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karen McNeal: Alright well it's 30 after so i'm going to go ahead and get started we've got a few minutes for an intro and then our first speaker will begin, I just want to thank you all for coming and joining us on our climate resilience session here the southeast geological survey.

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karen McNeal: And conference and we're just excited to be here this session kind of came out of our funded and SF.

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karen McNeal: Research traineeship that we have it all burn and many of the speakers are trainees on the traineeship what we have a few other speakers to that are joining and we're very excited that i've submitted to this session, so we can hear number of perspectives on.

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karen McNeal: Climate resilience and we're going to hear about national parks we're going to hear about agriculture we're going to hear about drought ecosystem services urban growth.

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karen McNeal: Climate adaption science user engagement and experiences and co development, as well as usability as an educational tools, so I think we have a really cool and diverse.

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karen McNeal: lineup today and we did manage time for a break, so that we won't be meaning to sit on our bombs, the whole time and in case I forget maybe katie can help me put in the link if during that break you want to actually stand up and do some fun activity, we have set up a yoga.

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karen McNeal: online activity with one of our instructors here at all burn and we can throw that link in one break time gets closer.

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karen McNeal: So that if you want to stand up and learn a little bit about geology within the area, as well as do some stretching and yoga.

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karen McNeal: That might be a nice alternative just do something different, instead of staying in checking your email like I often do on these things when I should be getting up.

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karen McNeal: um but yeah so before we begin, it might be nice because we've got about two minutes for just to go round really quick and have everyone introduce themselves that are here, so my name is Karen mcneil i'm a professor in geosciences at auburn China go ahead.

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Chandana Mitra Geoscience: And jump in, let me turn associate professor in the department of geosciences.

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karen McNeal: Hannah go ahead.

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Hannah Stewart?: hi amanda Stewart I am a master student at auburn university studying rural sociology.

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karen McNeal: Right de.

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Di Tian: De young assistant professor in the department called soil, and you know sciences.

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karen McNeal: Right brandon.

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Brandon Ryan: hi.

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Brandon Ryan: i'm brandon brandon Ryan i'm a first year masters student in the department of geography here at auburn.

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karen McNeal: Right Taylor.

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Tayler Schillerberg: Sorry hi i'm Taylor, I am a second year PhD student at auburn university and i'm studying your climatology.

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karen McNeal: Thanks Tyler.

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Tyler Smith: Tyler Smith i'm a second year PhD student here at auburn studying geoscience education NGO cognition.

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karen McNeal: That.

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Steph Courtney (she/her): somewhere to Tyler I am a second year PhD student at auburn studying climate change education NGO cognition.

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karen McNeal: ui.

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Elijah Johnson (he/him): I see him as the to evolve Elijah Johnson PhD student here second year studying spatial thinking in the geosciences.

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karen McNeal: katie.

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Katie Brown: hi everyone i'm the program coordinator for the nsf research, training ship program here at all burns for eagle.

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karen McNeal: haven.

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Haven Cashwell: Everyone i'm a second year masters student at auburn University in geography per room and i'm currently doing research on I tracking a decision support systems.

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alley.

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Ally Brown: Everyone, I am a first year PhD student and i'm also doing geoscience education.

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karen McNeal: Blair.

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Blair Tormey: hey everyone i'm I play for me i'm a coastal research scientist at Western Carolina university and i've been very thoroughly impressed with the program that auburn has put forward so far.

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karen McNeal: Thank you so much that Nevada thing is that right.

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karen McNeal: you're on mute.

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karen McNeal: unmute.

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Nivanthi Mihindukulasooriya: Oh, thank you i'm sorry I i'm an assistant professor of geology at northern Kentucky University in Highland heights Kentucky.

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karen McNeal: awesome thanks for joining us, and I see lynette.

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Lynnette Martek: hi i'm lynette martek and I teach.

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Lynnette Martek: intro to weather and geology at the University of South Carolina Lancaster campus.

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karen McNeal: rate, and I think I have two more broad broad Nick.

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karen McNeal: know.

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karen McNeal: How about Michael Ferris.

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karen McNeal: that's okay.

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karen McNeal: And Michaela is.

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karen McNeal: A last person, and I think bill hames is here as well.

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karen McNeal: Michaela do you want to say hello.

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karen McNeal: that's okay all right we're going to go ahead and get started with our first speaker now that it's exactly time to.

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karen McNeal: get going um.

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karen McNeal: So Blair, if you want to go ahead and share your screen um Thank you all for being here and hopefully we'll continue to.

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karen McNeal: meet more of you, maybe during the break go ahead and do your full.

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karen McNeal: screen, so we can see your PowerPoint presentation.

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Blair Tormey: Okay, can everyone hear me okay.

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karen McNeal: yep perfect alright so.

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karen McNeal: This.

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karen McNeal: Year torney telling us about climate change and coastal hazard vulnerability of instructor and national parks adaptation along the southeast and golf course go ahead, Blair.

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Blair Tormey: Okay, so thank you Karen and I.

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Blair Tormey: want to thank everyone for putting on this really great session guys have done a great job with Conference so far and i'm looking forward to the whole session so.

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Blair Tormey: My name is Tommy like I said I when I knowledge my co authors katie and rob we all work at the program for study have developed shorelines at Western Carolina university.

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Blair Tormey: And today i'll be talking to you about the coastal hazard vulnerability of infrastructure and national parks, with an eye towards adaptation.

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Blair Tormey: screens not advancing.

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Blair Tormey: Here we go so before again I it's important to kind of explain the basic terminology used and vulnerability assessments.

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Blair Tormey: So in natural systems vulnerability is defined as the degree to which a system is susceptible to certain stressors.

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Blair Tormey: it's often calculated as a function of three components exposure, which is whether it resources located in area experiencing hazard.

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Blair Tormey: sensitivity, how the resource fares when exposed to that hazard and then the adaptive capacity, so the ability of the resource to adjust or cope with the hazard or impacts of the hazard.

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Blair Tormey: So that's how vulnerability is and the formula for vulnerability supplied and natural systems in the built environment, however, it can be a little more complicated.

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Blair Tormey: So exposure and sensitivity are relatively straightforward to quantify where exposure is scored based on the location location relative to the hazard.

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Blair Tormey: So, for example, is the building located in a flood zone sensitivity is scored based on inherent physical properties of the asset so that's things like its condition, whether it's got storm resistant construction, etc.

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Blair Tormey: and

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Blair Tormey: However, I trying to score adaptive capacity for an asset can be somewhat complicated, so if we take the the Cape hatteras lighthouse for example here.

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Blair Tormey: don't one was trying to try to list and score the adaptive capacity for the lighthouse he need to consider numerous factors so things like physical logistics of.

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Blair Tormey: Locating the the relocating that the lighthouse what the cost is what the politics are surrounding the the lighthouse what priorities the park might have.

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Blair Tormey: The park mission sentiment is a huge factor here this this tall pile of bricks is important to a lot of people here in North Carolina it's it's on our license plates and drivers license for for crying out loud so.

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Blair Tormey: A lot of people have are are tied to this structure and that weighs in on on some of the adaptive capacity considerations.

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Blair Tormey: So all of these factors as you're hopefully starting to see our our kind of difficult to assign a numeric score to.

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Blair Tormey: I and then add in the factor of time, each one of these things could change over time park priorities will change over time, the park mission sentiment about the lighthouse might change over time, so all these things become really, really hard to assign a score to.

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Blair Tormey: So when when, because of this problem.

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Blair Tormey: vulnerability, we found when assessing vulnerability for infrastructure it's best calculated as a combination of only exposure and sensitivity, so we only look at the exposure.

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Blair Tormey: And the sensitivity of the asset, and this is most meaningful when it's applied on an asset by asset basis so looking at individual assets, rather than looking at a region or a portion of the park we actually hone in on specific assets.

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Blair Tormey: This isn't this doesn't mean that adaptive capacity isn't important.

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Blair Tormey: We just choose to address it separately, because it's so hard to quantify and therefore we don't include it in a in a final vulnerability score.

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Blair Tormey: However, adaptive actions that can be that can be taken for assets will in fact help reduce the vulnerability score either by.

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Blair Tormey: Reducing exposure reducing sensitivity, or both, so if you want to lower the vulnerability of an asset, you can look at reducing its exposure reducing the sensitivity and the any adaptation actions you take for those those two will actually lower the overall vulnerability.

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Blair Tormey: So, like I said exposure scoring exposure for infrastructure is relatively straightforward it's probably the easiest thing generally a desktop GIs type exercise.

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Blair Tormey: involves evaluating whether the the assets located in an area that will experience experiences specific hazard so in order to score exposure at the asset level it's critical that the hazard be manageable if we don't have maps hazard maps for an area then it's hard to score the exposure.

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Blair Tormey: And at the same time, the assets themselves have to be manageable so actually knowing the locations of these particular assets is useful.

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Blair Tormey: And that's often where where things run into difficulty with parks.

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Blair Tormey: A park typically manages hundreds of infrastructure assets and it's probably not all that surprising that they don't always know the exact longitude and latitude of every little tool shed and.

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Blair Tormey: restroom in the park, so a lot of times there's there's a fair amount of time spent on the ground locating these assets getting longitudes and latitudes and.

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Blair Tormey: and actually loading those into a GIs platform.

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Blair Tormey: roads and trails because of their linear they're being linear features present present additional challenges as well.

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Blair Tormey: And oftentimes you have to segment roads and trails in order to try and score the the exposure as it, you know as a road may go in and out of a hazard area so here's an example from Cape hatteras, this is just the fema hazard zones.

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Blair Tormey: mapped over the fema i'm sorry the Cape hatteras assets, you can see the.

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Blair Tormey: Lives are located here and white, and then you may be able to make out various roads are going in and out of the hazard zones each of those roads would have to be segmented in order to show which segments are running through the hazard zone and which ones aren't.

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Blair Tormey: Point assets like buildings are a lot easier to score they're either in or out of the of the hazards them.

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Blair Tormey: So, in short, exposures all about it's We borrow the phrase from real estate it's all about location location location right it's whether the assets located in an area that's exposing it to a hazard or not.

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Blair Tormey: And the specific characteristics of the asset itself don't really matter, which is why you know it's just a bunch of points in lines as we're doing this analysis.

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Blair Tormey: scoring sensitivity, we go can be a little more complicated sensitivity basically remember evaluates how a particular asset will fare when exposed to a hazard.

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Blair Tormey: This may require the collection of data on the inherent properties of the assets, including condition construction design, etc.

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Blair Tormey: This data could be mined from you know, in the park service has a facilities management database that has a fair amount of information about each one of their buildings and infrastructure assets.

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Blair Tormey: So we can mine information from that but we've often found it most useful to collect the institutional knowledge from park staff.

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Blair Tormey: On an asset by asset basis using a questionnaire, so we send them a pretty extensive questionnaire about each asset and they they fill out that questionnaire and we score the.

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Blair Tormey: score the sensitivity, based on the responses to that questionnaire.

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Blair Tormey: So, for example here, this is building this is in biscayne National Park national seashore and in Florida.

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Blair Tormey: The building on the Right has, as several several factors that give it a low sensitivity overall it's elevated above base flood it's got a store relatively storm resistant design is in relatively good condition.

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Blair Tormey: has no history of past damaged by storms, however, in this case there's one strike against it in that it doesn't have any sort of protective engineering that's protecting it from storms and erosion.

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Blair Tormey: As a geologist I tend to dislike protective engineering on coastlines, but um, we have to recognize that, from an engineering perspective and protecting the infrastructure.

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Blair Tormey: Things like bulkheads and refinements and sea walls actually do protect to a to a degree, so we do actually give it.

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Blair Tormey: A positive score for sense to be there.

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Blair Tormey: In contrast, the road here is is not, this is the entrance road into this game it's not elevated.

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Blair Tormey: it's not inherently road storm resistant has poorly maintained and has a history of storm damage, so in this case, the only thing that that really lowers that sensitivity, or is favorable in this case.

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Blair Tormey: would be the presence of the protective engineering and the form of rap rap here in the front of the road.

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Blair Tormey: So overall this this road, whatever relatively high sensitivities to storm surge in this case so as opposed to exposure sensitivity, has nothing to do with location it's really all about the inherent properties of the asset itself.

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Blair Tormey: So our methodology basically evaluates the vulnerability of parks of service infrastructure to forming climate and coastal hazards sea level rise coastal flooding extreme flooding events such as storm surge or tsunami.

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Blair Tormey: And then coastal erosion and then depending on the available data, the vulnerability can be evaluated over multiple scenarios or multiple time frames.

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Blair Tormey: For example, a park for planning and prioritization purposes may want to know its vulnerability to say sea level rise by the year 2050 versus the year 2100.

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Blair Tormey: You know, in a park if you're trying to adapt or evaluate your sense your overall vulnerability, it might be more useful to know your vulnerability over a 30 year planning timeframe, rather than 100 years out because those buildings may not have 100 year lifespan.

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Blair Tormey: So when possible it's best to use consistent well maintained data sources typically we get those from fema or Noah or other governments cetaceans.

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Blair Tormey: The park service also has developed its own set of storm surge and sea level rise models for several parks and we use that data as well in our GIs analysis.

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Blair Tormey: Finally, conducting vulnerability assessments at the asset scales most useful to park management.

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Blair Tormey: Because it really helps them prioritize adaptation strategies for specific assets so for example here at sick national historic park and Alaska.

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Blair Tormey: The facility managers can quickly look at our vulnerability assessment and figure out which totem Poles on their totem walk.

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Blair Tormey: Have the highest vulnerability and perhaps start evaluating them for for adaptations.

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Blair Tormey: They can also see that you know places like the visitor Center may not be a good option in terms of storing these things long term.

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Blair Tormey: They might need to seek some higher ground sites for long term storage totals in fact what they've actually done here.

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Blair Tormey: Their solution is that they've taken all the original historic totem Poles and place them into museum storage well out of the tsunami hazard zone.

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Blair Tormey: In the polls that are actually that you see when you take the total walk are actually um.

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Blair Tormey: duplicates that have been carved by a by a local native artists, so when one of those gets washed away or out since you buy it by storm they hire contract, the local artists to carve another and the original historic Paul is preserved in their museum.

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Blair Tormey: So we started conducting these vulnerability assessments that coastal parks and.

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Blair Tormey: To date, we've done completed assessments at 25 coastal parks under our current round of funding we've been tasked with completing another 17 in the.

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Blair Tormey: 17 of the remaining coastal parks in the southeast region, we expect to complete those in the southeast this year and then we're going to be moving on to the Northeast region parks so by the end of this year will have completed roughly 40 parks for coastal hazards.

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Blair Tormey: So, for the rest of the talk i'd like to focus on the results of.

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Blair Tormey: Our vulnerability assessment at one park the Cape lookout national seashore which we completed in 2017 and specifically we're going to look at how the impacts of hurricane dorian.

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Blair Tormey: confirmed our assessment just two years later, so in 2017 we did the assessment in two years later, they were hit by Hurricane dorian and it kind of confirmed a lot of the results that we had from that initial assessment.

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Blair Tormey: So Cape lookout national seashore comprises roughly 300 miles of relatively undeveloped shoreline on three barrier islands got North core banks.

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Blair Tormey: Then South core banks and shackelford banks park hosts over half a million visitors annually and the main attractions include the historic villages that Cape lookout down here in the south ports with village up at the north end, these are historic villages.

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Blair Tormey: Then camp recreational campgrounds at long point and great island, in addition to you know hundreds of miles of pristine undeveloped beaches marshes and dune environments.

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Blair Tormey: park service also has an overarching guiding policy to protect the natural environments and in the national see shores and parks.

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Blair Tormey: So natural processes are allowed to continue without interference which makes these national seashore is in parks are really outstanding natural laboratory to observe coastal gene morphic processes so lots of classes go here to observe these things.

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Blair Tormey: To support visitation the park maintains mentally intrusive infrastructure and the bear islands are only accessible by boat driving on sand roads is permitted, but tightly controlled, especially during nesting seasons for birds and turtles.

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Blair Tormey: So, based on our results.

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Blair Tormey: We we completed that vulnerability assessment and 2017 we looked at over 200 buildings roads parking lots on trails etc.

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Blair Tormey: and found that over 67% were found of high vulnerability another 26% moderate vulnerability and only 7% low vulnerability so taken together that's 93% of the assets in the park have either hire moderate vulnerability.

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Blair Tormey: And among the highly vulnerable assets, the value is a replacement value is currently estimated over $40 million, so this is no small amount of money and assets.

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Blair Tormey: we're going to focus on three areas in the park great island long point in Portsmouth.

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Blair Tormey: and

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Blair Tormey: Just real quickly i'll take a look at the the hurricane track for dorian hurricane dorian tracked roughly parallel to the coastline, the entire shoreline as a category three storm.

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Blair Tormey: And then briefly made landfall at Cape hatteras as a category one storm.

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karen McNeal: Or you know your about two minutes remaining.

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Blair Tormey: Great Okay, thank you.

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Blair Tormey: Locally storm surge is up to seven feet, you can see that from high water marks measured it at Portsmouth village and then.

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Blair Tormey: The south of portland spillage these huge ED channels were cut as the as the storm surge cut through and across the island we'll just do main real quickly here.

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Blair Tormey: And you can see those large channels cut across the island.

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Blair Tormey: At long point to the south, our vulnerability assessment was was largely confirmed almost 21 of the almost all the buildings were damaged three were washed away in these in these channels and then roads docks and utilities were destroyed at the campground.

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Blair Tormey: At Portsmouth again I our vulnerability assessment almost all the assets for high vulnerability there, except for just three buildings.

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Blair Tormey: And dorian confirm that almost all the 27 buildings were damaged by about two meters of storm surge and you can see why that most of the elevations here below two meters so i'm very, very low elevation area and just inundated.

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Blair Tormey: So looking towards adaptation.

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Blair Tormey: The park service's developed a set of adaptation options for long point Portsmouth and great island campgrounds.

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Blair Tormey: Right now, the options being looked at most heavily are relocation of the campground for long point and possibly decommissioning and abandoning at Portsmouth village they're going to repair.

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Blair Tormey: The minimally damaged buildings and then decommission and abandoned and remove any of the buildings that were to severely damaged right now there are no plans to relocate the site, or any of the historic buildings in Portsmouth and then finally in a progressive action.

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Blair Tormey: Even though the campground at great island was not badly impacted by dorian the park service decided to.

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Blair Tormey: Based on her phone ability assessment to evaluate five potential sites for relocation so here's the current location and then these five potential sites that we're helping them to evaluate.

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Blair Tormey: So, in short, the campgrounds and summary campgrounds the Cape lookout continue to be a very popular visitor attraction.

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Blair Tormey: Hopefully, with some adaptation with our vulnerability study and some adaptation options will be able to find an adaptive solution to lower the overall vulnerability of these.

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Blair Tormey: assets and in in this will serve as a test case for a lot of the other coastal parks, that we worked in and hopefully the park service will be able to preserve a lot of those assets in the in the years to come.

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Blair Tormey: So I think with with that said i'll open it up to questions if there's time.

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karen McNeal: I don't think we have time, but you have two questions already in the chat that maybe you can respond to and we can move on to if you wouldn't mind stop sharing.

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Blair Tormey: On a perfect i'll do that thank.

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karen McNeal: You so much that it was a very really cool dog i'd love to talk to you more about some of the work you're doing.

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karen McNeal: Our next speaker Taylor if you can go ahead and share your screen.

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karen McNeal: Taylor schellenberg is going to talk to us about the changes of aggro climatology farm oh my gosh changes or climatic conditions and associated crop failures over global croplands lots of seeds going on there.

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karen McNeal: Taylor i'll let you go ahead and kick it off.

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karen McNeal: Okay, thank you.

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Tayler Schillerberg: So she said, my name is Taylor sheila Berg and my co author is the team.

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Tayler Schillerberg: So first anthropogenic and prices are changing current climate conditions global temperatures have increased 1.8 degrees and the last century, and the United States alone has increased 1.2 degrees and last few decades.

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Tayler Schillerberg: The changes in temperature and precipitation result and whether.

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Tayler Schillerberg: Whether burner variability resulting in an increased risk of droughts, floods, heat waves and other extreme events 100 degrees.

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Tayler Schillerberg: Here we go 100 degree meridian line and the United States kind of separates the arid West regions from the rain bed regions of the researchers have found that the 100 degree meridian mine has had an eastward propagation bringing with it the arid conditions of the West.

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Tayler Schillerberg: Your cultural productivity will be reduced because of increased temperatures increased precipitation variability flooding droughts and drought duration droughts put an added strain on already stressed water resources, because of the demand for human and echoes ecosystem needs.

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Tayler Schillerberg: Currently, one third of yield variability is due to climate is due to climate variability best changes in climate increase the vulnerability of agricultural systems today nearly 75% of the world's cropland as proof is produced on less than one quarter of the total cropland.

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Tayler Schillerberg: increase vulnerability of these systems.

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Tayler Schillerberg: will result in an increase of a failure event.

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Tayler Schillerberg: There are super regions around the world of high productivity, that are commonly referred to as bread baskets for the staple crops and the figure on the right, we can see that both the combination on the US and China, contribute to over 50% of the world's maize production.

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Tayler Schillerberg: An acre climate and to see is an indicator that has a specific agricultural significance, for example, the last spring, the last spring frost and first of all frost donate dates for the climatological growing season and can be indicative as to which crop can be grown in the season.

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Tayler Schillerberg: Research on agro climate and the seeds have been spatially limited and conducted at regional skills that are most common egger climate indices are represented, for example on the right can make no looked at the last spring frost and they found that there was a decrease and.

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Tayler Schillerberg: occurrence of the last spring frost with a later occurrence of the first of all for us together this results and a longer climb illogical growing season seen ever read.

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Tayler Schillerberg: can make at all found that egg or climate conditions and Europe have become on favorable with time, both of these example focus on your variability rather than crop failure.

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Tayler Schillerberg: clap it all 2002 2020 I use climatic variables of temperature precipitation solar radiation to determine crop failure occurrences and global bread baskets for maze rice soybeans and wheat below is an image that shows the location of bread baskets for maize and soybeans.

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Tayler Schillerberg: For their for their analysis they focus on two different time periods.

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Tayler Schillerberg: and the resulting change between these two time periods, they determined that there has been an increase in the number of bread baskets that.

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Tayler Schillerberg: Face simultaneously experience climatic conditions that can result in crop failure for maze soybeans and wheat.

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Tayler Schillerberg: So the passing out pass analyses of these agro climate indices.

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Tayler Schillerberg: have focused on limited regions with.

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Tayler Schillerberg: There is no global emphasis the regional studies often only focus on a limited number of beggar climate indices.

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Tayler Schillerberg: Crop failure which events which have an important impact on global food security, have not been look that in detail in relation to it.

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Tayler Schillerberg: Like your climate and the seeds this analysis can provide information as to which microclimate variables, are the most important and preventing crop failure events and Peter climates.

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Tayler Schillerberg: Therefore, shaping the mitigation and adoption strategies to kemper that can prevent future crop failure occurrences.

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Tayler Schillerberg: So with that the objectives of the study is to determine the connection between egger Clement indices and crop yield focusing on crop failure and regions of high productivity and food insecure regions.

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Tayler Schillerberg: And so, with that i'll go into the data on there has been daily maximum minimum temperature and precipitation are available at half a degree resolution from the NASA power website.

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Tayler Schillerberg: surface soil moisture was gathered from glean version 3.38.

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Tayler Schillerberg: And then to determine regions of agricultural cropland which is pictured in orange and as well as agriculturally significant regions of high productivity and food insecure regions on are in the bounding boxes and blue this data.

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Tayler Schillerberg: was obtained from the production data of are provided my mom for.

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Tayler Schillerberg: Planting and harvesting dates and ranges for maze rice soybeans and wheat are available through sex at all 2010 and meal data for these crops was available.

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Tayler Schillerberg: From 1982 2016 is a man sucky.

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Tayler Schillerberg: So without there will be several egg or climate and the seeds that were calculated for this study this slide displays five acre Clement indices better defend it on the hemisphere, of the growing season for that hemisphere so, for example, for last spring frost.

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Tayler Schillerberg: The first last spring frost is determined from the July 15 which represents the middle of the growing season and then progression backward and for this Southern Hemisphere it's from January 15 which represents the middle of their growing season and then progression backward.

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Tayler Schillerberg: So the remaining of the egg or climate indices that are calculated are based upon the plants planting and harvesting beats and ranges and then dependent on the grid cell location, as well as the individual plant threshold.

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Tayler Schillerberg: The final acre climate in the sea is composed of three different indices, this is the field conditions at planting during the mid like middle of the season so between planting and harvesting and then the final one is field conditions at harvest.

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So.

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Tayler Schillerberg: After to after the data was aggregated into a common grid spacing the egg or climate and the seeds were calculated for the years 1982 2016.

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Tayler Schillerberg: Next man candle non parametric test was performed at the grid several local food cell level to determine monotonic increasing and decreasing trends, the trends, where do you determine significant with Monte Carlo shuffling of 10,000 times.

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Tayler Schillerberg: That graphic to the right displays global crop failure events for maze of failure event occurs when the yield is below the when the deep trended yield is below the lower quartile of field for that so.

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Tayler Schillerberg: The machine learning algorithm other random forest is used to train and test the data determine classification of a crop fire events.

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Tayler Schillerberg: and variable importance for each of the Agriculture agriculturally significant regions mentioned earlier.

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Tayler Schillerberg: And then, finally and analyses of the crop player of crop failure events and high yielding noodles years will be conducted for each region, based upon the variable importance to determine if there is a trend of the distribution of beggar in the sea.

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Tayler Schillerberg: Right, so these next results are displayed of the man candle result, the man candle trend analysis.

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Tayler Schillerberg: The figure shows the trend.

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Tayler Schillerberg: At more colored regions represent significant values better at least or a greater than point 0.1.

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Tayler Schillerberg: So values that are on the warm color scale and the K, a positive trend.

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Tayler Schillerberg: So, increasing in value, whereas nate cool colors represent a negative trend regions that are Gray or regions are non significant and regions that are white indicate.

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Tayler Schillerberg: That there a requirement, and this, he was not calculated in that region because of lack of data or there is no cropland in that region.

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Tayler Schillerberg: So for accumulated frost days, there is a general decrease in accumulation across, especially in the northern latitudes.

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Tayler Schillerberg: And regions of Australia China Europe India and the United States all experienced 50% or more of the crop and experiencing a significant decrease in the number of accumulated frost days this drops below 20% for the majority when the p value is increased to a two 0.01.

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Tayler Schillerberg: The decline in accumulated frosties can have an impact on insect and weed control.

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Tayler Schillerberg: This result is also complemented by earlier occurrences of the last spring frost and later occurrence of the first fall frost, resulting in a longer climatological growing season, this can impact the type of crops grown into the region.

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Tayler Schillerberg: For made there is a general over.

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Tayler Schillerberg: There is an overall increase in the number of growing degree days that are expected experienced during the growing season, there are only three regions of agricultural interest that do not experienced significant.

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Tayler Schillerberg: increases greater than 50% of the cropland experiencing increases.

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Tayler Schillerberg: Their only a few regions.

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Tayler Schillerberg: That experience a decrease, and that is notably in southern the southern portion out of Central America.

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Tayler Schillerberg: and general increase in growing degree days also occurs for rice soybeans and wheat, with the same region and Central America experiencing a decrease.

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Tayler Schillerberg: The increasing growing degree days can lead to a new varieties being grown that have a longer period longer growing season, to reach maturity.

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Tayler Schillerberg: For both precipitation and dry days, there is a varied response, especially seen in these two regions.

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Tayler Schillerberg: Only and South East Africa initial risk be experienced significant.

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Tayler Schillerberg: trends and.

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Tayler Schillerberg: Experienced regions of over 50% of their cropland having significant trends, the occurrence of dry days is important for which crops grow in the region and how much irrigation and other strategies are needed to supplement water supply.

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Tayler Schillerberg: positive trends in field conditions indicate that there are more days allowed to be in the field, either planting crops or or harvesting crops field conditions that a negative trend would indicate fewer days to be out in the field.

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Tayler Schillerberg: The field conditions at harvest overall show only minor regions of significance for the majority of the crops.

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Tayler Schillerberg: For soybeans harvesting season, the largest significant regions occur in Brazil and Mexico or less than a third of the region experiences significant trends.

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Tayler Schillerberg: heat stress can increase the demand for water and in sufficient if.

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Tayler Schillerberg: it's sufficient mater is not available, I yield reduction can occur, there are three regions that experienced significant trends are over 50% of the cropland.

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Tayler Schillerberg: Over 50% of the crop and experiences significant trend these trends and the locations indicate that they're like we heat just occurring.

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Tayler Schillerberg: One interesting region is this region and Africa, where there is an increase in heat stress, as well as an increase in dry days experience during the growing region this area may may experience more unfavorable conditions and the future.

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Tayler Schillerberg: This figure shows the percentage of maize cropland that is experiencing crop failure during a given year again, this is the lower quartile of the D trended yield.

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Tayler Schillerberg: Each column is shaded in proportion to the regional crop failure events several years, especially stick out globally and for the US, in particular, these years or 1983 1988 93 2002 and 2012.

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Tayler Schillerberg: So this last figure shows a variable importance, this is the result from the random forest variable ranking.

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Tayler Schillerberg: variables and yellow are most important and determining crop failure well variables and purple are least important from this bigger, we can see that.

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Tayler Schillerberg: indices that rely on precipitation are among the most important, however, when looking at the European Union and the United States, they can see that the first of all processes more important.

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Tayler Schillerberg: So with this some next steps include looking further analysis of the important variables for each region and determining how the egg or climate indices changed between those events.

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Tayler Schillerberg: So far this research has concluded that I can Clement indices have changed globally and that increases related to the growing season length growing degree days and heat stress, as well as reductions and the frost related climate acre climate and variables.

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Tayler Schillerberg: Anger climate variables related to precipitation are most important in regions that do not experience across.

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Tayler Schillerberg: As far as research has began to establish how egg requirement and indices related to crop so crop failure events and will provide a future direction as to which and what can be taken in order to reduce the occurrence of crop failure in the future.

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Tayler Schillerberg: So with that i'd like to thank the auburn university and our program and i'll take any questions.

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karen McNeal: awesome thanks so much Taylor john and I go ahead and questions yeah I think there's two minutes.

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Chandana Mitra Geoscience: Okay, I don't see any question in the chat um so, but anyone, I have a question, but if anyone else has a question, you can put in the chat or not, but if you have it, you can unmute yourself and ask.

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Chandana Mitra Geoscience: dealer I something something in that regarding the slide number ago there was one in which you showed, and I, my focus was more on India, and I saw that there was, you can really Oh, all of the information that you share with us shows that India has gone through.

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Chandana Mitra Geoscience: Failure a reduction in maize production, but the one which you, which was men can do, one which was 1982 onwards, the you know the.

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Chandana Mitra Geoscience: How would I say the video sort of thing you showed us how it has changed over the years in that one there was nothing over India, it was still like a white spot is that something different data set or.

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Tayler Schillerberg: yeah so that actually depends on what were the cropland is where the maze cropland is so for India it's located from my data set is located near the northern border so looking at the man candle it was only comparing MAIs cropland amaze cropland.

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Tayler Schillerberg: So, since that's a such a small area, there was a larger portion that experienced significant trends.

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Chandana Mitra Geoscience: Thank you.

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karen McNeal: gonna say I think we're right at two so Taylor if you wouldn't mind sharing and if anyone has another question feel free to pop it in the chat for Taylor.

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karen McNeal: call you can go ahead and start.

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karen McNeal: Start sharing Why introduce you.

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karen McNeal: Our next speaker is pilot pilot messenger he's going to talk to us about flash throughout spatial temporal variability in the continental United States and it's prediction from Atlantic and Pacific sea surface temperatures.

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karen McNeal: Go ahead.

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Kyle Lesinger: I have to unmute myself first, of course.

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Kyle Lesinger: share my screen.

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Kyle Lesinger: Alright, so thank you for introducing me, my name is kyle messenger and i'm working with Dr D tn and i'm a first year PhD student at auburn university.

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Kyle Lesinger: So my outline for my research today i'll give you an introduction as to what flash drought is a little bit of motivation, what the current state of literature is.

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Kyle Lesinger: i'll go over some of my objectives, as well as the data methods that I used and finally do tell you a few of my results and then summarize.

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Kyle Lesinger: So flash droughts So these are droughts that occur with a sudden onset and rapid intensification, so this is both.

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Kyle Lesinger: A sudden onset over time and then a rapid intensification over space and the magnitude of the drought So these are generally.

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Kyle Lesinger: have high soil moisture loss underwater limited conditions and i'll break that down in just a few minutes for those who are aware.

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Kyle Lesinger: But typically these droughts, they can be spurred on by high temperatures very windy conditions with low precipitation as well as large scale patterns, such as rossby wave trains were just persistent high pressure systems.

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Kyle Lesinger: So some of the major effects the Flash drought or wildfires so when you have a loss of soil moisture and they're.

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Kyle Lesinger: able to start a fire much more quickly and spread we have high crop losses, especially if it's during the growing season of specific crops.

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Kyle Lesinger: and also for cities and municipal water, we can have reduction in reservoirs and this can actually affect humans from a the water perspective from available drinking water.

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Kyle Lesinger: So it's really important for us to understand these events, so I seek to improve our understanding of past events that are started by quick decreases in soil moisture or evaporated demand.

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Kyle Lesinger: So that's my overall goal, so a little bit of the literature review so on the Left you're going to see just a few.

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Kyle Lesinger: articles that have been written about how people look at rapid changes in soil moisture so.

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Kyle Lesinger: The first one, we see that within 20 days, which is the fourth contents, so if we see rapid changes between the I think they did the 40th percentile to the 20th percentile.

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Kyle Lesinger: Within 20 days, then this is considered a blast route.

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Kyle Lesinger: One at all, they looked at 315 day length in terms time they looked at actually a four week link and they looked at multiple drought pair indicators and found that so moisture was a really good index.

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Kyle Lesinger: So, to improve upon this research, there is a new smyrna root zone so moisture product that I will be using.

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Kyle Lesinger: And because it's been shown as an effective indicator, so I will improve upon some previous research but at some my own definitions.

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Kyle Lesinger: So for evaporate of demand i'll get into that a little bit later if you're not aware of what that is so.

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Kyle Lesinger: Several studies of look at this specific.

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Kyle Lesinger: Data product which tells you evaporated demand anomalies, but currently no study has looked at both soil moisture and evaporated demand, at the same time, so we definitely want those comparison of the driver and the impact of properly classify a flash drought.

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Kyle Lesinger: And then clustering, so this is a technique that i'll be using it's been used before on.

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Kyle Lesinger: geospatial data, and you can compare it with different indexes so that we can get a better idea of when.

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Kyle Lesinger: Events tend to occur at the same time, so Currently, this has not been done with the operative demand data so there's definitely room for us understanding what happens with that kind of data.

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Kyle Lesinger: So by objectives like first quantify the spatial temporal variability of flash droughts using two different data sets the evaporator demand drought index and the submerge root zone store moisture.

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Kyle Lesinger: My second objective is i'm going to look at both of them well first we look at them separately and then I will look at them at.

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Kyle Lesinger: Combined to see both the driver and the impact and to see what difference there is between when I look at them separately versus when I look at them together.

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Kyle Lesinger: And then I did near the end I would want to look at when these events occur over time so spectral analysis of the density functions that.

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Kyle Lesinger: describe when these events that frequency occurs most often So when I hypothesize that the frequency intensity and duration of droughts, has been increasing.

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Kyle Lesinger: So elaborate of demand, so my data set is from no other physical science laboratory so it's both a merge green analysis, so it has NTT data.

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Kyle Lesinger: But in this has been modeled and there's also a satellite data that's within it so it's a really good product at very fine resolution that's available from 1981 through the President.

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Kyle Lesinger: And so it's multi scale or in it, you can look at one week to 12 weeks or one month to 12 months.

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Kyle Lesinger: But you can also look at each individual pixel or you can look at a group of pixels as an average so it's really versatile product that you can use that.

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Kyle Lesinger: 180 degree resolution so really good data set for looking at it man, and so what evaporated man is it's called in several papers, the thirst of the atmosphere so.

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Kyle Lesinger: What we see here on the left is the room is actually start right here on the right side, so this is an energy limited regime so there's sufficient water in the ground so that most of the energy the solar energy is partition into latency as opposed to sensible heat so.

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Kyle Lesinger: As the evaporate.

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Kyle Lesinger: As evapotranspiration increases, there will be a parallel relationship between evaporated demand and.

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Kyle Lesinger: transportation, but then eventually once there is no more soil moisture then we enter a water limited limited regime and.

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Kyle Lesinger: apple transpiration will actually decrease well if operative demand will increase, and so this is actually a telltale sign of flash drought, because.

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Kyle Lesinger: Initially, when the drought start there is going to be a lag because they're still is efficient solar moisture in the ground so by looking at very quick Spikes and evaporate evaporated demand this gives a very good indicator of what the soil moisture state is.

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Kyle Lesinger: So also have every demand there's a lot of connecting carts that relate to the feedbacks that are recurring so.

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Kyle Lesinger: This is just a simple schematic of several multiple positive and negative feedback system so initially evaporated demand kind of falls somewhere within this so as soon as.

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Kyle Lesinger: evaporation starts decreasing right here we'll start So you can see it start decreasing will then there's going to be a positive feedback with vapor pressure deficit.

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Kyle Lesinger: With vapor pressure deficit, which leads to positive feedback so it'll cause less precipitation which will cause more drawing of the land, which will cause less.

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Kyle Lesinger: evaporation so there's just this feedback that will amplify the effect once there's already these high evaporation evaporated demand rate.

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Kyle Lesinger: So so moisture is a really good impact data source, so we can actually look at what's happening on the ground, so the root zone so on wisher from submerge.

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Kyle Lesinger: come from the Goddard earth system of earth sciences and it's also emerging analysis and it has NTT data they compared that simulated it with satellite data so it's on the same time scale and it's actually looking at volumetric water content, so the.

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Kyle Lesinger: relationship premium volume of soil in the ground and the volume of water in the ground and so with this we can look at and this product was needed because there wasn't.

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Kyle Lesinger: with certain microwave or I think that's what it is they, they only gets from zero to five centimeters in the ground so that's good for the shallow layer, but then you also need the deeper layers, and so this product was definitely needed.

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Kyle Lesinger: On a daily timescale of the contiguous United States for agriculture, specifically, and then it has been tested against the normalized difference vegetation index and in situ observations and it can also be used for.

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Kyle Lesinger: If we know the soil moisture that's in the ground it's a good predictor for how much storm water runoff you may have if the storms are greater than 25 millimeters a day so it's a very versatile product as well.

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Kyle Lesinger: So I first step, going to look at the spatial temporal variability of both the eddie and roots as well wisher just to see what are the classification differences between.

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Kyle Lesinger: These two indexes for the number of flash drought events or the frequency of occurrence, with a lot of different things we can look at with variability.

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Kyle Lesinger: So some of the methods first i'll make an automated algorithm for identifying when is a flash drive occurring.

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Kyle Lesinger: Then we can cluster this data, to see what areas of space what actual physical locations in the United States are experiencing these conditions, at the same time.

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Kyle Lesinger: And then validate it with some optimal cluster algorithms look at variability and then compare it with previous class drought events that have occurred see how well we've classified.

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Kyle Lesinger: So there's actually two different because I have two different products and looking at two different definitions so first the word man got index definition is.

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Kyle Lesinger: A 50% increase in the vdi over two weeks, and then we sustain that for at least another two weeks so we're looking at changes that occur on a weekly scale, but it has to occur over at least a month.

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Kyle Lesinger: And so, these are very quick on the sub seasonal seasonal timescale and then so for so moisture if the percentile drops from above the 40th percentile to below the 20th in four weeks or less so we're going to look at the same time scale of a month with on a weekly basis.

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Kyle Lesinger: So this is just a.

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Kyle Lesinger: picture of the adaptive man drought index, so you can actually see on the left is from October 14 of last year, so this is for a one month, so they were.

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Kyle Lesinger: The easiest very dark so it's ED four categories So these are above the 98th percentile of evaporated demand and now.

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Kyle Lesinger: And so, this was last October, and you see here on the right, this is actually this March so currently if you know any you've heard about the drought that suffering right now there is a very large affected of the southwest that is in drought and this could have been very well.

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Kyle Lesinger: initiated or assisted by the lack of soil moisture that could have been drained in October, so there is not sufficient rainfall or Pacific terror snow and occurs of the winter months, and this these effects to linger on to the next season so.

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Kyle Lesinger: I will now get into the clustering so those who don't aren't familiar with clustering, this is a diagram so for hierarchical clustering.

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Kyle Lesinger: All of my grid points actually start off as their own cluster so each one is considered a single cluster and then we will.

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Kyle Lesinger: First, look at the distance between each grid cell and each every other grid so, so this is actually considered the car distance and so the formula is kind of it's one minus and then the X in intersection with Why is.

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Kyle Lesinger: The number of times they share a feature divided by the number of features that they have so it's a actually a fairly simple equation that the some packages do that for you.

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Kyle Lesinger: So, then, the second part is to merge clusters by distance method so from what i've learned is there's not necessarily the you can't do just one.

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Kyle Lesinger: Cluster distance method, because some of them, if you have very large data it's kind of hard to say which one's the best until you process them and looked at the maps.

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Kyle Lesinger: So I actually did seven different cluster schemes and here's just three of them so for clustering you know when we actually start merging clusters.

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Kyle Lesinger: We can choose to merge them by the clusters that have the smallest minimum distance and it just imagine there's hundreds of clusters, at some point.

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Kyle Lesinger: We can choose to cluster them by the furthest distance and then we choose the minimum of all the furthest distances, or like with average it takes the average so.

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Kyle Lesinger: there's several different methods that we can, and then we want to confirm what's the best number of clusters, because this is an unsupervised classification.

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Kyle Lesinger: And so we can do that with several methods, the elbow the Silhouette and index is like the fray.

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Kyle Lesinger: So i'll show you just a moment the so the top left you see the method, and this is actually not my data, this is just a poor representation so.

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Kyle Lesinger: We want to look at the within some Square, so we want each cluster to have the smallest variability compared to every other cluster so.

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Kyle Lesinger: The distance between each point in the cluster is calculated and sound and that's actually what this within some of squares is so.

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Kyle Lesinger: The the ones, why there's a band right here, because we want to at some point to see that no more variability is explained by adding more clusters.

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Kyle Lesinger: And so the Silhouette method on the bottom left you'll see that this is actually a ratio of the.

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Kyle Lesinger: Cluster separation, to the cluster cohesion, so we want objects within a cluster to be more like.

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Kyle Lesinger: With each other, and we will objects outside of a cluster to be more different than every other cluster.

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Kyle Lesinger: So that's kind of the general idea and we can use several there's new over 40 pack or 40 different indexes that we could use i've selected several of them so.

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Kyle Lesinger: The fray index that would it would show you the mean outside cluster distance divided by the within cluster distance and so, for all of them it's mainly about we want objects within the cluster to be more similar to each other.

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Kyle Lesinger: So some of my results, so this was actually with the words cluster method, the method that minimizes variance So you see eight clusters here and it's actually it does a really good job considering.

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Kyle Lesinger: The climate regions of the United States, so we can definitely see these clusters that were flash droughts tend to occur at the same time, and this is over, you know all the time.

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Kyle Lesinger: And so apologies for the phone I couldn't early graphics I couldn't find a good color palette for this that made it any better, but.

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Kyle Lesinger: This is a image of the total number of weeks that are in flash drive, so the important part of this is that we see the MID the great plains region as the highest density of flash drought occurrences over.

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Kyle Lesinger: So this is kind of something that i've seen in other literature, where they this is definitely the hot spot, so my data didn't match up with that and then so for future.

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Kyle Lesinger: Value evaluation of for prediction, so we can look at each cluster individually and look at averages, we can make these time series and so that's my next step is i'll be looking at ways that we can look at the variability over years.

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Kyle Lesinger: And then try to use some of these for prediction so that would be for.

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Kyle Lesinger: At least initially, for spectral analysis, we can look at any peaks that return at certain intervals, or we could look at sea surface temperature for prediction.

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Kyle Lesinger: So to summarize my research i'm clustering and looking at the variability due to different data sets I want to find any patterns of recurrence.

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Kyle Lesinger: And then also my overall goal is to that, once we have understood these actual events, then we can use this for prediction and create models with that.

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Kyle Lesinger: So there's a lot of things that are occurring to increase our vulnerability in our system so whether it's agricultural system or for urban systems so climate change and then you add.

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Kyle Lesinger: little understanding of wendy's Dr events have occurred and we don't can't predict when they're going to occur, then all of these together they increase our vulnerability so it's definitely important for us to get an understanding of.

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Kyle Lesinger: Specifically flash droughts and to find the best index that can relate to that so for future research.

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Kyle Lesinger: There are several different things that we can do as an extension of this, so we can look at sea surface temperatures and look at different lags to see which ones are more correlated with flash droughts.

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Kyle Lesinger: We can look at a causal effect that works and so these are algorithms that can disentangle causal relationships between several different variables and then maybe we'll get into some machine learning for prediction based on.

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Kyle Lesinger: Other climate projected climate data sets.

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Kyle Lesinger: So who could benefit from this knowledge anyone has a an interest in water resource or a risk management we're anyone that's.

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Kyle Lesinger: deals with the agricultural sectors, because this is a really important issue that can affect water security and food security and so anyone that's interested in water related fields, this would definitely be interested in.

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Kyle Lesinger: So thank you for listening to me today and I will take questions.

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karen McNeal: Alright, thanks brandon tonight I think there's about two minutes.

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Chandana Mitra Geoscience: Okay, so.

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Chandana Mitra Geoscience: We don't see any questions in the chat box and I have a question for you and others if you have any questions, please you can unmute yourself in the next one minute and ask time.

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Chandana Mitra Geoscience: So my question is, is there a particular season, when we see more flat routes.

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Kyle Lesinger: Yes, so typically the literature, states that between March and October to the growing season is definitely the highest time for flash droughts, but.

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Kyle Lesinger: that's mainly because a lot of people like to talk about flash drops from agricultural state so technically it can happen any part of the year, but it's most impactful during the growing season.

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Chandana Mitra Geoscience: Okay.

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Kyle Lesinger: That would be your thing, and I can also look at well.

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Kyle Lesinger: I divided into months and seasons, we just do a growing season, to be really easy to assess that.

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Chandana Mitra Geoscience: And my second question was is this flash notes.

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Chandana Mitra Geoscience: Is a particular area is it all over the world, which is flat flat ground up are you find it in the literature, or is more specific to the US only because the subtropical temperate region.

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Kyle Lesinger: It is definitely not specific to the US so China it's been called the super boy it's been known for hundreds of years, so they they know about it and it mainly is a lot of high winds over there, but then Australia they've had documents other.

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Kyle Lesinger: I mean, so it definitely happens all over the world, but it would less likely to happen in areas where there's generally hi it's almost all year round.

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Chandana Mitra Geoscience: So listen the tropical region more than this, but.

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Kyle Lesinger: um.

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Kyle Lesinger: So that's actually a good question, I am not sure about the tropics I know about the larger countries that have had their studies, but that would be something interesting to look at.

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Chandana Mitra Geoscience: Thank you.

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karen McNeal: All right, great can you stop sharing kyle and then eli can go ahead share his screen.

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karen McNeal: As they're doing that our next speaker is Elijah Johnson and he's going to be talking to us about the ecosystem services of green spaces and the southeastern United States and we can see it perfectly so great timing all right, you lie go ahead.

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Elijah Johnson (he/him): Alright, thanks, so much so, yes, Mr Johnson and i'll be talking today about the ecosystem services of green spaces and the southeastern us.

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Elijah Johnson (he/him): And I want to shout out my collaborators on this, so my undergraduate researcher mason petri.

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Elijah Johnson (he/him): As well as doctors model and me, trying to make neo which have been instrumental in helping this project forward, so this is a project that's funded by the southeast climate adaptation science Center.

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Elijah Johnson (he/him): and basically what I wanted to do was find these ideal areas for green spaces in urban settings, so I will it.

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Elijah Johnson (he/him): Alright, so just a bit of a roadmap, so we know where we're going today basically i'll talk about ecosystem services, what the do some of the challenges that come from ecosystem services like climate change.

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Elijah Johnson (he/him): talk specifically about the climate challenges in the southeast, especially when it comes to urban heat.

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Elijah Johnson (he/him): And then we'll talk about how green spaces essentially what they do for urban heat, what are the benefits they have to offer.

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Elijah Johnson (he/him): US and then i'll go into research objective and then methods specifically suitability modeling and then i'll talk about these areas of interest that probably need further investigation.

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Elijah Johnson (he/him): And then i'll wrap it up with conclusions and future work and this picture down here is just an example of one of many ways to incorporate green spaces into an urban landscapes and you see some shrubbery.

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Elijah Johnson (he/him): down here some tree canopy and also some wild grasses as well just ways to incorporate these into an urban setting.

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Elijah Johnson (he/him): Alright, so a little bit of background so ecosystem services, essentially, are all the resources that are available, by nature, for humans to you, so this includes air, soil land and water and when they're well managed, they can have a lot of benefits for people.

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Elijah Johnson (he/him): Including clean air, clean and plentiful water so readily available water natural hazard mitigation so thinking about, for example, men growth protect.

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Elijah Johnson (he/him): Protect shorelines from erosion and from natural hazards like bad weather, and then we have climate stimulus stabilization so balancing earth's energy budget.

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Elijah Johnson (he/him): recreation, culture and aesthetics, or more of the social infrastructure and then nutrition and physical infrastructure as well, so food, fuel and materials and.

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Elijah Johnson (he/him): yc biodiversity conservation, but when these are not well managed, when these ecosystem services are not well managed, we can have certain things that can negatively impact.

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Elijah Johnson (he/him): both the environment and then ultimately humans, which use these ecosystem services so some of those things that come into play, or policy, who can have water, who has access to clean air pollution, how we polluting these ecosystems that we ultimately you know demand so much from.

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Elijah Johnson (he/him): intensely and us so agriculture agricultural practices, but also permanent urbanization which we'll talk about a little bit later and then kind of overlying all this is climate.

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Elijah Johnson (he/him): So, think about climate change, specifically in the southeast United States is an annual average temperature map, so we have degrees Fahrenheit on the y axis and then the years from 1895 to 2009 on the X axis and basically what we've seen here is sort of a decreasing trend in.

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Elijah Johnson (he/him): average temperature and the last 100 years, largely due to transition from these row crops to actual four cents and mini to a forestry dominated ecosystem service model.

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Elijah Johnson (he/him): But in the last 40 years we're seeing something a little bit different where we see this kind of increasing trend.

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Elijah Johnson (he/him): And that's largely going to be due to urbanization so if we're thinking about the it five corridor looking at.

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Elijah Johnson (he/him): Some cities that are around that we have raleigh Durham that's an expanding Charlotte as well greenville South Carolina Atlanta, of course, and then you know coming into Alabama we have you know our brains been growing rapidly Montgomery and then to the northern has Birmingham.

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Elijah Johnson (he/him): And this is just a animation showing basically housing densities, of how this has changed.

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Elijah Johnson (he/him): Over time, so, starting with 1940s moving through time we see increasing housing density, especially in these urban centers like auburn like emory and Birmingham and even projecting into the future, we can see even more.

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Elijah Johnson (he/him): indicators of growth and housing density.

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Elijah Johnson (he/him): Alright, so the urban heat island effect is what comes into play when we think about you know extreme heat and the effect that urban surfaces have.

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Elijah Johnson (he/him): So the urban heat island effect is basically that urban centers are warmer than surrounding areas so, for example, this is late afternoon temperature on the y axis here and then various Land Rover types on the X axis, so we see that rural areas are having.

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Elijah Johnson (he/him): Lower late afternoon temperatures compared to the suburban residential so we're increasing so in these urban building materials we're going to increase these temperatures and then, as we continue to get more and more urban more and more densely.

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Elijah Johnson (he/him): populated and density denser buildings, I guess, building density increases then we're going to have more.

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Elijah Johnson (he/him): amplified urban heat island effect so higher temperatures here, and then we can see as we're decreasing going down decreasing in.

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Elijah Johnson (he/him): Urban building material so urban residential year and suburban residential Here we see this large green space that's a park.

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Elijah Johnson (he/him): showing this decline in temperatures and then an increase, as we go back to suburban residential and then decreasing through rural farmland.

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Elijah Johnson (he/him): There are a lot of factors that play into the urban heat island effect and why urban centers contribute a lot to this effect.

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Elijah Johnson (he/him): Mostly because of this low libido from urban building materials we think about blacktop pavement things like that are going to be absorbing a lot more heat they have higher heat storage.

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Elijah Johnson (he/him): And if we think about ground he eats slugs absorbing all that heat during the day and then releasing are radiating that heat at night.

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Elijah Johnson (he/him): it's called ground heat flux and then not being able to basically move that heat away from the area to to these tall buildings kind of trapping that heat there.

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Elijah Johnson (he/him): Another thing to note in city centers there are a lot of people, a lot of buildings, so that just means there's more you know machines devices.

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Elijah Johnson (he/him): heating and cooling cars, all these things are in people that are actually also giving off heat, all the while and they're concentrated in these urban centers so they're going to create sort of a challenge for.

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Elijah Johnson (he/him): heat in those areas.

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Elijah Johnson (he/him): So some of the factors are going to affect you H ayes is by no means an exhaustive list, but some of the.

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Elijah Johnson (he/him): ones that we wanted to discuss are addressing this study we're basically population are building density.

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Elijah Johnson (he/him): subsea now that population of building density have a positive relationship with you, each so more densely populated buildings are going to have.

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Elijah Johnson (he/him): Her guess amplifier increase the urban heat island effect that's more of a density measure, and then we have land cover.

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Elijah Johnson (he/him): Where we know urban surfaces have higher you H I then vegetated surfaces, so we wanted to kind of look at basically what the land cover with the surface of the land looks like and then another factor that we want to look at was more of a socio economic factor which is income.

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Elijah Johnson (he/him): Per capita, which has a negative relationship with you, H I, which basically means that income, when you have lower income you're more likely to have be more vulnerable to be, which is for various reasons, including you know, access to health care.

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Elijah Johnson (he/him): And you know ability to seek treatment if there something was physically or health wrong with you from this urban heat island effect.

438
01:14:14.340 --> 01:14:21.300
Elijah Johnson (he/him): Okay, so the direct impacts as human health include you know these morbidity is that are directly caused by extreme heat.

439
01:14:22.050 --> 01:14:34.200
Elijah Johnson (he/him): Like heat stroke and dehydration and these other human health, more videos that are exacerbated by extreme heats everything about respiratory disease like asthma COPD cardiovascular disease.

440
01:14:34.950 --> 01:14:46.230
Elijah Johnson (he/him): And kidney disease, disease and then also mental health challenges that come along with extreme heat and human health impacts that are becoming more and more talked about.

441
01:14:47.760 --> 01:14:51.450
Elijah Johnson (he/him): When we discussed the urban heat island effect and it impacts, the human health.

442
01:14:53.970 --> 01:15:04.590
Elijah Johnson (he/him): But Greens pieces have these environmental economics and health benefits that we've seen in the literature to, for example, environmental benefits include increased CO2 draw down.

443
01:15:06.390 --> 01:15:11.610
Elijah Johnson (he/him): Where these photosynthesis photosynthesis is basically drawing CO2 in the atmosphere.

444
01:15:13.470 --> 01:15:19.680
Elijah Johnson (he/him): which can contribute to balancing the earth's energy budget and that's also absorbing heat through evapotranspiration.

445
01:15:20.460 --> 01:15:35.610
Elijah Johnson (he/him): An economic example here would be lower energy demand cause for cooling, so if we're having a lower so if i'm in urban Center and we incorporate green spaces pretty much lowering the temperature of the area, which means that we're going to lower the demand for these cooling services.

446
01:15:36.840 --> 01:15:47.820
Elijah Johnson (he/him): And then also helsel enclosure health is a little bit tricky to find direct benefits for, but this access to green spaces basically increase.

447
01:15:48.420 --> 01:16:02.130
Elijah Johnson (he/him): can increase physical activity which may have better health outcomes for those morbidity that were discussed before also lower stressed and improved quality of life and also mental health improvements.

448
01:16:04.890 --> 01:16:16.260
Elijah Johnson (he/him): Are alright, so the objective for this part of the study was to sort of identify these respective areas with high suitability rankings for installing green spaces, so this was exploratory in nature, we wanted to take.

449
01:16:16.680 --> 01:16:23.610
Elijah Johnson (he/him): Three test counties to basically see if we can even identify these locales Given these factors that we have.

450
01:16:24.060 --> 01:16:41.670
Elijah Johnson (he/him): So three test counties that we use are all in Alabama so Jefferson county there are varying sizes as well, so Jefferson county population about 650 8000 Montgomery county population about 226,000 and Lee county population about 160 4000 this is as of 2019.

451
01:16:45.120 --> 01:16:58.590
Elijah Johnson (he/him): Alright, so data collection, so our three sources here we're per capita income, population density and land cover so our per capita income came from at the census tract geographic level came from the 2010 American Community survey.

452
01:16:59.820 --> 01:17:10.980
Elijah Johnson (he/him): total population for this population density calculation came from the 2010 census and inland area came from the US census bureau tiger line service.

453
01:17:12.810 --> 01:17:26.460
Elijah Johnson (he/him): And then land cover for Alabama was gathered from the national and cover data set and 2011 via the United States geological survey and all of these data were readily available to kind of provide a sort of proof of concept.

454
01:17:29.310 --> 01:17:31.950
Elijah Johnson (he/him): So the methods that are used in.

455
01:17:33.360 --> 01:17:40.620
Elijah Johnson (he/him): to sort of evaluate these suitable areas are are a guest is suitability modeling and rg is pro.

456
01:17:41.520 --> 01:17:51.360
Elijah Johnson (he/him): So basically suitability models use identify these ideal locations for some specific purpose, based on certain characteristics of the areas that certain factors or criteria that you define.

457
01:17:52.020 --> 01:18:03.450
Elijah Johnson (he/him): So essentially what you want to do is define your objective, so you know where where does a location or Where would you want some thing to be thinking about some habitat.

458
01:18:04.530 --> 01:18:13.500
Elijah Johnson (he/him): ideal habitat location for some endangered species that would probably be my objective, so I want to find where these suitable areas are these ideal areas are.

459
01:18:14.550 --> 01:18:17.580
Elijah Johnson (he/him): criteria that i'm going to include are probably going to be something like.

460
01:18:18.840 --> 01:18:25.590
Elijah Johnson (he/him): slope i'm probably going to want also access to water, so proximity to water things like that.

461
01:18:27.300 --> 01:18:33.420
Elijah Johnson (he/him): And then I want to probably derive my data, so if i'm looking at something like slope, but I only have as a DM which is my.

462
01:18:33.840 --> 01:18:50.400
Elijah Johnson (he/him): input data or digital elevation model that I can derive slope from my digital innovation model, and that will be my derivation essentially and then I want to transform that data, so I can compare my data sets together, so if I have say land cover type and there are you know.

463
01:18:51.570 --> 01:18:57.570
Elijah Johnson (he/him): 13 land cover types in this data set and then I have my slope, and I have.

464
01:18:59.940 --> 01:19:10.170
Elijah Johnson (he/him): One 290 degrees are doing a 90 degrees and slope I can't really compare those but if I can create a range of one to five of suitability ranking, then I can then compare both of those sets together.

465
01:19:11.910 --> 01:19:22.620
Elijah Johnson (he/him): So, then, I want to wait them, so if I know that access to water or proximity to water is important to them a weight that a little bit more than my other factors in my model then ultimately I want to locate.

466
01:19:23.880 --> 01:19:41.430
Elijah Johnson (he/him): So, looking at that specifically for my study my objective was to find suitable areas for urban green spaces my criteria here were low per capita income high population density and urban or barren land covers I want to target areas that are originating nature have intense.

467
01:19:42.750 --> 01:19:43.650
Elijah Johnson (he/him): urban development.

468
01:19:44.910 --> 01:19:56.640
Elijah Johnson (he/him): And then I wanted to derive my data so for population density my data sets where population to a population persons is tracked and then land area, so I divide my data by basically doing a population, population density.

469
01:19:57.420 --> 01:20:08.850
Elijah Johnson (he/him): I should also note that per capita income and population density were vector data files, so I needed to convert them to raster data files that I can use them or cellular data file, so I can.

470
01:20:10.890 --> 01:20:14.760
Elijah Johnson (he/him): Use them in this models of the model only uses cellular raster data.

471
01:20:16.080 --> 01:20:22.920
Elijah Johnson (he/him): And then I wanted to transform transform that data so low per capita income, I wanted a range of classes, where one was.

472
01:20:24.060 --> 01:20:33.870
Elijah Johnson (he/him): High per capita income and five was low per capita income if i'm looking for suitability for low suitability, at one and a higher suitability ranking five.

473
01:20:34.380 --> 01:20:41.850
Elijah Johnson (he/him): And then for population density, one would be low population density and five will be high population density.

474
01:20:42.240 --> 01:20:50.940
Elijah Johnson (he/him): And then the transformation for this urban and barren land cover so that was categorical so I ranked basically forced it heavily forested areas.

475
01:20:51.540 --> 01:21:05.370
Elijah Johnson (he/him): water bodies and things like that, as a one and then my more highly intense intensely developed areas we're going to be five and then, since this is exploratory studying we just wanted to see kind of what happens from this, we.

476
01:21:06.870 --> 01:21:09.630
Elijah Johnson (he/him): inputted basically an equal weight across the board.

477
01:21:11.010 --> 01:21:17.250
Elijah Johnson (he/him): With urban or barren land land cover having maybe 1% more and then ultimately we located our areas so.

478
01:21:18.210 --> 01:21:25.380
Elijah Johnson (he/him): Some results here, so this is a suitability model output for Jefferson county and Alabama so we see here we have Birmingham.

479
01:21:26.190 --> 01:21:39.570
Elijah Johnson (he/him): Is the city Center of Birmingham, and then we have some areas around Birmingham so we're looking at sort of the central to South East parts of the county that are highly suitable for.

480
01:21:41.160 --> 01:21:50.310
Elijah Johnson (he/him): Urban green spaces to some of these communities include central park the shields area inglenook area forest Park Area and what does my window wouldn't met us.

481
01:21:52.530 --> 01:22:00.390
Elijah Johnson (he/him): So just looking at areas for further investigation as to you know what other services exist there can we get higher resolution imagery.

482
01:22:01.710 --> 01:22:05.820
Elijah Johnson (he/him): for further investigation, the next one here is.

483
01:22:07.110 --> 01:22:25.170
Elijah Johnson (he/him): Montgomery county and Alabama we have this yellow dog being sort of the city Center and then around the city Center we see higher priority areas for green spaces old clover Dale and a little bit north of the woodland hills area that would be ideal location for.

484
01:22:27.450 --> 01:22:38.820
Elijah Johnson (he/him): green spaces and then Lastly, our county here at lake county where auburn is so this is auburn to the Center, and this is where our bonus so just west of auburn university basically.

485
01:22:40.500 --> 01:22:45.630
Elijah Johnson (he/him): it's down by mlk boulevard that's their suitable areas for green spaces as well.

486
01:22:47.820 --> 01:22:48.030
Elijah Johnson (he/him): yeah.

487
01:22:49.950 --> 01:22:56.220
Elijah Johnson (he/him): So basically what what we see here that suit suitability models can help identify ideal locations for green spaces.

488
01:22:56.610 --> 01:23:11.850
Elijah Johnson (he/him): And there are several areas within deficit in Montgomery county is that may make great potential sites for green spaces that require further investigation higher resolution data so going forward that's what we want to do we want to probably look at something a little bit more.

489
01:23:13.140 --> 01:23:18.360
Elijah Johnson (he/him): Higher resolution geographic levels, instead of census tract i've been go even smaller possible.

490
01:23:19.980 --> 01:23:27.300
Elijah Johnson (he/him): And then, introduce more factors, so we only use three factors representing three different sort of areas of things that affect urban heat.

491
01:23:27.900 --> 01:23:34.110
Elijah Johnson (he/him): But what we want to do is basically look more deeply into these other factors that may be contributing or do contribute.

492
01:23:34.440 --> 01:23:43.590
Elijah Johnson (he/him): And then refined these weeds maybe land covers more important than, say per capita income so maybe we need to tweak things a little bit differently, which requires a little bit more investigation.

493
01:23:44.130 --> 01:23:54.780
Elijah Johnson (he/him): And then, once those are done, we want to expand this modeling into other cities throughout the southeast us basically to help city planners and Community members decide, you know how to best.

494
01:23:56.430 --> 01:24:00.090
Elijah Johnson (he/him): serve basically underserved populations in cities and how.

495
01:24:01.740 --> 01:24:10.050
Elijah Johnson (he/him): We can help to reduce urban eat and you know increase you know, economic and environmental and human health benefits.

496
01:24:11.340 --> 01:24:16.560
Elijah Johnson (he/him): So with that i'll hand it back over, but thank you and I can i'll take questions and feedback.

497
01:24:18.270 --> 01:24:25.170
karen McNeal: awesome thanks eli I think there might be one time for one or two in the chat if you want to go ahead and.

498
01:24:25.350 --> 01:24:25.920
karen McNeal: Ask one.

499
01:24:25.950 --> 01:24:36.630
Chandana Mitra Geoscience: Yes, sure i'm the first one, which is from Hannah she asked how did you decide to wait categories like demographics and organization, using a one to five value.

500
01:24:39.090 --> 01:24:44.280
Elijah Johnson (he/him): So basically those are like set ranges to go back to that.

501
01:24:46.980 --> 01:24:49.770
Elijah Johnson (he/him): she's a range of classes, so you basically have this continuous.

502
01:24:51.690 --> 01:25:05.250
Elijah Johnson (he/him): Data set of per capita income and what this basically does is just sets up the classes one through five and make it a PIC one through 10 I could have picked you know any number of ranges, but I just decided on one through five just to keep it a little bit more simple.

503
01:25:06.300 --> 01:25:20.580
Elijah Johnson (he/him): But basically, since I know that per capita income is sort of inversely related to you agi That means that one would be higher per capita income because it's low suitability versus.

504
01:25:22.050 --> 01:25:28.530
Elijah Johnson (he/him): High per capita income or I guess low per capita income, which would be a five which will be high suitability so they're just like.

505
01:25:29.820 --> 01:25:30.570
Elijah Johnson (he/him): predetermine.

506
01:25:32.100 --> 01:25:32.940
Elijah Johnson (he/him): categories of.

507
01:25:34.440 --> 01:25:37.830
Elijah Johnson (he/him): Those values same for our population density.

508
01:25:38.910 --> 01:25:43.140
Elijah Johnson (he/him): Then urban or barren land cover those categories were basically just.

509
01:25:43.800 --> 01:25:56.610
Elijah Johnson (he/him): The areas that would not benefit from green spaces so forest aren't going to benefit from green spaces oceans are are against bodies bodies of water and going to benefit from green spaces as much as say a highly developed urban area.

510
01:25:58.080 --> 01:26:00.480
Elijah Johnson (he/him): that's all I got thanks for your question.

511
01:26:01.890 --> 01:26:05.220
Chandana Mitra Geoscience: Okay question for kyle you can answer in the chat.

512
01:26:06.510 --> 01:26:06.840
Chandana Mitra Geoscience: And then.

513
01:26:07.290 --> 01:26:10.260
Elijah Johnson (he/him): yeah I can answer in the chat yes amount of time yep.

514
01:26:10.890 --> 01:26:17.130
karen McNeal: awesome Thank you and you have another one, there too, so you'll be busy on chat for a little bit here Okay, I am supposed to share my screen.

515
01:26:17.730 --> 01:26:18.480
karen McNeal: With the.

516
01:26:19.680 --> 01:26:24.750
karen McNeal: First, he poster we have some going to try to do that, hopefully successfully.

517
01:26:26.850 --> 01:26:29.220
karen McNeal: And looks like mature sounds on.

518
01:26:30.240 --> 01:26:38.280
karen McNeal: So let me know if you can hear this and I can't see you very well so shauna or do you can just turn off your MIC to tell me that it's working, that would be great.

519
01:26:40.710 --> 01:26:51.990
Di Tian: Oh good afternoon everyone, my name is brandon Ryan, and today i'm going to talk to you about the analysis of future urban growth of the Columbus dothan and Panama city urban climate archipelago.

520
01:26:52.470 --> 01:27:04.290
RISE- Audrey Heun GSA: Of before he was working the nsf climate resilience traineeship here at auburn university, as well as the auburn university geosciences department and my advisor Dr chandon in vitro for helping me with this poster.

521
01:27:05.970 --> 01:27:16.590
karen McNeal: So, as we know, urban centers tend to trap he and this idea of urban heat island is the difference between that temperature in urban areas and rural areas.

522
01:27:17.460 --> 01:27:26.730
karen McNeal: vote, we can start thinking as population and urban sprawl continue the future that these cities might potentially become more interconnected in the future.

523
01:27:27.180 --> 01:27:30.090
karen McNeal: And might start linking these urban heat islands together.

524
01:27:30.450 --> 01:27:38.700
karen McNeal: So that's where this idea of an archipelago comes in, much like a chain of islands is called an archipelago, we can start changing these urban heat islands together the future.

525
01:27:39.090 --> 01:27:46.770
karen McNeal: And analyzing what sort of impasse these chains of islands might have on the climate system, whether the urban heat or been flooding and things of that nature.

526
01:27:47.820 --> 01:27:52.020
And the figure figure one, we can see kind of a larger.

527
01:27:53.760 --> 01:28:05.310
karen McNeal: Urban climate archipelago, which will potentially develop in the future, called Charlie into which extends from the Charlotte to Atlanta those cities, so this image is projected growth into.

528
01:28:06.150 --> 01:28:11.790
karen McNeal: And as we can see those cities are going to potentially become very, very interconnected with one another.

529
01:28:12.570 --> 01:28:23.490
karen McNeal: of my study area in particular, we are looking at kind of smaller urban climate archipelagos which might develop into the future, so i'm looking at again Columbus dothan in Panama city.

530
01:28:24.450 --> 01:28:31.950
karen McNeal: Do you cities were chosen for a few reasons, first, they are all experiencing some amount of sustained population growth second.

531
01:28:32.400 --> 01:28:41.040
karen McNeal: lot of these cities are really easy to reach from coastal communities and coastal cities as well as being enlightened and on a main kind of.

532
01:28:41.700 --> 01:28:54.480
karen McNeal: interstate roadway system towards Atlanta which a lot of people might travel on and, thirdly, these again are kind of smaller to medium sized cities which don't get as much analysis as larger cities.

533
01:28:56.100 --> 01:29:03.120
However, in the future, they will be impacted just the same by climate change and potentially climate induce migration.

534
01:29:04.290 --> 01:29:13.530
karen McNeal: Therefore it's important to study these cities, because they might not have as much infrastructure or economics or financials there to support.

535
01:29:15.540 --> 01:29:30.000
What might happen in the future so analyzing them now is definitely important so for my methods we did an unsupervised classification of land use from 2005 to 2020 using landsat imagery.

536
01:29:31.140 --> 01:29:45.750
karen McNeal: which then was input into the terrorists at modeling software which allows us to project future urbanization into the years 2035 and 2050 so the way this model works is it uses to input years we use 2015 and 2020.

537
01:29:47.760 --> 01:29:56.130
And then determines what sells might convert from one land use to another, so in this case urban vegetation from urban.

538
01:29:56.610 --> 01:30:05.910
And where urbanization might take place, you can also input, a lot of driver variables, such as slope the distance from roadways that will kind of also influence.

539
01:30:06.600 --> 01:30:24.960
What your output will look like so in the general land use classification, we can see and based on the table Columbus and Panama city, are the two that have experienced kind of more rapid growth over the past decade or so, while both and kind of experience, more sustained development.

540
01:30:25.980 --> 01:30:37.440
This can also be seen in the future projections as well that Columbus and Panama city will likely experience kind of more rapid an expansive urbanization as opposed to the depth and area.

541
01:30:38.940 --> 01:30:43.350
These future projections, in particular for for future research are really important.

542
01:30:44.940 --> 01:30:52.680
karen McNeal: because it will help inform community leaders and individuals what their cities might look like in the future, but we can also start thinking about how.

543
01:30:53.250 --> 01:31:01.770
karen McNeal: Potential sea level rise and other weather events might induce migration into these cities, so the figure at the bottom.

544
01:31:02.730 --> 01:31:17.190
karen McNeal: figure six is of county inflow of people into Panama city and, as we can see there's a large kind of county to Panama city migration like a nearness to Panama city, as well as kind of a southeastern flow.

545
01:31:18.300 --> 01:31:25.860
as well, so when we start thinking about these smaller medium sized cities, we need to start thinking about this potential influx of people.

546
01:31:26.160 --> 01:31:35.070
And how that might impact urbanization and drive and where it is urbanization and how to better protect these people from potential hazards, they may face in these cities.

547
01:31:42.480 --> 01:31:55.350
karen McNeal: Alright, so thank you brandon we have five minutes for questions about four minutes, so if anybody has any questions for brandon feel free to unmute or put it in the chat.

548
01:31:59.370 --> 01:32:01.080
Chandana Mitra Geoscience: anyone has any questions.

549
01:32:05.610 --> 01:32:18.720
Chandana Mitra Geoscience: So brandon I have a question for you, so I know i'm with you on this project but um So what are the different kind of fall hazards, which you think the impact this particular area video.

550
01:32:23.850 --> 01:32:34.740
Brandon Ryan: yeah so I mean I guess there's definitely a lot of potential things obviously heat waves and droughts are going to be pretty significant potential shifting of.

551
01:32:36.030 --> 01:32:51.120
Brandon Ryan: The tornado alley or like new development of a new tornado alley through kind of Alabama Mississippi things like that might potentially get worse, so I guess more mezzo scale events might also become an issue Panama city is pretty obvious with sea level rise.

552
01:32:52.980 --> 01:33:00.630
Brandon Ryan: So yeah so there's definitely a few different things, obviously it's pretty localized in a sense that some hazards that will affect.

553
01:33:01.650 --> 01:33:03.570
Brandon Ryan: paying them on may not affect both and.

554
01:33:04.020 --> 01:33:12.600
Brandon Ryan: So on for Columbus you know Columbus has a pretty major river that runs through it so kind of more localized flooding, as a result of increased precipitation in Columbus.

555
01:33:13.170 --> 01:33:21.360
Brandon Ryan: Maybe a hazard that other places, may not experience so yeah, so it is kind of a wide breadth of things that might potentially impact the area.

556
01:33:23.670 --> 01:33:24.120
Chandana Mitra Geoscience: You.

557
01:33:27.120 --> 01:33:28.350
Chandana Mitra Geoscience: have a question for Brendan.

558
01:33:37.320 --> 01:33:42.510
Chandana Mitra Geoscience: Not any move on to the next speaker or, I think, is it is a break after this and.

559
01:33:42.810 --> 01:34:01.530
karen McNeal: yeah there actually is a break, and I put in the chat the stretch break that Dr Marilyn vocal put together that I mentioned at the beginning of the talk if you haven't tried this first break it I highly recommend it it's a lot of fun and I will probably do a little bit as well.

560
01:34:02.670 --> 01:34:08.550
karen McNeal: So we haven't chill let me double check the schedule, I think till.

561
01:34:10.200 --> 01:34:10.980
karen McNeal: Three 320.

562
01:34:11.040 --> 01:34:11.760
Brandon Ryan: Let me check.

563
01:34:12.240 --> 01:34:21.810
karen McNeal: We have until 320 So if you want to stay connected, you can just turn off your video and mute yourself and go take a break for a little while.

564
01:34:22.650 --> 01:34:32.160
karen McNeal: And then we're going to be starting promptly at 320 so if you're a speaker after the break i'd recommend you coming back about five minutes till just to make sure everybody's here.

565
01:34:34.440 --> 01:34:44.820
karen McNeal: And yes, thanks thanks eli you eli still answering some questions and putting in some things, and if you have any other questions for any of our speakers, please feel free to put those in chat.

566
01:34:45.180 --> 01:34:55.000
karen McNeal: And they can respond as well alright well we'll take a quick break and see you at or a little before 320.

567
01:34:55.001 --> 01:35:02.100
karen McNeal: And eli if you're here, I think there was another comment I don't know if you saw it in the chat for you.

568
01:35:03.030 --> 01:35:04.890
Elijah Johnson (he/him): yeah so i'm responding to it, right now, thank you.

569
01:35:04.950 --> 01:35:06.600
Elijah Johnson (he/him): Perfect yes.

570
01:35:07.680 --> 01:35:13.380
karen McNeal: All right, I can see everything just fine stuff I see presenter view so we'll go ahead and get started.

571
01:35:13.890 --> 01:35:30.780
karen McNeal: So our first speaker for the second half of this session after the break is definitely Courtney, and she is going to talk to us about climate adaption science to action measuring use of a cask funded research so go ahead and take it away.

572
01:35:32.430 --> 01:35:33.450
Steph Courtney (she/her): Okay sounds good.

573
01:35:34.560 --> 01:35:42.510
Steph Courtney (she/her): Can it so my title so we're good there and i'll just emphasize on the first screen that that this is a team project, including.

574
01:35:42.960 --> 01:35:55.830
Steph Courtney (she/her): folks here at auburn and YouTube knoxville and some other places i'll talk about in a second and if you'd like to view our lab website, we have a qr code right there that will be recurring just if you're curious.

575
01:35:57.330 --> 01:35:58.470
Steph Courtney (she/her): So.

576
01:35:59.520 --> 01:36:12.870
Steph Courtney (she/her): There we go, this is a big paragraph, but it really sums up what the climate adaptation science Center to do so, I called him casco time that was what was in the title there so i'm just going to read all of it bear with me, none of its really credible.

577
01:36:13.650 --> 01:36:25.290
Steph Courtney (she/her): The big ideas as a partnership driven program i've highlighted that and we'll come back to it that team scientific researchers with natural, cultural resource managers and local communities, so lots of parties involved.

578
01:36:25.620 --> 01:36:36.480
Steph Courtney (she/her): To help fish wildlife waters and lands across the country adapt to changing and conditions and by changing conditions largely What that means is climate change and urbanization, hence the climate adaptation.

579
01:36:37.230 --> 01:36:47.520
Steph Courtney (she/her): Part of the name, so there are these eight regional centers running funded by the usgs, but we are specifically a consortium Member here in auburn.

580
01:36:47.850 --> 01:36:59.550
Steph Courtney (she/her): Of the south east network, so the southeast climate adaptation science Center so we'll call it see cask sometimes and there are other consortium members and the university and.

581
01:37:00.420 --> 01:37:11.490
Steph Courtney (she/her): All the research funded in the first round of the cast, which was what are receiving in on here total the $7 million so five years 28 projects totaling it 7 million.

582
01:37:11.970 --> 01:37:22.320
Steph Courtney (she/her): So really the motivation for this project, just to get you thinking about that right away, is what impact did all these public funds have, how can we look at that and learn more about that impact.

583
01:37:22.770 --> 01:37:30.270
Steph Courtney (she/her): And then zooming out a little bit What about all the other conservation spending there's seven other regional casks and there's a lot of similar.

584
01:37:30.630 --> 01:37:44.010
Steph Courtney (she/her): You know efforts within forest service fish and wildlife all sorts of different federal with places that there's both climate and conservation science funded and we want to learn about how to make that funding possibly more effective.

585
01:37:45.660 --> 01:38:01.080
Steph Courtney (she/her): So when I highlighted partnership in the very first cast definition here that is relevant to this first bullet here, where the big idea in the the casks is this co production model where.

586
01:38:01.500 --> 01:38:08.100
Steph Courtney (she/her): The science creators and the science users are working together collaborating throughout the process of the science project.

587
01:38:08.400 --> 01:38:15.870
Steph Courtney (she/her): And that is intended to produce more actionable climate conservation science so that's The idea is that we're not just making science.

588
01:38:16.110 --> 01:38:24.540
Steph Courtney (she/her): But science that can be directly applicable to the problems and obstacles that we are facing with climate change and conservation.

589
01:38:25.260 --> 01:38:38.520
Steph Courtney (she/her): And co production is hopefully a way to make it more actionable so the previous literature in a quick nutshell is that long term relationships between those parties ample communication between the users and creators.

590
01:38:39.000 --> 01:38:45.750
Steph Courtney (she/her): and keeping the work centered on the applications of information is how you get transparent credible and usable research.

591
01:38:46.140 --> 01:38:55.110
Steph Courtney (she/her): And i'll note here to that these these terms of science creator and science user a little clunky normally we would just want to say you know scientist and manager or something like that.

592
01:38:55.500 --> 01:39:05.640
Steph Courtney (she/her): But the reality is that when you're doing research collaboratively the line there gets kind of blurry a lot of managers and you know people who are.

593
01:39:06.030 --> 01:39:09.090
Steph Courtney (she/her): biologists ecologists out in the field are scientists too.

594
01:39:09.810 --> 01:39:23.370
Steph Courtney (she/her): So these terms, become a little clunky, but it really just further illustrates the fact that if we're we're working together on this, then the roles become much more blended and we all are collaborating and you know playing a part in many different stages of the process.

595
01:39:25.740 --> 01:39:36.840
Steph Courtney (she/her): So, ultimately, if we're talking about actionable science how it's being used, we want to be measuring that use so there's a lot of literature about measuring the use of knowledge and a really broad sense.

596
01:39:37.530 --> 01:39:41.100
Steph Courtney (she/her): All different kinds of knowledge, not just you know conservation science.

597
01:39:41.910 --> 01:39:50.250
Steph Courtney (she/her): or even natural science at all, but this is certainly pretty difficult to measure, so a lot of times you'll see qualitative approaches case study approaches.

598
01:39:50.460 --> 01:39:58.830
Steph Courtney (she/her): So, for example, you know, following one research project and interviewing the researchers, the people who use it, the people who hear about it down the line.

599
01:39:59.250 --> 01:40:09.300
Steph Courtney (she/her): That kind of approach, but there are some frameworks that are a little bit more transferable so, the main one, for that would be this evaluation typology of the different kinds of use.

600
01:40:10.500 --> 01:40:18.270
Steph Courtney (she/her): So we'll just go through, one by one there's conceptual use, which is just about becoming better informed building your knowledge base.

601
01:40:18.750 --> 01:40:27.210
Steph Courtney (she/her): instrumental use in the darker green hear us to directly inform a new decision or new action, so this is what we tend to think of is.

602
01:40:27.480 --> 01:40:33.960
Steph Courtney (she/her): you're doing something new, or something differently, because you read this paper or you know use this web tool, what have you.

603
01:40:34.500 --> 01:40:40.260
Steph Courtney (she/her): And then lastly justification use, which is also called political or symbolic use a lot of times.

604
01:40:40.830 --> 01:40:46.680
Steph Courtney (she/her): Where you using this information to substantiate a decision or action already made to sort of back yourself up.

605
01:40:47.070 --> 01:41:02.400
Steph Courtney (she/her): justify why you're doing something, but it didn't necessarily you know make you change course on what you're doing so, those are the sort of big three we think about with use of knowledge by those managers stakeholders, the public, etc.

606
01:41:03.540 --> 01:41:13.200
Steph Courtney (she/her): However, when we're talking about science projects, we also have you know a lot of times the research done by people in academic settings, for example, universities.

607
01:41:14.310 --> 01:41:19.500
Steph Courtney (she/her): who are looking to publish and a lot of the times their metrics for success are more about scientific exam.

608
01:41:20.550 --> 01:41:31.530
Steph Courtney (she/her): scientific advance if are we filling a research gap Is this something novel is there, new methods being used those are sorts of things that a lot of times the researchers have to care about for their own careers.

609
01:41:31.890 --> 01:41:46.830
Steph Courtney (she/her): So we're thinking about effective projects with as being composers kind of those two variables, the useable disparate decision making by various parties and then also scientific advance, so this is just this is not real data, this is just a way to think about it basically.

610
01:41:48.660 --> 01:41:51.420
Steph Courtney (she/her): Excuse me, and we can think about the.

611
01:41:52.620 --> 01:42:02.940
Steph Courtney (she/her): relationship between those two kinds of effectiveness or success and think about testing different hypotheses they're like, for example, are there projects that are usually.

612
01:42:03.210 --> 01:42:14.820
Steph Courtney (she/her): Successful in both if it's more useful third decision making is it usually also scientifically novel or or useful or other trade offs are different kinds of projects successful.

613
01:42:15.600 --> 01:42:20.970
Steph Courtney (she/her): In each of these realms in different ways, and maybe only in one or the other realm.

614
01:42:21.870 --> 01:42:31.200
Steph Courtney (she/her): And then that can lead us to sort of think about why that might be what sort of project characteristics, can lead to those kinds of outcomes, so this is another just important framework to this work.

615
01:42:31.830 --> 01:42:38.790
Steph Courtney (she/her): And this led us to the current project that i'm actually talking about shorthand title being best practices for research design.

616
01:42:39.600 --> 01:42:50.760
Steph Courtney (she/her): So there are three objectives, the first one being to make a transferable or reproducible evaluation approach for actionable climate science projects so hopefully you've got a handle on those buzzwords now.

617
01:42:51.270 --> 01:42:57.450
Steph Courtney (she/her): And we're trying to specifically make a quantitative approach, not because that's the best way in the qualitative ones aren't any good.

618
01:42:57.720 --> 01:43:07.590
Steph Courtney (she/her): But quantitative is usually cheaper easier and easier to transfer and reproduce in different settings so we're hoping, this is something that can be shared and useful for different parties.

619
01:43:08.520 --> 01:43:18.720
Steph Courtney (she/her): And then we're specifically using those see cask phase one projects, I talked about before 28 projects $7 million dollars to illustrate this approach so it's on one hand, a pilot.

620
01:43:19.800 --> 01:43:27.810
Steph Courtney (she/her): Case essentially for this method to sort of refine the method and and figure out what we might want to do better before we hand it out and distributed.

621
01:43:28.230 --> 01:43:41.070
Steph Courtney (she/her): For Objective number one, but we will, of course, hopefully get some interesting findings out of number two and this hope to also answer this question number three what common characteristics are there between these effective projects.

622
01:43:41.880 --> 01:43:52.470
Steph Courtney (she/her): And this project is in a working group model so we're hoping to sort of follow practice what we preach with co production there, so we have folks at auburn and university of Tennessee knoxville.

623
01:43:52.800 --> 01:43:56.580
Steph Courtney (she/her): And then we also have monthly meetings that are working hand in hand with.

624
01:43:57.330 --> 01:44:11.460
Steph Courtney (she/her): Employees at the SE cask so we hope to both learn from them because they're the ones on the ground, funding and working with these projects and then also just make sure that whatever we produce will be useful to them our stakeholders.

625
01:44:12.150 --> 01:44:22.530
Steph Courtney (she/her): However, this project is ongoing, we do not have final results yet so today i'll just kind of be talking about this evaluation approach and then have a little bit of luminary results to share.

626
01:44:24.600 --> 01:44:27.570
Steph Courtney (she/her): So this is going to be a lot of content so i've laid it out short.

627
01:44:28.440 --> 01:44:42.270
Steph Courtney (she/her): The theory of change for any program activity is just a framework often visual for what is happening and what that can lead us to find out what impact it has and that can help us to figure out how to measure it.

628
01:44:42.840 --> 01:44:50.880
Steph Courtney (she/her): So if we think about the typical research project, this is just a shorthand version of our theory of change for each of these ones, he cast funded projects.

629
01:44:51.270 --> 01:44:58.680
Steph Courtney (she/her): You have certain inputs and then there's the process of actually doing the research which has components like meetings.

630
01:44:59.130 --> 01:45:05.880
Steph Courtney (she/her): And then that has outputs where you know they create a publication, create a website, what have you, so this is maybe the typical life cycle.

631
01:45:06.270 --> 01:45:10.920
Steph Courtney (she/her): Of a research funding proposal and then your final report, where you follow up and say here's what we did.

632
01:45:11.880 --> 01:45:14.850
Steph Courtney (she/her): and out of that we're hoping that will lead to certain outcomes.

633
01:45:15.240 --> 01:45:23.100
Steph Courtney (she/her): So we're talking about citations and maybe sparking more research as being sort of our scientific advance outcomes and then use by national cultural resource managers.

634
01:45:23.520 --> 01:45:27.030
Steph Courtney (she/her): Being our application outcomes, but there are a couple other parts.

635
01:45:27.630 --> 01:45:36.360
Steph Courtney (she/her): Realistically, that are happening that we also have to model if we want to capture how this happens in the real world so some of these are just sort of external controls, so the easiest example is.

636
01:45:36.690 --> 01:45:46.050
Steph Courtney (she/her): A project that ended only last year, probably won't have any citations and that's not because it's a bad project it's just because it's been too short of a time period since ended.

637
01:45:47.280 --> 01:45:57.690
Steph Courtney (she/her): And then ultimately what we don't necessarily acknowledge that often is that we're doing all of these things in the green to lead to the purple we're hoping to actually make healthier more resilient ecosystems.

638
01:45:58.110 --> 01:46:07.560
Steph Courtney (she/her): But that's really hard to measure, few people have been able to figure out if one project leads to a happier ecosystem that's a really difficult scope so we're not handling that.

639
01:46:08.400 --> 01:46:16.830
Steph Courtney (she/her): Otherwise, all the green boxes, we can actually measure by going through the reports and serving the p eyes and look for a lot of public data sources.

640
01:46:17.220 --> 01:46:30.420
Steph Courtney (she/her): But some of this is not possible that way, so the use part, we need a new instrument to be able to look at that better and so that was my chunk of this project is my chunk and that's what we'll be talking about in a little more detail.

641
01:46:32.520 --> 01:46:42.330
Steph Courtney (she/her): So this is just a quick sort of summary, we actually followed an eight step survey development process i'm actually not even gonna spend time on these because i'm a little short on time.

642
01:46:42.660 --> 01:46:47.040
Steph Courtney (she/her): But basically we've been following a specific process that you can look at this paper for more details.

643
01:46:48.000 --> 01:46:55.530
Steph Courtney (she/her): Of instrument and item development it's not just coming up with questions and hoping they measure what we measure so we've been doing this for a while.

644
01:46:56.430 --> 01:47:11.580
Steph Courtney (she/her): If you take the survey, which is live now, this is a quick little preview of what it would look like so we're asking to what degree, is this project, for example, influence your decisions to change any habitat habitat or species management practices.

645
01:47:12.030 --> 01:47:17.760
Steph Courtney (she/her): And then you have liquored scale response options or not applicable my organization doesn't do this.

646
01:47:18.150 --> 01:47:24.870
Steph Courtney (she/her): And we did end up using those three constructs from the literature that I mentioned earlier that conceptual instrumental and justification use.

647
01:47:25.350 --> 01:47:35.790
Steph Courtney (she/her): But then we have a couple boundary items where, after all of our literature review interviewing stakeholders, all this there's some that were just not totally sure what what constructed fits under.

648
01:47:36.330 --> 01:47:48.630
Steph Courtney (she/her): And that's where the confirmation by factor analysis will come in, so we can test a couple different models of how these items aligned with each other and and hopefully learn some new things about the constructs and responses from that.

649
01:47:51.090 --> 01:48:03.990
Steph Courtney (she/her): So the sample we scoured the pii surveys that we talked about the reports that were out there and snowball sampling meaning asking the respondents hey do you know anyone else who can talk to us about this project or.

650
01:48:04.320 --> 01:48:16.110
Steph Courtney (she/her): Or has used this tool and can talk about it, and ultimately sent out 230 unique invites and are currently sitting on 77 complete responses from 21 projects so that's 21 out of the 28.

651
01:48:16.740 --> 01:48:23.670
Steph Courtney (she/her): And the only reason i'm even bringing up these numbers is because one of our sort of tasks and this process is figuring out.

652
01:48:25.140 --> 01:48:31.950
Steph Courtney (she/her): If our response distribution, has to do more with our sampling and if we did a good job at sampling or introduce certain sampling biases.

653
01:48:32.370 --> 01:48:40.020
Steph Courtney (she/her): Or if our response to restoration is a true reflection of the use of each project so for example here is our.

654
01:48:40.410 --> 01:48:49.890
Steph Courtney (she/her): Current responses for each project as they sit so each bar is a project, so this project 26 here has 10 responses, for example, that's the highest number.

655
01:48:50.640 --> 01:49:03.810
Steph Courtney (she/her): Whereas the project 27 has zero responses and it's our job to basically be competent enough in our sampling and and hopefully i've gone through the right sampling procedures and done our best to find respondents.

656
01:49:04.140 --> 01:49:14.580
Steph Courtney (she/her): to figure out if we just didn't find users have 27 or if there's truly fewer users of 27 if that's a genuine finding such as something to look at.

657
01:49:15.360 --> 01:49:26.010
Steph Courtney (she/her): And then I also have are just where it sits right now summary of the Youth scores, so in this orange here we have the conceptual use across all responses of all the projects.

658
01:49:26.340 --> 01:49:30.180
Steph Courtney (she/her): Just a quick summary, the green is instrumental and the yellow justification.

659
01:49:30.960 --> 01:49:38.730
Steph Courtney (she/her): So, the main thing I just pulled from this right now is that we have a complete spreads someone answered no use for every type of use.

660
01:49:39.060 --> 01:49:45.180
Steph Courtney (she/her): For some project, and someone answered a great deal of use, we have a full spread and use of the wide variability of it.

661
01:49:45.660 --> 01:49:58.170
Steph Courtney (she/her): And then also that you know where the averages sit do very so instrumental us has the lowest median being that bar and the lowest mean, which is the little X, which might look like a duck field depending how zoom did you are.

662
01:49:59.340 --> 01:50:14.070
Steph Courtney (she/her): which makes sense to us, based on you know the interviews and development we've done and what the literature says that it's a lot easier to say that a project, you know I learned something new from it than it made me change how I was actually doing something in the real world.

663
01:50:15.330 --> 01:50:20.250
Steph Courtney (she/her): So this is just sort of good to start looking at and we're going to keep looking at it in more depth.

664
01:50:20.700 --> 01:50:29.640
Steph Courtney (she/her): In the meantime, though, we are also trying to fill in data gaps and and learn more about those projects for which we have zero users so we're going to be serving the castle leadership.

665
01:50:30.060 --> 01:50:37.200
Steph Courtney (she/her): about their perception of use of the projects for which we don't have respondents and see if that can tell us anything new, or if that would be a valid data input.

666
01:50:37.710 --> 01:50:45.570
Steph Courtney (she/her): And then ultimately do that factor analysis that I mentioned to figure out how our measurement of this is going and then ultimately the structural equation model.

667
01:50:46.050 --> 01:50:59.280
Steph Courtney (she/her): Roughly mimicking the theory of change, which I put another summary version at the bottom here to learn about again how the characteristics of the projects might relate to these outcomes abuse and scientific impact.

668
01:51:00.420 --> 01:51:07.710
Steph Courtney (she/her): And with that I left you with sort of our our big questions here and that data again and I would love to take any questions.

669
01:51:12.420 --> 01:51:13.920
karen McNeal: awesome Thank you.

670
01:51:17.340 --> 01:51:18.390
Chandana Mitra Geoscience: Okay, so.

671
01:51:19.800 --> 01:51:23.130
Chandana Mitra Geoscience: I hope you have time for questions, and I can see Karen has a question.

672
01:51:24.480 --> 01:51:28.710
Chandana Mitra Geoscience: And the question is any as to why instrument instrumental uses.

673
01:51:29.220 --> 01:51:30.270
justification.

674
01:51:32.520 --> 01:51:41.850
Steph Courtney (she/her): I do have thoughts, yes I briefly hinted at this were just a lot of the actions that people are actually taking their jobs, which would be the instrumental.

675
01:51:42.150 --> 01:51:49.380
Steph Courtney (she/her): Are you know informed by their years of education or how it's been done or professional development, what have you.

676
01:51:50.190 --> 01:51:56.280
Steph Courtney (she/her): So it's a lot harder to change those things and to say it's had an impact or influence and changed, what I do.

677
01:51:57.210 --> 01:52:04.980
Steph Courtney (she/her): But it is interesting that justification, where maybe you didn't actually use it to change a decision or action, but then you use it to justify anyway.

678
01:52:05.370 --> 01:52:14.610
Steph Courtney (she/her): And I do sort of an in a way, think of justification is just say you put in a funding proposal, and you cite this paper as a way to back you up additionally.

679
01:52:15.570 --> 01:52:24.780
Steph Courtney (she/her): there's just a lot of ways that you can use a project to justify what you're doing, but ultimately I don't really have that's interesting and yeah we'll do more interviews and figure it out.

680
01:52:29.490 --> 01:52:34.350
Chandana Mitra Geoscience: So anyone else any question for Steve Austin.

681
01:52:37.440 --> 01:52:51.120
Chandana Mitra Geoscience: let's see if I could just more more maybe you already mentioned this, but what I wanted to kind of understand from this particular graph that you're going here is so all the projects war.

682
01:52:51.840 --> 01:53:08.610
Chandana Mitra Geoscience: What I mean to say is for every single project did they all check on concepts and instrument instrument instrument us and justification to same price evaluated, for all these three categories or there was supposed to be plugged into different categories.

683
01:53:09.270 --> 01:53:20.010
Steph Courtney (she/her): No yeah so every person responded about one project for all these questions and there they could always say na but I don't think anyone said na for an.

684
01:53:20.010 --> 01:53:23.010
Steph Courtney (she/her): entire category so everyone.

685
01:53:23.130 --> 01:53:31.530
Steph Courtney (she/her): At least i've heard you know six questions that's conceptual six I think instrumental seven justification, whatever that balance was.

686
01:53:33.420 --> 01:53:35.250
Chandana Mitra Geoscience: That make sense, thank you.

687
01:53:37.620 --> 01:53:38.430
Chandana Mitra Geoscience: come back to you.

688
01:53:40.290 --> 01:53:41.520
karen McNeal: Okay i'm.

689
01:53:42.900 --> 01:53:45.180
karen McNeal: About two minutes um.

690
01:53:46.980 --> 01:53:51.030
karen McNeal: me other questions before we have stuff stop sharing.

691
01:53:52.140 --> 01:53:52.920
don't think so.

692
01:53:54.840 --> 01:53:55.170
karen McNeal: Okay.

693
01:53:56.280 --> 01:54:00.630
karen McNeal: So go ahead and take that and then our next speaker.

694
01:54:00.660 --> 01:54:01.110
Is.

695
01:54:02.310 --> 01:54:11.580
karen McNeal: haven cash well so haven, if you want to go ahead and start sharing and will be, maybe a little few seconds early, but I think we'll be all right.

696
01:54:19.050 --> 01:54:20.100
karen McNeal: All right, you get heaven.

697
01:54:21.600 --> 01:54:38.940
karen McNeal: yeah Okay, so our next speaker is haven cash well and her title of her talk is user engagement with a web based decision support tools to support us fish and wildlife service scientists development of species status assessments.

698
01:54:41.310 --> 01:54:43.410
Haven Cashwell: Great so hi everyone, my name is.

699
01:54:43.410 --> 01:54:54.090
Haven Cashwell: haven cash well and before we get started, I would like to acknowledge the collaborators on this research, which were Dr JEREMY Neil Dr Cathy dello and Dr Ryan boils.

700
01:54:56.160 --> 01:55:05.730
Haven Cashwell: So the decision support system that was evaluated for this project was known as campus and Campus stands for climate analysis and visualization for the assessment of species status.

701
01:55:06.540 --> 01:55:10.590
Haven Cashwell: This decision support system was developed by the State climate office of North Carolina.

702
01:55:11.430 --> 01:55:24.750
Haven Cashwell: And it's this entire research project is in partnership with scientists from both the US fish and wildlife service and the US geological survey and i'm very thankful that this research is being funded by the southeast climate adaptation science Center.

703
01:55:26.130 --> 01:55:35.730
Haven Cashwell: So why was campus developed in the first place well the US fish and wildlife service must evaluate the status of that risk plants and animals under the endangered species act.

704
01:55:36.090 --> 01:55:40.590
Haven Cashwell: And they do this by producing species status assessments, also known as essays.

705
01:55:41.340 --> 01:55:48.420
Haven Cashwell: And these assessments are prepared for at risk species to help inform a range of management decisions under the endangered species act.

706
01:55:48.960 --> 01:56:01.110
Haven Cashwell: And these assessments can be based on history biology and vulnerabilities within a species and they tend to focus on the three r's which are resiliency redundancy and representation of species.

707
01:56:02.220 --> 01:56:03.930
Haven Cashwell: So a couple of examples.

708
01:56:05.340 --> 01:56:16.500
Haven Cashwell: of species that do have species status assessments are the brutal darter which is this top right photo that's actually a screenshot of the species status, assessment and then the gopher tortoise.

709
01:56:19.320 --> 01:56:28.230
Haven Cashwell: So, ultimately, for this project two different versions of canvas were developed by the State climate office, so that we could evaluate each version and see which one was more effective.

710
01:56:28.830 --> 01:56:32.880
Haven Cashwell: So what these two versions, the version on the left is known as the box swap version.

711
01:56:33.600 --> 01:56:40.740
Haven Cashwell: And the version on the left or the right, excuse me as soon as the beta version, and there are two distinct differences between these two versions.

712
01:56:41.040 --> 01:56:49.260
Haven Cashwell: The first being how the climate projections data was graphically displayed so in the box swap version on the left, you can see, it was displayed in a box and whisker format.

713
01:56:50.490 --> 01:56:54.990
Haven Cashwell: browsing the fate of bar version of the right, you can see that it was displayed in a faded bar format.

714
01:56:56.220 --> 01:57:02.820
Haven Cashwell: The second distinct difference between these two versions were the two color schemes use for the climate snapshot on the bottom portion of canvas.

715
01:57:03.360 --> 01:57:13.080
Haven Cashwell: And the box plot version, you can see, on the climate snapshot color scheme was more sequential in nature, going from white to a dark red to a dark purple.

716
01:57:13.800 --> 01:57:21.780
Haven Cashwell: And then the fate of our color scheme here on the right and started off with a dark blue going to it a lighter yellow and then eventually to a dark Gray.

717
01:57:24.720 --> 01:57:35.400
Haven Cashwell: So canvas was loosely based on a previous evaluation of a decision decision support system known as pie map in two screenshots of prime Member located on the right hand side.

718
01:57:36.630 --> 01:57:43.320
Haven Cashwell: And for our graphical design differences, we wanted to make sure that we chose two distinct differences to display error within our data.

719
01:57:43.920 --> 01:57:53.820
Haven Cashwell: However, our previous research is mainly focused on the differences between bar graphs and line graphs, and so we just chose the box and whisker plot, and then the fate of bar to display.

720
01:57:54.930 --> 01:57:55.710
Haven Cashwell: This arrow.

721
01:57:56.880 --> 01:58:06.060
Haven Cashwell: And then, with color schemes on previous research has shown that sequential color schemes like for the box plot version of canvas are more effective than color schemes.

722
01:58:06.690 --> 01:58:15.180
Haven Cashwell: More so like the fate of our version of canvas where we have a divergent color scheme, so we chose these two color schemes to also test that theory as well.

723
01:58:17.940 --> 01:58:30.810
Haven Cashwell: So, in total, I had to research questions that I would have wanted to answer the first being how do novice users and, in our case undergraduates engage with the different versions of canvas, and this was tested through I tracking.

724
01:58:31.530 --> 01:58:48.060
Haven Cashwell: Now I would like to note we had undergraduates as our novice users as a convenient sample, but we did choose to target the College of forestry and wildlife students, because we felt like they would have had some familiarity with the impacts of climate change on species.

725
01:58:49.620 --> 01:59:00.210
Haven Cashwell: And then the second research question I would like to answer was how can the usability of campus be improved, and this was measured through I tracking and thematic coding of undergraduate interviews.

726
01:59:01.050 --> 01:59:06.570
Haven Cashwell: So assessing the usability pertaining to research question to can be divided into three different metrics.

727
01:59:07.650 --> 01:59:11.880
Haven Cashwell: The first been efficiency, so how long it takes for a participant to choose an answer.

728
01:59:12.390 --> 01:59:21.510
Haven Cashwell: The second being effectiveness so whether or not the participant chose the correct answer, and the third being satisfaction, which was the overall perceived usefulness of campus.

729
01:59:22.110 --> 01:59:31.080
Haven Cashwell: Now, both efficiency and effectiveness were measured using eye tracking metrics and satisfaction was measured through the medic coding of undergraduate interviews.

730
01:59:32.970 --> 01:59:44.490
Haven Cashwell: Now, with our tracking we use the toby ti X 300 eye tracker which is located here attached to the bottom of the computer monitor and it safely measure to participants visual focus on the computer monitor.

731
01:59:46.200 --> 01:59:54.900
Haven Cashwell: All research was conducted in the auburn GEO cognition lamb on auburn's campus and we made sure to follow all recommended CDC guidelines at that particular time of the study.

732
01:59:57.030 --> 02:00:05.400
Haven Cashwell: So for my study procedure I first had a pilot study, where I have four members of the auburn GEO cognition lab come in and complete the entirety of the study.

733
02:00:05.820 --> 02:00:14.700
Haven Cashwell: From that we made to necessary changes, the first being too short in the free exploration time period from 120 seconds to 75 seconds.

734
02:00:15.450 --> 02:00:23.910
Haven Cashwell: We have free exploration so that participants could get acquainted with what they were looking at before we started asking the questions on it, so we shorten that time link.

735
02:00:24.780 --> 02:00:35.880
Haven Cashwell: We also noticed a discrepancy between the wording of both the box law and the fate of bar graph or fate of bar version of canvas, and so we made the change to match that wording across versions.

736
02:00:36.660 --> 02:00:45.510
Haven Cashwell: So once those necessary changes were made, we then moved on to I tracking participants and participants were randomly assign a version depending on when they came into the lab.

737
02:00:45.780 --> 02:00:51.600
Haven Cashwell: So they they were either randomly assigned the entirety of the beta bar version or the entirety of the box bought version.

738
02:00:52.830 --> 02:00:59.220
Haven Cashwell: Once they were done I tracking I think conducted approximately 15 minute long interviews with the participants.

739
02:00:59.550 --> 02:01:15.360
Haven Cashwell: were used retrospective verbal protocol so in this case I displayed their eye tracking data in order to spark a conversation between myself and the participant so using this I could see if they were looking at a particular area or not, and ask them questions pertaining to that.

740
02:01:17.460 --> 02:01:28.950
Haven Cashwell: So, in total, I had 39 participants come into the lab, however, two of those participants data had to be thrown out to them, not meeting the greater than 70% way to gay sample that we established at the beginning of the study.

741
02:01:29.790 --> 02:01:44.820
Haven Cashwell: So for my analysis I had 37 participants 19 of those in the fate of bar version and 18 of those in the box bought version of canvas out of those 37 participants majority of them were 19 years old, with a fairly even spread upon ages, among them.

742
02:01:46.050 --> 02:01:48.690
Haven Cashwell: And then, ethnicity, a majority of them were white.

743
02:01:49.920 --> 02:01:59.190
Haven Cashwell: With my grade classification, there was a fairly even spread from freshman to senior and majority of my participants came from the College of engineering, however, I would also like to note.

744
02:02:00.060 --> 02:02:07.560
Haven Cashwell: The second most college represented was the forest dream wildlife college, which is who our target participants were for this study.

745
02:02:10.800 --> 02:02:26.970
Haven Cashwell: So the way that I was able to obtain I tracking metrics or through areas of interest, also known as a wise and so here i'm showing a blank version of what the box swap version of canvas looks like and now i'm just overlaying areas of interest for one particular question.

746
02:02:28.230 --> 02:02:34.530
Haven Cashwell: Now these areas of interest, allow us to collect I tracking data in these particular boxes, so I drew these boxes.

747
02:02:35.310 --> 02:02:47.340
Haven Cashwell: For each question and they either determined correct location and correct location or some of them were even neutral ao is which pertain to, for instance, the question and the answer, or maybe an associated legend.

748
02:02:48.750 --> 02:02:55.410
Haven Cashwell: I should note that areas of interest remained the same across versions, but not necessarily the same across questions so.

749
02:02:56.850 --> 02:03:08.580
Haven Cashwell: Meaning that Question one in the fate of bar version had the same areas of interest as question one in the box plot, however question one in the box block may not contain the same areas of interest as question to in the box plot.

750
02:03:10.050 --> 02:03:20.970
Haven Cashwell: So with these areas of interest, I chose to analyze three different eye tracking metrics, the first being time to first fixation so how long it took participants to initially fixate on a certain day ally.

751
02:03:21.600 --> 02:03:32.970
Haven Cashwell: The second being total fixation duration, so how long participants spent fixated on a certain day ally, meaning they could have initially fixated on it left and then came back and that total time would have been added together.

752
02:03:33.930 --> 02:03:39.240
Haven Cashwell: And then finally time to first mouse click and that was how long it took for participants to select an answer.

753
02:03:40.110 --> 02:03:55.980
Haven Cashwell: What these three I tracking metrics I initially did a bulk analysis to see if there were any obvious differences between groups, unfortunately I didn't receive statistical significance, however, I did receive a larger effect size for total fixation duration, when I calculated Cohen Steve.

754
02:03:57.300 --> 02:04:06.300
Haven Cashwell: So, since I didn't receive any statistical significance, I decided to do a finer tuned analysis on both time to first fixation and total fixation duration.

755
02:04:07.110 --> 02:04:21.060
Haven Cashwell: And I made sure to normalize this data by time to first mouse click because I wanted to get an accurate representation for how long participants were spending on a particular question since since we didn't limit the amount of time they could spend on each question.

756
02:04:22.320 --> 02:04:27.570
Haven Cashwell: Once again I didn't receive any statistical significance, so I looked at averages and effect sizes.

757
02:04:28.140 --> 02:04:42.990
Haven Cashwell: And so on average fate of our participants took longer initially fixating so time to first fixation on correct locations and also on average box plot participants fixated for longer so total fixation duration on correct locations.

758
02:04:45.900 --> 02:04:59.460
Haven Cashwell: So here are the larger effect sizes that we found from that finer tuned analysis with time to first fixation we ended up having five tasks with the larger effect size and then total fixation duration, we only had three tasks with the larger effect size.

759
02:05:00.690 --> 02:05:12.570
Haven Cashwell: With looking at these tasks, I noticed that there was one task test 3.3 that overlap between the two, so I wanted to look at that a bit further, to see what was going on with that particular task.

760
02:05:13.890 --> 02:05:22.470
Haven Cashwell: To task 3.3 asked to select the best answer for the multimodal mean for 2010 to 2039 for the gopher tortoise and auburn Alabama.

761
02:05:24.570 --> 02:05:32.940
Haven Cashwell: So here i'm showing a heat map for both the box plot version of canvas and the fate of our version and a heat map is showing how long participants were.

762
02:05:33.240 --> 02:05:46.890
Haven Cashwell: Looking in a particular area, so the warmer the colors and the longer the participants were looking there, so the red colors that you're seeing versus the cooler colors which would be the blue, the less the participants were looking in that particular area.

763
02:05:48.090 --> 02:05:54.690
Haven Cashwell: So now, I have over lane a black box on the screen, which shows the correct location for this particular question.

764
02:05:55.410 --> 02:06:06.570
Haven Cashwell: And if we look at the box plot version of canvas on the left, you can see that there was a concentration of those participants, looking at the correct location and even the associated access label in order to answer the question.

765
02:06:07.740 --> 02:06:14.820
Haven Cashwell: However, what the fate of bar version on the right we're not really seeing that concentration of participants looking there nor looking at the associated.

766
02:06:15.840 --> 02:06:17.340
Haven Cashwell: legend to answer the question.

767
02:06:19.770 --> 02:06:30.600
Haven Cashwell: So in this case task 3.3 was able to show us that the box plot participants noodle look at the correct location, rather than the fate of our participants not looking at the correct location.

768
02:06:32.670 --> 02:06:37.500
Haven Cashwell: So now i'm going to move on to my second research question was which was about the usability of canvas.

769
02:06:38.160 --> 02:06:50.700
Haven Cashwell: And first i'll start off with efficiency, so how long it took for participants to select an answer, and this was measured using time to first mouse click unfortunately there was no statistical significance, so I looked at averages.

770
02:06:52.140 --> 02:07:00.090
Haven Cashwell: and out of the averages 10 out of the 15 questions took fate of our participants longer to select an answer, so in this table this top row.

771
02:07:00.540 --> 02:07:16.170
Haven Cashwell: Then questions that are shaded orange in the top row show the 10 questions where it took faded bar participants longer to select an answer and in the bottom row i'm highlighted orange show the five questions where it took box pop participants longer to select an answer.

772
02:07:17.430 --> 02:07:24.030
Haven Cashwell: So in this case the box plot version of canvas was deemed more efficient than the fate of our version of canvas.

773
02:07:26.100 --> 02:07:31.590
Haven Cashwell: With effectiveness, this was measured on whether or not the participant chose the correct answer.

774
02:07:31.890 --> 02:07:42.000
Haven Cashwell: Since my sample size only different by one participant, I wanted to make sure that I chose an accurate way of displaying this data, so I chose to display it as number of participants who answered incorrectly.

775
02:07:43.650 --> 02:07:56.430
Haven Cashwell: So, as you can see the top row is once again showing the fate of our participants, the questions that are highlighted orange are the ones where the fate of bark participants had a larger number of participants answer incorrectly.

776
02:07:57.900 --> 02:08:07.380
Haven Cashwell: And then, with the box plot version of canvas and once again highlighted orange or the four questions where the box plot had a large number of participants answer incorrectly.

777
02:08:08.280 --> 02:08:13.740
Haven Cashwell: I should note that there were five questions were both versions of canvas had the same number of participants answer incorrectly.

778
02:08:15.000 --> 02:08:25.680
Haven Cashwell: So in this case the box plot was deemed more effective than the faded bar version of canvas because they had a less number of questions where there was a larger number of those participants answer incorrectly.

779
02:08:27.690 --> 02:08:37.710
Haven Cashwell: And then, finally, the last metric of usability is satisfaction, so I wanted to look at difficult to understand versus easy to understand and that the magic interviews.

780
02:08:38.970 --> 02:08:39.450
Haven Cashwell: So.

781
02:08:40.980 --> 02:08:48.570
Haven Cashwell: difficult to understand, was brought up at five times in these interviews rather than easy to understand only bringing brought up 58 times.

782
02:08:48.900 --> 02:08:52.920
Haven Cashwell: And I wanted to show an example of what these particular quotes could have looked like.

783
02:08:53.490 --> 02:09:02.340
Haven Cashwell: So if difficult to understand here's a quote from a participant stating I just didn't understand the bottom I don't know why I couldn't understand it, I was confused about what goes with what.

784
02:09:03.210 --> 02:09:10.800
Haven Cashwell: In this case, the participant was talking about the climate snapshot portion on the bottom and they weren't really understanding that that particular data.

785
02:09:12.270 --> 02:09:20.340
Haven Cashwell: Whereas an easy to understand quote looked like having the colors and the legend pertain to what was being seen on the graph was helpful and it was easier to stay focused.

786
02:09:21.210 --> 02:09:35.070
Haven Cashwell: So in this case, this participant is talking about the the colors and the climate projections those same colors being the same and the access label and the graphically displayed data they felt like that was helpful and help them understand.

787
02:09:37.770 --> 02:09:43.290
Haven Cashwell: So now i'm going to kind of go through and compare the two versions of canvas so starting with time to first fixation.

788
02:09:44.160 --> 02:09:59.910
Haven Cashwell: On average, it took feta bar participants longer to initially fixate on the correct location, does the box plot version being better for time to first fixation for total fixation duration on average box plot participants fixated longer on correct locations.

789
02:10:00.930 --> 02:10:05.490
Haven Cashwell: US sorry, making the fate of our version better and that I tracking metric.

790
02:10:06.390 --> 02:10:16.440
Haven Cashwell: When it comes to usability with efficiency 10 out of the 15 questions to fate of our participants longer to select an answer that's making the box plot version of canvas more efficient.

791
02:10:17.340 --> 02:10:24.210
Haven Cashwell: With effectiveness, there were more questions where the fate of bar version had a larger number of participants answer incorrectly.

792
02:10:25.140 --> 02:10:36.450
Haven Cashwell: Thus, making the box plot version of canvas more effective and then, finally, with satisfaction, since the box plot was more efficient and more effective it just made it more satisfying.

793
02:10:37.830 --> 02:10:44.220
Haven Cashwell: So my recommended version of canvas to be tested in the future, would be the box pop version of canvas where we have a box and whisker.

794
02:10:45.690 --> 02:10:57.570
Haven Cashwell: For graphically displaying data and then having a sequential color scheme for the climate snapshot portion of canvas where the colors go from white to read to a dark purple.

795
02:10:59.220 --> 02:11:06.960
Haven Cashwell: And with future research, I could analyze more I tracking metrics in the futures, such as fixation counter total visit duration.

796
02:11:07.530 --> 02:11:19.530
Haven Cashwell: I also can look at high and low performers within this first iteration of testing to see what the differences were and, ultimately, we want to convert canvas to a working website so I tracking the navigational website can be helpful.

797
02:11:21.060 --> 02:11:27.990
Haven Cashwell: I also want to attract canvas with stakeholders in the US fish and wildlife service since ultimately this tool is being made for them in the future.

798
02:11:29.190 --> 02:11:36.030
Haven Cashwell: And then another extension of this project is that I am currently interviewing authors of species status assessments.

799
02:11:36.360 --> 02:11:45.210
Haven Cashwell: In order to understand exactly what climate information they used to use and what they would like to use in the future and really what climate information they need to complete these assessments.

800
02:11:46.320 --> 02:11:50.310
Haven Cashwell: So with that I will leave you with a few thoughts and ask for questions.

801
02:11:54.870 --> 02:11:57.120
karen McNeal: awesome Thank you so much.

802
02:12:01.530 --> 02:12:02.490
Chandana Mitra Geoscience: For him.

803
02:12:03.120 --> 02:12:06.240
karen McNeal: We have three three minutes.

804
02:12:07.920 --> 02:12:09.930
Chandana Mitra Geoscience: Okay, so anyone any question.

805
02:12:11.070 --> 02:12:12.600
Chandana Mitra Geoscience: Or, I have a question for me I didn't.

806
02:12:15.270 --> 02:12:15.660
Chandana Mitra Geoscience: write.

807
02:12:16.110 --> 02:12:26.370
Chandana Mitra Geoscience: It in about his faded clubs or bars and is that a common common one, which is used for research, when you do comparisons of the socks, especially with I tracking.

808
02:12:27.600 --> 02:12:34.410
Haven Cashwell: yeah so um when looking at previous research I wasn't really able to find any research on fate of bar graphs in general.

809
02:12:36.120 --> 02:12:50.820
Haven Cashwell: dice with this research, we really wanted to choose two distinct graphically design differences but yeah there hasn't really been much literature out there about the fate of bar for a graphical design.

810
02:12:52.080 --> 02:12:54.960
Chandana Mitra Geoscience: and great so it means a lot to add to some literature now.

811
02:12:55.410 --> 02:12:56.610
Haven Cashwell: Yes, exactly.

812
02:12:58.230 --> 02:13:01.800
Chandana Mitra Geoscience: Okay anyone else has a question for haven.

813
02:13:04.140 --> 02:13:06.690
karen McNeal: I was sort of thinking about stuff and haven.

814
02:13:07.650 --> 02:13:20.790
karen McNeal: kind of highlight co production and for the scientist who wants to make a product that's useful what would be for for some stakeholders.

815
02:13:21.270 --> 02:13:39.300
karen McNeal: I guess to you guys and they don't really have the money to invest in evaluation to say hey, this is what I need to do what would be your recommendation if they can't really do a research, you know evaluation product, and I know steph you're trying to get at this by.

816
02:13:41.040 --> 02:13:48.480
karen McNeal: Getting the funders to kind of think about what is most effective but based on what you know now, and this is for either of you to answer.

817
02:13:48.780 --> 02:14:03.570
karen McNeal: What would be some of your recommendations for scientists to do you want to do want to make a product for a stakeholder to use but don't have you know need the first few steps and want to be the most efficient and effective at doing it.

818
02:14:07.830 --> 02:14:11.130
Haven Cashwell: step to do you want to answer doing only been to me.

819
02:14:14.880 --> 02:14:16.230
Steph Courtney (she/her): i'm here, but you got it.

820
02:14:17.670 --> 02:14:30.390
Haven Cashwell: Okay, so I guess with first steps with the scientists wanting to make a valuable product I would think talking with the stakeholders in general is going to be beneficial in order to really.

821
02:14:31.710 --> 02:14:40.110
Haven Cashwell: understand the stakeholders needs, and I feel like having just you know conversations back and forth is going to be really beneficial.

822
02:14:41.550 --> 02:14:53.040
Haven Cashwell: With yeah just assessing needs and understanding what exactly stakeholders, would like to see out of a beneficial product I think would be like an important first step, hopefully, that answered the question.

823
02:14:54.090 --> 02:14:58.740
karen McNeal: yeah so yeah and he said, you have any to add to it and then maybe 10 seconds.

824
02:15:01.950 --> 02:15:03.120
Steph Courtney (she/her): is slightly missed the.

825
02:15:03.120 --> 02:15:04.590
Steph Courtney (she/her): Question i'll be fully honest.

826
02:15:04.680 --> 02:15:09.090
karen McNeal: Oh yeah that's fine I think haven got it so first talks to the stakeholder number one.

827
02:15:10.290 --> 02:15:13.980
karen McNeal: got it all right haven, you can go ahead and stop sharing and.

828
02:15:20.340 --> 02:15:24.000
karen McNeal: I just needed myself i'm sorry Lillian if you want to go ahead and share your screen.

829
02:15:24.840 --> 02:15:26.190
Lily Howie: All right, thank you very much.

830
02:15:26.610 --> 02:15:41.220
karen McNeal: For that working yeah all right great and so while she's doing that i'll introduce her talk it's titled climate experiences and perspectives, a survey of participants and viewers of the 2020 South Carolina seven expedition.

831
02:15:42.900 --> 02:15:45.840
karen McNeal: very interested in looking forward to it, I don't see your screen yet.

832
02:15:45.870 --> 02:15:47.700
Lily Howie: i'm getting it up just.

833
02:15:47.760 --> 02:15:50.190
karen McNeal: Okay, great perfect just wanted to make sure we're good.

834
02:15:51.930 --> 02:15:52.110
karen McNeal: yeah.

835
02:15:56.160 --> 02:16:03.930
Lily Howie: i'm lily how he and I work with coastal Carolina university and the dire Institute for leadership.

836
02:16:04.950 --> 02:16:15.300
Lily Howie: Here at coastal and so we were looking at the South Carolina seven expedition this past summer South Carolina seven that will advance the slide.

837
02:16:15.960 --> 02:16:24.150
Lily Howie: It stands for the South Carolina Seven Wonders expedition, and this is an expedition that was created by Tom mulliken.

838
02:16:24.780 --> 02:16:37.500
Lily Howie: He is a research professor at coastal Carolina university and he's also the chairman for the South Carolina floodwater Commission so he's been working for for several years now, with the flood water Commission.

839
02:16:39.330 --> 02:16:50.610
Lily Howie: talking to and working with communities in South Carolina who've been affected by flooding, whether it's hurricane related storm related storm surge and.

840
02:16:51.150 --> 02:17:02.190
Lily Howie: nuisance flooding title related there's a lot going on and he's seen the need for awareness in in terms of the natural resources that we have in South Carolina.

841
02:17:02.580 --> 02:17:15.330
Lily Howie: And one great way to do that is to get people involved in their local public public lands parks state parks, we have condrey National Park.

842
02:17:15.840 --> 02:17:29.250
Lily Howie: There is one of the Seven Wonders of South Carolina so Professor mole can put together this expedition which is 30 days it was in the inaugural expedition was in July of 2020.

843
02:17:30.240 --> 02:17:40.200
Lily Howie: And each day has an activity which was a lot of hiking, but they also had some horseback riding river rafting scuba diving.

844
02:17:41.010 --> 02:17:55.200
Lily Howie: And they also had public outreach where they talked about issues like conservation development flood water issues fitness issues with people in South Carolina getting out.

845
02:17:56.670 --> 02:18:06.120
Lily Howie: Getting out and involved in nature and how that helps with certain medical conditions there's there's a lot going on and so part of.

846
02:18:06.930 --> 02:18:14.790
Lily Howie: What they wanted to do with the South Carolina seven expedition was not only get information out to people in South Carolina.

847
02:18:15.090 --> 02:18:23.850
Lily Howie: But also get information from people in South Carolina what they think about the environment, how they're involved in climate.

848
02:18:24.300 --> 02:18:29.550
Lily Howie: How climate has affected them, so the South Carolina seven program reached out to.

849
02:18:30.120 --> 02:18:42.030
Lily Howie: The dire institute at coastal Carolina to create a survey, this was our mountains to see survey that we were distributing to participants and viewers because.

850
02:18:42.870 --> 02:18:51.900
Lily Howie: Obviously, with the coronavirus issues we weren't able to get the engagement in person engagement that we were hoping for, with the.

851
02:18:52.350 --> 02:19:06.660
Lily Howie: program people weren't able to travel as much so, we were able to reach out to people on social media who are following along with the photos and video that were being posted each each leg of the trip.

852
02:19:08.160 --> 02:19:15.720
Lily Howie: So we developed a survey that asks about belief in climate change, specifically anthropogenic climate change.

853
02:19:16.410 --> 02:19:34.350
Lily Howie: perceived threat of certain consequences of climate change, for South Carolina particular opinions of government response state and federal level opinions on the inclusion of climate change curriculum in public schools, whether or not the responded had.

854
02:19:35.490 --> 02:19:44.490
Lily Howie: Personal experience with extreme weather events and we left it kind of open ended with extreme weather events, because different things.

855
02:19:45.000 --> 02:19:57.420
Lily Howie: are different in different parts of the State different types of extreme weather affect people differently, and then we asked for some demographic information their county where they live, age, gender.

856
02:19:58.290 --> 02:20:09.750
Lily Howie: race, ethnicity household income and the highest level of education that they had completed so starting out we're just going to go over some of the.

857
02:20:10.620 --> 02:20:23.700
Lily Howie: raw information, the numbers that we bought before we get into some of the more statistics statistical analyses we did, but when we asked them about their opinion on climate change 80%.

858
02:20:24.090 --> 02:20:34.890
Lily Howie: Just over 80% said that yes, they believe in climate change in specifically anthropogenic climate change, the human activity is affecting.

859
02:20:36.480 --> 02:20:39.030
Lily Howie: whoops so someone left something in chat.

860
02:20:41.520 --> 02:20:43.260
Lily Howie: That human activity is affecting.

861
02:20:44.490 --> 02:21:02.910
Lily Howie: Climate issues another 11% said that yes, they do believe in climate change natural climate change, not necessarily human affected, and when we ask people to look ahead to think about climate in the coming five years 25 years.

862
02:21:04.290 --> 02:21:23.130
Lily Howie: 72% said yes, they expect that they'll be personally affected by climate change issues in five years and then that went up to 88% saying that they expect that they or their family their relatives, will be affected, within the next 25 years with a lot of people.

863
02:21:24.990 --> 02:21:33.960
Lily Howie: We also asked about opinions of government response these questions were modeled after a survey that was done in Australia in 2011.

864
02:21:34.560 --> 02:21:41.160
Lily Howie: That asked about responses, so it was expressing satisfaction and dissatisfaction, but with.

865
02:21:41.490 --> 02:21:50.850
Lily Howie: A directional element to it, do they think the government is not doing enough, are they doing too much, and some people said yes, but we noticed that.

866
02:21:51.360 --> 02:22:10.380
Lily Howie: More people were unsatisfied on both sides with the Federal response than they were with the South Carolina response see 70 70% thinking, the Federal Government is not doing enough versus 68% thinking South carolina's not doing enough.

867
02:22:11.610 --> 02:22:19.980
Lily Howie: So more people were satisfied or thinking that the South Carolina government was doing enough versus the federal government.

868
02:22:20.220 --> 02:22:33.750
Lily Howie: But also, there were more people unsure of what the federal the South Carolina government was doing what I think is fair, with the way that we get our news nowadays there's not as much emphasis on local news as there is.

869
02:22:34.890 --> 02:22:43.230
Lily Howie: Global and national level news but it's we, we can see that South Carolina does seem to be doing something right.

870
02:22:44.610 --> 02:22:47.610
Lily Howie: That that we want to continue obviously moving forward.

871
02:22:49.470 --> 02:22:56.310
Lily Howie: So when we asked about South Carolina schools and curriculum in public schools, we had 86% saying that.

872
02:22:56.760 --> 02:23:06.390
Lily Howie: Yes, they do believe that education on both natural and man made positive climate change should be taught or should be provided in public schools.

873
02:23:07.260 --> 02:23:17.340
Lily Howie: In their curriculum, we had some people say just natural causes of climate change, we had some people say avoid education on climate change completely.

874
02:23:18.480 --> 02:23:28.020
Lily Howie: But the vast majority were supporting natural and anthropogenic climate change in schools, and then we asked people just.

875
02:23:29.070 --> 02:23:38.520
Lily Howie: Generally, are you interested in learning more about climate and over 70% said yes, they were, which is is not only.

876
02:23:39.030 --> 02:23:44.940
Lily Howie: Just a question to say hey do you want to know more, are you are you looking for information.

877
02:23:45.810 --> 02:23:54.690
Lily Howie: to figure out how we can best direct them but also if you're trying to get funding and get support for educational programs.

878
02:23:55.320 --> 02:24:07.770
Lily Howie: You can look at this and say, not only is there, you would think a demand for this sort of information, there are a need for it, there is a demand for it people, people do want information.

879
02:24:09.810 --> 02:24:19.050
Lily Howie: So here's where things I think was the most interesting part we asked people about it they've been personally affected by extreme weather.

880
02:24:19.410 --> 02:24:26.730
Lily Howie: And, most people had, which is, I think, natural with the way that South Carolina has been hit with hurricanes.

881
02:24:27.720 --> 02:24:46.590
Lily Howie: nor'easters all kinds of storms, particularly in the past five years and, but if we look at this map and we do, we can also see some some of where the gaps, where where we didn't have respondents from certain counties, but when it came to the counties, we did have responses from.

882
02:24:47.910 --> 02:25:09.030
Lily Howie: When you get upstate and the fall line is pretty high up most of South Carolina is coastal plain, but if we're looking just at the counties here along the coast almost everyone, we talked to, with the exception of georgetown county which had, I believe, about 70% of the people we.

883
02:25:10.530 --> 02:25:26.580
Lily Howie: People who responded to the survey there had reported experience, all of these counties along the coast over 75% of people responding from those counties said yes, they had been personally affected by extreme weather events.

884
02:25:27.840 --> 02:25:34.620
Lily Howie: So that that was a geographic association that we don't see as clearly when you get further upstate.

885
02:25:37.500 --> 02:25:50.100
Lily Howie: Now we're looking at associations and we took the demographic information, as well as their reports of whether or not they were experienced extreme weather.

886
02:25:51.240 --> 02:26:03.720
Lily Howie: And we used a unit variance analysis analysis of variance so this tells us how things change in correlation with one another there's no causation implied.

887
02:26:04.290 --> 02:26:16.080
Lily Howie: But it just tells us that two variables go together, or that they they change in together with one another, so when we asked about the opinion on climate change.

888
02:26:17.250 --> 02:26:30.900
Lily Howie: There were several significant relationships we had a business significant relationship younger respondents were more likely to believe in anthropogenic climate change it's not a huge surprise education level again.

889
02:26:31.620 --> 02:26:39.360
Lily Howie: responded to a completed higher level at levels of education or, more likely to report belief in anthropogenic climate change.

890
02:26:40.350 --> 02:26:53.430
Lily Howie: But if you look at the F values you'll notice this last one is very significant, the extreme weather experience people who reported personal experience with extreme weather were more likely.

891
02:26:54.510 --> 02:27:10.590
Lily Howie: A significant amount to report, believing in anthropogenic climate change, people who have seen this firsthand and who have been affected by the extreme weather that South Carolina has had in the past decade we didn't give a time limit on it.

892
02:27:11.790 --> 02:27:21.000
Lily Howie: But these are the people who are saying hey something's going on something's happening, they have recognized it because they've seen it themselves.

893
02:27:22.140 --> 02:27:34.710
Lily Howie: And that came through in more of these questions as we looked at it, the climate change curriculum people who had completed higher levels of education were more likely to advocate for.

894
02:27:35.760 --> 02:27:37.770
Lily Howie: anthropogenic climate change curriculum.

895
02:27:39.090 --> 02:27:54.510
Lily Howie: And again extreme weather with this higher value responded to reported experience with extreme weather more likely to advocate for the climate change curriculum and then, when we ask people are they interested in learning more about climate.

896
02:27:55.680 --> 02:28:06.900
Lily Howie: Again, we saw the education level, it was the people who had completed higher levels of education, who were more educated, who are more likely to say yes, I want more education.

897
02:28:07.260 --> 02:28:18.210
Lily Howie: And we can wax philosophical I guess about that a while you can say, well, they have a greater appreciation for education or they're more aware of what they know and don't know.

898
02:28:18.870 --> 02:28:30.750
Lily Howie: But that's something to be aware of, I think, and then also people who have experienced extreme weather are more likely to be interested in more information learning more about climate.

899
02:28:33.210 --> 02:28:33.690
Lily Howie: Another.

900
02:28:35.190 --> 02:28:44.130
Lily Howie: statistical analysis, we did we did a reliability analysis and this is just to tell us to make sure that people weren't marking answers at random.

901
02:28:45.090 --> 02:28:55.470
Lily Howie: we're looking at a measure of internal consistency between several items that would go together in a perfect.

902
02:28:55.980 --> 02:29:04.800
Lily Howie: survey, these would all be the same question that responded to the answer multiple times, but you can't actually do that to real people.

903
02:29:05.670 --> 02:29:18.210
Lily Howie: So we were looking at the rating of severity of consequences of climate change and we did see with chromebooks alpha it's a measure of internal consistency.

904
02:29:18.810 --> 02:29:29.790
Lily Howie: measure between zero to one and values of above point eight are usually considered reliable, so we did have a reliable item.

905
02:29:30.420 --> 02:29:36.030
Lily Howie: Here that's telling us now that just tells us that it's consistent that people were not marking answers at random.

906
02:29:36.420 --> 02:29:48.900
Lily Howie: Then the question is how applicable are these findings to the general population of South Carolina for that we want to look at our limitations and potential for error and bias.

907
02:29:49.380 --> 02:30:04.950
Lily Howie: first thing to note this is possibly a textbook example of selection bias, because we were serving people who already had chosen to be a part of this expedition they had chosen whether to.

908
02:30:05.400 --> 02:30:14.160
Lily Howie: visit in person or join in person or they had follow eight started following the Facebook page the social media pages.

909
02:30:14.430 --> 02:30:22.050
Lily Howie: We were serving people who are already part of each sub set because they had actively chosen to participate.

910
02:30:22.440 --> 02:30:34.890
Lily Howie: Another thing is when we use the demographics, we look at the demographics, not only to do those correlations with the questions, but also to say what do our respondents look like.

911
02:30:35.730 --> 02:30:46.530
Lily Howie: And we had the question on ethnicity and race, we had someone right in an answer on the the other to say why does this matter.

912
02:30:50.160 --> 02:30:53.940
Lily Howie: Well, I will show you why it matters, because this is not good.

913
02:30:55.200 --> 02:31:11.850
Lily Howie: This is a, this is a graph that shows the the online surveys, so this is just the online ones, but you can see, when it comes to ethnicity, we had 87.1% white respondents and when you have a sample size of over 400.

914
02:31:13.080 --> 02:31:14.220
Lily Howie: we're clearly.

915
02:31:15.420 --> 02:31:24.960
Lily Howie: surveying a specific subset of the population, and there is a large portion of South carolina's incredibly diverse population that we're not eating.

916
02:31:26.040 --> 02:31:32.280
Lily Howie: So that's something to keep in mind when we're looking at how we can apply the results of the survey.

917
02:31:33.360 --> 02:31:34.020
Lily Howie: To.

918
02:31:35.250 --> 02:31:40.500
Lily Howie: The people of South Carolina we also are looking at we just looked at soonest to tell us.

919
02:31:42.570 --> 02:31:59.700
Lily Howie: The how our demographics varied we had pretty even split on gender, we had pretty even normally distributed income levels, the respondents were relatively young and they were very well educated.

920
02:32:00.180 --> 02:32:03.210
Lily Howie: In general, so this is the the people that were getting.

921
02:32:03.930 --> 02:32:13.260
Lily Howie: that's, not to say that there's nothing of value in the survey results, but we have to realize that this is a subset of people that we're looking at.

922
02:32:13.560 --> 02:32:27.450
Lily Howie: And we have to figure out if we want to continue that survey and continue to build the South Carolina seven program in the future, how are we going to reach out to these groups that we have not reached yet.

923
02:32:28.740 --> 02:32:37.650
Lily Howie: And so that's our question, looking forward, as we look to continue this program we're already plans are in motion for the.

924
02:32:39.750 --> 02:32:41.100
Lily Howie: expedition in July.

925
02:32:42.150 --> 02:32:49.830
Lily Howie: figuring out how to reach more people how to get the message out and how to get people involved.

926
02:32:50.880 --> 02:32:58.410
Lily Howie: In climate in South carolina's resources and appreciating what the state has to offer.

927
02:33:00.510 --> 02:33:02.280
karen McNeal: awesome Thank you so much.

928
02:33:04.980 --> 02:33:22.170
Chandana Mitra Geoscience: and killing me, this was a great presentation and we have two questions for you in the chat one is from Karen her question is how did it develop the survey, did you modify any existing CC surveys that asked similar questions to help you validate.

929
02:33:23.430 --> 02:33:24.450
Lily Howie: Oh, we did.

930
02:33:25.980 --> 02:33:28.920
Lily Howie: We did modify a little bit a survey that.

931
02:33:30.030 --> 02:33:36.420
Lily Howie: We had created I had worked on with Tom mulliken for the South Carolina floodwater mission.

932
02:33:37.560 --> 02:33:42.780
Lily Howie: In 2019 there was a survey that we did that that we created ourselves.

933
02:33:43.710 --> 02:34:02.610
Lily Howie: For the county of Marion county which has been hit very hard with flooding, and so a lot of these questions were adapted from that survey, which was specifically about flooding and taken and made a little bit broader to ask about climate in general, so we were working from.

934
02:34:02.940 --> 02:34:08.070
Lily Howie: A survey that we had worked on that we had done in the past.

935
02:34:09.690 --> 02:34:10.620
karen McNeal: Okay, thanks.

936
02:34:11.490 --> 02:34:25.050
Chandana Mitra Geoscience: And I, we have one minute, and so we can quickly go into hannah's question was there a compatible representation of survey respondents for each county in South Carolina or or certain companies over our understanding.

937
02:34:25.710 --> 02:34:30.390
Lily Howie: that's a good question, yes, there, there were certain counties certainly that were.

938
02:34:30.870 --> 02:34:32.220
Lily Howie: Under sampled certain were.

939
02:34:32.220 --> 02:34:33.060
Lily Howie: Over sampled.

940
02:34:33.810 --> 02:34:43.500
Lily Howie: I did I did some calculations on that I don't have the data on me right now, but it was not, it was not comparable represented.

941
02:34:44.640 --> 02:34:45.510
Lily Howie: For the.

942
02:34:47.400 --> 02:35:02.220
Lily Howie: The population, we did have especially a couple of the coastal counties, I believe, call it in County williamsburg county and Marion county all have over 30,000 people, and we did not have any response from any of those counties.

943
02:35:06.510 --> 02:35:13.920
karen McNeal: All right, thank you so much, and we're going to move on at two posters left, so thank you all for hanging in.

944
02:35:15.300 --> 02:35:30.510
karen McNeal: The posters are going to i'm going to play from my computer and the first one is going to be from Hannah Stewart so i'm going to bring that up share my screen yep and.

945
02:35:32.100 --> 02:35:34.050
karen McNeal: start, I hope you can all see that.

946
02:35:35.250 --> 02:35:40.530
karen McNeal: And i'm going to start playing in Somebody tell me if you're not hearing us or seeing something like you should.

947
02:35:43.710 --> 02:35:50.340
Hello everybody and welcome my name is Hannah steward and i'm a graduate student at auburn studying rural sociology.

948
02:35:51.120 --> 02:35:59.400
My thesis work will be based on a larger project, which was awarded to auburn by and rcs called the conservation innovation grant.

949
02:36:00.060 --> 02:36:11.010
Led by the future farmer team, specifically in my role on the project pretends to the barriers and limitations which prevent the implementation of climate smart technology on farms.

950
02:36:11.640 --> 02:36:18.120
And, before I go too much into my research, I want to introduce the problems which prompted this study.

951
02:36:18.780 --> 02:36:27.210
So m unpredictable weather patterns due to climate change have caused issues such as drought increase participant precipitation.

952
02:36:27.690 --> 02:36:38.790
rising global temperatures increased erosion, on which all have an influence on the nutrient availability soil by biodiversity and general farming success.

953
02:36:39.090 --> 02:36:49.740
So, because these climate of pressures may be largely acknowledged by the geoscience and agro signs communities producers are known to have some versions and.

954
02:36:51.480 --> 02:36:54.720
Some are maybe slower to recognize.

955
02:36:55.740 --> 02:37:08.370
These issues, so there has been a push to use new ways of information dissemination to help build networks and create sustainable change, but this is what makes this project so important.

956
02:37:09.210 --> 02:37:28.890
So we will be using a Co development of knowledge or basically a participatory approach to this project, where our research participants are not only subjects, but they're committed co researchers as well, so this kind of creates a transcendence from Trent traditional top down methodology.

957
02:37:30.060 --> 02:37:39.750
which allows for very strong engagement and learning from a bottom up approach, where our participants are also included.

958
02:37:40.320 --> 02:37:52.920
So over the span of a five year of five years, the future farmer team will conduct several on farm trials peer to peer discussions and interviews to co develop knowledge.

959
02:37:53.790 --> 02:38:03.060
about the use of climate smart technology, which includes variable rate irrigation erosion control and cover cropping and other nutrient management strategies.

960
02:38:03.540 --> 02:38:12.420
So these events will be held in North, South and Central Alabama where we will record and transcribe the the.

961
02:38:12.720 --> 02:38:22.140
meetings and interviews and upload them to a qualitative software called in vivo where we can parse out themes associated with those limitations and barriers.

962
02:38:22.980 --> 02:38:31.470
As they emerge from the data and what we know from the literature, we can also create social network diagrams which i'll explain our preliminary one later.

963
02:38:32.250 --> 02:38:46.890
And gaffey which will map how Community dynamics change over time and grow over time so using a social network analysis, not only will allow for a comprehensive evaluation and visualization of growth patterns.

964
02:38:47.760 --> 02:38:59.190
But it will help with our communication strategies as well, so, as you can see from figure one on this network analysis, specifically, we had six regional future farmer meetings.

965
02:38:59.640 --> 02:39:11.340
And we had 65 total participants with 110 links between them, so that kind of indicates some sort of communication, we were able to wait, the.

966
02:39:12.120 --> 02:39:23.310
context of purse participation of everyone associated with the meetings and discussions from zero to one or one indicates the highest level of engagement and participation.

967
02:39:24.990 --> 02:39:34.560
Based on the data generated from this analysis, the graph density or the existing ties between participants.

968
02:39:35.130 --> 02:39:48.480
Was point two, and the modularity which measures, the strength of clustering was point 397 so those though these are not strong measures of Community connection and social development.

969
02:39:49.140 --> 02:40:07.170
We hope that this expands and grows over time, as we conduct more meetings and farmers become producers, farmers and rcs agents and our future farmer to become more intertwined and more engaged within the project, so this is so important.

970
02:40:08.220 --> 02:40:16.470
Not only for the sustainable development of agriculture in Alabama, which is extremely vulnerable, but this is kind of a new.

971
02:40:16.710 --> 02:40:24.930
way in which, as researchers, we are not only disseminating information which is very helpful and sustainable but we're learning.

972
02:40:25.200 --> 02:40:35.670
ways in which to improve ourselves in education in general, what a collaborative space which allows for deep learning so thank you so much for tuning in and listening and I hope you guys enjoy the rest of the.

973
02:40:37.410 --> 02:40:37.890
conference.

974
02:40:42.030 --> 02:40:43.710
karen McNeal: Alright, thanks so much Hannah.

975
02:40:46.590 --> 02:40:48.420
Di Tian: go over any questions.

976
02:40:49.980 --> 02:40:51.030
In the chat box.

977
02:40:53.100 --> 02:40:53.340
karen McNeal: and

978
02:40:53.730 --> 02:41:11.220
karen McNeal: And I was, I was going to ask one if there's no one else i'm still obsessed with your network analysis and, as I was looking closer at the result you have are the great dots in the middle that were shown are those the API team, or those representative of other players.

979
02:41:11.880 --> 02:41:24.900
Hannah Stewart?: that's a great question So yes, because rpi team is includes extension agents professors myself other researchers were present at all of those meetings so of course.

980
02:41:25.260 --> 02:41:28.710
Hannah Stewart?: Those measures, measures of central city or more weighted in the Center.

981
02:41:29.520 --> 02:41:38.160
Hannah Stewart?: We also had one specific participant who wasn't and rcs agent he's also found in the middle, because he attended every single meeting as well.

982
02:41:38.430 --> 02:41:45.540
Hannah Stewart?: So we can see from this wonderful network analysis that the more participation and more engagement, you have.

983
02:41:46.050 --> 02:41:51.990
Hannah Stewart?: Of course, p eyes will probably have more in the beginning and they're going to be centered in this.

984
02:41:52.530 --> 02:42:05.700
Hannah Stewart?: In the middle of the graph We hope that, eventually toward the end of the project will see more farmers and people on the outside move closer to the middle as they start to be more confident and share and grown participation level.

985
02:42:06.750 --> 02:42:08.610
karen McNeal: yeah that makes sense, thanks yeah.

986
02:42:09.510 --> 02:42:15.090
Hannah Stewart?: it's a really cool um analysis program and i'm really glad that we have access to it because.

987
02:42:16.290 --> 02:42:26.910
Hannah Stewart?: The way we can wait people's participation, we can display that and the several in a variety of different ways which allows for pretty easy interpretation to see who those.

988
02:42:28.620 --> 02:42:37.710
Hannah Stewart?: High participants are and there's also some talk of printing out the social network analysis and actually going to our participants and having them look.

989
02:42:38.130 --> 02:42:47.820
Hannah Stewart?: look at it, even though they won't be labeled due to IRB protocol and protection of names, but just to see how how that influences their participation as well.

990
02:42:51.120 --> 02:42:51.570
karen McNeal: Thanks.

991
02:43:06.990 --> 02:43:09.570
Di Tian: Currently, water water last one or yeah.

992
02:43:09.720 --> 02:43:14.220
karen McNeal: yeah that's fine if you don't see any other questions that works for me all right, and our and our.

993
02:43:15.270 --> 02:43:20.400
karen McNeal: home run hitter here i'm Ali brown will be our last presenter for this session.

994
02:43:20.910 --> 02:43:33.300
karen McNeal: and her talk is titled evaluating the usability of the easy gcm climate modeling toolkit and its impact on undergraduate students understanding of the climate modeling process and climate change science.

995
02:43:33.660 --> 02:43:47.190
karen McNeal: And I got through all the titles, which I just want to you know give myself a little shoulder pads there because some of these are pretty long but alright i'm going to screen share and hopefully it all works well, like last time and.

996
02:43:48.420 --> 02:43:51.060
karen McNeal: we'll all have a virtual party after this.

997
02:44:13.770 --> 02:44:15.930
karen McNeal: I forgot the screens here try, one more time.

998
02:44:22.920 --> 02:44:27.600
Di Tian: Good morning, everyone, my name is Ali Brown and i'm a first year PhD student at auburn university.

999
02:44:27.930 --> 02:44:38.760
Di Tian: Today, has been presenting my preliminary research plans and methodology for evaluating the user experience, as well as the cognitive and affective impacts of an online global climate modeling tool, known as easy gcm.

1000
02:44:39.690 --> 02:44:52.920
Di Tian: First, let me give us some background on real global climate models for gcs complex gcs are one of the primary tools used by climate scientists to make projections about the earth and how it will respond under different emission pathways these models are based on the same.

1001
02:44:52.920 --> 02:44:56.670
karen McNeal: Laws and equations that underpin scientists understanding of the earth system.

1002
02:44:57.420 --> 02:45:04.590
karen McNeal: However, calculating all of these equations requires the use of a supercomputer like the one shown here, which is house in NASA and runs these gcm.

1003
02:45:05.370 --> 02:45:11.940
karen McNeal: However, this requirement for high computational power often leaves the inner workings of these gcm reserved for upper level scientists.

1004
02:45:12.330 --> 02:45:19.980
karen McNeal: and leaves many students unaware of the decision making process that climate scientists employed when using them, it is for this reason that easy gcm was created.

1005
02:45:21.000 --> 02:45:28.740
from its inception, the main purpose of easy gcm has been to allow students to work with authentic NASA global climate modeling data and tools.

1006
02:45:29.040 --> 02:45:37.260
Using easy DCM students participate in the complete scientific process and in doing so demystify the methods by which scientists forecast climate change.

1007
02:45:37.920 --> 02:45:41.910
here's some screenshots from the website displaying with students even they navigate through the task given.

1008
02:45:42.360 --> 02:45:51.630
It begins with a run simulations step we're different admission scenarios can be chosen and various variable responses, such as CO2 levels ice cover or air temperature can be shown over time.

1009
02:45:52.350 --> 02:46:00.930
Next is the computational step of post processing where students can average large amounts of data over various temporal scales and extract variables that they'd like to account for.

1010
02:46:01.560 --> 02:46:10.260
And finally, the visualizations that were users can use their average data and any variables they'd like to create striking visualizations just like real life NASA scientists.

1011
02:46:11.400 --> 02:46:22.410
in regards to this tool i'm especially interested in how design changes such as embedded information different locations for buttons and even varying colors between two versions of the software will improve user experience.

1012
02:46:23.010 --> 02:46:30.630
Additionally, i'd like to answer the question how does interacting with easy gcm improve students understanding about climate change and the modeling process.

1013
02:46:31.140 --> 02:46:39.450
And finally, through this research, I hope to gain insight into how students trust and perceptions of climate scientists change after interacting with us EG Sam.

1014
02:46:40.680 --> 02:46:48.990
To measure user experience undergraduate participants will first watch a short video tutorial a PC gcm and then be randomly assigned an A or B version of the software.

1015
02:46:49.320 --> 02:46:53.370
Each with intentional design variations to us well completing the same task.

1016
02:46:54.150 --> 02:47:03.480
As students advanced through easy gcm I tracking will allow us to follow their eye movements capturing data about where, when and for how long users, look at various parts of the computer screen.

1017
02:47:03.840 --> 02:47:09.210
In order to identify possible characteristics of the website that detract from the effective delivery of climate information.

1018
02:47:10.350 --> 02:47:16.830
This data produces what are known as heat maps and example heat map as shown here where red indicates areas that we're focused on heavily.

1019
02:47:17.760 --> 02:47:26.790
after finishing, I will play back the eye tracking session and asked the participants to describe their thought processes challenges and strategies in relations to user experience in a recorded interview.

1020
02:47:28.050 --> 02:47:34.980
analysis for the eye tracking portion will be performed on the produce heat maps fixation counts fixation duration and cicada analyses.

1021
02:47:35.520 --> 02:47:45.090
Additionally, the magic analysis will be used on the interviews to measure user experience and satisfaction between the two versions and ultimately provide feedback to the designers for improvements.

1022
02:47:46.230 --> 02:47:53.910
Along with this, I want to measure how much students are learning from interacting with easy gcm and if their perceptions of climate science change before and after using the tool.

1023
02:47:54.810 --> 02:48:02.700
A knowledge inventory assessment will be created using questions addressing climate specific knowledge modeling knowledge and perceptions and beliefs towards climate scientists.

1024
02:48:03.750 --> 02:48:10.950
After collecting demographic information students will be administered that adapted knowledge inventory test to assess their existing knowledge as a baseline.

1025
02:48:11.520 --> 02:48:16.500
participants will then move through the easy gcm toolkit performing a task with embedded climate information.

1026
02:48:17.280 --> 02:48:29.370
After they will be given a similar post test, as well as a qualitative interview to identify any changes to test in effect sizes will be used to analyze the difference and pre post test and the magic analysis will be performed on the interviews.

1027
02:48:30.390 --> 02:48:35.100
The pilot of the study will begin at the end of the Semester and I hope to be collecting data by fall 2021.

1028
02:48:36.630 --> 02:48:42.510
Thank you for your attention and also special thanks to the geo cognition lab and the National Science Foundation for supporting this research.

1029
02:48:47.460 --> 02:48:48.150
karen McNeal: fabulous.

1030
02:48:49.860 --> 02:48:54.000
karen McNeal: All right, alley you got to two questions in there, and you can.

1031
02:48:55.800 --> 02:48:57.390
Ally Brown: Sure yeah i'm.

1032
02:48:58.440 --> 02:49:10.050
Ally Brown: The biggest challenges of this research, I would say is going to be finding changes between the two versions that are easily testable so like havens I tracking.

1033
02:49:11.700 --> 02:49:17.910
Ally Brown: experiment that she went over earlier, where she picked the color map and the different graphical displays.

1034
02:49:18.180 --> 02:49:23.340
Ally Brown: Easy gcm is a little bit more complex than ssh and they've got a lot more moving pieces.

1035
02:49:23.580 --> 02:49:36.030
Ally Brown: So finding things that we can change and between the two versions and then easily test is a challenge that i'm definitely expecting to come across and also making those changes useful to the stakeholder.

1036
02:49:36.600 --> 02:49:45.330
Ally Brown: You know, we don't just want to go changing things that are relevant for us to test and don't mean anything to him and help the improvement of this tool as well.

1037
02:49:46.260 --> 02:49:50.940
Ally Brown: And then on the next question relevant demographics by that we're going to be.

1038
02:49:51.930 --> 02:50:05.550
Ally Brown: Collecting information about their previous experience and stem so we want to know if they have any previous modeling experience the kind of classes science classes that they've taken in the past, and along with that that'll go like their major.

1039
02:50:06.750 --> 02:50:13.740
Ally Brown: Probably their year and age as well that goes with year but also race.

1040
02:50:16.080 --> 02:50:17.820
Ally Brown: that's probably gender.

1041
02:50:29.040 --> 02:50:33.690
karen McNeal: Any other questions that are alley.

1042
02:50:37.380 --> 02:50:54.270
karen McNeal: About for any of our speakers that have gone today we've had a nice lineup of about 10 speakers and we still have technically five minutes um so if anybody wants to ask for those that are remaining any questions overall.

1043
02:51:06.330 --> 02:51:13.620
karen McNeal: I just want to say that these were a great set of presentations and we've got a range like I really love the range.

1044
02:51:15.570 --> 02:51:18.480
karen McNeal: You know of perspectives that we have here.

1045
02:51:20.400 --> 02:51:32.790
karen McNeal: I guess as a as the educator in me if everyone could just type in chat kind of one takeaway from this session, that would be awesome just to see kind of what are one new thing that you learned.

1046
02:51:33.240 --> 02:51:39.330
karen McNeal: or found out about that you might not have previously known about and we can have that all in chat that would be really neat to see.

1047
02:51:40.800 --> 02:51:46.830
karen McNeal: And it might be more challenging for some of those speakers who have seen each other's talks already, but there are some new ones that peppered in there, so.

1048
02:52:48.780 --> 02:52:57.720
karen McNeal: Good we've got some really good now we're not all brain dead yet maybe getting close but not yet so i'm just going to read a couple loud as they come in.

1049
02:52:58.980 --> 02:53:09.510
karen McNeal: Some ideas about methods and communication and dissemination of information and that there's a variety of methods to advocate for climate change.

1050
02:53:11.700 --> 02:53:24.720
karen McNeal: The cultural heritage and preservation yeah I agree with that one the network analysis and hannah's talk the benefits of cover crops and changes to ag practices and.

1051
02:53:26.610 --> 02:53:29.700
karen McNeal: everybody's to be an energy program yes come join.

1052
02:53:30.720 --> 02:53:40.590
karen McNeal: The environmental green space and in urban spaces and how we can benefit from that and how that helps commit resilience so.

1053
02:53:40.980 --> 02:53:55.740
karen McNeal: Good job everyone, I just want to say thank you and if we were in person, we would go all have a drink but we'll have a virtual drink and have your choice and see you back, maybe tomorrow at some of the sessions.

1054
02:53:59.190 --> 02:54:00.390
Lily Howie: Thank you very much.

1055
02:54:02.670 --> 02:54:02.000
Thank you.