Tracy Gartner: Welcome everyone to our second Nefa virtual event. This one focused on data sets, and we're excited to host another set of really exciting speakers today.
Tracy Gartner: Just a few moments. I'm not gonna take much of your time. I'm gonna turn it over to them very quickly. But just a few bits of information about we are on Zoom. So if you could please ensure that you are muted during presentations.
Tracy Gartner: we are recording this so that we can share it with other participants later, and you can. We do have closed captioning available that you can control yourself at the bottom of the screen. There should be a button that you can select for that.
Tracy Gartner: And then, just as a reminder, this is a professional conference, and so we will behave appropriately with night. The proper etiquette and all of those things. So please, just as a a general rule of thumb. Keep that in mind. It's easy when we're sitting in our living rooms to forget that.
Tracy Gartner: And so I'm gonna turn it over to Lori and Lori Anderson is going to introduce Nefa and tell us a little bit more about our first speaker.
Laurie Anderson: Thank you very much, Tracy, and thank you to everyone for attending. We're really delighted to have you here at our second event.
Laurie Anderson: As Tracy said, My name is Lori Anderson, and I'm. A plant Ecology professor at Ohio, Wesleyan University, a small college in Central Ohio.
Laurie Anderson: and I'm. Also the lead pi of the Macro Systems ecology for all research, coordination network.
Laurie Anderson: And this event is our second virtual event that's kicking off the Mefa project in our first year.
Laurie Anderson: so i'm going to give a brief overview of the me for project. and then I will go ahead and introduce our first speaker.
Laurie Anderson: and we'll go from there. Also, I'll just remind you that after we hear our speakers we will go into breakout rooms to discuss more about macro systems ecology.
Laurie Anderson: So let me go ahead and share my screen.
Laurie Anderson: Just take a moment here
Laurie Anderson: and let me go into the presentation mode.
Laurie Anderson: Let's see to move My.
Laurie Anderson: there we go. I think we're all set
Laurie Anderson: all right. So what is the Macro systems ecology for all, or mifa network?
Laurie Anderson: Well, this network supports faculty from all backgrounds, and we especially focus on faculty who are in teaching oriented positions. But we welcome those from all backgrounds
in developing collaborative macro systems. Ecology projects for both research and teaching.
Laurie Anderson: And we have a special focus on diversity, equity, inclusion, and justice. As we work on these projects and collaborate together.
Laurie Anderson: Mifa is a spin-off of the ecological research as education Network or Aaron, and many of the people who are administering Mfa come from the Aaron network.
Laurie Anderson: But this is a new initiative. And our funding for Mfa runs from 2,022 through 2,027. So we are indeed just starting out.
Laurie Anderson: Let me make sure I can
Laurie Anderson: we go advance my select?
Laurie Anderson: So the next question i'd like to address is, what is Macro Systems ecology? Well, i'm using a definition here from Heffernan at all. An excellent paper that I strongly recommend.
Laurie Anderson: This is the study of diverse ecological phenomena at the scale of regions to continents and their interactions with phenomena at other scales.
Laurie Anderson: and Heffernan has a lot of great examples to think about for Macro systems ecology. But the one that I've chosen for this presentation is thinking about butterfly population dynamics.
and you can think of if you're an ecologist, a lot of different local factors that might affect the size and growth rates
Laurie Anderson: or migration patterns, for example, of a local butterfly population. Things like the micro climate in which the larvae are are developing host plants that are available predators pathogens.
all of those kinds of things happening at the local scale.
Laurie Anderson: But we are, of course, aware that there are things happening at the Macro scale, like climate change and habitat loss that are impacting these local factors that are changing the influences of these local factors in important ways.
Laurie Anderson: So we have cross scale interactions, and we have patterns that appear at the larger scales that are informed by what's happening at the local scales and vice versa.
Laurie Anderson: So we're hoping with a macro systems focus to think more about these cross scale interactions and help both students and faculty in our network. Be more aware of these kinds of interactions.
Laurie Anderson: So these are the goals of our network. We want to expand faculty training in macro systems. Ecology, because we feel like this is a an emerging field, and we like to make sure that faculty who are not in research oriented positions
Laurie Anderson: can be trained in macro systems principles, and also think about the existing under or the existing environmental data sets that are often used in this kind of research, and that's a major focus of our event today.
Laurie Anderson: We also want to create a diversity, equity, inclusion, and justice focused network and be accessible and inclusive for scientists and students from all backgrounds.
Laurie Anderson: We want to take a page from the Aaron playbook and really think about collaborative research where we work in teams to develop research projects.
So
Laurie Anderson: teams that work across institutions to design scientifically compelling, but also diversity, equity, inclusion, and justice-centered work.
Laurie Anderson: And think about you know macro systems themes that we can address kind of in this framework.
Laurie Anderson: We also, since many of us are in teaching oriented positions, we want to think about educating through research. How can we create out of our projects models for teaching through authentic research experiences.
Laurie Anderson: So in our network we hope that we'll have a lot of conversation about teaching as well.
Laurie Anderson: So we're really interested in facilitating the development of these projects. And that means that the ideal Mefa project is going to have macro systems. Ecology concepts that underly it
a plan, a diversity, equity, inclusion, and justice plan for collaborative science.
Laurie Anderson: We want to use the vast amount of existing environmental data that's emerging from programs like Neon and available through other types of programs as well.
We want to
Laurie Anderson: take into account local community concerns as we design these projects.
Laurie Anderson: and we want to facilitate student learning through authentic research.
Laurie Anderson: So this is a tall order. These projects are doing a lot of different things. We think this is going to be challenging, but I really want to emphasize that we are not assuming expertise here.
Laurie Anderson: We really want to build this expertise together through our training events and through our working groups that we are going to be starting this summer.
So you can think of our Rcn. As basically the collaboration platform.
Laurie Anderson: and those of us who are administering the Rcn. As collaboration partners, and we're learning with you. I don't consider myself an expert in Macro systems ecology, but I find it fascinating. I'm. Ready to learn.
Laurie Anderson: I'd like to learn with people who have similar goals and priorities that I do as somebody at a teaching oriented institution. So you know we we invite you to join us in in learning about this exciting new field.
Laurie Anderson: So what are the benefits of learning about this field? It's an important emerging area. It's very relevant to environmental problems. It has great potential for collaboration. We think
Laurie Anderson: what large data sets are actually a great equalizer. They allow research and teaching, even without a field site. And there's also rich opportunities to teach data science which is becoming an increasingly important skill for E colleges.
Laurie Anderson: There are a number of challenges. Many ecologists, myself included, are trained originally as single site researchers
Laurie Anderson: and thinking about these cross-scale connections can be really challenging when you're coming out of that type of lens.
also working with large data sets, can require more faculty training.
Laurie Anderson: We all know that student quantitative skills in biology can be extremely variable. So we really want to support our students as they engage with this type of data
Laurie Anderson: and also collaborative projects do take time and skill to manage. So there is a a major commitment when you work in that in that type of venue.
Laurie Anderson: But we hope to provide training to address the challenges, so that more faculty can be part of this emerging field.
Laurie Anderson: So how do you get involved? Well, you can join our network for free. That's a that's a great place to start, and I've put the website and our QR. Code that will get you there
Laurie Anderson: also. If you're already part of Aaron, you're welcome to join me for as well, because we will operate those as complementary but separate entities.
Laurie Anderson: We're also having a summer meeting, July 20, fifth, through the 20 seventh at Swarthmore College in Philadelphia, PA. And we're going to have an online application available by April tenth, and we are going to have significant travel coverage.
Laurie Anderson: If you want to attend in person. There will also be a hybrid option for that attendance, so you can attend virtually as well.
Laurie Anderson: and the focus of that meeting will to will be to start building our project working groups
Laurie Anderson: and think about how we might apply these projects in a teaching context as well, and also think about principles for designing our projects with diversity, equity, inclusion, and justice in mind.
Laurie Anderson: We're also going to have more virtual events in fall 2,023 and beyond. So if you're in our network, you can get a immediate notification when those are going to be available to you, and we hope that you'll continue to join us.
Laurie Anderson: So we will start right now thinking about macro systems. Ecology, especially macro systems. Data sets.
Laurie Anderson: and I will go ahead and introduce our first speaker. so i'll stop sharing my screen
Laurie Anderson: and
Laurie Anderson: escape out of there, so I can navigate a bit more easily.
Laurie Anderson: and i'm going to introduce to start out Dr. Claire. Lunch from the National Ecological Observatory Network. Dr. Lunch is a trained plant, eco physiologist at Neon. Dr. Lunch oversees the science needs of the data pipeline for observational data
Laurie Anderson: and develops open source code and instructional materials to assist the user community in working with neon data.
Laurie Anderson: So Claire feel free to take it away.
Claire Lunch: Right thanks, Lori, for that introduction.
Claire Lunch: So I am going to give a very brief introduction to Neon and what it can do for you.
Claire Lunch: So Neon is a network of 81 sites, organized in 20 eco-climatic domains
Claire Lunch: that are distributed across the continental us plus Alaska hawaii and puerto rico
Claire Lunch: those 81 sites include both terrestrial and aquatic sites, and at those sites neon collects more than 180 data products, all of which are freely and openly available for anyone to use.
Claire Lunch: We we categorize neon data into 3 systems based on the data collection methods.
Claire Lunch: So the streaming sensor data, these are permanently installed sensors that are continuously and autonomously streaming data.
Claire Lunch: The observational system. This is anything collected by a human. So either a a direct measurement like measuring Dbh. On a tree or a a physical sample that's collected, and then sent out for analysis.
Claire Lunch: And then the third is the remote sensing system, which you will also see referred to as Aop for airborne observation platform. That is a low altitude, aircraft, collecting remote sensing data.
Claire Lunch: just sort of a high level overview of the types of data that each of these 3 systems collect.
Claire Lunch: So the streaming sensors
Claire Lunch: are collecting carbon and energy fluxes, sort of basic meteorology at the aquatic sites. They are monitoring hydrological parameters that kind of thing.
Claire Lunch: The observational system makes measurements of the populations of targeted taxonomic groups. There's a suite of measurements focused on plant biomass and production by geochemical cycles and a little bit of microbial sequencing.
Claire Lunch: And finally, the remote sensing system includes both a lidar and hyper spectral imaging instruments, as well as a very high resolution digital camera in the visible spectrum.
Claire Lunch: In addition to the data that Neon collects directly, there's also the opportunity to take advantage of me on infrastructure to do additional types of measurements. So this includes things like attaching additional instruments to the towers.
Claire Lunch: or having a neon plane fly a location that it normally wouldn't so. These do require
Claire Lunch: P. I. Funding to cover that additional work. But the idea is that it should, you know, still be cheaper than setting up your own network of towers, or, you know, having your own plane.
Claire Lunch: Finally, in in addition to those components.
Claire Lunch: physical samples that neon collects. A large subset of those are then archived at the Neon by a repository where
Claire Lunch: they are available for use. And this is a really exciting thing about the by repository is that this isn't a traditional museum collection. These are samples that are available for destructive analyses. So if you want to, you know.
Claire Lunch: perform an analysis on neon samples that isn't part of the the main neon suite of analyses
Claire Lunch: neon is a community resource, and we want to help people, you know.
Claire Lunch: use the data. So this is just a sort of brief overview of some of the the resources that we have to help. You do that we have self guided tutorials
Claire Lunch: that can introduce you to the data. Events, like workshops and webinars, and many of those workshops and webinars are recorded and are available on the Neon Youtube Channel for you to follow there as well.
Claire Lunch: And then, lastly, this is this is really the most important slide that I have that neon staff are also a resource to help you use neon and neon data.
Claire Lunch: I think most of the the neon science staff really agree that working with the research community is the best part of the job. We are here to help you, so do not hesitate to reach out.
Claire Lunch: All right. Thank you.
Laurie Anderson: Thank you very much, Claire, and I just want to reiterate that.
Laurie Anderson: Indeed, I've found the staff of Neon to be wonderfully welcoming and very willing to discuss the data at the many different study sites. I've also been lucky enough to visit some of the neon sites with my students and had a great experience
Laurie Anderson: being shown around and having the instruments explained to my undergrads. So please take advantage of me on as a resource, it's it's really fabulous.
Laurie Anderson: all right. I'm going to now go on to our next set of 3 speakers, and I just want to let the audience know that these speakers will each talk for 10 min, and I will actually interrupt verbally at 8 min in just to remind the speakers that I that they have 2 min remaining.
Laurie Anderson: So just so. You're not surprised when I chime in all of a sudden. so I will now introduce Dr. John Zubitz from Augsburg University.
Laurie Anderson: Dr. Zovitz is a mathematician who is focused on mathematical modeling. In biology and research, in environmental science.
He is collaboratively studying soil respiration in partnership with neon
Laurie Anderson: currently. He is leading a new faculty, mentoring cohort through bio quest slash cubes with Dr. Zimmerman, who folk who spoke in our first
Laurie Anderson: me that event.
Laurie Anderson: So take it away, John.
John Zobitz: All right. Thank you very much. I'm getting ready to share my screen and excuse my voice. I am recovering from Covid here, so i'm also isolated at the same time.
John Zobitz: But thank you for the introduction. I'm happy to speak about my project in partnership with Neon, also funded through the National Science Foundation under Macro Systems Grant.
John Zobitz: and what we are doing with my colleague, Napaka Zimmerman, at the University of San Francisco, with the host of undergraduate collaborators. is to develop a soil efflux product, using neon data.
John Zobitz: So to provide some of the context and research objectives. soils hold the largest reservoirs the carbon. But there is a lot of uncertainty in the mechanisms that describe processes
John Zobitz: that influence the transformation of sole carbon. So how do microbes take freshly labeled carbon from? You know that comes into the ground and transform it and
John Zobitz: release soil. Co. 2 through soil. Respiration.
John Zobitz: Part of what we want to do in this project is to leverage existing data sets provided by neon I'm. In order to quantify the soil, efflux, or sometimes what we call soil, respiration will be used, is a modified format of fixed law
John Zobitz: kind of shown below, where the rate of change from the soil is multiplied by, of the diffusion coefficient, multiplied by the change in Co. 2 vertically through the soil profile.
John Zobitz: So this project has 2 objectives. One that I've already mentioned is that we want to develop a continuous soil. Efforts product at Neon is sort of an add on to their existing measurements.
John Zobitz: And then the second part is, you know, beyond just using the end data to produce a new product. We also want to validate it. using direct measurements where possible. So part of the sampling plan
John Zobitz: is that in the summer of last year, and then this year
John Zobitz: excuse me. Napaka went to 3 different neon sites along the west coast
John Zobitz: directly. Measure the soil e flux, using a light core machine, and then this summer he is visiting the great plains in the midwest to also do the same thing, and at concurrently at the same time, I'm. Developed in the product.
John Zobitz: using neon data at these 3 different sites. So we can have an apples to apples comparison.
John Zobitz: So in terms of some of the methods. You see a picture here. From this past summer we got as close to the Neon soil sensor array as allowed, and could be possible, and Napaka and his undergraduates, and sell the like or soil flux Chamber
John Zobitz: and and I was using these 7 different neon products and measurements located down below.
John Zobitz: and
John Zobitz: bring in them from the the Neon servers. And then Re wrote code that takes that data and then produces the soil e flux product, as shown below. And so part of this is doing a lot of data processing and sort of analyzing the data
John Zobitz: and then combine it to make a new product.
John Zobitz: And so this is partly a measurement project as well as a data science Computer Science Mathematics Project at the same time.
John Zobitz: So in one thing that we quickly learned, and my students learned is that neon data is really robust. It's diverse. And there's a lot of considerations you need to, you know, work through in order to
John Zobitz: kind of produce this data product. And so they develop this flow chart there, I think, very much in the computer science mindset of what are the different sites. What comes in? How do we interpolate it? Because we're looking across the soil profile? So we have to interpolate across the different depths.
John Zobitz: and then also think, through kind of advanced structures, using our to do some nesting, as well as applying iterative loops to produce a continuous soil. Co. 2 product.
John Zobitz: And and so with this project, right? These students didn't have much training and biology. They're all computer scientists, maybe data science majors
John Zobitz: a keen interest in understanding sort of how to manage these large data sets and excitement in terms of being able to apply it to a problem where, like I didn't necessarily know how the results would work out as well
John Zobitz: as you know, some of the complications that just naturally arise along the way when you do projects like this.
John Zobitz: and so kind of a a key. Result: Here is graphs shown along these 2 panels. And so i'm gonna I'll kinda describe this, and they'll kind of give a zoomed in picture, but the left panel shows the 3 different sites that the pocket measured in in last summer
John Zobitz: as and so along with the soil Co. 2 product that my students developed at the same time. So we were. Did an apples to Apples comparison, both in terms of a Time series as well as
John Zobitz: looking at how well do the model product match up with the measurements that we got as close the soil sensor rate.
John Zobitz: So if you zoom in a little bit, and this is maybe a clear picture that we do get a diurnal time series of the Co. 2 flux, and this is the the the on site Sr. And the Santa Rita Experimental range.
John Zobitz: And so we can compare. How well does this measure, like or flux compared to the model flux value.
John Zobitz: No surprise, right? It Data are messy, and you know it. We're still trying to understand, you know, any differences or discrepancies between them, but it also gives us a chance to revisit sort of the code that we had developed, and to look for additional refinements and improvements.
John Zobitz: so kind of advice that I would give when working with sort of large macro systems, data sets or neon data sets is first of all, just a willingness to develop or engage your skills and data or computer science as well as sort of like these other quantitative skills, statistics, mathematics.
John Zobitz: I think the students that I had were very eager to flex those muscles and to try that out.
John Zobitz: Also, Data are messy, right? So we had to really plan for lots of stops and restarts, and to kind of think through. You know why the did not the final product produce what we wanted and kind of backtrack along the way
John Zobitz: we learned that you know it's better to remix code rather than recreate it from scratch, and so add errors at Neon also had some code that you know had been developed previously. But my my students kinda took that code and then redeveloped it to sort of apply their own data science skills to that and that sort of launched them forward. Then just kind of starting right from the beginning.
John Zobitz: One thing that we emphasize is that we really want to develop sustainable workflow, so that code developers chart was really helpful because we could pinpoint certain places or areas for improvement rather than having one long sort of file that did everything, because
John Zobitz: you know, it's really hard to parse that out.
John Zobitz: And then kinda I. And you know this. What Claire mentioned. This, too, is that, you know, working in partnership with Neon Staff and scientists, I is very important. They have the best institutional knowledge of the measurements, and some of the nuances with it kind of speaking from experience. At 1 point
John Zobitz: last summer we were really frustrated with kind of the results that we're getting. And so I sent an email off to add, and you know he was able to guide us through. Why this data product wasn't downloading correctly, and we're able to sort of parse it out and to kind of move forward. If I didn't have that expertise on hand, it really would have, you know, stopped the project as it is.
John Zobitz: So with that. Thank you.
John Zobitz: And i'm happy to discuss more and sort of later.
Laurie Anderson: Thank you very much, John. You you actually just got in as I was about to give my 2 min warning. So well done. And I also just wanted to say that I think this is an excellent example of what we're hoping to achieve within mifa, because I can really see that
Laurie Anderson: the big data component, the broad Continental scale working with the neon data, the educational component. And I love the fact that the undergrads
Laurie Anderson: are doing both hands on work. But then also, seeing
John Zobitz: seeing the connection to the, to the more complex large data sets so very exciting work. Thank you so much for sharing it with us.
Laurie Anderson: And also I wanted to mention to people that we are going to go into breakout sessions after the 3 talks, so there will be a chance to interact with with the speakers and ask questions and get more detail about their presentations.
So that's why we're not causing for questions now. But hopefully, you will save them for a vigorous discussion in the breakout rooms.
Laurie Anderson: Okay, so i'm going to introduce our next speaker.
Laurie Anderson: I'm. Very pleased to introduce Dr. Sparkle Malone from Yale University.
Laurie Anderson: Dr. Malone's Research centers on the impacts of climate, hydrology and land management on ecosystem structure and function. He uses big data systems to study ecosystems across space and time to build data, driven strategies. Excuse me
Laurie Anderson: that improve ecosystem conditions, sustainability and resilience to climate extremes
Laurie Anderson: sparkle. Please take it away.
Sparkle Malone: Oh.
Sparkle Malone: thank you for the introduction.
Sparkle Malone: So we know that
Sparkle Malone: atmospheric methane has been increasing at really alarming rate since about 2,006, and we're not exactly sure as to why that is.
Sparkle Malone: And so i'm interested in understanding how sources and syncs of methane dynamics are changing, and when we look at both bottom up and top down estimates of syncs and sources of methane, the current largest discrepancy happens for our other natural sources or sinks of methane category. And that's because, while atmospheric researchers
Sparkle Malone: might have the answer. They know how atmospheric methane concentrations are changing the bottom up. Researchers are measuring how different sources and sinks are changing. But our estimates are very different.
Sparkle Malone: and I believe part of that reason is because we're not actually capturing the whole range of natural dynamics. And so my project is really focused on understanding what are the natural emissions. And for this talk I'm going to focus on the Us.
Sparkle Malone: In order to come up with good estimates of what natural emission rates are. I'm using data from Fluxnet and Fluxnet is actually a group of groups of
Sparkle Malone: researchers and networks that have come together to report flux data from pi manage, or network managed towers that are all over the world.
Sparkle Malone: These towers are placed and ecosystems, and at the ecosystem scale they're measuring how fluxes of carbon water energy is moving between the sis ecosystems and the atmosphere. And so, if you have enough towers placed throughout different types of ecosystems, you can look at the relationship between fluxes and conditions and scale those flux measurements up to large regions, and our goal for today is going to be the
Sparkle Malone: it up to the
Sparkle Malone: size of the Us. We want a budget for the Us. For natural methane fluxes, and we want to create a budget that can be dynamic, and show us how fluxes might be changing over time. And so to do this we have to first look at whether or not we have sufficient infrastructure within the Us, and then, understanding the distribution of infrastructure. We can then go ahead and build a budget.
Sparkle Malone: And so to start working on that budget. One of the first things that we do is evaluate gaps in current infrastructure, and for this we took all of the tower locations that exist within the Us.
Sparkle Malone: We obtain land cover data from the national land cover database. We obtain climate data from a climate. Our package that gives you access to information from day met, and a number of other climate sources, including climate change projections.
Sparkle Malone: We put all this information together to measure dissimilarity, and so to understand whether or not the towers are currently representative of all of the space, climate, space, land, cover, space that exists within the Us. First, we have to measure, or can take all of the information that we know about that space. What are the land cover types that exist within that space? And what are the climatic conditions that occur over that space, and then understand
Sparkle Malone: how dissimilar one location is to another. We then need to divide up the Us. Into a climatic regions, and then across those regions we can measure dissimilarity, and then evaluate how well current infrastructure is representative of that dissimilarity.
Sparkle Malone: The first thing we did with our cluster analysis is, figure out how many clusters exist within the us, and for a combination of climate and land cover we're looking at about 10 clusters throughout the entire Us. And so next, we want to understand how is infrastructure distributed across these clusters, so that we know how to better build our models. And so we calculated the similarity, noting that there are clusters that have higher dissimilarity than is experienced on average. And so.
Sparkle Malone: on average, you're looking at a dissimilarity of about 0 point 0 4 within clusters.
Sparkle Malone: But there are clusters in the southeast and Alaska in the northeast, and also considering Hawaii that will have larger dissimilarity ranges, and that's largely because those areas could almost break out into another cluster, but there aren't enough area within that cluster for it to actually fully separate. And so we know that when we're looking at the distribution of tower infrastructure for those regions that have a higher dissemination.
Sparkle Malone: Similarity, they're going to actually require more towers. And the question for this project is, are the towers there, because we're not actually putting in more towers. We're just using the infrastructure that exists. And so, when we look at whether or not we have enough towers
Sparkle Malone: within a region, we find that there are actually only 2 regions that have sufficient infrastructure. Our eb region, which is shown here in green, and then the northeastern, a region which is shown here in blue, and these are the currently the only regions with us infrastructure that are sufficient to build a model that we could believe.
Sparkle Malone: And when we look at how infrastructure exists within that network, whether those towers are even reporting data, whether they belong to neon, or whether they're just reporting methane data to general networks that's available to the wider scientific community. We see that
Sparkle Malone: again. There isn't really enough representativeness.
Sparkle Malone: And you can see that if you look at the black line it's showing the total amount of dissimilarity that is experience within a cluster, and then the line next to it is showing how towers represent that dissimilarity. And so generally you'd want to see that these colored areas stretch the entire range of a clusters to similarity, giving you an indication that towers are distributed across those clusters. But that's not really what we're seeing.
Sparkle Malone: and so that means that if I want to build a model estimating natural methane emissions for the United States. I can't just use towers that exist within the Us. I have to then go to towers that exist globally and try to line that up the best I can with climatic conditions and land cover.
Sparkle Malone: And so, in order to do that, I start off
Sparkle Malone: selecting information, and so I start by limiting my towers to flux that methane that data is already processed
Sparkle Malone: and ready to go, and so I can download flux, net methane data, for currently over 80 towers, and some of these towers have been running anywhere from 2,006 to 2,018. There is my met data that comes along with those towers. And so I can develop models using site level information, using a vegetation index
Sparkle Malone: and the normalized difference, vegetation, index, and for this model I'm. Using modus derived data. The climate data is the same source that I would use for the previous analysis. And so, using this information, I start off first testing for multicolinearity to figure out how I can reduce the large number of explanatory variables that I would consider in my model. And then, once I have done that, i'll move on to the
Sparkle Malone: machine, learning random forest model development, and then validating, observed, versus predicted. And to do this analysis I actually need to create at least 2 different models. But really I created 3. I created one model that evaluates how Well, I can even predict fluxes at a site level, but that site level model cannot be scaled in space, because i'm not applying that site in space. I need to remove the site information and then try to develop a model
Sparkle Malone: that I can
Sparkle Malone: thank you that a model that I can use vegetation in order to scale the information in addition to climatic conditions and our spectral indices. And then I create a model. That is just the conditions where what would happen if I actually didn't know the land cover, or I didn't think I could cover the land cover sufficiently. And so for this analysis I created 3 different models.
Sparkle Malone: And when I look at the model results I can see that I can actually model quite well a site, level model. And so here i'm showing the various explained by my site level model. And then that model you can see compared to a model without side level information, but just with vegetation, class and climatic conditions and spectral information that various explain drops from about 75 to about 44 and then drops even further without vegetation
Sparkle Malone: class suggesting that I definitely need to be considering vegetation class in my model, and this is pretty much the best model that I can come up with.
Sparkle Malone: If we look at what predictors are used in this model. We're looking at minimum temperature vegetation class, Our normalized different vegetation, index, are enhanced vegetation, index, and then the vapor pressure deficit, and all of these models are making estimates on a monthly basis and scaling them out in time.
Sparkle Malone: When I look further into my model, while it might look like the model might be overall looking pretty good for the different models that I can develop. There is a real limitation in this model in that I don't. I still even using global infrastructure, Don't have enough towers distributed across certain vegetation classes. And so I know that I can adequately represent classes like grasslands and wetlands quite well.
Sparkle Malone: but that falls off when you go to classes like everglings ever green broadly for us. They're not represented well, and so I know that my model is better for some land covers than others.
Sparkle Malone: but if I move forward with taking those model estimates at the Flux Towers, I can still project them in space, using the data I downloaded from climate, R and from modus.
Sparkle Malone: And what i'm able to see is that in 2,020 natural emissions are estimated to be about 6.0, 3 technograms of carbon.
Sparkle Malone: and that's all just from methane fluxes.
Sparkle Malone: When I look at ways of incorporating uncertainty. I make those model estimates as if I don't know what the land cover is, or that any land cover could actually exist within my cell, which is true at a one kilometer. And so my uncertainty estimate is currently about 1.9, 3 technograms of carbon.
Sparkle Malone: And so this project really utilized data from a number of different sources. And in order to develop these dynamic models, we have to do that. But figuring out how to integrate that data properly is the most important thing, and figuring out how to work in that data integration, uncertainty into your actual uncertainty estimates. It's also a big challenge with working with this data, and my number, One source of advice would be to consider reproducibility early on in your design of your workflows, grabbing data from all these different sources, and figuring out
Sparkle Malone: how to put it together, because the reality is, is
Sparkle Malone: not only is the reproducibility important for other people who might attempt to create similar budgets, but it's important for you, because you'll actually have to redesign your budgets over and over again as new products become available. I did this model over spring break 2 weeks ago, and then since then a product became available, and I had to rerun the model to improve my estimates. And so it's kind of unbelievable how quickly things are coming online, and to take advantage of that, you have to build
Sparkle Malone: like John said sustainable workflows that you can respond to and update, and that would be the first thing. I would sit down and work out before I started working on code to develop projects like this.
Sparkle Malone: Thank you.
Laurie Anderson: Thank you very much. Sparkle. I I feel like your talk is given us so much to think about, and i'm really liking that you ended with the slide about integrating multiple sources of data, because I think there's so much potential there.
Laurie Anderson: and yet so many of us are not ready to tackle that. And it's exciting to see.
Laurie Anderson: you know, to see taking that on and and to have you serve as a as a role model for what we're trying to do. Also, I appreciate that. I'm: i'm teaching methane in my class this afternoon after we finish this. So yes, I'm very excited to get updated. Thank you very much.
Laurie Anderson: All right. I will introduce our final speaker.
Laurie Anderson: Give me a moment here. all right.
Laurie Anderson: I'd like to introduce Dr. Runjin Luther Christian on from Boston University. Dr. Mutha, Christian integrates empirical and theoretical models to understand the interconnected processes that control ecosystems and evaluate systems as a whole
Laurie Anderson: as a broadly trained ecologist. His work is mainly focused on the dynamics of major shifts in ecological communities. Influenced by anthropogenic impacts.
Laurie Anderson: He incorporates the use of large, publicly available data sets to evaluate these ecological relationships and processes
Laurie Anderson: and run it. Take it away.
Ranjan Muthukrishnan: Thank you. Okay, let me get my screen going.
Ranjan Muthukrishnan: Okay, hopefully. You can see all of that, and I can see my
Ranjan Muthukrishnan: cursor. Okay? Well. yeah, thank you for
Ranjan Muthukrishnan: giving me a little bit of time to tell you a little bit about some of my work, particularly thinking about biodiversity theory using neon data. But what I also kind of wanted to do today was, tell you a little bit about how I think about
Ranjan Muthukrishnan: doing macro systems, ecology, and particular how I develop sort of those approaches as somebody who I think a lot of like a lot of you like started as a single site field ecologist, and how I sort of use that background and experience to build into the sort of macro systems, questions and work that I do.
Ranjan Muthukrishnan: So
Ranjan Muthukrishnan: in that context, like this is probably something that most of you have seen. This is probably, you know, pretty standard the idea that you got an experiment with a bunch of different treatment levels, and you've got multiple replicates of each of those treatments in, you know, separate plots or individuals, or whatever it might look like.
Ranjan Muthukrishnan: And then you might get data that looks something like this. You know different levels of response at each of your different treatments with some variation. And you know you probably know exactly what to do with this scenario. You you're gonna sort of
Ranjan Muthukrishnan: run a like look for a trend line estimate, or do a linear regression. Estimate your coefficients and your you know your hypothesis. Is that that trend, or that coefficient is, you know, different from 0. That's you know.
Ranjan Muthukrishnan: This is probably what you all are well versed in know how to do. But so i'm gonna suggest. Okay, Well, what if you essentially do that? But now you include a second site, and you know again, this is, you know you probably don't to do. You run your linear regression for site, one
Ranjan Muthukrishnan: and then for the star site, and they maybe are a little bit different. Maybe you got a third site, and you're starting to get some variation
Ranjan Muthukrishnan: in those sort of relationships between your treatment and response. But still you probably know to do this probably looks familiar. You would just still do a linear regression, but add an an interaction term, and you can see if that there are significant differences between sites which is cool. It makes a ton of sense.
Ranjan Muthukrishnan: But what I would suggest is, you know, another strategy is to actually just estimate the Betas estimate those relationships at each individual site.
Ranjan Muthukrishnan: And now, if you do that, you can sort of consider those individual betas or relationships as data points themselves, and from that perspective you could start thinking about a different type or a different set of questions that you might be able to ask about
Ranjan Muthukrishnan: really about the variation, and why these relationships are different, or have, you know, different strengths under different conditions? And I think of these as something like a meta hypothesis. You know, really this idea of when and how do those relationships vary? And do they vary in some sort of consistent way?
Ranjan Muthukrishnan: And what you might think of is, or you know, one way to think about that is, Are there some characteristics about your different sites, you know, to mean temperature
Ranjan Muthukrishnan: that relates to the different responses that you're getting at these different sites. And so.
Ranjan Muthukrishnan: once you start thinking about like that, you can use a lot of the same methods that you probably are. You know that you're already familiar with. To think about testing these Meta hypotheses. You might want to see if you know. Response. Strength is a function of you. The environmental conditions, you know higher latitudes, stronger response.
Ranjan Muthukrishnan: But in this case you may also find out there isn't a relationship with these environmental characteristics. But that's actually quite interesting, too, because it's telling you that this is a really robust response. So it's a robust across a lot of conditions, and that's actually really interesting and important and valuable to know.
Ranjan Muthukrishnan: You might have other hypotheses like. Maybe this response only happens in certain kinds of habitat. Here we've got a response that it, you know it's significant in forest, but doesn't really happen, and there isn't strong in Grasslands, or maybe you could think about different scales, you know small single meter plots versus entire regions, and and so you can start, you know, using the skills and the methods you already have to ask these sort of Meta questions.
Ranjan Muthukrishnan: So I want to tell you a little bit about one is study that I've done kind of in this context. Again, I use neon it's got a bunch of sites. I won't belabor it because Claire is on a good job of presenting them already. I'll say so I focused, at least for this work
Ranjan Muthukrishnan: on the terrestrial data set or the terrestrial sites and terrestrial data. And in particular, I focused on the plant community and plant diversity data.
Ranjan Muthukrishnan: partly because, you know that's the kind of data that i'm already familiar with. That's the kind of data that I would be collecting if I was in the field myself. And this is my, you know. One of my big takeaways is like play to your strengths as you try to become a macroeologist.
Ranjan Muthukrishnan: Also. The plan to be data, i'd say, is one of the you know, most robust data sets collected by Neon. It's got really extensive spatial replication within the site. I think there's there's usually around 30 diversity plots. It's also gonna have annual sampling. So you'll have really robust spatial and temporal replication.
Ranjan Muthukrishnan: It's also really cool, because it has a nested design with one meter plots inside of 10 meter plots inside of 100 meter plots inside of you know 400 meter plots. And so you can look at these multiple scales.
Ranjan Muthukrishnan: and there's a whole bunch of other parameters that are being measured in a co-located fashion. You know bird abundances, Mac. Small mammals ticks soil chemistry but it's also important to remember that the replication isn't always exactly the same. There's, 30, you know diversity plots.
Ranjan Muthukrishnan: but you know 5 or 6 bird grids, and and thinking about how to align those different co-located data sets, and like at what scale or their co-located is gonna be an important thing to think about in a challenge.
Ranjan Muthukrishnan: Then you just have to sort of figure out how to deal with.
Ranjan Muthukrishnan: And again, you know the way I would say this especially for this type of data. It's useful to remember that me on data. It's a lot like the data that you're already collecting. You know it's built on the same sort of approaches. It's just bigger.
Ranjan Muthukrishnan: So my advice is, do what you know, you know. Start with maybe just one site and try to analyze it the way you'd analyze data that you collected yourself, or maybe take all of the neon data and just treat it like one site. Ignore the different sites and analyze it like you would.
Ranjan Muthukrishnan: And then, when you see something interesting, you can go deeper into whatever those practical questions are. Also, you know, Claire's done the hard part she's written the the functions, and are to like. Go from all of neon pretty quickly into data sets. You know you can make an excel spreadsheet that's gonna look pretty similar
Ranjan Muthukrishnan: to the kind of spreadsheet you already work with. You know, this is soil, chemistry, data. Here's plant community data. It's got sites. It's got pop numbers, like all of that's going to be pretty similar to things you probably have some experience with.
Ranjan Muthukrishnan: So if you have that kind of data, you know, I would start by, you know, trying to sort of look at established theory and see if you can replicate it. I think about diversity and base ability relationships. The idea that you know diverse systems are less likely to be invaded there. Some classic work from Jay Stackowitz.
Ranjan Muthukrishnan: and so I just took all the plant. Diversity plots from me on and I through the number of native species up against the number of exotic species. And again, we can see this nice negative correlation.
Ranjan Muthukrishnan: you know, reproducing this expected pattern. We also know from literature that, like this, is actually a scale dependent pattern, and as you go to a larger and larger scales, that relationship goes from negative to positive.
Ranjan Muthukrishnan: And again, we can reproduce this in the neon data where we see at as you go from the sort of one meter plot up to the the 400 meter plot. It goes from negative to positive. The interesting important question is to dig in a little deeper to different sites, and can we see some variation?
Ranjan Muthukrishnan: And I'll? I'll talk about that, but it said it, but we won't. Do it with this data I want to present, or we can look at that with. This is a carbon sequestration, 5 versus ecosystem functioning relationship.
Ranjan Muthukrishnan: And so again, we've got here. You know the number of species on the X axis and soil organic carbon as our measure of carbon sequestration.
Ranjan Muthukrishnan: And again, we've got a nice relationship which is cool. But this one's actually weird because it's the opposite of the expected pattern theory would suggest that we would actually have higher levels under the in the more diverse plots. So what we did to go, you know, and dig further. Here is we actually did estimate the
Ranjan Muthukrishnan: you know, at each site what's that relationship? Look like, and you can see. Now, there's actually quite a lot of heterogeneity. Some sites that are negative, but some that are positive. And so we can start thinking about why this variation exists. And so now I've just plotted it against another parameter at the site level, which is in this case just latitude. And you can see
Ranjan Muthukrishnan: Actually, we have a really strong relationship here that the highest latitude sites do show that positive relationship we expected. But as you get to sort of these, me middle and lower latitude sites the that relationship gets negative. So it's explaining this variation, though we're still you know it doesn't. Necessarily.
Ranjan Muthukrishnan: you know there's still we we should look into why, that is.
Ranjan Muthukrishnan: And so just. I thought I would finish with, you know. Come some over overall arching thoughts as well, you know challenges is to really start thinking about these meta hypotheses.
Ranjan Muthukrishnan: You know, traditional experimental design. Isn't necessarily going to be significant or sufficient. And there's going to be challenges because you're working with observational data, integrating with co-located data that might have different replication. And the data is not always going to fit your standard expectations or physical expectations.
Ranjan Muthukrishnan: So what can you do? You know one? I think you have to start becoming more comfortable with adapting analytical approaches.
Ranjan Muthukrishnan: but
Ranjan Muthukrishnan: you know the key to that is, they think about. What are you losing by using these approaches? But what's the benefit? What's the trade off and like? Are there real problems? Or is there some utility in using these approaches? And I will again say, You know your empirical and system based strength or
Ranjan Muthukrishnan: system based? Experience is a strength like play to that, like your intuitions are useful. And then the last thing that I do a lot to help me sort of as I'm. Building new approaches is, I use a lot of simulations
Ranjan Muthukrishnan: to. If i'm trying out a new analytical approach. I run sort of a null model simulation to see, you know. Is it actually working the way that I think it is, or like? Are these deviations from expectations actually problematic? Or
Ranjan Muthukrishnan: are the analysis still robust, and so building that sort of toolkit is, I think, the way to do this. But so thank you. We'll. We'll have, I guess, some time for questions in the breakouts, which is probably gonna happen in just a minute.
Ranjan Muthukrishnan: Alright, Thank you.
Laurie Anderson: Thank you very much engine, and thank you for emphasizing the approachability of the neon data. And you're reminding me
Laurie Anderson: that
Laurie Anderson: you know what I think an anxiety that a lot of scientists have is you sort of feel like you have to encompass all of scientific knowledge within your own brain. I there's some kind of pressure. I remember feeling as I developed my career, and I really realized like.
Laurie Anderson: No, we need to. This is where the team aspect really helps. I mean, you can kind of bring your own specialties to the table along with other people who have different kinds of specialties, and that's that's a powerful
Laurie Anderson: collaborative approach. So thank you for pointing that out. All right, Tracy, I think, is going to tell us a little bit about the format for the breakout rooms.
Tracy Gartner: Yes, thank you. To all of our speakers. I echo Lori's comments. This has been really a great session. We are not done. We are going to break into breakout rooms and we're gonna stay in our breakout rooms until about a a very nice
Laurie Anderson: We're having a very nice conversation in my breakout room, and i'd like to thank everyone for
Laurie Anderson: joining us and staying
Laurie Anderson: to talk with us and share ideas about macro Systems ecology.
and I'm just going to share my screen for one last moment to remind you of ways to engage with us
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Laurie Anderson: so I will stop my sharing. and
Laurie Anderson: so I can see all of you and I'll thank you once again for joining us at this event, really appreciate it, and thank you once again to our speakers. Excellent talks, really informative, gave me a good sense of a lot of diverse types of macro systems research. So thank you very much.