 My great pleasure to introduce Dr. Brooke Nunn as a research professor in the Department of Genome Sciences at the University of Washington. Dr. Nunn pioneered mass spectrometry analysis for proteins in ocean ecosystems during her early work. And since then, her first star research has spanned environmental microbiome, metaproteomics, marine micro-macrophonal taxonomy and even extends into extraterrestrial life detection, co-leading NASA's network for life detection. She's also directing the University of Washington Environmental Proteomics Resource Center. I'd like to welcome Dr. Nunn to begin her presentation. Thank you. I'm honored that I was invited to speak. I'd first like to acknowledge my co-investigators, Emma, Mike and Bill and my PhD student Miranda Mudge who all listened to my crazy ideas and make them into testable hypotheses and working functional tools. I'd also like to thank Elise Sipkin who just spoke on data integration for ecosystem level studies. She set the stage nicely for my talk where I'll talk about using statistics to help investigators track and significantly identify significantly changing ecosystems using high resolution metaproteomic analyses. Now every living organism has a large percent of cellular carbon and nitrogen-dedicated proteins. And if we look at an example of a protein sequence, we know that there are highly conserved domains that are specific to a taxonomic group such as indicated here in yellow. And there are domains that are specific to functions due to folding requirements or catalytic sites. That means in many cases that the peptide sequence can tell us who is doing what at the time of collection. And this general concept supports the simultaneous analysis of a complex community like a microbiome. And metaproteomics is gaining popularity as a tool but that rapid rise to stardom has resulted in a lack of standard methodologies or statistically driven conclusions. And we tend to see that each article is published at a different level of taxonomic resolution. It's a little bit of the Wild West out there but landmark papers by Emma Timmons-Schiffman and Max Sido are standardizing these methods across groups. And specifically our team is leveraging the sequence conservation I spoke of and uses peptides like biomarkers. Now, when we analyze a microbiome and its 1000 species expressing a range of functions, rather than report on a select species or a specific enzyme, we collect quantitative peptide data and use statistical approaches to identify taxonomic and functional resolution to accurately report who is doing what. The goal is to use all of our data and let the data drive the science. And mass spectrometry peptide quantification is now faster and more accurate. The latest generation of mass spectrometers combines extremely high resolving power, higher mass accuracy and very high acquisition speeds. And recent methods in the Macaas lab at the University of Washington allow us to fragment multiple ions simultaneously which decreases the chance of data loss. So using these complex spectra, we can complete a signal extraction to then ID the peptides and quantify them using area under the curve. And that provides us with a very wide dynamic range as I'm showing here, capturing peptides present at low abundances and very high abundances. Our team developed Metagomics which is an open source peptide centric analysis tool for looking at and analyzing metaproteomic data. It takes all of your quantitative peptide data and reports significantly changing taxonomy functions, sorry, taxonomy and functions which is ideal for comparisons across ecosystems or if you're trying to identify change points across space or time. In addition to developing methods in our lab, we focus on time series in the ocean and we're currently using the technology to track the bacterial community in a bay that's plagued by a harmful algal bloom. Now, previous research has demonstrated that phytoplankton and bacteria are constantly interacting, exchanging chemicals and metabolites and we're leveraging several fundamental microbiome principles, species and metabolic diversity, their fast proteome remodeling and we're using these principles to test our hypothesis. Can we use the microbiome's responsive proteome as an in situ suite of diverse microsensors to detect what triggers harmful algal blooms? As we dive into a bay, we can see a complex ecosystem of phytoplankton and bacteria. Metabolites can be released or chemicals in that ecosystem can influence the resulting phytoplankton physiology and vice versa. So we collected samples at a constant depth of two meters at 100 meters offshore and the whole community of microorganisms needed to be sampled every four hours. So water was rapidly pumped to our field lab where we had to isolate these different size fractions that represented phytoplankton, bacteria and then the dissolved metabolites and the nutrient chemistry. This is so we can link the chemistry back to the microbiome's functions. Now, our goal was to capture the whole community with very high temporal resolution over a long period of time. So samples were collected every four hours for 22 days resulting in a total of 1200 samples to be analyzed for metabolomics, metaproteomics, metagenomics and metatranscriptomics. And we're using statistical approaches now to discover interactions driving these bloom formations in the changing community succession. So our 2021 field season was a huge success here. I'm showing chlorophyll over the 22 days. If we look at that chlorophyll trace we can see that we've captured the initiation of two harmful algal blooms. Miranda thus far has performed quantitative metaproteomics analysis on the last six days of that time series to identify bacterial peptides that represent significant changes prior to bloom initiation. This is why she's looking at the first six days before the bloom initiation. So then test these candidate peptides on the first bloom initiation to confirm if they're applicable to a similar system. Now our current data set consists of approximately 5,000 bacterial peptides quantified and present across all these time points. And she can distribute each one of these peptides into different taxonomic classes based on those sequences. So here we're showing peptide abundance over the final six days that were collected every four hours. And each one of these figures represents the peptide abundance from a specific bacterial class. Now she ran a linear regression model of the peptide data and she revealed that there are four bacterial classes that significantly change 48 hours prior to the bloom. So it's super interesting. So I'm now gonna zoom in on Virucho Microbio as an example. And as a reminder, so here you can see that 48 hours prior to hab initiation. As a reminder, I'm showing you chlorophyll trace again and you can see that at the bottom of this figure that the phytoplankton bloom starts on the last day of the time series. Now using a hidden Markov model on the peptide data, Miranda was able to identify a non reverting state shift in this data set that identifies time points for us to specifically focus on because something probably significantly changed in that water chemistry to trigger this change in state. Now you might be thinking you're using taxonomic information. You could do this with 16S or metagenomic data. And although the metagenomic data does confirm the presence of Virucho Microbio, it only reveals a gradual growth over those six days. And it doesn't provide any data on real time protein functions, what these organisms are doing and why they're succeeding in this ecosystem. Whereas metaproteomics does provide that. Now if we focus in on these time points that Miranda indicated earlier as a significant of importance, she can then do hidden Markov models on all of the functions that are from this bacterial class to identify time specific metabolic functions of Virucho Microbio. So there's great depth and breadth to this metaproteomic data set. And here I'm just showing one class of bacteria and one example of timed metabolic responses. Our next step is to work with the Kubernetes Lab at Georgia Tech to integrate all of this microbial data to the waterborne metabolite in nutrient data to understand chemical drivers of the harmful algal bloom. And that brings me to my first panel discussion point. Omic data as rich as it is cannot stand alone. To track an ecosystem, we need quality, chemical and physiological data to validate and make sense of the complex omic data structure that we get. So next gen experiments must be co-designed to provide meaningful data to modelers for future efforts. The oceanography field has been lately thinking about currency converters between metrics and ecosystems to track global processes. This is the idea of being able to lead land, go from the ocean to the land to the atmosphere with one particular metric, such as total calories or electron transfer. And then two general housekeeping administrative details, I would propose that omic data repositories need to include raw data and need to be merged and funded. This is so data sets can be re-interrogated in the future as methods and bioinformatic methods improve. Last, we need standardized methods of reporting. As I mentioned, metaproteomic work groups are underway, but I think these need to occur across a large range of different communities. Thank you, and I'm looking forward to the discussion. Thank you, Brooke, that was fantastic. We'll hold questions as usual for the Q&A at the end, and hopefully some of the Q&A suggestions that Brooke mentioned would stimulate your discussion forum. So next time I'm introducing Dr. Andrew Farnsworth from Cornell University. Dr. Andrew Farnsworth is a visiting scientist in the Center for Avian Population Studies at Cornell Lab of Orphanology. His research advances the use and application of rapidly expanding remote and direct sensing technologies to study bird movements across scales, including weather surveillance radar, audio and video recording and monitoring tools, citizen science data sets and machine learning. He mentors young researchers from elementary to postdoctoral stages and engages frequently with the media in communication about science. Andrew, over to you. All right, thank you, Jack. I appreciate it, and thank you all for being in the audience and for the opportunity to speak today. So as Jack said, I'll be talking about a project called Birdcast today, which is thinking about understanding large-scale patterns of bird migration, in particular, using radar technology. That's what I'll focus on today, weather surveillance radar, and how we can use that to study bird migration net, really across scales in space and in time. Next slide, please. For those not familiar, birdcast has a website, birdcast.info. This project has been happening now for the better part of the last 20 years. It started when it was way ahead of its time in terms of the potential for the internet and cloud-based computing and what was possible online and with big data in the late 1990s to really become this collaborative effort between orthologists on the one hand and computer scientists on the other, in particular, machine learning specialists. And really the idea is to capture the spectacle of bird migration. And as some of you may know and some may not, bird migration is this incredible phenomenon, a series of phenomena that happen at least twice every year. Most of it happens under the cover of darkness. So it needs some unique approaches to studying the magnitude in terms of the numbers that are involved and even understanding the basic natural history of how many birds we're talking about on the move, but also, of course, what those birds are, what the species are. There is no one method that does justice to really capturing all of this information. However, the method and the tool that we've focused on in this project most in a most public-facing way is weather surveillance radar. And let me move to the next slide and give a little bit of background on that. So in the US, we have this unique system, in this case, 143 weather surveillance radars that have been distributed around the continent in the contiguous US and elsewhere, but in the contiguous US anyway, for much of the time between the early to mid-1990s and present. It's been a near-continuous sampling of the atmosphere. As you can see in this mosaic slide, each dot in the center of where these blue and green patterns are, there's a radar there. These radars are excellent at detecting all sorts of things in the atmosphere that range from various meteorological phenomena, whether it's large hail or different types of rain, snow, what would they call hydrometeors in the meteorological parlance, to the biological, which include birds, bats, insects, and the success with which this network with these sensors, these radars, detect biological activity is absolutely amazing. And when you look at this map, you'll see some areas that I've highlighted in white here. That's where all the meteorology is happening. So those hydrometeors, the intense precipitation associated with boundaries between air masses, that's in the white sort of irregular patterns. Everything else you see here, for the most part, is biological activity. And this particular image, this mosaic, is from May 15. And that is a time when this biological activity is absolutely dominated by birds. So the thought of thinking about how to capture all of this information about biological activity, teaching various machine learning algorithms, how to recognize between meteorology and biology, and then to speak about the ornithology that's happening, the concept of birdcast was all about trying to do this from the scale of a single radar that samples approximately maybe 100 to 200 kilometers on the landscape and a few kilometers above the ground, all the way up to the continental scale, multiple thousands of kilometers when you consider the network in Hall. Next slide, please. So the combination or one combination of this 20 year project has been the ability to do that, to basically look at hundreds of millions of radar scans since the 1990s and do exactly that kind of extraction of taking away the meteorology and talk about the ornithology. And one of the upshots of that, this graphic comes from a paper that was published in 2018 by a colleague of mine at Cornell, Adrian Doctor, is that we can now quantify the flows of bird biomass in the continental US, in and out of it. So the green arrows represent the spring movements when spring migration is occurring across a transect in the south and a transect in the north. The orange arrows represent the fall movements after birds have produced young. The numbers we're talking about of birds migrating under the cover of darkness are simply staggering when it comes to thinking about vertebrates in the spring, two and a half to three and a half billion birds every spring in the fall, four to five billion birds. So all under the cover of darkness, a large array of species, massive, massive movements of bio flows. Next slide, please. Now, since we can do that and we can do this across all of these 143 radars and across this multi-decadal dataset, we can do some really interesting things with those data. So once we can characterize where the birds are, how many birds there are, various other metrics, we can relate that to patterns and we can create forecast models. And one of the things we've done in the last few years, this is the work of Benjamin Van Doren, who's about to be at the University of Illinois, Champaign-Urbana and Kyle Horton, who is at Colorado State University. They published a paper in 2018 talking about these forecast models, describing the model that basically relates bird migration intensity on radar to various weather factors you can sample and easily extract from the network of weather stations around the contiguous U.S. And that model is a wonderful one. It captures about 80 to 85% of the variation in bird migration intensity with a handful of weather factors. Most appropriate highest predictor and highest gain from the predictor is temperature. So in the spring warmer temperatures tend to facilitate spring migration, in the fall cooler temperatures tend to facilitate fall migration. But the concept here is creating basically a weather map, if you will, for bird migration. So where the real intense colors, the whites, the yellows you see, that's high intensity bird migration. And we can create these models every three to six hours as various cycles of meteorological weather factor data are updated and the model behaves very well across years and across space and time. Next slide, please. Now with that model and with the other background of being able to characterize the ornithology and meteorology, we can also talk about observed maps or live maps, if you will. So the forecast on the one hand, how does the forecast do? We can do that in near real time. Basically, as soon as the radar data are available for us to analyze, maybe a few minutes after the radar detects them and does a scan of the atmosphere, those data are stored at Amazon Web Services. We can access those. We can run these models in the cloud and within a few minutes have a bird migration intensity that's happened just moments ago. So the idea of the forecast map and the observed map in very close to real time is just this wonderful way of understanding migration at a totally new and a very large set of scales. Next slide, please. Since we can do that, we can also dig a bit deeper and think about, okay, the continental maps are one way of approaching where and when and how intense is bird migration. Observers, scientists, general public has this interest in birds, as you probably know, and the idea to dig down deeper into those data and think about what's happening at the county or state level. We can produce a dashboard that looks at the altitude of migration, the pattern of movements within a given night over a particular county, how that relates to the seasonal pattern at that particular location, what's speed and direction. Next slide, please. We can also use those forecasts to compel action. So we can use the science that says, oh, bird migration occurs during a particular window of time and we have a three-day warning of when high intensity migration may occur. Let's impel people to act. In this case, to turn off lights to protect nocturnally migrating birds. Light disorients and attracts birds. Turning it off is very positive for nocturnal migrants. Next slide. Of course, the reason we do this and think about these big kinds of scales also come from a paper that was and obviously sort of the underlying theme of at the lab of ornithology and many other places, the science to conservation action. We've lost so many birds over the last 50 years. Thinking about ways that understanding these patterns of migration, where and when birds are on the planet, hopefully it gives us the ability to, as you see on the right here, reverse that curve. Next slide. And finally, I would just like to highlight that I've been talking about birds from the continental perspective and also the decadal perspective to arrows, to hours or minutes. This network of weather surveillance radars is also really wonderful at detecting these other biological so not just birds, insects and bats. And in addition, we're talking about radar here, but of course the ability to think about leveraging the amazing community science data that's available to us on birds during the day in projects like eBird and also acoustic monitoring to integrate across all of these different platforms can give us a really powerful complimentary view of what's happening at the small to the largest scales that span the continent. Hopefully we'll have some time for some questions. I'd encourage you all to visit birdcast.info and maybe during the panel discussion, happy to talk more, but thank you very much for your time, appreciate it. Thank you, Andrew, and great talk. And yet we'll save questions for the Q&A. So next up, I have Patrick Mayfroyd from University Catholic du Levant. Dr. Mayfroyd is a research associate in the Belgian research funds and a professor at the University of Catholic du Levant in Belgium. I'm probably saying that wrong, I apologize. My research focuses on how land use, your research focuses on how land use and more broadly land systems can contribute to sustainability. His main research interests are land use transitions, linkages between globalization and land use, including supply chain interventions to halt deforestation and theories of land system change and socio-ecological feedbacks. Thank you, Dr. Mayfroyd. Thanks. Do you hear me well? You hear you perfectly, and I'm probably... Yeah, I'm sorry, I realized I'm a little bit in the dark because I'm outside. I will try to go inside for the planet. I will share my screen, all right? Do you want to share your screen, is that okay? Is it okay? Do you see my screen? It's coming on slowly. Yes. Okay, so I'm here to speak about generalization and theory in land system science. So I'm a geographer, I'm not a biologist, but I hope I can provide a slightly interestingly useful complementary perspective to all the different planets and the different speeches from today. So what do we mean about land system science? It's essentially the study of land use and it's related terms, land management, land cover change. And we studied that with an interdisciplinary perspective as complex social ecological systems. So we're working very much at the interface between natural biological systems that you probably are all familiar with and social human systems. And with a perspective that this understanding of land use and land use dynamics can help to address sustainability issues. Why should land use matter for biologists in principle? I'm building here on a recent synthesis paper that many authors published last year, well, including me. But essentially because we live on a used planet, so over three quarters of the terrestrial land surface have been transformed by land use and land management. So the biological systems that live on these lands have been impacted by human activities. So that's not including climate change or other forms of anthropogenic disturbances beyond land use and land management. And even though there's climate change and other disturbances, land use remains so far the main driver of terrestrial biodiversity loss. So it is a huge component of biological systems. And when we study land use and land systems, we realize that they are really complex systems. They behave as complex systems with interdependencies between social, economic, political, institutional, cultural components on the human sides, but also ecological, geomorphological, climatic components, et cetera, on the natural sides with feedbacks, non-linear interactions, and especially which is the focus of the day here, interactions between different scales and different places. And I just want to also highlight that there's also multiple values and meanings attached to land systems. So something like land degradation and land restoration may have a very clear definition and understanding for biologists related to biodiversity or the integrity of some ecosystem processes, but for other people, it may mean very different things. And when we have to deal with land systems, we have to acknowledge these different perspectives and try to work with them. So we're trying to look at land system and land use and land use change with an approach that's already a few decades old, which is commonly called people to pixels, which is basically taking some kinds of geospatial data about land use and land cover change, which can come from remote sensing to a large extent, and linking that to data and understanding about the human drivers and human dynamics, which can come from participatory approaches, surveys, interviews, et cetera, and with environmental data that we can gather from surveys, the kinds of surveys and sampling that you're probably familiar with. And linking that together, we try to understand the causes of land use change and the impacts of land use change. More and more, we're moving to something that I would call people to pixel 2.0, which is the goal is still to link what happens in landscape in terms of land use dynamics, biological and environmental processes with the people who are actually operating these changes, but more and more we realize that these people who are impacting, driving, causing these changes are far away, they can be consumers, very far away in a distant country that consume products that are produced in this landscape. They can be NGO executives in Washington that decide about some kind of conservation intervention in the landscape or a trader in London that decides about investing in an agribusiness. So these are all these kinds of things that for example, Jack Liu who's organizing or co-organizing this called telecoupling. So all these kinds of distant dynamics. From that, we look at these two different case studies and different scales. I don't have really time to enter into the details of all of these, but my point here is how do we generalize when you have made a study about some kind of land use dynamics happening in some place, which can be a village, can be a landscape, can be watershed, can be a jurisdiction, can be a continent. How do we generalize? Of course, we can do some empirical approaches that you're probably all familiar with also which is meta-analysis of study cases, but we also increasingly working with more theoretical approaches and trying to develop what we call middle range theories of land system change. So we're not looking for, sorry, we're not looking for generalization in theories that are like natural laws that cover laws that applies to all kinds of contexts like physical laws, but we're looking for context generalizations. We try to describe a bounded range of phenomena and describe the chains of course and mechanisms that can explain it and the conditions that can enable these causal chain. I will just take one example that aligns a little bit the complexity of these land systems as leakage from deforestation policies. So we have a number of policies that can intervene in a landscape to reduce deforestation of forests or other kinds of ecosystems that are conversion of other kinds of ecosystems. Sorry, sorry. That we consider as biologically valuable and these interventions can generate a leakage. So the displacement of deforestation to other places. So we have a lot of studies that shows that very well, for example, for the Brazilian Amazon who set up protected areas to conserve areas that have some biological value and the people who were about to clear or were clearing these areas moved to another place nearby and they cleared the forest in these other places that are unprotected. But what we show also in studies is that this kind of leakage dynamics can happen over much larger scales. For example, in Brazil, actors, dynamics processes, businesses can move from the Brazilian Amazon which is increasingly protected towards other biomes such as the dry forest in Savannah of the Cerrado. And we have evidence that people can move even further and the deforestation frontiers can move even further. So for example, to the Chaco ecosystems which are also dry forests in Central and South America and even further in the Miombo woodlands and dry forests of Southern Africa. So all these processes happen at different scales and through different dynamics and actors. How do we make sense of these? We try to develop some kind of middle range theory as I was saying, which will account for the different kinds of mechanism and conditions that can explain these leakage processes at different scales. So we have a policy that restrict line use. I don't have the time to explain all the mechanism in details but I'm just trying to illustrate. So we can have under some conditions we can have some forms of leakage that we can call activity leakage. So where exactly the same people who are clearing land in one place move to another place and clear land. They can move close by if there are for example, small holder farmers they can move further away if there are agribusiness companies. But we can also have other forms of leakage like commodity market leakage where because we have restricted deforestation and agriculture in one place we have restricted the supply of some products that people are producing, soy, cattle, et cetera. It increases the market price for these products and some other people elsewhere in the planet completely unrelated to these other guys will have incentives now to produce more of these products so, et cetera, and to deforest further. But under the right incentives or right conditions these same people instead of expanding agriculture might receive incentive to intensify and increase yields on land that is already cleared and so they will not create more deforestation. So all of that can be structured into a proper theory. I'm not going much further than that because my time is up but I just want to wrap up with these ideas that land use is a major force in biological systems so I think it's important for all biologists to account for that somehow to Australian biologists and that there are these increasingly clear linkages across places and scales and that we try to develop tools like middle range theories to generalize and integrate these processes at different scales and that's it. Thank you, Patrick, that's fantastic. Let's bring both Brooke and Andrew back in, where we are. And I've got a whole bunch of questions but I'd like to kickstart just by asking a more general question for everybody. So scientific research often requires collaboration across multiple disciplines. We see many different disciplines just in this small group of people, right? You know, I've got people focusing exclusively on birds, people looking at molecular processes, people trying to understand sociological factors and their influence on biodiversity. That's how do I get all three of you working together in a way that would actually produce productive and meritocritous outcomes? Anybody I can pick on someone? Brooke. The one relationship that I saw that was unique that I didn't expect was between Patrick's work and myself where he showed his last slide about, you have a kind of a model system where you're showing how land use is tracked and moves from one box to the other in what we do in the ocean, right? We do box models and we show how, you know, nutrients are moved from one box to the next or how thermoclines change, you know, different factors and rates. So as far as like translational information there, honestly would have never anticipated having that similarity with Patrick's work but very similar models that we're generating. I think you can also say it's between Andrew and Patrick as well, like, you know, being able to understand the sociological variables that might be influencing bird migration dynamics would be fundamental as well, it'd be really interesting. Yeah, I would agree with that. And I also think that there's a primary opportunity. We've been thinking about it at Cornell a little bit but I know some of my colleagues in the aerial ecology world have as well that the notion of connecting terrestrial, aerial and marine systems in some interesting ways that go back to first principles but then have manifestations at some scales of, well, whether it's hourly or a decadal or whatever is in between, that cycle of connecting those spheres on the planet I think is a really interesting space to explore that hasn't happened yet, but now increasingly I think it can. So I would suggest that even with what seems like a disparate group of the three of us that that notion of being able to think, okay, well, we've got some very solid information in each of our domains and we know that there is some feedback and some cycling that's occurring into these other domains where it blurs, just starting to think at basic levels about, well, how might organisms that connect or interconnect these different systems and act at the scales that we're all interested in, even just at a theoretical scale, how might that work and how might we be able to model that now that we've got big data that represent each of these really well? I think it's a really wonderful time to be thinking about that kind of integration. It's not something that a few years ago would have ever crossed my mind. So I appreciate both what my colleagues had said here today but also, Jack, what you're mentioning about how might we connect those things? I agree. Patrick, do you have anything to add to that or I'll jump to Stephanie. Well, I had several things, but one is, well, I'm interested by your comment, Brooke, because it's a representation of theory that speak to some people but that don't necessarily speak to other people. So we're trying to fit that that's this point of mid-range theory in social ecological system where the goal was that this was ideally understandable by everyone. But when you speak to economists, their theory is a set of equations. They don't need these kind of things. They have equations that relate their variables and they find with that. If you speak to anthropologists, their theory is narrative. And I think that kind of visualization is a useful bridge because it's a kind of systemic thinking with box and arrows that I think can speak to biologists that can ideally speak to economists because they have the equation but they can turn that into a causal diagram, if ideally, and that social scientists, yeah, in the best situations can adhere to. But that's just a way to say that the visualization of a theory is not necessarily so easy and that I'm happy that you feel that it speaks. Honestly, it looks like step one before you go to that mathematical model, right? Like you can envision those arrows getting narrow or thicker based on the rate of change between the boxes and just tell them that this is phase one of his equation. Stephanie, you have a question. Yeah, I had two and I was sitting here and trying to decide which one to ask. So I think maybe I'll ask a general question. You've talked, a lot of our panelists have talked about some real revolutions in technology that are then revolutionizing our knowledge of systems. So in one case, specifically thinking about some of the omics potential for understanding microbial dynamics or the use of radar in what seem to be unexpected ways. I think a general concern that we might wanna think about and potentially address is the extent to which technologies that really revolutionize our understanding of particular taxa then sort of leave behind other taxa or ecosystem processes for which we don't have great technologies to monitor. And I'd love to hear any of you speak on that point. I can't hear about the bugs that you can't see, Andrew. Yeah, I can jump in on that point. And Stephanie, that's a really good one. And I think about it not only from the taxa that may not be represented by the new sampling regimes or new sampling mechanism, but also some of the skill sets or the interaction with the information that also could be lost. So that idea of with radar, you know, I've studied migration my whole life, usually with binoculars, right? So I can actually see the bird and I'm present with the bird and I can understand what it's doing. Now I look at pixels, you know, so what is the connection between those two things? How do you maintain the connection? Not so much so that it's a limiting factor, but so it's an enhancement. I think it's a really important place to think about carrying the past, whatever that basic, the fundamental natural history or whatever the information you might gather, figuring out how to keep that connected and how to keep the observer connected and also the person observing the data and analyzing the data connected to those processes are really important. And I think one of the ways that we've tried to do that and I think we're starting to succeed is to make sure that when you have these new technologies evolving, that you find ways to connect them back and ways that connect back when it comes to the individual. So from the radar perspective, something as simple as being able to say to people, well, your observations that you're submitting to this community science database, those are really important in having us understand the size of targets that go into the analyses that the radar detects. Or if you're astronomically minded, point a telescope at the full moon, watch birds migrating at night and know that those data embody what the radar sees and you can make connections. So I think that that notion of connecting those those sorts of levels is really important. And I think it's a major issue where we could easily get lost, I agree, where you lose knowledge that may just sort of become an anachronism or seemingly so, or that you just lose out on an ecosystem that you may not be able to perceive as well or some series of behaviors. So really good question. I think there are a lot of opportunities from the bird and maybe an ecological world to think about models for how do you not lose that connection and how do you not lose those data. Patrick, do you want to respond? Just a small point, but just to say we're working a lot with data across a lot of scale from the very plot scale to the global scale. And it's really important to acknowledge that when you move across scale, especially global scale, you have data that are what they are worth. And we've seen today a lot of data that you can obtain at global scale, they are based on proxies of relations that you get from ground data. And when I see, I'm not a biologist, but when I see the amount and availability and richness of data that you have for some places like in Brazil compared to some places like Mozambique, I see global maps of biodiversity, species richness of abundance, et cetera. And you know that you have to be extremely careful because they really don't just don't capture the same thing because they don't have the same ground data to rely with in the first place. Definitely want to respond? No, I was wondering if Brooke might have some thoughts on that too. Yeah, so one of my points that I mentioned was that omic data, for example, because you talked about that and has become super popular to be funded, which is great for me because that's what I do. And I shoot myself in the foot, but I also would say that we really need to recognize the importance of basic physiology and the importance of physiologists for creating rates and really good quality chemical data to make the omic data stand on its own. And so combining those. And you can see it if you look at what gets funded and frequently that there's definitely an impetus towards one side. So I would, you know, if there's a way to encourage collaboration so that omics is trapped with the physiology data and I'd mentioned this concept of next generation assays that put the two together, that would really help as far as broader scale studies. So we've been trying that for 20 years and just can't, you know, outside of neon, it's hard to get things scaled with that kind of integration. Inez, do you have a question? Yes, thank you. I have a question for Patrick. It's about the middle range theories. That sounds like a really nice approach when we are thinking about working across scales. So I was wondering if you could develop that idea a little bit more, because it's a theoretical point of view. Thank you. Okay. Well, it's nothing new in incentives, just something that we borrowed from old school sociology. So Robert Merton came with that in the 1950s and 60s to kind of criticize what was the dominant approach in social science at the time, the kind of grand theory where one guy comes with one concept, with that concept, that explains everything, the whole social reality, whether it's family, markets, schools, transport, urban things, whatever comes with that concept. And the idea was just to say, forget about that, basically, and just say, let's start from a bounded reality. And if we manage to have a theory that explains this piece of reality correctly, we can have another theory that explains the next piece of reality, and then we can see maybe we can bundle these theories together and they will actually fit together and we can reconcile them and grow step by step. And it's... Yeah. Okay. In a sense, it's not so different probably from many biological theories, but it's quite a different approach from physical laws. But in a sense, I know that, I don't know you, if you want to engage in the discussion, I'm happy to learn about how much you feel that biological theories all rely on things like evolution or not, etc. But, okay, the basic point is to take it step by step. Jack, do you have a question? Yes. Thank you so much. This is a fantastic really great to hear all the interesting presentation and discussion. So my question is related to theories about what Ines just said. Many biological and ecological theories were developed without explicit consideration of socio-economical human factors, especially those factors far away as Patrick mentioned in his presentation. So this is a question for all of you. So how to revise and expand biological theory, ecological theory by incorporating socio-economic factors locally in the distant places. For example, how to revise or expand the migration animal migration theories by considering land system change in winter and the breeding sites and how to revise the niche theory by adding socio-economic factors and how to incorporate land system change in the theories related to marine systems. Thank you so much. Brick, do you take that one first? Sure. Yeah, so I didn't show an aerial view of what we're looking at, harmful algal blooms which you can see through satellite data, right? So right now we're not doing aerial views because we're trying to find what triggers the harmful algal bloom, which means you've got to get it before it becomes a satellite image. But in the long run, like something I could see as far as putting it back to land use, a lot of the people currently believe I think a good chunk of the population that the harmful algal bloom explosion that we've seen across the United States and actually across the world is a result of anthropogenic runoff, right? So going back to some of Patrick's work, figuring out land use and the relationship between land use and the occurrence of blooms and where those terrestrial runoff flows into and how does that affect the nutrient chemistry? Can we figure out what actually is triggering it? To me, that's the most logical system to put humans back in touch with what we're doing and it is an anthropogenic aspect in my case, which is harmful algal blooms. Andrew? Yeah, so let me run with the harmful algal blooms for a minute. So thinking about Patrick's work on land use and brookier work and thinking about where these occur, those harmful algal blooms have ramifications when it comes to bird populations, whether it's large die-offs of seabirds associated with diatom and their distribution and various toxins and so on. So they're thinking about the movements of birds, in particular in this case, sooty fearwater is the one I'm thinking of. But movements of birds relative to patterns that we see in a particular ecosystem relative to land use, I think there's a really logical connection there. And to go sort of a step further to the radar side and thinking about the connection to socioeconomic factors, one of the things that's been addressed to our group recently has been connecting the basic natural history of, for example, where migratory animals are when, in particular birds, relative to energy usage and impacts from urban areas and built area, built environment, and obviously all of the socioeconomic relationships that come from that. So we've done a little bit of work trying to think about those different layers that are overlapping. Where are the activity centers of humanity and where do those align with the real bottlenecks of intense migration and movement? Or does it create certain risks or hazards for populations of animals on the move? So it is a very much natural kind of a situation when we're talking about the scales that we're all working on to overlay that kind of information. I agree. It's really important and I think it's obviously the science for the sake of the science and understanding the system is critically important. We want to advance our knowledge that way but bringing it back to what's going to be important for the way we steward the planet and the way we sustain our activities and whatever our standards of living might be keeping that connection and being able to overlay that every place we can with these data is going to be really incredible. And I think there are some great migratory animal community and the potential connections across scale there I think offer a really nice opportunity to do that kind of work where you can directly connect those sort of the salient features of the socioeconomics that you could study from a satellite, whether it's daylight band, light data or part, you know, particulate matter or whatever it might be and then associating those with these broader scale down to smaller scale patterns is really valuable. So that's a great question and really great direction to be heading I think. Patrick, do you want to comment? Maybe just to say that we're doing a lot of work now where we're trying to link land use impacts in some place with some kind of distant forces like for example commodity supply chain where we can say okay this deforestation in Brazil for soil production was related to consumers in Europe or somewhere else and some companies in the supply chain and we can link that to impacts that happen on the ground like deforestation loss of some species of carbon water impacts but we're only scratching the surface of these kind of linkage between these kind of socioeconomic coupling of distant places with the ecological environmental coupling. So for example if you think about the migratory birds we can have maps and say this deforestation was linked to consumer in this country and it created a loss of habitats for some bird species over there but at least in the work that we're doing so far we're completely ignoring that these birds might actually matter a lot for people thousands of kilometers away because they are migratory birds and that we can connect the dots even further so that there's a lot further well just another example for example is these water teleconnections where of course if you clear a forest somewhere you can have impacts on the hydrological cycle in this very place and this is connected to consumers in distant places but in a third system that tech would call a spillover system right? Other places would maybe suffer from a decreased rainfall over there and that I think we're still not capturing one. Thank you, thank you I appreciate it. We've got five minutes left Shahid do you have a question? Yeah thanks it's made me think a lot hearing this afternoon session about tools so I have a somewhat important question to see if I will not lead to silence as I did with my last question but it seems to me that we're hearing a lot about advances in technology bringing out remote sensing by acoustic omics demography is something that I use LIDAR and coupled with great computational advances in storage speed and other things that have changed I ask this question because I'm an editor for a journal that gets 20,000 submissions a year that rejects about 95% of it that's what we're looking for and a lot of it has this technology driving this explosion of research there's a lot of cost that comes with it both monetarily as well as the need for capacity building in developed countries less developed countries that don't have this technology so here's my question I get the sense sometimes that this advancing technology which is very costly is actually improving our precision but not necessarily advancing our paradigms this sort of controversial idea that breakthrough science has kind of diminished for the last few decades I'm not sure I agree with that but just to ask you as a panel do you feel that since your experts at this new technology that it really has changed our world or has it improved our precision and our ability to tackle questions but not necessarily change the way we understand nature I'm going to pick one person to answer that so that Chris can ask his question in the time frame so Brooke I'm in the department of genome sciences so I'm the only environmental person surrounded by human health stuff so from my perspective don't do satellite data so on the omic front it's making a massive difference before you would have never known what a microbiome was and the oceanographers to be honest we developed the concept of studying microbiome before it became even known that we had one in the gut and now that's made massive advances I'd say in science and medicine in particular so in addition to that I also do life detection stuff so the advances we've done as far as making mass spectrometers capable of doing a wide range of things with higher mass accuracy etc now it's being pushed off planet so that we can do bigger potential life detections that really changes how we view science and basic theories on life I agree I think you add a lot more data a lot more data can look like it's just stamp collecting but honestly the questions we can ask are fundamentally different I'm going to jump to Chris just because we've only got two minutes left what's your last up Chris sure I just have a real quick question and I guess it follows up Andrew and Brooke and Shaheed's question and that's really wonderful technology but how many of us are just sitting on kind of really cool questions and it is that species data I mean I think Andrew and Brooke you pointed out like in my mind it's we can do this well for a couple of taxa but we can't answer the really big ecosystem level questions because we're simply missing not even the ABOD but really some of these other species data sets then and I think what have we learned from birds and what have we learned from Alga that we could go the next step if we could collect that on a handful of other taxa and is that really something we should be prioritizing the missing groups I think we addressed this one slightly earlier in terms of what it could do but Patrick do you have any comment upon trying to integrate missing information into models No not necessarily but yes and no I mean I think in a sense I don't know yeah okay I was going to say maybe in a sense it's easier in human systems in the sense that you can gather a range of information from different types you can use more qualitative information from a few interviews etc and try to fill some gaps in your understanding and use some kind of Bayesian methods to derive some kind of probabilities of getting the answer right etc but I was just going to say maybe you could do the same in biology and we had other sessions about participatory mapping etc why sometimes where if I mean it's not going to fill gaps about microbiomes data sets but about some other kind of biological questions maybe you can try to gather more data from the people living in places and that can help to fill some gaps at some points in a different way You can use these data and technologies to identify gaps that would actually help you to identify which things we're missing so it can be useful even if you're not directly looking for things which you're missing originally Oh yeah sorry Brooke I was going to respond to Chris a little bit more and it was purely like it depends on what resolution the person is looking for right like if you need to know what species are capable of being resilient to a particular ecosystem or an environmental change you need to go at the molecular underpinnings for example of that particular species if you need to know how an entire ecosystem is going to change my viewpoint is to understand the entire group I mean I use entire microbiomes and think that as a single cell that has you know a metabolism that's changing through time and I don't I can't envision tracking species over a long period of time because it's just it's honestly too complicated so you've got to reduce your resolution and look at potentially you know gene flow or you know functionality through this change I'm asking for I say perfect okay thank you Janine I was asking if I could have your question let's go for it So to follow up on those comments I mean we've heard today about AI being used to identify insects we've heard about the microbiome in oceans we've we've seen birds and acoustic signals and coupled with we've got camera traps and that coupled with AI and citizen science and we've seen remote sensing of plants across scales and when you think about all the biological realms and all the advancing technology in all of those realms I mean thinking about she hits question a bit but going back to what Jillian said in an earlier panel about we've got this gappy messy data and people study what they love like in a way all of you are talking about these advancing technologies to study the things that you are trained in or the things you love and as long as there are enough people really connected to the biology working on the advancing technology then the issue becomes making sure we're integrating all of that making sure the biology is connected with the technology and that all of these different dimensions and perspectives are getting integrated so the question then is how do we do this integration and synthesis across all of the different kinds of advancing technologies that we've been hearing about today Andrew do you want to take that one sure so that's a great question and I think the synthesis for in my small part of the world really was about actually it was about applying to a cross cutting program at National Science Foundation that brought two distinct parties together the computer scientists that wanted to look at messy data and didn't really have a desire to have it be anything in particular and the ornithologists that wanted to understand with some sort of more precision and accuracy than just what was up here and just what was here how to predict movements of birds and the discussion points of those to the sort of the creativity of those two groups and bringing them together toward some interesting tasks that just had to be framed in the right way that to one group it was like oh okay there are the messy data and to the other there's the methodology to actually get where we're going that was enormously valuable so I think part of this has to do with you know cross cutting opportunities where you have a particular problem that isn't necessarily domain specific but maybe more a little bit about the challenge of trying to handle some broader issue of analyzing a large amount of information or some pattern or something like that I think that there's a key element of that I also think that just getting to sit at the same table sometimes with these diverse fields whether it's at something like AGU of every year or some other meeting where you have the opportunity to bring in lots of different disciplines I think that's a huge part of the the way forward that allows for these kinds of unique and creative integrations to occur so that you can keep the people that are in their domains really interested in what they're doing and then get inspired by the other domains to think wait a minute we've got to think about this problem a whole different way you know and here's a method to do it and and so on for from my perspective that sort of opportunity to go with this very distinctly different non ecological group and to make advances both in the natural history and in the ways that we can do the science and then broadly shift the science you know in a new direction with the technology I think that was hugely important just to be able to sit at the same table I also just like to chime in there democratization of these technologies is vital for building resources into communities so they can leverage this it's become fundamental to our work in health sciences so you know working with American Indian tribes to place a metagenomic and genomic sequencing technology dietician technology in the tribal communities led by the tribes that they run and they monetize is absolutely essential and this speaks to two of the previous panels in making sure that we innovate in these technologies and then we distribute them effectively so they can play a broader role in reaching the subjects that they can be applied to alrighty I think that's it we're only six minutes over this is fantastic Natalie tells me it's great so that's wonderful over to you Jack I think to close up well thanks so much Jack all the speakers the moderator the committee members and the public audience for this really excellent presentation and discussions during this and all earlier panels and they are amazingly informative inspiring and exciting the insights are truly remarkable the committee has learned so much and this webinar together with the previous two webinars will be very hopeful to the report that the committee is going to write details about the report will be posted on the website of the national academies and when they are available also thanks so much to the staff members of the national academies for their great hope in planning this and the previous webinars and make them most successful and again thanks a million everybody and although there are many other interesting issues for us to discuss we have run out of time as Jack just mentioned so hope you enjoy the rest of the day and so bye bye thank you so much again