 So, as a paleontologist, I study life on this planet as it used to be, because everything I study is already dead. So, according... I'm sorry, can you all see the screen? Because I'm a mathematician, so I can either show formulas or pictures. So, seeing the pictures is really, really important to my talk. If you want to get the most of my talk, please come close enough so you can actually see the screen. As a mathematician, I should, accordingly to Hollywood, spend most of my time staring at numbers, solving impossible equations, and probably also develop a serious mental health issue. The thing is, this is all horrible cliché, and the reality is very, very different from that. And what I spent my time with is actually looking at the question, can we predict extinction risk? So, can we determine the probability that any species on this planet will vanish forever in the near future? And from my paleontology side, I use the fact that, well, everything I look at is already extinct. So, there has been six major mass extinctions on this planet, and I can take the fossils from all these extinctions and use them as a basis for my study. Whereas, from the mathematical part, I can use all the framework that is usually used to predict the weather or the stock market and trying to get the prediction part of predicting extinction risk right. And this is not only a purely academic question, it is actually a very, very interesting and important question for conservation, because we're not doing this for fun, we're doing this to identify species that are very vulnerable and will most likely go extinct in the near future, and then protect them from going extinct. And one of the major reasons to protect them is why they might go extinct is actually climate change. So, what you can hopefully see here is the predicted temperature change on Earth until the year 2100. And if we continue as we do at the moment, we will have a temperature change of four degrees on average until the year 2100. And that has really, really strong effect on all ecosystems and will put a lot of species at risk just because droughts are more severe, temperatures are higher, and a lot of the ecosystems will actually most likely collapse. So, there are different approaches to trying to assign extinction risk to a species, and one approach is what I just called this has happened before approach. And it's pretty straightforward. The idea is really simple. You just look at, come on. You just look at what is happening right now. So, what we are doing is putting a lot of CO2 into the atmosphere which leads to a global warming and to ocean acidification. And then as a paleontologist, we go back in life's history and try to identify time intervals where this has happened before. And there are actually really good analogs and these are time intervals where there was a lot of volcanism that also put a lot of CO2 into the atmosphere. And then we're looking at the species that went extinct during these intervals of a lot of volcanism. And then we try to find the analogs of these species in the current situation and then conclude that they will have a elevated extinction risk. Another approach that I will now continue talking about in this talk is the so-called life cycle approach. And I will focus on this just because this is my research topic. So, this is what I do all the time. And it is based on a very simple empirical pattern and that is that the abundance of a species, so how common a species is, is very low when the species first appears and then it slowly increases until it reaches a maximum at the middle of its lifespan and then it slowly decreases again until it's going extinct. And what's really striking is that this pattern is really, really symmetrical. The way to success of a species is also the way of downfall of a species. And a common hypothesis is that this pattern actually reflects the life cycle of a species. Just like humans have a life cycle, this species also has a life cycle. Starting with the teenage years close to the origination where the species is young and dynamic, so it will spread out, become very abundant, then the adulthood where it will become very established and very abundant, and then it will slowly approach retirement. There will be less and less of that species until it's going extinct. It's pretty much like humans. And using this pattern to predict extinction risk is also pretty straightforward. As an example, here's a curve of a species that still lives, so it's still around in the present and we trace how common it was in the past. So we see it's had its teenage years, it had its adulthood and it's slowly approaching retirement. And with the knowledge of that common pattern of the life cycle, we can then predict, okay, it will most likely go extinct in the near future, so it has a high extinction risk. As a counterpart, come on. Here the blue species is constantly increasing, so it's no sign of adulthood still in its teenage years, and if we make a projection into the future, we assume it will still constantly increase, so this species has a low extinction risk. And this is a pretty straightforward approach. You can do this for every species for which we have fossils and that are still around. The thing is, this is based on the hypothesis that this is actually a life cycle. And when I looked at this, I found a second hypothesis which we can use to explain this pattern, this very, very symmetrical pattern and that argues for that this is actually not a life cycle, but it's rather an expression of purely noise and an observation bias that we introduce, simply because, well, everything we look at is already dead. Of course, it has to drop before it's going extinct, right? And I will run you through this for the rest of my talk. And the effect of noise is best explained by the Brownian motion, which was discovered by a very wealthy British guy that looked through his microscope and realized that the pollen in the water he observed are not still, but they're actually moving. And if he traced his movement, they showed a very, very distinct zig-zaggy pattern. And so they moved away from their original position and came back and moved away again. And he first thought this is actually, well, the pollen are obviously alive, they're moving, right? But after a while, people thought into this and looked at the pollen more closely and it turns out this is not that the pollen are alive, but it's that they're like randomly shuffled around by the water molecules. Just because at some point more water molecules are bumping into the pollen from one side, it will move in the other. And at some point randomly more pollen will bump from the other side, so it will just always shuffle around. And this Brownian motion is very common in all systems that are governed by a lot of background noise. For example, if you look at the news from the stock market, you will always find curves that are very, very similar to this. And there's people arguing that the stock market is on short-time intervals actually behaving like a pollen under a microscope. But how does this actually connect to our extinction? And just assume there's a very simple example. Assume there's a group of individuals of one species that is living somewhere on this planet and it's relatively stable, so it's moving, it's not changing its abundance, and then at some point it's hitting by a catastrophe and you have this sudden drop and then slowly get a recovery. And then we add another group of individuals from the species that is also hit by a catastrophe, but just at some other point because they're living somewhere else. And when we combine these two groups, just because we don't have the resolution, we just want to combine them to get more information. Something very interesting happens and that is the catastrophe does not look that catastrophic at all and they look a lot more smoother and a lot more regular than the original curves. And you can continue this. For example, if I average 10 groups from all around the globe that are independent of each other, it looks a lot more smoother and if I add more, you will always end up with a Brownian motion. So in any system that develops through time and you add a lot of noise to it, you will always end up with a Brownian motion. So in this case there's a strong argument for the abundance of a species to actually follow a Brownian motion. And now comes the second part which is the observation bias. So we know this is a Brownian motion so this is how the abundance of a species is supposed to look like if there's a lot of random noise from all over the globe contributing to it. We know two very important pieces of information and the first is, well, at some point there is an origination and at some point there will also be an extinction. So it will originate, so this is where we set the curve to zero and it will also go extinct. At the moment the abundance drops to zero. And so now we have a curve that looks surprisingly symmetrical and we can retrospectively assign it teenage years, adulthood and retirement. So we tend to see these life cycles even in random patterns just because, well, it starts at zero and ends at zero. There's not that many different patterns than an increase and then a drop to zero again. And the cool thing is this is actually pretty independent where I choose the origination. So I can place it up here and it's always symmetrical or I can place it up here and it's still symmetrical. It will always look symmetrical just by introducing the additional information of the extinction. And this is kind of like a bummer as a scientist because we have this very beautiful empirical pattern. This is the data. There's nothing we can do about the data. The data is like unchangeable. It's like pristine and there's nothing we can do about it. We have two very, very different hypotheses that explain this pattern. One is, well, this is a life cycle and there's this teenage year, adulthood and retirement and this is borrowed from a metaphor that species are in a way like humans. On the other hand, we have this other hypothesis that is, well, we're just seeing background noise and like retrospectively attributing these life cycles into the data that are not really there. And so what do we do in science? There's a very fundamental principle in science that's called Occam's razor. And Occam's razor pretty much says if you have two hypotheses, then the simpler one is most likely the better one. And here a simple explicitly means not easier to understand, but does it requires less assumptions. So now the question is, which one of those hypotheses is simpler? Okay. Okay, everybody who thinks that the life cycle hypothesis is simpler, please raise their hands. Okay. Everybody who thinks that the noise hypothesis is simpler, raise their hands. This is perfect. Because actually this is not a rhetorical question. We simply don't know. This is my research, it's an ongoing debate. There's just two weeks ago a new paper has been published arguing for the life cycle hypothesis. But I can't tell. Maybe in five years, and this is actually for me the most exciting part about science, it's not finding the answer. The moment you have the answer, it's kind of boring because you know it. But it's not going to work on a really hard problem. Thank you. Nicholas, thank you for your talk. I really hope we don't get extinct. Otherwise, it might get rough. So, who of the audience has any questions to Nicholas? Please raise your hands. The lady on the first row. Sorry for that, by the way. Are there any contemporary species that you consider with that life cycle thing? Is it useful for things where you can already see the whole boat? The thing is, this is commonly used, but it's always arguable whether it's retrospectively assigned or whether it's a real life cycle. So, nobody really knows. Because you will always find a maximum and then assign, okay, this is adulthood. But the problem with this metaphor about humans is a bit that for a human you see whether they're adult. Nobody has an idea of what it looks like that a species is adult. So, that's the main difference. And so, we cannot go to a species and say, this is in its teenage years. It doesn't work like that. There is one more question from the guy over there. Where are human beings as a species on that curve? Have we reached the top or are we going to continue to destroy the planet? Very good question. I think we're probably at all of these stages because as a species we're extremely young from a paleontological perspective. So, for example, the data I'm working here with, the temporal resolution is 10 million years. So, that's like the finest I can achieve. And if you're trying to apply these methods to human, they just don't work. They don't have the resolution. Personally, I think we're pretty close to extinction. That's some bad news. Any more positive questions? There is one over there. I was wondering, when you talk about the species here, to what extent is evolution a part of that? The very same thing might change enough that you might recognize it as a different thing and count it differently maybe. Or some other animal is taking over the same role, basically. And then it doesn't matter if there's an extinction, because the same thing keeps getting done. That's a very good question. The thing is that this is what you would call macroevolution. Usually, evolution takes one individual and looks at its success. What we're doing is we look at one species and look at its success. The question you're asking is an ecological question that is most of the time at a much lower temporal resolution. And for the species, these are not strict species in what biologists do as species. These are more for species, so they're only distinct by their morphology. And most of the time, we don't have a species resolution, but we do it on a higher taxonomic level. Okay, we'll have time for one more question. Any willing ones? There is one raised hand over there. Do you see taxonomic consistency when you come and predict the life cycles? So perhaps at, I don't know, family levels? So everything is symmetric at all taxonomic levels, but it gets worse with the higher levels because the resolution gets smaller, that's the problem. What's probably more interesting is that the pattern does not persist through mass extinctions. If the moment you have a very big impulse, a very big destruction, the symmetry collapses, which is also an argument for the random noise argument. I think to translate the answer, species go extinct by chance. Great, thank you very much for this talk. If you have any further questions to him, you'll probably find him at the ice cream shop after the talk. So thank you very much.