Description of the video:
>> Terrific. Hey, thanks everybody for joining us this afternoon. Nothing better to do on a Friday afternoon in October than to spend two hours staring at a Zoom screen. But this is gonna be action packed and with just something for everyone, I'm convinced. We've had a lot of researchers who would like to share a little snippet of what they're doing in the second half of the data summit, so I'm really looking forward to it.
I'm Janet McCabe. I'm the director of the IU Environmental Resilience Institute. I'm happy to welcome you and kick this off. This, I think, maybe is our third data summit that our data manager, Justin Peters, has organized. I just wanna say that from the very beginning, data has been a key part of what the Environmental Resilience Institute is all about, and a part of our founding principles and philosophy that in addition to all the great work that everybody's doing at IU, that we make those data available as much as we can.
Both to people within the university to help foster interdisciplinary research and teams coming together that might not have thought that they were interested in what a plant ecologist was doing but it turns out they are, but also for people outside the university. And this grand challenge and the Environmental Resilience Institute are all about impact in the real world in Indiana.
And I'm so pleased to say that after three years and a little bit of this grand challenge, we are really starting to see that impact happening. We're also really starting to see our researchers deliver in terms of having some results, having some data to talk about, publishing papers, having meaningful work to get out there.
And that's really no surprise either. You don't produce work instantly. It took a while. So I think that these data summits and the data that we are able to put on our website is just gonna grow and grow and grow. And I'm very grateful to Justin and our student Kimberly Cook, who works with Justin, and to our communications manager Jonathan Hines, and Joe Lang, O'Neill grad student who works with him on the website that they are working hard to make sure that we're actually gonna have those data available in a very easy to access way, whether you're inside the university or outside the university.
To that end, Justin is starting to or has been meeting with a number of ERI researchers to talk about what data they have and whether it's ready to be placed on the ERI website. If he hasn't come knocking on your door yet, he will. So please open that door for him and invite him in cuz he can help you get a broader impact for the work that you're doing.
If you're on this data summit and you're not already somebody that's been part of a project funded by the grand challenge, that's okay. As far as I'm concerned, data that are relevant to environmental change and climate resilience in Indiana or beyond are definitely things that we'd like to consider making available through our website to just get more attention to those things.
So I think that that's all I wanted to say by way of starting. I wanna thank you again. We're in year 4 of this grand challenge. We're very much looking forward to life at ERI and life in the world of interdisciplinary climate resilience research at IU post the grand challenge.
You guys are all partners with us in making that happen. So I look forward to many more gatherings like this. So with that, I'm gonna turn it over to Justin and turn my video off and mute myself and start listening.
>> Thank you for the intro, Janet. For everyone that is joining us right now, we have about 25 participants.
We are expected to have about 15 data updates later on in the afternoon. So we are gonna have multiple people joining us a little bit later. But as of right now, we have two presentations to start with. And the first one is from Abraham Lauer and Ben Kravitz, A New Downscaled Climate Dataset for Indiana, and I am going to turn it over to Abraham.
>> All right. All right, hopefully, you're seeing this presentation. Cool, let's do this. All right, so as Justin said, my name is Abraham Lauer. I'm in my second year in the graduate studies in the Department of Earth and Atmospheric Science, and today I'm gonna talk about the project I've been working on for the past year, year and a half, which is looking at a new downscaled climate dataset for Indiana.
Let's see, there we go. So I said it's my project. It is a group of people's projects. Ben Kravitz is taking the lead on this, and we're joined by several faculty members, both from Atmospheric Science and Geography. And as Janet said earlier, the grand challenge has been going on for four years.
And I've only been here for a year and a half. So I'm carrying the torch that's been passed on to me by several other students that have been working on this. So what are we trying to do here? Essentially, at a very high level, we are trying to dynamically downscale climate output from the Community Earth System Model over Indiana and the surrounding areas to a three kilometer resolution.
That's a lot of words and I'll get into that, but essentially, the main questions we're trying to answer is, what's climate change actually going to look like over Indiana? In particular, what will extreme events look like, cuz those are often the most costly events that occur in terms of climate?
No one's ever done dynamical downscaling over Indiana. The closest thing we've got is a statistical downscaling study that was done by Notre Dame over Indiana. So we wanna look at what results we get and how they compare to the results that Notre Dame found and see if there's any guidance that we can provide.
What do we do better, what do their methods do better, and compare and contrast those. And then finally, this is the data summit. We wanna provide our dataset to any downstream applications that can use it. Any water resources, health impacts, agriculture, ecology. I'm sure you guys can fill out that list for yourselves.
So dynamical downscaling is climate modeling. So what's in a climate model? Essentially, a climate model divides the earth up into a bunch of little boxes, grid boxes, and you can see a representation here. The grid boxes are both horizontal, north, south, east, west, as well as vertical, looking at either height or pressure differences.
And within each of these grid boxes, the model is trying to solve equations, solving equations based on how much radiation is coming in. If there's water vapor or air movement going on, how much movement, how much momentum that is generating, different things like that. And so these models are all built on these physical processes.
And some of them are quite simple. Like, how much solar radiation is coming in from the sun, that's a one line equation based on the time of year and the latitude. That's not a hard thing to do. But if we get a little more complicated, is precipitation falling?
That can be More complex to try and solve that equation. Is it falling as snow or is it falling as rain? Is there exaction, is warm air being pushed up from the tropics or is cold air coming down from the poles? Now those are different things that the climate model is solving based on these equations and at the end of the day, it's figuring out what does the earth look like at this point?
What does the climate look like for these series of time steps that the climate models putting out. So dynamical downscaling generally has two different models that are used. The first one that we use is CESM, the Community Earth System Model. This is a global climate model, it has about a one degree resolution and that ends up being roughly 100 kilometers.
And it does the entire earth from 1950 to 2100 under various RCP, forcing scenarios. So we're using that to essentially carry out, or to provide the climate change signal. So we're not necessarily thinking about, when we're looking at Indiana, what's happening in the Pacific Ocean? But what's happening in the Pacific Ocean is really important cuz El Nino plays a vital role in weather all over the globe.
So that's definitely going to affect what's happening in Indiana. And so we start with this global climate model to kind of get a high level look at what's going on across the entire globe. And then from there, we take the output from that climate model. The output that we're using was actually from the 5th community model intercomparison project, which was done back in 2005.
And so we're taking output from that model, and we're plugging it into WRF. WRF is the Weather Research Forecast model, and our domain across Indiana has a resolution of three kilometers. So WRF is actually really good at taking weather conditions that CESM gives it and resolving those, really really high resolution equations and kind of balancing them on these really small scales.
And it can predict like a squall line of thunderstorms moving across the state or different things like that with 100 kilometer resolution, you're never going to get that sort of detail just because a line of thunderstorms is gonna be, maybe two or three or five kilometers thick. So this is kind of a generic downscaling slide to kind of give an idea of how this works.
So we define different domains within our methodology. And so outside this white box here, we might be using the CESM data that is 100 kilometers. And I think you can see down here it's 111 kilometres, and then inside that box, everything gets shrunk. So all the grid boxes get cut in half essentially it's 55 kilometres in there and then as you go into further domains, you go further and further and your grid sizes get smaller and smaller so you can see down here 55 to 28 into fourteen and then finally when we get to the region that we care about, we have the smallest domain and that's looks like it's seven kilometers in this study.
And so why is this important? Well if you think about place like California, California has a lot of different topographical features that will interact with weather and climate very differently. The coast and the ocean breezes coming off the coast. Got this mountain region, you've got the Central Valley, you've got the Sierra Nevada's both the windward side and the leeward side and all of those things will, if you have too big of a resolution, those features just kind of get washed out and this ends up being a flat chunk of land when we know it's not.
And so that's why we need a small resolution to be able to resolve these features. So Indiana obviously does not have such a varied topography. But there's definitely a difference if you look at someplace up near Lake Michigan where it's mostly agricultural, I don't think anyone's gonna say that areas up here are equal or very similar to areas in Southern Indiana where we get into the rolling hills.
And so this slide gives a rough idea of what this downscaling might look like where you've got these big grid cells, where essentially everything within each of these cells is exactly the same. So there's no topography difference from this point to this point. And we downscale and we end up with a grid that looks more like this.
Again this is a representation, so this is not actually a one degree and this is not actually three kilometers. But you get the idea of how adding in downscaling to these much finer grid can give us a lot more information. Are there any questions on downscaling and kind of the methodology that we're using Cool all right.
So what does this actually give us, if we want to look at, ideas and pictures are fine, but what do we actually get out of downscaling? So this is example of WRF output on the very first time step of when it's running. And so it has just taken data in from CESM, this global model and it's interpolated it onto our grid space and that's why you're not seeing these giant blocks.
And it's not really blocky, it's already smoothed out. But there's not a lot of information here. Yeah, you can see it's cold up in Wisconsin, and it's warm in Indiana it's even warmer down in Tennessee. It looks like there's maybe a frontier, but how well ,I mean, you're not getting a ton of information out of this.
So, the way though our methodology, they're basically I'm gonna shoot myself in the foot and say there are roughly two ways to do downscaling. One is you turn on WRF and you keep it running and you just let it run for 100 years and you update the boundary conditions outside of the domain.
And those the changes in the boundary conditions is what kind of feeds into the model and that updates the conditions. We're not doing that here, we're not interested in one continuous long run. We're more interested in making sure that our climate signals today is as close to what CESM is giving us as possible.
And so what we do is we turn on WRF and we run it for 12 hours. And we say okay the first 12 hours we're gonna throw those away because, the model needs some spin up time to equally iterate across the entire domain. And then the next 12 hours we say that's actual data that we're gonna save.
And then we restart WRF. So every 12 hours, we're basically moving the starting point forward and restarting it and then running it for another 12 hours. And so if you this is June 2nd at 7 P.M from 2025. And so if you go back 12 hours and say what does CESM look at 7 A.M and then start WRF and let it run for 12 hours.
That was supposed to be there we go. This is what you get when WRF has run for 12 hours and this is the same image. This is These are the same time stuff, I guess. So this is what downscaling is giving us. You can see there's the heat island from Indianapolis, that little yellowish dot there is Monroe.
You can actually see how strong, this is a crazy strong front right here, where it's15 degrees Celsius. So what we're talking 60 degrees here, and over here, what are we? We're above 80 by the time we get from Northern Indiana down to a third of the way down the state.
So WRF is able to resolve some of these really fine scale. And you can see it's quite very we have the the mountainous terrain down in Virginia and North Carolina, right? Yeah. And it's showing showing up the cooler temperatures that we're seeing down in the Smoky Mountains there.
So we have WRF getting us a bunch of data and it looks interesting. Is it right? So this brings in the question of how good WRF does at modeling the data. And I don't want to get too far into the weeds on how we're looking at and correcting morph.
But a rough idea is for the historical period, from 1950 to 2005. We have WRF output for every three hours. And we also know what actually happened because it was observed in history. And so there are these reanalysis data sets that say, okay, we're missing some data in different places in different areas.
And these data sets attempt to resolve all those differences and then we can use them as a quasi source of truth. For what the historical period actually looked like. And so this is February at 7AM, every February at 7AM from 1950 to 2005. Two meter temperature at Bloomington.
And so the blue is they are source of truth. And the orange is what WRF said. And so this is a histogram of what the temperatures might look like and what the distribution of temperatures look like. So clearly WRF is colder than it should be pretty often. Look at a different time step.
If we look at June at 1PM WRF is a little too high. So if we wanna be really, really vague and really make our assumptions off of two data points, which I don't recommend. We could say WRF looks like it's heating up too much in the summer in the daytime.
And it's cooling off too much in the winter overnight. And so what we do with that is we say, okay, let's push on these distributions of WRF. And let's look at the coldest temperatures of WRF, they're too cold. And so let's just push those in a little bit and add a couple of degrees onto those.
And by doing this and by doing this to different sections of the data by moving quantile by quantile. We're able to tell WRF to apply a correction to what WRF is outputting to line up these histograms pretty nicely. As we can see that WRF and our source of truth actually look really similar now.
And so once we know what, okay WRF is always four degrees too cold at February at 7AM. In the lowest, if the temperature is between 240 and 260 Kelvin. Then when we run this in the future, we can apply that same correction to all temperatures in February at 7AM that are between 240 and 260.
So we're creating this this method that finds the things that WRF does badly and corrects them. And that's all I'm gonna say about that because, it can get complicated. So let's get into the interesting part. I'm sure that you've all been just dying listening to me talk about that and you actually wanna know what we're producing.
So what sort of data output do we have? For the initial starting point, this is what we're developing our bias correction methods off of. And kind of quality checking and this is what we're starting with. We have to meter temperature for every three hours. We have daily maximum temperature, daily minimum temperature and precipitation amounts for every three hours from 1950 to 2100.
These are on the same grid that you saw earlier. So it's a three kilometer resolution. The fun part about using a climate model is that climate models output a lot of stuff. There's tons of things in there. And so if we wanna look at, this is just what we're starting with.
But once we know what other things we wanna look at, we can start doing other variables. For example, temperature at 2-meters every 3 hours and we're starting with that but what's the temperature? What's the vertical profile the temperature doing? What's humidity looking like? How much water is in the in the air?
What do winds look like? I have 10-meter winds listed here. But we could also do a vertical profile of winds. We can get radiation fluxes, we can get surface pressures and soil temperature and moisture and surface run on runoff. And there's I can probably think of five or 10 more variables that I didn't put on the slide.
So there's a lot of different things that WRF is outputting. Now, how good are those? We're not sure yet. We haven't looked into any of these down here. We've been focusing on temperature and precipitation so far. But these are all things that we have that might be usable.
They're definitely usable, so long as you understand what the assumptions are behind them and how they're being calculated. So those are the sorts of data that we have, how much data are we actually talking? So remember the historical period is from 1951 to 2005. And then the future period is 2006 to 2100.
We have run RCP 8.5. So the scary bad climate scenario is done. And that ended up being raw output about 245 terabytes of data. Now remember, about half that model spent up cuz we throw away 12 hours and now we use the next 12 hours of WRF. But close to half of that is gonna be the outer domain and the boundary conditions that WRF is using.
And so what we're left with is about 30 to 50 terabytes of data raw output from WRF that's actually usable. The historical is about half the time period, so it's gonna be about half of RCP 8.5. And if you think about, okay, I don't care about raw data, I wanna know what usable data looks like.
If I stripped down one year of temperature, so every three hours temperature data. I can get it down if I remove all the metadata, I can and still have it be usable so I can assimilate it back into something that's usable. I can get down to about 1.4 gigs.
More likely, what is gonna be used is something like full surface temperature. So every three hours temperature from 1950 to 2100 that ends up being about 300 gigs. Because it's split into multiple files and the metadata needs to be repeated. One final note. We have this across more than just Indiana.
Obviously, you can see we have data stretching all the way over into Pennsylvania, and then we've got several states around Indiana. So our data is not restricted just to the state. And the reason for that is a lot of things that happen in Illinois affect what happens in Indiana, and similarly, Michigan and Ohio and Kentucky.
Weather doesn't contained in Indiana. So one example of a application that go outside the state is if you wanna look at the Wabash River. Wabash River watershed covers most of Indiana, but it's not isolated to Indiana. If you look down, and a lot of this comes out of Illinois.
And so if we just cut off our study right here, we'd be missing all this data that is potentially applicable to different studies that people are trying to do. And so we do have surrounding areas as well. With that, that's everything I wanted to share today. Thank you very much.
And if anybody has any questions, or comments, or complaints, or concerns, or applications that they'd like to talk about, please, please get in touch with us. Cuz that will help us drive what sort of directions we're going, and what sort of data that we start looking into, and different steps that we can start taking next.
>> Thank you, Abraham. That was great. I do have a question, if you could. You spent some time talking about the size of the data that's being produced if you strip it down minimally. If myself or others in participation here wanted to use that data, What's the format of that data?
Or what would that data look like, a NetCDF file? Certainly not CSVs, or something like that? Could you say-
>> How to look at that.
>> Yeah, so currently, everything's in NetCDF format.
>> Okay, great. So we do have a question in the chat from Hannah.
Where do you store your data and run the models?
>> Yes, so we have a slate project where all the data is being stored, and that's where the models get, I guess the model data all get stored there. And then all the simulations have been run on Big Red 3.
And then if you can imagine, I'm doing a lot of model running on Big Red 3. So my queue time is pretty high, because I've been taking a lot of computation time. And so I've been doing a lot of data processing using Karst at this point. So that's kinda where where everything is located right now.
>> Great, and for those of you in the audience, UITS Research Technologies has allocated the grand challenge, the Grand Challenge, ERI, a large storage space for the slate project. So if others are in need of storage like this, like Abraham and Ben are using this large amounts of storage for research purposes, we can accommodate, or set you up with some sleep project space.
Okay, thank you guys again. We are at 1:30. We had on the schedule for 1:35 Michelle Graff to present. I see Michelle is present with us. Michelle, would you be ready to present? Okay, great. Thank you again Ben and Abraham. At this point, Michelle, I'll turn it over to you.
While you're getting ready Michelle, I guess I could introduce you and the name of your presentation. So Michelle Graff will be presenting on studying energy insecurity through survey research. And the floor is yours, Michelle. You are muted.
>> Here we go, now we can hear you.
>> Okay, awesome Can everyone see that, hopefully? Okay, great. Thank you so much. Well, first, thank you so much for having me. I sincerely appreciate the invitation to talk about our survey research that is looking at energy and security. My name is Michelle Graff. I am a PhD candidate over at the O'Neill School of Public Environmental Affairs.
I'm working with Dr. Sanya Carley, Dr. David Konisky, and another PhD student, Trevor Memmott, primarily on this research. So first and foremost, of course, I wanna thank ERI for providing project funding for part of this research. We also have funding from the NSF, Indiana University's VPR office, as well as the Alfred P Sloan Foundation.
So just wanna start off thanking everyone who has contributed and allowed us to conduct this research. So first of all, sort of jumpstart my presentation with a conceptualization of what energy insecurity is. And it's just this idea that somebody, or a household, an individual is unable to meet their monthly energy demands that is required to power medical devices.
Electronic devices, cook their meals, turn on their lights in their homes, basically unable to meet your typical household energy demands that somebody might require. And this might look like not being able to pay a monthly utility bill, or actually not even having access to electricity. We know that energy insecurity can lead to mental and physical health implications.
So people who suffer from energy and security are more likely to suffer from anxiety and stress, as well as increased rates of asthma, upper respiratory issues, and in really extreme circumstances, death. A pretty prominent new story that circulated in 2018 was a really terrible story about a new jersey woman who passed away, because her electricity provider turned off her power after she could not pay her electricity bill.
And unfortunately, she was unable to power her electronic device that provided her oxygen that she relied on, and unfortunately passed away. So in really extreme circumstances, obviously, you get really extreme outcomes. So the implications of energy and security sort of range across the US. We know that energy insecurity is pretty prevalent in the US from prior survey research that is conducted once every four or so years by the EIA.
It's called the Residential Energy Consumption Survey. It doesn't really specifically look at energy, poverty, or energy and security in the US, but it does show that one in three households have reported difficulty paying their utility bills, or keeping adequate temperatures in their household. And about 20% of US households report high energy bills.
And as a result, needs to forego other expenses, like buying nutritious food for their family, or going to a doctor, or purchasing their prescription medication, in order to keep the lights on in their home. So we know that this problem exists. It's a material hardship that people have to deal with every day.
But it's really under Under appreciated and not really well understood and that's really what the research that we're doing is aiming to help understand the scope of the problem. We also know that the impact of the COVID-19 pandemic is likely going to exacerbate the issues of energy and security.
You can probably see that I'm at home right now. I assume a lot of you are at home right now. I'm powering my electronic device from home, I'm cooking more at home. My lights are on more often, especially this past week in Bloomington when it was so rainy and dreary and dark.
So as professional personal schooling sort of issues move into the home, we're going to be powering these devices much more. And as a result our electricity use has been going up. We're actually seeing an increase in electricity use while you're at home where normally you'd be out of the house.
And as a result, we're expecting there to be an increase in corresponding energy bills. So if you can imagine people who potentially are losing their jobs, are now going to have to pay more money because they're staying at home for the utility bill. So the energy insecure population is likely to grow as a result of the COVID-19 pandemic, and that's sort of part of our expectations and hypothesis.
There have been some utility disconnection protections put in the US across the states. They vary across the states in what they require utilities to actually do. So in some states, it's really strict and stringent and they say you cannot shut off anybody's electricity. In some states they say, well, you voluntarily can choose not to shut off people's electricity.
In other states they say nothing, and so these really do vary and it's really uncommon, I think only one state has actually forgiven any sort of debt. So there are a lot of people that say, my utilities won't be shut off, that means I probably don't have to pay my bill.
But in fact, as these orders have and are expected to expire going into the winter months, unfortunately these bills are going to come due. And some people might be finding themselves in a lot of debt further exacerbating the issue of the energy insecure population. So what are the research questions we're aiming to answer with our survey research?
The data that we're gonna collect? How prevalent is energy insecurity in the US? So is it widespread? Has the pandemic made it worse? What factors have led households to be more or less energy insecure? So are there are certain correlates that explain why one household is energy insecure as opposed to another.
What are the implications for households when they are energy insecure? So how does this energy insecurity affected their material hardships? And how are we doing this? Through a quantitative analysis of a nationally representative sample of Americans at or below 200% of the federal poverty line. So this is household level data and we know what states that they come from.
We have a pre pandemic baseline and impact of COVID pandemic shock through multiple measures of energy insecurity that range in severity. So what are the different questions that we ask people? Could you pay your energy bill in the last month? Did you receive a disconnection notice or were you disconnected from utility provider?
So you can see that these really range in severity. So there's a difference between not being able to pay my bill and actually being disconnected from the grid, right? So we ask people, this range of energy insecurity to get an understanding of where people fall in these buckets as well as how these different buckets might affect different issues related to energy insecurity.
So our research design, what's our data collection, sort of the main scoop of this presentation. So we conducted a survey design. This is the first of its kind to track the same respondents over the course of the year. Again, all low income Americans at or below 200% of the federal poverty line all really trying to understand energy and security and the implications there of that issue for the households.
So our first wave actually happened at the end of April and the beginning of May, our second wave finished up in August and we just are reviewing all of the results for that. Our third wave is going to happen December January, where we're gonna ask folk specifically about some cold weather issues.
Whereas in the wave two, in the August, we talked to them about what was going on during these crazy heat waves that was happening across the US. And then our fourth wave is meant to be a year after the COVID-19 pandemic really began, so March 2021. So in the first wave, we were able to collect information from 2,381 adults again at or below 200% of the federal poverty line.
And we were hoping to generate a nationally representative sample of this population and we were able to get that through YouGov, which is an online contractor that administered the data online. We got measures of energy insecurity across our three measures of severity. We collected information on socioeconomic demographics, some behavior health issues, whether or not they receive government assistance specifically related to energy as well as other government assistance.
The big ones like Snap and TANF as well as housing conditions. So do people live in efficient or deficient housing conditions? Because that has been known to affect how you actually are able to use your energy in your own home. One good example is for say, if you have holes in your walls or your doors, you're obviously going to have a less efficient heating system, right?
Heat is going to escape and unfortunately you might lose some energy that way. Some people suffer from having broken equipment. So broken heating or cooling equipment and as a result, they need to resort to using something that's pretty dangerous like a space heater or turning on their oven.
Space heaters are the number one cause of household fires. So they sort of turned to these issues when they sort of have poor housing conditions, so we definitely wanted to collect information on that as well. We leverage different timeframes even in the first wave of this survey, which I think is really interesting.
So we asked questions about the last year, so starting in May of 2019, all the way through till about may 2020. Last three months, so the beginning period of the pandemic and then in the last month once the pandemic was really underway in the month of May. We have a separate representative sample of 2,000 Indiana households that we're also able to ask all of these questions.
And get a representative understanding of energy insecurity not only at the national level but also for the State of Indiana. So I'm gonna present some results of our survey. And I'll start with the wave one national sample and then I'll go to the wave one of the Indiana sample.
And then I'll present some preliminary results that we have from wave two. So this graph is meant to show, what energy insecurity looks like at the national level over the last month, which we see in yellow, the last three months which we see in orange and the last year that we see in blue.
What's the prevalence in our national sample? What we see is that the proportion of respondents that are suffering from energy insecurity in the last year, last three months and in the last month, is prevalent. It's widespread, right? So 25% of our respondents could not pay an energy bill in the last year and 13% of those were actually just in the last month, so that means May 2020, April 2020, meaning when the pandemic was really happening.
And what's actually kind of scary is if you go up to the disconnected portion of this graph, you see 11% could not or were disconnected from the electricity grid in the last year and 4% were disconnected in the last month. And this is really when those disconnection protections that I was talking about, were meant to be in place.
So it's really sort of this sad and scary things, to see how prevalence these things are even when there's should be protection across the US, sort of helping people deal with this issue. So what we did is we try to make them very broad approximations by filling our representative national sample to the US Population.
So how many households are actually suffering from this issue based on the proportions that I just showed you? In the last year, we see about 4.7 million households couldn't pay an energy bill and 2 million of those were disconnected. And in the last month, nearly 2.5 households could not pay an energy bill and nearly a million were actually disconnected from the grid.
So these proportions that I just showed you in this previous slide, are really scaling to like real households, right? This is a real large number of people. And it sort of amplifies itself if you look at the number of individuals which considers how many people actually live within a household.
So let's say you're a family of 5, right, the number of households counts to as 1, but the number of individuals number of counts to is 5, right? So you're looking at 24 million in the last year that couldn't pay energy bill, 10 million disconnected and 4 million of those were disconnected just in the last month.
So we're seeing these pretty large numbers in the last month and the last year. And again, in this last month, we sort of were hoping to see much closer to zero because these protections were sort of hoping to help people especially in that disconnected column. So the data has allowed us to really not only understand the prevalence within our survey, but because it's nationally representative, we're able to scale it up to get some approximations for the entire US population of American households that are within 200% of the population.
I should really actually take a moment and stress that, this is not for all US households. This is for these households that are at or below 200% of the federal poverty line, really vulnerable low income households we're talking about. So we were able to also break down the prevalence of this energy insecurity in the last year, last month and last three months over different social-demographic issues.
So it's sort of going from the bottom up of this graphic, what we see is black and Hispanic households are suffering from energy insecurity at a higher rate than white households across all three of our measures. We also see again, moving up in this graphic households that have children under five.
So young children, of course, are suffering at much higher rates than people that do not have children in their home. Those who rely on a medical device like that New Jersey women I spoke to you about who needed her oxygen, but was disconnected, also suffering at much higher rates.
And then the top two lines you're seeing those who live in deficient or inefficient housing conditions. So this could be again, holes in the wall, broken AC or heating equipment as well as things like rafty poor insulation mold in their household are also suffering at much higher rates than those who live in adequate housing conditions.
The full bar represents what's going on in the past year, the last 12 months and the dashed part of the line shows what's going on just in the last month. So the beginning part of the pandemic again, when these protections really should have been in place and hopefully helping people.
But we're still seeing obvious disconnections, which we see in blue, which is, for lack of a better word, a bit of a bummer. So what factors actually are predicting this energy insecurity? We run a statistical analysis of both the long term so over the past year, what we deem chronic and acute what we deem in the last month, due to the pandemic energy insecurity.
And what we find is very similar to this scripted graphic I just showed you, households with children under five, those that rely on an electronic device, Black and Hispanic households. So households of color, those that are sort of in the poorest group of our survey under 100% of the federal poverty line and those that live in inefficient housing conditions all are suffering higher rates of energy insecurity both in the last year and the last month.
So for lack of a better words, these already vulnerable households are suffering across all three of these measures from energy insecurity at a chronic and acute rate. So the question that we are curious about in sort of disentangling is if the COVID-19 pandemic has exacerbated this energy insecurity.
So this is again, a descriptive look at what is going on within our proportion of survey respondents, we break down some questions that we specifically asked about COVID. So for example, the top two lines are asking folks if they experienced any COVID symptoms or received a positive COVID test.
And what we see is those that did experience COVID symptoms or received a positive COVID test are experiencing higher rates of energy insecurity, those that lost job hours. So we're either furloughed, completely lost their job or had reduced hours were seeing higher rates of energy insecurity than those that were able to keep their job or move their job to the home, right, and continue working.
The next is sort of a factor score of a group of questions we asked specifically about COVID hardship. One of the most important ones was did you have to forego expenses in order to pay an energy bill like medical expenses, and of course we know in a pandemic medical expenses are probably really important to be able to take care of, or buying nutritious food.
Being able to cook nutritious meals, of course, during a pandemic is something that we'd like everyone to be able to do. And those experiencing these hardships did experience energy insecurity at higher rates. And the last bit of information looks specifically at the stimulus checkm the $1,200 stimulus check that was released by the US government from the Cares Act, I think it was in May.
And those who received See the stimulus check, were less likely to be energy insecure and much less likely to be disconnected from the electricity good than those that did not receive their check. And again, all of these households are within 200% of the federal poverty line. And as a result are likely income eligible for those checks, right?
So we see the stimulus check actually having a pretty important impact on people being able to pay their energy bill. We run another logistic regression here to look at how these impacts have affected energy and security in a statistical manner, showing that the COVID stimulus receipt of it actually lowered energy insecurity.
Like we find COVID hardship, losing hours from because of COVID and then experiencing COVID symptoms, all increase the prevalence of energy insecurity in the last month for these these households across all three of our measures. And I should say that this statistical analysis also includes all of the information that we included in our first regression analysis as well.
So those predictors remain statistically significant as well and in addition, we're seeing this impact in the last month. So what can we say about the COVID-19 pandemic from our data? We think that the pandemic deepened energy and security. We definitely think it made it worse and it has had an impact.
It remained significant even after controlling for economic issues, especially income. And the CARES Act stimulus check really stopped people from getting disconnected. So it prevented that most difficult form of energy and security for these households. So now I'm gonna present the results from the Indiana sample. And I just wanted to give an idea of where these folks from Indiana since we're all living here and know the state relatively well hopefully are coming from.
This is the Indiana workforce development's regional map so we divided up based on these regions the proportion of the sample. It's meant to be a representative sample based on proportions and other issues like race and education to be representative of the full state. So this is just a breakdown of where people are coming from and what the real proportion of the sample looks like.
So again, I'm going to start with a descriptive understanding of people who are suffering from energy insecurity in the state of Indiana. And again, just like in the national sample, we're seeing this as a problem in the last year, last three months, as well as the last month.
So starting again from the bottom moving up, we're seeing that 30% of households could not pay an energy bill in the state of Indiana in the last year. 16% of them could not pay the energy bill in the last month and then if you go all the way up to disconnected.
We're seeing pretty similar numbers, right? 13% of the state of Indiana were actually disconnected from the grid. Even though Governor Holcomb did actually have protections in place, 4% were disconnected in the last month. So we know sort of a little bit more about what was going on in the state of Indiana in terms of what Governor Holcomb's protections were.
I think they went all the way through to August 15. And still 4% were disconnected from the grid, which again, as we saw with the national sample does result in real household. Real people suffering from a loss of being able to turn on their lights or cook food.
Or keep their homes at adequate temperatures over the summer months and during the pandemic. Again, we run the same statistical analysis. We find that inefficient housing conditions as well as the black and Hispanic households in the state of Indiana. Those that rely on electronic device, all of these vulnerable populations are suffering more from energy insecurity in the last year, than white households, those who have adequate housing conditions and those that do not rely on electronic devices.
So it's a similar story to the national sample what we're seeing in the state of Indiana. In fact, state of Indiana might be a little bit more representative in terms of energy insecurity than we expected to what's going on with the national sample of these folks within 200% or of the federal poverty line.
So I want to give, I just wanna check my time, I want to give a very brief overview of the wave two, the preliminary results. We're looking at the August results. We see that 37% of respondents have some form of utility debt at a national level. Only 38 respondents across both waves have increased their stimulus check.
So these households that we think should be income eligible, only 38% of them have received their check. 7% were evicted and 64% require prescription medication and only 18% couldn't fill this information. So these are sort of the other implications that once we get more information from our future waves, to sort of understand how energy and security is correlated with some of these other issues.
And that's part of what this data is able to allow us to understand. Since the month of May, this is just more aggregate of wave one and wave two. 20% of the US national sample could not pay an energy bill. 15% received a notice. And 6.5 still disconnected.
We're seeing these disparities really worsen. So the black and Hispanic, Native American households are all suffering across all three measures more than white households. Since the month of May have energy and security, so some implications, we think that they're going to be seasonal implications. We're excited to gather wave three data and understand what's going on in the cold months as compared to the hot months.
We see these increasing racial disparities and worried that they might have long term implications. Again, these shutoff moratoriums are likely going to continue to expire across the US potentially making these disconnection numbers even worse. Debt accrual so people not really fully understanding that they still have to pay their bill or just simply not being able to because they lost their job or for various other reasons.
And we really need to start thinking about short and long term government assistance to help folks with energy and security. It's really a material hardship that needs to be considered. So future topics that we want to think about with this data, how do respondents cope with energy and security?
What is the differential impact of these disconnection policies? Can respondents access legal services to help them? And do respondents trust their utility provider? Does the utility provider matter across the US? And that's really everything for today. So I'll stop sharing and give the floor back to other folks.
>> Thank you, Michelle we did have a couple of questions in the chat. So first one, is the energy burden data from surveys or from some publicly available data set?
>> So the information that I just provided is all from the nationally representative and the Indiana sample that we are collecting, the Energy Justice Research Group through your project funding.
So everything that I basically presented in our results as well as sort of up front is for us. I think that our wave one sample is set to be, or at least a big portion of our wave one data is set to be made publicly available relatively soon.
We have a peer reviewed publication that I think was just accepted and I think the data sharing processes In transit if you will, so hopefully it will be publicly available.
>> Great. Thank you so much for your presentation.
>> Yeah, no problem. Thank you so much for having me.
>> Okay, everyone, so we have about one hour left. We wanna wrap this up at three, and it's a great thing, it's a great problem to have in that we have 15 data updates. So what are we calling? We're calling this speed data in. Each update will have one slide in three to five minutes.
And since we have 15 participants, 3 to 5 minutes is gonna roughly take up 45 to 75 minutes. So I'm on this schedule for 15 minutes, I'm going to try and breeze through my content really quickly. And provide you just some links in the recording of this so that you can come back and view my presentation and content at a later point.
So that being said, I'm going to go ahead and share my screen. And what I wanna focus on today is a new tools and data resources page, and then an application data platform that kind of brings several different data sources into one, I guess platform. So from the ERI landing page or from the ERI Homepage, there is the tools and resources navigation.
I'd like to point your attention to the ERI data resources link. So in that ERI data resources, this is a new page that Janet mentioned at the intro. That the data and communications teams are kinda working together to kind of put up a set of tools and resources for the data that is being generated as part of the ERI and Grand Challenge Initiative.
So what we have here is we have a link to this ERI data platform and a little bit about explainer about it. I'll get to this in a moment and I will spend a few minutes demonstrating it, but it does give you some information about what data is included on there.
So partners at the Indiana Geological and Water Survey, Indiana Map data that's all available within this ERI data platform, as well as some state government, geospatial data and then all of the data that is housed on the IU ArcGIS Online is accessible through this. Again, I'll come back and demonstrate this in a moment.
But what I wanna bring your attention to is these other, I guess resources within this data resources page. Several of the projects that are kind of self run outside of ERI, something like the purple air and the air quality applications, or the MOTUS towers, the wildlife tracking applications that have a standalone application or they have their own portal to distribute the data.
We kind of link directly out to there from here. The same with some of the projects like the Hoosier Life Survey and Future Water, we can go directly to those applications and link to the data. Where we have not yet received the data or it's ready for publication we are in the process of doing these inventories.
And Janet mentioned at the outset that I'm knocking on doors trying to get a good inventory of what data is being generated and then perhaps maybe a timeline about when that data will be available to the public. And each of those instances we're pointing off to the project.
But our plan is on these project pages, so I'll just open one of these project pages. Our plan is for this project page to not only include details about the project in the media and the collaborators. But another chunk or block somewhere in each of these pages to described the data that is being collected and the data that will be soon coming or forthcoming at some point.
So, I bring this to your attention because I've met with many of you or several of you and try to get a sense of what data is being collected and for those of you that I have not, this is the intention for those meetings with you. All right, so I wanna go back and just spend a few minutes on this ERI data application, the data platform application because we just kind of soft release of this application and I think we will have a news release coming out in a few weeks, but I wanna demonstrate it to you to the participants here.
So a little splash screen basically saying navigate to your area of interest, choose a base map, and then add your own data or use some tools here. Before I get into this, I wanna jump back quickly I didn't get all the way down to these data resources. So data in progress, and then I mentioned the ERI data partners from the Indiana Map.
And for those of you that were with us at last semesters data summit, we introduced this Planet Labs satellite imagery for the entire globe. ERI did enter into a contract with Planet Labs to get access to this data and data platform is one of the ways that users can access that.
So back to that data platform. This is your landing page I mentioned just go to an area, you can navigate to an area using the Zoom tools or you gonna type in an address book in Indiana and take me there. Once you're here, I wanna take you to maybe an area that we know there's been some substantial environmental change something like the, Mineral lake.
So the planet satellite imagery, I can choose an individual year and a month. So if I want to go look back at March of 2020, the actual aerial photography looked like in March 2020. There it is I wanna go back and look at what it looked like in January of 2020, or a separate year, 2016 maybe in April, I think you get the idea.
You can use these resources, this Planet Labs resource in this application. So this is one of the items that I wanna bring to your attention. I'm going to go ahead and collapse that. Well, before I do this let's go ahead and go back to a recent satellite image and then maybe some spring of March 2020.
And then I wanna point out some of the Indiana open data sources that are available here. So within the Indiana open data sources we have all the Indiana Map services. We mentioned our partners at the IGWS. We're tapping directly into the Indiana Map and you can see any of those Indiana map layers and bring them into the current map if you wanted to bring in right now, there are some contour lines, I'm gonna go ahead and remove that.
What I wanna show you is some of the historical photography. Like if we go back and look at maybe some 1998, aerial photography, and again, this is coming in from the Indiana map and it's requesting that right now. As that's coming in, once that imagery comes in, we should be able to transition from, okay, so this is what the Mineral Lake look like in 1998.
And if we just adjust the transparency versus what it looks like now and what March of 2020, or on our right we can kind of visualize this change. You can see there's a whole new road and multiple Houses built in here they obviously the docks have changed. So one example there are a lot of these historical aerial photography data sets that you can tap into and kind of cycle through the transparency and look at they are actually visualized.
The environmental change taking place. So I'm going to go ahead and remove that, so that was one, Indiana, the state Government has many of data sets too that are available here. I mentioned the ArcGIS Online and then some Indiana map layers that are hosted on ArcGIS Online. So we can tap in to kind of all of these and kind of bring them into this data platform.
I will mention that this IU ArcGIS Online, anyone at IU staff faculty, student has access to a host or publish content on there. So If you want to publish content on the IU ArcGIS online, and then bring it in to an application like this, you can do so.
ERI, we are also establishing, I guess layers for curated data sets, so let me just refresh this page real quick. And I know one of the ones we're getting at later we'll be looking at some of the artificial sky brightness or like pollution. So this is kind of a global data set of light pollution where that actually takes place.
So we can curate, we've a lot of curated data sets here already to just kind of display in the application. And I'm looking for input if you have particular data sets that you would like curated and available on this application, by all means, contact me. He's gonna refresh that page real quick get back to Indiana.
And then lastly, so the Indiana open data sets, the year curate data sets, and we can't possibly curate every data set there is. So the ability to add your own data, is something that we want to be able to add to that and I have here, a couple of layers I just wanted to demonstrate this one.
This is IU hosted on IU.mapsArcGIS.com, this is the Indiana COVID-19 daily dashboard. So we can tap directly into that data by going down here getting that data. And I just wanna copy this, go back to my add your own data and we have an ArcGIS Online feature service, I'm just going to paste that URL in there.
And then we should have there it is a layer of daily Indiana COVID cases. So for here in Monroe County on what's the date. There you go, 10, 29 don't have today's yet but yes, you get the idea. So the total number, so this data, we can tap into a lot of different data sets and that's really what I wanna bring to your attention.
We can hand code or put boxes in here for individual data sets. We can search for open data sets and then we have the ability to kind of bring your own data. So I think that is kind of all I want to really highlight on that with the limited time I do want to at least make this available in the video.
So since I have limited time there are videos that if you want to learn how to use this, I guess, your identity portal tool to kind of visualize environmental change. I have several case studies, these are usually 6 minute videos and this one is I69 section 6 construction update.
There's some other ones some Indiana Surface Mining taking place in Greene County. You could kind of walk through and visualize some of the environmental change taking place. And then also some video here of using those tools to visualize beach erosion along Lake Michigan. So I mentioned and this is the last thing I'll mention before I turn it on.
I mentioned that we could tap into or add data from multiple different sources, this is kind of a documentation. And the documentation is really kind of lacking the Data resources page. That'll be one of the next, I guess, areas of focus is documentation. I want to bring to your attention this list of data sets that can be, I guess, brought in to the Data platform.
Obviously I showed you some of the state of Indiana GIS servers, but they're state servers from well from every state. There's also here at the end environmental groups. So if we wanted to tap into, let's say, the nature conservancy's GIS data, we can kind of do that right here also.
So, these are just resources that will be made eventually on that Data resources page. And with that, I know we're running short on time. We only got 15 minutes for I guess 15 speed data in presentations that are going to be roughly three to five minutes. So I want to leave plenty of time for those.
And actually, the data management system, Kimberly Cook here is going to kind of take over and I guess, run each slide and each Guess slide owner will have a few minutes. Kimberly you want to take over at this point and talk about how it's fun to take place.
>> I'm going to share my screen Really quick and go into presentation mode. All right so welcome to speed, dental. So everybody will have approximately three minutes to talk about their slide. I have a schedule up here and take a quick look, so you can be prepared basically.
If you go over five minutes, I'm going to have to play hardball and cut you off because we want to hear about everybody's presentation. But we will make these slides available after the data summit. So if we don't get to someone's presentation, you will still be able to view it afterwards.
So we'll kick it off with the urban green infrastructure group. So Heather Reynolds, I know I saw her Yeah, I'd like to share your screen or would you?
>> Yeah, I have a screen to share.
>> Okay, go ahead. Trying to put that in presentation mode. There we go.
Can you see that?
>> Quickly I'm gonna be really quick cuz you asked for it. Research context is resilience of urban social ecological systems, so basically we're dealing with cities here. Cities is essentially ecological systems, our process, is we go to municipal partners, various other sorts of partner resources, consulting firms that do tree inventories for cities.
And we collect green space, climate and social spatial data from them. Or from uscensus.gov or we created ourselves if we have to by going out and GPS in green roofs or something. The data are cleaned, metadata is written and the data is uploaded to our spatial data platform.
And that brings me to our data deliverables, the main is a spatial data platform called Indiana Green City mapper. We have a link to this site which is well into development. We're exploring ways to integrate that with the ERI site, but it may just have to be a link.
So this is a spatial data platform that will enable cities to mitigate their climate change challenges, heat stress, flooding, food security, etc. By being able to visualize where their green infrastructure is, where the climate issues are, where the social vulnerabilities are, etc. The data is fine grained. The geographic scope varies from a couple of cities, to for some of the data we have, we're getting more statewide scope.
It can easily integrate with other sources via GIS web services. And products include story maps, which we already have two, that'll be featured on the platform. Technical articles on how to develop this type of platform. And scholarly articles associated with resilience analyses that we're doing. This data set is so rich, it's allowing us to look very differently at resilience questions.
And for example, we're now looking at a urban forest equity, by not just looking at the spatial distribution of urban forests, but quality of the urban forests, which is a new contribution. So I'll end there. Do I have to stop sharing? Sorry, it's in my way. Here I am.
>> Thank you, Heather, that's great.
>> Kimberly, you're muted.
>> Anybody else have that one on Zoom Bingo?
>> All right, so that's exciting. All right, going into presentation mode. So we're moving on to the migratory birds as transmission pathways over emerging zoonoses, Alex Young.
>> Do I need to share my screen or I guess-
>> Yep, I've got it up there for you.
>> Okay, so we're studying mostly migratory American robins that are widespread across the continent. We've been sampling them basically, to understand their movements using GPS trackers. And collecting blood from each one of them to collect disease data.
So, my colleagues on this are Dan Becker, a postdoc in Ellen Ketterson's lab and Ellen Ketterson. So we are focused mainly on understanding the role that American Robins are playing in dispersing tick borne diseases across North America. And yeah, so basically we're collecting movement data through GPS loggers and disease data through the blood.
So, the idea is to forecast how robins are spreading zoonotic diseases. And we're basically at a stage right now of data collection and preliminary, very preliminary analysis. The data that's come in so far is very promising. Robins here in Indiana appear to be partially migratory. So some go all the way to the Gulf of Mexico for the winter and some stay.
So that's interesting because it means that there's gonna be a lot of variation in how they're moving diseases around. And the latest update is we're about to start a collaboration with the Max Planck Institute in Germany to use satellite transmitters. So we should have a lot more data coming in since it'll be coming in every two days, hopefully, through the International Space Station.
And transmitting the data, instead of us having to wait to catch the robin all over again after it migrates. And that's it, thank you.
>> Perfect, thank you. Seems like you're gonna have a lot of very interesting data to share. So, all right next we have light at night, migration, and disease.
From the Ketterson Lab?
>> Yeah, i think that slide was submitted by Ellen and I think she was with this whether or not she is with us any longer.
>> Hi, I'm still here.
>> Be able to speak to for a moment on this slide.
>> Yeah, I wasn't expecting to speak on, I'm happy to.
This is work that's being led by Dan Becker and Dr. Devraj Singh. And they are studying the impact of light at night on the annual cycles of songbirds. Finding that light at night actually accelerates the rate at which they prepare for migration and reproduction. And whether light at night has an impact on latent.
I'll just call them malaria infections, which are known to kind of go down in the winter and then reemerge in the spring. And what the work showed that is depicted here in this slide, was that Junco was a songbird exposed to light at night, were more likely to exhibit elevated levels of the avian malaria.
So there are two data things here showing the increase, the tan line being what birds experienced if they were exposed to light at night. And there were two different populations compared one, that's resident and ones that's migrant. And the impact on these infections reemerging was similar on both populations.
So we hope that this will be useful for both suburban and urban landscapes, to know not just that nocturnal lighting, artificial nocturnal lighting, has an impact on timing of reproduction and breeding. But also has an impact on the likelihood of a bird being infected and therefore infecting other birds.
Because it's mosquitoes that pass the infections back and forth. This paper was just published in the Proceedings of the Royal Society, which is kind of a big deal. And Dan Becker was the lead on this paper. So that's it.
>> Great, thank you very much. We have a couple more slides from your group.
So if you don't feel comfortable presenting, just let us know. We've got window strikes at IU next.
>> Well, Sarah Wanamaker is here, I believe. Sarah would you like to talk about your slide?
>> Yeah, I'm here, I didn't know I would be presenting but that's okay.
>> So cool.
So I'm Sarah Wanamaker, I have this current project that is I'm working to quantify windows strikes on IU Bloomington campus. So what I'm doing is I chose six buildings on campus that are relatively high risk in that they have a lot of glass, they're very tall. And I'm monitoring those buildings a couple times a week to look for bird carcasses.
Been doing this for about a month and I'm at 54 dead birds that I've found at just those six buildings. So the end goal is to try to find a solution and work towards making at least some of these buildings on campus bird safe by using Window decals or one way transparency film.
I have talked to a building manager Pete Goodwin that works at the GISP building on campus that is a really bad offender. And he has, when using vocalizations, he plays bird playback from the top of the building. And apparently that worked at first, but then the birds became habituated to it.
So, we hope to find a cost effective but functional solution for IU campus. I also hope to develop a carcass persistence study and the purpose of that study would be to estimate basically. What is my search efficiency? How do I scale up the numbers that I'm finding to try to calculate what the actual number of birth fatalities are on campus?
I think that pretty much sums it up, thank you.
>> Okay, thank you for sharing this very exciting project and I'm excited to see what you find.
>> Thank you.
>> All right, next up, we have migration birds and pesticides.
>> I'll take this one again.
>> Thank you so much.
>> This project is being led by Allie Bird and Katie Talbot and again Dan Becker. And we're following up on a study of a songbird in Canada, that was measured during its halfway point on a migration. And half the birds that were captured, were given a dose of something that could have contained a pesticide but didn't.
So they were the controls and another set was injected with a pesticide and the dependent variable. The thing that they measured was, how long was it before the birds took off. Again to complete their migration, and they found that and this is the first study of its kind so it showed up in science.
They found that a small dose of pesticide known as in neonicotinoid, which is often used in agricultural settings, slow diverse down. It took them longer to depart from their what's called a stopover site. So we wanted to know whether the same thing held here in Indiana, we use the different species called the Junko, and we tracked their movements with a set of towers, known as MOTUS towers.
Some of which Albert has set up around here. So caught the birds, injected some or really gave them in their mouth, allowed them to swallow a dose of the pesticide and others not. And then put tags on them that are called most tags that can be picked up by the towers and let them go.
We also wanted to know whether they carried malaria, so this is another connection between disease ecology and migration that we've seen in Alex's work and the light at night work. So the graph on the left simply shows what the Infection level of malaria was in the two sets of birds those that before getting a dose of the pesticide.
And then afterwards, the ones that were diseased or not disease and when they all took off. So if they were diseased and got a dose of the pesticide, they were delayed in their spring departure for their migratory grounds. If they were not diseased and dosed with a pesticide, then they left at the same time as those that were given just a batch of soil.
So, interaction first described really perhaps anywhere between pesticide use and the presence of a disease in the birds. And the best pesticide to some of the pesticide is harder on diseased birds in terms of departure than it is on non diseased birds. So we'll work with other researchers who are studying wildlife disease dynamics in the future.
And we believe it'll be relevant to public health and to people that are attempting to monitor bird health and wildlife health on a global scale. So, thank you.
>> Thank you again.
>> Very exciting project all ready there we go. And this is just more information about that particular project.
And finally from the courtesan group, we have bird predation on livestock, the black vulture.
>> This is Matt Hauser present.
>> Yeah, I'm here.
>> Wanna talk about this slide?
>> Yeah, sure this one's a surprise for me.
>> Yeah, so two proposal right now where the short version of it is that there's this relatively new type of vaults are not new.
And new in the sense that it's much more frequent and present in places like Indiana and sort of the Lower Midwest and increasingly Upper Midwest. Then it used to be we're not quite sure why it's here but climate change is likely a factor. And black vultures which we're focusing on here compared to turkey vultures, there's some evidence that they're actually killing animals.
Killing cattle specifically calves as they're being born, causing quite a bit of financial harm to the cattle farmers across the region. So we put in a proposal we wanted to understand the most effective way essentially it's a very practical proposal this is under the USDA. The most effective way that farmers can manage and prepare for the likelihood that black vultures will be around them and potentially harming their cattle.
But also ensure that they're managing in a way that will not harm other migratory birds, we think there is a slight possibility that farmers will be using poisonous or something like that. So it's gonna be Abigail, Sullivan, myself as sort of a social scientist and then Early Bird, Sarah Wanamegaer, Whitney Schneigle from Biology and Owen.
All working together to try to tease out the social and biophysical dimensions of it. What are farmers doing? And then what are the consequences of those management responses for how birds act? And right now we're based on the grant, we're really focused on providing data back to the farmers.
So this one only involves sort of how effective was your response to the farmers we work with. We'll also hold field days that are open to farmers of the general public to come, and we'll teach them what we learned, and how they can implement those management practices on their farms.
>> Okay, very cool, thank you for sharing more information about that particular slide, any other comments on that?
>> Alan, I get everything?
>> I'm muted, I thought that was a great job, I love the project. I'm glad you're leading it so.
>> No you're leading it.
>> You can see folks We have something to work out
>> That's why we have these things, so everybody knows what's going on.
Alrighty, next we're gonna move on to the Pests and Invasive Species Research Cluster. First off, we have assessing vector competence for Indiana mosquito populations. Presenter?
>> Hi. So my name is Tamanash and I'll be presenting the data that is joined in collaboration with the Newton lab and the Hardy lab in the microbiology department here at IU.
So yeah, our goal is to assess the vector competence of the local mosquito populations that we have collected personally and also that has been collected over the past several years by Keith Clay's group. And the aims of the project are outlined here, where we wanna know the distribution of the different mosquito species in South Central Indiana.
We wanna know the prevalence of mosquito borne pathogens. We're primarily interested in viruses, but ultimately we wanna move on to doing a deep sequencing approach to get a whole idea of what other potential pathogens are being transmitted or there's a chance of being transported by these mosquitoes. And what is especially a topic of interest in the Newton and Hardy labs is the presence of this symbiotic bacteria Wolbachia pipientis.
So, over the past few years, this has been the crux of my project, thesis project. Presence of this endosymbiont essentially stops viruses from replicating inside the mosquito. So it's a really good way to control the transmission and the overall vectorial capacity of mosquitoes. The interesting thing is certain mosquito species are naturally infected with Wolbachia.
So, if they are present and we find them in our local mosquito population, that would allow us to gauge their vector competence. i.e., if they have a natural Wolbachia population present inside them, then we can sort of assume or infer based on our current data that it will reduce the transmission of certain kinds of RNA viruses.
Examples of such RNA viruses that are a potential threat are Eastern Equine Encephalitis virus, Chikungunya virus, Zika virus, West Nile, Yellow Fever, amongst others. So, additionally, we want to also determine how temperature and rainfall influence the mosquito populations and therefore its vector competence, including the presence of pathogenic viruses and Wolbachia.
So kind of have an assessment of how the changes in the current climate changes impact this sort of scenario. We are currently at the data collection stage and here I am showing some preliminary data that I have. We have screened a total, so this is a quantitative PCR based assay.
And the goal is to determine the presence of these RNA viruses, as well as the presence of Wolbachia from the same samples to sort of look at the correlative effect. As of now, out of the total 85 individual species of individual mosquitoes that have been screened, 32% of them, roughly a third of them, seem to be carrying Wolbachia, as detected by our PCR based assay.
And on the right we have a breakdown of the different species of mosquitoes. Some of the things that I would like to point out that is of a special interest is if you look at the Culex pipien's data point, half of them roughly that have been collected contain a natural Wolbachia infection and half do not.
The interesting thing is the half that do carry Wolbachia were all collected recently from urban settings within the Bloomington and local adjacent areas. The half that weren't found to harbor Wolbachia we're all collected from non urban settings, specifically from the Kent Farm area, and they were all Wolbachia negative.
So we're really excited about this trend that we're seeing. What this would mean is that, assuming functional blockage of RNA virus replication, we might expect a lower chance of virus transmission in urban as compared to non-urban settings. As of this moment, we're presently performing virus screenings on these samples as well, as well as expanding our overall data set.
And we're really excited about the findings in the future. And thank you.
>> Thank you, Tamanash. That was wonderful, so much exciting research happening. Alrighty, Project Vector Shield. Chris, are you around?
>> Yep, I'm here. All right. So, I'm Chris. I was the field biologist on project vector shield, the TI being Keith Clay and this project is aiming to survey the tick and mosquito communities throughout the southern half of Indiana in order to assess the disease risk posed in the context of vector borne disease by those communities.
So we collected tick and mosquito samples from 20 sites, mostly at state parks From the spring through the fall in 2018, 2019, and 2020. And then those samples, many of them, have been sorted and identified, so that we know where we caught things, when we got things, and how many of those things we caught.
As far as an update of what some of our data findings are, one of the big things that we found is that there seems to be a wider established distribution of two of the tick species that we frequently captured. One being Amblyomma americanum, the Lone Star tick, and the other being Ixodes scapularis, the Blacklegged Deer tick.
So, you can kinda see some of this in the map to the right, where each county has Four white squares, each representing a different tick species that we captured. The upper left white square may or may not have a black circle in it. If there is, that would mean that Amblyomma americanum is present in that in that county, according to our data.
The dark black meaning that it's an established population, grey just meaning that we just found enough individuals to report it, but, likely there's not a breeding population, at least according to what we found. So, as you can see, most of the counties have that circle representing Amblyomma americanum in them and previously, only the southern most counties in Indiana were thought to have these established populations.
A similar pattern is being seen by our data in Ixodes scapularis, where originally, in the late 80s and early 90s, the Black Legged Deer tick was only found in the northwestern counties of Indiana. And then, more recently, it's starting to be found also in the sorta central western section of Indiana, but we have found it in most of the counties that we sampled, denoted by the black diamond in the lower left Square of the sort of setup for each county.
And then we also have some pathogen data from these tick samples that we were able to sequence and we did find the presence of borrelia bacteria in both the amblyomma americanum and the Ixodes scapularis. So, when borrelia is in an amblyomma americanum, it's often borrelia lonestari, which Is thought to be connected with that southern tick associated rash illness.
And we did find that in many of the counties that are in red and also in purple. And then, you can also see specifically when borrelia was found in amblyomma americanum, there's that red or purple in the black circle. And then, also another borrelia species, borrelia burgdorferi is the causative agent of lyme disease, and that is found generally in ixodes scapularis.
And so, we also saw that in four counties Ripley, Jefferson, Posey and Union. So, pretty wide spread throughout the distribution of our sites. And then, finally, we also found ehrlichia species in amblyomma americanum, which causes ehrlichiosis, and we found that four counties as well. As far as deliverables being produced by the project, currently we're preparing a publication to describe those tick range expansions in the lone star tick and the black legged deer tick in Indiana.
And we will be making the data associated with that publication available through that ERI Data Portal that Justin had talked about earlier, and I think that would wrap up what I have for today.
>> Great, thank you very much for sharing. That wraps up the person invasive species, we're moving on to the MacBean lab.
So, we've got a couple of presentations here about semiarid ecosystems for stuff about biosphere model prediction.
>> Hello everyone, I am Kashif Mahmud, a postdoc with Natasha McBean in Department of Geography. Currently, we are working to improve the terrestrial biosphere model prediction of semiarid ecosystem carbon dioxide exchange.
Recent modeling studies have shown that semiarid ecosystems play a dominant role in the inter annual variability of global carbon sink. However, the global terrestial biosphere models used in these studies have not been extensively tested or optimised against semi arid field data. So, a recent study comparing a suit of these models to simulate site carbon oxide flux data showed that all models actually underestimate both the mean and low carbon budget, and the net carbon dioxide flux inter annual variability.
But it remains to be seen that whether these model data discrepancies are due to inaccurate model parameters or inaccurate model processes. To bridge this gap, we tested whether parameter optimization could alleviate this model data discrepancy in semiarid ecosystem. We also aim to identify the physiological processes causing these potential data model misfit.
To fulfill this goal, we utilized carbon oxide flux observations from 12 Ameriflux sites spanning southwest US semiarid grass shrub forest ecosystems. Here are photos of two of the sites with flux towers. The left one is called the Vcp, which is an evergreen needle leaf pine forest in New Mexico, and the right one is Whs, which is an open shrub land site in Arizona.
The data are open source fields data and process in NetCDF format after gap filling, flux partitioning and so on. The net ecosystem exchange, in short, NEE, data are shown by the gray lines in these two time series for both these two sides. Utilizing this data, we use the Bayesian data simulation framework to optimize carbon cycle related parameters of IRCHIDEE terrestrial biosphere model.
The prior and posterior models any time series are shown in green and red colors respectively in both these time series, which shows a massive improvement in model predictions. And the data deliverables, the optimization outputs including all the model parameters are stored in a GitHub repository for open access for all the ecosystem modelling groups to actually reliable estimates of semiarid ecosystem contributions to the global carbon cycle.
So, that's all for this slide. Thank you, everyone.
>> Thank you, Kashif. Next up, we have multi source remote sensing data fusion.
>> Hi, I'm Rubaya Pervin. I am a PhD candidate in the Department of Geography and working with Dr. McBain. So, I'm interested in visitation distribution in China and ecosystem, and in this project I'm working in is titled multi source remote sensing data fusion.
So, the visitation in these areas are mostly combination of grasses and smaller shrubs that you can see in these pictures, how they are distributed in between grasses and bare lands. And my research question is how does the classification method and combination of different remote sensing data type affect the sharp distribution accuracy in this heterogeneous environment?
So, from my research question and topic, you probably guessed that I mostly work with the more sensing data. And I am using the existing remote sensing data provided by neon, the National Ecological Observatory Network, and they provide a hyperspectral images that has hundreds of spectral bands. And these data is provided in a 05 file format and they also provided LIDAR data, I'm using their LIDAR canopy height data at one meter resolution.
So, in my unique approach, I am using the LIDAR height, the smaller shrub height information as the image layer within that hyperspectral image. So, you can see this diagram here, and I am using this method and I'm trying four different classification method to detect this smaller shrub accurately.
So, because of time limitation, I'm not going to discuss about the detail of this classification method. But using this classification that data deliverable that I will produce will be the factional shrub cover map and shrub grass bare land maps. So, for this classification, the total data I'm downloading from NEON is about 1 terabyte, that's why I use super computer to store my data and running my classification from there.
So I have written all of my classification codes in Python and I will make those code publicly available so that other researcher can use those codes and apply in other regions to detect visitation. And these land cover maps can be directly used in land surface modelling and also can be used to validate visitation demographic modelling and furthermore, the.
Restion managers can also use these maps to get the location of shops, so these are the broader implication of this project. And thank you for listening to me.
>> Thank you so much. We only have a couple of presentations left, we're a little bit over time, Justin, are we still okay?
Cuz I think we're-
>> I think we are over time, if we don't get to the I guess the needs discussion, I think that's okay.
>> Okay, next up we have Eric Sanwise and Matt Houser.
>> Thank you, Kimberly, thanks, everybody. We have old news and new news on our section.
I feel a little bit like the kid at the grownups stage at the data summit here, but it's data, and I hope interesting data at that Kimberly and Justin and others have helped us to create the HLS, Hoosiers life survey opinion map. Which is one expression of the data that we received highlights of the data that we receive from the big social survey that we put out late last year, and then analyzed in the spring of 2020.
So in terms of what's available of that data, as I said, highlights are spatially visualized on the opinion survey map, but the actual numbers in greater depth that we developed with our partners that I use Center for Survey Research Ashley and their colleagues. Those are also accessible through us for people who want to know more about how environmental change really is affecting Hoosiers at an everyday level, what they're hearing about it, what they believe about it, what they're willing to do about it.
So in terms of the old news, looking back, taking those data from the Hoosiers life survey, our goal has been in these most recent months to really express in everyday language, what the the results tell us. And so in articles like the ones that you see on the screen, and elsewhere, we are trying to put the word out especially with the able assistance of Jonathan Hines in the eri office, trying to reach reporters and editorial pages across these state, and tell them more about what we're finding about what Indiana believes, and what it wants to know more about.
And so, reaching out to places like Fort Wayne, South Bend, Evansville, Indianapolis and elsewhere, we're able to give them both the granular level of, here's what your community is thinking about. And then the more general level of, here's how it relates to broader opinions and actions across the state of Indiana.
So that's been our job looking back. Looking forward, we were most fortunate to get eri support, for continuing the survey in light of a fundamental and really major social shift, that happened after all of our results were in. And that of course, was the arrival of COVID 19 on these shores early in this year.
Based on that very fundamental shift and all of the things that have triggered in our economy, and in our political environment, we resolved to go back to a certain group of our respondents, and ask them again, what they're thinking about environmental change, in light of the changes in our society.
That's where Matt Houser picks up the story, so I will turn it over to him.
>> Yeah, thanks, Eric, I'll just note that we're calling this the second Hoosiers life survey, maybe a little bit of lack of creativity, but Hoosiers life survey to point out, we're gonna be following up with people.
We know from some past environmental research that these major shifts it's usually related to economic crises, we have one of those in the workings, but we also have a public health crisis. We know these shifts tend to lead people to change their attitudes and their concerns about the environment.
And so this is a really unique opportunity considering that we got the final responses for the first Hoosiers life survey, just before COVID really became an issue here, to see how since that time point people in the state have shifted over time and how they're thinking about climate change.
So, that survey we're very close to having that one out in the field, right now we just completed our final draft of the questionnaire, and hopefully by 2021, we're able to put out another layer in the climate change opinions map on the year II website. Update what we currently have and give you a before and after COVID look, at how the state is viewing climate change and a variety of other related issues.
>> Awesome, thank you, Eric and Matt. Matt, would you mind discussing your soil microbes and farmers project?
>> Yeah, yes, so this is a National Science Foundation funded project called resilience to drought, or a drought of resilience. It's being led by Jen Lau in the biology department along with myself, Lizzie Grunting Browning, J Lennon and then a couple of people at Michigan State University, our collaborators there.
This is a five year project that begins in January, we're really excited about it. And the short version of it is, drought is coming to the Midwest, it's gonna happen more frequently and more significantly, a lot of it will be called agricultural drought. So it won't be an actual physical drought, but crops will experience it like a drought.
So what we expect yield declines because of this, and the degree to which agriculture and farmers will be impacted by these, it depends on soil adaptation. So how microbial communities respond, it depends on how farmers respond, whether or not they're using practices like cover crops, and no till or irrigation.
And it depends on how those two things interact. So we think there's a possibility that irrigation, which is one of the most widely used adaptation responses to drought, actually, and I'm a social scientist, so keep that in mind, actually makes soil microbial communities dumber. It leads them to evolve to provide less resilience to drought over time, creating a positive feedback loop where you need more and more irrigation over time to get the same benefits.
So we're gonna go out and do interviews with farmers, to understand their current adaptation responses. Biologists will come out with the social scientists, and we'll measure soil microbial communities from these farmers, to get a sense of how soil health management versus irrigation leads to different types of microbial communities.
Lizzie Grunting Browning is gonna do oral histories with farmers, to understand how they view and have adapted to drought and other climactic extremes over their tenure. And keep in mind the average farmer in the Midwest is like 60 years old. So they're gonna have some wonderful perspectives really rich understanding, of climate change over time.
And then we're gonna feed that information the soil microbial information back to farmers in like year four of the project to see if that shifts their views at all if we can show them that they are eroding their soils capacity. To provide resilience to drought over time, do they want to and more importantly, do they feel like they can change their practices we expect again, this will be related to irrigators.
We have a number of public data availability ideas going right now. So Lizzy I think is going to be doing a very exciting dimension of this where she's going to be going to like state and county fairs. With a history exhibit showing the oral histories of farmers, the social science research with the interviews and the soil data will be provided to Indiana maps.
We'll be able to give a layer essentially to the India maps and they'll show the some of the qualitative data and the soil microbial data there. That will be publicly available. And we're also planning on doing a workshop at ERI but also at the Kellogg Biological Station. Which is our Michigan State partner, to show modeling techniques and convergence ideas on how to do a social and ecological systems model.
Bringing together two data sets towards understanding adaptation and evolution over time. It'll probably be highly focused on structural equation modeling, multi level structural equation modeling. But that's down the road a little bit. We'll get it on everyone's schedule whenever we get there.
>> That sounds like a very highly multidisciplinary, collaborative project and that's very exciting.
Thank you very much.
>> Thank you.
>> All right, so we had one more slide. However, this person is not here, I don't believe so it will be part of the slide deck that we can send around and I will stop my screen share. Thank you everybody for being good sports about the time.
Everything, all of your research sounds amazing. I can't wait to see where it goes. Yeah, thank you, everybody.
>> I'd like to thank you so much for the participation. It was great to see so many people willing to update their projects and the data that is being produced.
It's really great to see so much diverse data and research taking place. We have the last item as kind of an open discussion about data needs. I don't know that we really have time to give that a proper discussion. But if there are particular data needs or you just like to meet up with me and talk data myself and Kimberly\g, I'd be happy to set up an interview or set up a zoom chat with you about that.
With that, we made it through everything else and have a happy Halloween everyone. I think that's it. Thank you so much for again for your participation and for sticking around