Three Preconditions for Helping Wild Animals at Scale

By William McAuliffe, Rethink Priorities @ 2024-06-12T20:32 (+43)

 Executive Summary

1. A theory of change specifies how a social movement will achieve the change it desires. The theory first posits preconditions that are necessary for meeting its goals. It then explains how the movement's activities help meet the preconditions. This report lays out the preconditions for the wild animal welfare movement to help wild animals at scale. 
2. The movement's main goal is to promote the interests of individual nonhuman animals not under the direct control of humans as ends in themselves.  
   1. The movement's fundamental normative assumption is that speciesism is ethically unjustifiable. 
   2. The fundamental empirical assumption is that wild animals face a number of anthropogenic and non-anthropogenic threats.
3. Humans already have the ability to help some wild animals with some problems. But three preconditions must be developed in order to help a substantial fraction of wild animals with the conditions have the biggest negative impact on their welfare:
   1. Valid measurement: Knowledge of (a) how to measure well-being among wild animals and (b) the causal relationships among the factors that influence it.
   2. Technical Ability: Technology and skill to implement and evaluate interventions to help wild animals at scale, while minimizing unintended negative consequences.
   3. Stakeholder Buy-In: Consent from stakeholders with veto power, and collaboration from stakeholders who can implement scalable interventions.

Who Should Read This Report

• Newcomers to wild animal welfare who want a primer that is fairly comprehensive and up-to-date.
• Readers of A Landscape Analysis of Wild Animal Welfare who want a justification for the preconditions we use to contextualize the movement's ongoing activities.

Introduction

There is growing concern that wild animals do not receive the degree of moral consideration they deserve.^[1] However, there is little common understanding of what minimal conditions must be met to improve wild animal welfare. The process of articulating a "theory of change" can help clarify assumptions about how a movement's activities will help it achieve its long-term goals (Weiss, 1995). A theory of change is a  "comprehensive description and illustration of how and why a desired change is expected to happen in a particular context" (Center for Theory of Change, n.d.). In essence, one reasons backwards from the desired outcome to the preconditions required to obtain that outcome, and in turn to the activities required to bring about those preconditions. 

We claim that, at the broadest level of analysis, there are three preconditions for helping wild animals, which we label Valid Measurement, Technical Ability, and Stakeholder Buy-In. In our companion report (Elmore & McAuliffe, 2024), we describe ongoing activities to help meet these preconditions.

A Primer on Wild Animal Welfare

Defining Wild Animal Welfare 

We define wild animal welfare as a movement that aims to promote the interests of individual nonhuman animals not under the direct control of humans as ends in themselves. In particular, the goal is to help at scale so that all wild animal populations can benefit and all major threats can be addressed. The phrase "not under the direct control of humans" refers to both (a) "wild" animals, who live in habitats that were not intentionally constructed by humans, and (b) "liminal" animals, who are free-ranging within areas settled by humans (Donaldson & Kymlicka, 2011, p. 210). The term "interests" is intentionally broad, in order to accommodate disagreement about what welfare consists of. The emphasis on "individuals" distinguishes wild animal welfare from those that focus on collectives. For example, although many conservationists assign moral worth to the experiences of individual wild animals, they also place independent value on preventing endangered species from going extinct (Coghlan & Cardilini, 2022). Treating the welfare of wild animals "as an end in itself" distinguishes wild animal welfare from initiatives where helping wild animals is instrumental to some other goal, such as safeguarding ecosystem services that benefit humans. 

The Case for Prioritizing Wild Animal Welfare 

All else equal, a movement does more good per unit of effort by helping larger groups of needy individuals.^[2] Lumped together, wild animals far outnumber other potential beneficiaries, such as farmed animals or humans (Tomasik, 2009). Of course, all else might not be equal. For one, the most abundant groups of wild animals include fishes and invertebrates (see Table S1 of Bar-On et al., 2018), which many have assumed are not sentient (i.e., the capacity to have positive and/or negative experiences; Diggles et al., 2024; Eisemann et al., 1984). That said, recent literature reviews conclude that the probability of sentience for many orders of these animals is higher than commonly believed (Birch et al., 2021; Gibbons et al., 2022; Sneddon, 2019). Although the evidence base is still sparse, according to the Animal Sentience Precautionary Principle, "Where there are threats of serious, negative animal welfare outcomes, lack of full scientific certainty as to the sentience of the animals in question shall not be used as a reason for postponing cost-effective measures to prevent those outcomes" (Birch, 2017, p. 3). 

The crux for prioritizing wild animals, then, is whether, conditional on sentience, they face serious, negative outcomes.^[3] Wild animals seem to endure myriad sources of suffering. At the most coarse level, they can be categorized by whether they are anthropogenic in origin. Donaldson and Kymlicka (2011) identify three types of anthropogenic threats (pgs. 156-157). Direct, intentional violence towards wild animals is typically motivated by sport or the desire for some resource (e.g., hunting, fishing, trapping animals for fur, etc.). Humans usually target liminal animals for posing danger to humans or exhausting an economic or natural resource. For example, commensal rodents spread disease, damage infrastructure, and consume crops. Humans frequently use pesticides to eliminate rodents from human spaces, which cause prolonged pain (Baker et al., 2022).

Second, Donaldson and Kymlicka define habitat loss as "encroachment into animal-inhabited territory in ways which destroy habitat and deny animals the space, resources, and ecosystem viability they need for survival" (2011, p. 156). We prefer the broader term encroachment, because habitat loss is only one possible effect of human appropriation of resources. Reasons for encroachment include food production (planting crops, introducing grazing animals, converting mangroves to ponds, etc.), exploitation of natural resources (e.g., minerals, timber), and human habitation (e.g., homes, roads). The effects of encroachment can be decomposed into three categories (Caley et al., 2001). Loss is a quantitative reduction in the overall amount of habitat. As a result, animals have access to fewer resources. Fragmentation subdivides a habitat of a given size into smaller, discontinuous areas. Possible consequences include short-term effects, like reduced ability to exclude conspecifics from a territory, as well as longer-term effects, like novel selection pressures (Allock & Hecht, 2020). Degradation affects the qualitative features required to meet the needs of an animal population. Animals may have less nutritious food or worse water quality, for instance.

Third, spillover effects refer to "the countless ways in which human infrastructure andactivity impose risks on animals" (Donaldson & Kymlicka, 2011, p. 157).  Examples include pollution from oil spills, ocean noise due to ships and seismic surveys, ice caps melting due to greenhouse gas emissions, and so on. Byproducts of encroachment, such as roadkill due to habitat fragmentation from highways, also count as spillover effects.

At a superficial level, the only difference between anthropogenic and non-anthropogenic threats, such as extreme weather, natural disasters, and disease outbreaks, is that the latter are not caused by humans. But non-anthropogenic threats are also unique in that even in the absence of any specific hardship, scarcity will eventually set in. Absent some other limiting factor, populations grow when resources are ample, exhausting the surplus. As a result, some animals will starve, fail to find refuge from predators, and so on. Antagonistic behaviors that wild animals have evolved to cope with scarcity, such as cannibalism, territoriality, parasitism, and predation, exacerbate the problem further. 

The evolved traits that concern wild animal advocates the most are life history strategies (Ng, 1995; Tomasik, 2015), or alternative approaches for allocating a finite budget of energy to growth, maintenance, and reproduction. Although strategies in reality vary across multiple dimensions, many authors simplify the taxonomy to a single "fast-slow" dimension: "a fast life history as characterized by early reproduction, short generation time, short lifespan, small adult body size, small offspring size, and high fecundity, while a slow life history has the opposite characteristics" (Cuddington, 2019a). No matter where a species is on the fast-slow continuum, many more offspring are born than will go on to reproduce themselves, at least in the long run. However, the higher birth rates of fast strategists means that their juvenile mortality rates are also much higher. Humans are an example of a slow life history strategist. Before modern innovations reduced juvenile mortality rates, death prior to the completion of puberty was somewhere around 48% (Dattani et al., 2023). Atlantic salmon have a faster strategy. Adult females lay at least 2,000 and sometimes more than 10,000 eggs per spawning event.^[4] Survival to the smolt stage (just before adulthood) ranges from less than 1% to 11% (Bley, 1987, p. 15). Slow strategists also take longer to mature and may receive more parental investment, so death before maturity does not necessarily preclude an abundance of positive experiences prior to death. Unfortunately, the enormous birth rate of fast strategists means that the majority of individuals who are born are fast strategists. If most of their lives are bad, then the majority of wild animals that come into existence have bad lives. 

It could turn out that some of these welfare threats are overblown or misunderstood. However, the sheer number of threats and their potential severity is sufficient to justify learning more about the experiences of wild animals. Ultimately, even if the movement's worst fears about the lives of wild animals are confirmed, they might still decide it is too risky to intervene (McAuliffe, 2023). Until then, though, there is value in sketching what the movement would need to accomplish in order to even be in a position to help wild animals.

Preconditions For the Movement to Succeed 

We introduce three preconditions– Valid Measurement, Technical Ability, and Stakeholder Buy-in– that we posit are at least necessary for helping wild animals at scale. We leave it open whether these preconditions are also jointly sufficient, or if there are other preconditions that we failed to consider.  

What it takes to achieve one precondition will depend to some degree on what it takes to achieve others. For example, to get Stakeholder Buy-In from policymakers that prefer to base their decisions on scientific evidence, some improvements in Valid Measurement may be a prerequisite. For the sake of space, this report does not make any specific assumptions about the interactions between different preconditions. 

Valid Measurement

Valid Measurement is knowledge of (a) how to measure welfare and (b) the causal relationships among the factors that influence welfare. Measures of welfare provide a yardstick against which to assess the overall urgency of intervention, and whether implementation is successful or not. Knowledge of the factors affecting welfare generate ideas for how to intervene to improve it. It also provides the ability to predict the unintended consequences of interventions, as well as their likely effects on welfare. 

There are at least three metrics to consider when evaluating how a condition impacts wild animal welfare, and how an intervention might help. Abundance refers to the number of individuals in a population. All else equal, a larger population of well-off individuals has a greater quantity of positive welfare than a smaller population, and a larger population of struggling individuals has a greater quantity of negative welfare than a smaller population. Composition refers to the mix of species in a habitat, as well as the demographics of a given species (e.g., age distribution). Some species may generally have better welfare than others, and some demographic characteristics might also have a causal impact on well-being (Hecht, 2021). Third, environmental conditions refer to features of the habitat that affect quality of life. Temperature and disease prevalence are examples. Although these metrics are conceptually independent of each other, in practice they influence each other in ways that can be difficult to empirically disentangle. Composition affects environmental conditions by altering which ecosystem services are available. Abundance affects environmental conditions via density-dependent factors, such as the likelihood that disease spreads. Net primary productivity (NPP), or "the amount of phytomass that becomes available to heterotrophic organisms" (Smil, 2013, p. 33), puts a constraint on abundance because survival and reproduction require energy^[5].

Below, we outline the main steps to achieving sufficient Valid Measurement for helping wild animals at scale.

Sentience 

Which taxa are sentient may have a decisive influence on the aggregate value of natural ecosystems and how to design interventions to help the animals living in them. To illustrate, some of the most abundant taxa on Earth (see Table S1 of Bar-On et al., 2018) are arthropods (i.e., a phylum of invertebrates that includes the insects and crustaceans). Many arthropods would qualify as fast strategists (Bauer, 2023; Cuddington, 2019b), which could justify some pessimism about the overall quality of life in the wild. Also, the decision to implement a wild animal intervention would need to consider any side effects on arthropods, since they may well outnumber the actual beneficiaries of the intervention. But, if it turns out that arthropods are not sentient after all, then neither of these implications would follow. The question is not quite that straightforward, though, since sentience may not be an all-or-nothing trait. Perhaps some species have more intense experiences than others. This greater capacity for welfare may have implications for whose experiences deserve the most moral consideration (Fischer, 2023). 

How can scientists improve the measurement of sentience? Only one's own sentience can be directly verified. Philosophers debate how we know that other humans possess sentience, but to some degree we likely generalize from knowledge of our own sentience to the presumption that biologically similar beings are also sentient (for an overview, see Avramides, 2023). This argument from analogy does not work for all non-human animals, as their physiological and behavioral characteristics can differ greatly from that of humans. However, it is possible to at least assess whether non-human animals possess behaviors and cognitive processes that require sentience in humans (Dung, 2022). The downside of this strategy is that it may overlook processes that happen to require sentience in non-human animals, even if that process either does not exist in humans (e.g., echolocation) or does not require sentience in humans (e.g., operant conditioning; Waldhorn, 2019b). Alternatively, one can rely on a theoretical account of sentience to deduce what its observable manifestations are. The weakness here is that different theories of sentience disagree about what the key characteristics are (Schwitzgebel, 2020). 

Of course, just because a species eventually develops sentience over the life course does not mean that it is present by the time that most of its members die. In comparing the ontogeny of insects, Gibbons et al. (2022) observe interspecies variation in the timing of the brain integration presumably required for sentience. They hypothesize it is due to variation in the stage of development where autonomous behavior is required for survival: 

taxonomic variation in the development of integrative brain regions may be the result of the lower adaptive value of multisensory integration for larvae of species with fewer action selection opportunities….For example, the cognitive demands of a caterpillar that must find food and avoid predation may necessitate more sensory integration earlier in development, when compared to a honey bee larva being cared for by adults in a small wax cell in the hive. (pgs. 173-174). 

On this view, sentience has adaptive value only if individuals can increase their probability of survival via flexible behavioral choices (Farnsworth & Elwood, 2023). Very high mortality rates could mean that the causes of mortality are largely beyond what an autonomous individual can control. Hence, natural selection may not favor sentience during the life-stages with the highest risk of mortality  (Browning & Veit, 2023, p. 12; Groff and Ng, 2019, pgs. 6-10; see Tomasik, 2015, p. 141 for an opposing view).^[6]

Measures of Welfare 

Once it is determined that members of a species have welfare, the question turns to how to measure it. Animal welfare science has already developed a wide variety of tools for measuring welfare across a wide variety of species. However, adapting those measures for wild animals is still in a nascent stage of development. Beaulieu (2023) sampled three animal welfare journals and five animal conservation journals from 2013-2022 and found that only 6% of welfare studies examined wild animals, and only 1% of conservation studies mentioned a welfare-related term. One clear roadblock to progress is that many of the best measures of welfare are based on behavior in controlled settings (Dawkins, 2021), which is inconsistent with measuring the welfare of wild animals in their normal course of life. Physiological assays and automated analysis of vocalizations and videos are feasible approaches for measuring welfare non-invasively. In both cases, however, the causal relationships between the data and the underlying welfare state are still poorly understood. To the extent measures are valid, they mostly capture arousal (i.e., level of activation) rather than valence (i.e., whether welfare is positive or negative). Finally, most of the available information applies to mammals, and to a lesser extent other vertebrates, but not to invertebrates (Beaulieu, 2024; Debauche et al., 2021; McKay, 2021), which are the majority of the animals living in the wild.

How precise the measurement of welfare needs to be depends on the use case. Ordinal measurement is sufficient for concluding that one environment or intervention is better than another for an individual animal. However, in other cases cardinal information is necessary. When judging whether a particular life contained more enjoyment than suffering overall, a ratio measure of welfare is necessary. Similarly, when an intervention has both winners and losers, judging whether the aggregate impact is positive requires a ratio measure. It is still unclear whether welfare is even a cardinal trait (for an optimistic view, see Browning, 2022)^[7].

A final challenge comes from the fact that wild animal interventions will likely affect members of multiple species. The effects welfare has on physiology and behavior differs across species, and sometimes across different life-stages of the same individual. Consequently, scientists may need to develop measures of welfare that are tailored to each species of interest, and to particular life-stages when relevant.

Quality of Life 

With valid measures of welfare in hand, scientists can accumulate knowledge about what the daily lives of wild animals are like under different conditions. Although there are many questions to answer, we highlight a handful here that would impact the urgency of intervention. 

First, what are the lives of juvenile fast life history strategists like prior to death (assuming they are sentient by this point)? Many writers depict them as experiencing little other than their death. However, the mere fact that a death occurred during the juvenile stage does not mean that death was the predominant event of their life. Cuddington (2019b) points out that "the juvenile stage may be the longest life stage" for many terrestrial insect herbivores, who typically fall on the "fast" side of life history spectrum. She estimates that predation will represent only 0.007-1.0% of total lifespan for juveniles of shorter-lived species, and only 0.002-0.3% of total lifespan for juveniles of longer-lived species. But how positive are their early experiences?

Second, how much suffering does death cause? Assuming an animal's life prior to death had been positive overall, the severity of its death would have to be hundreds or thousands of times worse than the average of its previous moments for its life to have contained net-negative welfare (Plant, 2016). Commentators use unsanitized descriptions of predation to motivate the intuition that death really might be that bad, "Gulls peck out and eat the eyes of baby seals, leaving the blinded pups to die so they can feast on their remains. A shrew will paralyze his prey with venom so he can eat the helpless animal alive, bit by bit, for days" (Reese Anthis, 2015). However, it is unclear whether slow, grueling deaths are the rule or the exception. It is also uncertain how frequently shock attenuates the pain of death (Browning & Veit, 2023, p. 14).

Third, how well do adults cope with parasitism, malnutrition, physical injuries, and the like? Many wild animal advocates argue that even the wild animals who manage to survive to adulthood still face a constant barrage of threats. For example, if predators gather in resource-rich areas, potential prey must either constantly risk predation or hide in resource-poor areas. This "landscape of fear" means that anxiety, hunger, or thirst may loom large in the daily life of many wild animals (e.g., Faria, 2016, pgs. 73-74; Tomasik, 2015, p. 136). On the other hand, it could be that how wild animals react to challenges is relative to what they routinely encounter: 

While prey animals may suffer decreased welfare in the presence of predators, in terms of reduced opportunities, it does not necessarily follow that there is a constant feeling of fear. In fact, it seems unlikely that many animals live in a state of chronic stress; it is certainly not adaptive. Stress responses are bad for organisms – they interfere with other body processes, suppress the immune system and potentially even alter the epigenome of offspring – and for these reasons animals are likely to minimise them. (Browning and Veit, 2023, p. 7).

Ecosystem Dynamics 

Natural ecosystems feature a variety of interactions among different species: cleaner fish eat parasites off of their hosts; hawks kill and eat mice; hornbills disperse seeds, which grow into fruits that other species consume. Changes in the abundance, composition, or environmental conditions of a target species may therefore also affect non-target species. Indeed, conservation interventions frequently have unintended negative consequences (e.g., see examples in Chauvenet et al., 2012).

The relationships among different species may also change in systematic ways due to a "regime shift"  (Folke et al., 2004). For example, a lake can remain clear in the face of a certain amount of fertilizer runoff. Beyond a certain point, though, the system loses capacity to absorb phosphorus. Consequently, the water will become and stay turbid–possibly even if runoff is later reduced. The resulting toxic and anoxic conditions cause fish populations to decline, presumably affecting their aggregate welfare. 

To determine whether an intervention will have a positive impact overall, then, scientists cannot only forecast the short-term impact on the welfare of the target species. They must also account for how life will change for all interdependent species. Furthermore, they must account for the probability that the intervention will affect how species in the habitat relate to each other. Reliably predicting either is beyond the current capacity of ecologists (Delon & Purves, 2018; Ingram & Steele, 2010). 

Wild animal advocates have proposed two types of solutions for circumventing the complexity introduced by ecosystems. First, if the movement was confident that the aggregate welfare of a habitat was negative (positive) on the whole, then it could recommend interventions to simply reduce (increase) the habitat's net primary productivity:

Minimizing NPP is much simpler as a strategy than trying to analyze the complete causal impacts of changing a food web at a higher level than primary production. Real food webs are extremely complex…and changing one node in a food web can have many ripple effects that are hard to calculate…It is indeed very difficult to say how a given intervention (say, vaccinating wild mammals against diseases) will affect overall suffering in an ecosystem. However, ironically, this complexity is a reason in favor of reducing NPP…rather than trying to tweak existing ecosystems. (Tomasik, 2016).

Attempts to change net primary productivity may themselves have unintended impacts on the composition or number of animals. For example, some wild animal advocates suggest that preserving large herbivores would reduce suffering because they are slow life history strategists that consume a large amount of biomass that would otherwise be consumed by a larger number of fast life history strategists, such as insects (Faria, 2023). However, changes in the abundance of insects would presumably have other ripple effects that would need to be taken into account, given their myriad impacts on plant populations (Myers & Sarfraz, 2017).^[8] Our current ability to predict even the intended impacts of interventions is also fairly weak. In a meta-analysis of the effects of savanna elephants on other species, an increase in the number of elephants did significantly decrease the abundance of trees and herbs. However, there were no statistically significant effects on either the abundance or composition of vertebrates or invertebrates (Guldemond et al., 2017). 
 

Second, wild animal advocates prioritize interventions that would not affect the size or composition of any populations. For example, pest managers could use pesticides that kill "pest" populations more quickly or less painfully (Tomasik, 2007). If the efficacy of the "humane" pesticide was the same as the industry standard and it was applied in an analogous fashion, then the intervention would not have any effects beyond reducing suffering during death. Although leaving existing population dynamics entirely intact may be feasible in pest management, it is not clear how to apply to cases where humans are not already in control of how wild animals die. Humans could try to change how wild animals die from one non-anthropogenic cause to another, but that would presumably affect their predators, which may in turn have other consequences.

Technical Ability 

Technical Ability is the capability to actually deploy knowledge of how to help wild animals, while also minimizing any unintended negative consequences. Humans already have a rudimentary ability to help wild animals. Wildlife managers provision food to declining populations (Murray et al., 2016). Governmental agencies can vaccinate wild animals against some diseases (Slate et al., 2009). Governments regulate how humans use land in which wild animals live (Bracanga & Dahis, 2022). Technical ability is not fully realized, however, until the movement develops methods that are effective, scalable, reversible, and selective for addressing all major threats facing all wild animal populations.

Effective 

Effectiveness refers to the size of the reduction of the welfare threat. Imagine an initiative to vaccinate wild animals against a disease that reduces aggregate welfare. Does successful administration of the vaccine actually confer immunity? The properties of the vaccine itself (e.g., live vs. inactivated) likely matter most, though factors like delivery method also matter (see the Scalable section below). Effectiveness also depends on the likelihood of administration in the first place. For example, oral vaccines only confer immunity if wild animals are willing to consume them. Accordingly, scientists test different baits to place vaccines inside of to identify ones that the population finds attractive and palatable (e.g., Bonwitt et al., 2020). 

Scalable 

Scalability refers to the ability to implement an intervention on a larger scale. It often comes at the expense of effectiveness. For example, it is often inefficient to vaccinate wild animal populations using parenteral vaccines– not only is hiring human labor costly, but humans will generally have limited success capturing animals that are skittish or live in remote areas. Dispensing oral vaccines require fewer staff and can reach far more animals, especially if they can be airdropped en masse. Unfortunately, in some cases oral vaccines have lower efficacy than their parenteral counterparts (e.g., Smith et al., 2019). 

Selective 

Interventions tend to have effects above and beyond what is intended. One reason is due to the complex interdependencies discussed in the Ecosystem Dynamics section: Intervening upon one population will have ripple effects on abiotic elements and other populations with whom they interact. A potential strategy for minimizing ripple effects is to only intervene upon species who provide redundant ecosystem services to coresidents. 

A second reason is that many traits are evolutionarily conserved. Consider a contraceptive that could replace wildlife managers' use of culling.  Ideally, the intervention should not have any effects on non-target populations. So far, it has been difficult to identify contraceptives that are species-specific, given that the systems regulating reproduction are conserved across species to some degree. As a result, Massei & Cowan (2014) instead recommend targeted delivery methods (p. 7), which likely reduces scalability. 

Third, the mechanism by which an intervention has its effect may play multiple roles. For example, scientists have found that some contraceptives might make some wild animals more irritable or less sociable, which might reduce their welfare (Gray & Cameron, 2010). This could be because the same hormones that are involved in regulating reproduction are also involved in some aspects of social behavior. Ideal interventions would intervene upon mechanisms that are unique to the processes of interest.

Reversible 

There is always a chance that even a well-vetted intervention will turn out poorly. Perhaps it turns out to be less selective than pilot trials had suggested, reducing the welfare of a non-target species. Or, the intervention may be implemented exactly as intended, but scientists neglect to anticipate an indirect effect with costs that exceed the intervention's benefits. To account for these risks, it is ideal for interventions to possess reversibility. Reversibility is the extent to which it is possible to "minimize the differences between the world where the action was reversed and the world where the action was never taken" (Eckerström-Liedholm, 2019, p.3). 

When a species plays an important role in an ecosystem, the biggest threat to reversibility is driving a species extinct, given the current inability to reintroduce extinct species. Thus, interventions should be designed such that, even in the worse-case scenario, the species of interest does not go extinct. For example, there are gene drives under development that peter out over time rather than spreading until they reach fixation across all populations (ibid, p. 12). Even if using a gene drive to eradicate a local population turned out to be a mistake, it at least would not drive the whole species extinct. 

Stakeholder Buy-in 

Stakeholder Buy-in has a positive component and a negative component. The positive component refers to eliciting collaboration to implement wild animal interventions. To improve Valid Measurement and Technical Ability, life scientists need to agree to spend their time studying wild animal welfare rather than other topics. Science funders need to make a similar trade-off in how they allocate resources. When large-scale interventions finally become available, they may be expensive to implement. Interest groups like the animal advocacy movement could help secure buy-in from relevant stakeholders by proactively promoting wild animal interventions.

The negative component refers to consent to implement interventions from stakeholders with veto power. It reflects the reality that other stakeholders could squelch unilateral action to help wild animals. Most obviously, governments have laws about how humans can engage with wild animals. For example, in the U.S. individuals must have a permit in order to rehabilitate migratory birds, marine mammals, sea turtles, and endangered species (Willette et al., 2023). Other groups– such as the general public, or social movements that oppose intervention into natural ecosystems– could galvanize the government to forbid interventions, or pressure collaborators to desist.

Causes for Concern 

Why would anyone want to squelch interventions that would improve the welfare of wild animals? First, there may be low awareness of wild animal suffering. Horta (2010) observes that people generally have an "idyllic" view of nature: "there are many who believe that nature is a rich source of value because of the existence of nonhuman sentient animals who have happy lives" (p. 75). If true, support for interventions might increase once people are aware of the degree of suffering wild animals experience. In an online study, U.K. adults on average modestly endorsed items related to the idyllic view (e.g., "In their natural environment, unaffected by humans, wild animals live pleasant lives."). However, the idyllic view was (weakly) positively correlated with items related to support for intervention (e.g., "we should intervene in nature to reduce the natural hardships wild animals face"; Sleegers et al., 2023, table 6). Possibly, people are willing to address non-anthropogenic threats to wild animals even if natural environments generally make them happy. 

Second, other values may dictate non-interference with natural ecosystems. Some, such as environmentalist philosophers, believe that there is an obligation to limit interference with the lives of wild animals, even if their lives are of poor quality (Everett, 2001). Sleegers et al. (2023) asked U.K. adults to what extent they would support or oppose an organization that implements a variety of wild animal welfare interventions. The interventions that enjoyed the most support provided one-off help for acute problems: "Monitoring wildlife areas to rescue trapped and injured wild animals" and "Helping wild animals in fires and natural disasters." The least popular interventions were the most invasive: "controlling the fertility of wild animals to manage their population size" and (especially) "genetically modifying wild animals to improve their welfare." It is difficult to tell whether respondents are objecting to invasiveness per se, or rather to use of "artificial" methods. 

Third, there may be skepticism of intervention efficacy. People may believe interventions are doomed to fail because ecosystems are too complex to predict. Sleegers et al. (2023) found that respondents on average slightly disagreed with items like, "no matter our intentions, helping animals in the wild will probably do more harm than good." Without knowing the underlying reasoning, it is difficult to know what to make of this finding. Optimistic respondents might have been thinking about interventions like wildlife rescue, rather than interventions with the potential to fundamentally alter ecosystems. Conversely, respondents who agreed that interventions will backfire might be referring to humans' current capacity to intervene on a large scale. Possibly, they would be more optimistic if asked whether humans are capable of developing the Valid Measurement and Technical Ability required to do more good than harm.

Fourth, people may place little intrinsic value on the individual well-being of wild animals. Sleegers et al. (2023) found that respondents on average did endorse items such as "I care about wild animals that are in pain, no matter whether their suffering is due to human or natural causes." However, a positive valuation of wild animal welfare is consistent with prioritizing other ends. For example, if people have partiality towards a certain charismatic species, they might not favor an intervention that would decrease their abundance, even if they acknowledge that aggregate welfare would improve overall. 

Fifth, people may perceive helping wild animals as risky to humans.  Conflicts of interest with liminal animals are particularly salient, given their proximity to humans. A desire to keep human spaces disease-free partly explains opposition to bans on rodenticide among the general public (Elmore et al., 2023). However, even interventions into remote wilderness could negatively affect humans. For example, a government may believe that using a gene drive to eliminate an invasive population would improve aggregate welfare. However, the gene drive could escape its borders and affect native populations that provide ecosystem services to humans in other countries. Governments will need to engage in international cooperation before implementing wild animal interventions that may have international effects (Esvelt & Gemmel, 2017). Finally, there is always a conflict of interest insofar as devoting more resources to helping wild animals means that there are fewer resources for helping humans. 

Strategies for Increasing Buy-in

One way to defuse concerns about wild animal interventions is to highlight overlap between stakeholders' preexisting concerns and the interests of wild animals. The One Health initiative emphasizes that "human, animal and plant health are interdependent and bound to the health of the ecosystems in which they exist." The initiative's focus on the health of ecosystems is compatible with the concerns of environmentalists and conservationists. The focus on human health is compatible with human self-interest. A limitation of focusing on win-wins is that some interventions involve genuine trade-offs. While habitat preservation efforts help wild animals under certain assumptions, they engender opposition when they negatively affect humans' economic interests (e.g., Gup, 1990; Leber, 2015). 

An alternative approach is to shift stakeholders' values such that they give wild animals' interests due weight in decision-making. The feasibility of shifting values at scale is unclear. Humans have an incentive to prioritize the needs of humans over nonhuman animals, which may lead to motivated reasoning that justifies the status quo. 

Wild animal advocates need not use the same approach to obtain the required level of aid or acquiescence from all relevant stakeholders. They also likely do not need to address every issue with every stakeholder. Imagine that the general public is not strongly opposed to policymakers allocating resources to wild animal interventions, so long as they do not pose a risk to human health. Under these conditions, advocates could limit their public-oriented efforts to demonstrating that wild animal interventions are safe for humans, and focus more on addressing the myriad demands of policymakers. 

Conclusion

We articulated preconditions for helping wild animals in order to contextualize the current activities of organizations working on wild animal welfare (see Elmore & McAuliffe, 2024). However, there are other potential applications as well. The framework can help anticipate the barriers they will face when implementing interventions. As a brief illustration, Table 1 categorizes barriers based on the four preconditions for several interventions that wild animal advocates have discussed. Organizations can orient their strategies around reducing these barriers. Knowledge of the preconditions for success may inform judgments of the movement's tractability (Eskander, 2018)– that is, how likely it is to succeed if it allocates resources efficiently. If meeting one or more of the preconditions seems intractable, then it may be more cost-effective to work towards other goals. Finally, thinking about the movement in terms of its preconditions may give new members of the movements ideas about how they can personally contribute. Helping wild animals at large seems difficult to pursue directly. The framework presented here shows that, while the movement's goals are indeed ambitious, it might be possible to break up the task into more manageable parts. 

Table 1. Selected wild animal interventions and barriers to successful implementation 

Acknowledgments

This post is a project of Rethink Priorities–a think tank dedicated to informing decisions made by high-impact organizations and funders across various cause areas. It was written by William McAuliffe, and managed by Daniela Waldhorn. Thanks to Mal Graham, Michael Beaulieu, Simon Eckerström-Liedholm, Bob Fischer, Willem Sleegers, David Moss, and Neil Dullaghan for comments on earlier versions of this report. If you are interested in RP’s work, please visit our research database and subscribe to our newsletter. 

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1. ^{^}

   See this video course, multiple monographs (e.g., Faria, 2023; Johannsen, 2020), profiles within guides to career development and charitable giving (Animal Charity Evaluators, 2023; Forristal, 2022; Giving What We Can, n.d.), an interactive website, the "Reducing Suffering" blog, and numerous journal articles (e.g., Soryl et al., 2021; Waldhorn, 2019a). 

2. ^{^}

   Here, we are assuming that the concept of aggregating welfare across individuals is philosophically coherent. For a dissenting perspective, see Korsgaard (2018).

3. ^{^}

   Dawkins (2017) argues that animals can still have welfare even if they are not sentient. Thus, scientists do not need to determine whether animals are sentient to justify caring about their health and desires. While resolving this issue is beyond the scope of this essay, we assume here that sentience is necessary for possessing welfare.

4. ^{^}

    Some species who have few offspring per spawning event still have a high lifetime reproductive output, because they frequently spawn. 

5. ^{^}

   Phytomass refers to all standing plant material, live and dead. Heterotrophs are organisms that cannot produce their own food, which includes all animals. They stand in contrast to autotrophs such as algae, who can produce their food via processes like photosynthesis. Autotrophs create the phytomass that heterotrophs depend on for survival and reproduction.  

6. ^{^}

   All of the evolutionary reasoning employed in this essay is somewhat speculative. Moreover, selection is not the only relevant evolutionary force. Sentience could be present early in development for non-adaptive reasons (e.g., genetic drift when a population was small). Conversely, sentience may fail to evolve even if it would confer a fitness advantage, perhaps because the relevant mutations never arose.

7. ^{^}

   Note that a measure may still be ordinal with respect to measuring welfare even if it uses a proxy that is itself on a ratio scale. For example, imagine counting the number of parasites on an animal to measure their welfare. This is a ratio scale that may have a monotonic relationship with experienced welfare. However, it may still only be an ordinal measure of welfare, because there is no known function relating changes in the number of parasites to changes in welfare level.  

8. ^{^}

   An alternative approach would be to simply allow human encroachment to continue unfettered (Tomasik, 2015). But again, unintended side effects may cause the approach to backfire. In particular, habitat destruction may favor fast strategists over slow strategists (Keesing & Ostfeld, 2021, p. 6). 

9. ^{^}

   E.g., Gonzalez-Crespo & Lavin, 2016; Massei et al., 2023; Rutberg, 2013; Yang et al., 2023.

10. ^{^}

    E.g., McMahan, 2010; Delon & Purves, 2018.

11. ^{^}

     E.g., Klem Jr., 2015; De Groot et al., 2022.

12. ^{^}

    Šimčikas, 2022.