Thumbnail image: Molly Harwood

[Ian Owens] Hi, everybody in the room and online. I believe we’ve got quite a few people watching on Zoom and Facebook as well today. Before we begin the lecture program properly, I’d like to do our land acknowledgment that we use for significant meetings such as this.

Cornell University is located on the traditional homelands of the Gayogohó:nǫɁ, the Cayuga Nation. The Gayogohó:nǫɁ are members of the Haudenosaunee Confederacy, an alliance of six sovereign nations with historic and contemporary presence on this land. The Confederacy precedes the establishment of Cornell University, New York State, and the United States of America. We acknowledge the painful history of the Gayogohó:nǫɁ dispossession and honor the ongoing connection of Gayogohó:nǫɁ people, past people, past and present, to these lands and waters.

So welcome everybody. It is an enormous pleasure that we are here at the 2022 Mundinger lecture. We haven’t been able to do this for a couple of years for reasons I think you’re all aware of, so it is really good to be back in a room here at Cornell, lecturing, inviting people from around the world to talk to us.

I guess it’s part of that pandemic that I referred, I guess one of the positive flip sides of that is now, when we do this, we are also transmitting on Zoom and Facebook. We can record the lecture, so therefore, it is probably actually seen by more people than it was before. So I’m not sure that’s a full benefit for all of that, for everything we’ve been through, but we have that positive side.

The Mundinger lecture, of course, is in honor of Paul Mundinger, who was a graduate of neurobiology and behavior here at Cornell, graduating his PhD in 1966– ’67. I believe that some of the Mundinger lecture, some of the Mundinger family are online, watching online today.

If so, welcome, and thank you very much for your support for this lecture. It’s particularly appropriate because Paul Mundinger is a– I’ve got a photograph of him. Here is a really good article about him there in New York as well.

Paul Mundinger looked at the interactions between genes and culture in terms of evolution, so a long way ahead of his time in terms of looking at that interaction both being important, often using song as his model system because of the cultural component was relatively well established and often using species such as house finches and, later, canaries to do that. So I think that– and what I’ve seen of Ben’s talk so far, it’s a great fit with that Mundinger spirit.

Coming to Ben himself, Ben, I’ll go through the field characteristics of Ben, if you like. First started his migration in Cambridge in the UK, where he did a natural sciences degree, went from Cambridge to Sheffield to do his PhD on copulation behavior and some of the first molecular genetics on chaffinches. That’s when Ben and I think first met. We were both PhD students, and there’s no stories to be told from those years. So that’s a mutual detente on that.

And then at that stage, Ben having been educated in the UK, he did kind of what, I think, we all considered a bold thing. And that was traveling to Sweden and starting to work at Uppsala, which was very much at that stage, the center of behavioral ecology, population biology, and so on. But still, going to Sweden, Uppsala was a slightly wild thing to do.

And Ben spent a few years at Uppsala, particularly with the behavioral ecologists, and there was a growing molecular team there but also had a few years, I think about seven years, between Uppsala and Edinburgh University. And for those of you that know that long-established quantitative genetics group in Edinburgh, that was, I think, a powerful combination– behavioral ecology, population biology, and some pretty hardcore quantitative genetics put on top of that. So yes, having known him before all of that, I really saw the imprint of those amazing places to travel to.

I think that was about seven years or so. Then Ben, much to the celebration actually in the UK, came back to the UK, took a Royal Society University Research Fellowship, so a kind of big, quite flexible fellowship, and came back to Oxford, came to Oxford University. Since coming on on that fellowship, he subsequently became the director of the Edward Grey Institute for Field Ornithology, which certainly, as a young Brit birder, birdwatcher, that was the place that you would go to meet.

As we meet each other here at Cornell, Oxford, EGI, as we called it, was the place. So he became director of that. He also became the inaugural Luke Hoffman Professor in Ornithology, which was established, I think, with Ben in mind. And then recently, he’s been chair of the Oxford Zoology and now Biology department. So we just realized that the day– how long have you been at Oxford?

[Audience] 22 and 1/2 years.

[Ian Owens] 22 and 1/2 years. That’s a long time at Oxford. So that’s Ben’s career if you like. Having known each other since we were in our early 20s, I guess, the great strengths I’ve seen in Ben throughout that period is despite the fact he’s largely an empiricist, he has always been unusually interested in theory. Whereas with a lot of empiricists you’re talking about what the method is you might be using, I always remember conversations with Ben were mostly about theory, actually and what the current theory was saying, what it wasn’t saying– what had been tested and what hadn’t been tested.

So first of all, for an empiricist, very interested in theory. The second thing, always looking turns out a long way ahead in terms of what the big questions were. When a lot of the field were looking at what’s topical now, I always felt that Ben didn’t realize that it was at the time. He was looking at the next question, which I have to admit, when someone first brings that up, why is he talking about that? Why is it that heritability of fitness is going to be important? All these. Suddenly a few years later, we’re all working on that field.

So looking ahead, probably from– I’m reading the theory. And then I guess the third component I would add to that is having done all of that, then doing really large scale field tests. And I guess I saw that really begin with those Swedish studies. So if any of you have visited places like Gotland, they have vast nest populations that they’re working with and doing really big manipulations across broods and so on.

So with that combination of theory looking ahead and a large scale field experiment, well, it’s built a hell of a career and impact. Talking about impacts, I would say that places where– you see Ben always in the literature are around evolution of wild populations. Ben his collaborators, often the people that are testing– OK, what is the real role of genes in evolution in a wild population? Big tests of that.

Also reproductive trade within individuals– do manipulative experiments looking at the costs of reproduction. More recently, I think he’ll talk about today social networks in the wild. What is the impact of social networks? Like Paul Mundinger looked at. The relative role of culture versus genes. And I hear now he’s starting to look at interactions between different trophic levels– in this case between birds, trees, and caterpillars and so on.

So those impacts– some people have a single truck. During Ben’s career, he has worked across a series of substantial lives. Throughout that, I’ve also always been very impressed by the fact that some of that is very technical stuff, and yet when I hear Ben talk, he’s often coming back to, yeah, but natural history is also important. It’s good to have very fancy big tests so you know how genes work in populations but really in the natural history and particularly around Watson Wood. I think he’s really championed the role of Natural History and of course, long term population studies.

Also if you look Ben up, as well as all of that, you will see that he’s also spoken, I think, from the heart about the role of being a parent and carer in the academic world, the stresses that puts one under, and what departments and colleagues should do to support parents. And it’s not often you get that combination. So it’s not surprising– given everything that I’ve just said, it’s not surprising that Ben has also been recognized for those sorts of things. So I’m going to embarrass you Ben and read up a few of these. I’m not going to try and go through it all.

He’s also very often one of the years– highly cited researchers. He’s been given to ERC fellowships. So those are fellowships given by the European Research Council across all fields, all senior stages to Europe’s very top researchers. He’s been given two of those. I believe he’s got one just starting now. He’s given the E.O Wilson award from the American Society of Naturalists, the Linnaean medal of the Linnaean Society of London, and this year he was elected a fellow of the Royal Society in the UK, which is like being elected a member of the Academy here– so stupendous. Is that a big enough build up? OK. So only remains now to hand over to Ben. Thank you for coming here Ben and presenting the Mundinger lecture.

[APPLAUSE]

[Ben Sheldon] Thank you very much, and can I just check you can hear me OK at the back? Yeah, great. Fantastic. In fact, I first met Ian in– I think it was January 1989. I think I might have seen the first scientific talk he ever gave, which was as an undergraduate at this conference in Oxford on alarm calling and blackbirds. Is that right?

[Ian Owens] Yeah.

[Ben Sheldon] Yeah, yeah, OK So there you go. Anyway, thanks very much and very kind introduction, and I’m delighted to come here and talk to you today. I’ve had a really fun visit so far. Great time out at the Lab of Ornithology yesterday and today meeting people. I’m really impressed by the kind of tremendous breadth of activities going on there, really, across all global species and really thinking about big scale patterns there. What we’re going to try and do to today is to talk about in a sense perhaps a different scientific approach, which is the approach that I have pursued having inherited this study based at Oxford, which is a real in-depth dive into the ecology evolution and behavior of one species in one place as well.

Let’s talk about the kind of scientific insights that you can gain from that kind of work. Of course, when I start talking about a 75-year study, I need to put some history on that. It’s– I shall say however old I look, I’m not quite that old. The study was started by a British ornithologist called David Lack, and Lack was appointed as the director of the Edward Grey Institute that Ian mentioned in 1945. He was– this was his first academic appointment, but he’d already established quite a reputation as– I put it in inverted commas– amateur ornithologist. He’d actually done some really fundamental work– for example, studying individually marked birds– this classic book on the life of the Robin and actually, particularly on the interface between ecology and evolution.

So many people think that actually Lack’s work in the ’40s, thinking about Darwin’s finches, was actually one of the key things that re-established them as a kind of model in ecology and evolution. Anyway, so Lack starting in Oxford in 1945 was thinking about a model system that he could use to do some fundamental work on population biology in birds. And he’s very clear this is the first paper that he published describing the long-term study in Wytham in 1964.

But the idea to start this study came from a visit to colleagues in the Netherlands and to Hans Kluijver, and they had actually already discovered that if you put up nest boxes in woodlands, you got almost the whole population of great tits coming to use those nest boxes. Then Lack realized that this was the fantastic species. It’s common– widespread across Europe and lots of different habitats. It’s relatively undisturbed by human intervention and so on, so a great potential model for him to work on.

And Lack carried out the first decade or so of the study, and then he bequeathed the Wytham study to Chris Perrins, who carried out his Dphil research on the population. And he greatly expanded the study, so more than five-fold increase in the population size and really set it up in a way that’s very much still in place today. There aren’t many photos of David Lack and Chris Perrins together in the field, and this is the one photo I could find, and I was really thrilled to discover the coincidence that– if you read the caption, this was actually taken here in Ithaca more than 60 years ago in 1962, when the International Ornithological Congress was held here.

So there’s David and Chris inspecting a chimney swift nest here in Ithaca. One of the greatest pleasures for me of running this study for the last 20 years or so is that Chris is very much still around. So here’s Chris this spring– the 65th year that he worked in Wytham Woods. If you know Chris at all, you’ll be able to interpret this expression on his face. He’s sharing a little bit of wisdom with me, telling me that basically I don’t know anything yet. Anyway, so Chris has been a wonderful kind of presence throughout the time here.

So the concept and the value of long term study was something that’s fairly quickly became apparent to people like David Lack and Chris Perrins. This is a quote from the 1964 paper. As the Marley study is only in its 17th year, there are many points on which it’s not yet possible to draw firm conclusions. And then he points out that the 15th year was a pretty unusual year. So the idea that actually one needed long time series to make sense of what was happening– something that quickly became apparent.

And it’s interesting– I don’t really know quite how this emerged, but around the time the Lack was writing that, a number of other long term population studies were being founded. So here’s a few of them here, and I don’t know whether Fitz is online listening, but the Florida scrub jay is one where, of course, he was involved with for many, many decades as well.

These studies were not just of birds. They were also studies of mammalian populations, primates, carnivores, and ungulates here. And the thing that all of those studies ultimately have in common is a realization that a very simple bit of information leads to profound scientific insights. And that simple bit of information is exemplified by this picture here. This is a fledgling Great Tits It’s being ringed as we say in Britain– not bounded. We say ringed.

So it’s been fitted with an individually identifiable mark, and that means any time that bird’s caught again, you know exactly who it is. And catch it enough, you can put together all sorts of information about it. And having information about individuals and be able to study those individuals repeatedly over time opens all kinds of scientific doors, all kinds of scientific questions. So for example, it enables you to analyze the age structure in populations, the linkage between different life history stages, to quantify social structure, social organization. Also to measure fitness over lifetimes, to measure natural or sexual selection in the field, and also to link together generations as well.

And really it’s these common features that have been the things that have led long-term population studies like the Great Tit one but like those other ones that I mentioned and many others, in fact, to be scientifically successful and influential. So going back to the Wytham great tit study– so it’s just over 1,000 less boxes distributed in this deciduous Woodland just outside Oxford. 385 hectares, which is about 4 square kilometers in size.

And the great advantage of these birds, they breed in these nest boxes, which means you can easily and quickly and under very standardized ways collect data on things like how many eggs there are in a clutch, when those eggs hatch, how well they grow. You can band the– sorry. Ring the young, catch the parents, read their ring numbers and so on. And over time, you build up a data set with lots and lots of data about those individuals. So more than 20,000 breeding attempts and all life history data for more than 114,000 birds now for this population.

Can you still hear me OK at the back? Yeah, OK, great. So as we had our 75th birthday, we put together a few summary graphics to summarize some of that. So these are sort of fun facts– 114,000 birds, as I said. 147,000 eggs that we can track to a female that laid them. So the most eggs that any female laid– 74 over a lifetime. The most offspring– actually a different bird, 67 over her lifetime. And by banding the young– I’m going to say banding, actually. Somehow I’ve switched into that. –banding the young and the parents, you can trace back lineages as much as 35 generations in this population. The oldest bird lived for nine years– so these kinds of facts.

Of course, collecting that data is an enormous amount of work, and it’s also resulted in a lot of science as well. So we tried to work out what the kind of cumulative effort was. These are sort slightly back of the envelope calculations for how much work was involved in collecting that data, but what we do with some precision is that 71 PhDs done on this population and 367 papers published. I’m not going to try and go through all of that work tonight, so this is really going to be a kind of attempt to skip through the years– kind of highlighting some of the prominent themes in that work and particularly focusing a little bit more on some of the recent work.

We sat down and looked at those papers. Actually, identified really six separate themes that had developed in this study. And what I mean by developed is not that population ecology was dropped in the 1970s when behavior became interesting. Often those themes were started and they continue. It’s just the ones added in extra kind of perspectives, and so now we can look at all of these things simultaneously.

But yeah, the initial work on the population– really think about population ecology, population biology. And this is for me exemplified by what I think is one of the best scientific papers in Ornithology or at least population ecology. If you haven’t read this, I really recommend it. This is Chris Perrrins’ 1965 paper. It comes from a time when publication of papers was in a different world than today.

So this is a single author paper– 47 pages long. No supplementary information those days. You stack all of your figures and all your tables in the same manuscript. 25,000 words long, so imagine trying to submit a paper like that these days. It would come straight back to you with– split this up into five papers please, sir. And actually if you look at what’s in this paper, it contains a remarkable number of scientific findings. So what Chris was trying to do in this paper was to try and understand why the population fluctuated over time but also to establish some key relationships that are really things that people have spent decades working on.

So he showed, for example, that the laying date of the great tits varied with spring temperature but also female age, that birds that bred late in the season were younger– laid fewer eggs. And that if you timed your breeding to match the peak in caterpillars, you did better, and that survival of birds was very strongly influenced by crops of beech mast from year to year. So fantastic amount of information there. And again, I’d urge you to read that paper.

Skipping on to another theme– so I’m not going to say actually very much about the behavioral work that started in the 1970s, other than to namecheck John Krebs as he was. Now Baron krebs of Wytham– someone who became so successful in science that he was actually ennobled in the British Society. So John Krebs who carried out some classic work on initially territoriality and how it regulated populations, but then things like song variation and also behavioral variation in space use.

I’ll say a bit more about behavior later on in the talk. I’m going to think about work that was focused on understanding individual variation. And then also from the 2000 onwards– particular when I arrived in Oxford starting to use an evolutionary framework to try and think about that variation. And this population, like many others, if you study it for as long as this, you collect lots and lots of data on the phenotypes of individuals. So this is one trait in particular. This is the laying date– the first egg date of the greater population in Wytham plotted in every year from 1960 to last year.

And if you squint at that for long enough, you can see all sorts of interesting stuff in there. Remember, this is about 20,000 individual females that have made some decision that you’re recording here. So you can see, for example, that the mean of the distribution is shifted to the left over time but not in a steady way. It’s oscillated backwards and forwards but moved to the left. You can also see tremendous variation in the shape of that distribution over time, and almost any phenotypic character you look at, you see variation like this.

And it’s a fundamental part of evolution in ecology to understand where does this variation come from, what does it mean for fitness, and what does it mean for the way the population works. So we’ve been very interested in those questions, and there’s something that has been particularly facilitated by this ability to link parents and offspring through the generations. So this great tit here, which we’re banding, we’ll know who the parents are, and of course, if that comes back and breeds again as an adult, we’ll then know who its offspring are and so on.

We can take those individual records, and we can build from those pedigrees for the population. So this slightly abstract looking figure here is a part of the pedigree for the Wytham Great Tit population. And I’ve just zoomed into a little– see if that’s working. The little red square there is a little zoomed in portion. You can see expanded out here. Each of the Black lines there represents an individual bird. They’re nested together, because there are broods there. Each of the blue lines represents a paternal link to another generation. Each of the yellow lines, a maternal link to another generation.

And you can see that there’s in a sense an enormous amount of information there, and you can use this kind of information for all sorts of things. So this data, one of my Dphil students at the moment, Carys Jones, is working on, there’s about 14 and one half 1,000 individuals, and there are lots and lots of maternal and paternal links and so on. So she’s working on what determines the variation in learning data, and how it interacts with spatial variation in the environment.

I’ve said a tiny bit about the quantitative genetic work, but actually other things you can use these pedigrees for is, for example, to study processes like inbreeding. And inbreeding is– inbreeding depression is this process that occurs when close relatives breed together and produce offspring, and the extent of inbreeding depression is the extent to which fitness is depressed in relation to how closely related parents are of an individual offspring. Marta Szulkin a few years ago did her PhD with me, trying to study the determinants of inbreeding depression and its consequences in the Great Tit population.

Now, one of the challenges about studying inbreeding in wild populations is often it’s actually quite a rare event. Partly you need certain kinds of data to establish that it’s happening, but then even if you have that, it turns out it doesn’t happen very often. And so using at that time, a little bit over four decades worth of data and using a subset– so this had to be birds where we knew the grandparents of all the individuals involved in order to work out whether they were related or not.

And what Marta found in the entire data set was that there were just 45 cases of what we call close inbreeding– so between brothers and sisters or parents and offspring. But that’s still enough actually to do some analysis, comparing the effect of inbreeding with non-inbreeding. And what Marta showed is that taking those data and actually putting them in a context of studying fitness over the whole lifetime, you could show that inbreeding depression– and this is the difference between the gray bars of the outbred birds and the black bars are the inbred birds– the closely inbred birds.

As you go through the different reproductive stages from the brood size to the number fledged, the number recruited to the population as adults, the number of offspring they themselves had, the number of their offspring that they themselves recruited to the population. So you’ve now gone three generations down the lineage. You can see that the effect of inbreeding actually is seen at each of those steps. So actually, if we think about the fitness cost of inbreeding, we might perhaps have measured it in terms of the number of fledged young. And we’d say, well, it’s an 18% decrease in fitness.

That’s not trivial, but actually if we measure it right through the whole life history and actually even to the next generation, we can see that effect is actually as high as 61%. And I think this is one of the strengths of these long-term studies. Is that an event there’s actually quite rare– so roughly speaking there’s about one of these events per year. So imagine being the PhD student that’s sent out to study a thing that only happens three times in your entire PhD fieldwork period. Of course, these data allowed a sufficient [AUDIO OUT]. So it turns out inbreeding depression is very, very strong in this population.

We can do something– I will say something a little bit about the quantitative genetic work. So one of the things that we can do with those pedigrees is to estimate the heritability of different characters– how much of the variation in the population can be explained by additive genetic effects that are inherited from parents to offspring. If you do that across different characters– this is for male and female great tits in the long-term data. You find that there’s a quite clear relationship between how heritable a trait is, how much genetic variation it has on the y-axis, and how strongly that trait is under selection, which is represented by its correlation with Lifetime Fitness.

Traits that have clear relationships– there’s a clear relationship between the genetic variants in the traits and the strength of selection on that trait, and this is the pattern that we’ve now seen replicated and probably a dozen or more species, where we’ve got data. We can look for this. The quantitative genetic approach is one that some people find is slightly mysterious and always ask questions about, well, what about the genetic basis for these traits? And I’m not going to say much about it, other than we have been able to take those approaches also in this population.

And that’s been particularly through collaboration with other scientists working on great tits in the Netherlands and other scientists working on genetics elsewhere in the UK, particularly John Slade at Sheffield. So we did some work fairly recently, where we took a quite large sample of little over 3,000 birds– great tits from the UK and the Netherlands. And we genotyped them at nearly half a million single nucleotide polymorphisms. And by doing that, we could show that there were lots of regions of genetic differentiation between the UK and the Netherlands. This is the green plot here. Each of those spikes represents an area of high genetic differentiation.

We could combine those data, the genotype data, with the phenotype data we had from the Oxford population to look at how genetic variation within that population explained variation phenotypic traits– in this case, particularly beak size. And these peaks you can see here are cases where there was a strong genetic signal for variation in beak size within the Oxford population. And some of those peaks coincide with the peaks representing differentiation between the Dutch and British Great Tit population.

And I don’t have time to go into the rest of the study, other than to say that other analyzes in the paper– and you could read it if you want to– suggested that there have been actually quite recent rapid evolutionary differentiation in beak length between these populations, and particularly this locus here called 485, might have been a key player in that.

What I want to say more about is more recent work on understanding the effects of seasonality and climate change on populations, and for which this population has been one that’s been– allowed us a lot of insights into those processes in wild populations. And this is a population– these birds breed in deciduous woodlands in a seasonal place, so it’s seasonal and pretty much the way it is around here, where key events in the annual cycle are the way in which the timing of events is linked between different trophic levels.

And the classic system that we often think about in Europe is involving deciduous trees, like the oak tree here. The caterpillars of a Guild of , but particularly, the winter moth which are adapted to feed on the newly emerged leaves of oak trees. And then great tits, which themselves are laying eggs to hatch at around the time that the peak and abundance of the winter moth caterpillars is there. So you’ve got three different trophic levels and where the synchronization between them, and it presents a really fascinating problem to understand the behavior, the ecology, the evolutionary interactions between those different systems– how they vary in time and space, and particularly, how robust they are to perturbation to changes.

And so you think about this problem– we’ve got the bud-burst state of the oak trees up here. The caterpillars have to hatch just around when those buds burst, because as the oak leaves age, they put down tannins into the leaves. They become far less palatable to the caterpillars. So if you’re a caterpillar and you hatch too late, the oak leaves are not palatable to you, so you don’t do very well at all. And those caterpillars grow to reach late instar stage through the spring.

The problem for the birds is that the interval between when they lay their eggs and when the chicks have their peak food demand is about a month or so. So they need to time their reproduction– their egg laying– so that the peak demand of their young coincides with this peak of caterpillars in the environment. And that can be a difficult problem, because what we know, first of all, from year to year the timing of the bud-burst date of these trees varies. We know that a lot of that is driven by annual variation in the spring climate.

What we’ve also seen, of course, and it won’t be a surprise to many of you, I think, is that in recent years, there’s been quite systematic changes in the timing of those events. And that’s driven by changes in the average spring temperature in this kind of early spring period. So this is the mean spring temperature in March and April from 1963 to 2020, and you can see there’s a clear increasing over there. There’s a really fascinating recent thing that we’ve started to become interested in, which is not just an increase in the mean but also you can see an increase in the variance in recent years as well.

So that’s in a sense a second order problem these birds are having to deal with– not just the birds. The caterpillars and trees as well, of course. So you can see the temperatures got warmer in recent years, and you see this response at the population level in the great tits– that the average laying for the population. Each dot is the population in one year. Has advanced over time. So now, they’re breeding on average somewhere between 15 and 20 days earlier now than there were at the start of the study.

So the question really is how is this change occurring? What are its consequences? What are the mechanisms by which it’s achieved? And this is work done particularly by Anne Charmantier and Ella Cole, who’ve been postdocs at me. So we know for the great tits, actually, that we can predict their behavior, their timing, by simple climatic indices, like spring temperature. This is just another form of spring temperature plotted here. So if we plot the annual laying day to the great tits against the measure of spring temperature, we see what– I would think for an ecologist, if you get a relationship that explains getting on for 3/4 of the variance, you’re pretty happy. It’s not an experiment, but you’re pretty confident you’re sort of on the right track in terms of causal effects there.

The really nice thing is that if you look at the same measure of spring temperature for the caterpillar timing, which we also have, as it happens, data fall over many years. You can see also a very strong relationship for them as well. So both the birds and the caterpillars appear to be responding to the same q in terms of spring temperature. And not surprisingly, if you then put those together and say, how is the half full date of the caterpillars? That’s a standard measure of timing for them and the laying date of the birds. How are those related? You see pretty tight correlation between them.

So in a sense, the answer to how these birds have– what’s happened to them over time is that they’ve moved their laying date forward as Springs have become systematically warmer. But fortunately for them, their food– the food that they’re relying on has also moved at pretty much the same rate. So this means that we’re not seeing any kind of threat to this population yet in terms of advancement of breeding due to climate change.

But the key scientific point I want to make is that the long-term study allowed us to also dive into the data and ask how this change in the great tits was accomplished. What was the mechanism by which these birds shifted the population to track this changing resource that they’re tracking? And we could do that by using the fact that we had observations of multiple individuals over years. And by doing that, you can just look at those individuals and say, OK, we’ve got this individual in two years that differed in their environmental conditions. How much did that individual shift its behavior by in those two years?

And that effectively was plotted here. This is the difference in the laying date within individual great tits between pairs of successive years, and this is the difference in the warmth sum between pairs of successive years. And if you work out the slope through those points, you come up with a slope of 0.071– minus 0.071 days per degree centigrade. So that tells you about the rate of response within individual birds to the environmental variation.

A really nice thing was that if you measure the rates of response at the population level to the same environmental cue, you see a slope statistically indistinguishable from within individual slope. What that tells us is that this population level response here can be entirely explained by individuals shifting their behavior. We don’t need to invoke any other processes like adaptive evolution or range shift or anything like that. Great tits have an ability to respond at least to the environmental conditions we’ve seen so far that allows them to track the key resource that they’re needing to track to reproduce successfully.

OK. So that’s enough about climate change for now. So a little bit now about some of the more recent work we’ve been doing on social behavior in this population and particularly thinking about social behavior as a template that helps us understand social learning and the way that culture can develop in populations. And again, this is a bit of work that’s been facilitated by knowing about individual identities. So the alert among you will have noticed this great tit here as well as a metal band on this leg carries a plastic band on that leg there. So that plastic band contains an RFID tag. It’s the same thing that if you have your pet microchipped. It’s basically the same technology as that.

And these RFID tags, sometimes called pet tags, they are– stands for passive integrated transponder. They’re inert, so there’s no power there. But if you get them within range of an electromagnetic field, basically, there’s a signal of identity that can be detected. So you can use these as a way to identify where individuals are if you set up the right kind of equipment. And we set up because we were interested in trying to understand social relationships between birds– standardized protocol to collect data on how individuals were interacting with each other.

And this was based on putting out for quite a few years a grid of 65 data loggers across the whole of Wytham Woods, which were automated. So they would open and close– open at dawn and close at dusk, and they’d do that two days in every seven– we did that over the core period of the winter. And what happens at one of those feeders looks like this, so this is the kind of thing that you see. So here’s a feeder. It’s inside a cage there.

It’s inside the cage because we have enormous problems from a not so welcome American import– the great squirrel, which destroys field equipment if it can get to it. So the cages keep the squirrels out. The birds, as you can see, are not really bothered by it. As you can see, each bird as it lands there just rushes up the little pet tag code and put out these. You can collect lots and lots of data about where birds are. So there’s a blue tit landing there. It’s not just great tits. There was a marsh tit landing. So a bit like black-capped chickadee really so–

So over the years, we developed this. So we really collected lots and lots of data. So we were getting up to in excess of 10 million detections of individuals getting on for 2,000 or 3,000 individuals in some winters. And you could use those data to construct social networks, which are basically pictures of how individuals are associated socially. So from that 18,000 birds that were tagged over those years, about 40 million records. And what you do with those is basically you detect cases where birds are occurring statistically more often with some individuals than others and when this pattern differs from random association.

And from doing that, you can build a social network for these birds. So this is what a social network– a part of the population– would look like. This is a multi-species one– so involving blue tits, great tits, marsh tits, and coal tits. And you can, of course, do all things with that and ask about why it varies and why it has the structure it does and so on. I actually want to talk about some work that we did that was basically using these social networks to ask how we could use them to understand how individuals learnt from one another and how cultural differences could emerge in populations.

And this work was really inspired by this classic observation from Britain in the middle years of the 20th century, which is in the days when people had milk delivered to doorsteps in Britain– had a foil cap. And in the 1920s, actually, the first case was recorded of a tit learning to peck through that cap to get at the cream that was underneath it. And there’s a really fascinating paper from 1949 that basically argues that this behavior probably must have spread through social learning. Probably multiple individuals in different parts of the country individually innovating to find this new food source.

So my graduate student and post-doc, Lucy Aplin, designed an experiment to really try and test what was happening with this kind of process and whether we could get cultural evolution within a population of great tits. The experiment involved taking great tits briefly into captivity just for a day or two and training those birds to solve a puzzle that they would never have seen before to get food. And the food reward was a meal worm– the greatest really like mealworms, so they were motivated to do that.

Once the bird had learnt the puzzle, it was then released back into the wild in its local population with the puzzle boxes that it had learnt how to solve. And the question was, would it then– would the behavior it had been taught then spread through the population? So we had, basically, these puzzle boxes where there was one action called action A, another action, action B, and then a control where there was no action at all. So some birds have been taught action A. Some have been taught action B, and then there were controls that were just taking them to captivity but not taught anything.

So we had– we set up eight replicates of this experiment in Wytham, and the crucial thing about the experiment is that these different red and blue options here involve solutions to this puzzle box that have equal difficulty. It’s sliding a door same amount in a different direction though– an equal reward as well. So in a sense, there was no reason to prefer one over the other if you knew both existed. And we seeded each of these different bits of population. So three populations were seeded with birds that had been taught the red solution and two with the blue solution, and there were three control populations.

And this is just a little video showing what’s happening at these devices. So this is action A, which is pushing the door to the right or the blue side as well. And the loggers record who is the individual and what they were doing on the device. So here you can see a great tit is opening the little puzzle box. It takes a mealworm out and flies off, and then the box will close for another bird to come along. So great, because you can collect lots and lots of information about how these birds solve this puzzle across lots of individuals.

And just to show that this is the other solution– so here’s a bird that knows how to open the device. Here’s a bird that has learned that if it’s really quick, it can sneak in and scrounge something before the door closes, and here’s a third bird that hasn’t yet figured out the puzzle. So really fascinating to see how these behaviors would spread in the population.

The first thing to do is to look at the control population. So here, remember that the birds have just been in captivity but hadn’t been taught anything, but we put the puzzle boxes out anyway into the populations. And what we see in the controls is that you do get some spread of the solution to this puzzle, but basically, it happens very slowly, and it relies on a bird spontaneously innovating at this device. And that’s actually something that great tits are very good at. They explore their world, and if there’s something food-related, they’ll probably find a way to find it eventually.

Once a bird discovers that, you start to see this take off in terms of the proportion of the population that knows how to open this box. But it’s very, very different in the populations where you’ve seeded the solution with a knowledgeable individual. So the five populations there, you can see that’s really rapidly– within 20 days, this has spread to between 62% and 81% of the population. And also that in many of the populations, there’s a characteristic sigmoid shape to the spread, which is basically slow initially but then it reaches a stage where a lot of individuals know the solution, then it spreads incredibly rapidly almost until it saturates those individuals who are going to learn at all.

So it spreads rapidly in these experimental populations, but the crucial test really is whether those birds that learn the solution whether they show a side bias that corresponds with that of the bird that was introduced into their population. And this is one of those experiments that you see it and think, well, yeah, we don’t really introduce statistics here, because it’s such a clear result. So what we’ve got here is the– option A, this is tutor that demonstrated– taught the blue solution. You can see the vast majority of birds learn the blue solution– those populations where the demonstrator was taught the red solution, the vast majority of birds learn that solution.

And in the control, interestingly, it tends to be more even, and that’s where in a sense there’s no reason to expect a particular bias in a case– in fact, in some cases, different individuals independently innovated different solutions, and then they would start to spread through the population. So really clear evidence there that this specific bit of information has been established and spread in this population.

And we could use our social networks to actually look at how the behavior would spread through the population over time. So here’s a social network for one of the experiments– just one of the replicates. And the yellow dot is the individual that was taught and is knowledgeable. And you’ll see over time as that spreads, the nodes– the dots which are individual birds start turning red. And it jumps into other sort of subparts of the network.

And this– by looking at the spread over that network, you can calculate things like the relative likelihood of learning from a knowledgeable neighbor and so on. And it turns out that having a knowledgeable neighbor makes you about 12 times more likely to learn the behavior in the next time step, so really strong effects of social learning here. The really interesting question, though, is how long this behavior that we’ve introduced would persist in the population. So if you recall, these experiments are quite short term. So we did them over just actually a few weeks in one winter. We went back the following winter to the population to retest some of the areas where we’d establish these new behavioral traditions.

And the thing about the great tits– large clutch size is relatively low survival rates from year to year, so lots of turnover in the population. So from one year to the next, about 60% of the birds in the population will be entirely new and naive. So you’re testing a population where there’s been a lot of new birds arriving. And yeah, if we looked at the persistence of these behaviors– so what we’ve got here is the initial spread– the bias in year one to blue in this population, and it biased to red in this population. This is the second year on testing here.

And these are the second years of spread without green lines. You can see that the relative rate of spread in the second year is even faster than in the first year. And actually, it looks as if the populations are even– sample size is small here. –maybe even more unanimous in their preference. So this was really fascinating to us, because it showed that you could establish this long-term cultural differences in these populations. And that those we did test in the third year as well, those are actually stable over time.

Now, this is admittedly a rather artificial situation. What does it say about cultural variation in natural population? So I want to just talk quickly about some current work that’s going on led by field student Nilo Recalde. And this is work that’s underway, so these are kind of preliminary glimpses of what’s happening here. But really excited by this work and where it can go. So what Nilo is trying to do is to study the cultural evolution of learning in birdsong by combining really dense sampling of the population with the information we have about the history of birds, where they’ve come from, their parents are, what the demography of the population is and so on.

So the aim of the project really is to try and understand change and diversification in song in this population but really asking how things like local scale turnover and dispersal and immigration drive cultural evolution from year to year in the population. That’s not so easy to do in natural populations, because you need to combine really dense sampling of traits with lots of information about life histories as well. And so what’s happening in the great tits here is– oh, yeah. I should play you some songs here. So here’s some Great Tit songs.

[Great Tit CHIRPING]

Those of you that have been in Europe in the spring will know this song. This is just an example of the kind of diversity you’d find in the population in one year. So it’s not the world’s most varied song, but actually, if you measure enough birds, you’ll find lots and lots of variation out there. And what Nilo’s work is–

[Great Tit CHIRPING]

Yeah, OK, come on. What Nilo’s work is doing is taking account of the fact that we know, for example, where birds were born, where they’ve dispersed to, and we know the environment in which they’re learning their songs over time. We also know that birds disperse different amounts, so this is the dispersal distance of birds– natal dispersal distance within Wytham. But also there’s immigrants that come into the population as well. So it’s potentially lots of processes that are reshuffling the song combinations in the population, and his aim is to try to understand how all those drive diversity.

This involved exhaustive field work– so up to 60 automatic acoustic loggers out. So I think he’s recorded 2.7 years worth of song data from these populations. He’s then done some stuff. I’m not going to begin to say that I understand quite the methods here– some pretty fancy kind of neural net analysis of the song variation to classify songs and then use those to measure diversity. So just to give you an example, this is what low diversity means if you’re a great tit– an individual great tit. So this is the lowest diversity bird in one year.

[Great Tit CHIRPING]

You’ve got the high diversity one.

[Great Tit CHIRPING]

Anyway, so we’re trying to explain this within between individual variation in diversity. And again, these are preliminary results, and I should be seeing more of these soon. But what Nilo is finding is that there’s very interesting small scale spatial variation– the cultural similarity in songs between neighbors that are not unexpected effect that you see greater similarity for birds that are relatively close together in the population. That’s clearly what we’d kind of expect. But I think some of the more interesting findings already are that if we compare the repertoire size of birds that are born in Wytham versus those that moved into Wytham, there’s a systematic difference in repertoire size.

And actually, if we look at local measures of cultural diversity and similarity in the population that those are being driven by proportion of local immigrants in the population. So there’s a really interesting story, hopefully, going to emerge about role of these demographic processes in driving a cultural change over time. Anyway, that’s for another day. So this was a quick tour through some of the work that we’ve done in the Wytham population, and I think I’m going to close with some speculation about what are the themes they’re going to develop in the next few years in this population study.

So and I think some of these will perhaps be obvious– particularly I think to some of the people here, where some of these approaches are being really pioneered at the moment in Ornithology. So I think we’re going to see much more use of artificial intelligence to tell us things about behavior and ecology that currently were not accessible to us. I think certainly I and I think others actually doing long term population studies have actually been guilty– perhaps not thinking enough about the effects of spatial scale in some of the processes that we’re studying here. And that’s partly because tools to really study some aspects of spatial processes haven’t really been available to us until recently.

And the last thing is that the use of data like ours in combination with other studies to do what you might call meta or mega population analysis, and I’ll just illustrate those points quickly. So these are just some examples of using AI deep learning methods to tell us things about individuals. So this is a cool paper by Andre Ferreira recently, where he used analysis of videos of birds– in fact of great tit is one of the species he did this for and showed that you could identify individual birds with pretty high reliability from video data.

And for me, it’s kind of potentially quite revolutionary, because it means we might actually not ever need to in a sense catch birds and handle them to identify, and that introduces all kinds of biases into what we know about which individuals are where. I mentioned already the song analysis that Nilo is doing using these deep learning methods. But also– and this is a drone shot of parts of Wytham Woods. What we can think about is how we can classify environments over large scales using these kinds of techniques as well.

In terms of the scale of things– so much of what I told you about was actually, for example, the work looking at climate change. Was analysis, which boiled the whole population down to a single data point, but actually that kind of approach has all sorts of limitations. So we think about the way that great tits or other birds or other organisms sample their environment. They actually sample really a very small part of often the large study sites that we use. And by collapsing all that variation down to a single point, we’re not really acknowledging the scale over which biological interactions occur.

And scale dependence of course, has lots of potentially important effects for evolution and ecological processes. So one of the things that we’re doing, hopefully, over the next five years is to try to use drone-based mapping of the trees across the whole forest. There’s about 170,000 trees, so it’s quite a lot of trees to map to identify them– to try to extract tree level measures of phenology across the entire Woodland. And the idea is to relate this to ecology and evolution occurring both in the insects but also the bird consumers of those insects and how this scales in space. So this is attempt to address this neglect for sort of spatial scale I mentioned.

The last area– and this is the thing which we’re already seeing, I think, some really sort of fruit’s being born out of some collaborations across these long-term population studies. These are studies that combine data across multiple long-term studies. So and a recent one here by Timothy Bonnet in science that took some new methods for analyzing genetic variation in fitness. Applied this across 22 populations, including the great tits in Wytham and derive some really new understanding for how genetic variation in fitness was distributed across populations. Here’s one that’s actually just done on grass and blue chip populations– blue tits related to the Great Tits. And he’s asking how sensitivity to climate change relates to exposure to climate change in the past.

And each of those points there is an estimate for a long-term study of a great and blue tit population across– it covers most of the European continent. So by combining these studies, we can really start to ask large scale questions about variation right across populations. So I’m going to finish just by reflecting a little bit on why these long-term studies, I think, have been scientifically valuable. One of those is that the long-term nature of the work– this enables you to analyze rather than static states or events, processes going on. So you can see right through an entire life history, for example.

The more you study a system, the more you think about the way that these different processes can actually be linked together in the way that whole system is behaving. So think about, for example, the trophic linkage between trees, caterpillars, and birds. The other thing is that this a long-term term perspective. It enables us to think a lot about the fact that systems are not static. They change over time. They’re dynamic. But also occasionally, would enable us to understand that rare events can actually be extremely important and can tell us a lot about the way that things are happening in populations. And I think this is one of the reasons why I think these studies will continue to be a really valuable contribution to our understanding in ecology and evolution generally.

So I’m just going to close by acknowledging and thanking, of course, the enormous effort that’s gone into the work that I’ve talked about. So this is our field team in 2022. There’s 11 people there, but what you should think about is that each of the years of data that I’ve talked about has involved a team of about that size putting in thousands and thousands of hours in the field– so literally hundreds of people’s effort over all those years. And a study like this clearly wouldn’t be possible without that enormous dedication from all of those people, so thank you to them and thanks for your time.

[APPLAUSE]

[Ian Owens] Thank you, Ben. Fantastic talk. Incredibly wide ranging. Lots of different angles. And then now, we’ve got a bit of time for questions and maybe even answers. We do have– I think we’ve got– we’ve got an exam in here later, so we really need to be out by 7 o’clock, but let’s do some questions and answers. At least so copies at the back tuned in to our Zoom and Facebook listeners, but let’s start in the room. Anybody got any questions they want to start with? Irby?

[Audience] I’ve got a question. So in your learning study, you may need to repeat my question for that. In your learning study, why did the learning asymptote around 80%? How come it didn’t go to fixation? What was happening with that 20%–

[INTERPOSING VOICES]

[Ben Sheldon] So I’ll repeat the question in case people couldn’t hear it, but Irby is asking why when we’re establishing this population that spreads so quickly, why doesn’t it reach 100% fixation, and that’s a very interesting question. I think partly it’s that some of the individuals we count as the population are actually transients that probably were not there for very long. But actually, if you remember the spread, I showed through the network, there were individual nodes that didn’t go red, even though they were very close to the origin of the behavior.

And what we’ve seen in each of those experiments, each of the replicas, is that there are individuals that despite being surrounded by other individuals that have learned this technique and are doing it all the time don’t learn to open that device themselves. Some of them do what you saw in one of the videos– is they develop specializations as scroungers. So about 10% of the population develops the scrounging phenotype– so not opening themselves. In a sense, they’ve learnt a different thing, but it’s not the solution to the problem.

The other thing that we see as well, and this is in a sense a smaller set of birds but equally fascinating, is that there are some birds that despite the fact that everybody else is opening this device by sliding it that way, they– so what we see is most birds will discover that they can actually open it the other way as well, and the really interesting thing is they stay using the solution that’s the majority in the population. But a small number of individuals, in a sense, stubbornly continue to do the opposite of what everyone else does.

I think we can all think of people who are a little bit like that in our lives, but one of the really fascinating things about this is that you’re interested in this learning where you learn very rapidly and copy what everyone else does. It’s sometimes called conformist learning. So you could get stuck in a kind of state there, where you can’t escape it. But if you have a small number of individuals that actually are anti-conformist, in a sense your population– and this is not attempt to argue a group selection explanation for that, but actually what it means is your population actually can escape from this state quite easily. And we did some follow up work that I haven’t had time to talk about, where we tested how easily the populations could switch to another overall strategy.

One last thing I will say is that– obviously, it’s one of the strongest bits of evidence for that conformist learning being important was that– remember, we had eight replicates of this experiment. And mostly, the birds stayed in one local area, but occasionally, birds would switch between areas. And I think it was about 15 birds that switched between populations, where different traditions have been established, so they went from red to blue or blue to red. And I think 11 out of 15– may have misremembered that, but something like 11 out of 15 when they switched population would adopt the behavior of the population they moved into. So they would switch their behavior to match that of the population they’d dispersed into, so it’s a real kind of case of when in Rome do what the Romans do for great tits.

So there’s some fascinating diversity hidden in those summary statistics of birds that scrounge, birds that in a sense are maybe transient, and then these birds that are stubbornly doing something else. So there’s a lot of interesting other variation to look at there as well. Yeah.

[Ian Owens] Thanks, Ben. Let’s take another question from the room then we’ll go on to Zoom. Mike?

[Audience] That is a fascinating result. Excellent talk by the way. Irby asked a question I was interested in, and I have a follow up question, which is these individuals that stubbornly refuse to learn or individuals who figure out how to open the box on their own, have you looked back at the fitness of those individuals to look at there’s any difference between them and others in the population?

[Ben Sheldon] Actually, we haven’t– now, when you ask the question, I’m thinking why have we not done this? And I can’t really think why we haven’t done that, but we haven’t actually explored very much the kind of whether there are sort of long-term fitness correlates of this variation in behavior. What I do know is that we tried– Lucy Aplin, particularly, tried to explain whether birds adopted an opening versus a scrounging strategy. And basically, almost nothing seemed to predict that. It just seemed to be a kind of rather idiosyncratic thing that individuals got onto this track of being either an opener or a scrounger, and they would just stick on that.

And actually even if you tested them a year later, they immediately adopted that strategy. Even though they haven’t been using these devices for a whole year, the moment you put them out there, the scroungers are scrounging still, and the openers are opening. Very occasional switches between them, but basically, it’s a thing that once they’ve adopted it, they stick with it. But we couldn’t really find anything to do with age or sex or subsequent survival or anything like that that would predict it’s different. So it may just be down to rather small scale details in what you experience initially sets you down a particular track, which is– yeah.

[Ian Owens] Lisa.

[Lisa Kopp] Yeah. I have a question from Zoom. It says, I’ve always thought that one of the advantages of a long-term study is that one can correct previous errors, sometimes with new technologies, sometimes for other reasons. Are there any such cases in the great tits study that you are aware of?

[Ian Owens] Do you want to repeat that?

[Ben Sheldon] Yeah. So the question was, I’ve always thought one of the advantages of a long-term study was that one could correct previous errors, or I think, with new technologies. I think that was the gist of the question. Are there any examples of that? Yeah. Let me think about that. It’s an interesting question. I guess, I would say that there are, for example, the– let’s give you an example of this case.

So how do we identify the birds that are in the population? You have to go out– for most of the study, you had to go out and actually catch those birds physically. So for example, the breeding population, you would assess by going to a nest box and catching the breeding birds there and so on. Now, that creates all kinds of potential biases in terms of catchability of individuals and the stage at which you catch them and so on.

So when we introduce the process of putting RFID tags on the whole population, we had a second way in which we could go and identify birds without having to catch them. In fact, we could go much earlier in the breeding cycle to identify birds at individual breeding attempts. And when we did that, we actually found that the set of birds that we were identifying using the automatic method– the RFID tag method– was systematically different than the set of birds we identified using the traditional catching methods.

So our traditional methods underestimated the proportion of young individuals in the population. It underestimated the proportion of immigrant individuals in the population– probably because those individuals were either harder to catch, or they may have actually been failing reproduction slightly at a higher rate than the older or locally born individuals. So that was an example of what turned out to be a systematic bias that we discovered, which we could then– of course, we can– in a sense, we can’t very easily correct the old data fall for that bias, but that’s one example and probably there are many more if I could. It’s an interesting question. I should think more about that particular aspect, so thank you for that question.

[Ian Owens] If you could go back now, when did when did Lack start the Wytham?

[Ben Sheldon] 1947, yeah.

[Ian Owens] So if you were wandering around Wytham with Lack, David Lack, is there any advice you would give him about–

[Ben Sheldon] What I’d say to him was, David, there’s a stuff called DNA, right?

[LAUGHTER]

Do this. Because actually, in 1947, it hadn’t even been established that DNA was the hereditary material. It’s obviously six years pre-Watson. It’s a really trivial chemical as far as you’re concerned, but it will turn out to be of the most tremendous importance for understanding things using technologies you can’t imagine. But actually if you could start collecting blood samples for me–

[INTERPOSING VOICES]

[LAUGHTER]

[Ben Sheldon] –that would be totally phenomenal. Because that’s the one thing I would say, and it it’s in some ways astonishing to think about how recent the idea of doing genetics and populations is. I mean, I think you and I and without revealing our age, will sort of felt we were pretty much at the cutting edge when the days of DNA fingerprinting wild populations using techniques, and which I was telling some of the post-docs at the lab on yesterday. I think they couldn’t quite believe how clunky and old-fashioned it sounded. Waiting two weeks to get your results from the sort of freezer– all this kind of stuff. So I think– I mean, I think that’s probably the one thing that we wish we had the full record of genetic material going right back to day one. It would be phenomenal.

[Ian Owens] You’ve got the pedigree, but you haven’t got the– you can’t look at individual–

[Ben Sheldon] Yeah, yeah, pedigree. Pedigree is great if you can do certain kinds of things with it, and I’m a great fan of actually that quantity of genetic analysis. But in fact, one of the things we’ve done by combining the two approaches is to actually show that slightly technical thing– what you do is you can estimate using the combining quantitative genetic and the SNP genotyping. You can estimate how much of the genetic variants for each of your traits is associated with each chromosome.

And what we showed is that basically, there’s across eight traits and two populations of great tits– the correlation between chromosome size and the amount of genetic variants that gets partitioned to that chromosome, which in a sense is quite important bit of evidence that one of the key assumptions of quantitative genetics, which is that there’s a very large number of alleles of small effect. That supports that assumption in this population. So it actually– it’s a sort of– it really means that genetic approach has real viability and power in populations like this, so that’s one of the reasons why I think it still had a lot to say.

[Ian Owens] Any other questions in the room or on Zoom?

[Audience] I was wondering– you said that there’s this close tracking between– in terms of the winter moth caterpillars and [INAUDIBLE] only individual behavior. So what’s the cue that the individuals are using to delay at that exactly the right time?

[Ben Sheldon] Yeah, now that’s a good– that’s a very good question. So I will– what I’ll do is, I’ll I guess refer to some work done by a close colleague of mine in the Netherlands– Marcel Visser, who has a amazing setup where they can actually breathe– sorry, I’m caught on the drawer. They can breed great tits in captivity and actually vary environmental conditions. So they’ve done a series of experiments that have tested potential cues. So for example, is it to do with leaf development, for example?

And in that experiment, actually, they found no evidence in captivity that that’s cue. And actually, I think the best– the strongest response they’ve got is from experiments, where what they varied is the rate of temperature increase, and that is the thing that apparently produces the strongest egg laying response in great tits in captivity, which I think it’s amazingly hard work to do. These birds, they’re great, but there are drawbacks in terms of working on them in the lab in captivity.

I guess, I think that’s really important evidence, but I always, in a sense, for me on the little bit how much we can break down a system of free ranging birds into individual cues in laboratory. So it’s a really hard question to answer, I think, and I think I’ll often answer it and since don’t really have a better answer than, well, there’s some laboratory experiments suggesting there’s a rate of temperature increase, which is important. But more than that, we don’t know. And of course, they were limited in how many queues they could test, because those experiments are time-consuming and take a lot of work.

[Ian Owens] I was really struck by that result that within individual– the adaptive plasticity seems sufficient to explain the cross-year correlation. The slopes were so similar and so on. So what’s your interpretation of that, and what do you think it means for longer-term adaptation to environmental change? Are they really doing it almost entirely through plasticity, and will they continue to do that?

[Ben Sheldon] I guess what we can say is that the timespan over which we’ve studied the birds and can calculate that response– environments in which that they’ve seen, we can compare those two. I think it’s still the longest instrumental record of temperature anywhere in the world, which is in Central England. So this is a record going back to 1650 or so. So it’s 370 years or something. And the 50 years or so– 60 years or so that we can calculate that response over, basically, there haven’t been any years more extreme than the most extreme years in that time series.

So actually, that time of year is very variable between years, and actually over a relatively short period of time, you experience a very wide span of environmental conditions. So that’s just about in a sense saying that what they see is eye variation, and they’re just about to do that. The question, I guess, is about what happens in the future, and some work I haven’t talked about– Dphil student, Emily Simmons, did together with Tim Coulson, who is a colleague of mine. Tried to sort of model what would happen if you were– so what plasticity was doing to evolutionary responses.

And one of the results from that is that there’s in a sense a cost to plasticity, because it means that it’s hiding the effect of selection or variation. But to good at– we’re too good at adjusting. Selection never sees the underlying variation, and that can actually mean that you’re not ever actually able to adapt fast enough if things really start to change very rapidly.

So there can be– some of those models suggests that actually plasticity can be a cost in terms of preventing an adaptation, and that’s based on applying integrated population models to the data to look at changes over time in the phenotypes under different environmental conditions, so yeah. So I think, in some sense, that’s, I guess– we could say about an expectation there that there’s potentially a conflict between plasticity and evolution and the really rapid change, but it depends a bit on what climate model you run and so on, so there’s a lot of uncertainty there.

[Ian Owens] Yeah. I guess I was also struck how different that is from some North American birds that are not responding in the same way. OK. I’m conscious of the time. We should give the people doing the exam access to the room. So thank you, again, Ben for excellent talk. Another round of applause, please.

[APPLAUSE]

 

End of transcript

The 2022 Paul C. Mundinger Distinguished Lectureship was given by Ben Sheldon, Professor of Field Ornithology at the University of Oxford.

For decades, long-term population studies have enabled scientists to explore a wealth of ecological and evolutionary processes. One of the most influential has been a 75-year study of Great Tits (Parus major) in Wytham Woods, UK. From early scientists working with binoculars and leg bands, to modern researchers equipped with DNA analyses, tracking tags, and remote sensing tools, studies of Great Tits have yielded insight after insight. In this lecture, Professor Sheldon recounts some of the highlights, look at why long-term studies are so successful, and explore how to keep them viable for the next 75 years. 

This lectureship was established in honor of the late Paul Mundinger, who received his Ph.D. in Evolutionary Biology from Cornell University.