The expected value of wild animal welfare is framed by age-specific survivorship

by lbbhecht 17d4th Nov 20194 min read5 comments

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This post summarizes work conducted at both Wild Animal Initiative and Animal Ethics. The first part can be found here and continues here. A pre-print paper (currently under review) can be found here.

Introduction

The documentary series "Our Planet" opens with a flamingo chick whose legs have become caked with salt from the mud flats. The young bird can't keep up with the rest of their flock and is left to die. At the same time, other chicks and healthy adults seem to be living reasonably contented lives, able to find food and overcome other challenges. It seems plausible that an adult flamingo has lived a life characterized more by pleasure than suffering. This chick, though – and some proportion of all flamingos who have been born – never got to experience their best years.

As part of an ongoing project to understand the welfare of wild animals, I analyzed age-specific mortality rates and considered how they might relate 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.

Welfare expectancy

To understand the balance of pleasure and suffering in nature, we need to understand what proportion of animals experience different welfare-relevant outcomes. 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.

Lifespan is a blunt way of quantifying welfare outcomes, because two animals may have very different experiences and yet die at the same age. However, within the same species, the frequency of different lifespans is likely to reflect common challenges associated with specific stages of life. Since only living animals are capable of experiencing welfare, lifespan is effectively an upper bound on the amount of affectively positive or negative experience an animal can accrue.

If average welfare levels are constant throughout life, then life expectancy is the only welfare-relevant metric we can derive from patterns of age-specific mortality. However, welfare likely does vary with age, as juveniles, sub-adults, reproductive adults and senescent animals face different levels and forms of disease, competition, predation and environmental hardship. This potential for variation calls for a distinct concept of welfare expectancy.

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.

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 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.

Age-specific mortality

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 in the paper associated with this project, average lifespans were on average 16% of a species' maximum lifespan, with only 5% of populations having life expectancies >33% of their maximum. 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.

Patterns of age-specific mortality are too diverse to assign universal classifications to large taxonomic groups. For example, even some insect species have relatively high rates of juvenile survivorship. However, while age-specific patterns vary, animals of certain groups, such as the ray-finned fishes (actinopterygii), do have considerably shorter life expectancies and lower average annual survival rates than others, including birds (aves) and mammals. Importantly, life expectancy is only an average of the lifespans. As explained above, not only the average, but the precise distribution of lifespans is important to consider because of how it may correspond to the distribution of age-specific welfare. For a detailed discussion of some examples of age-specific mortality patterns, see the post by Animal Ethics or the pre-print manuscript.

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 could also be important to account for.

Conclusion

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.

The field of welfare biology is at a very early stage, and little dedicated work from the life sciences has been invested up until recently. While progress is still limited by the lack of empirical studies of wild animal welfare, knowledge of age-specific mortality patterns and a predictive understanding of population ecology will be essential for contextualizing this information, as well as evaluating and prioritizing among interventions that differentially affect various age groups.

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