would be taken as experiencing the lowest levels of cumulative welfare.

Three speculative line graphs, each with three lines, indicating the rate of biological age accumulation for fish under low-, medium- and high-density conditions in a hypothetical experiment. The leftmost plot shows hypothetical results for schooling fish and shows slowest accumulation in the mid-density condition, indicating a preference for moderate density for those fish. The middle plot shows results for solitary fish and shows the rate of accumulation increasing progressively with density, showing that these fish are negatively affected by any level of population density above the minimum. Finally, the rightmost plot shows results for solitary fish with vaccination, indicating that vaccination improves welfare at all density levels with an especially strong effect at higher densities. All results are speculative.

Plots showing speculative results of a hypothetical experiment investigating the effect of crowding on fish welfare, indicating that biological age accumulates slowest (indicating highest welfare) at low densities for solitary fish and middling densities for schooling fish, with a positive welfare effect of vaccination. The shape of the curves is arbitrary.

To make up some totally speculative results, these biological ageing data might indicate that naturally solitary fish exhibit significant welfare declines from any level of crowding, while schooling fish have a preferred crowding level and experience reduced welfare above or below this level. These welfare declines may or may not be abrogated by interventions such as vaccination, better water filtration, or changes in feeding schedule. By collapsing all (or most) of an animal’s welfare experience into a single, objective, cumulative measure, many different experiments of this kind could be performed quickly and efficiently, providing a more comprehensive picture of the welfare effects of crowding on fish wellbeing.

While the idea that crowding is bad for fish welfare may not be particularly controversial, this approach would allow researchers to empirically quantify how bad that effect is compared to other aspects of a farmed fish’s life, and assess the efficacy of different interventions (such as vaccination or water oxygenation) for mitigating that welfare impact. The same technique could be applied to help resolve active empirical controversies in the animal welfare movement, such as the relative welfare levels of caged vs cage-free chickens, the relative importance of water oxygenation in the welfare of farmed fishes, or the net welfare impact of predator reintroduction on prey species.

Limitations and caveats

While I was initially sceptical about the applicability of biological ageing markers as measures of animal welfare, I have generally been convinced that this represents a novel and important advance in the field. Nevertheless, there are a number of important limitations or difficulties I anticipate in actually applying the method, which I think it is important to be aware of and mitigate where possible. I have divided these limitations into two categories: contexts where the prerequisites of applying the method may be difficult or expensive to obtain, and contexts where the link between ageing and welfare may be weakened or broken entirely.

Difficulties in application

The need for biological ageing clocks

In order to use biological ageing as a cumulative welfare measure in a given species, we need some kind of biological ageing measure for that species. As discussed above, these range in complexity from simple metrics like telomere length to highly complex machine-learning-based predictors, and there is likely to be a tradeoff between the accuracy and comprehensiveness of a measure on the one hand and its affordability and transferability on the other. As funding is very limited in many animal-welfare contexts, it is likely that simpler, cheaper metrics that can be transferred between species with relatively little validation will be preferred; however, it is important to remember that these may only provide a partial measure of biological age.

The best biomarkers of ageing to use for these experiments will depend on the species being tested and its relationship to other well-validated model systems. In many vertebrate species, and probably most mammals, the markers established in humans and laboratory mice are likely to be the best option. Conversely, in species very distantly related to humans the validity of these markers may be limited: insects, for example, are largely post-mitotic in their adult form (limiting the usefulness of telomere attrition as a biomarker) and have very different neuroanatomy from vertebrates (preventing the use of hippocampal volume). If it is desirable to apply these ageing-based methods to assess welfare in these species, alternative biomarkers (such as accumulation of fluorescent advanced glycation end products in Drosophila 20) will need to be developed and validated as welfare measures, substantially increasing the upfront cost.

Experimental controls

While biological ageing is a promising measure of cumulative welfare, it is important to remember that welfare is not the only thing affecting biological age. Most obviously, chronological age has a very strong effect on biological age, and studies should always compare age-matched individuals when possible. Genetic variability is also an important factor: many species exhibit substantially different lifespans in different populations, and polymorphisms within a population can also have a substantial effect. As a result, ageing-based welfare measures will be most reliable in contexts where all individuals are genetically homogeneous, or at least where there is no systematic difference in genetic composition between different experimental groups. Finally, there should of course be as little systematic difference in environment as possible between the groups being compared, other than whatever exposure is being investigated for its welfare effects.

Of these control requirements, the need for genetic comparability between experimental groups is the most frustrating, as it appears to exclude a lot of factors that are widely thought to be important for animal welfare, particularly in domesticated contexts where animals’ genotypes have been substantially modified by humans through selective breeding. On the face of it, biological-ageing methods seem to be unable to address this, as the groups being compared are not genetically comparable. However, given the importance of genetic effects on welfare in many contexts, any way to overcome this limitation would be very useful, and further investigation on this front seems quite valuable in expectation.

Limitations to validity

Death and other acute events

In many cases, a large portion of the suffering experienced by an animal, whether domesticated or wild, is suspected to take place acutely at the moment of death. If this death is sufficiently gruesome, the suffering so engendered could potentially outweigh the entire net welfare of a life that is otherwise worth living. Despite its importance, however, it seems unlikely that the suffering entailed by dying would be adequately reflected in ageing-based measures of cumulative welfare. For one thing, there would be no opportunity for the negative experiences associated with the animal’s death to be reflected as a subsequent increase in the rate of biological ageing; for another, the extensive physiological damage resulting from death would prevent an accurate postmortem assessment of biological age in many cases. Hence, an important limitation of ageing-based methods of measuring welfare may be their inability to incorporate the affective experience of dying.

A similar concern might apply, albeit to a much lesser extent, to other highly acute exposures, i.e. those with large but short-lived effects on welfare. Many of these will be reflected in ongoing cumulative welfare to some extent (e.g. as physical or psychological trauma in the case of negative events), but it’s not clear to me that the cumulative readout of welfare given by ageing biomarkers will always incorporate them adequately. More research may be needed here.

Differences between juveniles and adults

In both wild and domesticated contexts, many of the animals of greatest concern from a welfare perspective are juveniles who have not yet reached reproductive maturation. These immature individuals are often much more numerous than adults, and have less chance to accumulate positive experiences to outweigh the pain of dying.

For animals that die shortly after birth or hatching, the cumulative welfare of their lives is likely to be dominated by the affective experience of dying, and this experience will not be adequately reflected in biological ageing markers. Ageing-based approaches therefore seem unable to effectively address the welfare of these individuals; on the other hand, it seems likely that any measure of cumulative welfare will run into the same problem.

The situation for individuals that have relatively long lives as juveniles is more complex. In many species, juvenile and adult individuals differ substantially in their biology, and the question of whether juveniles are “ageing” is somewhat fraught. There are certainly dramatic changes taking place over the course of development, some of which could be interpreted as an accumulation of damage: telomeres, for example, shorten rapidly during the period of juvenile growth 18. On the other hand, many clinical biomarkers of ageing do not begin to accumulate until adulthood 19. This means that the applicability of biological-ageing measures to juveniles depends on the specific biomarkers being used, and different markers are likely to be most appropriate for measuring juvenile vs adult wellbeing 29.

Animals very different from humans

The evidence supporting the use of biological ageing markers as measures of cumulative welfare falls into three broad categories: a theoretical, evolutionary argument linking the affective status of an exposure to its effect on ageing via its effect on somatic damage; extensive empirical research in humans associating biological ageing markers with directly reported affective mood (e.g. stress, depression or anxiety) and experiences known to impact mood (e.g. trauma, pain, exercise, and sleep); and more limited empirical research in animals linking these markers to experiences that are both plausibly affectively relevant and associated with other widely-used welfare measures.

The second of these, empirical data on humans, is particularly important, as only humans are capable of directly reporting their affective state to human researchers and so directly confirming a link between biological ageing and welfare. As one moves away from humans in terms of the species under investigation, the less weight can be put on this source of evidence in support of this link, and the more one has to rely on the first and third sources of evidence outlined above. The more distant and dissimilar a species is from those species in which ageing-base techniques have been empirically studied, the more our confidence in those techniques should decrease towards the level of confidence we have in the theoretical argument alone. This poses an issue, since the vast majority of animals on the planet fall into this category.

There are two particular groups of animals for which I think the existing empirical data provides relatively little support for ageing-based welfare measures: invertebrates, and those animals (both vertebrate and invertebrate) whose pattern of lifetime ageing differs substantially from that of humans. The reasons for scepticism in the first case are clear: invertebrates are very different from vertebrates in many aspects of their biology, differ substantially from mammals in terms of their biomarkers of ageing (see above), are very diverse amongst themselves, and are almost totally unstudied as objects of welfare concern. My concerns about the second group, however, are likely to be less clear to someone outside the ageing field, and I will try to briefly explain my reasons below.

Typically, under simple assumptions that are frequently roughly met in real animals, we expect to see mortality progressively increase and fecundity progressively decrease with time after reproductive maturation 16. Many animals, including humans and nearly all common domesticated species, follow this pattern, but this does not apply universally to all animals. Some species, most famously the green hydra 14 and more recently the naked mole rat 15, do not appear to age at all, while some (including various corals, reptiles and amphibians 9) seem to “age in reverse”, exhibiting declining mortality with age until death.

These differences in life history could pose major problems for the use of biological ageing markers as a measure of cumulative welfare in these species. To begin with, it isn’t clear how to define the concept of biological age, let alone measure it, in an animal that does not age in any conventional way. Some subset of conventional biomarkers of ageing may still accumulate with time in these species, but that subset is likely to differ from taxon to taxon depending on what biological methods they have used to overcome the ageing process. Worse, even the theoretical argument in support of ageing-based welfare measures in these species may be greatly weakened: for an animal to exhibit no ageing or even reverse ageing over a prolonged time period, they must be either extremely resistant to somatic damage or have extremely good mechanisms in place to repair that damage, meaning the relationship between damaging (and therefore aversive) experiences and ageing may be largely or entirely severed.

These issues will pose little difficulty to researchers and activists concerned with the welfare of agricultural, experimental or other captive animals, most or all of which, to my knowledge, follow the conventional pattern of ageing exhibited by humans, mice and Drosophila. However, they could turn out to be significant for researchers interested in quantifying and improving the welfare of wild animals, who will inevitably have to tackle the welfare of large numbers of animals very different from humans. The extent to which these “unconventional” life histories are widespread in the natural world is unclear to me at present, and could be an important factor affecting the applicability of these methods in certain contexts.


Finding better methods with which to quantitatively measure the cumulative welfare experience of nonhuman animals would represent a major advance in the study of animal welfare. As a potential route to a better and more objective measure of cumulative welfare, biomarkers of ageing are potentially very promising.

Many of the caveats I outlined above are educated guesses and may well turn out to be circumventable with sufficient thought and care. If they are not, how serious a problem would this pose for the practical usefulness of this method? In the case of domesticated animals, an inability to compare groups differing systematically in their genetics or adequately incorporate the badness of death are all significant limitations, but would still leave us with a tool which could be gainfully applied in many important contexts. For wild animals, the issues are more serious: there are vastly more species for which we would have to develop methods of measuring biological age, it is much harder to perform well-controlled longitudinal experiments, and many more of the animals of concern fall into categories for which I am more sceptical about the theoretical applicability of the method. A further concern is that measuring chronological age accurately is often difficult for wild animals, potentially undermining one of the foundations of ageing-based welfare measures. Nevertheless, of all the methods we might think of for measuring the cumulative welfare of wild animals, biomarkers of ageing seem to be among the least hopeless, and among those most worth developing further in the hopes of overcoming some of these pervasive issues.

Overall, I am currently very optimistic about the value of applying these methods in domesticated contexts, and cautiously optimistic about applying them to wild ecosystems. I would recommend that researchers interested in the welfare of either domesticated or wild animals take note of these techniques and consider their applicability in their own domains, and that funders in this space seriously consider funding their further development and application to new contexts. On a meta level, I think the surprisingly strong applicability of techniques from the biology of ageing to animal welfare science should encourage us all to look for innovative, unexpected and interdisciplinary ways to help nonhuman animals: as is often the case in science, the crucial insights and discoveries may not be at all where we expect them to be.


This report was funded by the Wild Animal Initiative, and many WAI researchers (including Luke Hecht, Michelle Graham, Hollis Howe and Jane Capozzelli) contributed their time to reviewing and suggesting improvements to the draft. Prof. Melissa Bateson, the author of the key sources for the report, also generously read the draft and provided crucial feedback.

  1. Bateson, M., & Poirier, C. (2019). Can biomarkers of biological age be used to assess cumulative lifetime experience? Anim. Welf. 28: 41-56. doi: 10.7120/09627286.28.1.041 

  2. Medawar, P. B.(1952). An unsolved problem of biology. London: HK Lewis & Co 

  3. One major category of negative experiences which do not appear to be directly damaging (but are nevertheless important to an animal’s wellbeing) are social experiences. For many reasons, an animal’s social status and relationships are very important to their survival and reproduction, but are not typically the direct cause of bodily damage. However, negative social experiences (low status, rejection by mates, ostracism) do give rise to significant levels of stress in many species, and this stress is well known to be physiologically damaging. 

  4. Bateson, M. (2016). Cumulative stress in research animals: Telomere attrition as a biomarker in a welfare context?. BioEssays 38 (2): 201-212. 

  5. Belsky, D. W., et al. (2017). Eleven telomere, epigenetic clock, and biomarker-composite quantifications of biological aging: do they measure the same thing? American Journal of Epidemiology 187 (6): 1220-1230. 

  6. Cole, J. H. et al. (2018). Brain age predicts mortality. Molecular Psychiatry 23 (5): 1385-1392. 

  7. Ashley, P. J.(2007). Fish welfare: current issues in aquaculture. Applied Animal Behaviour Science 104 (3-4): 199-235. 

  8. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013). The Hallmarks of Aging. Cell 153 (6): 1194-1217. 

  9. Jones, O. R., et al. (2014). Diversity of ageing across the tree of life. Nature 505 (7482): 169. 

  10. Jia, L., Zhang, W., & Chen, X. (2017). Common methods of biological age estimation. Clinical Interventions in Aging 12: 759-772. 

  11. Horvath, S., & Raj, K. (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics 19 (6): 371-384. 

  12. Mamoshina, P., et al. (2019). Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers. Scientific Reports 9: 142. 

  13. Langkilde, T., & Shine, R. (2006). How much stress do researchers inflict on their study animals? A case study using a scincid lizard, Eulamprus heatwolei. Journal of Experimental Biology 209 (6): 1035-1043. 

  14. Dańko, M. J., Kozłowski, J., & Schaible, R. (2015). Unraveling the non-senescence phenomenon in Hydra. Journal of Theoretical Biology 382: 137-149. 

  15. Ruby, J. G., Smith, M., & Buffenstein, R. (2018). Naked mole-rat mortality rates defy Gompertzian laws by not increasing with age. eLife 7:e31157. 

  16. Charlesworth, B. (2000). Fisher, Medawar, Hamilton and the evolution of aging. Genetics 156 (3): 927-931. 

  17. Williams, G. C.(1957). Pleiotropy, natural selection, and the evolution of senescence. Evolution 11(4): 398-411. 

  18. Zeichner, S. L., et al. (1999). Rapid telomere shortening in children. Blood 93 (9): 2824-2830. 

  19. Hollingsworth, J. W., Hashizume, A., & Jablon, S. (1965). Correlations between tests of aging in Hiroshima subjects—an attempt to define “physiologic age”. Yale J Biol Med 38 (1): 11-26. 

  20. Jacobson, J., et al. (2010). Biomarkers of aging in Drosophila. Aging Cell 9 (4): 466-477. 

  21. Morozova, A., et al. (2016) Ultrasound of alternating frequencies and variable emotional impact evokes depressive syndrome in mice and rats. Progress in Neuropsychopharmacology and Biological Psychiatry 68: 52-63. 

  22. Smith, E.L., et al. (2005) Effect of repetitive visual stimuli on behaviour and plasma corticosterone of European starlings. Animal Biology 55: 245-258. 

  23. Throughout this piece I am assuming that welfare is synonymous with affect, i.e. with subjectively positive and negative experiences. This is not an uncontroversial position, and there are many in the animal-welfare field who prefer a concept of welfare which incorporates both affective wellbeing and physical health, or even avoids questions of subjective experience altogether 24. While I’m sceptical about this, it’s worth noting that measures of cumulative wellbeing are also essential when using such a definition, so it doesn’t necessarily change many of the conclusions of this report. 

  24. Dawkins, M. S.(2017) Animal welfare with and without consciousness. Journal of Zoology 301: 1-10. 

  25. Pepper, G.V., et al. (2018) Telomeres as integrative markers of exposure to stress and aversity: a systematic review and meta-analysis. Royal Society Open Science 5: 180744. 

  26. It’s important to stress here that the great majority of research into the association between lifestyle and telomere length is cross-sectional and correlational: individuals with greater exposure to adversity have shorter telomeres. This finding is robust, but not causal: it doesn’t demonstrate that these adverse experiences cause shorter telomeres. Evidence from longitudinal studies, for example on smoking 27, tend to find a much smaller effect; more on this later. 

  27. Bateson, M. et al. (2019). Smoking does not accelerate leucocyte telomere attrition: a meta-analysis of 18 longitudinal cohorts. Royal Society Open Science 6: 190420. 

  28. Melissa Bateson (pers. comm.) points out that a “complex and multi-modal” measure of biological ageing does not necessarily entail expensive and expertise-heavy multi-omics methods. An alternative approach, which captures the goal of assessing many different aspects of an animal’s biology while being much cheaper, is a “biomarker panel” approach, in which a large number of different easy-to-measure features that are associated with poor health in old age are collected from the same individuals. These could then be used as input to a machine-learning model which has been trained to use them to predict biological age. This approach has many advantages, including potentially high reliability and low cost. However, it seems to require a fairly high level of interaction with the animal (at least in humans and lab animals, many of the markers used are often behavioural), making it difficult to use in wild contexts, and is probably quite species-specific. Both of these issues could be overcome to some extent through the choice of biomarkers used. 

  29. In the case of telomere attrition, juveniles may actually be more appropriate subjects than adults (M. Bateson, pers. comm.): the base rate of telomere attrition is much higher, which makes changes in the attrition rate due to welfare factors easier to detect. It is also much easier, quicker and cheaper to do longitudinal studies on juveniles, avoiding many of the interpretation issues associated with cross-sectional studies. 

At the LessWrong European Community Weekend 2018, I gave a talk explaining the intuition behind non-adaptive theories of the evolution of ageing. This blog post and its followup are adapted from that presentation.

When people find out that I did my PhD in the biology of ageing, they tend to ask one of two questions. First, they ask what they can do to live longer. Second, they ask why people age in the first place. My answer to the first question is unfortunately fairly boring at present — don’t smoke, eat well, get enough exercise, get enough sleep, et cetera — but when it comes to the second I think I have more of interest to say. To get this blog rolling (see what I did there), let’s dive into the important and fractious question of why we age.

What is ageing?

It is a curious thing that there is no word in the English language that stands for the mere increase of years; that is, for ageing without its connotations of increasing deterioration and decay.

—Peter Medawar, “An Unsolved Problem in Biology”

When people talk about “ageing”, there are broadly speaking three different things they might mean1. Firstly, there is the simple process of getting older — of the amount of time since you were born inexorably increasing. Let’s call this process “temporal ageing”. Ageing in this way has a lot of benefits: more memories, more experience, and with luck more self-knowledge and more wisdom.

Unfortunately, the benefits conferred by temporal ageing are currently inextricably tied to the physical changes denoted by the second meaning of “ageing”: a generalised physiological deterioration, characterised by a wide range of unfortunate symptoms affecting almost every system of the body. As a result of this second kind of ageing, we become slower, more fragile, more prone to disease, and generally more likely to experience impaired health and wellbeing as we get older, eventually leading to death. As we as a civilisation have gradually eliminated more and more extrinsic forms of suffering and death, the depredations of ageing have gradually become the primary cause of ill health and death in developed countries by an overwhelming margin. This is the kind of ageing people mean when they worry about getting cancer or dementia, buy “anti-ageing” skin cream, or invest in real anti-ageing research; it’s the province of doctors, physiologists, and molecular biologists. Let’s call it “physiological ageing”.

Finally, the individual changes taking place due to physiological ageing give rise to a distinctive statistical pattern at the level of entire populations of humans or other animals: a progressive increase in mortality (probability of dying) and decrease in fecundity (expected number of offspring) in older age cohorts. This pattern is what gives rise to plots like the ones below, and it’s what demographers, actuaries and evolutionary biologists generally mean when they talk about “ageing”. From this perspective, the specific functional changes underlying these changes in survival and reproduction are less important than the high-level functional changes that result: changes in the rates of reproduction, illness, disability, and death. From an evolutionary perspective it is the first and last of these, reproduction and death rates, that are the most important. We can call this final meaning of the word ageing “demographic ageing”.

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Logarithmic mortality curves for British and American populations at different points in the 20th century. The \(y\)-axes give the log-probability of dying for individuals in a given age-class in the year and country indicated. Source: US Office of Retirement and Disability Policy

These two phenomena, of physiological and demographic ageing, are tightly interlinked in any given population but are nevertheless conceptually distinct: two different species (an insect and a mammal, say) could undergo very different physiological ageing processes but exhibit very similar patterns of demographic ageing. Physiological and demographic ageing also give us very different perspectives on the question of why we age. From the perspective of physiological ageing, the question is generally asking about the specific genetic, molecular, histological or physiological mechanisms underlying the changes we observe: what particular aspect of our biology is causing our bodies to deteriorate with age in this or that particular way? From the perspective of demographic ageing, the relevant why question is simpler and more fundamental: given that ageing appears to be pretty deleterious to the survival and reproduction chances of any individual experiencing it, how could we have evolved to exhibit declining functionality with age at all?

In these posts, I’ll be focusing on the second kind of why question, discussing the evolutionary teleology of the ageing process. As we’ll see, from that perspective, ageing is frankly pretty weird.

Three puzzles of ageing

When we look at nature, or at ourselves, we observe something surprising: animals get old. In many, many different species, old age is accompanied by a progressive decline in functionality, leading to higher rates of death2 and lower rates of reproduction. This pattern is seen almost everywhere you look in the animal kingdom, suggesting that it has either evolved again and again independently or been retained after inheritance from a common ancestor. Yet despite this commonality, there is profound variation in the details of the ageing process: even closely related species can differ dramatically in how quickly they age and how long they tend to live. And here and there, we see species that seem to have escaped the iron grip of ageing, exhibiting mortality that stays constant or even declines over time.

Alt Text

Distribution of lifespans across all mouse and bat species from the AnAge database (accessed 2018-09-01). Despite their relatively close relationship, similar size, and similar metabolic rates, bats live dramatically longer than mice.

This, then, is the threefold puzzle of ageing. Why should a process that appears to be so deleterious to the individuals experiencing it have evolved to be so widespread in nature? Given this ubiquity, which implies there is some compelling evolutionary reason for ageing to exist, why do different animals vary so much in their lifespans? And how, when ageing has either evolved or been retained in so many different lineages, have some animals evolved to escape it?

Any successful theory of the evolution of ageing must be able to convincingly answer all these questions. A number of attempts have been made over the years, none of which has managed to capture the consensus of the academic community. These attempted explanations can be broadly divided into two groups: those that propose with some reason why ageing, which seems so deleterious, is adaptive after all, and those that accept that ageing is deleterious and attempt to explain why it might evolve anyway. I’ll discuss the main representatives of each group in separate blog posts, but first I want to tackle one simple non-adaptive theory that doesn’t quite manage to do the job.

Why ageing is not (just) wear and tear

The senescence of human organs consists not of their wearing out but of their lack of replacement when worn out.

George C. Williams, “Pleiotropy, Natural Selection, and the Evolution of Senescence”

One common folk theory of ageing is that it is simply wear and tear: like a car, the body is a machine, and like any machine it wears out over time. Exposure to the environment naturally leads to the accumulation of damage, which progressively impairs the function of the machine until it breaks down (i.e. we die). Any imperfection in the machine’s components will hasten this process, either by generating more damage or by becoming progressively more dysfunctional over time. This progressive degradation is inevitable: we can keep a car in working order with regular maintenance and repair, but we are not (yet) capable of doing this for the kinds of wear and tear that accumulate in the body. Hence, ageing.

This explanation of ageing is intuitive, and parts of it are true as far as they go. There are certainly various kinds of damage and dysregulation that accumulate in the body with age: genetic mutations, senescent cells, shortened telomeres, cross-linked chemical aggregates, degraded stem-cell niches, and on and on. If we could remove and correct some or all of these issues the way we can replace a dodgy spark plug, we’d go a long way towards addressing the problem of physiological ageing.

But as an explanation of why ageing exists in the first place, “wear and tear is inevitable” just doesn’t cut it, because a living body is not like a car. Where a car is dead matter shaped by external tools, a body is a dynamic, self-generating system with incredible powers of self-repair. These self-repair processes are awe-inspiringly good: of the tens of thousands of genetic mutations that occur per cell every day in the human body, virtually all are accurately repaired. Our bodies can repair wounds, fight off infections, kill and replace malfunctioning cells, partially regrow (some) organs, even remodel their bones to best respond to the forces they experience. Many of these regenerative processes decline as we age, but that decline is itself part of the ageing process: young children are amazingly good at healing without scarring, for example.

So while bodily damage is inevitable as part of the daily business of living, our bodies successfully repair almost all of it, especially when we’re young. Evolutionarily speaking, the question is not why the damage occurs, but why it is permitted to accumulate. It seems our bodies’ repair processes are not quite perfect, and allow damage and dysregulation to progressively accumulate over time. Why aren’t they better?

Could our bodies’ repair systems be better? They could certainly be worse: there are many, many mammal species with much shorter lifespans than humans’, even when kept in very safe conditions. These animals age faster than humans because they accumulate damage and dysregulation faster; for whatever reason, their monitoring and repair systems have evolved to be that much sloppier than ours3. Conversely, there are at least a few mammals (such as bowhead whales) that live longer than we do; clearly they have something going for them that we don’t, but why? And that’s without going into animals like green hydra or naked mole rats that don’t seem to age at all: if they can do it, why can’t we?

Because it’s not an evolutionary theory, wear-and-tear is incapable of addressing these questions. Yes, damage is inevitable, but why does this result in such different rates of ageing in different species? If one species can evolve to remove this damage so efficiently that it doesn’t age at all, what is preventing most other species from doing the same? The answers to these questions don’t lie in the eternal inevitability of molecular damage, but in the selective pressures each species is exposed to across evolutionary time. In the rest of this series, I’ll address theories of ageing that attempt to explain ageing in these terms.

  1. Actually, there’s a fourth meaning that gets used in the media quite a lot: “population ageing”, by which is meant an increase in the median age of a population and the proportion of old people due to changes in social conditions. This is distinct from my “demographic ageing” in that the former is looking at the age composition of the whole population, while the latter is comparing different age groups within the population. I don’t plan on talking about population ageing here. 

  2. This increase in death rate is both intrinsic and extrinsic: older individuals are more likely to die from heart attack, stroke, cancer and so on, but are also more vulnerable to predation, starvation and disease. 

  3. I’ve left out an important consideration here, which is that rather than worse repair systems, these other mammals might be experiencing higher rates of damage, perhaps due to a higher metabolic rate. A repair system with the same stringency that is exposed to a higher level of damage will let more damage events through. Even if this is true, though, the question remains of why these animals haven’t evolved better repair mechanisms to cope with this higher rate of damage. 

  4. In addition to being longer-lived than humans, bowhead whales are also larger. This also raises the classic “why don’t all whales get cancer” problem: if cancer is a matter of mutations, mutations are a matter of chance, and whales have more cells in which the right mutations can accumulate, why don’t they all get horrible tumours? There are various theories about this problem, too, none of which I intend to discuss here.