 of soil, not at the exclusion, but it handles things like war on the soil chemistry, soil on agricultural inputs, and those types of things. It's a different class of models that are typically available or known to the hydrologic and engineering communities. I don't know that there's a good definition for it, so I'm sort of winging it. Thank you, Michael. We're going to move on now. So our next speaker is one of our chairs, and it's Kim DeMutser, who's going to be talking about modeling a coastal environment with human elements. Thank you. We'll save this microphone with a hold up. So yeah, I figured when I was invited to do the keynote here to really think about the modeling I do and how humans fit in. And in a way, it was actually really easy. A lot of my modeling I do in the Louisiana coastal area, and if there's any area I can think of where people are integrated with the coastal environment, it's there. So I have to admit, I am not modeling humans. I do ecological modeling, but I'd like to point out here and there where those links are to potential coupling with these people that do model the human elements. So to give you a bit of an idea of the type of modeling I do, it's ecological modeling. Specifically, my type of ecological modeling is very much like this image that you see right here. So this is a food web. When we create an ecosystem model, it's really a virtual representation of the trophic flows through a food web. We call it ecosystem model, Rotterdam food web model, because we include abiotic factors as well. So how would anything in the environment affect these interactions that I'm showing here? So the lines that you see are mostly predator prey. So this guy is eaten by this guy and so on. So it's a flow of energy that can be expressed in biomass, in carbon that's going through a system like that. And you can see here, this is mostly my link to humans is fishing. So once you have a virtual representation like that, you can then create dynamic simulations and maybe increase or decrease fishing effort and figure out how that changes your ecosystem. So these ecological models have been around for a long time, but it's actually pretty recent that these models have actually been applied and being used in real-world issues. And recently, I met with a group of scientists that will work in this integrated ecosystem assessment in the Gulf of Mexico. And we try to figure out so what is really the right application of these models and let's write a paper about it. And two main teams emerged, which was ecosystem-based management of fisheries and ecosystem restorations as really the two areas where these type of models can be applied right now. And that paper actually just came out last Friday. So that would be a good read for anybody who's interested. So I actually work in both teams. But what I want to focus on today is the second one, ecosystem restoration, especially again with Louisiana in mind, where ecosystem restoration is extremely important. What you're looking at right here is a map. It's a combination of historic land loss and projected land loss up to 2050. And all the red areas that you see is either already lost since 1935 or projected to be lost before 2050. So lots of areas of land being lost. So what's really at stake here is that this indeed is an area that is very much part of the culture of people in Louisiana. There's recreational fishing, commercial fishing. Just generally, culturally, people are really connected to the land. So there's a lot at stake here. These aren't just areas. It's nobody's using for anything. So what are we doing about that? Well, first of all, luckily, the governor of Louisiana realized that this is a real issue. And actually, literally just this year in January, he declared a state of emergency that was really about this land loss. So the thought behind that was to really speed up the parenting process of restoration projects that are being designed to mitigate this land loss. So what is being done about that? There is indeed a plan. So this is the Louisiana's Coastal Master Plan that is basically sums up what would be the best approaches to mitigate this land loss. And also in the plan, everybody's very much aware how much ingrained people are with the land and how much is at stake when we are losing that land. So what you see here is just several examples. And obviously, many is the best way to express the value of any land. Several examples of what it would cost to lose this land. So here, for example, at the bottom, the asset value of the Mississippi River Delta. So an evaluation of what the value of these systems are expressed in dollars and basically to show that that's what at stake when these lands are lost. So how do we go about doing that? And where do these models come in then? So here you see, I guess, a lot of information on a slide where coastal projects are being submitted to the state of Louisiana. And then that's where these predictive models come in. Basically, hundreds of scenarios are run with different combinations of these projects. And there's various different types of models that look at that where really only a small component is the ecosystem outcome. So I'm the sub-task leader of that particular component. But they will look at mainly land building, which is the main idea behind doing these projects, but also everything else. What is the effect of doing that? So it's really, I guess, too much to really look at in a 25-minute presentation. So I want to focus on one big component of these projects, which is sediment diversions, and actually use as a case study another study I've been working on outside of the Coastal Master Plan to look at what the effects of the sediment diversions are on the fish and fisheries and those receiving basins. And that is the Mississippi River Delta Management Project that was, it had already been a project that has been on the way for a while. It's a collaborative project between the Corps of Engineers and the Louisiana Coastal Protection and Restoration Authority. And really the main goal here is looking at how sediment diversions build land, restore these wetlands. And eventually, they pulled my group in to see, like, well, what if we do that? What would the effect be on the ecology of the system? And this is the main idea behind that, that what you're looking at here is the Mississippi River. And this is just like a diagram, not a real one that is actually in existence. But the idea is that if you were to open up the Mississippi River and let fresh water, nutrients, and sediments flow out into these areas, you actually have kind of a natural way of building land again. Because historically, that's how the whole Delta was built. The Mississippi River would change scores. There was overbank flooding. That's how the whole area was built. So this would be basically the most natural way to rebuild these lost areas. Because the land erosion, partially, is just natural. The land is just sinking. The problem is, by having levees all along the Mississippi River, there is no way to replenish that anymore and mitigate that land loss. Now, obviously, it's been a long time that the system has been in this way. There was no fresh water inflow in these estuaries that are being lost. So the ecosystem has changed into this new state. And people are used to what's around now. So people are now worried, well, what if we are going to let this water back in? What's going to happen to the fish? I'm used to shrimping over here. I'm used to catching oysters over here. What's going to happen? So that's exactly what we came in to look at. And the question we tried to answer was how does a select combination of sediment diversion, so they have a few proposed large sediment diversions in this area, effective fish and shellfish in those receiving basins. And we developed an ecosystem model for that reason, accounting for the effects of the environmental changes that would occur once they're open, but also include predator-prey interactions. So perhaps one species is affected by the salinity, while another species is more tolerant if the salinity decreases, for example. That second species could be impacted in some way if it's the predator or the prey of that species. So there's a lot of indirect effects you wouldn't know about if you wouldn't use an ecosystem model like that. Now, how do we know how the environment is changing? That's actually what another model is doing. And in this case, it was a DELF-3D hydrodynamic model that was already coupled to biogeochemical models and vegetation models that would play out a scenario like that of opening the sediment diversions in different places and then provided us with output of how would chlorophyll A change? How would salinity change? Dettles of suspended solids, for example, is one that can affect fish and oysters. Percent wetland coverty. All ideas about land and fishes use that, those wetlands as nurseries. So all that, we could then load into our model. So it was kind of a loose coupling and used those environmental changes to see how it would affect our fishes. So to build such an ecosystem model, I used EcoPath with EcoSim and EcoSpace. So that's freeware. And I will, together with Joe sitting here, we'll give a clinic about that tomorrow if you'd like to learn more. But real brief, it consists of these three components. EcoPath is a mass balance snapshot of your ecosystem. That's where you rebuild basically your ecosystem on your computer. You have a virtual representation now. You could stop there or you can go into EcoSim and then look through time. What if you change something? How do the biomasses of these different species change? In this case, we used it for model calibration. So we ran a true time that had already happened. We had data of biomass of that time so we could calibrate our model to observations. And then there's EcoSpace, which was the framework of this model. Then you have to develop a base map of some kind with a grid. It's going to be a square grid but the size of the grid cells you can decide upon yourself. And what that allows you to do is to actually look at spatial and temporal changes in time. So your fishes may change through time but they also change in space. The distribution may change if something happens somewhere in your model area where these fishes react to. So that was kind of the ideal situation to use a model like that for this research question. So this was our model area. The model area is really the entire square. The yellow that you'll see is that the sponsors are very much interested in receiving information on these sub regions as well. So we'd be subdivided in different regions. What do you see here too? These were these proposed large sediment diversions. So in this upper area that one would flow into Bretton Sound, one flow into Barotaria Bay and then two lower one where one was flowing into Barotaria Bay and one flowing into Bretton Sound. So we tested various scenarios with that and of course I need to show my obligatory flow chart which is this one. I guess for a presentation it has a little bit too much information. So what I did is simplify it a bit. So what you have on top there is a Delft 3D model. Obviously it's real goal is to see what the land gain per scenario is. But while that is doing that, it is actually providing these environmental drivers I was talking about. Salinity, total suspended solids, chlorophyll A, percent wetland that we would grab on a monthly basis to drive our model. Then with our model, we could look at fish biomass distribution and landings that would be the result of any of these scenarios. And here one little thing I wanna add that we realized for oysters to load in these monthly drivers that was the time scale wasn't quite right for that. Oysters can actually disappear if the salinity is too fresh for about two weeks. So we wouldn't be able to capture that. So what we ended up doing is creating this little sub-routine where we would receive daily output because Delft 3D creates a lot more output than just these monthly drivers. And then created an extra layer. It was almost like a habitat suitability index for oysters based on these daily values of salinity, TSS and temperature. And then we would use that as a driver on a monthly basis. So that way we kind of had to work around our monthly time step. And also an extra thing was that oysters really are focused on the substrate and cultures, oysters and broken pieces of oysters. That would be the only locations why they would settle. So that would be another driver. So you can include habitat as drivers as well. And the last thing I wanted to add, this would be another location where I could think, where I think a coupling to more of a human dimension component of the modeling would be very appropriate. Because we can come to landings, we can even get to revenue by putting price per pound on the fish. But it's actually really simple. And to look here, what if this is your fish, biomass distributions and landings to really link it much better to the human dimensions of what does that do to the communities that live here? How does that money trickle up if you do have this amount of landings? How will they do economically? I think that would be very interesting to pursue. All right. So to give you a better idea of what this model really is, I wasn't going to put the big pools and flows of biomass diagram with this amount of species that looks like a bowl of spaghetti. So that wasn't going to do you any good. So I just listed the species here just to give you an idea what it entails. You're not looking at this one fish. You're looking at a bunch of fish in the entire community. How does that respond to these changes in the environment? And even these fishes are then split based on age classes. So juveniles can maybe react completely differently than their adults. And you can capture that by splitting those up. So since there's too much to talk about, there were a few that people were particularly interested in, a gold manate, a large mouth bass, red drum, spotted sea trout, blue crab, and brown shrimp. So I'll focus with the results on those species. First real brief, so I talked about these environmental drivers. Well, how do these species then respond to these environmental drivers? And really, and in different ways, you can come up with these response curves. Every species has some type of tolerance curve for any of the environmental drivers that you're including in the model. So for example, if this is salinity, maybe this is the optimum salinity of one species. This is kind of their range. It can handle. And for every species, these curves look different. For every environmental driver, these curves look different. So all that comes together creates what we call habitat capacity, which is very similar to a habitat suitability index for each of these species for every time step in the model. So this is an example of a habitat capacity map. So first, the capacity map is created. Warm collars would be good areas. Cold collars would be bad areas. In this case, I put juvenile brown shrimp up there. That was really, had a high affinity for marsh edge, which was one of the drivers we added in there. So you can kind of see how those are the good areas for this species to occur. And then this is not the end of it. Then you run your model and through these trophic interactions, you can see what the real biomass is. This is one of the drivers that will decide which areas are good or bad for each of these species in the model. The operation plan that I will be showing you here, and remember, this is about these diversions here, is to have all four of them open for 50 years. The operation plan, we varied that as well. This particular one is that the opening was triggered by 600,000 cubic feet per second in the river. So you see it's not open at all times. It just has a large pulse whenever the river flow was high enough. And then we would compare it with a future without action. So what if we don't do any of this and what the biomass would be? Here I picked juvenile gold manhaden to show you some of the results. So high biomass would be warm collars, cold collars, low biomass. This is a future without action. So this is kind of what would happen without the diversions. You open the diversions, you see that they would avoid these particular areas. This was in June. If you remember the operation plan, that was when they had the maximum amount of fresh water. So this would be for these particular species when they really have the maximum capacity of the diversions. What's interesting here is that the juvenile gold manhaden actually can handle these low salinities. What happened though is that the TSS really shaded the phytoplankton, reduced phytoplankton growth, reduced juvenile gold manhaden growth. So there's various processes going on that are being captured with these models. What was interesting too, though, we look at October now, future without action, that October with the diversions of that same year didn't look that bad at all. So they actually moved back. So there's a lot of movement instead of just complete mortality of these species, and they can move back into that area if there is at least a time of closure of those diversions. So if we looked at a couple of these species that I just mentioned, and then looked at the different subregions, so black is the entire model area, the y-axis is the relative change compared to a future without action, and those colors are just different subregions in the model, is that what we would see overall is that species that like higher salinities would have, indeed, a reduction in biomass. Some species in large mouthbasses, an example of that, that prefer lower salinities, had an increase in biomass, but also that's a lot of that instead of complete extirpation or just general high mortality was a redistribution of the species because if we were to look at the entire model area, the effects would really be dampened so that they would move elsewhere even in a particular smaller region if the biomass would go down. So that's basically what our main conclusions were. So indeed, we did see losses of higher species that prefer higher salinities, most of them just moving in other areas though. So this could also be used to inform people where you cannot expect certain species anymore and where you can expect them and maybe help people adjust. Really the main goal is land building. So it is kind of a trade-off. Some of these fishes may not actually thrive well right near these diversions, but also that the magnitude of change was dampened on a larger spatial scale. There was a redistribution of species. We also saw a large relative change in areas with low biomass. So if there's a big difference between future without action and those diversion, it didn't mean necessarily absolute biomass was the biggest change there. There was maybe a few individuals there and if 50% of them gone, that's a big change. But for total biomass, it wasn't necessarily always changing the most. Also, we saw that the two lower diversions were mostly responsible for the total biomass changes. So with this, but also in combination with other models, that indeed showed that those upper diversions probably most efficiently build lands, the decision was made to not continue with those lower diversions, focus on the upper diversions and that even with just the upper diversions, so we ran those scenarios as well, you would have some distribution changes, some loss in biomass and those were decisions obviously by the managers. The land building capacity benefits really outweigh the biomass losses there and then at least that information can be used. Okay, this area will not be suitable for oysters anymore. This area, however, is something where they will still thrive. A lot of these leases are basically, people basically set their spat in those areas. So you can choose where to do that. So the information would also be useful in that regard. So I think because this is such a science-based approach and we have these different models determining what might make change, if we open these diversions, this mid-bariteria sediment diversion, so one of the upper ones going into Bariteria Bay has now been granted really just in January, fast-track permitting. So they're gonna start doing this. Again, just like Scott Hagen was saying yesterday, our playing out difference in the areas is really to inform policy. We don't make any of the final decisions but it really helps them focus on what to move forward with, what to not move forward with. The permitting processes will still include conducting environmental impact statement that I won't be doing. So there will be more looking into the ecology of the system but we can really help speed up the process and help decision makers make the best science-informed decisions. All right, so with that I would like to point out that the photographs were provided by a local photographer. So I've lived in Louisiana and you can really see how there's such a pride with living in this coastal environment that's so beautiful and thriving and providing all these resources and that can be really seen from the pictures. I hope that I have kind of put in this presentation how beautiful it is. So with that, thank you for your attention and I'd like to take any questions. Thank you, Kim. We have time for questions. So my question is about the way that you calibrate the fisheries models. So I don't really know how this works but I know that the positioning or the location of the landings data sort of collects, integrates very wide areas and do you have to integrate landings data with sampling data so that you get a better understanding of how it's just a really fundamental thing about these models that I never quite understood. So we're lucky in this area. So what I've seen the acknowledgement here, I have data from the Louisiana Department of Wildlife and Fisheries. They actually very, very intensively surveyed the area. So I haven't, I don't calibrate with landings data. I actually calibrate with surveys that are a very good representation of the biomass of these species. Also not all of these species are commercially targeted. So you can actually have all the species of the model. You have some information through these biomass surveys that they do. So most of the calibration was done on the survey data and they have multiple stations in every one of these basins. So it's very high resolution, also temporal high resolution, they go out every month. They really use it to maybe decide the opening of the shrimping season, but you can do a data request and get all of that information and I've used it a lot throughout my career. It's a very useful data set. We do use some landings data as well. So we have biomass in there, but also obviously landings in there. Also the Louisiana Department of Wildlife and Fisheries has triptychic data, which you do, there is some aggregation. Data are classified if the any data point is based on less than three commercial fishing boats, but as soon as it's four in that particular area, you can have the data. So DAB actually for certain of these targeted species, where there's a lot of fishing going on, we could still get a lot of those data, where the aggregate really just means three or more boats, which is not that negative. Okay, thank you. Right, right. I think our main way to do it is we have, we have certain known uncertainties. For example, we can do Monte Carlo runs with when we have the biomass, I put up the fishes. We can vary that within a certain range and see if we run that over 50 years. How much does that vary? There's certain other inputs that we don't really know exactly what the uncertainty is. So what we end up doing if we can't really quantify the uncertainty is change how we bring our output. Of course, the model was very precise. I can get the grams for me to square every fish in any year and any month. That's not how you provide the information to the policy makers. So what we do, we run multiple scenarios of voice comparatively and we do have confidence in, well, because we have at least as much uncertainty, but probably more, you'll have at least more biomass in this scenario of this particular fish than in this one and not say, okay, you'll have 10 grams per meter square in 2050 if you do this exactly. So I think by having to work indeed with a high amount of uncertainty and even an unknown part of the uncertainty, we also don't know how fishing output is exactly gonna change in the future. So that is a big effect. So we can't always know exactly our uncertainty. We adjust our message. Like this would be more biomass or this would be less biomass. A lot of them becoming really precise.