1 00:00:00,090 --> 00:00:08,280 So good afternoon. I wish I were able to do this in person, but obviously it goes without, then it wouldn't be safe. 2 00:00:08,280 --> 00:00:15,510 Let me start by thanking returns and the organisers of this similar series for inviting me to share with you some of their research staff. 3 00:00:15,510 --> 00:00:30,260 My group carries out we are based at the Department of Zoology and we examine senescence or agan, as it's often referred to as the Tree of Life. 4 00:00:30,260 --> 00:00:37,400 And we do work with mathematics and ecological and evolutionary modelling quite a bit. 5 00:00:37,400 --> 00:00:41,330 I've tried to tone that down a little bit for this presentation. 6 00:00:41,330 --> 00:00:46,250 But obviously, if you've got questions about the stats or the maths, very happy to answer them. 7 00:00:46,250 --> 00:00:54,530 Before I jump into it, I would like to reveal that, take a message, and that is that senescence or agent is not universal. 8 00:00:54,530 --> 00:01:03,800 It does happen in humans, but we are amongst the very few species that can form an exception to the rule. 9 00:01:03,800 --> 00:01:07,610 And this is big news because for a very long time, as you will see today, 10 00:01:07,610 --> 00:01:15,180 senescence was believed to be universal, to be found no matter where you look at it. 11 00:01:15,180 --> 00:01:19,730 And because it's a really old question, no pun intended, 12 00:01:19,730 --> 00:01:28,490 I thought it will be fit in to start by showing you a piece of art that does the fountain of eternal youth. 13 00:01:28,490 --> 00:01:35,870 I think that this really tabulates the main research that has been paying attention to the causes of ageing. 14 00:01:35,870 --> 00:01:48,320 We biologists, medical scientists, evolutionary biologists all have been obsessed with trying to find where the fountain of eternal youth lies ahead. 15 00:01:48,320 --> 00:01:58,910 And the myth behind this was that the lucky one to find it and to either base in it or to drink from it would show eternal youth, 16 00:01:58,910 --> 00:02:07,000 which is formerly kind of a increase or least stability in your vital functions. 17 00:02:07,000 --> 00:02:14,780 This is a little bit of a summary of the research that we conduct in my in my group and other than their words, 18 00:02:14,780 --> 00:02:22,420 senescence from the Latin PSINet cheddar, which means to become decrepit, you'll find other words such as plants. 19 00:02:22,420 --> 00:02:27,640 And so one other piece. One of the efforts I'm going to try to carry out today with with the audience 20 00:02:27,640 --> 00:02:35,710 here is to emphasise the message that humans are not the centre of it all. 21 00:02:35,710 --> 00:02:42,340 Obviously, we've got a strong emphasis in trying to prolong lifespan, in prolong health spun as well. 22 00:02:42,340 --> 00:02:44,630 That is the use of good quality of life. 23 00:02:44,630 --> 00:02:54,850 But we may learn one or two or more things by looking around and stop looking into our belly button to look for that fountain of eternal youth. 24 00:02:54,850 --> 00:03:00,580 So this, in essence, is one of the oldest questions that philosophers and researchers have looked into. 25 00:03:00,580 --> 00:03:09,160 The first written recording of senescence caused by two 350 B.C. and it was first formulated that we know of by. 26 00:03:09,160 --> 00:03:17,650 Of course, of all our feltl who back in the day, apparently roads, attacks, saying that if you want to live long and prosper, 27 00:03:17,650 --> 00:03:22,210 which of course, back in the day was no more than 35 to 40 years, keep that in perspective. 28 00:03:22,210 --> 00:03:29,110 You would do well in going for long walks everyday and then not eating too much. 29 00:03:29,110 --> 00:03:30,940 I'm going to touch on that. Not eating too much. 30 00:03:30,940 --> 00:03:40,300 Later on in this talk, from our democratic perspectives, that is in terms of how individuals in a population may suffer or not from senescence. 31 00:03:40,300 --> 00:03:53,470 So in essence, is define what happens between when you first become an adult, until when 99 percent of individuals in that cohorts die out. 32 00:03:53,470 --> 00:03:56,500 So in a depiction, you can see senescence here. 33 00:03:56,500 --> 00:04:04,060 You can see that fertility in blue is declining after a mean peak of reproduction with age, and that the risk of mortality, 34 00:04:04,060 --> 00:04:09,700 that is the odds that you can survive to the next year, increased quite exponentially with age. 35 00:04:09,700 --> 00:04:15,330 Of course, you can see that the mortality risk is quite high early on, but that's not what senescence is looking at. 36 00:04:15,330 --> 00:04:22,630 Senescence is just looking at what happens between when you first become an adult until you die of. 37 00:04:22,630 --> 00:04:25,390 So in essence, it's very important for many very reasons, 38 00:04:25,390 --> 00:04:32,560 one of them is how old you are and what country your base and know what gender or what socio economic 39 00:04:32,560 --> 00:04:38,620 background or what neighbourhood you live will affect how big a loan you can get from a bank. 40 00:04:38,620 --> 00:04:45,820 And all of that is these are natural scientists, which predicts how likely you are to die if you are likely to die young than your requite, 41 00:04:45,820 --> 00:04:51,880 unlikely to be able to return the bank loan and dangerous. So your interest will go higher. 42 00:04:51,880 --> 00:04:55,280 But in a sense, it's also equally important for a species. 43 00:04:55,280 --> 00:05:00,190 Hecker's, the tree of life, not just humans. Think about what you had for breakfast this morning. 44 00:05:00,190 --> 00:05:03,340 You probably have some orange juice. You. 45 00:05:03,340 --> 00:05:07,890 But I had some other fruits coming from the plank in them. Perhaps. Yes, for dinner as I did. 46 00:05:07,890 --> 00:05:14,650 You had a lovely bottle of of French wine and productivity. 47 00:05:14,650 --> 00:05:20,470 That is brought to you by the planking, them by the animal kingdom and other kingdoms can be affected by senescence. 48 00:05:20,470 --> 00:05:27,580 If a suspicious and NASA very quickly chances that that species will remain productive. 49 00:05:27,580 --> 00:05:34,090 That oranges or pineapples or whatever have you that you're eating, that will go down and you will not benefit from that much. 50 00:05:34,090 --> 00:05:42,600 So how much and how long species live is key to the ecosystem services that species provide to us humans. 51 00:05:42,600 --> 00:05:46,600 And he also has really important consequences for, again, back to we humans. 52 00:05:46,600 --> 00:05:54,190 How long you leave, obviously, under what conditions you are going to be leaving, because then the years of health span and also importantly, 53 00:05:54,190 --> 00:06:04,150 how long will society, the man that you work, not just in terms of years, but also how many hours per day, how many days per week, for instance? 54 00:06:04,150 --> 00:06:11,410 Couple of years ago, a rather controversial but I think scientifically sounds pet was put forward by Professor Rejean Bob Bell, 55 00:06:11,410 --> 00:06:18,430 the previous director of the Max Planck Institute for Demographic Research and one of my previous supervisors. 56 00:06:18,430 --> 00:06:24,550 He had observed that longevity in Germany was really skyrocketing. 57 00:06:24,550 --> 00:06:34,000 And as a consequence of that, he said, no, no paupers in compacting the working life to 40, 48 hours per week. 58 00:06:34,000 --> 00:06:40,720 Instead of what he proposes, you know, working fewer hours, they work fewer hours per week, but work for longer. 59 00:06:40,720 --> 00:06:48,550 So retire at the leisure time. You can imagine that was not very well received in the German media, but there you go. 60 00:06:48,550 --> 00:06:54,100 The type of consequences and logical thought that underlines the prolongation 61 00:06:54,100 --> 00:06:58,840 of lifespan in humans that we are currently witnessing in many countries, 62 00:06:58,840 --> 00:07:05,080 not only Germany. What are the key question, since an essence, of course, is why does it happen? 63 00:07:05,080 --> 00:07:14,470 Why have senescence evolved? No, I'm gonna point you out to this paper by method from 1990 and one guess in 1990 happened yesterday. 64 00:07:14,470 --> 00:07:23,110 But in fact, it's 30 years ago now. Metaverse made a really nice review of how many theories have been put forward and tried 65 00:07:23,110 --> 00:07:29,500 to define to understand what are the mechanisms for why senescence or ageing happen. 66 00:07:29,500 --> 00:07:34,150 And at that point, 30 years ago, he counted 300 theories. 67 00:07:34,150 --> 00:07:41,370 If you do the same exercise nowadays, you're much likely to find probably close to four digit numbers. 68 00:07:41,370 --> 00:07:44,460 Still, in order to understand the main theories of senescence, 69 00:07:44,460 --> 00:07:52,560 I thought that it will be interesting to just discuss them very briefly before we jump into into the research that I want to present today. 70 00:07:52,560 --> 00:08:00,810 The first one is mutation accumulation. So the mutation accumulation theory states that organisms become more decrepit 71 00:08:00,810 --> 00:08:05,640 simply because of all the mutations that can happen in your in your in your body. 72 00:08:05,640 --> 00:08:09,900 Some of them will be deleterious. They will affect your performance negatively. 73 00:08:09,900 --> 00:08:14,630 They will affect your fitness and their genetic machinery that we have evolved. 74 00:08:14,630 --> 00:08:23,370 Now we have in place to try to fix two rebote or two, cancel out those negative mutations doesn't aren't fast enough. 75 00:08:23,370 --> 00:08:29,880 So the rates of mutation of negative mutations is much higher than the rate at which we can fix them or NALDEN. 76 00:08:29,880 --> 00:08:34,980 That's a consequence of that. Individuals that are lucky to get to live long. 77 00:08:34,980 --> 00:08:40,140 Accumulate those mutations and therefore they become more kapit. 78 00:08:40,140 --> 00:08:44,040 The second one is called antagonistic play chopping. 79 00:08:44,040 --> 00:08:50,070 And this is a really interesting theory that has been proven to be true in quite a few animals humans. 80 00:08:50,070 --> 00:08:56,490 Amongst them is based on the idea that genes can carry out more than just one function. 81 00:08:56,490 --> 00:09:03,450 Those genes so-called play a tropic gene given gene can have a function early on in your life. 82 00:09:03,450 --> 00:09:08,520 And the same gene can have a different function later on in your life. 83 00:09:08,520 --> 00:09:14,970 It's been shown with evolutionary models that a gene that has a positive effect in your lifespan. 84 00:09:14,970 --> 00:09:23,820 When you're young will be selected for by natural selection, even if the same gene has got negative effects on your fitness. 85 00:09:23,820 --> 00:09:29,100 That in principle, natural selection should try to wipe out. 86 00:09:29,100 --> 00:09:36,120 So one of the reasons why Antagonistically Trophy explains senescence evolve is if a gene has two functions. 87 00:09:36,120 --> 00:09:44,580 One is positive early on, but negative later in life, the negative ones will become very much blind to natural selection. 88 00:09:44,580 --> 00:09:49,890 And that's one of the reasons why and how Tennyson's can evolve. 89 00:09:49,890 --> 00:10:02,220 The third mean classical theory of the evolution of senescence is that of that is possible soma and it is possible some of these mors organisms, 90 00:10:02,220 --> 00:10:10,110 not plants. I will show you in a second. But most organisms can be broadly categorised into a germ line. 91 00:10:10,110 --> 00:10:17,160 Namely, you're going to your reproductive organs and Soma needs some alone, which is everything Elaman, 92 00:10:17,160 --> 00:10:24,390 everything else in your body that helps you meet and pass on your genes to the next generation. 93 00:10:24,390 --> 00:10:33,030 Investments in energy, which is obviously a limited resource into Sohmer or into germ, is inefficient. 94 00:10:33,030 --> 00:10:36,960 And as a consequence of the cumulative accumulation of these errors, 95 00:10:36,960 --> 00:10:43,560 in terms of how energies accumulated through time, the individual tends to become less optimal than it ought to be. 96 00:10:43,560 --> 00:10:50,560 If he had an effective machinery. So as a consequence of that, again, if you live long, you become more decrepit. 97 00:10:50,560 --> 00:10:52,120 What's come into this three theories, 98 00:10:52,120 --> 00:11:00,250 other than the fact that historically they've been very well accepted along the age in research and the age community? 99 00:11:00,250 --> 00:11:07,330 Well, they all expect they'll predict that senescence is universal. 100 00:11:07,330 --> 00:11:14,950 And against the backdrop of that is the fact that while we know that senescence has got a genetical a genetic basis, 101 00:11:14,950 --> 00:11:27,910 here you have a study looking at what happens to the survivorship in the Y axis as a function of time or age in the x axis 102 00:11:27,910 --> 00:11:35,560 of different cohorts of the same slide species for which one has been treated genetically to knock down a specific gene. 103 00:11:35,560 --> 00:11:44,050 If you knock down a specific thing, you can see the bloodline cohort will get to the longer up to the age of 90 days instead of 80 days. 104 00:11:44,050 --> 00:11:50,470 So senators that have a genetic basis, but it also has an environmental basis. 105 00:11:50,470 --> 00:11:53,680 It also has an ecological basis where you live, where you eat, 106 00:11:53,680 --> 00:11:59,950 where you hang out with kind of say how long and in what conditions you will get to this. 107 00:11:59,950 --> 00:12:09,370 And one of the reasons or one of the ways in which that can be demonstrated is with calorie restriction theory are subtle. 108 00:12:09,370 --> 00:12:17,440 I will remind you already pointed out to us that if you go for long walks and you don't eat too much, you'll get to live long. 109 00:12:17,440 --> 00:12:24,430 If you watch how your diet or if you reduce the amount of calories that you intake, 110 00:12:24,430 --> 00:12:29,470 if you restrain, if you restrict your calorie intake, you get to live longer. 111 00:12:29,470 --> 00:12:33,280 This has been shown in mice. It has also been shown in flies. 112 00:12:33,280 --> 00:12:40,150 It has been shown in bacteria as well. This field of calorie restriction, however, is a bit controversial. 113 00:12:40,150 --> 00:12:46,290 There's been a few studies that have shown for the rhesus monkey that it either has no effect. 114 00:12:46,290 --> 00:12:50,820 Or that he has a positive affect, so we can talk about that later, if you would like. 115 00:12:50,820 --> 00:13:00,570 But the main thing is the understanding that genetics. That is nature and the environment nurture come together in almost like a 116 00:13:00,570 --> 00:13:07,860 perfect marriage to shape the Age-Based trajectory of mortality and fertility. 117 00:13:07,860 --> 00:13:16,090 Your fitness components, in other words. Still, this three theories, and particularly one of the fathers of these three theories. 118 00:13:16,090 --> 00:13:22,790 Hamilton, they are so strong and they're so sure in their prediction that citizens should be universal. 119 00:13:22,790 --> 00:13:28,880 That Hamilton back in 1966, published in a peer review manuscript. 120 00:13:28,880 --> 00:13:36,370 Senescence is inevitable. It will be found even in the farthest reaches of the universe. 121 00:13:36,370 --> 00:13:42,880 And the quote continues. Also observed, even in the most bizarre of creatures, ikaros the tree of Life. 122 00:13:42,880 --> 00:13:47,860 Well, today I went to this. Wolf, back to you. Today, I want to show you that humans are not the rule. 123 00:13:47,860 --> 00:13:55,390 I think many other aspects of science. So what it means is that this pattern that you can see here, which is what we humans follow, 124 00:13:55,390 --> 00:14:04,150 increasing fertility and then a drastic decline in increases in mortality with age is not really what every creature across the tree of life. 125 00:14:04,150 --> 00:14:12,310 Followups. We published a paper in Nature a few years ago where we show evidence for this. 126 00:14:12,310 --> 00:14:18,890 There are some species that do follow the predictions on the evolution of innocence by Hamilton. 127 00:14:18,890 --> 00:14:26,650 I'll give you some examples here, but I'll dive into them in a second. So you've got modern human populations such as Japanese. 128 00:14:26,650 --> 00:14:32,770 You've got hunter gatherers and you've got other non-human species such as guppies and fulminates. 129 00:14:32,770 --> 00:14:38,740 They do show an increase in mortality and declining fertility with age. 130 00:14:38,740 --> 00:14:43,470 There are other species such as dimness firms like pine trees, 131 00:14:43,470 --> 00:14:52,430 the Crookwell Dial and the Nine Crocodile and the Yellowbelly Marmot's in Yellowstone National Park U.S. were. 132 00:14:52,430 --> 00:14:58,060 The increases in mortality do happen, but food safety also increases with age, 133 00:14:58,060 --> 00:15:04,250 which is something that it's not predicted by a possible series of senescence. 134 00:15:04,250 --> 00:15:05,580 Let's flip that around. 135 00:15:05,580 --> 00:15:19,590 There are also other species like this, a tortoise, oh, it's brown algae in the intertidal of the Atlantic Ocean that show a decline in mortality. 136 00:15:19,590 --> 00:15:23,670 We'll also have an increase in fertility as they grow older. 137 00:15:23,670 --> 00:15:33,150 So what this means effectively is that there are some species where senescence not only does not happen. 138 00:15:33,150 --> 00:15:38,370 Fitness component, which is what natural selection acts upon the other way, improve with age. 139 00:15:38,370 --> 00:15:44,430 So just like a good bottle of French wine, the older the better. 140 00:15:44,430 --> 00:15:48,840 If you're interested in the documents in that publication, you can take a look at that. 141 00:15:48,840 --> 00:15:52,530 In the interest of time, I'm going to jump very quickly through this. 142 00:15:52,530 --> 00:16:01,350 I will just mention that we looked at this paper in 48 different species and we found, in essence, is a second opinion. 143 00:16:01,350 --> 00:16:09,750 Some species do on their goals in essence, and species don't. Some species have a flat projection of mortality risks and fertility to age. 144 00:16:09,750 --> 00:16:13,530 And it takes all different kinds of combinations of fitness components. 145 00:16:13,530 --> 00:16:21,000 So I'm just going to jump through all of this and I'm just going to make a call for how important it is to understand the mechanisms for a long time. 146 00:16:21,000 --> 00:16:27,420 The fourth, on understanding the mechanisms has been placed on why we ask why humans finance. 147 00:16:27,420 --> 00:16:31,410 And let us assume that everything else looks like a human mammals. 148 00:16:31,410 --> 00:16:39,050 Birds sometimes will also sniff. But what we show in this paper is that we need to start thinking about how can the other species stop? 149 00:16:39,050 --> 00:16:45,220 And specifically, even if he might sound like science fiction, what can we learn from them? 150 00:16:45,220 --> 00:16:54,730 In order to prolong our lifespan and more importantly for long, our health span, the years of good quality of life. 151 00:16:54,730 --> 00:17:02,920 So some of the approaches that we're taking my group are primarily inspired in three different pillars of science to tackle this question. 152 00:17:02,920 --> 00:17:09,520 One has to do with using big data on the demography of multiple species in plants and animals. 153 00:17:09,520 --> 00:17:15,190 The other one has to do with following individuals in their natural populations through a long time. 154 00:17:15,190 --> 00:17:25,240 We've been following populations of birds, of trees, of fungi for up to 15 years now. 155 00:17:25,240 --> 00:17:34,700 And another one has to do with understanding the molecular basis of why some species and they, in essence and other escape from it. 156 00:17:34,700 --> 00:17:43,030 So I'm gonna tell you a brief overlook about how we're tackling this question from a big data point of view. 157 00:17:43,030 --> 00:17:51,320 All this work is in a first instance, it was supported by the Max Planck Institute for Demographic Research, where I'm still affiliated. 158 00:17:51,320 --> 00:17:57,820 And you've got there are photograph of the first group that started building databases, compadre, 159 00:17:57,820 --> 00:18:04,990 which contains demography of plants and comadre, which contains demographic information for animals, humans included. 160 00:18:04,990 --> 00:18:13,790 Back in Rostock, Germany. And this research is now based here in Europe and the Department of Social. 161 00:18:13,790 --> 00:18:19,400 So what we have done in this project is for the last over 10 years, really, it's been it's been quite a few years now. 162 00:18:19,400 --> 00:18:29,790 We have been following an archive in peer review publication that contains information on the demography that is their rates of survival. 163 00:18:29,790 --> 00:18:35,590 The rates of development and the rates of reproduction of animals and plants. 164 00:18:35,590 --> 00:18:42,620 Where you can see on the top left is a world map of the G.P.S. locations where the data were 165 00:18:42,620 --> 00:18:47,780 collected by either researchers around the globe as well as by people in my group and myself. 166 00:18:47,780 --> 00:18:52,790 Where you can see on the bottom is the amount of publications that contain demographic 167 00:18:52,790 --> 00:18:58,160 information that we specifically target to address this question for plants and for animals. 168 00:18:58,160 --> 00:19:02,360 You can see that it really has increase in a cumulative manner quite drastically. 169 00:19:02,360 --> 00:19:09,860 So together we have got access to thousands of records on the natural population that I. 170 00:19:09,860 --> 00:19:19,470 And the information about survival chances and reproductive rates of individuals in their natural environments. 171 00:19:19,470 --> 00:19:25,830 The way in which this information is our taste, a matrix. 172 00:19:25,830 --> 00:19:31,110 All right. And I promise that this will be perhaps the first or the second only time they'll talk about mathematics. 173 00:19:31,110 --> 00:19:35,360 If you're interested in this model, I'm happy to talk with you later. 174 00:19:35,360 --> 00:19:41,580 The main thrust of these models is the classified, the visuals in a population, according to some speech. 175 00:19:41,580 --> 00:19:50,550 So how old you are is typically one of them. And in them, we track survival, development and reproduction from those. 176 00:19:50,550 --> 00:19:57,480 We can then apply algorithms that we've been developing in my group and with some collaborators to precisely 177 00:19:57,480 --> 00:20:04,860 look at the shape of mortality and fertility and to evaluate how many species do adhere to their prediction. 178 00:20:04,860 --> 00:20:12,320 By Hamilton on Yes dude [INAUDIBLE] and arse. And which ones don't? 179 00:20:12,320 --> 00:20:17,990 Now, when you're in grad school, the typically teach to the rule of one in a presentation. 180 00:20:17,990 --> 00:20:27,170 The rule of one is thou shall only go and say thou shall only show figure with one main object in one's life. 181 00:20:27,170 --> 00:20:35,240 Here I am breaking the rule of one big time. And the purpose of this is to try to overwhelm you, if I may. 182 00:20:35,240 --> 00:20:42,380 With the amount of information that we have. Where you have here is I think it was three hundred and sixty. 183 00:20:42,380 --> 00:20:47,810 Plant and animal species. Each little. 184 00:20:47,810 --> 00:20:50,840 Each square has a collar. 185 00:20:50,840 --> 00:20:58,130 In the background, the background has been classified according to the colour of the attacks on any groups to which that species belongs. 186 00:20:58,130 --> 00:21:03,980 So you've got in light blue, for instance. You've got algae and turquoise. 187 00:21:03,980 --> 00:21:07,580 You've got trees lighter too close. You've got palm in brown. 188 00:21:07,580 --> 00:21:14,420 You've got mammals. And this graph is organised from the top left. 189 00:21:14,420 --> 00:21:22,640 You can hopefully see an icon of a baby. And at the bottom right, you've got an icon of an elderly person. 190 00:21:22,640 --> 00:21:30,710 So this graph is organised according to the chances that a species escapes from senescence on the left. 191 00:21:30,710 --> 00:21:38,690 And in that continuum way increases the forces of mortality and declines fertility as it grows older. 192 00:21:38,690 --> 00:21:41,040 And that's what it's meant by that old person. 193 00:21:41,040 --> 00:21:47,510 I can see where you can see here is that there's a vast amount of variation in terms of the senators trajectories. 194 00:21:47,510 --> 00:21:55,450 In fact, if we were to classify species according to black or white to you and there goes in essence or do you not undergoes an effort to get France? 195 00:21:55,450 --> 00:22:09,620 In essence, what we find in this study is we know you go 307 species is 160 escape from Tennyson and 147 undergo senescence, 196 00:22:09,620 --> 00:22:11,510 but they do so with different intensities. 197 00:22:11,510 --> 00:22:19,850 So with a quick overview, you in big data for demographics, you can see that large amount of them if they don't undergo senescence. 198 00:22:19,850 --> 00:22:25,510 Of course, in this graph, humans will be in the side that to other guys and. 199 00:22:25,510 --> 00:22:31,660 I've made the claim that we can not only classify species according to whether they undergo Theravance or not, 200 00:22:31,660 --> 00:22:36,100 but we can also measure just how hard they undergo. 201 00:22:36,100 --> 00:22:42,340 And this is the second time where I'm going to be talking about some mathematical algorithm things as last time either way. 202 00:22:42,340 --> 00:22:51,970 So we're going to use a metaphorical, if it is entropy, to classify when individuals die from the moment that they are born until they die. 203 00:22:51,970 --> 00:22:59,540 You can picture on the y axis and survivorship of a cohort of 100 individuals. 204 00:22:59,540 --> 00:23:05,680 What we're mission exactly is how quickly does that curve decline with age from 205 00:23:05,680 --> 00:23:11,530 the age that Dana just begun mature until well under what we call in demography, 206 00:23:11,530 --> 00:23:19,600 the wheel of death? Well, if this is the age at which in getting cohort, 99 percent of the individuals have died. 207 00:23:19,600 --> 00:23:24,670 So because we start with 100 individuals, in this case, 99 individuals have died. 208 00:23:24,670 --> 00:23:31,750 Well, you can see there is overall all this began three different types of curves of survivorship. 209 00:23:31,750 --> 00:23:38,260 Type one. Type two, three. In fact, one. Most of the mortality event happened late in life. 210 00:23:38,260 --> 00:23:42,730 That's where you see the decline of death. Right hand types in type two. 211 00:23:42,730 --> 00:23:49,570 What you can see is that the loss of individuals, the rate of mortality is constant through time. 212 00:23:49,570 --> 00:23:53,590 And in type three, all you can see is that the curve actually plateaus. 213 00:23:53,590 --> 00:24:02,290 So as you get older and you're less likely to die. So the division between type two looking up in type two, looking down, 214 00:24:02,290 --> 00:24:11,430 helps us understand how strongly do you see this or how strongly can you actually escape from senescence? 215 00:24:11,430 --> 00:24:15,550 And that's the measurements that we have seen from this measure of entropy. 216 00:24:15,550 --> 00:24:21,310 It's essentially correspond to a hedge value of minus one equals two one or a greater than one. 217 00:24:21,310 --> 00:24:33,920 If this species finesses has negligible senescence, that is the performance of flat with age or if it escapes from senescence, respectively. 218 00:24:33,920 --> 00:24:38,480 We can apply this measure if it is entropy across the tree of life now. 219 00:24:38,480 --> 00:24:45,020 So we have got the information for different biological and taxonomic classes. 220 00:24:45,020 --> 00:24:48,710 So on the bottom, you've got I've been up to Righi. 221 00:24:48,710 --> 00:24:54,290 Those are bony fish you've got. And those are those are a coral. 222 00:24:54,290 --> 00:25:00,020 You've got sacred sites. I got a fighter. Those are cycads by not today. 223 00:25:00,020 --> 00:25:11,150 Those are pine trees. They're broadly categorised according to whether they belong to the animal kingdom in blue or to their plank in them in green. 224 00:25:11,150 --> 00:25:17,570 And there are groups are going to those classes. Right. So if the bar crosses over there one line and it's significantly a bow, 225 00:25:17,570 --> 00:25:22,510 then it means that that specific tax on a group overall escapes from senescence. 226 00:25:22,510 --> 00:25:28,310 And if it's below, it undergoes or either evolved. Senescence may show you some of those examples. 227 00:25:28,310 --> 00:25:41,660 So bony fish, bird plums, insects and mammals to which we humans belong have a strong propensity towards undergoing senescent rs2. 228 00:25:41,660 --> 00:25:46,670 So flowering plants like money Magnoli oxidised. 229 00:25:46,670 --> 00:25:55,100 There are other groups, for instance, like sponges and reptiles which belong to the animal kingdom, as you know. 230 00:25:55,100 --> 00:26:04,190 And three other groups of the plank propensity to either have negligible senescence or to escape from senescence all together. 231 00:26:04,190 --> 00:26:13,930 So in our exploration of what are the main drivers of senescence, one of the null hypothesis was will animals undergo senescence plans? 232 00:26:13,930 --> 00:26:18,650 Don't. And what this graph shows is it's not that clear cut. 233 00:26:18,650 --> 00:26:25,190 There are some examples of animals that escaped from senescence. And there are some examples of plants that do on their goals, in essence. 234 00:26:25,190 --> 00:26:34,760 So it must be something different. Shall we continue walking through some of the mechanisms? 235 00:26:34,760 --> 00:26:48,500 One of the most pervasive theories in demography and in life history theory is that of the fast flow continuum, the fast, slow continuum, 236 00:26:48,500 --> 00:26:59,060 which is a theory put forward for us by the stern states that individuals allocate into reproduction or maintenance of themselves in a really, 237 00:26:59,060 --> 00:27:04,520 well, tight and orchestrated Trade-Off. So a Trade-Off is by Gitari compromise. 238 00:27:04,520 --> 00:27:08,480 So you've got six pots of energy and no more than that. 239 00:27:08,480 --> 00:27:16,070 And you choose to either allocate everything to reproduction, everything to maintenance, or somewhere in the middle. 240 00:27:16,070 --> 00:27:21,050 How much you allocate to each of those functions structures a continuum. 241 00:27:21,050 --> 00:27:27,440 There are some species like C. elegans on the top left that allocates mostly to reproduction, very little to maintenance. 242 00:27:27,440 --> 00:27:30,080 There are some other species like the one on the top, right. 243 00:27:30,080 --> 00:27:35,930 Hunter gatherers and other human populations were most of the allocation of resources into maintenance. 244 00:27:35,930 --> 00:27:42,320 And there are species in between. A colleague of mine showed. 245 00:27:42,320 --> 00:27:53,870 If you understand where species are run across this continuum, which can be used through a proxy called generation time. 246 00:27:53,870 --> 00:28:00,340 I'll define that in a second. You can predict whether the species has a high rate of senescence on the way axis or not. 247 00:28:00,340 --> 00:28:07,780 So the species with a high generation time would be one that takes a long time to replace itself. 248 00:28:07,780 --> 00:28:15,700 So creatures like Sequoia's creatures, like the bristlecone pine in the Dead Valley in the US, 249 00:28:15,700 --> 00:28:22,990 which point since has a life expectancy of maximum life expectancy of over 5000 years. 250 00:28:22,990 --> 00:28:32,020 Creatures like the Nile crocodile and to a lesser extent, creatures like you and me, humans tend to have a middle or a really high generation time. 251 00:28:32,020 --> 00:28:37,890 Creatures like C. elegans or fruit flies bacteria might have a short generation. 252 00:28:37,890 --> 00:28:46,100 So a species with a short generation time undergoes senescence much faster than species with a long generation time. 253 00:28:46,100 --> 00:28:50,910 And you've got some depiction there of the species that Michael looked up. Yes. 254 00:28:50,910 --> 00:29:02,260 And some really cute non-human animals as well. Well, as we interested in evaluating whether the Fast Logan team also applies to the plant kingdom, 255 00:29:02,260 --> 00:29:07,180 as you know, the plant kingdom is another really diverse group of organisms, 256 00:29:07,180 --> 00:29:16,180 and they deserve just as much attention in terms of our understanding of their biology as do humans and other non-human animals. 257 00:29:16,180 --> 00:29:24,220 So, again, we've got a lot of information for the demography of hundreds, if not thousands of plant species. 258 00:29:24,220 --> 00:29:31,930 We can derive from this demographic information, information about generation time, information about allocations into longevity, 259 00:29:31,930 --> 00:29:37,060 like the rate of senescence or at the time at which individuals first become adults. 260 00:29:37,060 --> 00:29:47,440 We can classify different moments of reproduction. We can explicitly look at how much individuals these every time that they reproduce. 261 00:29:47,440 --> 00:29:53,770 So do we have one offspring or Talison? We're going to evaluate the degree or the frequency of reproduction. 262 00:29:53,770 --> 00:29:58,540 That's the degree of either party or some species reproduce just once and then they die. 263 00:29:58,540 --> 00:30:02,140 Other species reproduce quite a few times before they die. 264 00:30:02,140 --> 00:30:09,580 We're going to value the reproductive output, which is the expected number of offspring to give a mother throughout her lifetime. 265 00:30:09,580 --> 00:30:16,560 And we can also value the reproductive window, which is how long can you actually remain reproductively active? 266 00:30:16,560 --> 00:30:20,380 And because we're also measuring plans were size. 267 00:30:20,380 --> 00:30:24,400 It's a really good predictor of survival and reproduction. 268 00:30:24,400 --> 00:30:33,280 We evaluated the rate of growth of each individual in the natural population as well as the rate of shrinkage. 269 00:30:33,280 --> 00:30:37,900 And before I proceed, I want to take a second to explain what I mean by the rate of shrinkage. 270 00:30:37,900 --> 00:30:43,810 I would assume that the audience that I'm speaking with today might not think 271 00:30:43,810 --> 00:30:50,590 about how important size or loss of size is for the fitness of an individual. 272 00:30:50,590 --> 00:30:58,390 Well, shrinkage, that is, the loss in biomass through time happens quite frequently in not just the plant kingdom. 273 00:30:58,390 --> 00:30:59,980 Also in the animal kingdom, for instance, 274 00:30:59,980 --> 00:31:10,030 there was a paper published in Nature a decade ago that evaluated changes in size in the Galapagos Island iguana. 275 00:31:10,030 --> 00:31:20,050 It's been shown that the years following a La Nina event, it went to individuals that decrease in size have a higher chance of survival. 276 00:31:20,050 --> 00:31:24,130 If you know anything about climate change and the media in that part of the world, 277 00:31:24,130 --> 00:31:29,380 you'll know that when a media event happens, less food is available. 278 00:31:29,380 --> 00:31:33,700 So what the researchers were able to found to find and what made this paper on Nature paper, 279 00:31:33,700 --> 00:31:40,930 it was the Coplin of the fact that individuals that decrease in size more had a higher chance of survival. 280 00:31:40,930 --> 00:31:45,820 They got to live longer. They postponed the onset of senescence as well. 281 00:31:45,820 --> 00:31:51,090 So obviously, you don't tend to think of a shrinkage in humans other than when we go on a diet. 282 00:31:51,090 --> 00:31:56,500 Right. But the type of diet, quote unquote, that these iguanas are able to undergo is quite drastic. 283 00:31:56,500 --> 00:32:01,420 The ways in which signs were measured in this iguana is the length from this now. 284 00:32:01,420 --> 00:32:06,850 That's the tip of the the face of the Delta reptile until the cloaca. 285 00:32:06,850 --> 00:32:13,400 That's the organ that allows them to both defaecate and sexual intercourse with their authors. 286 00:32:13,400 --> 00:32:21,370 We're able to measure is that distance and show that some individuals were shrinking by 20 percent in that length. 287 00:32:21,370 --> 00:32:26,950 Now, can you imagine what it would mean for you to get 20 percent shorter from one year to the next? 288 00:32:26,950 --> 00:32:31,620 Let that sink in for a second. How do you do that? 289 00:32:31,620 --> 00:32:39,090 They're waiting was in which this one is were undergoing shrinkage, is by literally eating themselves inside. 290 00:32:39,090 --> 00:32:44,490 They were exposed to low resources because of landing Evan. 291 00:32:44,490 --> 00:32:53,190 And they started to restore to resource of the calcium that had been stored in their bones in order to be able to survive to the next year. 292 00:32:53,190 --> 00:32:57,150 So one of the ways in which you can live longer is if you decrease in sight. 293 00:32:57,150 --> 00:33:04,980 So in the blank in them, that also happens. They raise whether understanding the shrinkage can deviate predictions from 294 00:33:04,980 --> 00:33:12,110 the Parslow continuum and the importance of generation time for senescence. It might seem that you have a better chance. 295 00:33:12,110 --> 00:33:15,740 You have got a lot of roads. The roads could be maybe. 296 00:33:15,740 --> 00:33:20,840 Information about human individual income and commerce could be different, vulnerable. 297 00:33:20,840 --> 00:33:27,120 So you could halve anything that you look at maybe in blood samples, concentration of oxygen. 298 00:33:27,120 --> 00:33:31,510 There could be any volume information about each human individual. 299 00:33:31,510 --> 00:33:38,270 You've got you've got a data set that contains a thousand individual human records and then 10 Durrell's. 300 00:33:38,270 --> 00:33:42,980 You might suspect that a lot of those variables may be correlated against each other in 301 00:33:42,980 --> 00:33:47,420 such a way that if you want to understand what makes an individual healthy or unhealthy, 302 00:33:47,420 --> 00:33:51,410 you ought to reduce all of that rich data. 303 00:33:51,410 --> 00:34:00,410 Well that's what ipca the principal component analysis is, the statistical way, multivariate way to reduce dimensions in a data set. 304 00:34:00,410 --> 00:34:10,730 So just like the example that I gave you in humans here, we've got a lot of different moments for demographic performance in plants. 305 00:34:10,730 --> 00:34:16,130 So the first question was, let's see if we can reduce that according to the Fastenal Continuum. 306 00:34:16,130 --> 00:34:23,960 If we did, you should find a single axis of variation where everything that has to do with investing on reproduction, 307 00:34:23,960 --> 00:34:28,640 which in this graph is depicted in red, will be to one site. 308 00:34:28,640 --> 00:34:38,930 So you look at lots of reproduction and everything that is related to survival, which is in grey and in black, that black is generation. 309 00:34:38,930 --> 00:34:46,680 Time, by the way, will be to one site such a way that individuals or populations in this case, each point is one species will have to trade off. 310 00:34:46,680 --> 00:34:53,340 They can see each other and only one axis will be important. That access is what we call before the fast flow continuum. 311 00:34:53,340 --> 00:34:58,500 And here, well, you can see with plants, there's hundred in ten different species here, by the way, 312 00:34:58,500 --> 00:35:04,640 is that the data are bit more dimensional, meaning the Fastenal continuum is not enough to explain. 313 00:35:04,640 --> 00:35:08,810 The demography is a different plant species. There's something else going on here. 314 00:35:08,810 --> 00:35:12,980 Well, you can find here is that little Greek symbol to the left in blue called Gunma. 315 00:35:12,980 --> 00:35:23,510 That's growth. If individuals grow fast to the left did on these long, which is the value of our generation, time to the right. 316 00:35:23,510 --> 00:35:30,470 There's a Trade-Off there that the fossil will. But every single moment of reproduction in the plant kingdom is decouple, 317 00:35:30,470 --> 00:35:37,400 is independent or as we call it in that this is orthogonal to how long you live. 318 00:35:37,400 --> 00:35:41,900 Every single arrow that is read has to do with different limits of reproduction and 319 00:35:41,900 --> 00:35:46,160 they're running or someone else for that minute that whether a plunderer on their growth, 320 00:35:46,160 --> 00:35:52,520 in essence or not, is probably not dictated by these trade-offs. 321 00:35:52,520 --> 00:35:56,670 Well, you can see on the second axis is what we call the reproductive strategy, 322 00:35:56,670 --> 00:36:03,160 its axis is that you absorb twenty one percent of that variance and it forms a compromise 323 00:36:03,160 --> 00:36:07,450 between how long do you reproduce specious to the tub rupee's for a long time. 324 00:36:07,450 --> 00:36:14,260 And how likely are you to undergo shrinkage? That's the Greek letter row at the bottom. 325 00:36:14,260 --> 00:36:21,190 So it just again, two highlights. You've got individuals that grow very fast on the left or 18 high generation times and individuals 326 00:36:21,190 --> 00:36:26,410 that have got high moments of reproduction under TARP or have the ability to decrease in size. 327 00:36:26,410 --> 00:36:32,650 And together, these two axes explain about, well, close to 60 percent of the variance. 328 00:36:32,650 --> 00:36:45,930 Now we can measure through very simple mathematics. How far away are the orientations of generation time from different moments of reproduction? 329 00:36:45,930 --> 00:36:51,160 OK. So here you've got the degree of either party s. Here you've got the net reproductive output are not. 330 00:36:51,160 --> 00:36:57,970 Which, by the way, is indeed the same term that epidemiologists use in reports of covenanted. 331 00:36:57,970 --> 00:37:05,050 Where you can see here is another study that I published a few years ago in this case, 650 plant species. 332 00:37:05,050 --> 00:37:10,300 Again, these are organised, according to the Fastenal Continuum, from left to right. 333 00:37:10,300 --> 00:37:13,570 And the reproductive strategy is continuum from bottom to right. 334 00:37:13,570 --> 00:37:23,170 Just as I show you before. Well, you can see is that on the third dimension, on foot in the forces of selection for or against mortality. 335 00:37:23,170 --> 00:37:28,660 So Hamilton forces of selection states that they can come in three different flavours. 336 00:37:28,660 --> 00:37:35,380 They can be confident with each. They can decline with age resulting in species on they're going senescence. 337 00:37:35,380 --> 00:37:41,890 That corresponds to a negative slope. So a dog collar, a black collar on that graph to the right. 338 00:37:41,890 --> 00:37:49,350 Or you can increase with age, therefore selecting for species that don't ever on their go senescence. 339 00:37:49,350 --> 00:37:52,990 Disney Coplin that I mentioned before between the Fosco continuum, 340 00:37:52,990 --> 00:37:58,840 not trailing off with reproductive strategies is necessary to predict whether our plant 341 00:37:58,840 --> 00:38:08,060 species will undergo senescence towards the bottom left in black or we'll get from it or. 342 00:38:08,060 --> 00:38:20,290 In Yellow Points. Just like we can do that for quite a few plant species, we can, and we have also done that for animal species, including in humans. 343 00:38:20,290 --> 00:38:26,020 Because you're wondering, you human are here in the pink, not so. 344 00:38:26,020 --> 00:38:33,970 You tend to live relatively long and you have somewhat narrow window of opportunity for reproduction compared to other organisms. 345 00:38:33,970 --> 00:38:39,980 Both animals and very specifically also plants. 346 00:38:39,980 --> 00:38:44,320 That slave is actually outdated, outdated. It says that this manuscript is in preparation. 347 00:38:44,320 --> 00:38:47,830 It was it was published last year in Nature, Ecology and Evolution. 348 00:38:47,830 --> 00:38:55,090 And then it's what we show is that just like it happens with plans to understand that demography is of different animals, 349 00:38:55,090 --> 00:38:59,110 humans included, you need to understand their prostate gland, continue attributions. 350 00:38:59,110 --> 00:39:07,840 And also how frequently do they reproduce? OK, so we know that the investment and the trade offs are important to understand. 351 00:39:07,840 --> 00:39:11,240 In essence, I know not your question that you might want to ask is, OK. 352 00:39:11,240 --> 00:39:19,280 So if a species that is really closely related to me undergoes senescence, I also likely to undergo senescence. 353 00:39:19,280 --> 00:39:24,980 And the answer's no. By the way. So in this paper currently in review article you that there is what we did is 354 00:39:24,980 --> 00:39:30,860 we evaluated whether the species on there once and essence for mortality in. 355 00:39:30,860 --> 00:39:36,150 Right. Whether they had a constant rate of mortality with age. 356 00:39:36,150 --> 00:39:39,990 In yellow or whether they escaped from senescence. 357 00:39:39,990 --> 00:39:47,550 That is a declining mortality with age. And we've done this exercise for vertebrates and for invertebrates. 358 00:39:47,550 --> 00:39:51,810 The expectation here was that the collars anchors these phylogeny, 359 00:39:51,810 --> 00:39:57,390 which is a way to depict graphically how closely related you are to another species. 360 00:39:57,390 --> 00:40:03,960 Those collars in the outer ring should be clustered. It's all the rights should be together. 361 00:40:03,960 --> 00:40:08,310 All the blues should be together. All the yellows should be together. 362 00:40:08,310 --> 00:40:15,930 But they are not so phylogeny. Our understanding of our sister species is not a good mechanism. 363 00:40:15,930 --> 00:40:25,800 It's not a good mindset to predict senescence or its. We've also done the same analysis for plants and the same thing is true, 364 00:40:25,800 --> 00:40:35,960 senescence is not explained by phylogenetic distance to assist our species within the animal or the plank in them. 365 00:40:35,960 --> 00:40:39,560 Then what? What explains that? So we talked about trade-offs. 366 00:40:39,560 --> 00:40:47,450 We know that some species and there goes and really strong trade offs such that if you locate a law into reproduction in red, 367 00:40:47,450 --> 00:40:51,950 you cannot allocate a log into survival or generation time. 368 00:40:51,950 --> 00:40:56,840 Ingrey, those species would be you and me, the rest of the mammal class. 369 00:40:56,840 --> 00:41:00,370 In some it's. We can estimate the Orridge. 370 00:41:00,370 --> 00:41:03,150 That is the strength of that Trade-Off. 371 00:41:03,150 --> 00:41:12,090 We also know some other species like flowering plants, insects, reptiles and bony fish have got a slightly more soft and trails. 372 00:41:12,090 --> 00:41:19,680 So when you do the SPCA analysis on this glass, the decoupling is not full, but it's green, not very strong. 373 00:41:19,680 --> 00:41:25,650 And there are other species like dimness worm and the grasses where the decoupling is complete. 374 00:41:25,650 --> 00:41:31,520 You can invest a lot into survival and a lot into reproduction. 375 00:41:31,520 --> 00:41:37,820 We can then evaluate Kiritsis entropy that measure that mathematical measure of how lucky you are to escape or two on their growth, 376 00:41:37,820 --> 00:41:43,110 in essence, to remind you if you're to the left of one. 377 00:41:43,110 --> 00:41:49,110 You on their goals, in essence, if you are to the right of one, you escape from senescence. 378 00:41:49,110 --> 00:41:57,880 So understanding how species. Suffer or benefit from the lack of trade-offs is key, a key predictor. 379 00:41:57,880 --> 00:42:05,360 So when there's an Asian. There was indeed a paper put together by some of my collaborators in the Max 380 00:42:05,360 --> 00:42:10,600 Planck that was calling for a more mechanistic understanding of senescence. 381 00:42:10,600 --> 00:42:18,820 We now know that senescence is no universal. What's the next thing is, well, why do some species escape from it? 382 00:42:18,820 --> 00:42:22,840 Where are they found both around the globe and across the tree of life? 383 00:42:22,840 --> 00:42:28,080 And what makes them special is there's something that we can learn from them. 384 00:42:28,080 --> 00:42:34,390 I'm to expose you're going I'm going to keep on pushing you guys to think about plants for a second. 385 00:42:34,390 --> 00:42:43,680 The founding father of Modern Plants Ecology, John Harper, once said that plants are waiting out there waiting to be counted in content. 386 00:42:43,680 --> 00:42:48,910 By the way, is the bread and butter of demography, too. 387 00:42:48,910 --> 00:42:58,840 Each will take their licence if I'm allowed to odds in many other less kind of charismatic species like fungi, insect also within the animal kingdom. 388 00:42:58,840 --> 00:43:01,790 And the list continues. 389 00:43:01,790 --> 00:43:06,840 So I'm not going to talk about the second angle here in the interest of time, but I do want to talk about molecular mechanisms. 390 00:43:06,840 --> 00:43:18,750 I want to talk about long term individual records. I will target, I mean, the following minutes to you guys about some of the molecular underpinnings. 391 00:43:18,750 --> 00:43:20,520 Through this exercise with Big Data, 392 00:43:20,520 --> 00:43:28,140 we've been able to identify and isolate some likely candidates around this periphery of cloud of data of species that have got, 393 00:43:28,140 --> 00:43:33,740 quote unquote, altered demographic behaviours. If you remember, humans were more or less in the centre, 394 00:43:33,740 --> 00:43:41,010 but there are a wealth of species that are situated around that cloud of what's possible demographically across the tree of life. 395 00:43:41,010 --> 00:43:46,050 We've got some carnivorous plant species. We've got some orchids and other disciplines. 396 00:43:46,050 --> 00:43:52,380 We've got some corals and sponges that we're currently studying in the Great Coral Reef in Australia. 397 00:43:52,380 --> 00:43:57,300 We've got some birds that spend most of their lives. 398 00:43:57,300 --> 00:44:03,060 Who knows? We're out in the sea in the league for a very long time. 399 00:44:03,060 --> 00:44:09,930 So I want to talk to you about another plant species called Sistas. I'll do this or the Mediterranean rosebuds. 400 00:44:09,930 --> 00:44:16,860 This is our plan that is found across the western end of the Mediterranean basin. 401 00:44:16,860 --> 00:44:23,320 I've been following population that I mix of about 600 individuals of these endemic plant species 402 00:44:23,320 --> 00:44:32,460 nearby Barcelona and these rather dramatic landscapes called Montserrat Mountain National Park. 403 00:44:32,460 --> 00:44:38,310 Specifically, what I was interested in was to test the hypothesis of mutation and cumulation. 404 00:44:38,310 --> 00:44:42,030 I'll remind you of mutation. Accumulation is one of the main theories of senescence. 405 00:44:42,030 --> 00:44:47,010 And what it states is that, you know, goes in essence, you become more decrepit. 406 00:44:47,010 --> 00:44:55,870 Your vital functions go down with age because you're stuck with deleterious mutations, negative mutations. 407 00:44:55,870 --> 00:45:06,390 One of the ways with which some organisms like long live plants can get rid of mutated tissue is by shedding parts of their anatomy. 408 00:45:06,390 --> 00:45:08,460 Obviously, you know that I'm gonna lose my way, 409 00:45:08,460 --> 00:45:17,730 but if I cut your arm set on your right arm is full of mutations, which is compromising your performance. 410 00:45:17,730 --> 00:45:25,050 So the whole individual that you are, if I cut it and I don't take it immediately, I don't take you immediately to the hospital. 411 00:45:25,050 --> 00:45:32,970 You will die. However, the ability to cut modules and continue live in is quite common in other creatures. 412 00:45:32,970 --> 00:45:37,260 Reptiles can do this. Plants do that as well. Very often. 413 00:45:37,260 --> 00:45:47,960 So what I was interested in experimenting with is what happens if I cut off modules or plants and then I continue tracking them to time. 414 00:45:47,960 --> 00:45:54,630 Will plants that have on their go forced shedding of modules in the bottom row 415 00:45:54,630 --> 00:46:03,670 indicated with the Cesur have a higher rate of performance and lower rate of senescent? 416 00:46:03,670 --> 00:46:09,750 Similarly, inspired by Aristotle's work, I wanted to test how likely you are to escape or it's on their growth, 417 00:46:09,750 --> 00:46:14,670 in essence, if I give you less or more resources. So calorie restriction. 418 00:46:14,670 --> 00:46:23,250 Now, obviously, I cannot physically put a plant on a diet in the field and do something of it in an easy way. 419 00:46:23,250 --> 00:46:27,600 It's impossible to extract nutrients and minerals with the soil without destroying 420 00:46:27,600 --> 00:46:31,140 the root system and therefore compromising the demography of the individual. 421 00:46:31,140 --> 00:46:37,880 So instead of extracting resources to put the plan on a diet, what I did is I gave it more resources. 422 00:46:37,880 --> 00:46:41,880 And in this ecosystem, the resource that limits the growth of the plant is phosphorus. 423 00:46:41,880 --> 00:46:43,980 This is a phosphorus depleted soil. 424 00:46:43,980 --> 00:46:51,960 So I gave 200 individuals of the of the population pockets of phosphorus so that they could take them from the resources of the roots. 425 00:46:51,960 --> 00:46:57,150 And the expectation in this case is that by virtue of having more resources, they will live longer. 426 00:46:57,150 --> 00:47:03,310 So they will be faster. And there was cineaste faster as well. 427 00:47:03,310 --> 00:47:07,930 For each of these 600 individuals start in 2014 and still ongoing. 428 00:47:07,930 --> 00:47:13,980 By the way, despite Korvettes within measuring the biochemical activity. 429 00:47:13,980 --> 00:47:20,150 Of its performance within measuring the physiological activity of its performance and with a mission, 430 00:47:20,150 --> 00:47:28,430 different rates of the demography of the individual survival grow the probability that each year the reproduce if the reproduce, 431 00:47:28,430 --> 00:47:35,240 how many seats do they produce? If the seats are produce are how many embryos within each seed? 432 00:47:35,240 --> 00:47:39,170 Are the embryos viable? Can they germinate? And do they recruit? 433 00:47:39,170 --> 00:47:48,950 So just to give you the short of it, it's really hard work and it's work that happens in the summer under really hot Spanish sun. 434 00:47:48,950 --> 00:47:54,320 Of course, too, sometimes 40 Celsius degrees for each of these individuals. 435 00:47:54,320 --> 00:48:01,190 We have codebreakers of each. And their ways with which we can do that is by taking ventral ecological measurements. 436 00:48:01,190 --> 00:48:10,190 So, as you know, some trees have the ability to keep a record of how much they're growing every year with the rings. 437 00:48:10,190 --> 00:48:16,040 So ad hoc study. At the end of the study, we can take a subset of the individuals, age them, 438 00:48:16,040 --> 00:48:20,750 counting the rings and associate where we have measured before to how old they were. 439 00:48:20,750 --> 00:48:28,270 OK. So you're going to see here is for each of the functions, biochemistry. 440 00:48:28,270 --> 00:48:37,990 Physiology and demography, whether the functions decline with age now will be represented with an old person, 441 00:48:37,990 --> 00:48:44,830 I can inbreds whether the functions remain constant with age and that's Roob 442 00:48:44,830 --> 00:48:50,800 a sentence with a black icon of medium age woman and whether the functions, 443 00:48:50,800 --> 00:48:56,250 the vitality of those functions improve with age. And that's what we call negative senescence. 444 00:48:56,250 --> 00:49:03,850 And that's represented with a green icon of a baby. So what you can see here is that's where they control individuals to which we have nothing. 445 00:49:03,850 --> 00:49:09,760 We merely follow them through time. Some functions do decline with age. 446 00:49:09,760 --> 00:49:14,860 Some functions stay the same and some functions improve with age, 447 00:49:14,860 --> 00:49:21,280 such as the probability of reproduction and the number of seeds for the youth producing the flower. 448 00:49:21,280 --> 00:49:29,320 Contrast that with what happens to individuals for which we had fores shedding. 449 00:49:29,320 --> 00:49:37,270 So this worrying to me is where I cut a third of distance and I just waited for them to regrow them again. 450 00:49:37,270 --> 00:49:42,840 Well, you can see here is that the probability of undergoing senescence goes on. 451 00:49:42,840 --> 00:49:48,510 So this are more likely to either have negligible or in some cases negative citizens. 452 00:49:48,510 --> 00:49:56,640 And last but not least, the individuals to which we gave additional resources, we had expected that they will undergo something faster. 453 00:49:56,640 --> 00:50:04,080 But what we found, in fact, is that they don't. There were Harborough, the Delta, to have an increase in vitality in many functions. 454 00:50:04,080 --> 00:50:14,670 They control individuals, did not. What was interesting to me from from from this analysis is that depending on what level of biological organisation, 455 00:50:14,670 --> 00:50:19,710 the biochemistry and physiology or the demography, the signatures could be the couple. 456 00:50:19,710 --> 00:50:23,130 You could be undergoing senescence at one level, but not the other. 457 00:50:23,130 --> 00:50:29,970 And we would have expected four different levels of biological organisation to be coupled. 458 00:50:29,970 --> 00:50:33,690 One after the other, such that the processes will upscale from fuselier. 459 00:50:33,690 --> 00:50:40,610 So from biochemistry to fishier to demob theory predicts, we don't find that. 460 00:50:40,610 --> 00:50:47,670 And talking about modularity, I want you to think of plans and many other organisms is like a pile of strops. 461 00:50:47,670 --> 00:50:53,340 The main function of a plant such as this one. 462 00:50:53,340 --> 00:50:59,220 Hopefully you can see that. So bio on the way a plant works whose. 463 00:50:59,220 --> 00:51:04,140 It takes water and neutrons from the soil. It transports them in the vasculature. 464 00:51:04,140 --> 00:51:08,620 You can think about them in terms of arteries. To the leaves. In the leaves. 465 00:51:08,620 --> 00:51:20,850 The nutrients. The water will be transform into carbohydrates with the help of energy coming from the sun and oxygen, which is then reduced to CO2. 466 00:51:20,850 --> 00:51:28,020 Right. There are ways in which those nutrients are transported from the ground up to the leaf is through a collection of pipes. 467 00:51:28,020 --> 00:51:35,010 So a plant is nothing but a collection of pipes from which the leaves are sucking to take those resources up. 468 00:51:35,010 --> 00:51:42,240 One of the things that we know is that different plants and different animals as well can have an ability to act more or less independent. 469 00:51:42,240 --> 00:51:51,870 So you can take from one stroke, but not from the other. The independence between those strong results in a higher degree of modularity. 470 00:51:51,870 --> 00:51:56,220 And the expectation here is that organisms that behave more independently may 471 00:51:56,220 --> 00:52:01,460 be able to shed modules and maybe you'll able to optimise ageing trajectories. 472 00:52:01,460 --> 00:52:07,560 OK. So we tested this hypothesis. We've got ways to assign a degree of modularity, 473 00:52:07,560 --> 00:52:14,460 mainly by taking a cross-section of the plant or the animal and looking at how independent different modules within it are. 474 00:52:14,460 --> 00:52:19,710 We can then, for the same species, estimate the rates of senescence and the short of it is. 475 00:52:19,710 --> 00:52:26,400 We found that plans that are more molecule are planned to have got a higher degree of internal 476 00:52:26,400 --> 00:52:33,180 independence escape from senescence likely driven through the ability to shed modules, 477 00:52:33,180 --> 00:52:42,780 to shed units of their anatomy. When things get ugly, we try to do the sensing in animals and we failed miserably. 478 00:52:42,780 --> 00:52:48,990 We didn't find any signature there. I've got some ideas as the way that could have happened and why. 479 00:52:48,990 --> 00:52:51,570 But in the interest of time, I'm just gonna skip through this. 480 00:52:51,570 --> 00:52:55,890 I'm just going to bring up your attention to the fact that a lot of creatures that perhaps you 481 00:52:55,890 --> 00:53:03,600 don't think about in terms of being closely related to humans are involved in the animal kingdom, 482 00:53:03,600 --> 00:53:09,360 sponges and corals and also insects, some of which are colonial. 483 00:53:09,360 --> 00:53:14,960 And in fact, what they represent is just a collection of different units that are tightly synchronised. 484 00:53:14,960 --> 00:53:22,140 It's been shown that insects, by the way, that have a strong sense of sociality, that build colonies live longer, 485 00:53:22,140 --> 00:53:27,330 that insects have the same ecological conditions that don't that are completely in isolation. 486 00:53:27,330 --> 00:53:30,390 So the sense of society, the sense of community, 487 00:53:30,390 --> 00:53:41,640 the sense of integration is likely a cause that should be explored further if we are to prolong lifespan and health spent within human populations is 488 00:53:41,640 --> 00:53:45,060 going to move to that is going to showcase the fact that we've got a lot of 489 00:53:45,060 --> 00:53:49,410 different creatures that we're looking at doing still work in South Africa, 490 00:53:49,410 --> 00:53:56,550 doing the work here in Oxford, looking at a calorie restriction with flatworms on the top left and going to read 491 00:53:56,550 --> 00:54:02,420 exotic places like Australia to evaluate the role of modularity on senescence. 492 00:54:02,420 --> 00:54:06,890 Take a message from the top. From this, I just want to close up Tennyson's is not common. 493 00:54:06,890 --> 00:54:17,030 In fact, that argue is the exception Asias that most of the high quality data that we have is based on animals like us where senescence is a thing. 494 00:54:17,030 --> 00:54:28,250 So in order to understand. Why and how you undergo senescence, we need to look at how organisms allocate resources into survival and reproduction. 495 00:54:28,250 --> 00:54:33,520 Understanding the biochemistry is ultimately the holy grail of Asian research, 496 00:54:33,520 --> 00:54:40,630 and very little research has been infected or perform under interface between biochemistry and the demography of the species. 497 00:54:40,630 --> 00:54:49,600 The more should be should be implemented. And as I showed in their last part of this talk, Senator, is this really multifaceted, multifaceted? 498 00:54:49,600 --> 00:54:58,390 This controls with genetics. It's also controlled through the environment. A key question here is understanding to what extent. 499 00:54:58,390 --> 00:55:05,150 One can win over the other. What condition is there genetics more important than the environment when we know that? 500 00:55:05,150 --> 00:55:14,080 And only one we know that will we be able to deliver the benefits of ageing research to human societies. 501 00:55:14,080 --> 00:55:19,327 On that, I'd like to conclude to thank you for your time, and I'll be very happy to take questions.