Generous support we get from the Simons Foundation.

So I want to go back and think, you know, start thinking about why am I even talking about. Let me see if I can change like there we go. Why am I even talking about?

Particular in the surface ocean, the elemental composition and this actually comes back. I think very neatly, and I was excited when you invited me to talk in this sort in this year brings us back very neatly to Liverpool and its centenary.

And just talk for a moment about James Johnston. I'm sure most of you, if not all of you are more familiar than I with James Johnston, but he was a fascinating person, have been learning more about recently. He was an apprentice woodcarver in Scotland who was clearly academically inclined. Got a scholarship, went to London and studied natural Sciences, and ended up at.

As a biologist and marine biologist came to Liverpool to work at the Fisheries Lab and then was appointed professor at the University in 1920. So I guess with the.

Beginnings of the Oceanography Department. There he was right. He was right there. Very interesting character. He he wrote his written. He wrote several books on general biology and marine biology, marine biogeography, all sorts of interesting aspects to this to this man in his career.

So in 1932 a he died and then there was a memorial volume published by University of the full press in 1934. A collection of papers.

In his in his memory, touching on topics that he was interested in.

And so one of the papers in that volume, which is a hugely influential paper, was by Alfred Redfield, who is a professor at Harvard and worked with Polish. New Graphic Institute.

And it's about the I can't read the title 'cause the zoom stuff is in front of my screen, but it's something like the transformations of organic derivatives in seawater and their relation to the composition of plankton. I don't want you to read this page, but what I want you to notice that this paragraph at the top here is a quote from James Johnston. You know this part of the book and he's touching on Johnston's emphasis on the interplay between biology and chemistry of the oceans and how important that interplay.

So James Johnson, Liverpool are right at the beginning of this. This is a hugely influential paper that that that that Redfield Rd Redfield's point in this paper was to he measured the elemental composition of organic particles from the surface ocean of plankton, and he found that on average their elemental composition was 106 carbons to 16 nitrogens to one phosphorus molar ratio.

And this is the typical elemental composition, and you find that in the different pools of, particularly in the phytoplankton in the zoo plankton, there's some variation around that, of course, and this became known as the Redfield ratio.

What was interesting in the paper also is that he pointed out that when you measure the relative variations in the inorganic dissolved forms of these elements, you find the same ratios more or less.

And so that you know, just highlights the tight coupling between the biological cycle and the global cycling. The global rest of words of these elements in the ocean. And I'll talk a bit more about the impact of carbon in a moment.

So this ratio is called the red fill ratios Canonical number through. You know, for 100 years almost.

Generations have been learning about the red fill ratio and for decades this has been kind of used in as a as a tool for carbon cycle models in, particularly because we think about when we think about the the.

The flow of the biological flow of carbon in the ocean carbon is fixed by primary producers. Phytoplankton in the surface ocean where there's sunlight.

And.

These organisms are consuming, you know, if we think about the idea idealistically, they're consuming inorganic forms of nitrogen, phosphorus, CO2, and making organic molecules. Those organic molecules form the fuel for the higher trophic levels and also ultimately the source of the sinking material that leads to a store of carbon in the deep ocean are biologically mediated store of carbon in the deep ocean.

Now we typically think of the surface ocean not being limited by the availability of carbon, but by the availability of nitrogen or phosphorus or iron or some other element. And so if you think about a nitrogen limited system.

The more carbons attached to each organic nitrogen sinking out of the surface ocean, the more carbon ends up being stored in the deep ocean. In this so called biological pump.

And so the elemental composition of the material in the surface ocean and the sinking material particular has a profound impact on the carbon partitioning between ocean and atmosphere. An if we use some simple model, this is just a redrawing of something that in the equation that appears in the book that reckon I wrote. But you can think about from a simple model point view the change in atmospheric CO2 at some steady state as a function of the average.

Carbon to phosphorus ratio in sinking particles.

Redfield ratios about the middle of the growth here an if we doubled that ratio on average, then you could imagine that the models which suggests that you take another 50 or 60 parts per million of CO2 out of the atmosphere.

So this this ratio exerts a powerful leverage on the carbon cycle and potential for variation. For example, in Perth, climate soar with warming, for example, can lead to feedbacks, modifications of the global carbon cycle climate, and so one of the reasons we care about these elemental composition and being owner understand and even be able to predict it is because we'd like to be able to.

Think about how whether this was important or not, and these post variations how my future oceans respond in this regard, and so we'd like to understand this. We'd like to have quantitative models that can explain and predict the elemental composition.

So as I said, for many years, many others have leveraged this kind of relative constancy of the average Redfield ratio.

Polaris practical purposes, but in fact the composition of particular in the surface ocean, it varies and it varies systematically, but it's hard to observe, and it's not been terribly comprehensively observed, but because of that in a recent compilation from Adam Marty, this.

Graph on the right shows the distribution of the distribution of the samples that were used in this compilation.

Adam martini tried to make a global scale of.

View of variations in the C&P ratio.

Some of his colleagues at UC Irvine in the paper from Tang did the same thing, but they use an inverse model to infer from the interior concentrations and flows. What the elemental composition of that sinking particulate material must be. They give a rather comprehensive, rather similar view, and I'm just this graph is very busy and complicated, but basically you can see there are some trends here. These dark green regions are the subtropical gyre's and the light green regions of the tropics.

And subarctic regions.

The box and whisker plots indicate the direct data from martini's compilation and the red.

The dots and their error bars indicate the inference from an inverse model, but what you can see is there inferring, you know there's a lot of variability. Local variability in these ratios, but there are also some systematic variations implied with elevated here. See to P ratios in the sub tropics, so these are almost twice the Canonical Redfield value, or more than twice.

And so there are systematic variations implied around the Globe, and so you know, there's a couple of things that are interesting about this. One is we'd really like to understand what drives those variations, and in doing so, that might help us think about the more General Dynamics of what's controlling these ratios, and therefore how they might change in response to other kinds of changes to the system.

So, so we're kind of thinking about the Redfield ratio in a more regional.

Sense now nan, how to how that can help us understand the underlying dynamics?

So yeah, just the big questions. Then what drives these regional variations of the elemental composition of surface ocean particular?

There are a number of contributions to that I'm actually going to focus here just for the interest of time and sort of clarity on the role of phytoplankton in particularly acclimation.

Of Phytoplankton to their local environment. I just want to say here that I know that's not the only potential influence and we're happy to discuss other influences at the end of the talk, I'm just keeping the story clean here.

Anne.

Can we understand these variations well enough to write down quantitative models that enable us to interpret them quantitatively and actually have some predictive for interpretive power that we could use for carbon cycle climate studies, for example? So these are kind of big questions and goals that we kind of going towards. So just again focusing on the fighter plankton and the acclamation of phytoplankton. So if you grow so, this is.

A fairly busy plot and I'm going to spend a few minutes on it. It will be relevant as we move forward, but if you grow the bottom line of this plot is that if you grow phytoplankton in the lab and it's subject them to a variety of environmental conditions, you find that their elemental composition composition varies widely in response to those changes. So there's an acclimation response, and So what these data show our experimental data that were published by Healy in 1985 in a very beautiful paper.

In which they measured the carbon, nitrogen, and phosphorus and chlorophyll composition of actually a freshwater primary producer, Cynical Caucus. Linares in the lab in continuous culture. So they grew them to steady stay in a in a vessel with medium flowing in and out flow as a death term. If you like grew them to steady state and.

Each point on this graph represents the measured nitrogen to carbon ratio. I've turned that into carbon to nitrogen, which seems a bit more natural on the other on that on the right hand side here.

As a function of growth rate, which in the chemostat can be controlled by the dilution rate.

And as a function of the light intensity. So I forgot to put the units. I'm sorry, but this is micromoles of photons per meter squared per second or something like that. But a high number indicates a higher photon flux.

So first thing couple of things to know about this and we'll come back to these points first of all.

You know, so this is this has been known for a long time. I'm reviewing some very, very elegant about old data now, but at first of all the nitrogen carbon ratio has varied by a factor of five, just through different growth and light conditions here, and so you can see that there's a very high potential for variations in the environment driving variations in there.

Elemental composition of the primary producers in the real world, the Redfield ratios kind of somewhere in the middle of this plot, couple of things that will come back to is for a fixed light intensity. Say look at the red dots. Here there's a more or less linear increase in nitrogen to carbon as the growth rate increases.

At to maintain the same growth rate at decreasing light intensity, there's an increase in nitrogen to carbon, so high growth rate or low light intensity leads to an increase in enter C and that could be as much as a factor of five. Of course, there are other potential influences as well, so we're going to kind of use this as a starting point for some of the model model. I'll show you in a minute or two.

So let's try and dive in to interpreting what's going on in a conceptual way, and then try and build. I'll try and give you the flavor of a simple model that builds around that so.

What controls the elemental composition of an individual cell? Well, we can break that down into a slightly different way and think about the macromolecular composition of a cell. So an individual phytoplankton cell has perhaps half of its mass, say, is in protein.

Which is nitrogen rich? Good fraction might be in carbohydrate and lipids, which you know, let's say free of nitrogen.

Lipids, phospholipids and nucleic acids, DNA and are a rich in phosphorus and so most of the phosphorus in a cell is in these in these molecules. So these broadbrush pools of biologically important molecules linked to Elemental Sto Kiamat RE. So variations in the relative abundance of these different molecule molecular pools is going to be intimately tide to the overall study geometry of the cell.

And these broadbrush pools account for almost all of the carbon, nitrogen phosphorus in our cell. Most most of it, let's say.

And so breaking this cell down into these few pools, this half a dozen pools makes a lot of sense in terms of linking to the.

The still kiamat re the second nice thing about this is that the allocation to these molecules relates to Physiology, so a cell that's growing first is likely to have a higher compliment of RNA because it needs that machinery to produce new proteins, new molecules, a cell that is under low light might invest more in.

A pigments to harvest light when the photon flux is lower and there will be an associated increase in the light. Harvesting proteins for example.

So there's a nice link between elemental composition, macromolecular composition and physiological state and environmental conditions, and this has been a long noted, and there's some beautiful works that have kind of connected these things. They just lovely book by Elsenz Turner called ecological soy kiama tree, which I think really encapsulates this general idea they've been.

Studies like Guidina Roche looked specifically at this allocation within the phytoplankton, and models have used this kind of concept a little bit, but I think in our field we haven't really fully exploited this nice conceptual framework into quantitative models in the way that we that we might, and so that's part of what we've been trying to do.

So, ah, yeah. And so I wanted to talk next little bit for a few minutes about a case where we've taken this conceptual view.

Linked it to the Healy data and tried to build a quantitative for mechanistic model that that can link elemental store country macromolecular allocation and Physiology, and this is work from Cayenne, amora or is a former graduate student of mine and just this just came out a couple months ago.

So I want to go back to healeys data set to set this up just a little more. So we talked a lot about this. Nitrogen to carbon ratio as a function of growth rate and light intensity. You can see there's this linear increase with growth rate. OK, more faster growth, more investment in biosynthetic protein which are nitrogen rich, so the nitrogen content goes up. Light intensity goes down.

More pigments and proteins are needed to harvest light, and so the nitrogen content goes up. So qualitatively this all makes sense and we can see a parallel variation in the chlorophyll to carbon ratio. Again, faster growth requires more energy, therefore more light harvesting more pigments, lower light requires more pigments, so these two sort of rather parallel piece of data.

By the way, that you can find in multiple studies in the literature, including many marine organisms, the same qualitative behavior. So this is not strictly some artifact of this particular Organism.

So let's try and get under the skin of these these particular relationships a little bit, and so I'm going to walk through, but in very broad terms, the model. So we take the conceptual model I've already described.

Think about cells as being 6 pools of these broadbrush macromolecular pools.

Each each macromolecular pool is assigned a fixed but different.

Elemental composition and this is rooted in empirical data.

We're actually going to resolve three pools of protein, so split the protein, pull up into a light harvesting photosynthesis, allocation, biosynthesis and growth allocation, and kind of other. You know your basic metabolic machinery, things that don't not flexibel some cool that does not vary.

This, you know, Richard Gaider, and collaborators. People like this have also taken this approach in the past, so this is kind of collecting together a few pieces that are already out there.

We're going to basically use conservation of carbon, nitrogen, and phosphorus at the cellular scale. So if you reduce or increase one of these pools, then others have to accordingly decrease or increase so that the total.

The in some sense the total is conserved.

So you build in tradeoffs by writing down statements of conservation for each of these elements, and then we need some rules to say how the allocation of these elements relate to Physiology. So I'm going to talk about four empirically supported or at least three of them are empirically supported simple rules that relate.

Allocation to protein to light intensity or growth, for example.

And then we kind of write down a set of equations that encapsulate these rules and these conservation principles and use them to predict the carbon, nitrogen, phosphorus and chlorophyll to carbon as a function of growth rate in light.

Witcher predictions of what he leads? A data set beautifully observed an we can ask if the model is qualitatively consistent and we can also optimize the parameters of that model if we wish to best match. He leads data and that's kind of what we're going to do in the next couple of minutes.

So I just want to give a flavor. I know I'm going to go first through this. Then I just want to give a flavor of the equations underlying the system, but we're not going to do that thoroughly. But what I wanted to say is that it's actually simple. It's all at steady state, so it ends up being some algebra an the most complicated thing you end up with is solving the root of a quadratic, so it's a very simple model, although.

It's kind of got various moving parts.

So we write down a set of statements for Conservation that says total carbon in the cell is equal to the sum of contributions from these different macromolecular pools.

So again, if we increase one, others have to decrease. We can do the same for nitrogen and phosphorus. The total nitrogen, phosphorus and carbon of the wholesale can actually vary. We reference everything to carbon, so we were actually working ratios.

And then let's think about these four rules that relate.

Allocation and Physiology or the four physiological rules we need.

One is that photosynthesis is a saturating function of a radiance, so there's lots of empirical data for this. Lots of work has been done to describe these curves and get beneath the details of these kind of curves, but essentially we can describe this relationship by a maximum photosynthesis rate and a parameter a here that describes the curvature of the slope at the origin. So we always assuming is that.

That the photosynthesis versus a radiance rate has a form of this type. There are two parameters which we can get some apriori knowledge of from various culture data. But we also can solve them optimize them by fitting our model to the heelys data, which is what we will do here.

So it's two parameters.

The second rule that we're going to invoke is that the investment in biosynthetic protein proteins are related to buy synthesis and reproduction increase linearly with growth rate, and so mathematically that just says that investment in this biosynthetic protein pool is a linear function of mu, the growth rate, and there's a coefficient a here which represents somehow that relationship. There's some beautiful data from recent studies with proteomics.

That have demonstrated this linearity or this close to linear relationship is a very reasonable assumption and we can get some sort of prior information about this. This coefficient, although this is data from a different Organism. But we can use that prior as a first guess for our model and then refine it.

So that's a second very simple rule. The third simple rule, for which I actually have least empirical data, but I think it's very logical, but probably not right in. In truth is that the allocation of protein to the light harvesting and photosynthetic protein complement is linearly proportional to the.

Amount of chlorophyll in the cell.

So that introduces another another.

Tunable or parameter that needs to be evaluated. So it's basically just saying that you know if you if the light gets lower and you increase the pigments then you're going to have to have a whole bunch of proteins and thylakoid membranes and stuff that it was also increased in Association with those pigments, and we're assuming that the ratio in which they vary is fixed, and I'm sure that's not completely true. It's an approximation that we begin with.

And it introduces this one more parameter.

Into the model that we need to fix.

The fourth rule that we are going to invoke is that the RNA to protein ratio is linear, varies linearly with the growth rate.

There's empirical evidence for that. There's lots of evidence for that in heterotrophic bacteria and a growing body of evidence. This is also holds for Algy and Phytoplankton. Here's one example from a paper of Nick Licien Steinberg.

If we kind of re, we can write that down and re twist it around a little bit. But what it says is that the phosphorus investment in RNA is proportional to the product of the growth rate and the protein. The total protein in the cell.

So we end up actually with two parameters. Here at constant proportionality here and and a minimum value.

This is interesting 'cause This is basically what it's kind of simple to understand. Basically, the faster you grow you you want to produce that you want to reproduce the same number of proteins, but first there you probably need more machinery to do that.

So you increase your investment in RNA proportional to growth rate, but as we saw before, the protein investment in a faster growing there's a bigger protein pool in a faster growing cell already because it also needs more protein to grow faster.

And so this C protein term is actually proportional to growth rate, mu itself. And it turns out the investment in phosphorus for RNA. According to this simple empirically driven model would be quadratic with growth rate.

And well, I'm not going to say anymore about that except that it leads to a nonlinear relationship between C to P and growth, which is something that's pleasing.

OK, so I've tried to layout the conceptual foundations. We write down some conservation laws we've written a few rules based on reasonable interpretations of observations in the lab.

We can write down an equation that talks about the carbon flow and again the details I know are going to go to 1st, but if we say that the rate of change of total carbon in the cell is zero. In other words, the average size of a cell in some steady state is constant.

Then the input of carbon to that cell is through photosynthesis, which depends on chlorophyll.

And the losses of carbon are due to division. So splitting into two smaller cells and the respiration cost of growth and division and any maintenance. So we basically write down our carbon budget for the cell and we can.

Turn that into an expression for the carbon. The chlorophyll to carbon ratio.

This goes back again. This has been done long time ago. The nice thing that you get, I just the point to make out of this is that the this very, very simple model already proved.

It's that the carbon to chlorophyll ratio, the chlorophyll tacom ratio should be linear with the growth rate mu and these two constants here are functions of the light intensity.

And so immediately that equation predicts these linear relationships with different curves for different light intensities on this graph.

To the extent that we know Apriori some of the constants that go into this P, chlorophyll, and this growth efficiency and the maintenance, we can, we can show that we can qualitatively match the predicted relationship. The solid lines with the dots which are healeys data. In fact, what we've done here is using a metropolis Hastings algorithm, found the values of those few parameters which.

Make the model most closest fit the data, or more closely, fit the data.

So there's a little bit of optimization going on here, so we've got a very simple model that already makes a qualitative prediction of the chlorophyll to carbon as a function of growth and light intensity. I'll talk about in a minute, but actually the model also predicts the termination of these lines. The growth rate at the maximum growth rate at any given light intensity. I'll say how that works in a minute, but that's a very useful piece of the model.

If we add into that I'm now glossing over the details, but if we add into that statements about nitrogen conservation and recognize that most of the nitrogen in the cell is in the protein with a little bit more algebra, we can also find a linear prediction of a linear relationship between growth and nitrogen to carbon.

More or less linear and with the by using the data, we can optimize those allocation parameters. The data can tell us what the allocation parameters to the different pools of protein need to be in order to best match this data set.

And so, as growth increases, we're allocating more protein for biosynthesis and for photosynthesis and light harvesting. And so the protein goes up with growth rate as the light goes down. We're investing more in a light harvesting proteins, so all these conceptual things that we talked about for we basically got a mathematical description that is qualitatively reasonable, an now being calibrated by laboratory data set.

I mentioned that the maximum growth rate is now a prediction from the model. Once we've calibrated those allocation parameters, we can look at the carbon allocation for example as a function of growth rate, and I've been this into some slightly more home homogenized pools. We called storage, which is basically carbs and lipids biosynthetic machinery.

Photosynthesis and light harvesting and then this general metabolic machinery that's constant, we're assuming is constant.

What you can see is as the growth rate gets faster, the allocation towards functional machinery has to get higher and higher, and at some point you know that's always being traded off against these. What you might say is non essential if you like storage pools of Carbs and possibly lipids and at some point so the slopes here are governed by the allocation parameters which have been trained by the data at some point we can't add anymore protein.

There's nowhere else to go, and that's the maximum growth rate and at low light we reach that more quickly because the investment in light harvesting proteins so much so much higher.

So yeah, I just wanted to just to round off on this model and I know I've kind of whizzed through this a little bit, but I just want to say we've taken this conceptual view. It's a relatively simple model, we can write it all down as some algebraic expressions and solve it very simply. We're able to fit it to a data set and have a working model which both qualitatively and quantitatively captures the observed variations of.

Nitrogen to carbon phosphorus to carbon, chlorophyll to carbon as observed in this one particular data set.

That data set is representative qualitatively of many other datasets. We think there's a lot of generality in these trends. The specific numbers will vary, and so now we have a model which can help us interpret the acclamation of cells, and we could apply this model in an environmental context, which I'll show in a few minutes to try and interpret some field data where it's getting a little bit.

Pushing it a bit far at that point, but let let's go there.

So that's the piece about the The The Model, so I I hope that it's sort of clear what we've tried to do there.

Let's switch back to logic and I want to get back to the large scale patterns and pull these things together and I'm just going to spend 5 minutes doing that. Have been part of a team working on a series of transects, ocean observations going North from Hawaii through the nutrient starved subtropical gyre. This is surface nitrate and into the transition zone towards the subarctic, where the nitrate concentration increases. At the same time, the.

The surface chlorophyll goes from very low to higher abundance of surface chlorophyll and bio mass and more productive region higher nutrient supply. This is the gradient cruise spearheaded by Ginger Armbrust Angel white, number of others. So we finkles group have been looking at the macromolecular composition of the particles. Angel whites grouper been looking at the Elemental store kiama tree, the particles and one of the hypothesis for this.

Cruise or series of cruises have been to try and demonstrate that in the environment. The particular variations, in particular elemental composition, are consistent, consistently matched by the changes in the macromolecular composition of those particles.

So let's just look at look at the data briefly. This is from Angel White stab. This is an unpublished data, but manuscripts in preparation for all of this.

You can see the see here. We see the C to N in the red dots as a function of latitude along the cruise. The Black Line is the underway salinity, just as a kind of an indicator of where we are. You can see we go from the high salinity, the sub tropics into the transition zone towards the subpolar region.

There's a lot of scatter as with the martini compilation, there's a lot of scatter in the elemental composition, which is very interesting in itself. What's causing those local variation? Those small scale variations? There's also a systematic change which is consistent with the martini data set where you go to from a slightly higher to a slightly lower seat, AN&C to P as you go from the sub tropics into the transition sub polar region.

So that big signature that's in the compilation is found here. We've actually got, you know, a higher density of data and so on so forth. So it's kind of it's kind of nice.

Let's look at that in terms of the macromolecule. So Justin Leifer, who's at Mount Allison and Zoe Franklin, her lab, who spearheaded this Dell Howze.

At the same time, but with an independent set of samples measured the measured the macromolecular content of the particles, and so the colored lines here indicate the cumulative contribution from their assays of carbohydrates, protein, lipid and other other molecules. They basically measured those six pools. We talked about the black dots here indicate.

The carbon in nanograms per milliliter from Angel weights data set a direct measure of particular carbon, and this cumulative here is the inference by summing up the contributions from the measured macromolecules. So the nice thing is that we can account for a large fraction, if not most of the carbon, nitrogen and phosphorus in the measured in bulk from the.

Contributions from these various macromolecules so it says this macromolecular interpretation is going to be perhaps useful if most of the carbon nitrogen in unrecognizable forms, which is what I think happens when you get into the deep ocean. Things that aren't as measured in these assays, then we would have a harder time to try and use this framework to interpret.

So the first thing is that you know the particulate carbon increases to the North as we expect, as it gets nutrient rich. Higher bio mass and the contribution from the macromolecules more or less explains a large fraction of the increase that.

You see?

If we think about what's causing this change in the elemental ratio, we can look at the change in the contributions from those different macromolecular pools. So this is Justin and Zoe's data, now mapped as a function of the percent contribution to the total particular carbon, or the total of the cumulative macromolecular carbon, right? There's a bit that didn't quite captured in this.

As a function of latitude and the pink is protein, the yellow is carbohydrate.

The Brown is lipid and then these other molecules and what you can see is that over the transect protein accounts for a large fraction carbs and lipids count for most of the rest. And then there's a tradeoff. As you go northwards between carbs being reduced as a fractional contribution and proteins increasing as a fraction of contribution, and that is totally consistent. Of course with the.

Reduction in C to end that we see and so the predicted see to end from. This is a good match with the observed. See to and from the direct measurement.

So I'm going to go out on a limb here that I've just got a minute or two here more. I'm going to go out on a limb and take the model that we developed in the last few minutes of the talk Kase model. We can take Kase model and try and ask how might the fighter plankton acclamation have contributed to this change? This switch, but you know this tradeoff between protein and carbohydrate.

So what we did we took Kase model and.

Estimated the growth rate. If we know we think the system is Nitrogen Limited.

Amendment experiments suggest that it was so on most of that transect. If we estimate the potential growth rate from the nitrogen concentration that was measured.

And then we use that growth rate in a look up table from our model to think about the macromolecular allocation, assuming that all of these samples are in the surface, so they're all getting a based in the light saturated part of the curve.

We can make a prediction from the model driven by the environmental nitrogen concentration about the contributions of these different macromolecular pools to the fighter plankton bio mass, and what you do see this is very early days. This is just sort of hot off the presses with thinking about this, but sort of pleasingly. The model is suggesting the right order of magnitude in the relative contributions of the different macromolecular pools.

And as you go North, we're seeing an increase in the contribution of.

Protein and a reduction in Carbs and Carbs and lipids in the model. And so there's some qualitative connection to what we're seeing in the data, and maybe this is part of the story. Maybe fighter plankton acclimation along that transect is part of the story of the changing.

C to N.

So low nitrogen supply at leads to low potential growth rates. Anhelo protein allocation and the opposite is true at the opposite end of transect.

So what I've tried to do here is come full circle and think about a little bit about how their acclamation of the primary producers may be a linked to these large scale observe patterns. It's clearly quite a bit of work to do to make to make sense of all that, and there are other things going on as well.

So I'll just finish by restating, I think, a couple of key points.

From the data that we just saw, the variations in the carbon, nitrogen, phosphorus of surface ocean particular consistent with changing the relative abundance of major macromolecular pools, and this is great. This gives us some leverage and think about the biological mechanisms underpinning those changes.

One of those biological mechanisms might be to do with the acclamation of the primary producers an we can suggest, based on the models calibrated by Labora tree data, that decreasing C to N as we go North is to do with an increasing growth rate and an increasing allocation to protein. So I will finish there and thank you for your patience just to thanks my various.

The collaborators once again.

Thanks Mick.

Never easy is talking to the computer for a long period of time, so thanks for not getting distracted by email. Pinging and all those. All those things. If you if you'd like to ask me a question, I'd ask you to raise your hand or Alert me via the kind of chat box and I'll do my best to kind of manage that. So Rick traditional in our seminars, Rick has a question, so let's start again, alrich.

Thanks very much Mike. You know fantastic kind of run through and showing the power of a simple conceptual model was particularly interested in this kind of trade off towards the end of your talk between the proteins.

The tradeoff leading to this CN variation.

So.

You ended by saying is rich is it really than the physical imprint of the background dynamics in dictating the nutrient supply? Or is there any competition with the latitudinal variations of light?

So I I think it I think I'm you know that I'm, you know, there's all of the above are possible, right? But but I think the first thing you said is what I would but I'm you know I'm filtering things through to write if we think about those those observations were made at 8 meters in the surface and then in the spring or the summer. And so basically the light. The light is not varying that great deal, but for the experience of these plankton.

Along that transect, yes, there's going to be weather and all sorts of stuff, but but essentially that we could, we we've tried to let that we think we can put that aside as the controlling factor. But what you do see, you know, and that massive increase in particular reflects that you see a massive change from a sort of trickle of nutrients to a host by big nutrients. Exactly what you said in the first piece. So again, yeah, you're right. The physics is ultimately going to play into that very strongly.

And so this is connected to that. You see that change at the transition zone because you're seeing us a change in the nutrient supply that that that is a hypothesis. But that's the one that I feel is, is is sort of emerging from this picture. Great, thanks very much mate.

I think George was George Wolf was next on the list. Feel free to turn your video on George if you want to Imic Angel. How you doing that was great. Thanks very much kind of answer. My question actually already, but I'll push this anyway so the the observations were all made a meters in. Just yeah yeah.

That's correct, sorry. I think you frozen George Hello Hello sorry yeah I lost you from yeah.

So you could turn, you can turn your video off George maybe, and just re ask the question.

Thank you, yeah so sorry I. So the measurements were made at 8 meters.

You know, moving from the subtropics North? Yeah, you know, there's going to be.

Presumably a compositional change with depth as well, because you know, in the subtropics you get the chlorophyll Max. Whatever it is, 10s of meters.

Ultimately, how?

Have you got a feel for how that might vary?

Well my you know it's a great question and so from the company, but my feeling is that the you know as we go into the dark then you'd expect to ramp up of protein.

With light, but probably a slower growth rates, there's going to be some.

Some tradeoff between lower growth rate but higher light invest acquisition, investment with depth, and so it's kind of not so clear to me. It would be kind of simple to try tinkering with the model. We haven't really done that on the depth profile. Think about that from the point of view of cruises.

In the cruise that went this year, I believe they did a depth profile of the macromolecular composition, at least at one station with A view to trying to get an empirical handle on that.

So but but my my intuition is that it will be a little more complicated 'cause you know the the increase in light and the decrease in growth rate are going to compensate for one another a bit as you go down through the water column. So I don't have an obvious.

Prediction to make.

On that.

OK, thank you.

Thanks, George Jonathan, you've got your hand raised and encourage anyone else who wants to ask the question. Quite international participants, so don't be shy.

With them yeah thanks mate. Yeah it was really good. Thank you. So I was just thinking sort of mention what you're talking about here with the earlier work that you've done on that, Rick talked about the introduction that sort of emotion ecosystem model I sort of got to the end of this talk, thinking actually you could have a global model that just models the carbon, nitrogen and phosphorus pools. Yeah, just need a model that deals with the proteins in the pigments.

So how how would you match that up with a model that worries about species? You're almost saying at the end of this that you don't need to worry about different species. You just need to worry about the sort of.

The mechanics of what's going on inside the cell in it's the growth rate and the partitioning between CMP. That's important. Yeah, must be important.

Yeah, I said I'm sorry so it is one of the things I just omitted. I kind of had a slide about that a little bit. They took out so taxonomy does matter so that you know Antonio to Quigg and so we think we've done some work that you know that looks at compilations of data. And indeed there are systematic variations in the elemental composition of different. You know, between Edina Flageolets and Pico. Sign back to here, for example, and Zoe's work suggests that a lot of that might have to do with the way they deal with cell wall or protection and stuff.

So there is a systematic, potentially a systematic contribution from that to and we've thought quite a bit about that, and we actually have.

On that gradient cruise we have quite a bit of data from cytometry and now from optical instruments we are getting a picture of the taxonomic breakdown. Our first cut at that is that the acclimation variations would.

Seemed to be likely a bigger.

Influence, but I think you're quite right. I don't think you know. I do think there will be an influence from the taxonomic variation to there. There are systematic variations.

I kind of glossed over that for the purpose of this tool, but I I my personal feeling based on the evidence I have now is that the acclamation will be the bigger player.

No, thank you. I guess it interesting side comment nammack is that it some organisms cause have their requirements hardwired evolutionarily and then there's some some of these requirements are flexible and can acclimate to local conditions and that would be quite interesting to think about, I think.

I agree, so those coefficients that sort of determine allocation. You could think of those as traits almost right, and for some things there will be more flexibility than others. I think is what you're saying and there's a. There's an adaptation piece of that and super interesting. Yeah, I'd be interesting to link that with the, you know, the Darwin style.

Multi PST modeling. I'm not sure if any other so I'm gonna take the Liberty of asking my question. I think since I'm here I'm looking at the iron mic. Don't worry.

I'm gonna ask about the growth rate, which is the key, so I was when you were speaking. I was wondering OK how is he going to link these allocations to the growth rate. But actually the other way around right at the growth rate is kind of.

Imposing some of these outcomes in terms of the allocations and so just some thoughts on how my liberate ourselves from that happens to have that assumption and actually have the growth rate as an emergent property of the model. It would make the confrontation with the field data alot easier, right global?

Well, what we did in that in that simple kind of application, the models to assume that the growth rate depended on the supply rate of the limiting resource, which we were pretty clear about is is nitrogen. You know the experimental evidence along the along that transect that shows that an. So if we use a size based you know empirically informed estimate of the.

The potential growth rate for nitrogen based on some uptake model. Then we use that as our indicator what the possible growth rate could be.

So I guess what I'm saying is you can think about the internal structuring as a piece, which in the key mistake that we actually didn't worry about that I'll take it all 'cause it was all balanced, but you could use the kind of uptake kinetics or the availability of photons as your as your guide for what the potential growth rate could be. I don't think I've really answered your question, but that's kind of how we've been thinking about it.

Yeah, thanks. I mean, I guess it's it's a difficult thing, right? I mean, yeah, we're trying to work on that a little bit ourselves in terms of thinking of different ways to represent growth, moving away from the likely big assumptions and these kinds of things. Annoyingly, I'm writing proposal with the same same title as your project here, so I don't know if you've copyrighted the gradients.

Great minds think alike. I might have to come up with a new title. I just hope that doesn't send it. Depends who you're sending it to, right? If you don't send it to Simons Foundation, you probably are right. Yeah? And then.

He might be OK there. Appears you put a question in the chat. You might as well ask it in person to virtually in person to mix appears if you're.

Hello, available to connect a PS I mean thanks for the talk. It was really interesting.

I just want to hear about what any next steps that you're thinking.

Um, using the model 4 like I guess the depth question is interesting also.

Expanding it to use include macromolecular, sorry.

My macro nutrients and micronutrients, yeah and yeah, any next steps? You're thinking? Yeah, so case the case, been thinking about the micronutrient. You know we didn't. I don't think we really reached that regime in the observations, but there's a regime. If we got a little bit further North, we might. We expect that would probably iron constraint, and so you know that made is very thoughtful about what to do there. So I know. I know AL's been involved in some work that is kind of related this, but.

I think you could think about a stoichiometries of iron. For example, trace metals associated with different protein pools, and so you could get some leverage potential to extend things in that way. So that's definitely something that's on the radar.

No depth as you know.

Is it Jonathan or George who's asking about that? Sorry, I'm like George, so that's a good question and we really ought to exploit depth more, right? It's kind of much easier to go down the water column A few feet.

Quite that's about right, and so that would be, you know, we ought to probably exploit that more and stuff. I would you know, as we were talking, I was thinking that we ought to make some predictions with this model about what that would. What that would mean for the macromolecular distribution. We could. We could have a prediction that could be testable. I haven't been able to find a data set that clearly could test that if people know about one, that would be fun.

People a lot of interest in diurnal cycle. Whether these you know, sort of these storage pools are varying a lot over the day. So how are we worried about that?

I think from the bigger picture is I sort of showed you a diagnostic application of the model where we kind of estimated the growth rate from some observed nutrient data and then applied it. But the other thing we're trying to do is a little bit along the lines of Jonathan was mentioning which is plugged this into A.

Like an ecosystem carbon cycle model, I would like the fundamental physiological basis of at least some of these models to switch to something more like this for the very reason that we would have a chance of moving towards some predictive capability or interpretive capability.

For the, for the.

Elemental composition, which is clearly something that.

Potentially important, but you know, we don't quite have the tools to explore. I think. Yeah, that sounds great. Thank you, thank you.

Thanks Pierce, but don't have any other questions as far as I can see.

You know, a final remark might be, you know, we always want to think about global change climate change. How is this new perspective on modeling of the Physiology of the cell going to alter the predictions that you know that existing models were very heavily parameterized in terms of how reduced nutrient supply would affect growth rates and primary production? Wonder if you thought about warming maker by temperature actually got a really strong temperature gradient on that cruise track, and how?

That might play into any of the adaptation. Also, it's not including the model at present, but have you thought about that in terms of its impact on enzymes and their efficiencies? We have a little bit. Then there's potentially some differential between the protein and the and the RNA that would be interesting too. So there's an. There's some kind of works about that you can find here and there, so been thinking a little bit about that. It's not been a major focus, but we're kind of quite aware of.

And so if.

If temperature enabled growth rates to you know, say, RNA to work more efficiently or something like that, and then growth rates could get quicker, then you have to think about what that means in terms of investing in protein to the to make that possible and so and that kind of multiplicative term in that RNA to protein relationship makes things interesting, so I don't have a really clear story there, but it's very interesting and it's not.

It wasn't intuitively obvious to think about, so we've been thinking about that a little bit and think Richard Guiders published some nice data that showed things compensated somehow.

Yeah, and it's interesting because the temperature is one thing really know is going to change, right? It's going to warm up. We might wonder away her arms about what's going to happen to the availability of nitrogen or iron or even lights, but things are going to warm up and it's quite interesting. I think that we don't have been lacking a kind of direct physiological parameterisation about that effect on the cell beyond the kind of applicatives, right? Yeah, that's right. Yeah, I mean I don't have any clear things to say here, but you're making me want to go back and think about that a lot more deeply.

So yeah, let's do that good perfect.

Brilliant, I've got a message from George George on to say it's loaded onto announce what's coming up as part of the centenary. Make him up, put you off duty now so you can relax. This is also part of the centenary celebrations and these are meant to be in person talks, but there's a bunch plans and it maybe George you want to talk to that? Yeah I was just going to remind everybody that there will be some more talks coming along. There's one from Nikki grouper and one from Eric Achterberg.

Which will probably bring the year to extend. We have been planning a centenary dinner at the Maritime Museum, but sadly we've had to cancel that. So just watch this space. We haven't got dates set yet, but will advertise it in the normal way once.

Once we once we have those dates.

Brilliant thanks George. And with that I'd like to really extend my sincere thanks Mick for giving us this really thought provoking and entertaining presentation and I know it's not easy and so thanks very much. I'll give you a kind of virtual round of applause and wish you well on the other side. The Palm. Good luck with the roof repairs and.

Hope to catch up soon, thanks. Thanks for joining us and thanks everybody for joining the seminar. Thank you. Yeah, thank you very much for the opportunity. I really have enjoyed the see you in person. I hope to yeah sometime soon. Hopefully thank you very much. Cheers bye bye.