Carter Lab

Last year, I attended a symposium hosted by Peter Kappeler at the German Primate Center on the topic of “social complexity”. A bunch of evolutionary and behavioral ecologists from different backgrounds got together to argue about stuff like ‘How should we define social complexity?’, ‘Is the brain size of a species a good of measure of social complexity? (or anything at all?) and “Why does Germany have so much more science funding than us?”

This was actually one of the best conferences I attended, I met a bunch of people whose work I’d read, and I started useful collaborations, including one with a Msc student working on neophobia, who convinced me that bats reacting to novel objects was actually interesting and helped me write this paper. With others from Damien Farine’s lab, I wrote my opinion about the conference here.

What’s more socially complex: a bee or a chimp? (Answer: vampire bat). Biologists tend to define social complexity in such as way that their animal is one of the ‘complex ones’. One possible solution to this that I liked for talking about social complexity was the terms proposed by Dieter Lukas and Tim Clutton-Brock: “organizational complexity” (exemplified by cooperative breeders and eusocial insects which have a division of reproduction and labor) and “relational complexity” (exemplified by animals with individualized relationships like some primates, elephants, and of course vampire bats). They published evidence for these distinct dimensions of complexity in mammals here. Others suggested other frameworks.*

The invited speakers were asked to write up their talks as papers for this special issue in Behavioral Ecology and Sociobiology. I gave a talk about vampire bats and I contributed to an invited talk/paper on social complexity across bats, led by Jerry Wilkinson.

Here’s the link to that paper, Kinship, association, and social complexity in bats (if that link doesn’t work look under “publications” above). The basic idea is that comparing social networks across species is actually quite difficult and rarely done because different studies measure ‘groups’ and ‘associations’ differently across species. But bat researchers tend to do the same thing: we individually mark bats, then observe which bats are in a roost across different days, and when we mark them, we collect a tissue sample to estimate their genetic relatedness. Several people have done this over several years. So Jerry gathered all the data together from different researchers that have done long-term studies** and we did the same basic social network analyses in each species to see if anything interesting came up.

To be honest, there wasn’t anything too surprising if you know the social and genetic structures of these different bat species, but it was quite nice to put it all together in one place and to measure all these species using the same metrics. For some species, it did change my picture of their social structures as being a bit more ‘messy’ that I thought. I also noticed was that we actually had different conclusions about relatedness and association for some species than previous published analyses, suggesting that the details of how you measure relatedness and association can determine what you conclude. Another lesson was that the link between relatedness and association can depend a lot on what your null model accounts for. This is something Damien has often written about. For example, if two individuals are always seen together but always in the same roost, then do they actually prefer roosting near each other or do they just prefer the same roost and they don’t actually care about each other? It’s actually easier to infer social structure for animals that switch roosts and move around because you can account for spatial effects. Another example is that a null model that does not account for time effects could lead to the idea that two individuals are highly associated simply because they both died in the first year of the study. Social networks are inherently correlational, and it’s quite easy to draw the wrong conclusions if you don’t think about and correct for these kinds of biases.

Another nice thing that you don’t see in the paper is that we ran all the same analyses in both Matlab (‘Socprog’ by Hal Whitehead) and R (‘asnipe’ by Damien Farine) to test if both packages gave the same results and if not, why not. Given all the possible ways to do things, not all of the analyses we did ended up in the paper, and I think there’s a lot more that could possibly be tested. For example, if we could get reliable maternity data, perhaps we could test for evidence of within-group maternal inheritance of associations (if you’re reading this and want to see if this analysis is feasible, feel free to email me).

*_One could also look at animal social complexity not as just understood by us biologists but more from the perspective of mathematicians who study ‘complexity science’. Liz Hobson and others at the amazing and lovely Santa Fe Institute wrote a paper on this (preprint here).

** Jerry Wilkinson had data on evening bats in the USA. Wilkinson and Kisi Bohn’s had data on greater-spear nosed bats in Trinidad. Mirjam Knörnschild, Linus Günther, Barbara Caspers, Martina Nagy, and Frieder Mayer provided data on sac-winged bats and proboscis bats in Costa Rica. Gloriana Chaverri had data on disc-winged bats in Costa Rica. Gerald Kerth had data on Bechstein’s bats in Germany. Jorge Ortega had data on Jamaican fruit bats kin Mexico. Krista Patriquin had data on Northern long-eared bats in Canada. Bryan Arnold (Pallid bats) and Dina Dechmann (Lophostoma silvicolum, the bats that live in active termite nests) also contributed data but were not included in the study because the data were too sparse to estimate good networks. Victoria Flores and Rachel Page will soon be adding the frog-eating bat to this comparative dataset.

This week I’ve been working with a team of bat researchers in Lamanai, Belize (an archaeological site of the ruins of a Mayan city).  We are collecting data for a study on the effects of sickness behavior on social associations in wild vampire bats. Last year, PhD student Sebastian “Basti” Stockmaier and I conducted two projects on how social behavior is affected by lipopolysaccharide (LPS)—a bacterial endotoxin that challenges the immune system and induces sickness behavior. LPS can cause symptoms of sickness such as fever and lethargy, but the effects are temporary and the animal makes a full recovery because there is no actual pathogen. The effect of LPS on social networks was first studied by Patricia Lopes. She injected mice with LPS or saline and tracked their nest-sharing associations. Mice that were injected with LPS were less socially connected to others. This work is important because it shows that sickness behavior can reshape social networks and therefore change how a pathogen might spread.

Basti and I conducted three studies on the effects of LPS on vampire bat social behavior. In one study, we injected vampire bats with either saline (control) or LPS then isolated them and counted how many contact calls they produced. In another study, we individually fasted bats in a large captive colony housed in a flight cage, injected them with LPS or saline, and then looked at whether they received more or less food from their group-mates. We also looked at whether the sick-feeling bats gave or received more social grooming. Finally, to experimentally remove the effect of association, we tested the effects of LPS effects on grooming given and received when bats were forced into constant association by keeping them with one or 3 others in close proximity. We did this test because sickness behavior might have an effect on both social associations (being in the same place at the same time) and social interactions (e.g. mating, fighting, grooming, food sharing) and although people often use associations as a proxy for interactions, they are not the same thing. These studies were all done while I was in Rachel Page’s Lab at the Smithsonian Tropical Research Institute in Panama, and they should be published this year and next.

Next, I thought it would be good to complement these captive studies by looking at effects of LPS on vampire bats in the wild. So Simon Ripperger, my wife Michelle Nowak, and I joined a field trip that Brock Fenton and Nancy Simmons take to Lamanai every year.  Simon Ripperger is the only person who could track social associations between more than 30 vampire bats simultaneously. We also teamed up with disease ecologist Daniel Becker who has been banding and monitoring the physiology of the vampire bats at Lamanai the last few years.

Brock knew about a large hollow tree full of insect-eating Saccopteryx bilineata, insect and nectar-feeding Glossophaga soricina, and our target: the blood-feeding Desmodus rotundus. To catch the emerging bats, we strung up mist-nets using a jerry-rigged pulley system. Saccopteryx emerged at dusk, followed by the Glossophaga. Next, we began to capture males going in and out of the tree (see images below taken on previous years at the same roost exit by Brock Fenton).

Vampire bat exiting the roost (above and below). Photos by Brock Fenton.

After a slow period, we began to catch females exiting around 2 am. By the end of the night we had captured more than 40 females and even more males, and we stopped because we ran out of bags and had more than we needed (image below).

cloth bags each holding a vampire bat

We released 34 female vampire bats with proximity loggers, half being injected with LPS and the other half with saline. Daniel Becker banded both males and females and took blood and hair samples for his long-term studies.

Using Simon’s proximity logger system, we could remotely download encounters between bats over the next 4 nights, without having to disturb or recapture them. The loggers document the duration and distance estimates of encounters among up to 60 individuals at distances from touching to 10 meters. My prediction is that the immune-challenged bats will have fewer encounters with others because they will be less active, and that the effects on association will not be as dramatic as what can be seen when observing the actual interactions.

Vampire bat with 1.5 g proximity sensor (above and below). Photos by Brock Fenton.