Cooperation Among Nonhuman Animals

I have been traveling this week, and so this week’s post is a day late and on the short site. This week’s goal is to wrap up a loose end from the recent series of the origins of large-scale cooperation in human societies and consider cooperation in nonhuman animals. Such cooperation is rare, and it further underscores the importance in understanding human cooperation. Once again, we are diving into the worlds of anthropology and biology, neither of which I understand very well, and so please let me know if you understand these fields and see that I made a mistake or overlooked an important piece of research.

Today’s post is based closely on a post on the main Scaling in Human Societies site. We will examine three issues in cooperation in the animal kingdom: fission-fusion dynamics in animal societies, what drives cooperation and what holds it back, and whether relatedness is a necessary driver of cooperation. As usual, this work is supported by a Living Literature Review grant from Coefficient Giving, but the conclusions are my own and not those of CG. Also, I have used Anthropic’s Claude as a research aide, but there is no AI writing in this piece.

Robinson and Barker (2017) analyze cooperation among humans and among ants. They define a group as “aggregation of cooperating individuals that is stable with respect to the timescale of cooperation” and distinguish between within-group cooperation and inter-group cooperation. The authors identify two key drivers of inter-group cooperation that are common across species: protection against threats, such as predators and harsh climates; and resource sharing. As discussed by Robinson and Barker (2017), there are, in turn, two possible collective responses among multiple groups to shared threats. One response is fusionism, or for multiple groups to fuse into a single group. This behavior is seen among many types of animals, as is not intergroup cooperation under definition of Robinson and Barker (2017). A second response is intergroup cooperation, whereby groups remain distinct but work together.

Wittemyer, Douglas-Hamilton, and Getz (2005) apply cluster analysis to the species Loxodonta africana of elephant and find four tiers of social organization. The lower two tiers are stable across seasons, but the upper two tiers are more prone to fission-fusion dynamics. Smith et al. (2008) identify fission-fusion dynamics among populations of spotted hyenas, with the sharing of resources and protection from lions key drivers of fusion, where aggression is a key of fission. Thus spotted hyena population are set by an equilibrium between the two forces, with populations being larger at times of food abundance.

Schwartz and Hoeksema (1998) argue that, under the model of comparative advantage, geographic differences in the distribution of resources should drive mutualisms. Here, a “mutualism” is a mutually beneficial biological interaction between individuals, and as the paper uses plants and mycorrhizal fungi as an example, mutualisms are not necessary conscious cooperation. Under comparative advantage, if two distinct groups can produce two distinct resources at different relative costs, then it is advantageous for the two groups to trade, provided that transaction costs are low. Furthermore, intergroup trade would lead to specialization among the different groups.

The argument of Schwartz and Hoeksema (1998) should sound very much like the basic economics reasoning behind free trade, and it is interesting to see the same phenomenon appear in nature in two very different forms. However, Robinson and Barker (2017) argue that this phenomenon is rare among nonhuman animals, and indeed, specialization across groups is what makes human intergroup cooperation unique.

Another example of a driver of cooperation is advantage in mating. Connor et al. (2022) show that male bottlenose dolphins in Shark Bay, Western Australia form the largest known multilevel alliance network outside of humans, with unrelated males cooperating across three alliance levels. The study shows that cooperation between groups, not just within them, increases male access to females. The authors argue this represents a striking case of convergent evolution with human intergroup alliance behavior. Connor et al. (2022) reach their conclusion by tracking the association behavior of 121 male dolphins.

Powers and Lehmann (2017), which I discussed a few weeks ago, consider large-scale cooperation from a genetic perspective and propose three mechanisms to drive it: that organisms gain directly from cooperation, that organisms gain indirectly through reciprocity, and that organisms propagate their genes through large scale cooperation when large numbers of individuals are related. Since insects tend to be related to many other individual insects, the third mechanism might explain large scale insect cooperation, such as among ants as discussed by Robinson and Barker (2017). Reeve and Hölldobler (2007) model cooperation among insects as a dynamic between individual cooperation between groups and intergroup cooperation, and they find that intergroup cooperation increases as the level of relatedness between groups increases. However, they also find that intergroup competition is a driver of within-group cooperation. Wilson and Hölldobler (2005) reach the same conclusion in studying eusocial insects, such as ants and termites.

Samuni, Crockford, and Wittig (2021) track intergroup relations between chimpanzees and find that intergroup cooperation is more likely to occur among related individuals than between unrelated individuals. They furthermore find that social bonds increase levels of cooperation between both kin and non-kin individuals.

Although relatedness helps explain cooperation among nonhuman animals, it is not strictly necessary. Bernasconi and Strassmann (1999) observe large-scale non-kin cooperation among ant foundress associations (an ant foundress is a queen who founds a new colony), showing that relatedness is not necessary for intergroup cooperation.

The ecological conditions that select for large-scale cooperation are rare, as argue Rodrigues, Barker, and Robinson (2023), and thus such cooperation is rare. They find that dispersal patterns are a major factor in determining how cooperation unfolds. In local dispersal, individuals do not move far from their birthplace, and thus individuals entering a new group are more likely to be kin. In long-distance dispersal, individuals are more likely to travel a long distance from their birth place and thus interact with individuals who are not kin. Species that show more local dispersal are more likely to show intergroup cooperation, which the authors argue is based on relatedness.

However, Rodrigues, Barker, and Robinson (2023) also show that localized dispersal creates a negative feedback to cooperation. Mostly local dispersal, also known as high population viscosity, creates additional competition for resources, which undermines intergroup cooperation. For this reason, the authors’ model shows that intergroup cooperation is unstable and thus rare.

Smith et al. (2012) consider cooperation in terms of hunting. Considering 87 species of carnivores, an order of mammals, they find a positive relationship between a carnivorous diet and cooperative behavior. Spotted hyenas, in particular, show high levels of social hunting and cooperation with nonkin individuals.

Pisor and Surbeck (2019) compare intergroup interactions between humans and nonhuman great apes from an evolutionary perspective, which can take the form of aggression or tolerance. They find that the human foraging ecology, with high variability in the availability of resources, is especially conducive to intergroup cooperation, which took the form of a greater number of intergroup relationships reinforced by status acquisition and cultural institutions.

Taking a step back, I find it interesting how may examples there are in biology of equilibria set by a balance of opposing forces. For example, in considering the size of businesses in an economy, which I did a few months ago, we avoid the two extremes, which are that every person is their own independent contractor and that all business activity in the world is under a single corporation. Equilibrium business sizes are set by a tension between the benefits of combination (efficient, synergistic operation) and the drawbacks of combination (higher bureaucracy cost), and the ideal size depends on the specific industry, regulatory climate, and other factors. Likewise, as I have discussed here many times regarding optimum city size, we avoid the extremes of every person living separately and the the entire world’s population living in a single megacity. Ideal city size is again set by a tension between the benefits of size (agglomeration) and the drawbacks of size (crime, congestion, etc.).