Research within EEL focuses on why animals do the things they do. This means we study animal behaviour, and how it relates to ecological and evolutionary processes. We have a particular interest in social behaviour, so why two or more animals interact, what happens when they do so, and what consequences this has for their lives and the evolution of populations.
Social networks of animals
Animals interact in diverse ways, making analysing social interactions consistently across species and contexts difficult. One solution is to use “social network analysis”. A social network is a way of representing interactions among individuals, with individuals depicted as “nodes” or “vertices” and links between them as “edges, “links” or “ties” if they interact. Researchers have often tried to estimate the importance of these social networks for selection and fitness in animal populations. In parallel, those interested in quantitative genetics have tried to understand how social interactions influence evolution and adaptation. Andew McAdam and I reviewed what these two different approaches can offer each other.
Although papers using social network analysis have tended to focus on highly social animals that live in groups, and interact in various cooperative ways, e.g. dolphins and chimpanzees, there is no reason to limit the use of social network analysis to only highly social animals. I have applied social network analysis to multiple generations of the cricket Gryllus campestris, to study whether they social networks are stable across generations, despite the complete turnover of individuals, to investigate classic questions in sexual selection using the tools of network analysis, and to examine how mating and fighting interactions are related in dynamic networks. All work on the crickets was with Prof. Tom Tregenza and Dr. Rolando Rodríguez-Muñoz, who make up the Wild Crickets project. We’ve recently published four papers on senecence in wild crickets (1, 2, 3, 4).
Social network analysis was deveoped in sociology, and there are still many statistical techiques animal researchers are not using. Myself and Dr. Matthew Silk thought this was a shame, and so we have written a couple of review articles, firstly reviewing how stochastic actor-oriented models can be used to analyse social networks changing through time (co-written with Dr. Amiyall Ilany and Tom Tregenza), and then reviewing how exponential random graph models (ERGMs) can be used to understand animal social structure. We also worked with Dr. Julian Evans to compare ERGMs to other commonly used methods of network analysis.
I am also interested in multilayer networks, a way of analysing interactions of different kinds and between different types of individuals. Myself, Matt Silk, and Dr Matthew Hasenjager have guest edited a special issue in Current Zoology about the topic. Our topic introduction is here, while I also contributed an article with Dr Noa Pinter-Wollman on using multilayer network analysis to examine temporal dynamics of networks in the social spider Stegodyphus dumicola.
Evolution when animals interact
Most of the time in evolutionary biology, we consider how an organism’s traits relate to its fitness, and how its genes influence the expression of that trait, and therefore predict how that trait may evolve. However, when animals interact with one-another the picture can get more complicated.
First, although we mostly consider individal-selection, selection is in fact “multilevel”, acting on genes, aggregations of those genes in individuals, and aggregations of individuals into groups. This extends to individuals that do not live in groups, but to solitary animals that can be aggregated at different spatial scales. I found selection in North American red squirrels (Tamiasciurus hudsonicus) is for earlier breeding in the spring at a local level (so you want to breed earlier than others within 130m) but at larger spatial scales selection is for later breeding, presumably as breeding very early in the Yukon spring is very difficult. Local selection for early breeding was more intense when population densities were high, suggesting the benefit of early breeding is to allow pups to acquire a vacant territory.
Secondly, when animals interact, they can influence each other’s traits. If this effect has a genetic basis (i.e. individuals who influence others more have relatives who do likewise) then some of an individual’s traits are determined by the genes of those it interacts with, not just its own genes. In red squirrels I have found that individuals influence how early their neighbours give birth, but only at high population densities. There is some suggestion this effect has a genetic basis, but uncertainty is high.
We also found that the previous owner of a squirrel’s territory can influence it, “from beyond the grave”. Males cache the most white spruce cones (Picea glauca – a red squirrel’s favourite food), and leave the most behind when they die, meaning the next owner of the territory has more cones, can give birth earlier, and has a higher lifetime fitness. Its pretty amazing that a dead squirrel can have such an effect on a live one, especially as they may never have met!
I also worked with Jack Hendrix and April Robin Martinig to demonstrate that holding a territory helps juvenile red squirels avoid predators over winter. All work on red squirrels has been with Andrew McAdam , as well as all members of the Kluane Red Squirrel Project.
Social interactions can mean evolution does strange things. I have shown how social interactions can cause evolution to move in the opposite direction to direct selection, allow trait evolution and adaptation to be de-coupled, and allow maladaptation (where fitness evolves to be lower across generations, which previously wasn’t thought to be compatible with evolutionary theory).
The behaviour of an animal could in theory change completely from one moment to the next, perfectly matcing the requirements of a situation. However, it does not. Individuals are limited in their behavioural repertoires compared to what occurs across an entire population, so some individuals are consistently risk-taking, or active, or aggressive, compared to other individuals (known as their “behavioural type”, “personality” or “coping style”).
In crickets for instance, some individuals emerge quite quickly out of shelter in a lab, while others may not emerge at all, while once out, some crickets run around a lot, while others do very little. I, working with Dr Morgan David, demonstrated these differences are somewhat consistent over the adult lifetime of individuals, and in fact among-individual differences tend to get greater as individuals age. These among-individual differences in activity have consequences in the wild too; working with Adèle James I found that crickets that ran around a box in the lab more also ran around in the wild more the next day. I also found that those that were consistently more active also had shorter lives, suggesting all that activity lead them into trouble. They did however have equal lifetime mating success, suggesting they got more done in their shorter time on earth.
Consistent differences are not just limited to individuals. Working with Jonathan Pruitt, James Lichtenstein, Raul Costa Pereira, and Justin Yegar, I have found that colonies of the social spider Anelosimus eximus in Ecuador have consistently different collective behaviours, these behaviours influence colony survival in different ways at different elevations (see also this related paper lead by Brendan McEwen), and may be passed onto “offspring” colonies after an artificial fision event. Consistent differences are also present in the structures animals make. Longer individuals of the orb-weaver Micrathena vigorsii build consistently wider and less dense webs, and may also be more stuborn in the face of a predation threat.
Reading and writing about among-individual differences all the time, even among clones raised in identical environments, got me thinking that they should be considered the norm, not an unusual phenomenon requiring explanation. But could there be something that causes among-individual differences to arise out of essentially equivalent beginnings? Working with Joseph Burant, and Matthew Brachmann, I have written a forum piece, discussing how insights from chaos theory, and the study of complex systems, could bring light to this problem.
I’ve also reviewed the insights we could gain for animal populations by adopting some of the ideas from complex systems theory.