Long-term design considerations for wild animal welfare interventions

Persistence and reversibility

When assessing the merits of different interventions to improve wild animal welfare, two prominent factors are (1) how persistent a certain intervention is and (2) how easy it is to reverse the effects given a bad outcome.

Reversibility is important because we can’t always predict upfront exactly how an intervention will turn out. If things go wrong, we want to be able to reverse the intervention to contain the damage. We anticipate having a better empirical understanding in the future, so to preserve option value, we probably want to delay persistent and hard-to-reverse interventions until we’re highly confident they’ll have positive effects.

Persistence, or the duration of the effect in expectation, is important because it will affect how much effort we would have to put into improving wild animal welfare by a certain amount. If the effects are very persistent, we only have to initiate the process once, then we can reap the benefits over a long period of time. 

However, the benefits of higher cost-effectiveness of persistent interventions seem to be strongly outweighed by other factors in most cases. Firstly, most highly persistent interventions are very hard to reverse, which means we have to give up a lot of option value. Secondly, trying to implement highly persistent and hard-to-reverse interventions could stir up a lot of animosity, which would be damaging for the long-term stability of the wild animal welfare project.

Thus, it seems like if we have to choose between an intervention with low persistence but high reversibility and an intervention with high persistence but low reversibility, we should choose the former rather than the latter.

Striking a balance

There might be a way, however, to increase persistence in some scenarios without paying with reduced reversibility. More precisely: if we could find interventions that are persistent in the face of natural processes, while at the same time being highly reversible by humans, we could get some of the benefits of persistence, while avoiding the negative consequences of implementing irreversible interventions. 

With continued work on their efficacy and safety, gene drives might possess these characteristics in the future. A gene drive is a genetic modification that makes itself more likely to be inherited. Gene drives can be persistent because they are able to modify the genome of every individual in a population or species. Gene drives can also, under some circumstances, be reversible. A secondary gene drive can target the primary gene drive, halting its spread and removing the changes it made. However, it is important to note that the safety and reversibility of gene drives is critically dependent on how much effort is put into making them safe and reversible, which is one reason why the development of policies regulating gene drives will be of high importance.

Despite gene drives being an important exception, most actions will probably be either non-persistent and reversible, or persistent and non-reversible. In my report, I explore three examples in the second category: extinctions, climate change, and energy efficiency improvements in plants. Even though all of these examples have somewhat unclear effects on wild animal welfare, they should probably be avoided due to the fact that they seem very hard to reverse, and will persist for a long time.

Cover photo: Adapted from “Loggerhead sea turtle hatchlings crawl to sea,” © Blair Witherington, Creative Commons

Optimal population density: trading off the quality and quantity of welfare

Introduction

Largely as a result of human development, the sizes of many wild animal populations have been falling.¹ Much of this decline has resulted not from direct killing of wild animals by humans, but indirectly by fragmenting their habitats and cutting animals off from the resources that sustained their historical population sizes.^2,3 This has immediate consequences for the welfare of individuals, who may face starvation, competition for resources, or conflict with humans as they try to access resources in their former habitat (e.g. urban pigeons⁴; fur seal⁵; black bear⁶). According to many ethical views, it is better to have more happy animals than fewer equally happy animals; but as a population grows, so does the number of individuals who may be adversely affected by further growth, requiring us to think carefully about trade-offs between the quantity and quality of welfare. A thorough understanding of individual welfare levels in a given population and how they are affected by changes in population density could eventually enable us to estimate the population size which would maximize the welfare experienced in that population.

Population modeling and density-dependence

The density of a population is defined by the ratio between the number of individuals and the amount of habitat or a limiting resource available to them; if a population grows or its habitat declines, population density increases. The population size (N) at which a balance between birth rate and death rate is achieved—its maximum sustainable size—is known as the “carrying capacity” (K) under given environmental conditions (Box 1).

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Box 1

A population’s growth rate per generation (r) is equal to the difference between the number of births (b) and the number of deaths (d): r = b - d. The growth rate before any effects of density are taken into account is denoted r₀. To the extent that r₀ exceeds zero, density-dependent effects must reduce age-specific survival or fecundity rates to achieve a stable population. The population size (N) at which r = 0 and the population remains stable long-term is known as the carrying capacity (K).

These parameters come together in the logistic growth equation, where any excess of births over deaths shrinks to zero as N approaches K: dn/dt = r₀N(K - N)/(K).

While this equation captures how extremes of population density relate to population growth rates, it is certainly incomplete. For example, intermediate densities may have less consistent effects than the simple logistic function suggests, especially in populations facing strong density-independent stressors. Populations inhabiting an environment that changes much more rapidly than the time between their generations may never reach their carrying capacity at any given time, but may still encounter strong density effects during periods when N > K, as might occur during a drought or similar period of low productivity.

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Death rates may rise and birth rates may fall as a result of stressors experienced by the animals in a population. These stressors can be density-dependent or density-independent. For example, competition for food can lead to starvation and limit the energy available for reproduction⁷, and crowding in early life can cause an animal to age prematurely⁸. Infectious diseases may also spread more rapidly in a dense population⁹.

Other causes of mortality are density-independent. In a river, water temperature or pollutants might kill fish at a rate that is independent of the number of fish in the river¹⁰. Indeed, density-independent factors can make an environment so hostile that the maximum stable population size is very low, and the surviving individuals face no meaningful competition for resources.

Whether a certain factor is density-dependent or density-independent can also vary according to the circumstances. For example, the direct threat posed to any individual by fierce winds and lightning during a storm is independent of density, but density-dependent mortality might follow in the wake of the storm if a limiting resource has been damaged. Population density within a certain range can also have positive effects for individual welfare by providing safety in numbers and more accessible social interactions. For example, having fellow newts around during their transition to maturity means that individuals of Triturus cristatus are less likely to undertake dangerous migrations to find mates¹¹.

Welfare implications of population density

In a recent article, I proposed the concept of welfare expectancy as a way to link age-specific survival rates and welfare. Welfare expectancy is a sum of the lifetime welfare an individual born into a given population can expect to experience. Age-specific survival rates may also be correlated with age-specific welfare, suggesting that any density-related impairment of survival could greatly reduce welfare expectancy, especially when it affects juvenile animals.

A general expectation of declining welfare expectancy per individual as a population grows towards its carrying capacity raises the possibility that the population size which maximizes the total welfare of a population may be smaller than its maximum sustainable size. Whether this occurs depends on how many individuals the population would contain at its carrying capacity and how average welfare varies with population size (Box 2). The conditions are most likely to be met if the strongest negative effects of population density on welfare manifest at high densities (i.e., as N approaches K; Figure 1). This could occur under the following three conditions:

1. if lower densities primarily limit reproduction while higher densities affect survival rates.
2. if density-dependent mortality disproportionately affects older animals at lower densities but young animals at high densities; or
3. if density-independent effects are nearly sufficient to stabilize the population’s growth rate, so any negative effects of density are only felt near the carrying capacity.

In general, the shape of average welfare expectancy with respect to density depends on the particular ecology and life history of the population in question, especially whether density preferentially affects fecundity versus survival rates or the survival rates of young versus old individuals. If increased density is mainly accommodated by reduced fecundity or late-life survival, for instance, then average welfare might not decline so steeply as a population grows. Theory and reviews of demographic models for a variety of large vertebrates suggest that juvenile survivorship is usually the first casualty of high population densities^12,13,14, which is one set of conditions under which we might expect the welfare-optimal population density to be lower than the population’s carrying capacity. Part of the explanation for this pattern in large vertebrates is the competitive advantage older animals typically have over younger animals who are smaller and less experienced. However, it is not yet known how broadly these age dynamics apply among the much more numerous smaller-bodied, “fast-living” animals, where there are examples of adult advantage¹⁵ and disadvantage¹⁶. More research is needed into the expected shape and mechanisms of density-dependence among their populations.

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Box 2

Let W_N represent the average welfare expectancy at population size N and let K represent the population size at carrying capacity. Then, the welfare-optimal population size will be less than the carrying capacity if W_K-1/W_K > K/(K - 1).

The welfare expectancy per individual in a population depends on the life expectancy from birth and the welfare they typically experience over that lifespan. If average welfare is positive, average welfare expectancy will correlate with life expectancy. If an increase in population density disproportionately harms the youngest animals, this can greatly reduce life expectancy.

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Implications for welfare interventions

If we are completely uncertain about the absolute welfare level of animals in a population, then it follows that we are completely uncertain about whether their lives are dominated by pleasure or suffering. If their lives contain more goodness on average, then increasing the population size would be expected to increase the total amount of welfare experienced within the population as a whole. On the other hand, if their lives are primarily unhappy , then adding more unhappy individuals through population growth would actually subtract from the population’s total welfare. From a standpoint of complete uncertainty, then, the expected value of population growth or decline is symmetrical¹⁷. Whether or not population growth increases or decreases total welfare of the population depends on the balance of the positive and negative experiences of the individuals added by that growth.

However, the existence of a density-dependent decline in average welfare would set up an asymmetry in favor of relatively smaller populations, since increasing population density would reduce the probability that average welfare remains net-positive, giving population growth a lower expected value than population reduction. Of course, this heuristic assumes complete ignorance of the population’s welfare characteristics, which is rarely the case.

On the other hand, if we knew the exact average welfare expectancy for all possible population sizes (W_N), we could predict its optimal density and also define a ‘margin of error’ on either side within which total welfare expectancy would be expected to at least equal that obtained at carrying capacity (Figure 1). This could provide a wide target for interventions aiming to improve welfare by managing population density. Notably, the largest range and scope for improvement in total welfare over what would occur naturally is possible when the most aversive effects of population density only manifest at high densities. This suggests that a review of evidence on the shapes of density-dependence curves for various species could indicate how impactful managing around population density might be as a form of welfare intervention.

Figure 1. Left: Hypothetical curves depict how average (per-individual) welfare expectancy might decline with increasing population density, depending on whether the most aversive effects of density develop at low densities (“early” in population growth), at a constant rate (“continuous”), or only at high population densities (“late”). Right: Average welfare under each of these three scenarios is multiplied by population density to give total welfare. Total welfare below the level achieved at the maximum sustainable size (K) is shaded. Despite average welfare always being positive, the welfare-optimal population size (O) is less than the maximum sustainable population size under the “late” (O=0.75) and “continuous” (O=0.81) scenarios. Total welfare continues to increase up to K under the “early” model (O=1.00), where higher population densities have a smaller effect on average welfare.

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Figure 2. Left: As the population grows from 0 to K average welfare declines from net-positive to net-negative (red shading). A population of wild animals might appear to have very poor welfare at their natural population density, yet have the potential for good welfare at lower population density. Right: In such a case, their total welfare would be maximized by sustaining a medium-sized population, with “Late” density effects increasing the population’s optimal size and potential welfare.

Population density could be managed (and any effects ameliorated) by reducing birth rates, culling individuals from the existing population, or increasing resource availability. Culling is currently the default method for managing animal densities, usually targeting those considered “pests.” Although welfare is occasionally considered, this approach is suboptimal because it involves inflicting suffering on some animals and cutting their lives short (even though culling methods may be less painful than natural causes of death¹⁸). Non-lethal reduction of population density could be achieved through fertility control, directly limiting the number of animals born without shortening the lives of extant individuals. A complementary approach is reducing the effective density of a population by increasing the quantity or quality of its habitat using the principles of restoration ecology. This could reduce density and improve welfare in the short-term without requiring any reduction in population size. However, without additional measures artificially limiting their growth rate, populations would simply return to their natural sizes and any associated harmful effects of density, so to be sustainable long-term this would still need to be paired with fertility control or another method. A combination of fertility control and habitat improvement could form the basis of a welfare-focused approach to wildlife management. Such an approach must also take into account non-target impacts on other species before being put into practice¹⁹.

Conclusions

It is currently technologically infeasible to make population management decisions that consider the needs of every individual wild animal. We therefore need to determine guiding principles to help us predict the likely results of various population management projects, even under uncertainty about the absolute level of welfare the relevant animals experience. Understanding the density-dependence of welfare in a given population provides one such principle. Furthermore, given sufficient information about absolute welfare levels, this approach could also be used to identify a window of effect sizes where an intervention is expected to increase total welfare relative to the natural long-term size of a population.

The effects of varying levels of density on survival and reproductive rates are exceedingly difficult to determine for any population, given the generational timescales involved and the fact that any population density other than K must by definition be unstable without outside intervention. As a result, precise estimates of density-dependence are rare in the literature, though there is empirical and theoretical support for certain generalizations. Direct evidence on wild animal welfare is also lacking, though we can make inferences from survival rates, body condition and behavior. Research in both of these areas could be high-impact, as an understanding of how welfare expectancy varies with population size would be crucial for planning many large-scale wild animal welfare interventions.

References

1. World Wildlife Federation. 2018. Living Planet Report - 2018: Aiming Higher.  ↩

2. Andren, H. (1994). Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos, 355-366. ↩

3. Ripple, W. J., Wolf, C., Newsome, T. M., Hoffmann, M., Wirsing, A. J., & McCauley, D. J. (2017). Extinction risk is most acute for the world’s largest and smallest vertebrates. Proceedings of the National Academy of Sciences, 114(40), 10678-10683. ↩

4. Senar, J. C., Montalvo, T., Pascual, J., & Peracho, V. (2017). Reducing the availability of food to control feral pigeons: changes in population size and composition. Pest management science, 73(2), 313-317. ↩

5. Doidge, D. W., Croxall, J. P., & Baker, J. R. (1984). Density‐dependent pup mortality in the Antarctic fur seal Arctocephalus gazellu at South Georgia. Journal of Zoology, 202(3), 449-460. ↩

6. Murphy, S. M., Augustine, B. C., Ulrey, W. A., Guthrie, J. M., Scheick, B. K., McCown, J. W., & Cox, J. J. (2017). Consequences of severe habitat fragmentation on density, genetics, and spatial capture-recapture analysis of a small bear population. PloS one, 12(7), e0181849. ↩

7. McMahon, C. R., Harcourt, R. G., Burton, H. R., Daniel, O., & Hindell, M. A. (2017). Seal mothers expend more on offspring under favourable conditions and less when resources are limited. Journal of Animal Ecology, 86(2), 359-370. ↩

8. Nettle, D., Monaghan, P., Gillespie, R., Brilot, B., Bedford, T., & Bateson, M. (2015). An experimental demonstration that early-life competitive disadvantage accelerates telomere loss. Proceedings of the Royal Society B: Biological Sciences, 282(1798), 20141610. ↩

9. Civitello, D. J., Allman, B. E., Morozumi, C., & Rohr, J. R. (2018). Assessing the direct and indirect effects of food provisioning and nutrient enrichment on wildlife infectious disease dynamics. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1745), 20170101. ↩

10. Boulenger, C., Acou, A., Gimenez, O., Charrier, F., Tremblay, J., & Feunteun, E. (2016). Factors determining survival of European eels in two unexploited sub‐populations. Freshwater Biology, 61(6), 947-962. ↩

11. Cayuela, H., Schmidt, B. R., Weinbach, A., Besnard, A., & Joly, P. (2019). Multiple density‐dependent processes shape the dynamics of a spatially structured amphibian population. Journal of Animal Ecology, 88(1), 164-177. ↩

12. Eberhardt, L. L. (1977). "Optimal" Management Policies for Marine Mammals. Wildlife Society Bulletin, 162-169. ↩

13. Gaillard, J. M., Festa-Bianchet, M., & Yoccoz, N. G. (1998). Population dynamics of large herbivores: variable recruitment with constant adult survival. Trends in Ecology & Evolution, 13(2), 58-63. ↩

14. Bergman, E. J., Doherty, P. F., White, G. C., & Holland, A. A. (2015). Density dependence in mule deer: a review of evidence. Wildlife Biology, 21(1), 18-30. ↩

15. Figler, M. H., Cheverton, H. M., & Blank, G. S. (1999). Shelter competition in juvenile red swamp crayfish (Procambarus clarkii): the influences of sex differences, relative size, and prior residence. Aquaculture, 178(1-2), 63-75. ↩

16. Fletcher, W. J. (1988). Intraspecific interactions between adults and juveniles of the subtidal limpet, Patelloida mufria. Oecologia, 75(2), 272-277. ↩

17. Briggs, R. A., "Normative Theories of Rational Choice: Expected Utility", The Stanford Encyclopedia of Philosophy (Fall 2019 Edition), Edward N. Zalta (ed.). ↩

18. Bradshaw, E. L., & Bateson, P. (2000). Welfare implications of culling red deer (Cervus elaphus). Animal Welfare, 9(1), 3-24. ↩

19. Clarke, M., & Ng, Y. K. (2006). Population dynamics and animal welfare: issues raised by the culling of kangaroos in Puckapunyal. Social Choice and Welfare, 27(2), 407-422. ↩

Age-specific survivorship frames the expected value of wild animal welfare

This project is a continuation of work that began at Animal Ethics, which is covered here. We thank them for their collaboration.

As part of an ongoing project to understand the welfare of wild animals, I analyzed age-specific survivorship as it relates to welfare, introducing a new concept for understanding the lives of wild animals: welfare expectancy. Welfare expectancy can serve as a framework for weighing up the different levels of well-being animals might experience over the course of their lives, helping to model the welfare consequences of interventions and natural pressures, such as predation, that may disproportionately affect animals of particular ages.

Wild animals’ lives are extraordinarily diverse. Individuals of different species occupy different habitats, consume different resources, and engage in different behaviors. Even within species, their fortunes vary depending on their circumstances of birth and exposure to chance events which lead to differential survival, mating success, and welfare. 

Organisms must make trade-offs between their own survival and reproduction, and the survival of their offspring. These trade-offs evolve to maximize lifetime reproductive output, which defines an individual’s fitness. However, it is crucial to recognize that fitness and welfare are not the same. For example, a strategy which maximizes the average fitness of offspring could lead to reproductive success in adulthood for a few, but short lives for most. 

In order to identify safe and tractable ways to improve wild animals' lives, welfare biology needs to identify the major threats to their welfare. In the face of the diversity of individual experiences wild animals may have, we need a way to tally them up in order to assess the overall welfare of a population. Expected value does this by taking the sum of the value of each outcome multiplied by its probability. For example, to calculate the life expectancy of a population (i.e. the expected value of lifespan), one would multiply the proportion of individuals who die at a certain age by the number of years they lived and sum this across all possible lifespans. 

In my forthcoming paper (previous version), I introduce a similar concept of “welfare expectancy” to formalize the relationship between age-specific survivorship and lifetime welfare. Consider a species for which welfare is poor in early life, but high in adulthood. If the probability of surviving early life is high, then the lifetime expected value of welfare for an individual born into that population may be high, because most individuals are going to have a chance to live out their best years in adulthood. If the probability of surviving early life is low, then most individuals will only live to experience the juvenile period of poor welfare. Conversely, in a species where welfare is higher in early life than in adulthood (e.g. due to good parental care), the net welfare of even a short-lived animal could be relatively high. 

We are profoundly uncertain about whether most animals’ lives are dominated by pleasure or suffering, or even how to go about weighing these up. Therefore, it may be prudent to concentrate on a measure of “relative welfare expectancy” (RWE), representing the normalized welfare expectancy of a population divided by its life expectancy. For a fixed life expectancy, the highest welfare expectancy is achieved by maximizing the proportion of animals living to experience the best years of life while minimizing the proportion experiencing the worst years, as illustrated in Figure 1.

Figure 1: Assuming a fixed maximum lifespan, relative welfare expectancy is maximized when the majority of individuals live through ages characterized by above-average welfare. For example, in the “bad” case, the highest-welfare periods come late in life; most individuals in such a population only experience the poorer-welfare early ages.

As part of an investigation of age-specific mortality jointly supported by Animal Ethics and Wild Animal Initiative, I analyzed published demographic models for 152 populations of 88 species to survey patterns of age-specific mortality and used them to illustrate the implications of age-specific welfare. The vast majority of animals live very short lives; not only in absolute terms, but also relative to the longest-lived members of their species. In fact, of the populations analyzed, average lifespans were on average 16% of a species’ maximum lifespan, with only 5% of populations having life expectancies >33% of their maximum (Figure 2). Importantly, this represents an average across populations, not across individuals. Because short-lived species tend to be more populous, lives in nature are likely to be cut short far more often than these numbers suggest. Depending on how welfare varies with age in their respective species, especially short-lived individuals will be missing out on a great deal of positive and/or negative experience.

Figure 2: Histogram of life expectancy as a proportion of the species’ theoretical lifespan. Most animals live only a small fraction of their potential lives, even in species where the theoretical lifespan is already relatively short.

Confident application of the welfare expectancy concept will require empirical data on values of age-specific welfare, which are currently scarce for wild animal populations. A plausible working hypothesis, however, is that the average day-to-day welfare experienced by an animal of a given age is proportional to their probability of surviving that period of life. The justification for this is that the same factors which lead to mortality (e.g. disease, vulnerability to predators, competition for food) have been shown to lead to chronic stress and poor physical condition.

Although we do not know where to draw the line between net-positive and net-negative welfare for any species, under this hypothesis the probability that welfare over a given period is net positive would also be proportional to the survival probability. For example, a year in which an animal has a 60% chance of surviving might be twice as likely to be net-positive as a year which they have a 30% chance of surviving. In the absence of evidence, for the purposes of this report we decided to represent age-specific welfare exclusively as a positive number ranging from 0 to 1 (equal to the survival rate), for illustrative purposes only. Readers are free to adjust the welfare expectancy values given in Table A1 of the report according to where they feel the line between net-positive and net-negative welfare may lie for any particular species by subtracting the product of its life expectancy and the minimum annual survival rate they feel would correspond to net-positive welfare.

For an animal to have an enjoyable life on net, they must experience enough pleasure to compensate for the pain of their death. Cause of death, and therefore the duration and pain of an animal’s experience of dying, may also vary with age similarly to welfare. In a hypothetical species, juveniles might be most likely to starve while adults are most likely to be predated, with the relative probabilities of these and other mortality factors shifting over a lifetime. If the pain of death is a sufficiently strong factor to negate some of the positive welfare an animal might have experienced while alive, age-specific variation in the incidence of various manners of death and their severity would also be important to account for. (See Michael Plant and Brian Tomasik for contrasting views on this.)

At the individual level, welfare expectancy unites two distinct concepts: day-to-day quality of life and the quantity of welfare experienced over an individual’s lifetime. However, a similar quantity-quality distinction applies at the level of populations, with welfare expectancy addressing the quality side of the argument and quantity being determined by the population size. Ideally, a population should be managed in such a way that maximizes its total welfare expectancy. A predictive understanding of population ecology is therefore crucial for contextualizing information on wild animal welfare, as well as evaluating and prioritizing among interventions that differentially affect various age or stage groups. The welfare expectancy framework might serve as an early paradigm for welfare biology.

Assessing biomarkers of aging as measures of cumulative animal welfare

Summary

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 aging 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 aging 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 aging 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 ².
• Exposure to environmental ultrasound frequencies that are undetectable to humans causes depression-like symptoms in laboratory rats and mice ³.
• The flickering of some fluorescent lights at frequencies above the human flicker-fusion rate raises stress-hormone levels in starlings ⁴.
• 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 ⁵.

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 ⁶ 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 behavioral measures such as the presence of stereotypies, and high-level behavioral 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 behavioral 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 aging process.

Biological aging 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 ⁷. These deteriorative processes, which occur in broadly similar ways in many different animal species ⁸, are collectively known as aging.

Among researchers studying the biology of aging, it is well-known that simple chronological age is a less-than-perfect measure of the aspects of aging 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 ⁹. 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) ^{9, 10}. So-called “biological aging clocks”, which incorporate a variety of different types of biological data, are an active area of research in the study of human aging, and the best such clocks can predict health and other outcomes much more accurately than chronological age alone ¹⁰.

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 aging-like symptoms and has recently been shown to substantially increase biological age in young humans ¹¹. 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 ^{12, 1, 13, 14}. 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 ¹².

In addition to this and other empirical data, there are good theoretical reasons to expect the rate of biological aging 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 ¹⁵, with a similar connection between physiologically-protective experiences and positive affect. Since aging is characterized 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 aging. 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 aging 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 aging process will manifest itself in a plethora of different phenotypes, many of which can be used to try to quantify biological aging. In humans, a wide variety of biological readouts have been used as biomarkers of aging, 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 ^{1, 16, 17}. While many of these biomarkers predict health outcomes better than chronological age, they often reflect different aspects of the aging process and do not always correlate well with one another ^{1, 16}. Combining different measures (e.g. with machine-learning-based prediction tools) can overcome these problems and improve the ability of a biological aging measure to predict health outcomes ^{17, 11}.

As different biomarkers of aging track different aspects of the aging 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 ^{12, 1}. 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 ¹⁸. Bateson & Pourier ^{12, 1} 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 ². 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 aging 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.

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 aging 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 skeptical about the applicability of biological aging 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 aging and welfare may be weakened or broken entirely.

Difficulties in application

The need for biological aging clocks

In order to use biological aging as a cumulative welfare measure in a given species, we need some kind of biological aging 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 aging 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 aging-based methods to assess welfare in these species, alternative biomarkers (such as accumulation of fluorescent advanced glycation end products in Drosophila ¹⁹) will need to be developed and validated as welfare measures, substantially increasing the upfront cost.

Experimental controls

While biological aging 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, aging-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-aging 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 aging-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 aging; 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 aging-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 aging 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 aging markers. aging-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 “aging” 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 ²⁰. On the other hand, many clinical biomarkers of aging do not begin to accumulate until adulthood ²¹. This means that the applicability of biological-aging 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 ²².

Animals very different from humans

The evidence supporting the use of biological aging 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 aging via its effect on somatic damage; extensive empirical research in humans associating biological aging 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 aging 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 aging-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 aging-based welfare measures: invertebrates, and those animals (both vertebrate and invertebrate) whose pattern of lifetime aging differs substantially from that of humans. The reasons for skepticism 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 aging (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 aging 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 ²³. 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 ²⁴ and more recently the naked mole rat ²⁵, do not appear to age at all, while some (including various corals, reptiles and amphibians ⁸) 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 aging 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 aging 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 aging process. Worse, even the theoretical argument in support of aging-based welfare measures in these species may be greatly weakened: for an animal to exhibit no aging or even reverse aging 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 aging 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 aging 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.

Conclusion

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 aging 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 skeptical 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 aging-based welfare measures. Nevertheless, of all the methods we might think of for measuring the cumulative welfare of wild animals, biomarkers of aging 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 aging 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.

Acknowledgements

Professor Melissa Bateson, the author of the key sources for the report, generously read the draft of this report and provided crucial feedback on several important points.

References

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6. 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 ²⁶. While I’m skeptical 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. ↩︎

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14. 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. ↩︎

15. 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. ↩︎

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18. Melissa Bateson (pers. comm.) points out that a “complex and multi-modal” measure of biological aging 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 behavioral), 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. ↩︎

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22. 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. ↩︎

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

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. ↩︎