 to your talk. Good morning, everybody. Thanks for the invitation to be here. Thanks to the organizers and all the tech support. Seems to be going very smoothly, so that's much appreciated. Yeah, so actually, I'm a professor at Towson University, which is in Baltimore, Maryland. And if Simon were on, he would know what I'm talking about. But I do spend also some time, usually every summer, as a senior research scholar at IASA in Austria. And for the last dozen years or so, have been the scientific coordinator of their Young Scientist Summer Program, which I will give a quick plug about because any of you PhD students or mentors of PhD students should know that we host this summer program, which really runs all summer June to the end of August, which allows PhD students to come to IASA and work with a IASA scientist. And they can get funding from one of the national member organizations of IASA. And the thing to keep in mind is a deadline for the coming summer is always around mid-January. So by November, December, the call is open, and you can apply for that. If anyone has any questions, you're welcome to ask me about that at some point as well, too. In fact, IASA is celebrating its 50th anniversary this year. I have the little pin that says that. And it's the 45th anniversary of this Young Scientist Summer Program. So it's really been quite successful. Probably over 2,000 PhD students have gone through this in the 45 years. Since we're such a diverse crowd today, I thought I would say a little bit about my background. I had a double major for my bachelor's in physics and aeronautics. And I actually started an MS program in aeronautical engineering until I realized that mostly I would be working for defense contractors. And I really wanted to do something to make the world a better place rather than blowing it up. That's not entirely true, but that's what it seemed like at the time. So I switched to environmental science and with the focus on energy resources bringing in my physics background that way. During that time, as many people do in grad school, scour the libraries back when you were in shelves and finding stuff that was all next to each other, I thought that was great. So you just kept reading along the shelf everything from systems ecology, ecological economics, and urban areas, and complexity, and so on. And that led me then to a PhD in systems ecology, mostly focused on network methods that allow for a whole system analysis, which is what I'm currently doing now, but really now with ecological systems, mostly with socioeconomic and ecological systems, a lot with urban metabolism, food energy, water, nexus, regenerative economy, and so on. Since 2004, Tauss and I have been teaching a course I created called Human Ecology and Sustainability, which sounds very fresh today, but actually back in 2004, there weren't too many courses with that title and with that in the book that I found, which was from 2001, which is pretty good, Gerald Martin's Human Ecology book. And what he stresses in there and what I'm bringing to I think the perspective of this conference about human ecology is that what we're trying to do is trace the chain of effects and feedback through ecosystems in human societies, primarily to be able to anticipate long range environmental consequences and hopefully avoid disastrous surprises along the way. We can also generate new ideas for dealing with environmental problems, and in general, we wanna make the world a better place, right? Maintain a livable and sustainable relationship with the environment. So that's kind of my underlying idea of what human ecology is. I think one of the reasons why we're also here today, and we saw this in Partha's opening talk, was that we are at a point in time where human footprint is greater than carrying capacity. And I think we all know that, we all, at least everybody in this room, acknowledges that, that that's not a sustainable way to do things. It's not common knowledge, but one of the ways it's trying to get attention to this is the idea of overshoot day. And I'm not sure if you've heard of that, but overshoot day is the day at which we have now used the Earth's resources for that year. Ironically, it's tomorrow. So I guess I should have been asked to talk tomorrow, but anyways, we have one more day to live it up, I guess, is the point. Notice the overshoot day has been, oh, I can do this now, right? I was looking forward to that, so cool. Overshoot day has been earlier and earlier throughout the year, the last several decades. Actually, 1970 was the year that we were basically one Earth. The last year we were one Earth. And since then, we're now, we heard a slightly different number earlier, but 1.6, 1.75 Earths. There was a slight decrease in 2020 because we used 8% less energy for travel because of COVID, but then it immediately jumped back again. So, we didn't build back better. We built back the way we did. And we know this because we can measure the ecological footprint on the planet through various measures. And we also know the basic biocapacity that the Earth can generate for us. Again, that was the discussion of the first day that that's really the first principle of sustainability. If we're over that, we're not sustainable. I mean, it's that simple. Another point that I try to bring into my talks is that I see the problems we're dealing with today as really symptoms of other deeper underlying issues. And cleaning up messes and treating symptoms is never also going to get us to where we need to go. And there's a deeper flawed relationship, I would say, that we have with nature and also with ourselves that leads to us continuing to make problems and make these symptoms rather than addressing them at the source. Another way of looking at that is that the problems that we have today were actually solutions to yesterday's problems. Almost invariably, the messes we're cleaning up today were thought to be good ideas a few generations ago or a few decades ago. And I'm afraid we're going to do the same thing going forward unless we change this fundamental relationship that we have with nature and with ourselves. And one of those is, I think, very eloquently put by Wendell Berry, who is a poet and wonderful author and farmer. And it's just like this, that we don't understand the word environment very well. We don't understand, we use it wrong. And he's saying the concept of country or homeland dwelling place becomes simplified as the environment. That what surrounds us. And once we see our place as surrounding us, we've already made a profound division between it and ourselves. We've given up the understanding and dropped it out of our language and so therefore out of our thought that we in our country create one another, depend on one another and are literally part of one another. That our land passes in and out of our bodies as our bodies pass in and out of our land that we in our land are part of one another. So we who are all living neighbors here, human, plant and animal are part of one another. I mean, it sounds almost like, sure, we all know that but I think we forget that and not to pick on anything but one of the earlier talks also the word abiotic we use, I think it was Pablo's talk. Sorry, Pablo. In your sociological diagram, I was going to ask a question about this. You mentioned abiotic and a lot of people think about abiotic parameters as temperature and soil pH and rainfall and those are all affected by life on this planet. They're not without life. Abiotic means without life. I'm trying to push this new term, conbiotic because basically everything on our living planet is affected by life and when we create words and use words like abiotic it generates a separation that is not right, it's not there. And that also comes about from how we draw our system boundaries. So whatever system we make, we put the environment as that stuff outside of our system boundary. Okay, well that's convenient and we kind of have to do that but one of the things that network analysis allows us to do is to make the node not within, separate from the environment but actually the center of two different environments. And so every object is receiving something from its environment and generating something out into its environment which then allows us to ask questions about where it comes from and where it goes. And immediately you see that's a very different perspective on how we look at the world because it's not fragmented from its environment, it's interconnected with its environment. And then you can build up multiple of these, right? Each one of those nodes is connected to other ones. And so there's this theory out there called environ theory which has been developed back since the 1970s and there's quite a few papers out there which allows you to look at all these interactions, again I would say in a more holistic sense. This is what my PhD work was on and a lot of my early papers as well too. And let me just give you one example from that. I know there's some network experts in the room so this may be trivial for you but just so we all have some firm basis. So this is a well-studied ecosystem food web. It's of an oyster reef model. So these six compartments represent different parts of the food web starting with the filter feeders. Again I can point there at that. And then there's detritus and there's other parts of the feeding chain and maybe to make it look a little more pretty to see what's actually out there. It's these kinds of species. And what we know from this diagram is the who eats who. Where the energy is going. In this case what is it? Achilles calories I think per square meter per day. And so we can generate an adjacency matrix of the direct connections between each of those compartments in that model. And one of the nice things about the network approach is that we can take powers of the adjacency matrix which will tell us the indirect pathways. So the A matrix was the direct pathways. A squared is the number of pathways between any two nodes that take two steps. So in this case you see that between two and two that I ignore the self-loops between two and two there's exactly two pathways, two distinct pathways of two steps. If you continue on that exercise you see now there's four pathways of three steps between two and two then seven pathways between two and two and so on. And what you notice when you do this exercise is how quickly, this isn't an exponential growth. This is common rhetorical growth. It's actually faster than exponential. So it doesn't take very far to go indirectly back into the matrix into the network to get huge numbers of distinct pathways. We're into the hundreds if we take 10 steps and if we even take 20 steps we're already into the millions of different distinct pathways between two nodes in a very simple six compartment network. This is not a complex, I mean it is a complex network but it's not that complex, it's only six nodes and yet we're having 1.5 million different pathways, unique distinct pathways to get from one node to another. So that's the complexity I guess that emerges that we can then dive into and identify and track the influence of those pathways. So I'll come back to networks a little bit later but I also wanted to point out then the, when you're thinking about the environment it's also often useful to think about these major spheres that are out there. I would say not to partition them but really for completeness to make sure that we're covering all of our bases and your system boundaries should be including parts of each of those but then of course recognizing the topic of human ecology we can't leave humans out of that. The humans are the active integrator of the other spheres. We like to take stuff from one sphere and move it around to other ones. We're really good at that, right? So the other, the spheres interact without us as they had done before our presence here but we're really good at accelerating and embellishing those flows. Okay, now thank you also those of you that spoke earlier I can go even quicker through some of this stuff. It's also the 50th anniversary of the Limits to Growth book which I think was a very important, profound work in this field. We've already heard about that so I won't go into that. Luis showed us the picture of the earth sunrise or I'm sorry yeah, earth sunrise. I actually prefer the blue marble when I talk about limits to growth and this is it, this is the limits, right? This is the whole planet in one photo. A few years later it was Apollo 17, not Apollo 8. So 1972 also the 50th anniversary, right? It's a confluence of 50th anniversaries. Limits to Growth, blue marble, yasa. So anyways, in this one picture and it did galvanize the environmental movement as we heard earlier to have people recognize that this is it. This is our spaceship flying through space and we better take care of it. So the problem with, oh I shouldn't say problem the problem with how the Limits to Growth book was received was very negative, was very doom and gloom and very, they don't wanna hear about that, especially economists and politicians don't wanna hear that there's any kinds of limits. That was a poor selling point I think on that book and maybe it was just the times but more recent work such as that of Jane Jacobs who's a very influential systems thinker has talked about limits but she refers to limits as invitations to work along with them. I think that's a really nice way of looking at it because sure once you know the knowledge of the Limit then work with it, deal with it, embrace it. So that spin is what we're looking for and a few years ago some co-authors myself published a book called Flourishing Within Limits to Growth which is the same idea that yes there are limits but we can flourish within these limits and our basis was following nature because ecosystems do this all the time. They have to deal with the real time solar energy constraints and other resources and they clearly flourish, right? The complexity, the diversity, the information accumulation and so on. So trying to turn the tables on growth or limits to growth is not a bad thing and I think that's the key message here. So one of the things that we did in that book was to talk about what are some of these properties that we can observe in ecosystems and so we boiled it down into nine properties. I don't necessarily like lists because I know you always either forget one or have one too many or something but we spent about a decade actually. My assistant's ecologist friends and I but I doubt it's the final word but we're happy with it for the moment. The first three are basically what we've already heard today as far as the first and second laws of thermodynamics and the periodic table. These are the two I think really interesting ones. It sets biology apart from physics is the fact that ecosystems use surplus energy to move further from thermodynamic equilibrium. We refer to that as a physically driven biological aspect because it's something biology does but with the physics around it. The second one is that ecosystems also can co-evolve and adapt and modify their environment which is a biologically driven biological aspect. So that's also something that biology does that the physical systems don't do but biologically. So anyways, I think those are the two key and trying to understand those is still a lot of interesting work to be done. The last four properties have to do with just what we also observed that there's a lot of diversity in nature, that there are a lot of networks out there, that there's hierarchies out there and there's a lot of information. So a lot of my research and interests now are trying to really understand these better and apply them to not just ecosystems but to socioeconomic systems with the understanding that nature is sustainable as best as the best model we have for sustainability to date I think is nature. And so maybe if we can design and organize our socioeconomic systems along these that we would be better off. Yeah, I'll just go through these really quickly but as I said, the first one was that there's conservation of matter and energy. The first law of thermodynamics is quite useful. Second law and a corollary of that is that there are no trash cans in nature. Everything ends up going somewhere. The matter is conserved. It gets reused over and over again through functional couplings. The second law is basically saying that all processes are dissipative and the implication there is that these are all have to be open systems and as open systems they require a continuous inflow and through flow of energy, of new high quality energy driving them. So keeping in mind open systems is the important thing there which drive all of the biogeochemical cycles that we have. Yeah, interestingly we've got 92 natural elements but life uses about 25 of them and uses them in fairly much, pretty much the same way to do certain things. Maybe in another planet life could be different but here it's organized around a certain similar biochemistry. All right, so then these were the other ones that I mentioned that really I think are the unique ones that things grow. I mean that's what's so cool about nature is that we take it for granted that at the beginning of the growing season there's an empty field and by the end of the growing season there's corn stalks that are taller than we are. I mean that's an amazing working against equilibrium process that's going on by converting sunlight into biomass. And then the other one is the fact that we've got evolution not just of the organism but evolution of the organism environed complex. I think that Gregory Bateson made a nice point about that we have the wrong unit of evolution that it needs to include the environment as well too. Yeah networks I had already talked about that we do see networks everywhere. Hierarchies that are existing in nature I think is also well observed. So now why is that relevant to human ecology? And the point is that we all have to abide by these. Nature and humans alike have to respect mass energy conservation, have only irreversible processes where work energy is lost, are open and need the input of work energy for maintenance, have hierarchies, have diversity, have networks and have information. Where they differ what we pointed out in the book was that ecosystems are really much, much better at recycling than we are. And I know there's efforts about circular economy and about improving that, but really the rates of cycling that human systems do is paltry compared to what needs to be done and what we see in nature. Another one is that this idea of growth is that ecosystems do take the surplus work energy and construct the biomass as I mentioned in the fields and they increase the organizational complexities and we clearly see that in human societies but the problem and the challenge we have is that the energies that are driving that complexity aggradation are non-renewable energies. And so we're in this trap that we're making more structure that needs to be supported and yet we're doing it with energies that we know aren't going to be available to us in the not so distant future. So although the same process is happening, the input vectors to those are quite a bit different. This is an interesting point that maybe we can unpack a little bit later is that you get economic rewards whether you're building or exploiting a gradient. So there's a lot of gradients out there that we can capitalize on and the farmer is growing a gradient and then cutting it down and getting returned from that. Other people just go out in the forest and cut down the trees and they exploit those gradients without necessarily having to grow it in the first place. So I think we need, our economic systems need to better differentiate between if you're actually building something or if you're just exploiting something. And then there was also a very interesting talk earlier this week too about the difference in growth and development. I think that's an important thing to point out is that ecosystems do both. They do grow, they do increase in biomass but was just said earlier. They reach kind of a climax biomass. That old growth forest or that savanna ecosystem is going to reach a, based on the net primary production abilities of that region, it's going to reach a limit of growth but then it continues to develop. Information still continues to increase. Again, in terms of either genetic diversity or biochemical diversity or network diversity. And so there's a lot of development to take place even past growth. Whereas, I think that human societies are relying more on growth, focusing more on growth and largely by just putting more into the system. You can always get the system bigger if you put more into the system in the first place. And that's a key difference between nature and humans. All right, coming back to this idea of fragmentation that I mentioned and how we see the environment wrong. And I think that's what leads to the tragedy of the comments because we see the environment, again, is out there. So we have this paradigm that we separate life from the environment in mind and action. And then once it's fragmented, it's very easy to treat environment less than life, right? Because it's out there, we'll get to it later but it's not part of the endogenous evaluation that we have. And therefore we consume and degrade the environment. I mean, it's almost a natural outcome of how we view this. And I said this is one kind of manifestation of tragedy of the comments. What we're promoting is a new paradigm for life, right? Something really simple. But the idea that we need a more internal endogenous recursive nature of nature where we are defining a single life environment system and using a hyper set formulation. And this is more of a conceptual model but life environment as one thing is environment at the macro scale but within environment or ecosystems, within ecosystems or organisms, but again within organisms is the environment, right? We're taking the environment all the time we're made up of the environment. And by having environment both at the inner circle and at the outer circle, you can't ignore it. You're gonna wanna be a little more protective of it, I would think. And then that could lead potentially to bounty of the comments. Not a tragedy of the comments but actually a win-win situation. That's a little hokey to talk about win-win but I think there's some good examples out there in nature. So I look at bounty of the comments as positive spillover effect. Things that you weren't anticipating that happened that are really good for the system. And what's interesting is that is those network methodologies that I referred to earlier, we actually have a way of identifying the amount of network mutualism that exists in any complex network. And this is based on the, again, those direct and indirect pathways that I referred to. So we're gonna use Power Series to get there. And as a result, what was interesting was that most of the ecosystems that we've studied actually have network mutualism. They have a net positive mutualistic interactions which is, I'll give you an example of that and then I'll make the conclusion there. So coming back to our oyster reef model again. So there's the adjacency matrix. This is just the structure and a lot of network analysis done just on the structure and you can do a lot of interesting things about centrality and betweenness and connectivity. Most of the networks that we deal with are weighted digraphs so we know not just the structure but we know the amount of flows between any two compartments. So I've revealed now that this data set actually tells you how much is there. So instead of just an adjacency matrix, you actually have a flow matrix driven by inputs and also then receiving kind of final demand or outputs from that. Now you can take that F matrix and do a lot more interesting things with it I think than you can with just the A matrix and one of those is this network analysis that talks about or reveals the types of relations that you have. So in the case of the oyster reef, the direct relations are always zero sum. Any two pairwise that you pull out, there's a winner and a loser. Something's gaining, something's losing. I mean that's nature and it's basic sense, right? So you get zeros if they're not interacting. You read these that pairwise across the main diagonal. So there's zero, zero there, but there's a plus, minus there, plus, minus there. There's a minus plus there. But these are all pairwise as either plus, minus, minus plus or zero, zero. If you run the analysis and try to understand that the impact of the indirect pathways, you get this as your final matrix. Well a couple of things jump out right away. One is there no zeros, right? It's the cliche of everything that is connected to everything else actually shows up mathematically that everything is connected to everything else. And you see a lot of positive signs. Of the 36 possible signs in there, 25 of them are now positive. Whereas before it was an equal number of positive and negative signs. So we would refer to this as network mutualism because of the case that you have now more positive signs than negative signs. And you see that because a lot of those zeros, those neutralisms, actually ended up becoming mutualism in four of the five cases where there was no direct interaction. They were actually indirect mutualists with each other. So being part of the network, they were helping another part of the network and getting help from it. In one case it was an indirect predation, right? I mean, there's no guarantee it's gonna be mutualistic, it is what it is. But what's also interesting, well as I said, so this is really contrary to conventionalism, even the ecological literature. We're kind of heretics there too. But it's really full of mutualistic interactions. Sorry, Brian. Yes. So is this a property of this particular network or is this a generic property? I mean, if you require a random, this network, would this property be... So network mutualism is not guaranteed to happen, but it usually happens in the well-defined ecosystem networks that we have. In the ones that you observe in nature. Yeah, so the data sets, I mean, some of the other network scientists are probably even no better than I do, but there's a couple hundred really, like high quality ecological networks that there's databases for. And the majority of those would... Not random networks. No, no, yeah. These are from data from ecological systems. But I'm curious then, we're applying this now to human systems, like trade networks or to economic systems, to urban metabolism systems and seeing, and we're finding a lot less mutualism actually in the human systems. That's actually another talk, but that's an interesting line of work with this. Brian, sorry, can you just quickly repeat how you built the second matrix? Yes, so the second matrix is built by... So we take each pairwise interaction between the, in the direct sense and we normalize that by the through flow of the node. So we know the flow value. So it's the net flow divided by the through flow to give a fractional exchange flow and then take the power series to get the second order effects, third order effects, fourth order effects and sum that infinite series. The series actually converges. So we get the effect of all the possible indirect pathways into a final matrix. And this is just the signs of that final matrix. So there's numbers behind there, but the numbers just kind of complicate things. So I'm just showing you the signs. But yeah, thank you for that as well too. One other point I wanted to make on this one, which is really anti-intuitive is that sometimes the direct relations actually will flip in the indirect sense. So here you had a predation, a plus minus, but in the integral one it was a mutualism. So the ecologist that's out there measuring something in the field and measures an organism eating another organism is writing down that's a predation relation and that's what they're witnessing. But when you actually run the indirect influences, they're actually those two species or functional groups could be helping each other in this case. Or in this case a predation actually ended up being competition. So the fact direct relations flip is really like, oh wow, that's pretty cool. And something we should probably be paying attention to. But I don't know how I'm doing on time. I meant to start mine and I didn't. Five minutes, okay. So this was just kind of a deep dive into some of the other mathematics that you can do with this. I was curious about whether the network pattern can say something about it. So if you have just a simple chain model, it turns out that the amount of mutualism that you have in your integral matrix depends on whether it's an even or odd chain, but in the limit you get about three times as many positive signs as negative signs if you have just a chain. So actually that's not so bad. I mean we think linear systems are not as useful, but still you're getting some mutualism out of there. But what's interesting is the system that has the most mutualism is a circle, is a cycle. So again you can run through the math here and in the case of an infinite cycle you actually get infinite mutualism. So in fact everybody is mutualistic with everybody except the one who's taking from you, but you're actually helping everybody else in the network because you're keeping the network going. So what comes around goes around and you only have one negative interaction and all the rest are positive. Again, pretty cool, right? So what are some of these activities that can potentially alter the network patterns? If we're trying to look for network patterns that are more beneficial, then we've heard talks already about fragmentation in terms of habitat loss and loss of diversity. And we know that these are taking away. We saw a very good one of how the change over time from Stephanie about how much more simple the networks were becoming due to human activities and interventions. So those networks are probably going to have much less mutualism than they would if they were more natural. Also the fact that network mutualism is a positive externality. And another point that I'm just kind of toying with now and jumping onto is that we focus a lot on negative externalities. We wanna reduce negative externalities. I actually think it's a bigger thing to do is to try to reinstate the positive externalities that were already out there in nature. There was a lot of positive spillover that got snipped along the way. And we didn't realize that it was gone until it was gone and now we've forgotten about it. We're trying to reduce the bad but we're not trying to promote the good enough if that makes sense. And that's more of a theoretical thing than a specific example, but maybe that makes sense. All right, so let me just then end with this idea of the people, planet, prosperity, these pillars of sustainable development. I will do my quick note though that the nested hierarchy is really a much more accurate view of sustainability because again, environment is everywhere and society is a subset of environment and economy is only a subset of economy, of society. But still, I mean, if we've got these three main pillars of sustainable development, what I'm really interested in collaborating with the demographers in the room now is that historically, ecology was really the only discipline that dealt in cycles that was dealing with limits, that was dealing with carrying capacity constraints, it was dealing with the pulsing of cycles. Both human population was growing and the economy was growing. They were team mates together and ecology was always the outside character in that discussion. What interesting thing has happened, right? We're hearing from all the demographers that that's not the case anymore. The human population is now leveling off. So what does that mean? It means economy is the outsider. So the demographers should team with ecologists and be on the same team and say, hey, we both got stable models basically, stable futures. How do we deal with that? And we heard earlier some of the problems. How do you deal with pensions? How do you deal with one child being spoiled or whatever as population is reduced in financing that? But anyways, I think this is really encouraging that there can be much more concerted, coordinated effort with ecology and demography trying to work together with that. So that's why I think for future research is realigning with the realignment of demography away from economics and the growth paradigm but towards ecology with cycles and limits by adding environmental caring capacity to demographic models. We've heard the question the very first day to the first demographer, where's ecology in your model? Same question I had. How will economics then respond to being the lonely discipline? There's some economists here that maybe can help with that. And then as I said, let's try to investigate more of the positive spillover benefits in the networks and trying to re-establish those network connections. Another way to think about limits which I really know that will have arrived there is I love this quote, they are limits, let's celebrate the limits, right? I mean, don't be afraid of limits, celebrate them. And when I hear Japan finally saying, yeah, we got a stable population, then I'll know that we've arrived that that's the right outcome. Oh my God, it's a stable population. What are we gonna do? What are we gonna do? And that's how it's treated now. Whenever we get close to a limit, there's kind of a freak out that goes on. So at the same time, we are confronted with working against limits, right? So, okay, that's it. That was a picture I took at 6.30 this morning. Thank you very much for this interesting presentation. And now it's time for questions. Thank you for your very thought-provoking talk. I have two related questions regarding the positive externalities. First, if your paradigm says that everything is connected and there's no environment, what's the meaning of externality in this context? That's the first. And the second is if you could give concrete examples of positive externalities, we need to reinforce. Yes, thank you. Way back at the beginning, maybe I'm going the wrong way, aren't I? No, that should be the wrong way. Everything is connected, everything else, within your system model, and there still has to be a practical system boundary. I'm just trying to go back to where, the very first time I showed the conceptual model of how we do systems, and it was second or third slide, so apologies for that. But yeah, so I didn't, actually in the longer version of this talk, I have this model, but at some point you have to draw a system boundary around this. And we have the discussion like, oh, let's model the universe every time. And then it's like, now that's a little bit too hard. So do you want to include all the major processes that you think are going to influence, but practically you do have to include a system boundary? So that's how we get around the fact in that sense. Positive spillover, I think soil formation is a really good example of that. So as a result of the organisms working together and doing their thing, they're grading soils and they're making richer, more organic soils, nutrient rich soils. And so as you start to snip away some of the activities, if you add too much pesticides and you're killing the microbial activity in the soils, you're losing that benefit. And so you still might be able to get, in the short term, faster, not healthier, but yeah, faster plants growing, but you're actually killing away this idea that soil formation was a natural part of the cycle before. So for one example, you only ask for one, so. Other questions? There's something from the... Fabio, do you have one Fabio? Yes, you showed the case of the oyster reef and you found out that eventually mutualism prevails over others. Would you say that this is a feature of all systems that are stable? I mean, you can build a network, an ecosystem that does not have this property. Would you say that this ecosystem will eventually collapse or die if there is no... Would you conjecture, if not prove, that prevailing mutualism is a condition in Equanon is a condition in Equanon, this system can remain stable as opposite to collapse? Yeah, I wouldn't use the word stable, but I would say it might be evolutionarily selected for. And it might be moving into that space where that is the outcome. And so, yeah, I mean, that's kind of the strong hypothesis that we're going by, is that the ones that exhibit mutualism and more mutualism are, again, not necessarily maybe stable, better systems, they're functioning in a way, and it's what we observe in most natural ecosystems. And as I said, what's interesting is it's not what we're observing in most of the human networks that we're looking at. But yeah, so I mean, I think you're thinking the same way we are about it, is that there's some features about those networks that are inherently moving in that direction, and then they will be selected for or be stable, if you want to use that. We can talk later. So thank you again, Brian. And if there are no other questions, we move to the coffee break and we meet again at 10 past 11. Thank you.