 on one of these different boxes in my field instead of trying to sort of connect across them. And so what I try to do is take these computational approaches and really connect between all these different boxes and sort of get a better picture of this question about where animals live in the ocean. Another thing I try to do in my work in terms of my approach is oftentimes we have really detailed work at one site. So this might be a marine lab or a field station and sort of, if we understand the mechanisms there, how do we translate that over a much larger area? So we're not just finding out what's happening at one location, but we're translating that into much larger spatial scales. The other thing is oftentimes we're looking at one point in time, unless it's a long-term time series. So it'll be the scale of like a remasters or a PhD. And so you're just going to have a brief period of time. But also in terms of understanding those underlying mechanisms, projecting that over longer periods of time. So we can actually make projections about what the effects of climate change are going to be in the future. So those are some of the things about my approach about what I try to do to answer this question. And I've worked in a bunch of different topics. I'm not really tied to one particular organism or system. I've worked in the Plagic Ocean and the coastal ocean mainly, though. And then I've worked on bacteria, sinking particles. I've worked on mussels, not these types of mussels, but the mussels that are shellfish along the coast. And then what I'm going to talk to you about today is epoxy intolerance in the Plagic Ocean. And so what I'm going to do is talk about the approach that I'm using and then talk about across many different animals applying this approach and then focusing in then on tuna species shown here in terms of making projections for what will happen with climate change in the future. So if we're talking about hypoxia, I first want to describe exactly what I'm talking about in the Plagic Ocean. So this is a map of oxygen pressure at 500 meters depth in the ocean. And the ocean is three-dimensional, which adds sort of an extra complexity to looking at where animals live and what they're doing. So this is at 500 meters depth. And so what this map shows is that there's large areas of the Pacific where oxygen is nearly zero. And this is also true over here in the Bay of the Gal and the Arabian Sea. And this feature is really long term. It's caused by the natural process of the ocean circulation. And so these are persistently there in the ocean as a feature. Oh, and I also wanted to say, so in terms of comparing this maybe to land, here when we're on land, we take kind of oxygen for granted. We have plenty of it. We don't really think about it that much. And so in terms of going into different systems, like the ocean where it actually gets down to really low levels of oxygen like this, the only really comparable equivalents are if you're a mountaineer and you're climbing a mountain and you get to those high altitudes and all of a sudden oxygen is not as abundant as it once was or the pressure has decreased. That's one area. And also going to deeper depths in soil where microbes use up a lot of the oxygen and turn the soil anoxic. For the most part, we take oxygen on land kind of for granted. So thinking about in the ocean where these are a real challenge for the animals that live there. So just taking a transect. So I said the ocean's 3D and just showing how oxygen looks in sort of a three-dimensional environment. So this is showing depth over here on this axis. And then this is latitude on this axis here. And what this is showing is, so this yellow area here is, oxygen is abundant there and animals have ready access to it. But in some regions, particularly here in the north, just north of the equator, these oxygen levels drop really low and they can be really low at 100 to 200 meters depth. And so, and but then, so they're really low here that extends into northward. But then you get down here and there's less oxygen or there's, there's still less oxygen over here but there's also more oxygen than there is up here. And what's happening is, in this region here is organic matter sinking in the water column, bacteria using up all the oxygen. And so, but they've used up all the organic matter by these depths. And so there's not really organic matter left for them to be using oxygen at those deeper depths. And so, and most of the oxygen is created, it's either absorbed into the ocean up here at the surface or it's created as a byproduct of photosynthesis up here at the surface ocean. So that's where the oxygen is coming in to the system. And so, that's why it's much higher up there. So going into the future, so what they've been observing in the ocean is that these areas, these hypoxic regions are increasing and they're increasing in size and they're getting shallower in depth. And I've noticed that in several of these regions across the world. And so they've developed this hypothesis that these low oxygen regions are expanding and they're gonna decrease. So this is where all the animals live up here near the surface. That's where most of the biomass is. And it's gonna be compressed in the future. And ecologically, this could have a really big impact on the ecological interactions in terms of predator prey. If prey had a refuge before by going deeper into these zones, but the predators are now able to access them that could change the dynamics there in terms of competition. Maybe there was niche, there was partitioning among animals for their different food resources in that vertical water column. And now with this happening in the future, there's gonna be this compression of that habitat and they might be more in competition with each other. So the dynamics of this changing could have a really big impact on the plagic ocean habitat. And so what I'm gonna describe is it turns out, the first thing when I sort of got interested in this question was looking at what is a hypoxia threshold? It turns out that's not really an easy question to answer especially if you wanna look at a range of diverse organisms. And so first I'll talk about the approach I'm gonna use for that. Then I'll talk about how that can be applied across a bunch of different animals and then I'll focus in on how climate change is gonna affect tuna in particular. So one way to measure hypoxia tolerance for animals is to measure the critical oxygen pressure. And so when animals are swimming around, they're consuming oxygen at a particular rate. And depending on the activity, whether it's routine or just sitting there not doing anything or maybe they're growing or reproducing or just swimming around, that changes the amount of oxygen they're consuming. And so, but the routine metabolic rate is one right here. And this is sort of an animal at rest not doing anything in particular. And so this critical oxygen pressure is the pressure of oxygen in the water, the point at which they can no longer maintain the regular oxygen consumption rate. So that has a really defined point here and it can be compared across a bunch of different animals. And it's often used as a measure of hypoxia tolerance for animals in the ocean environment. And so how do you make these measurements? So you take an animal, in this case a fish, put it in a closed tank and then either you draw down oxygen via a gas exchange column or you can actually have the animal just consume oxygen on its own and eventually it'll reach that point where it can no longer maintain that routine oxygen consumption rate and then you'll have a measure of your critical oxygen. And so I, at the beginning of this year, had an opportunity to go on a ship. So I was on a ship for a month between mid January and mid February. And we, so this is the RV Secouliac and we were off the coast of Mexico, 200 miles. And we were there for an entire month and what we were doing is we were collecting animals and we were making measures of critical oxygen. So this is a krill in one of these respiration chambers. And so this is sort of one of my segue, so away from just talking about the science in terms of the data. So this is kind of new technology here on the side. So this is, this band right here is holding a fiber optic cable to a strip on the vial. And what that strip, what the fiber optic cable does is releases light and measures fluorescence on that strip to measure oxygen in these vials. And so this technology has been developed in the last few years. And so just talking to scientists on the boat, they used to be measuring oxygen at the beginning of the end of these experiments. And now they get continuous measurements at one second time intervals, which the software forces you to do. So we were like, we don't really need one second time intervals, but that wasn't even really an option in the software to change that. And so on one day you can get 86,400 measurements. These often go for two to three days. So you can see there's an order of magnitude, several orders of magnitude have changed and the amount of number of measurements that you're gonna get out of it. And so scientists, which you used to be doing all their analyses in Excel, all of a sudden can't even open their files. And so this is definitely providing some new challenges at least in the field that I'm working in. So looking at hypoxia thresholds. So I, there's a lot of animals that you can't just put in a vial like a krill. And so I got interested in sort of looking at, and particularly something like a tuna, I got interested in looking at what other measures of hypoxia could there be? And so one is looking at the blood of animals in terms of its oxygen affinity. And so this is PO2 in the blood. And this is, as you increase the PO2 in the blood, the percentage of the blood that's oxygenated increases. And the way that we measure, sort of do comparisons between different types of blood is using this measure called the P50, which is the oxygen pressure at which blood is 50% oxygenated. And so if you have a lower P50, so on the scale, you have a greater affinity for oxygen and you're more tolerant of low oxygen. So you're more hypoxia tolerant. And if you have a higher P50, then you're less hypoxia tolerant. So there's a, but there's a trade-off here. If you're able, if oxygen binds better to your blood pigments, it releases slower. So if you need to do something like a burst swimming, where you need rapid oxygen delivery to your tissues, you don't want it to bind so tightly that it takes a long time for that, the rates for that to happen. So there are trade-offs here. So that's why not every animal has the lowest P50 possible because there are different adaptations of different animals. And so there was this really neat study by Mandik et al in 2009. And they were really interested in understanding these different measures of hypoxia tolerance and how they're connected together. And so what they did was they went and found, so this is a sculpin, it's a little tiny fish. And they went and they found closely related fish. So they're all sort of have the same morphological features, but they went to different habitats. These animals are living in different environments. And they got a bunch of different fish. They were different species, but closely related. And then they started trying to figure out whether this critical oxygen pressure and how that's related to P50. And what they found was that, well they actually measured critical oxygen pressure against a bunch of different other measures such as gill surface area. But what they found was that P50 was the best predictor of this organismal. So this is a cellular level measure, but this was doing the best measure of the critical oxygen pressure, which is a more organismal measure. So I'm talking about blood pigments, just to give you an idea of what they look like. This is a hemoglobin. This is the one that we have as humans in our blood. And it has iron as the binding site for oxygen. And this is a hemocyanin, which is what a lot of marine invertebrates have in their blood for binding. And so there is, and you can see there's quite a bit of difference between these two molecules, but they do have the same overall function. And so this reaction between hemoglobin and oxygen is, can be exothermic, which means that heat is released as this binding occurs. And that means that blockchain binding can be favored in cooler conditions. So animals as they're swimming around the environment are exposed to different temperatures, which can have an effect on this reaction. For other species, the hemoglobin combines with oxygen, but heat needs to be absorbed as part of the process. And in that, in order to bind oxygen with the hemoglobin. So in this case, blood oxygen binding would be favored under warmer conditions. So just to show that in pictorial form, this is the animal, they're in a warmer temperature and they're swimming to cooler temperatures. And there's this shift in the P50 as they're moving around. So as animals are swimming in the ocean, these hypoxia tolerances could be affected as they move to different temperature conditions. And how do I calculate that? So this is typically calculated using the Vanthoff equation to calculate the apparent heat of oxygenation. And you have, and there's both, what you measure P50 at two different temperatures. And that can determine the heat of oxygenation. So moving on to the next step. Looking at P50, blood oxygen binding across a range of different animals. And so in terms of talking about some of the data science aspects of it, so this is the case study that I wrote about in the practice of reproducible research, if any of you were involved in that. That was in the book by Justin, that was added by Justin Kitzes, Dan Turrick and Fatma Denise. And so what I'll be talking about today is some of the stuff on this side. And then I'll be showing the figures and the interactive graph that I used as part of the project. Just to show some of the data. So what I did was looked at for blood oxygen binding measurements in the literature. And so this is just showing what the data looks like in the literature. They often would have different, each individual fish that or whatever animal they measured, they would often only measure up to like six fish per measurement. And so, and then they could put all the data just right into the paper. And so you could actually get the data right from the paper. And then also what they did was they would take averages and sometimes you would just get averages, but they often would be on no more than like six animals. So these measurements are typically much fewer in number than some other measurements that, or other data I'll be talking today about today. So what I did is I went to the literature and I looked for both benthic and pelagic animals. And what I found was about 50, just over 50 animals. And so what I plotted them here and what I was looking for was whether they were, they were very different from each other. So whether they had a very different P50 or a very different heat of oxygenation. And whether these animals had a diversity in that. And what this graph shows is yes they do. They have, there's animals with a broad range of different parameters for these two measures. But I'm also gonna digress here in terms of talking about some of the data science stuff I've done. But so I made this graph and I worked with Jeff Hare's group at the University of Washington and they developed software to make interactive graphs. And so, and because you can't really see what's going on in this graph, I mean I have little numbers. You can go to a table and find out what those numbers are. But this just seemed to be a really good graph to turn into an interactive graph. So it would be much more streamlined in terms of being able to access that information more readily. And so I'm gonna show you, I made an interactive graph using the D3 library. So this is an interactive graph I made. I tried to get them to publish it as part of the journal article. They didn't really have the resources to do that but I do have a link in the article that you can go and see this interactive graph at the point of where the graph is in the paper. And so what this is showing is that same graph but now because there are tooltips you can actually hover over each of the points and actually get all the basic information from there. You can get what species this is, what the common name is, what the reference was. And then I was also able to add in additional information including the habitat, whether these were benthic or pelagic animals, what blood pigment was, it is. So I was looking at both hemoglobin and hemocyanin, whether it's invertebrate or invertebrate, and how much they move upwards and downwards in the water column. So now if someone was looking at this interactive graph they could get a lot more information out of it a lot easier. And so this is like a really good way to use interactive graphs in my field and I hope it'll be used more often in the future. And so looking at this graph I wanna point out some of the, now moving back to the science in terms of the features. So I've been talking about pelagic animals. So what it turns out is krill are some of the outliers on this plot. So this is a krill here. This is a jumbo squid. And here's another krill and here's another squid. So there's definitely in the pelagic animals a lot of diversity and they tend to be outliers when I started looking at doing this analysis. And then also I'm looking at tuna. So this is a southern bluefin tuna here. So this is a zero line between whether this blood oxygen binding reactions are endothermic above here or exothermic down here. And so tuna fall on both sides of this line. And so that is also really an interesting feature that I discovered when I was exploring this graph, this interactive graph. And also note that most animals are kind of down here in this quadrant of the graph. Switching back to the presentation. So just to summarize, there's the krill and the squid are these outliers in the graph and then there's tuna that are here in the middle. So for the next few slides I'm gonna talk about different physiological types. And so this is a real tongue twister whenever I say it. And I've been talked to people and they've wanted me to say like squid-like or krill-like but the problem is that that doesn't really represent what they are and it might give the wrong impression. So I tend to go with the tongue twister version of this is a low P50 endothermic, low P50 exothermic, low P50 or high P50 exothermic. But I will in all the slides point out which one is the most animals one, this one down in this quadrant. So you have an idea of what most animals are and then in the other two we're talking more about outliers. And so looking at these animals in terms of, so if animals have acclimated to this particular temperature and that animal swims like to a lower or higher temperature there's a lot of temperature variation in the ocean. That P50 is gonna switch. So they're gonna decrease, the P50 is gonna decrease with decreasing temperature and increasing with increasing temperature for this most animals category. However, in comparison for an animal that has a low P50 that's endothermic the opposite is gonna happen. So if they're swimming to warmer water the P50 is gonna decrease and if they're swimming to cooler water the P50 is going to increase. So these animals depending on their adaptations are gonna be experiencing the environment very differently. So next looking at one that's high P50 and a blood oxygen binding reaction that's exothermic so it's very similar to the most animals category here but the slope of that curve is much cheaper so they're gonna experience much bigger ships in P50 as they're swimming between different waters of different temperature. So what does this mean for an animal who's living in the ocean? That's living in the ocean. So looking at this one spot here sort of in the central tropical Pacific. So this is looking in the water column. So this is depth. And so there's oxygen and temperature are both decreasing rapidly between the surface and deeper depths. And this panel over here is just a zoomed in version of this panel there. And so what I'm gonna show is these different physiological types. So this is showing the most animals one and so at the surface it's a two kilopascal P50 but as it swims to deeper, if it's swimming to deeper depths that P50 is decreasing. And so the P50 is always lower than the PO2. That's not the case for this other circumstance where the animal has a low P50 and a blood oxygen binding reaction that's endothermic. Instead this animal, the P50 is increasing is that animal is moving in the water column and eventually reaches a point where blood would be less than 50% oxygenated. And that's the same as true for this animal with a high P50 and blood action binding reaction that's exothermic. The decrease in P50 is much more rapid but it still reaches a point where the blood would be less than 50% oxygenated. And so looking at this, taking this out of the water column and looking at it more broadly, I'm gonna be talking about something called a P50 depth. There's a point in the water column at which P50 equals PO2. And so that's what I'm gonna use to map in the ocean where the physiological differences are between different animals. And so to do that, in oceanography we have a lot of data that's just broadly available to anybody and one of these resources we have is called the World Ocean Atlas. And so this is data that's been objectively analyzed to a one degree grid and then you can get a bunch of different variables from that and just showing sort of like a grid like this and how they create these grids is they take averages and interpolate to this grid and then they make that available as a resource in terms of grid data. The nice thing about using this type of data is it's all uniform, it's been processed into a product where you're not trying to just find the closest data to your site and cobble things together. So this is definitely a product that can be reused and used for a bunch of different applications. The data is stored in net CDF files. So in the computer science group that I work with at the University of Washington, I, they all went to a conference and they came back and someone at the conference had been presenting software they had developed showing a bunch of the different environmental variables and they said, this is so cool, they're showing temperature all over the globe and look at this tool that they developed and then they were talking about it and then they're like, yeah and they use these really obscure file formats called a net CDF. And I said, that's not obscure, that's like our like currency in my field, like everybody uses it and it was just really funny just to see the different interactions between people from the computer science field or like, oh this is totally obscure and then like in the geosciences where we use these files on a daily basis. So definitely like in terms of being interacting with interdisciplinary interactions through like the eScience Institute it was really kind of funny to have that kind of interaction. And these files are great. It's nice cause they have a lot of libraries and resources that you can access them. And then all this data is widely available including ocean data, you can get climate model results, weather forecast, satellite data and so and this is available to the entire community and you can access it using Python or R. So it's really great. Today I'm going to be just talking about ocean temperature and ocean oxygen but there's a whole bunch of other variables that you could get from these types of files. So going with the same physiological types that I've been going talking about so far. So this is the most animals version here on the top left and this white area set. So this is showing that P50 depth measure that I defined earlier. And so what the white area means is there's no P50 depth at all. So for most animals if they're moving vertically in the water column they might would never encounter P50 depth except for right here in the Eastern Pacific and maybe in here over here in the Arabian Sea. And so most of the ocean they would not be affected at least by that parameter. However, if I'm potting an animal with low P50 and blood oxygen binary reaction endothermic the picture changes. There's still a lot of the ocean where this animal would not reach a P50 depth but in the North Pacific it would reach a P50 depth through most of the North Pacific and then over here in the Arabian Sea in the Bay of Bengal. And so these two animals are living in the same environment in the same place but they would be experiencing environment very differently especially as they're moving vertically in the water column. And the same is true for an animal with a high P15 of blood oxygen binary reaction that's exothermic, it's slightly different but definitely more like this one. And then the last thing I wanna show is just in oceanography often they just pick a threshold such as 60 micro moles per kilogram. They say animals below this animals are gonna be affected by hypoxia but the reality is that most animals might not be affected by hypoxia at this threshold. It might be just describing some subset of them and that this is really important to consider that there's all this variability and how animals perceive the environment in terms of their tolerance of hypoxia. So looking at a transect along 140 west. So this is just gonna show that in the tropics right here two of these physiological types have very similar P50 depths but moving to a different location for their north they would have very different P50 depths. So geography matters in terms of how the environment changes as moving along, while moving along the transect. And this is showing my horrible color maps. I was pretty good throughout the whole presentation but I made these prior to finding out about perceptually uniform color maps. So I was trying like yes a couple days ago was like maybe I should change them so I don't show people at Berkeley but anyway, this is what's in the paper so you might discover it anyway. Anyway, so I realize these are terrible color maps but anyways, but what they show is that PO2 the gradients in PO2 and temperature change along the gradients from north to south and so that has a big impact on where the P50 depth is. So ecological implications of this work are that there's verticals in the nation of this physiological threshold in the ocean environment which occurs in the Plagic Ocean. And that varies geographically. Also just picking a threshold doesn't really represent the diversity of adaptations that animals have for living in the ocean. So on to talking about tuna. So tuna are a great species for, one for this subject is because there are actually a lot of measurements on them. People are really interested, they're physiologically interesting but they're also a commercially important fishery so people really focus in on them as a species to study. They have these large scale geographic ranges and the model results that I'm gonna talk about really are best used over the largest of spatial scales so if it was just an animal that lived in this tiny little region I wouldn't be able to make as good of assessments. And then competition, tuna are competing for each other for similar prey resources. And so this is just showing, using data from the IUCN, just showing where the tuna geographic ranges are and there's a lot of areas where there's up to six tuna species that are living in the same location. So these tuna are actually living in the same location but what they've done is they partitioned up the vertical environment in terms of where they're forging. So any sort of changes in terms of the oxygen might have a really big impact where these animals can feed and what their prey are doing could also have an impact on them and whether or not they're coming into greater competition with each other. So this is the physiological parameters for tuna. There's a cluster down here, both southern bluefin and big eye have the same P-50 at acclimation temperatures but then all the other tuna species that were available in the literature have different P-50s. And then also in terms of the heat of oxygenation there's some variability there actually big eye tuna has a blood oxygen binding reaction that's exothermic, southern bluefin has a blood oxygen binding reaction that's endothermic and the skipjack and yellowfin have blood oxygen binding reactions that are independent of each other. And so I've gotten this question before is that tuna are heterotherm so some of their body tissues are warmer than others but at the gills the blood is the same temperature as the environment so that's how we can make assessments using this approach. So just looking at a transect along fun 52 West this is comparing the two species with the greatest differences and blood heat of oxygenation. Big eye tuna this is how they would perceive the environment so this is showing depth and latitude and then the white contours are the pressure of oxygen in the water and the pink line here is the P-50 depth. So this animal as it's moving around and this animal is known to spend more time at deeper depths as it's moving up and down in the water calm it's not P-50 isn't really changing all that much it's basically seeing the environment the same towards the surface as it would at deeper depths. However for the southern bluefin tuna it would be seeing the environment very differently if it was moving from the surface and the tropics to deeper depths and in fact this animal doesn't even live in the tropics it mostly lives down here where interestingly enough it looks much more like it does for the big eye tuna and so how these animals these adaptations really would change the way do change the way the animals interact with the environment. So how what is so using that world ocean atlas data that I mentioned before what would the P-50 depths be for these animals and so for Atlantic bluefin tuna the stippling here is showing where the animals are found so there's no overlap there's P-50 depths over here but that's not where these animals live so it doesn't really have an impact on them. For some species like the Pacific bluefin tuna the entire range of this animal has a P-50 depth and then for some of the other species there's both areas without a P-50 depth that's the gray area and the areas with the P-50 depth so these animals could exploit different environments and then lastly there's a southern bluefin tuna which most of its range does not have a P-50 depth except for in its spawning region and so then what I did was I took the model projections projections from the climate model intercomparison project five from the last climate change report and this data is available online openly available and then I use six of the earth system models which have the variables that I was needed which is oxygen and temperature and then I took averages across all of them and that's what I'm going to be showing you in the following graphs. So this is set up similar to the other plot. The red means that the P-50 depths are getting shallower so that compression that I was talking about in the initial hypothesis is potentially going to occur. If it's blue it means it's actually P-50 depths are getting deeper so there could be some expansions and so what this plot shows is well Atlantic bluefin tuna doesn't matter because it doesn't have any overlap but over here in the North Pacific where a lot of these animals live there's going to be actually up to 100 to 200 meters getting the P-50 depths are going to get shallower which would have an impact on these species and it's going to affect a lot of the different species that live there. There's also a P-50 depths is getting shallower and for the southern bluefin tuna in the spawning region but I have a graph later that shows that a little more specifically. So looking at the changes in P-50 depths so this is showing all the grid cells from the previous plot just combined into a box plot just looking to see whether it's getting shallower overall or deeper and what this shows is that for all tuna species the P-50 depths are getting for the most part shallower not huge amounts shallower but slightly shallower and then for the one Pacific bluefin and the southern bluefin they're actually getting quite a bit shallower so there's a lot of shoaling occurring and this is actually the southern bluefin actually had to be on a different axis so this is only one that's on this axis and so looking at the spawning ground specifically of the southern bluefin tuna this is showing the stippling is the habitat area again and so this is showing the present day but the world ocean Atlas data and so the P-50 depths are quite deep and there's very small bit of overlap here but moving into the future there's going to be a lot more overlap and then also the P-50 depths are getting a lot shallower which is what's causing that change in this plot here that is being shown so those are the implications of this implications are that climate change is not necessarily going to have the same impact on all tuna species so definitely doing a case by case basis looking at some specific and important theological parameters could give us better assessments of what tuna are going to experience in the future and in particular what this assessment shows is that we should be focusing on the Pacific bluefin tuna which is actually one of the most endangered tuna species it's the one that is worth the most money and is most desired and so they're also under a lot of fishing pressure but it also looks like they're going to be more impacted by climate change than the other species and then also looking at the spawning ground in the southern bluefin tuna a lot of times adaptation can really occur in areas where reproduction occurs so this could be a good species to look at to see if there are any adaptations in these parameters that are occurring as the climate is changing and so going back to my initial approach about whether connecting responses across levels of biological organization and over spatial and temporal scales so looking at tuna I didn't look at population level responses but I definitely connected across protein cell organism and up to the community level looking at how different tuna living in the same environment are going to be affected and so and also here looking over ocean basin scales globally and then in terms of time looking at the present and then into the future and then so talking about some of the future stuff I have a graduate student Shili Liang and so I was just showing you averages across all the models we're not even looking at the extreme events such as El Nino and La Nina and so what she's going to do is look at what the effects of El Nino are and this is some preliminary results from what she found what she found is that oxygen levels are a lot lower shallower during El Nino years and so that could have a really big impact looking at these more extreme events rather than these long-term averages in the models and so she's going to be working on that I think one of the next big steps in this is trying to figure out predators versus prey if prey are shifting and the predators are shifting the same maybe they're not going to be that big of an effect but maybe if the prey and the predators aren't shifting in the same ways there could be actually much bigger effects so depending on what the predators versus prey are doing could have an impact also making this connection to behavior right now I was just looking at sort of physiological changes as these animals are moving around but can this can be connected to what the animals are actually doing there's two ways of doing that either looking in the lab and using lab-based behavioral observations or there's a lot of tagging data from tunas if that can be used to also look at this I've been trying to work find some people who would be willing to share data which has been a bit of a challenge but there is a potential to actually use the tagging data to look more into this I would like to acknowledge my co-authors and my funding sources and then I'd be happy to answer any questions thanks