We could identify the most severe diseases through research and target them with vaccination campaigns. Yes, eliminating a disease would lead to population growth until another factor limits the populationâbut since it was such an unusually severe limiting factor, the net effect is likely to be positive.
This is interesting and seems possible to me, but Iâd probably want to look more into any particular case and see population modelling to verify the logic more generally.
If this does work, I wonder if weâd have a general and reliable path forward for reducing wild animal suffering (whether disease or another cause, it sounds like youâre more agnostic about it being diseases in the paper): just iteratively and incrementally reduce the causes of suffering in a population, roughly in order from the most severe (worst conditional on their occurrence[1]) to the mildest.
However, the worst conditional on their occurrrence could be rare enough that this wouldnât look cost-effective. In that case, we might look for another animal population where it does look cost-effective to reduce the most severe cause of suffering.
I feel less confident in this specific approach â I think downstream effects are just often very contingent. Itâs easy to imagine scenarios like: eliminate very bad disease 1, population goes up, now very bad disease 2 emerges or is transmitted from some other population because contact occurs, etc.
I suppose this could be less of an issue for the very worst diseases/âissues, etc., but I suspect those by their nature are less common (e.g. could be self-limiting, etc), and it seems like the method would only work insofar as changing population levels, etc. doesnât increase the risk of novel worse things, which seems only possible for the very worst things.
That being said I am very sympathetic to just trying more things, at least for some animals, especially vaccinations.
My argument is that while such outcomes are possible, the outcomes of the opposite effect are also possible, and often there just doesnât seem reason to assume one to be more likely than the other (this is in simplest terms what people mean when they talk about cluelessness). Given that, I think it makes sense to assume the effects which we cannot predict are on expectation neutral (most likely, they will be a mix of good and bad) and then pursue it if the effects we can predict are clearly good.
So for example, treating chronic wasting disease in deer will surely affect populations of very small mammals, reptiles, and insects in subtle but nonetheless large ways, due to the numbers of these species. But itâs going to be a mix of benefits and harms, which are incredibly hard to predict. The benefit for the deer, however, is really certain, so if it can be done without great environmental disruption, then Iâd say the expected value is pretty good.
Iâve been thinking about this approach since last year, and havenât had time to prioritize it to do detailed work on a framework, but I have some initial thoughts. I think youâre right that, if youâre comfortable with that sort of cluelessness, this kind of thing is relatively safe to do (although as @Michael St Jules đ¸ notes, Iâd also want to do some proper population modeling in a highly studied ecosystem to get some grounding for the idea).
But I think you can actually do better than focusing on only the very worst diseases depending on population parameters. For example, in populations that are top-down regulated (i.e., the population size is held under the carrying capacity of the resource by an external factor), you would not expect increases in starvation as a result of removing a disease (caveat: if that disease *is* the top down regulator, than you would have a problemâwhich unfortunately is the case in many CWD contexts). So then the disease doesnât need to be worse than both starvation and predation, say, but rather just worse than predation. The population size would equilibriate somewhere a bit higher, but the top-down regulation creates a buffer between population size and resource carrying capacity, and at high enough predation pressures you might reasonably expect almost no population increase.
So I think in an ideal case, youâd identify (1) a high suffering disease that (2) affects a population primarily controlled by intense predation pressure in (3) a predator that mainly eats the target population (so the increases in predator population sizes donât affect other animals, who arenât having a disease treated and for whom this would just represent an increase in suffering).
Of course, if you have a population with high predation pressure, the target population probably dies very quickly after getting the disease, so the suffering caused by the disease might not be very long in duration. But if its a really awful disease that could still be a lot of suffering.
I donât think anyoneâs done a scan of the literature for diseases with these properties, and I doubt youâd easily find a perfect caseâmost populations are a mixture of top-down and bottom-up regulation. But I also think that probably my few hours of playing around with these ideas on the side of my other work are not likely to be the final word on the question :) so Iâm optimistic someone spending a lot more time with this could identify other âecological profilesâ of diseases that make them âsaferâ in indirect effects terms to work on than others (I think thereâs some things to say about bottom-up regulated populations as well, for exampleâprobably there you would want a disease that is a lot worse than starvation).
Hey Mal, this is a great point, I completely agree. The disease doesnât have to be worse than all possible ways of dying if you know that the counterfactual is likely to be a particular mid-intensity harm. Although the welfare gains should still be significant in order to justify the ecological risks.
Thank you all for the very interesting discussion.
I think addressing the greatest sources of suffering is a promising approach to robustly increase welfare. However, I believe the focus should be on the greatest sources of suffering in the ecosystem, not in any given population, such that effects on non-target organisms can be neglected. Electrically stunning farmed shrimps arguably addresses one of the greatest sources of suffering of farmed shrimps, and the ratio between its effects on target and non-target organisms is much larger than for the vast majority of interventions, but I still do not know whether it increases or decreases welfare (even in expectation) due to potentially dominant effects on soil animals and microorganisms.
I expect the greatest sources of suffering in the ecosystem to be found in the organisms accounting for the most suffering in the ecosystem. However, I would say much more research on comparing welfare across species is needed to identify such organisms. I can see them being vertebrates, invertebrates, trees, or microorganisms.
I worry very specific unrealistic conditions will be needed to ensure the effects on non-target organisms can be neglected if it is not known which organisms account for the most suffering in the ecosystem. So I would prioritise research on comparing welfare across species over mapping sources of suffering in ecosystems.
Re: your footnote: I think this depends heavily on how severe we are talking. I donât have a strong opinion, because I really think no one has looked at it, about how much more severe things can get from disease than from something like keel bone fractures. A priori it doesnât seem unreasonable to assume that the artificial conditions of factory farming enable a chicken to live in pain much longer, and therefore have higher overall suffering, than we would ever see in the wildâbut Iâm not that confident in that idea, so it would be good to look at more diseases. The point being that a severe enough disease could still be worth working on in dalys/âdollar terms even if it doesnât affect that many individuals, and that would also make it more ecologically inert in many cases (since changing the circumstances of very large numbers of animals seems riskier).
WAI facilitated a grant from Coefficient (then OP) years ago to look at disease severity; they came out with a few papers recently here and here. As is perhaps unsurprising, but disappointing, much of the research on disease in wildlife doesnât provide enough info to do a good job estimating the welfare burden. But the high scoring bacterial zoonoses in the first paper could be a good place to start a research project attempting to better assess the severity and numerosity compared to FAW conditions (as a cost effectiveness bar).
Thanks Michael! And definitely, all of these interventions should ideally be pursued only after trying to predict as many of the effects as possible. I gave a bit more of an answer on this point below.
I can also imagine incrementally moving down the ladder of worst harms⌠but I expect things will get harder as interventions become more pervasive, and at some point we would need more comprehensive modelling or to really think about how we want to shape the ecosystem as a whole.
Thanks for sharing!
This is interesting and seems possible to me, but Iâd probably want to look more into any particular case and see population modelling to verify the logic more generally.
If this does work, I wonder if weâd have a general and reliable path forward for reducing wild animal suffering (whether disease or another cause, it sounds like youâre more agnostic about it being diseases in the paper): just iteratively and incrementally reduce the causes of suffering in a population, roughly in order from the most severe (worst conditional on their occurrence[1]) to the mildest.
However, the worst conditional on their occurrrence could be rare enough that this wouldnât look cost-effective. In that case, we might look for another animal population where it does look cost-effective to reduce the most severe cause of suffering.
I feel less confident in this specific approach â I think downstream effects are just often very contingent. Itâs easy to imagine scenarios like: eliminate very bad disease 1, population goes up, now very bad disease 2 emerges or is transmitted from some other population because contact occurs, etc.
I suppose this could be less of an issue for the very worst diseases/âissues, etc., but I suspect those by their nature are less common (e.g. could be self-limiting, etc), and it seems like the method would only work insofar as changing population levels, etc. doesnât increase the risk of novel worse things, which seems only possible for the very worst things.
That being said I am very sympathetic to just trying more things, at least for some animals, especially vaccinations.
Thanks for this point!
My argument is that while such outcomes are possible, the outcomes of the opposite effect are also possible, and often there just doesnât seem reason to assume one to be more likely than the other (this is in simplest terms what people mean when they talk about cluelessness). Given that, I think it makes sense to assume the effects which we cannot predict are on expectation neutral (most likely, they will be a mix of good and bad) and then pursue it if the effects we can predict are clearly good.
So for example, treating chronic wasting disease in deer will surely affect populations of very small mammals, reptiles, and insects in subtle but nonetheless large ways, due to the numbers of these species. But itâs going to be a mix of benefits and harms, which are incredibly hard to predict. The benefit for the deer, however, is really certain, so if it can be done without great environmental disruption, then Iâd say the expected value is pretty good.
Iâve been thinking about this approach since last year, and havenât had time to prioritize it to do detailed work on a framework, but I have some initial thoughts. I think youâre right that, if youâre comfortable with that sort of cluelessness, this kind of thing is relatively safe to do (although as @Michael St Jules đ¸ notes, Iâd also want to do some proper population modeling in a highly studied ecosystem to get some grounding for the idea).
But I think you can actually do better than focusing on only the very worst diseases depending on population parameters. For example, in populations that are top-down regulated (i.e., the population size is held under the carrying capacity of the resource by an external factor), you would not expect increases in starvation as a result of removing a disease (caveat: if that disease *is* the top down regulator, than you would have a problemâwhich unfortunately is the case in many CWD contexts). So then the disease doesnât need to be worse than both starvation and predation, say, but rather just worse than predation. The population size would equilibriate somewhere a bit higher, but the top-down regulation creates a buffer between population size and resource carrying capacity, and at high enough predation pressures you might reasonably expect almost no population increase.
So I think in an ideal case, youâd identify (1) a high suffering disease that (2) affects a population primarily controlled by intense predation pressure in (3) a predator that mainly eats the target population (so the increases in predator population sizes donât affect other animals, who arenât having a disease treated and for whom this would just represent an increase in suffering).
Of course, if you have a population with high predation pressure, the target population probably dies very quickly after getting the disease, so the suffering caused by the disease might not be very long in duration. But if its a really awful disease that could still be a lot of suffering.
I donât think anyoneâs done a scan of the literature for diseases with these properties, and I doubt youâd easily find a perfect caseâmost populations are a mixture of top-down and bottom-up regulation. But I also think that probably my few hours of playing around with these ideas on the side of my other work are not likely to be the final word on the question :) so Iâm optimistic someone spending a lot more time with this could identify other âecological profilesâ of diseases that make them âsaferâ in indirect effects terms to work on than others (I think thereâs some things to say about bottom-up regulated populations as well, for exampleâprobably there you would want a disease that is a lot worse than starvation).
Hey Mal, this is a great point, I completely agree. The disease doesnât have to be worse than all possible ways of dying if you know that the counterfactual is likely to be a particular mid-intensity harm. Although the welfare gains should still be significant in order to justify the ecological risks.
Thank you all for the very interesting discussion.
I think addressing the greatest sources of suffering is a promising approach to robustly increase welfare. However, I believe the focus should be on the greatest sources of suffering in the ecosystem, not in any given population, such that effects on non-target organisms can be neglected. Electrically stunning farmed shrimps arguably addresses one of the greatest sources of suffering of farmed shrimps, and the ratio between its effects on target and non-target organisms is much larger than for the vast majority of interventions, but I still do not know whether it increases or decreases welfare (even in expectation) due to potentially dominant effects on soil animals and microorganisms.
I expect the greatest sources of suffering in the ecosystem to be found in the organisms accounting for the most suffering in the ecosystem. However, I would say much more research on comparing welfare across species is needed to identify such organisms. I can see them being vertebrates, invertebrates, trees, or microorganisms.
I worry very specific unrealistic conditions will be needed to ensure the effects on non-target organisms can be neglected if it is not known which organisms account for the most suffering in the ecosystem. So I would prioritise research on comparing welfare across species over mapping sources of suffering in ecosystems.
Re: your footnote: I think this depends heavily on how severe we are talking. I donât have a strong opinion, because I really think no one has looked at it, about how much more severe things can get from disease than from something like keel bone fractures. A priori it doesnât seem unreasonable to assume that the artificial conditions of factory farming enable a chicken to live in pain much longer, and therefore have higher overall suffering, than we would ever see in the wildâbut Iâm not that confident in that idea, so it would be good to look at more diseases. The point being that a severe enough disease could still be worth working on in dalys/âdollar terms even if it doesnât affect that many individuals, and that would also make it more ecologically inert in many cases (since changing the circumstances of very large numbers of animals seems riskier).
WAI facilitated a grant from Coefficient (then OP) years ago to look at disease severity; they came out with a few papers recently here and here. As is perhaps unsurprising, but disappointing, much of the research on disease in wildlife doesnât provide enough info to do a good job estimating the welfare burden. But the high scoring bacterial zoonoses in the first paper could be a good place to start a research project attempting to better assess the severity and numerosity compared to FAW conditions (as a cost effectiveness bar).
Thanks Michael! And definitely, all of these interventions should ideally be pursued only after trying to predict as many of the effects as possible. I gave a bit more of an answer on this point below.
I can also imagine incrementally moving down the ladder of worst harms⌠but I expect things will get harder as interventions become more pervasive, and at some point we would need more comprehensive modelling or to really think about how we want to shape the ecosystem as a whole.