Kyle S. Van Houtan, PhD: "Place, Memory, and the Future of the Ocean" | Talks at Google
Thanks. For coming out of your lunch, break today I appreciate, everyone being here. Today. I want to talk about I want to give you a brief introduction, to some things some initiatives we're doing at the Monterey Bay Aquarium kind, of our approach to problem solving and. In. How. We're trying to inspire conservation, of the ocean as. Thanks, for the introduction Kyle as, Kyle mentioned I, oversee. Our research and science groups and so, I'm going to talk about some of the things that we've been doing for the past few years. One. Of the really important things to know about the aquarium is and even, I was surprised about this is we haven't been around that long we haven't even been around, 40. Years that but, our roots go actually back pretty deep and our roots are kind of based in two institutions, and one of them is Stanford. University. This is the Hopkins marine station that, started in 1892, 125. Years ago in Pacific Grove California. They, moved their, initial, lab which is what you see right here but what I love about this photo is that this is what a marine biologist, looks like in 1892, it. Looks rather Victorian, but you still see the, kind of the shenanigans of the people hanging out on the roofs everyone. Wants to be a marine biologist but, not everyone knows what a marine biologist looks like this is what a marine biologist looks like, we're. Also so, that spirit of. Investigation. And research is very alive in what we do the other institution, is one, much closer to where we are right now and that's hewlett-packard, HP. Founded. In a garage in Palo Alto arguably. One. Of the by, David Packard and Bill Hewlett David. Packard's daughters, were. The ones that founded the aquarium and so, the spirit of innovation from. Silicon, Valley and the investigation. From Stanford, is really where our roots are as an, institution. We've been science-based from day one and, and. We owe a lot to these two institutions David. Packard is sometimes, you. Know he's widely. Viewed as a, Titan, of Silicon Valley his supervisor. At Stanford, Turman, is widely. Referred to as the father of Silicon Valley and. Turman, got his PhD at MIT with. Vannevar. Bush if you have that name sounds from, he wrote science the endless frontier he was the first science advisor to a u.s. president, to FDR and so, our roots if you academics. Love to trace their roots back to where it started and vanir Bush at MIT is you. Could argue as that's maybe where the aquarium's started, so this is the aquarium this is a view of our back deck I would, say that our secret, sauce is that, we're about place we're. About the Monterey Bay and, as you can see maybe, this wasn't taken today because.
It's Raining but, it's a glorious place there's. A lot of amazing creatures and ecosystems. Just in the Monterey Bay and, it's an interesting story about. The history of conservation in Monterey it wasn't, always the, way it is today, it's, a story of, fisheries. Collapse, of immigrants. Coming in and bringing new technologies. Of the sociology of that always not being perfect but, working things out it's, a story of marine protected areas, and the, ecosystems actually returning, and the fisheries coming back and so. It's, not always been an. Easy story but the story of this place I think is very important, for. Marine conservation and, the story of the ocean so. That's. Where we are one. Of the things that we're working on in the science team is to. Solve problems and. Even. Though we have roots in academia and, in Silicon Valley. We. Are you. Know we're, a modest, operation, we there's only so much that we can do and so, we want to solve a problem of course we're going to bring science to bear on that problem that's what our research group helps, support but, we also bring policy. And we also do quite a bit of communication and so, our theory of change is, not necessarily. Focusing. On one of those but doing all of those and trying to do all of them really well we have around 2 million people that come into our physical building, every, year and we have you. Know. Too. Many people and we probably have about 50 times those engagements online so we have an incredible ability to reach people and educate them about the. Ocean, a great example of this combination. Of science and policy and communication, is the seafood watch, it's, an app it started, as a paper card. That your consumer, could use to make the best choices about. Sustainable. Shellfish. Aquaculture. Fish. Or wild cod fish and it. Kind, of works you, you, can if, you're interested in mahi-mahi you, can look at the variety of places that they come from the variety of ways they're caught and choose the best most, sustainable solution. And so, this is a great example we may not be able to reach. The scale, of research that, that a university, could do we, may not be able to do the scale, of policy, that a federal agency could do and we, may not have the communication. Reach of an organization, like Google but we try to do all of those at the same time to solve problems, so.
One A brief example, of that Kyle, mentioned bluefin, tuna we've been working on bluefin tuna in the Pacific, for 20, years and it's. An amazing creature, that. We. Don't that we didn't know a lot about at that time and we, learned about bluefin. Tuna through, tagging, them and through tagging, them by putting living, tags on them, satellite, tags that pop off and then report a lot of data to us how. Deep the fish went its. Migratory, paths its, body temperature. It's a cellar ama tree device. Is on there and, we. Tagged them in the East Pacific here, and we developed, these practices, we innovated, these techniques, these surgeries, to do these tags these. Fish are about 18. Months to five years of age we, tagged them in the West Pacific in Japan when they're of breeding age so much larger fish much more difficult to work with and we actually tagged the babies at the various stages and so the, getting access, to those places really helps us understand, where, they're going how long they're there and their complete life cycle and, I. Mentioned that we do more than a research our, goal of course is to recover the population, is over 97%. Depleted. From, its historical numbers and just past year we had a major victory in, getting. All the tuna fishing nations, in the Pacific Ocean to agree to, recover, the population, and so, while science. And communication, and working through seafood watch and celebrity, chefs, this. Is our goal now, that we have a political agreement we. Really have to focus on how we're gonna rebuild it everyone, says yes we want to rebuild the population, now how do we do now science and research is going to come back to the forefront so. This, is something that I really use to guide our team it's a quote from Ed Catmull who runs. Pixar, wrote a book called creativity, Inc I really love this because when, we bring scientists, in we expect, them to know, science yes we may bring them in we may train them and do some things but we're really going for art and what, I mean by that is give, you an example from Hawaii I lived, in Hawaii for seven years I worked in a variety different capacities, there but we had a problem of only having a limited data set of fish. Populations, in exploitation, it only went back to about 1950, after World War 2 Hawaii wasn't even a state yet and we, needed a longer-term.
Data. Set in which to make decisions to know about the status of different fish populations, and we had this gap you can see there's a gap on the x-axis between, we. Have two points in 1900, and 1902 when an economist, came out and surveyed, the fish markets in Hawaii and then we didn't have any other data until 1950. 1952 something like that so, what I did is I went back and I found data. In. Restaurant, menus in Waikiki. Restaurants, and we, got about 500. Different restaurant, menus and on, these menus were just a teeming, with marine life but, what was fascinating is, about it wasn't, always the same throughout time the early menus had all these fish on there that were a high-value fish, still today but. Just were not being served in the restaurants and the catch had gone down so, we took a bunch of data off them and we're actually able to fill in some of the blanks over time because, the menus went back before the official catch, records, did so, that was really cool and I have to tell you, finding. These menus. It's not like there's a Dewey Decimal number for, restaurant menus so, it's not like you just go to your Public Library I I. Met, some people in, you know in their garages they're there to to their grandma in Hawaiian, gate. I would give them a box, of something and then they had it in the attic or something like that we ended up getting 500, menus probably, from hundreds. Of different sources some. People collected, them as curios as souvenirs, of their trip to Hawaii a cruise, from San Diego in the 1930s. Or something like that I got them off eBay I got them off Craigslist all. Sorts of places I met a lot of great people heard a lot of great stories but the I'm showing you this because of, is because of this plot in the lower right the green plot those. Are the data but. This is my inspiration, for them I'm sort, of quoting, the Napali coast if that makes sense if anyone's been to the island of Kauai it has an astounding coastline. That's that, was I guess you could say it was sort of made famous in the movie Jurassic Park when the helicopter comes in. But, this these are the data here but we're trying to be artful about it and we're quoting we're like this is a paper about Hawaii, let's show Hawaii somehow, so the Napali coast is kind of embedded in this another. Thing. That. We've been looking at is modeling the risk to. Coral, reefs and the fisheries in the in those reefs sort of like the fisheries fallout from, the collapse of reefs if you want to think about it like that and we're, trying to think about all sorts of ways to visualize the, data and, there's, a map and here's the sort of major ecosystems, the.
Top 29 ecosystems, where reefs are and we're ranking them and trying to prioritize where risk lies the most, but. If you perhaps, are familiar, with this band from the early 80s we're, quoting this album, cover Joy, Division, and. There's. Actually a package. Within the the, software application are that, you can use it's called joy plot and, we're, just quoting this Joy Division, because they're kind of a cool band and it's actually a good way to visualize the data but. There's all sorts of ways to be artful, about how you discuss, the, story of the data and how you visualize it and so that's one of the things we're trying to do a, big. Thing for us is, how. Do we observe. And analyze, data autonomously. You. Guys are probably in a similar, business I would imagine, this, sort of story begins with Rachel Carson's book in the 1960s, Silent Spring and I think one of the things we really learned from that is that there's no such thing as a way what. We say there's no such thing as a way, and. If, you're thinking about sustainability you're, thinking about. Closing. The the, loop having, a circular economy. Waste. Equals food you've probably heard of before one, of my professors used to say and this. Consciousness really came because of Rachel Spring if you throw something in a river it, doesn't go away the river takes it to some other place right, and so, we're thinking about this in the North Pacific in, terms of microplastic, pollution it's, become, you've probably heard about plastic, in the ocean it breaks down into very small bits smaller than a millimeter then it's micro plastic and then it gets into the food web and it, has toxins, in the material of the plastic, it, absorbs. Toxins, from the water column so on the surface and then it gets into the food web you've probably heard about these Great. Pacific Garbage Patch, --is these gyres which sort of physically concentrate, the plastic, and one. Of the things that that. We're really fascinated, by is those. Are not areas. Of high biological. Importance, we. Sometimes refer them, as biological. Deserts. Actually, there's an area immediately, to the south of this gyre on the right and between. California, and Hawaii where. We've seen a lot of great white sharks congregate, it's called the white shark cafe and, we. Sort of refer to that is like the Burning Man for white sharks because, they're, going out to a lot of the males are going out to the desert we, don't know if it's for to, meet someone special or if, it's to get a bite to eat or to have an. Experience. But. This. Garbage Patch i. I've, I've not, been to the Garbage Patch but on the very periphery, of it is the Big Island of Hawaii and one, of the beaches there called Camilla Beach is very famous for its sand, essentially.
Being Plastic. Because. It's buffering, right on the edge of that gyre and it just sort of scrubs, like, friction the, plastic, out of the water as it hits the edge of that, Beach and I worked on sea turtles a lot in Hawaii, this. Little Hawksbill turtle is probably four months old it would fit in the palm of your hand about. Nine centimeters long was its shell and it, had 41, pieces of plastic, in its gastrointestinal, tract, it didn't make it and, so. This is a problem, with animals ingesting, it it's an acute challenge, but there's also this longer-term challenge, and we don't know the implications like I said there's, a lot of plastic in those tires but, the area up top where it goes from cool, green to sort of warmer yellow and red is this, thermic, line that we know is a biodiversity. Hotspot it's, a path along. Which, whales and turtles and large, fish like bluefin tuna and swordfish, travel, and it's where a lot of fisheries congregate, now, how do we get out there and measure. Plastic. At the scale, of thousands. Kilometers if we're gonna do that it's gonna be a drone. We. Can't afford to send big, hundreds. Of long foot, ships out there that, costs tens, of thousands, of dollars a day it's much more feasible to send a drone so, we're gonna work with them barri the Monterey Bay Aquarium Research Institute it's. Kind of like it's. Kind of like your younger sibling that gets all the cool toys that's. Kind of how Ambari is for us and they. Have an exceptional. Talent. Of creating. Sensors, and putting them on vehicles, to. Go observe the, places in the ocean at scale and, inaccessible. Places like the deep which is one of the things they focused on and so, we're developing technology. To monitor that. Transition. Zone that area of biological importance in the North Pacific to really survey it for plastics we, think that, what's going on with plastic, in that place is a lot, more important, than what's going on in the desert by comparison because that's an ecosystem. Biodiversity. Hotspot. So. Not only observing, the data but, interpreting, and analyzing the, data needs to be autonomous, we're. Using this, is you're not met you're not supposed to see all these individual, plots there's 268. Spectra. From microplastic, every, little piece of plastic that we have pulled out of the ocean we have to read with, a to. Read their spectra, to determine, what it is and we, cannot do that manually, so we're using machine learning algorithms. And neural networks to, process. These and identify, them at scale if we were to do this in the ocean at the scale of the problem, we, need to have we, need to match the scale of observation and, the scale of analysis, to the scale of the problem, and that's, an ocean so. We're using autonomous. Observation, drones and we're using autonomous, analysis, and machine learning. But. What I really want to talk about is this. You, all are familiar with Search Search, is. Everything. But. You can't do search if you don't have memory and so. If you don't have memory you have nothing what, we're trying to do we. Just opened a new lab in our conservation, research. Group and it's called the ocean memory lab and the, idea, behind this lab is that we're trying to build long-term, records. Ecological. Records of the ocean so we can have proper baselines. For ecosystem. Health if, we don't have that memory of what, a healthy ocean is not, just the last 10 years not, the last 40 years way beyond my lifetime. Hundreds. Of years how do we build that record because, that record is absolutely, integral. For understanding, where we are where. We need to get back to and, then, perhaps some paths on how to get there so. This, is an important. An incredibly. Important, plot it's this carbon. Dioxide concentration. In the atmosphere as, measured. From the. Mount Aloha Observatory, on the Big Island of Hawaii and so, this is just what. I want to show you is this magic, red line let's think of that as a baseline. 400. Parts per million of carbon dioxide in the atmosphere is, a line that we had kind of set several years ago as something we never wanted to cross what we've crossed it we, crossed it in 2015. I think for the first time and you, can see there's a seasonal cycle sort, of the air the, planet breathing, rather and, it.
Goes Up and down with the, northern hemisphere forests, in the winter being, deciduous, and losing their leaves and the ocean cycle, now as I go back and as I add more data to that you can see how far apart the historical, observation, is from, that red line that, we didn't went across in 400, we, go back to 1950, you, go back to 1900. We. Go back to, 1800. And we. Can even go back further now. How do you think that this graph was built you, can see there's sort of three different sections of the graph well. The first part oh he can go back even further the. First part is done. With. An instrument, measuring. It beginning, in 1958, you, know and you've made a good scientific, figure when they and bronze it and put it on this side of a building right it should be like every scientists goal that they'll and bronze, a figure that I've made and maybe my restaurant menus won't we'll get there someday but but. But. But not today so, Keeling. From Scripps, Institute oceanography, started these measurements and 18:58, and then, when they said you know you're onto something here, but, we need more data we wish you had done that in 1758. But of course we didn't have sensors so, how do you think they went back and built it they went through glacial, ice cores and went to Antarctica and, dozens. Of teams from all over the world of international collaborators, have worked to build this together but, this plot is called the, Keeling curve right. And this, is the absolute, important. Baseline, for us to measure climate, change and where we are today this is what began at all really and we. Don't have one of these for plastic, we. Don't have one of these for a lot of problems so how do we build this memory well. Let's. Go back to this idea of drones, there. Is a drone, inside. There's a payload, with, data. An, observation. Scheme some sort sensors. Some. Sort of propulsion, there. Is energy, there's battery of some sort and then probably some sort of way to transmit, the data back. To Moss Landing, - and barri well, what I will go out to embarass a, I've been working with drones my, entire professional life but. I've never touched one of those things I have touched one of these things this. Is a sea turtle shell and what, my my. Idea is that actually, animals. Are drones they. Are they have long. They have tissues, that, they take data on their ecosystem, experience, and are recording, it and are, learning all sorts of stuff about the conditions of the ocean so let me explain that a bit more, that's, a hawksbill, sea turtle right there this. Turtle. Died. Many years ago and was in a freezer in Hawaii when I got there and then, we did an e crop C on it been in a freezer since 1992, and I, said well let's learn something, about this animal that, met, a bad end and maybe we can get, some information about how to manage the population better, so, this animal obviously. A beautiful, show was. Not hunted. Historically, for its it's meat, it was used for its show and you. Can see why it's gorgeous and but. The thing is within that and within that shell my, idea is that there's probably a lot of data so, - first we. Need a we, need some sort of baseline record, of, and so. This is one way we go about doing this this is obviously, a nuclear, test in. Probably. Several kilometers, away from where that animal actually. Died, that I just showed you and this, is in the Marshall Islands you. Can see. That. This is injecting, all sorts of heavy carbon, into, the atmosphere radiocarbon. Or 14 carbon and what. We can do is we can get a map of the, memory, of that radioactive. Carbon. In the, system through, corals, and we did this in Hawaii and you can just see the levels of radiocarbon are, pretty much low background baseline, until about 1950, and then they start to uptick they, peak in the, late 60s, early 70s depending. On where you are on the planet and then, they start to gradually decline, with, the nuclear, test ban treaty right, but. This right here is a is, a very helpful. Calendar. Of radiocarbon. That, we can use to understand, the, timing, of these, different. Animals in the ocean some, of the most basic things about, wildlife. Like a bluefin tuna or a blue whale or, even a sea turtle is how old is it how, long is he gonna live where, did it come from, where, was it born where is it going to breed these basic, questions are really elusive this, is a cross-section, of one of those shells that I showed you earlier we, know this animal, died, in, 1976. But we can micro, sample, along the various gradients, kind, of like the, grains, on a log, of wood right, but. One line isn't necessarily, one year and so. What we do is we use tools much, like what your dentist will use to fix a cavity a very small precise drill and we, drill out powder from there and we analyze, the isotopes, and using.
The Radiocarbon, isotopes. Or bomb radiocarbon, we can actually go back and date each. One of those sections and say oh this, animal was born in 1953. At dot in 1976. It was 23 years old. Now, we can, say oh this, isn't just a sea turtle shell it's a clock. But. It's also a, passport. It's. Also. Recording. Toxins, and it's. Also recording, temperature. They're. Literally. Thousands of things we can get out of that shell to understand, the experience, of that individual. Turtle that. We can build a record of its environment. So. Let me give you another example, but before I do that I want to explain to you marine. Food webs in a very sort of cartoony, basic kind of way at. The very basic level we have the Sun which, is its trophic, position, or trophic level is zero something. That eats only the Sun is trophic position of one like phytoplankton, something. That only eats phytoplankton is, going to be - something, that only eats that like a small like a sardine or something is gonna be three a tuna. That eats tad is going to be four and then something that eats that is going to be five so. There are these change, that you when you go up the food web now, one of the things that people have looked over time is to measure the aggregate, performance of a fishery by, getting its mean trophic, levels you look at all the fish that were caught you, assign a trophic level to each one you, weight it by the mass and then. You just scale up now if you look at over time in different, parts of the ocean this is the Northwest Atlantic the Northeast Atlantic, the South East Pacific and, then, the Mediterranean, you can see as catch and fish goes up the trophic position, goes down that, means those food webs or those chains are getting smaller that's, something we want to avoid because. That means the systems are getting simpler less complex, less resilient and they're losing some of the top predators now, this has been a subject of a lot of debate gone. Back and forth because when you use fisheries, data, to. Measure the performance of a fishery, there, are inherent biases. What. You can see in this plot in the lower left is the importance, of anchovy, when the anchovy, catch goes up the, trophic. Position of the aggregate, fishery goes way down because it's a low foraging.
Fish Right and so, there's been a lot of debate about this well. One of the things we said is trophic, level kind of interesting idea but, maybe if we don't use fisheries, data to measure it maybe if we use because that has inherent, economic biases, what, if people decided. They don't want to use anchovy anymore, they want to use natural fertilizer instead of fish fertilizer what. If there is a moratorium in, the, US on a certain kind of fishery that will change the catch so the ecosystem isn't, changing is just the economic, rules, are changing right, so we actually want to measure what we're interested in measuring without artifacts, it's a huge issue in in observation. And measurement right so we, said why don't you look at these birds because, they're also interested. In eating fish and, they're like us they're searching the ocean for top predatorial. Fish but. They're not subject to economic constraints, like we are and one. Of the things about there's, cool drones that embark and other people make is they're. Fascinating, but they can't go back in time. They're. Not time machines but. Working with museums, and repositories. We can actually go back in time and that study I just showed you those kind, of weighing things they're based on fisheries, statistics, which are largely after World War two so you're limited to data back to 1950. Of course the ocean was changing before 1950. Of course we were fishing before 1950. Of course climate change was happening before 1950, so let's go back before 1950, but in order to do that we have to be creative and so, we started going to museums and getting, seabirds these birds are from the late 1800s, and we, can measure throughout. Time their, trophic position, by, looking, at the ratio of amino acids, in their, feathers so, these birds have thousands, of feathers these, are precious specimens. We don't want to harm them but we can just go in and take two feathers you. Ned the bird never knows we came and left right and even. An expert would not even know that we took those feathers we take body feathers we don't take the beautiful long primary. Flight feathers that like, the kind you're used to like dipping in an ink bottle and right writing with now these are like the downy feathers in the breast we take two of these there's, another nine hunters another 9,000 they are waiting left and. But. Importantly, these resources, are already there. Natural. History or posit or ease museums, collections. There. Are these are all over the world and they're literally just data, archives, sitting. In a drawer right, and so we're trying to tap some of the potential of these to understand long-term trends in the ocean we, went and used this technique called compound, specific stable, isotope, analysis and, amino acids kind, of rolls off the tongue but, what we can do is similar, to the heavy carbon theory, this, does not a radioisotope, is this is a stable isotope but we can look at where they are in the food level based on the nitrogen that they're accruing the heavy nitrogen and what, we found in looking at eight different species is that you, can see in the lower left that pink plot is an, ensemble, of all those pieces of birds their trophic position is declining.
And It's, almost, exactly the, same magnitude. Of decline as was measured in the fisheries data but, what's really cool about this is because. They're birds and people are fascinated, by birds and I'm studying for birds for a long time is we actually know, the, diet, of these birds because. Those that, dot on there with the huge error bars is one, point in time where. Some, of my colleagues in Hawaii actually captured, a lot of these birds and looked at their stomach contents and. Basically. Got the stomach contents of hundreds, of seabirds which you can imagine what that experience was like. It. Was it was a little pungent I would say but. Nonetheless they, reconstructed. The diets of all these animals, and we can then we have two sources of data now we have diet contents, which, are pretty primary, source and then we reconstructed. It from the amino, acids in their feathers beginning, in 1890, and then sequentially over time to today now what, we can do is we have the trophic levels, and we, have the diets we can actually solve for X and we, can actually look at their diets, over time we can reconstruct, what the diets would have been to, produce those, trophic, levels and what, we can see if you look in the lower left the ensemble, for the all species is that, squid, populations. In the North Pacific have, doubled. In the last, hundred and thirty years, so. That's just describing, what's happening, right through the feathers this, is an albatross, a Laysan albatross, of course, it's a very, well, known for its ability to fly very efficiently. Over long, areas. In search law huge. Areas, of the ocean with very little energy expended, you may not know this but its wing is the, archetype, for the Mars plane, that's being designed right now by NASA this. Plane has no propulsion it's just a glider they're gonna drop it in the outer atmosphere of Mars and it's just gonna descend hopefully. At a very slow rate they're hoping for three minutes because. The atmosphere has never been measured in Mars they have a Rover but they don't have a plane but, the design of his wing is called a Prandtl, wing and it's designed basically office. Principles, as an albatross wing so, it's a pretty good flier that's all I say. But, now that we have we can say the trophic positions have changed over time the. Fish that they're eating has changed, over time why. Is that happening, well. Here comes machine learning again we, take various measurements, of echo morphology, we, take various measurements, of the climate data in the North Pacific and we take various, ways, data, streams of Fisheries, and we can say all these things could be affecting, why these birds and their trophic position is declining it could be the birds themselves some. Of them are really great flyer is like albatross some, of them are a little chunkier and have shorter wings and can't fly as far then. We have all these things going on with climate we have all these things going on with fisheries so, what's causing it and so, we can run through and apply machine.
Learning Random. Forest algorithms, and build models to understand, this that the human eye or some more standard, statistical, methods like general. Linearized, models can't do and we can say that from. All of this I will tell you that all of those things matter, because. These animals don't get driven by one variable they get driven by many variables, because the ocean, is a multidisciplinary, space, so, if you're an albatross you can fly further but, climate change is also happening and, commercial. Fisheries are also taking, your preferred fish out of the ocean and so, we can do all of those things but. The important, point that I'm trying to say here is that we started, with feathers, we. Started, with birds in a drawer that weren't being used for anything and we, were able to build this whole story about how the ocean is changing and how squid populations, are growing just, from looking at amino acids, in bird, feathers so. Let me give you another. Example, back to home again. The Monterey Bay I mentioned. That we have our roots in the, Stanford. Marine, Lab which is right next door Hopkins, marine lab they've, been there at that location for a hundred years and they've been every morning, since, the early 1900s have been that the docent, in charge. Go. Out at 8 a.m. dip a bucket, of water into the ocean put. A thermometer in it and measure, the temperature now they did that every day of the week except Sunday for, about 10 years and then they were like well to hell with that was Oh Sunday too and so. We have this really cool data record but, we had this is going back to instruments. Versus proxies, right about, 1919. We have those data records on the temperature, of the ocean every, morning at 8 a.m. so. From those data we, can learn a few things we, can look at the average temperature, but, often what's really important, is the extremes, so. If I just look at that time series, of thermometer, in a bucket 8 a.m. from, our colleagues next door we, can see that the hottest week of the. Year has. Changed, and has become 2, degrees C hotter, today than. It was when they first started in 1919. Ok, so think about that it may say well 2 C isn't terrible, maybe. It's not great. But. Then if you look at another way of measuring it if we look at when that hot, week is not just how hot is that week the, extreme, you know the 50 the first hottest, week of the year but not just how hot is it but when does it occur and it occurs about, 10 days earlier. So. That's not only happening, it's, a higher magnitude, of heat but it's actually occurring earlier in the year not, a huge surprise either, of those things but then if you actually you say let's not look at weeks let's. Look at days and let's look at extreme heat days this, is where it really gets interesting, and we see there are now 68. More. Extreme. Heat days than there were a hundred years ago this is just one century in one, place and this is the value of data we, wouldn't be able to have one, be able to search these records, and come up with these statistics or have these metrics if we didn't have the memory right, but, we can actually go back further, than. When they were measuring, with a thermometer with. With. A human actually going out there with a sensor, and that's. Actually. With macroalgae. People. Have been collecting, algae probably. As long as people have been going into the ocean it's, really easy to do it, basically the procedure goes like this you reach down you pick up the algae and you, put in your pocket it's. A little bit more complex usually you press it between two pieces of paper but, we have an herbarium at the aquarium, called, the a barium aquarium. Herbarium and we've. Been collecting since the late 70s, when we started scoping, out what we're, when the aquarium started getting designed and build but, our neighbors next door have. Been collecting, algae, since the 1890s, and even before a little bit so, this is the same genesis. Of red algae calif, elise and you, can see that even though it's been kept between two pieces of paper for, a hundred in almost. 30 years, it, looks bread it still looks the same and it's a remarkably, good condition so. What we can do is, we, can actually recreate. The temperature, of the ocean in which that, alga. Specimen. Lived by, looking at the oxygen isotopes, we, can look at the nutrients. In that ocean we can look at the pollution in that, ocean we, can look at this, is a this, is an organism that roots in the bottom so it didn't migrate so we can't say oh what's its passport, did it go to japan and come back it did not do that we can say that right now with no analyses, but, this is really important, and so what we're now doing is, trying to say well these are all our specimens, over time we, didn't collect any equal amounts but, we can go back and use these, algae. Or.
We. Can use Birds or, we can use tuna we, can use otters, we can use sharks to really learn a ton about the ocean we, just need to, sort of go, into the memory find. The platform find, the drone whatever animal or plant that is get, the sensor out of it and then recreate that thank. You very much. Do. You have anything you, would call out. What. Sort of any interventions. Are you like, to improve like if we're going on this downward trend in, a lot of ways do, you have suggestions. Of, focus, areas you. Know for all of us as consumers or you, know I think. One of the before, we get there I think the it's. An important question and it's the obvious question I think how. Do you prioritize, what. Problems to address and then, because you can't do everything but. Let's just say it's difficult to do everything I. Would, say that one. Of the issues that's been really fascinating. As a scientist, and a conservation, is to observe. Is the. Public engagement on the issue of microplastic we. Were not really, concerned in talking about that ten years ago that. Is not a new issue it's been identified for a while it just really seems to have captured the. Environmental, consciousness, and even, the public, consciousness is what it the garbage patch and, I think the way that we think about that is now, that we know let's do, something about it right, that's what Sylvia, Earle and others would say something like that I think that's a it's a really good way now that we have the information now we have the data what, are we gonna what are gonna do about it I think, when it comes to plastic, the. Easiest, thing to do is to. Not use single-use. Plastic, a throwaway. Or recyclable water, bottle that basically has a one, use lifetime, and then it gets repurposed. To something else and. So what I would say is that's, the easiest thing to, address, but. It's pervasive, you. See single-use plastic, in a lot of different places, and. I think that would be something that you can immediately do I think the choices that we make when. It comes to climate change which. Probably. The. One, of the things that I have heard and, these are very difficult things to measure but, if we were to stop, looking. At the issue of climate change versus, issue of plastic what's more important if. We were to stop if we had a magic button that we could turn off all carbon, emissions we can go carbon neutral today. We. Would still be dealing with the problem of climate change 1,000 years from now because. The Earth's climate system is so complex and the, ocean, is a huge, reservoir of heat that. It takes a long time for. Things to equilibrate one. Of the reasons that perhaps climate change isn't, as severe today. As it we might think it would be is because, the ocean is holding so much of the heat and so it's like this buffer that's, controlling, our. Climate system so. If that wasn't used for climate if we were to do the same kind of thing and magically turn off our plastic, and micro, plastic, pollution switch, we, would probably be, dealing with this for another 40, years. Very. Different magnitude, of the problem. So. Climate is probably something that is going to affect everyone, and has been for decades and, I would say that the choices that we make, in terms of our carbon emissions and our carbon consumption are massive, the food that you eat the. Travel, that you do the people tuning in from other places that, didn't fly here that actually are just tuning in online great. Choice and, I think we just need to prioritize, those decisions, we're even looking at this in our research programs we. Have three different research, projects. That we could do and we're. Evaluating right now well how. Does sustainability factor, into this these, are all three very this, is the it's a we have a great we. Have a great problem we have three fascinating. Research programs, one, one. Project on axe one project on y one project on Z but, they're not all the same in terms of their carbon consumption some, of them would, be maybe require, more energy and that's into our consciousness right now about how we prioritize and choose projects, for.
Just For research. So. I'm a scientist, and we. Have a lot of policy, experts that we work with but I would say that. Policies. Addressing single-use, plastic, plastic is so pervasive in our society I probably I'm, holding plastic in my hand right now but, I'm not gonna recycle, it or throw it away I'm gonna use it for a very long time and I. Would say that policies. Relating, to single-use, plastic, are just things that we need to incentivize, that going, away first, of all thank you so much for coming, that took was amazing I think we'll agree one. Question I was wondering about ISM, so, you talked about this memory lab that your guys, aren't building is but. You also talked about the creative ways that you, use to gather all the data that is you, know hidden everywhere and very difficult to find so, I was just wondering is the goal. To open up, the lab to everyone. And I. Don't know if I have feathers in my room, from ten years ago like are you gonna encourage people to participate, and. Put. The data that they might have at. The disposal of the aquarium and also are. You gonna make the data available. To other scientists. And great other, people that's. A great question so the first thing is I want to say is I've had written above my desk for a long time more, data is more better right, even though the grammar isn't great the, people, get what I mean by that so. When. I was doing the menues project in Hawaii there's. This thing in Hawaii you may not have heard of that called coconut wireless, it's. The word getting out you, know it's. It's a it's a joke of course it's, the gossip train and when. I was working on this project, coconut, Wireless was in full effect and I, would get people, and. Then when we published, the the study and. It was on NPR I got a lot of random solicitations, people just sent me stuff in the mail but I would get calls and saying hey, I have five, menus from 1960, do you want them and I'm like yes of course I do so, when, it comes to wildlife it's a little trickier because G 1080 per minutes so, we wouldn't say hey here's, a here's an open data call for seabird feathers because we. Want the pros, to do that and, permitted, fashion, but, I would say that. Yes. There. Will be we, are going to be increasingly, communicating. With the general public about ways they can get involved and contribute. Data to. Our to our projects and as we do that I think that's an incredible. Point because crowdsourcing. And involving. Citizen science is a it's a very big, component, of where we're going on the second question about open open. Access open source. We're. A non-profit and, it's it's one of our core values to be open access to open source and barri. They, make some amazing devices and then they put the blueprints, online it's. Really amazing that they do that, because. They're the same there they have the same DNA as we do we. Want to be open access so we use Open Science framework we. Use github we. Use fixed share ResearchGate. We put all of our publications, online we're increasingly trying to work with, only open access journals, because, we want to work with people all over the world who, may not have access to a university, library, in which those things are free and openly available so. We it's a huge value of ours to make our data accessible. To. Invite people into, our research projects in a way that they can meaningfully contribute I mean be a co-author in, our work, we. Want to solve problems and we know we need more data and more experts we firmly believe in the theory of plenty at, the more eyes and the more Minds you have on a project that more comes out of it it's, not like we have to golem it so. We can get all the glory that's not our position at all we want to open. Science open access is, wherever you are so yes and we're. Building the data. Infrastructure, to, make that seamlessly, happen, and then the user interface, that's, very intuitive I, think. Some of the problems that were actually I'll go even a step further some, of the problems that we're dealing with every. One of this room maybe in the choir and maybe like super onboard but, there let's be honest there are some more difficult issues that aren't so obvious and have to get people with disparate, beliefs, and political. Persuasions. To agree on so one of the things that we're doing when we publish, a paper we're, now publishing. Transparent. Gooeys. Graphical. User interfaces, to, let people say well we, did the study this way but if you pull the dials in different ways you, can get a different answer so, some of the problems were solving we're trying to actually recruit, do, it a different way, and I think that's where science is going to be going with much more interactive, where. The the, analytical, framework, is on.
The Back end and you have a user interface and, you're, using machine. Learning you're, just pushing a little dials with your mouse and that's, where we're going to be going through things, like. The. Frameworks, usually on the art platform, where we can do this yeah. What. You're just referring to with, how you're. Building. Your. Research. Now so that it can be more. Presentable to people with other views is that how you're. Making. It more open to things like getting. Fisheries, on board for, things like sides-- with bluefin things like that because I know in the past it's been a challenge, to get the fisheries and. Other. People. Lobbyists, stuff like that on board with getting. Protected. Fisheries, all, of that all of that fun stuff integrated. Just. The challenge of having, species. Protected and your, scientists. Working together with, all, of that you, can put it more eloquently I'll let you do that so that's the main question is there a way to get, science. Working together with people who wish. To deny. Science. Or use it in a way. That. Supports fisheries. Versus, science sure. I mean it's, a great question I would say that. What. I would say is that a, lot. Of people care about bluefin, tuna for different reasons and, I. Think. Everyone pretty, much cares about bluefin tuna it's just four different incentives, and their, incentives, are prioritized, differently if you're a bluefin tuna fisherman you can't fish for. Bluefin anyway if there are no bluefin so it's. It's, in everyone's, interest for there to be bluefin tuna it's just how much are extracted, every year but I think that what you're getting out is exactly the point is that when we do analyses, and we come to a conclusion we. Not only want to be transparent. About the data about, how we got there but, then we say and here. Here's an interactive, where, you can choose your own incentives think about a situation like. Choosing. Where to put marine. Protected area preserve. In the ocean if. You're incentives, are to, protect climate, change or to. Rebuild, fisheries, or, to. Have a. Variety, of other things those. Places could show up differently in the ocean to protect biodiversity. And, what, we can do is we can say here are the 10 most. The. The top 10 market share of ideas, for, ocean. Conservation for. Marine protected areas we can build those data layers there exists, now and we, could say and you can weight them however you want this. One 10 this one zero this one five and then it gives you a map that's. What we're working on right now those. Kinds of Interactive's. It easier. To, show. Fisheries, and. So. They're you mentioned cabo pulmo there's some great success, stories, of conservation. Working I would, just look at the great whales in the Pacific Ocean blue, whale humpback, whale gray, whale these, are basically candidates, for removing, from the endangered species list now. Because. Their populations, have been growing at three to six percent a year ever. Since they put a moratorium on whaling and so, what's, amazing is if you don't kill whales they. Actually, do well and. I. Think that's something everyone agrees on and. So. There's a lot of examples like that and so it's just how do you get protection how do you get the how do you serve the dual mandate of. Commercial. Fisheries and, conservation. That's. The question and how much do you weigh in which one and I think when you move, towards a a data, rich, open.
Relatable. Interactive, framework, a lot, of the a lot of the disagreements. Can be a back be, can be about we, don't trust your data we, don't trust your methods and and. You're, doing it behind closed doors and you're not inviting us to be a part of that well all of those disagreements go away when. You make the data available when. You make the processes. Transparent. And rational and when you make their Interactive's, online. That, really anyone can can, very intuitively, understand, and that's what I think is we're trying to meet everyone where that because all of those complaints, are, totally, valid, we. Don't win secret data we don't want secret methods we don't win secret rooms with secret decisions when, it all to be out there the, whole point of science, is that it's it's, egalitarian. And, democratic and, accessible, and. So we need to have the infrastructures, and the platforms, to make it that way so. When you were talking about the Keeling, curve measuring, the co2, levels, and that. 1958. Non-words I think we, have an instrument that I guess is very very good at this and, before it it's more of a proxy, how. Do you align. The data from the two and possibly, even maybe many disparate, historical. Sources how, do you make sure that they. Are calibrated, or that. Sort of thing for something like this with the ocean when we left I think it's like a symphony you know bunch of different instruments and you're trying to make a coherent. Narrative. Out of it and when. It comes to I'm less of an expert on the co2 series, that. Is a there's, a lot of research groups from a lot of countries and a lot of NSF, funding a lot of major programs, that went into building that curve it may you may look at that curve and say oh it's, just a simple graph but it's so. Much work. Went into that graph sometimes, when you look at figures and we can just scrape.
Data Put. A cool viz together in like. An hour and then you, look at something like that and that is like. PhDs. And careers. And, awards. And huge, amount of effort went into that but, getting getting, together a proxy. And a. Calibrated. Data set is usually, the beginning of a lot of the thought of some of our projects, as we start, with some sort of training set and then, we start with something that we think is a good proxy and how do we Wed those two together so, that we can take the training set away and then we can do it at scale this is what we're doing with microplastic, when, we look at the Raman. Laser spectroscopy. We, train the spectra, unknown, types and then. We go out and we do the proxies the problem, is is that plastic, in the ocean is been. Tumbled. Around in the waves or at depth extreme, temperatures, different, acidities. In an, animal's stomach for, a while so, it's not not pristine so we but, that's actually part, of the, magic because, we can actually now using. Spectroscopy we're, working to age some of these particles, we could say not only is it nylon, but, it's nylon it's been the ocean for 40 years that's. Really helpful information as, opposed. To saying well 40% of it's nylon 40%. Of its polycarbonate. 40%, of its poly out of my door that's, more than hundred percent but but. You get the idea but I think just having those training sets when, you go back in time the. Error bars can get a little broader and so, it's just the. Question is just realizing, the limits of your data and using. A training set that is unassailable I say, that's just our general approach going, about it, some. Proxies are are, more difficult than others and sometimes it's a proxy if a proxy it's like a metro shkadov data and, then you get that little thing on the inside right and we want to if we wanted to kind of limit our proxies. But it's amazing what you can do if, you have some, data to begin with as opposed to nothing, thank. You very much. You.