 From the program, we are going to have three, and there is nothing you have to prepare for them, just popcorns and then watch the discussion and participate. So, without further delay, it's my pleasure to introduce Flavia Marchitti, who is currently a postdoc at the University of Campinas in Brazil. And today she's going to talk about the phylogenetic patterns and how to understand them using microevolutionary models. So thank you very much Flavia for being with us and for giving this seminar. Thank you Jacopo, thank you for the invitation and please check if the screen, share screen is working. Is it fine? So I'll be talking about the, just let me put you on the side here, sorry. So I'll be talking about these phylogenetic patterns using an evolutionary model. And I've been working with this topic in the last, in the past five years, but I'm very interested in many topics, explained and exposed here in this, in this winter school. And I'll be clear about that in the next slides. So, I'm very grateful to thank lots of collaborators and mentors that I had during my lifetime. The list is longer than, than you can see here, fortunately. And I'm also very happy to be, to have the finance of supporting grants in Brazil, and to have the opportunity to go overseas to study. And this is not a great time for science in Brazil and maybe not for many places around the world. So I'm very grateful to, to be financed by, by FAPAS pentups here. And so during this, this winter school, we've listened a lot about coexistence between different species. How can we have different species sharing resources and still coexist in the same area or in the same environment. And I'm very interested in this topic as well, but I'm also very interested in how these species form, how they appear the first time. And studying the species formation, how they, these species coexist. I've been working with geographic models of species, modes of speciation, and I've been working with neutral models to form a backbone to understand no neutral models, how we can learn from neutral models from the absence of any other forces to understand how these things will change these species formation. And, and the species diversity form patterns, for instance, patterns that can be observed in phylogenetic trees. We see different species forming and they are related to each other in such a way that can be described by these phylogenetic trees. And there are many forces shaping and process shaping these, these patterns that we observe. For instance, how species that could exist in the same area, how they diversify in different species. And what are the signatures that we can have, for instance, from species that don't share the same area. And with this, and the main line of my research nowadays, it's to understand how this micro, we can link the macroevolutionary patterns and the microevolutionary forces. This fuser gap that exists in the literature called the micro, macro gap. It's too hard for me to say that, but I think you got it. And when I talk about speciation, when we talk about diversification, usually we, we think about geographic configuration. We usually learn that species form when you have some barrier that separates individuals in two different areas. And these individuals accumulate differences. And once they are together again. They don't recognize each other as individuals of the same species. This is known as allopathic speciation, and usually is invoked to explain how the species form. And this is likely the process that happened with the chimpanzees and bonobos apes in Africa that were divided by the Congo river and speciate. And, but there is another form of speciation call it sympathetic speciation. So the sympathetic speciation happens, or it doesn't happen. It depends if it exists when individuals share the same area. And there's a debate in the literature if it is an important force for speciation to happen if it really exists. But I think before this discussion before this debate, we need to learn what are the, how it happens if it happens how it happens, and what are the signature it leaves for us to understand that it happened. So that's why I've been working with models in sympathetic speciation to understand how we can then deny this is the process that is shaping evolution in many species, or in many phylogenetic species. So one of the most well known sympathetic speciation models was proposed by Dickman the bad 1999 where they use it a model where they included assertive mating, which is the individuals, they choose sexual partners based on how similar they are. They also included genuine cage between the mating character and ecological character, and also included competition the model to understand if individuals sharing the same space could then split in different species. And they show that with these ingredients they could observe the species formation this bifurcations on the ecological character. So earlier than that, in 1991, Higgs and the Hida, also known as the Hida Higgs model, proposed that sympathetic speciation could happen under a neutral model. And maybe some of you will think how neutral model are users. They don't show us anything, but I think we can learn a lot from neutral models to then understand the neutral models. We don't like this model a lot. So the Hida Higgs model proposes that we can define individuals by a very large genome as large as infinite genome that have each gene, this genome can have positive one or minus one value in their positions. And they show, and I tell you how this population of any individuals can then split in groups of different individuals that we will call species here. And they do these require a simple thing that is a minimal similarity between the videos to reproduce. They do not prefer to reproduce with the most similar but they have, they must have a minimal similarity to reproduce. So what happens in this model is that we have two individuals that are a ploid individuals. So they have only one string that finds the genome, the genome, the genome is be ally because I told you, and we can compute the similarity between two individuals for the reproduction. So here individuals are often better. They have similarities in some positions here and here, but they have some dissimilarity in other positions for instance in here. But if they have a minimum required similarity for the reproduction, they can then mate and form an offspring for the next generation. So this offspring will have the alleles from their parents with a probability of a half it comes from parent alpha and a probability of a half it comes from the parent beta and eight of these alleles will have a chance of mutation, even by new. So here, for instance, the individual should be a positive one in this allele, but it should be a negative one in this allele but it became a positive one because of the mutation. And with this, I should stop the video first. Sorry. It's starting in a very, sorry, I don't know how to reproduce it again, but it's starting with a very high similarity. All individuals are very similar. So if we define with the sexual reproduction in with the mutation rate, we get individuals that have a lower similarity between each other than a given point that is defined by the, the, the minimal similarity that we define for the species. So these individuals that have dissimilarity between each other is that where we see a species formation. So we get individuals that cannot reproduce to each other. So with this simpler model, the hidden Higgs have shown that with the sexual reproduction with a minimum similarity between the sexual partners, and with a very large genome as large as infinite. We can observe that we will have groups of individuals that are dissimilar to each other and cannot reproduce to each other. We can observe the speciation with no geographic structuring here. In a symmetric mode of speciation. And, as you can see, the infinite genome is a very large cost paid here to have speciation under a symmetric situation. So something that started with Marcus again in the last, in the past 10 years, he decided studying this, the hidden Higgs model in a modified version. We try to work with a model where it's more real more close to real situation where genomes are no more infinite they are like finite genomes. So we have a genome that have like be lossy and exactly as in the same model of the Higgs we have two individuals that produce, they must have a minimum similarity for the reproduction. And there is a mutation rate that can change the offspring inherited allele. With this, Marcus shows that it's possible to have not infinite genomes and still have sympathetic speciation. So he shows that as we have a lower genome sizes for a fixed set number of individuals, we have to, we must have a lower mutation rate as well to have a speciation happening. And the other thing that is important is that for a given fixed mutation rate, as the genome size increases, the lower the number of individuals required to speciation to happen under a symmetric mode. And this is something important we show this is not it's not required anymore to have an infinite genome for for the sympathetic speciation to happen so it's more close to real work. And also, something that Marcus, Marcus decided study was to understand how, how we can enhance how can we force a speciation, putting some structure on the on the spatial on the spatial area where individuals leave. What about if we decrease the sympathy level somehow. And he has shown that if we put a radius around the neighborhood and in the neighborhood of a photo individual for the reproduction. Forcing this individual to look for a partner for a sexual partner around his radius, and we can change this radio for a small radius. Now can can we observe this to speciation happening so here we are decreasing the sympathy level, but yours to we still have individuals in the same in the same area. And he shows that when we have a mating ratios, smaller rating, mating radius for looking for the sexual partner, you observe that the genome size required for observing speciation is lower so we can model the sympathy somehow using small genomes, and some is structuring on the space for observing speciation. And that he shows that in a given amount of time. If the radius is small, we observe more species and species are more structure in the space than we if we have a larger radius. In the same time, observing this one. So, this was very interesting and very, very important to, to help us to develop the other research that we did in the past years. And one of them was trying to, to do that thing that I showed you before to look for some relationship between species, and how this relationship between species could be related to the geographic mode of speciation. So, we observed that species were forming through time in my spatial model. And we also the server that that I told you before, when we have a spatial configuration. We can observe that as larger is the, the radius of individuals to look for partners, the last species are format. So, when we observe a smaller genome. It takes a longer time to species to farm and find an equilibration of the number of species format. And this was very important and we were interesting and relating this. With the phylogenetic trees of the species that were formed in, in these special areas. And so we did, we developed methods to to construct the phylogenetic trees. And then we started this specification model. And this individual base of model. So, we developed a method where we could record all the station and extinction events, during the evolutionary time. And we could say which species came from whom and if, for instance, extinction events were happening, and how long it take to one species to form and then to form another one what are the, the times of this, of this lens of this branch lens. Developing this method we can also annotate more information during the evolutionary model and have a more concise information by the most recent common ancestor. And, and we observe that these two methods, which are the two phylogenetic tree he presented here by recording all this position events and annotating more concise information the most recent common ancestor. We could use information from the phylogenetic trees to compare these two methods. So, using, for instance, metrics to describe the branch lens. For instance, we have the, an acceleration metric or the, the gamma statistics that define us that the phylogenetic tree for us. And that describes to us that the phylogenetic trees has longer brands or smaller brands is more accelerated or this, this accelerated phylogenetic trees, and also information about the imbalance of this phylogenetic trees, how low is the phylogenetic trees and how caterpillar is the format of this phylogenetic trees. So using this, this metrics to describe the phylogenetic trees, we could see that annotating all the events are also annotating more concise information by the most recent common ancestor was very similar, we could use the most concise one. And here I show you very happily that this annotating all the events so having the true phylogenetic trees presents to us. The same using the most recent common ancestor present as the same trades out about the game is that it's statistics that is about the brand lens. And also about the balance of this phylogenetic tree so this methods are very similar and it helped us a lot to to get information and do simulations more fast. So, as I told you I was interested to understand how these radios of the mating range could change the phylogenetic trees. So, I would, I was interested in understanding how parapet can see petrification were different in terms of the phylogenetic trees formed after after the process. So, just reminding you, we observed that we would have less species under larger meeting radios, and also would take a longer time for small genomes to form the number of species that we observe in the calibration time. And something very interesting happened when we did the simulations after that. We observed that there is a signal here observed mainly in the radius, meaning the parapetrics in Patrick level of the spatial use. Where we observe that as more sympathetic is the phylogenetic tree the larger the radius. It means that more deeply are the phylogenetic trees they have this format here. These mother did the radios it means the market a Patrick is this position in our model, the more steamy are the phylogenetic trees. And we also observed that the genome size when we can observe here the same radius and different genome size that increases from here to here from orange to blue. We observe that it affects both the acceleration and the balance of phylogenetic trees. And we compare this to empirical phylogenetic trees adaptive and non adaptive phylogenetic trees. And we observe that we could have. We could observe some patterns in our data and observe me especially. This is these are the trees that we worked with. And we observe that the higher the genetic flow during this position time. The more balanced were the phylogenetic trees. Why are the last genetic flow happened during the dispensation time of this empirical trees. The more unbalanced were the phylogenetic trees. And this was very related to the radius of the mating range that we had here. So it seems. In our model we can observe some signatures of the geographic mode of speciation, and maybe there are some signatures out there and the phylogenetic trees that we have databases that we can relate to the geographic mode of speciation. So, I was very interested in this results that we have about the same but we and the para three, but I was also interested to understand okay, how can we tell about the barriers about islands about things if we have some barriers, defining our space and can we detect some something and in our phylogenetic trees. So, if we have a lot of three. So, going back more close to the real world, if we go to be a lot of three can, can we observe some pattern the phylogenetic trees. By the results that I've just shown you I was expecting that as we impose a lot of three, as we decrease the radius as we increase the structure in the space, I was expecting that we would observe that phylogenetic trees will become more steamy. So, I would expect that, given a low poetry speciation model, I would have more steamy phylogenetic trees. And we performed our model in this structure space and we observe that contrary that we will, of our expectations, you observe more balanced phylogenetic trees and I think here is where we can see it more, more clearly. We observe that groups of species, sometimes called deans were observable from the phylogenetic trees. And something very interesting could be detected when we put the balance of these phylogenetic trees, according to the number of species. It is well known that the balance of phylogenetic trees. It varies with the number of species, even in a in a normalized metric. So, when we did this, when we put the results of our simulations here, you observe that for a given number of species, those that were structured in more deans had a smaller balance or a balanced metric than those not structured in deans, just in one deans. And it was interesting to observe that for the Hawaii of some Hawaiian species, such as the Sylvio's Ward Alliance, the Tetragonapus spiders, and they stick to Lincoln, that are very, very splitted in different islands in the Hawaii. We could relate to that these species have been formatted in different deans. They did, and it could be detected by the balancing of the phylogenetic trees. However, for some other species, for instance, for the fish, for the diving fishes, we didn't observe the balance, we couldn't relate the balance of the phylogenetic trees with the many islands where they deformed. So maybe, and we explored it in the paper, maybe this information was erased from the phylogenetic trees. And contrary to our expectations, the alpha value that is related to the branch lengths of the phylogenetic trees didn't change a lot. It's true that the variation did, but we didn't see any change in the mean value of the alpha value. And so it was a surprising result. And when running these models, we observed that we are not only creating new species, we are also losing some diversity somehow. And the diversity was being lost by different methods. The most well known and expected one was the extinction. So some species were formed and passed at some time they would disappear as the number of individuals decreased. Other process such as reversal and fusion that I will call broadly as hybridization process. We observed that some species somehow were being splitted and then joined again in another species. So we were farming species but then we are losing them by hybridization. And together with the students we decided to study how frequent these things were. So these species could be defined somehow as the group of individuals that have some genetic flow between them. It is not necessarily a direct flow, but it must be some somehow some flow between different individuals, for instance, these one and this one, they have a genetic flow through this third individual here. So species could form and individuals were linked by genetic flow. And we observed that the hybridization could be defined that when we have a species formation such this blue one, but then reverse it somehow to the species that it came from. So this is the most typical hybridization process that is usually called fusion here. And the extinction is really at the disappearance of a group of individuals that by fluctuation they just disappear. So we could have in our melodramatic trees this three process, the extinction where individuals disappear just by fluctuation of the number of individuals and that species, the fusion where species is split into two and then fuses into one again. So the first one that requires two is the station process, which is easier to see here, and then a fusion between the oldest one with the new one. It's not a fusion between the newest species but a fusion hybridization between the most recent and the most. And the, or the species that originated these two. What we could see is that extinction happens all the time. And it's basically independent of genome size. We see some difference here we observe that smaller genomes have a lower extinction rate, but it's not so different as for instance fusion and reversal. So fusion and reversal, they happen this hybridization modes at this hybridization process happen all the time but fusion happens more and reversal gets an equilibration, and we observe that the fusion is more common as a smaller genome. And we also observed that, as expected the population size during the extinction happens more frequently in the small population sizes so just before the event happened the during if the event was an extinction. So fusion size is likely a small population size, while fusion and reversal, it happened and any population size that we had. And the extinction and fusion and reversal didn't change a lot on the brand land of the of the species that where that event happened more frequently. The extinction could happen in species that were very old or very new and fusion and reversal seems to happen more new species more recent species. So, I've shown here that the neutrality help us to understand how the genetic situation and population size situation, also known as genetic drift or ecological drift, cool it as to two structures of our genetic trees and how this extinction fusion and reversal could happen in during this process. And it helped us to understand to form the theory understand more broadly how we could then develop non neutral models and understand more understand better non neutral models. And in our group we did this in two different ways. And I will show you the results by Deborah principi, I think it's just participating here, and also work a work that is being developed by my students nowadays. So, we had the backbone to understand how genetic interactions, for instance, they meet the nuclear interactions to reflect on different genetic trees. And I, we've been study as well how the cooperation evolution is linked to the diversification or to the permission of new species. So I start with Deborah principi work. So they develop this very nice model, where the individuals they have, not only the normal genome that is called the nuclear DNA, but they also have the mitochondrial DNA. And usually the mitochondrial DNA is inherited by the mother. These DNAs, they must have some, some compatibility, the nuclear and the mitochondrial one, because the respiration a very important process is, it's a link between the proteins and everything that is necessary for the respiration process made by the nuclear and the mitochondrial DNA. So they must have some compatibility between them. And using this, the difference, the compatibility between these, these two DNAs that Deborah defined that the individuals that have a better smaller distance between these genomes or a more couple DNAs. They, they would have a larger fitness that should be fine by a function. And again, individuals can be defined in this space. And something very interesting that they observe that is that as we increase the force of selection. And, and the cell and the force of the selection is defined by the, how broad is this fitness here. So, as we increase the force of the selection on the genome and the nuclear and mitochondrial genomes. So here the larger, the smaller the value of sigma here the larger the selection force. And also, a species that were in the, oh, sorry, and the n i value here is when we have no interaction between the nuclear and the, and the mitochondria. And also they observed that extinction were more frequent when we had selection, but not exactly in the higher selection in the most for the greater force of selection. They observed that intermediate values of selection, provoked more extinctions and this model. And they also observed that the phylogenetic trees were affected by the force of selection between mitochondria and the nuclear. And so the comparing with when we have no interaction between these two genomes. When we have a selection for us the balance of the phylogenetic trees was the unbalanced metric of the phylogenetic trees was lower. And this is very interesting. And I think this is how we can summarize one of her results and when we, we compare the coupled part of the DNA. The mitochondria is strong selection and when we have no interaction between mitochondria and the nucleus, we observe that the mutation rate present on on the, on the genomes that were linked decrease the, the mutation rate of the mitochondria and not the nuclear DNA. And also comparing side the same individual, they observed that in the end the selection is decreasing the mutation rate is purifying somehow the DNA formed in this species. Something very interesting interesting about the mitochondrial DNA is that they, they can be used in biological environments and real environments for identifying species and this is something that they have shown that the mitochondrial DNA. It has a higher matching with the DNA of the nuclear part. And so someone could you could use the barcode the DNA the mitochondrial DNA for barcode. Yes, but it's not necessary and this is something very impressive. It's not necessary that these, these two genomes interact. Even when there is no interaction, there is a mutual ratio between these two DNAs. And just to to finish with my, my last work here. I've been working the last years to understand how cooperation evolves. And this is something impressive that happens in the most variety of species. You can observe for instance, I mean not to interact with each other. You can observe memos that cooperate with each other and understanding how cooperation happens in nature. Help us to understand how we can force cooperation in human populations. Maybe it's a dream, but it's something that we could say that we can learn for nature. So, usually the cooperation evolution is studied using evolutionary game theory. So an evolutionary game theory, we'd say that we have individuals that cooperate and some individuals that don't cooperate they're colored factors. And in a given amount of time, depending on the of the interactions between these individuals, we will observe that one of these strategies will be stable. And usually we stood how we, we studied systems where the defection is, is the stable strategy. And one of the most well known games is called it. It's represented by this by this payoff matrix, where we have the two strategies cooperators and the factors. And we say that a defector receives a payoff of five when interacting with cooperator, and it receives a payoff of one when interacting with another defector. And the cooperator here receives a payoff of three when interacting with another cooperator and the cooperator receives zero when interacting with the defector. And thinking that these columns here represent populations. We can see if the strategies in the rose can invade the populations defining the columns. And usually, and in this type of game call it the prisoners dilemma. What we observe is that independent of what happens the defection is the stable strategy, because the defector can invade population of cooperators and a defector can invade and be stable in a population of defectors. So this is the final result, usually, and something that has been studied in, I don't know, the last 3040 years, I cannot calculate things now, but in many years. And, and is how to how we can have the cooperation evolving situations where we were expecting the defection to be stable. And notebook and many collaborators have shown that the spatial structure can be an important force for establishing some cooperation where only defection was expected. So using this simpler form of the prisoners dilemma, where we have one here and the value that is varied here. No vote can collaborators have shown that even for values where we're expecting defection only, we could observe some cooperation, they think directions were defined in the space. So if the interactions and the payoffs gained by videos were defined by a neighborhood. And even for higher values of be here where only, again, only defection was expected, because of the spatial structure we could observe some, some information of this, this group of operators. And so we decided studying these in our model and our individual based models. So if we've included in the end of the genome is to trace the cooperation and the defection that defines the payoff that each individual gets and in their neighborhood. And using the same payoff matrix use it by Novok and collaborators. We observe that the spatial structure could form still form a species under this non neutral model, but under a game situation where individuals have some fitness because of interaction with others. The number of species farm is usually is usually is smaller. And the cooperation frequency maybe is not clear here because I've started in the number one and not in the number zero. We start with the same number of operators are the same frequency of operators under a neutral game or the absence of game or under neutral model. We observe that the cooperation keeps the same frequency for the, for the whole time, while for the game model, that is also special as Novok feed, we observe that there is a value of P, which defines values of P, where we were expecting defection but we still could observe some cooperation. And these values are different of those observed by Novok, and I'll explore more that we also observe that species could form so we could, we could draw their phylogenetic trees, and this is something very impressive. We observed a very different balance during the species formation, and also more accelerated phylogenetic trees. So we could detect somehow, as Deborah did in her work at that, when we include some, some selection here when we include some, some fitness here. There are things that can that are detectable in the phylogenetic trees. And so the phylogenetic trees here are reflecting how the spatial structure and cooperation evolution affect the phylogenetic tree. One thing we are still interesting is understanding how the absence of the structure can affect the, not only the, the, the formation of species but also the cooperation evolution. And so doing a greater neighborhood for the species to the mating range and also for computing the payoffs of these individuals. And Louise is developing this work, my student, I'm interested in, we are interested in understanding how species form, and this is not anymore a neutral model so species cannot form but we, we have many clues that they will form. And we are interested also if there are patterns in these species that are formed. Do we observe more defectors in some species, we observe my more cooperators in other species, and how these leave signatures in the phylogenetic trees. So this data conservative trade, can we relate the, the cooperation or a defection as an ancestral trade of a given, a given group of species here. And so this is the kind of thing we, we are exploring now. And, and in the end, another suit of mine will be studying how the cooperation and the hybrid, the cooperation can affect the extinction and hybridization rates. So, again, is it more frequent that cooperators will be extinct. Is it more frequent that hybridization happens in synaptic specification or in therapeutic specification. So this is the kind of questions that I'm, I'm interesting now. So, just to summarize, I think I have two minutes here. I've shown you that competition, or natural selection is not necessary for sympathetic specification. And this is a result of the Hida-Hins model. And sorry, I've shown you that even in finite genomes, when we have a given genome size, a given mutation rate and a given number of individuals. So we can have sympathetic specification, and especially structuring when we define a mating ratio, radius for, for the individuals we can have more species. We can find signatures of the geographic mode of speciation in phylogenetic trees. And I've shown you that allopatry and parapatry affect mainly the balance of the phylogenetic trees. And almost in the end, I've shown you that extinction and hybridizations, they can be characterized in neutral models, and we can then explore non-neutral models. And being interactions between the same, inside the same individual, or they can be through interactions between different individuals. But all this knowledge that we accumulated is said in neutral models has helped us to understand and to perform the non-neutral models. So, I'm ready for any questions from you. Thank you so much. Great. Thanks a lot, Flavia, for the nice talk. So we have time for questions. So there are a few from the chat, which I'll start fitting for you. And then if anyone wants to ask anything, can raise the end on Zoom. So Elvira is asking, do you use empirical phylogenies without dating to compare with the simulation of these models? The empirical data without dating to compare with simulation. No, it's with dating. And then there is Margaret, who is asking, I noticed you made use of the random forest model on the phylogenetic trees. I wish to understand why you made the choice. Also, did you try other models? What were your discoveries? Maybe I don't know what is a random forest model. I don't know. I don't know if maybe she can ask. Yeah, Margaret, please. Yeah, good afternoon. Thank you. Can you hear me? Yes. Okay, yeah. If you check, I don't know the slide number. When we were showing the inheritance from the phylogenetic trees is actually a random forest model that we used to model those trees. I don't know if I can go back to the slide. I can't really get the exact slide. Yeah, there's a branching that you had. Is it in the beginning? Yeah, kind of in the beginning. I'm sorry. Yeah, before the slide, yes. Backward. Okay, yeah, yeah, yeah, here. Yes, exactly. Yeah. And this one? Yeah, and there's a place that is actually stated that we use a random forest model to actually get the models out. So I actually understand why you choose the random forest. Oh, so this is how we represented this method that we call it. Also known as the most recent common ancestor. And I don't know if it's exactly the same thing of the random forest model, but we, we get the individuals of our simulation, and then we trace back from which species it came from. So for instance, this individual here. Let's say the yellow one here, maybe it's easier. The yellow one here when we trace back this group of individuals that are represented by this, this guy here. And when we trace back, it came from my specification that happened just in the beginning. And the blue one here, we can see that the individuals of the blue one here came from yellow individuals. So when we joined information, so we have information based on this individual here that the specification happened two times before present time. So we can say there is a distance between the blue and the yellow one that happened two times before now. And this is just like following back the, the species that identifies the species of an individual and which is the ancestor of the species of that individual. It's the same of the random forest they didn't know. But all the same I've signed to a private message and I hope that because I want to know your, your choice of model there are actually several models. So, you know, generating the random, for instance, like the most accurate, to change what you do, but there are other, also other models that will be the choice. So, I just wanted to understand the choice of all this. Yeah, just one more thing maybe it can help you to understand what are, what are our choice. So, this, this information, so using all the events so recording all the events. It takes a lot of time and takes a lot of information that we need to record in our computers. So doing this help us a lot. But we also explored, for instance, genetic distances by different metrics. Normalize random forest. Yeah, that was why I have to ask why random forest. So we have the information of the genomes of the individuals we can do this genetic distance between the videos of different species. But we've noticed that when we compare to the real for the genetic trees that is made during the evolutionary process when we can record all the events. So, I'm sure that is the phylogenetic trees made by the most recent common ancestor were more similar to the real phylogenetic trees. That's why we decided to use this method to construct our phylogenetic trees. Thank you so much. You're welcome. Thank you. Time for more questions. Since nobody's asking, I can ask my question. So, in one of the first plot you showed about the model where you compare with the empirical networks, you had adaptive and non adaptive networks. I mean, I'm not sure. I also say network, but it's three. Sorry. Yes. There is gene flow so they are also network in some sense. Yeah, yeah. So you have the adaptive and non adaptive. So I'm not sure how these two are classified, but I will that the word adaptive suggests that there should be some, at least, difference in the structure while it seems that you didn't see any difference between the two. So I was wondering. I can go back to that slide as well. But it's true. So when we decide to compare to adaptive and adaptive radiations below here. It was because we were evaluating our phylogenetic trees. Let me show here. We were evaluating our phylogenetic trees under this when when they reached the equilibration time. So we were interested in this radiation process when it takes longer time, you see that they don't form new species so the phylogenetic trees, you reach somehow some, some structure that is somehow fix it. So we decided to start how radiations could, how did, what are the signatures and during the radiation. And then when we go to the literature. You only find or mainly find adaptive radiation. Maybe it's because we believe that the main process happened happened under adaptive radiation. So usually when they say adaptive radiation they are informing us there is somehow some selection during the process. And the non adaptive radiation so this information of adaptive and non adaptive radiation were also gotten from the, got from the literature so as the authors inform us, oh it's adaptive it's not non adaptive. And if you see in our table. I mean, I should put in the presentation mode. We have more usually more adaptive and non adaptive networks. And it's true. We can see some structure here in the adaptive phylogenetic trees that are the triangle ones. That is given by the natural selection not by the spatial structure. It was interesting to understand and to see that maybe the genetic flow. That can be defined as lower high defines better how the structure of the phylogenetic more than if it's adaptive or non adaptive, maybe the genetic flow flow is the most important thing here. And we made this classification of the genetic flow based on the, how many islands or how, how was the structure doing the radiation process. Did I respond to your question. Yes, yes, no no it was, I mean, I think it was surprising that there was no difference between the adaptive and non adaptive right because they both match the neutral case. Yeah, and I think, because we have this information that make this space, the spatial structure is very important, we have more more. We are more prepared to evaluate the non neutral models understand what are the signatures on the non neutral models that are not given by space. Great. So, is there any more, any other question. Okay, so if not, let me thank Flavia again for the very nice seminar. And we are now going to take a break for one hour and a half so it's going to be a long break you can even take a walk out of the screen and go out. And we are going to start again at 345 with the second lecture by Alvaro Sanchez. So thanks to everyone. See you in our now. Bye bye.