Hi everyone! Good morning. Good to see so many of you here this morning after a holiday weekend. Thank you for braving the fog and the cold to be here. We're going to jump right in, I think, unless you have any burning logistical questions that I can help with. Questions from previous lectures. I'll be reviewing a few things in this lecture, so hopefully I'll address things you have questions about. And by all means, come by office hours. We're having a good time there if you want to ask extra questions in office hours. But anything on course logistics that I can help with right now? Yes. You do have discussions this week. Yes. Correct? That answers that. Yeah, okay. Right. When you have a holiday day off, yeah. Everything is canceled. So amazingly, you're still spent such a gradual entree into this course, which is nice. But starting next week, it's going to get really busy for you, okay? So I just want you to be prepared. I don't want you to be surprised by the pace once this class actually gets going. But no, no discussions this week, no labs this week. So you're still easing in. Okay. I should get right in. We've got a lot of slides. Cell phones off, including mine. Clicker today. Okay. So we're talking about interspecific relationships primarily today. Relationships between species. And this will be the foundation for us for our look at community ecology. So we will set up our categorization scheme for the types of interspecific relationships that exist. And we'll focus in turn on competition and provide some examples of competition in from field studies. We will modify our equations for logistic growth from last time in showing how competition between species can be modeled mathematically. We will then focus on predation, another major type of ecological interaction and subsuming under this herbivory. Not drawing a firm distinction there, as we'll explain between predation and herbivory. Where one animal, where one creature consumes another creature. And in talking about predation, we'll talk a bit about predator prey cycles again in the context of our studies of population growth. And then, yeah, we will talk about herbivory separately from that. But drawing out the similarities more than the differences with predation. And then finishing mostly with just pictures and stories concerning mutualism and commensalism. So before I review a couple of things from the last lecture, let's introduce the categories that we're going to be dealing with today. In our classification of interspecific interactions, you can classify such interactions in various ways. And none of them are perfect. None of them is perfect. We will focus on the effects of these interactions on the participants as a means for classifying the type of interaction. So we'll distinguish competition from predation and herbivory, from commensalism and mutualism. And as I just said, the categories are distinguished based on whether they have a negative, a positive, or a neutral effect on the participants. And we'll just use a plus and a minus and a zero if you want to indicate positive influence, negative influence, or no influence. And with regard to an influence on a species, what might I be referring to? A positive influence or a negative influence. Influencing what about a species or a population or an organism? Number of individuals, if you're talking about the population perhaps. Reproductive success, if you're talking about an individual perhaps. Rate of growth, if you're talking about an individual, which should reflect the success of that individual and ultimately its reproductive output. Longevity. Yeah. So you get the idea about these positive and negative effects. So we'll start with a focus on competition in part because competition has been a major focus of ecological studies in Darwin with incredible debate and swings of the pendulum in terms of the recognition of its importance or its deemphasis, its lack of importance in nature. Berkeley has been an institution that has focused a lot on the study of competition and by all accounts competition is real and occurs in nature but you just realize that ecologists vary a lot in the degree to which they consider it as a major structuring factor in nature. It has a long history, the study of competition going back to Darwin yes, but it really took off with some of the work of Gauss in the 30s, in the 1930s in particular. Laboratory studies primarily that lent themselves to theoretical insights and even philosophical insights about nature that provided foundations for later study in both lab and field and in theory. Some of Gauss' original data there cleaned up a bit for more general consumption. We can look at them here. So he's looking at paramecia in the laboratory growing them, growing two different species of paramecia in isolation and then together. Did I enlarge my cursor? Not so much but you can see it. So focusing on these curves, these are, these curves are of sigmoid shape right? So let's just, let's just use this opportunity to review a minute from what happened way back last week. So the lights could come up. Thank you. Let's just review a bit on population growth and our modeling, our modeling work from last time. Recall that in the absence of resource limitation, a population as a result of its own intrinsic capacity to increase over time in terms of its numbers will increase geometrically or exponentially, right? So we refer to this as geometric increase or exponential, exponential increase. And we modeled it, right? It's in the shadows here. I'm not sure if you'll be able to see that. As dn dt equal to r, the intrinsic rate of increase or the per capita rate of growth times n. And it steepens with time because n is becoming larger. And so you have a compounding of the growth rate, just like you would have a compounding of the interest in your bank account if you left an initial sum in there. The interest on your initial sum year after year will grow as a result of the growing base of money in the bank account. It's in the same way in a population. The growing population, the growing number of individuals will provide a larger base. And r times n as a result will increase more steeply with time. So that's most basically what we understand with geometric increase. And there are plenty of examples of that in the lab and in nature. But this cannot continue forever, of course. There are limits to growth. And these limits to growth act such that population growth through time, same axes here, is frequently seen to slow and to level off. Speaking ideally, things are often much noisier than this, more complicated than this. Still modelable but rarely so simple. And yet there are plenty of examples, particularly from lab settings, of this type of sigmoidal, sigmoid growth, or this is the logistic curve, the logistic model, where at some point here the effects of the limitations of the environment act on the growing population to limit its numbers in time. And we model this with the addition of an extra term in our, to our exponential growth model, right? And that's just with reference to the carrying capacity, K, which we called K, which is with reference to the existing population under study. The carrying capacity is the total number of individuals of that population in that environment locally at that time that can be supported. It's always contextual, the carrying capacity. The carrying capacity for, for ants on the Berkeley campus is some theoretical number right now. But at a different season, that carrying capacity might change and it will change. And 20 years from now, given changes in the environment with differences in other organisms that live here, differences in climate, the carrying capacity is likely to be different again. And the carrying capacity of ants here is different from the carrying capacity of ants on another university campus. In space it differs. So the carrying capacity is always with reference to particular places in particular times. But we can model this type of sigmoidal growth well with this simple equation. And what you need to do, if you haven't already, is understand how the effect of this term, K minus N over K, produces this, this gradual approach to, an asymptotic approach to the carrying capacity. And let me give you, if you haven't done that already, we had a couple of G whiz moments in office hours that were nice where people started to see just how that works. And it really is an aesthetically satisfying model. And so I really hope that you will, you will arrive at that understanding. So I just would like to suggest one way to do that. So let's assume, let's assume a fixed intrinsic rate of increase per capital growth rate of one for simplicity. So let's assume that R equals one. And let's assume that our carrying capacity, let's change it from ants to rabbits, just for fun, is 100 rabbits in this setting that we're talking about. Let's say on, down at the marina in the, in the park down at the marina where I got a bunch of jack rabbits now that they've restored that land to more natural conditions. 100 jack rabbits can live down there and they have an R of one. When that land was restored down there, and I can already see that I'm going to run out of room here. When that land was restored at the marina, there was only, only one rabbit. And if there were only one rabbit, what would our term K minus N over K be? If, if K equals 100, 99 over 100, right? Which is, which is close to one, 0.99. So when that term is very high like that, close to one, this equation is basically going to go to RN, the exponential. So you're going to see exponential growth when the population number is very, very low, like one. If it goes up to 50, if you have 50 rabbits after a certain amount of time, what will K minus N over K equal? Equal a half, right? 0.5. And that will have a halving effect on R times N, which will lead to this slowing of growth, right? And you can follow that out. You can follow it out all the way to, to 99 where you'd have the term go to 0.01, right? Radically slowing the growth as it approaches the carrying capacity there. Remember carrying capacity is 100, so you're almost there, or 200 itself. And at 100, you're going to have 100 minus 100 in the numerator, right? So growth will cease. The change in numbers over time will equal 0. Growth will cease, okay? So hopefully you can see that. And you should be able to if you, if you just work through it a couple of times. But the population dynamics are usually much more complicated. We present the, the simple case initially to build on. And modelers do come, you know, make more complex these, these, the mathematics here to better fit the reality of natural populations. But we'll stop at this point, you know, for our purposes really in modeling the growth of individual populations. I think I mentioned that in a previous lecture, lecture that some of what I was doing was dumb, dumbed down. I think I used that phrase. And someone asked me afterwards, are you really dumbing it down in here? And I am dumbing it down. That's, but that's the nature of introductory science. It's the nature of learning any skill really, is you learn, you learn the basics, the foundations on which to build.  So I, I, you know, I think that's just fine if, if what we're producing is something simple. And, and in some ways may be wrong because things have advanced beyond that. But you need to learn the basics so that you can learn how these things are simple or wrong or should be, should be challenged. All right. So back to Gauss. When he grew paramecia in isolation, they did follow a roughly sigmoid growth curve. But in growing them together, one species was seen to persist, whereas the other went to local extinction. And Gauss interpreted this as in terms of a competitive superiority of the one species in relation to the other. This led him to what became known as the competitive exclusion principle. The importance of this only, only was drawn out over time. And you're, you can find various definitions of it. One definition that produces a counterpoint to what's given in your book if you want it is, if two competing species coexist in a stable environment, they do so as a result of ecological differentiation. And it's been a very powerful principle in structuring ecological thought and ecological experiments. But please do realize that the importance of it in nature is, is hotly debated. So let's, let's focus on competition. If you want a definition, competition is an interaction between organisms based on a shared requirement that is in limited supply. You don't compete for something if it's not in limited supply. We don't tend to compete for oxygen. We human individuals on most, most parts of this planet. Right now there's enough and we don't tend to battle one another for it. In some areas we do compete for say water, which is in very short supply, fresh water in many places. And competition can be real and sometimes fierce for such a resource in limited supply. But let's distinguish a couple of types of competition. We'll distinguish interference competition from exploitative competition. When you think of interference competition, think of a behavioral interaction that causes a physical interaction. So think of direct physical interactions between individuals. Examples given here of a couple of Beatles vying for position, perhaps vying for position in relation to mating opportunities. Looking here at a lion and a hyena. If you thought cats and dogs didn't like each other, try lions and hyenas. They really don't like each other. And they will battle sometimes to the end in a competitive spirit. As a result, usually of competition for food. They eat similar things. There are differences that are very real where lions focus somewhat more on the soft tissues of prey. The hyenas will also consume the hard tissues, the bones. So a hyena might scavenge what's left after a pride of lions has made a kill. Hyenas are the consummate scavengers. They'll not just eat the bones and the hooves that remain, but they'll eat all the poo and everything else that all the previous consumers had left on the ground. So they'll just clear the area. So there are these differences that do exist. Real ecological differences. But the competition is real enough that when they encounter each other, there's usually some kind of direct conflict. You can't get them too close to each other. They get uncomfortable and there's a conflict. Interference competition or between two spiders. But what is competition for? Some resource and limited supply. It might be food. It might be mating opportunities. It might be a space in which to nest or dwell. It might be water. It might be nutrients. So we speak of it broadly, competition for resources. In exploitative competition, the participants may not encounter each other at all. That's what's meant to be implied by this diagram, where in this case, two consumers, a caterpillar and a grasshopper, are both leaf consumers, say. They're both consuming leaves, but perhaps they don't ever really physically encounter each other. But they're consuming the same stuff, and so they're limiting the resource available to the other by their own activities. As a result, they're competing because they're reducing the resource pool that's available to the other participant, even though they're not necessarily physically encountering each other. Exploitative competition. As a result of the exploitation of a common resource, they compete. Here you have a tangle of roots in a bank of soil. What kind of competition might you see here? Interference or exploitative? I heard both, and I think you could see both among these plants. The roots might physically try to occupy space to the exclusion of other root systems, and then in seeking similar nutrients or water, they may reduce the available supply of resources to all the other participants. So you can have interference and exploitation in setting like that. They're not mutually exclusive. This is a good point to introduce the concept of a niche. Hopefully I'll have some more time to look at the history of this concept a little bit, because it's very interesting. Stretching back to the founder of our museum here in this building, the Museum of Vertebrate Zoology, Joseph Grinnell, was one of the first people to formally define the niche. Later, ecologists such as Charles Elton and G. Evelyn Hutchinson built upon that, and hopefully I'll have some more time to talk about that when we talk about communities. But here, these classic studies, primarily of Joseph Connell from the 60s, helped to illustrate competition and the idea of a niche and the distinction between a fundamental niche and the realized niche. It's an example from your book. I'll draw a few examples of these good examples from your book. Your book tends to do very well in this section. So we have two types of barnacles. And barnacles, as you probably know, grow on a solid substrate. They are free-floating as larvae, and when they anchor, they form a shell around them, and they harden onto the substrate, and then they're immobile. So in this area, in this region where the ocean pictured here rises all the way to a high tide level of this point, and all the way down to a low tide level of this point, you have two species, two genera, in fact, of barnacle, chlamylus and ballanus, growing in spatially distinct parts of the habitat, with chlamylus growing all the way up to the high tide line, and ballanus all the way down to the low tide line. So it was possible to study the fundamental resource requirements and the tolerances of these organisms to different environmental factors through a manipulation experiment. Here's what we just saw. It's the same picture as before, really, just simplified a little bit, where what we see realized in nature is this configuration with chlamylus occupying the upper region and ballanus the lower region. And we can speak of this as the realized niches of these creatures, what these organisms, the spatial and the physical environmental spaces that these organisms come to occupy in the face of natural conditions. When ballanus was removed, the blue barnacle from the lower region, when ballanus was removed, chlamylus came to occupy the entire region. It moved into the territory that was previously occupied by ballanus. I don't have a figure to represent it, but if you remove chlamylus, ballanus doesn't budge. It stays in the same spatial configuration. So through a manipulation study where you actually remove one species or the other, you come to see that the fundamental niches of these organisms seem to differ. The fundamental niche in the sense that the broadest possible zone of occupation in the environment that an organism can occupy in the absence of competition and other factors. The fundamental niche of chlamylus is the entire region between low and high tide lines as evidenced by the removal experiment that took ballanus out where it came to occupy that whole region. But ballanus, on the other hand, its realized niche is equivalent to its fundamental niche. It didn't budge in the absence of its apparent competitor here. So we must realize that what we see in the natural world is a reflection of the dynamics between existing populations. And in the absence of competitors, the distribution and abundance of a particular population might differ very much. So I won't focus on this for long. But let's just note that this is our simple logistic model with this extra term here. So if you take this extra term out, you'll have your logistic model. What we want to show here is how you can model the co-occurrence of two populations in competition. They're called the Lutte Volterra equations named after an American and an Italian who simultaneously derived these equations for the study of natural populations. And it's named after them. They use them also for studies of predator prey dynamics, but here we're focused on competition. So just know what we're doing, and you can further discuss this in a discussion section, hopefully, because I don't think this is addressed in your book. We need a term to model the effect of a second species on our first species, represented by n sub 1. We need to calculate this effect of the second species in terms of the numbers of individuals of the first species, and we do that just using a coefficient, a competition coefficient, alpha, to represent the proportional effect that a second species has on this first species. And in the same way, we use the competition coefficient beta to reflect the effect of the first species back on the second species to model the dynamics of that second species in time. And the relative effects of one species on another might differ quite a lot. One species might have a very strong competitive effect on the first species, whereas the other has very little effect, and that is also something that can be empirically derived from studies in nature. You can conduct studies in nature to derive actual values, real world values for these coefficients. So if you know that one species has a very strong competitive effect on the other, you might model its competition coefficient as very high, as 0.9 say. And so 0.9 times the number of individuals in that population is going to produce a relatively large number to plug into this equation, and you can follow the logic in the model of the logistic curve, the effect that that would have. I don't want to spend too much more time on it. This stretches just beyond the limits of what you need to know here in terms of our modeling equations, and I won't provide any further complexity to these equations. So let's look at examples of interspecific relationships in nature that seem to highlight competition relationships. And one example comes from something that's very often seen in vertebrates, at least. It's very often seen in the organisms that I'm most interested in, in birds and mammals, as well as in lizard snakes and other vertebrates. The fact of resource partitioning, where the different species of a group, often a closely related group, like a genus of lizards here, a nollus lizards, occupy different parts of the habitat. In this case, just different physical parts of the habitat. Rekordii is up in the canopy of these trees. Distinctus is apparently a denizen of fence posts. Cybotes and atherigii occupy lower portions of the vegetation. Christophii and solitis are along the trunks of the trees and on the bark. Things like that. They're occupying different spatial parts of the habitat. They're differentiating space in this concrete way. They may not show much evidence for interaction at all. They may occasionally throw their do-lap out and do push-ups and make themselves visible, which they do. But physical encounters may be very rare. And so an ecologist might scratch his or her head and say, where's the competition? They're just occupying different spaces. They're dividing the community in terms of spatial resources. They're all eating relatively similar things, say. They're all eating insects that they can fit into their mouth, say. But they're doing it in different parts of the habitat and thus not interacting much. Well, ecologist number two might come by and say, well, this signals the fact that competition occurred in evolutionary time and led to the structuring of this group of species. It signals the ghost of competition past. And competition was a real force in structuring this group. You just don't see it now because it has acted and ensconced in these species particular natural history traits that they're exhibiting and that cause them to differentiate resources and enable them to coexist consistent with the competitive exclusion principle. Another maybe more concrete source of evidence for competition and its influence on individual morphology, the form of organisms, morphology just referring to the form of a creature, its appearance, the way it looks. Character displacement. Some great examples of character displacement. There aren't too many out there, but there are some good ones for birds and some good ones for mammals that I know of. Here's a nice one from the group of finches, Darwin finches, Geospisa finches from the Galapagos Islands.  It ranges from something like 7 to 16 millimeters, that depth. That depth of beak is known to be related to the things these birds eat. The things with deeper beaks tend to have greater capacity to crack hard objects and they eat heavier seeds. When fuliganosa and fortus occupy islands without the other species present, so when they exist in alopatri in distinct areas, just another term for living in different places, they have similar sized beaks when they're living alone without the presence of the other. But on islands such as Santa Maria and San Cristobal, where they live together, their beak sizes are quite different. The average, the mean size of their beaks, and if you ran a statistical test, these would almost certainly be distinct in terms of the size of the beaks of these two populations, when living sympathrically, when living together. The assumption being when they live together, they need to differentiate their use of resources and in doing so, natural selection has led to differentiation in morphology, in beak morphology, in the form of their bills related to their different diets. I've introduced a few terms here, which you'll use again and again in the evolution section. A famous old picture, I don't know where this picture ultimately came from, but I see it around fairly often. It's a great image to illustrate a few things. I just like the African savanna. I mean, maybe not a great image for all of you, but this is a fascinating location on the planet, this type of setting on the African plain, open country, and you'll see why I'm so interested in that kind of setting more and more, I think, as I go on and start to talk about human evolution. Humans evolved in this part of the world and we have a long history with these kinds of dynamics.  What's happening here? What do these arrows represent? Between the hyena and the vulture, what have we? We have interference competition potentially. If the hyena wants to run the vulture off the kill or if the vultures become so numerous that they cause the hyena problems, that's rarely true. It's usually more one-sided with the hyena running off the vulture. The hyena and the zebra, that arrow and the arrow could also run from the vulture to the zebra would represent consumption, food consumption and predation, right? But what if the lions are on the horizon? Who's gonna see them first? Probably a couple of the circling vultures and words gonna get down to the other vultures and then the hyenas are gonna become aware. So you might also think about that arrow between vulture and hyena as being something cooperative where they're both concerned about lions and the vulture might be the one to see them first or maybe it's a hyena by chance and they communicate it to the other. So you could see a cooperative arrangement between them as well. So for one thing this highlights for me is just the dynamism of these relationships where a relationship can be interference competition or exploitative competition. It can turn into cooperation. It can turn into other forms in time and it does. So moving into some of these other types and this is where I'll move somewhat briskly to just get through a lot of pictures and still all very important but I want to try to get through this material. The true predators have many hosts using that term hosts in perhaps an unfamiliar way. And the relationship is lethal in the sense that the predator kills the prey, the host. And it has to do so again and again to sustain itself. And just an example there, one of our world's great predators, the praying mantis which you can find around Berkeley. I found one on Telegraph last year, just in a little doorway into Telegraph Avenue. They're around town, great creatures. You can handle them.  They will look you in the eye and you'll just have this uncanny experience of their staring you in the eye. But the prey of predators go to great lengths to avoid being consumed, of course, because it's a lethal arrangement. They don't, it doesn't serve them very well in terms of their fitness to die. So a lot of times you will see morphological adaptations to avoid predation where they're just being very spiny and hard to, difficult to eat. Well in both all these cases, porcupines and fishers here, one of the only consistent predators of porcupines in North America, the fisher. So usually you have some type of predator that can overcome those defenses but attempt to defend themselves in various ways, a host will. Aposematism is just a reference to warning coloration that signals to a predator that I should not be eaten for some reason. In the case of these poison dart frogs, it's because of the toxins in their skin.  It can kill the consumer. So they advertise themselves. They look very striking and they stand out. They advertise themselves. That's what we mean in this context of aposematism. And they're often oranges and blacks or yellows and blacks. Or if you turn a corner and see this, you will jump and you jump. Well, maybe not if they're little babies and they're that cute, but if they're grown up and they're bigger and you see black and white when you turn a corner, it'll give you a startle. And it's an almost instinctual reaction to that type of warning coloration, black on white. But also things like crypticity and camouflage blending into the surroundings. That's often a defense against predators. Just so instead of being, instead of advertising yourself, you try to be overlooked. There's a frog in there, probably against a bunch of lichen. I distinguish crypticity from camouflage maybe because crypticity can be in behavior. If you watch a chameleon walk along a branch or a stick insect, they'll often shift back and forth. They'll shift back and forth as if they're blowing in the wind or something. They'll make themselves look cryptic as they move. Camouflage, maybe you can think of as just in terms of more of the color matching to the background. I'll give you the slide and you can look for the creatures that might be present. Something else you'll get in the evolution section, so I'll just gloss over it here. I think it's from this part of the book.  In the case of yellow jackets and wasps and things, black and yellow usually signals the fact that it can sting you. Other organisms coexisting with organisms with warning coloration may come to resemble them. I'll let this be dealt with in evolution primarily. But just know that this type of mimicry can be honest or dishonest. The mimicking organism might also have the defenses that the mimicked has, or it might be bluffing. And we distinguish malaria from Batesian mimicry to distinguish honest from dishonest types of mimicry. Here you have a larva, like a caterpillar, looking an awful lot like a snake. So if a bird's gonna come in and try to eat this and it flares up and looks like a snake, maybe that might just be a sufficient startle to the bird for it to fly away and leave it alone. That's a, it's bluffing, trying to look dangerous when in fact it's rather quite harmless. So we distinguish those two types of mimicry. Okay, looking at predator prey cycles and a couple of classic examples. From Lake Superior on Isle Royale, Lake Superior froze over hard early in the 1900s and moose walked across the frozen ice and came to occupy that island for the first time. And it turned out to be a bountiful island and population numbers increased quite a lot. There was a lot to eat. Moose liked to wade through the water and eat aquatic vegetation. And they were quite happy there until the lake froze again and the wolves arrived. And they crossed the ice and they joined the moose on Isle Royale and setting up a fascinating study for ecologists to study predator prey dynamics because wolf hunt in packs and eat moose. And there's a lot of food there and one moose kill can sustain a few wolves for a little while. So what ecologists were prepared to do in this instant was, instance was to monitor population numbers of both wolves and moose and to look at them, look at the correlations between them in time. And I'll let you explore this in your book as well. But note that these predator prey cycles often appear to be entrained in some way. They're correlated. And what becomes difficult to sort out is causal relationships between populations. They're very often clearly correlated but can you assume that the correlation is also causal where the changes in the numbers of one population are causing the changes in the numbers of the other. In this case, when moose were seen to increase here the wolves were seen to undergo a parallel but just slightly staggered in time increase also to a high point of many, many wolves on the one island in the, in around 1980. This caused, apparently, this was correlated with a decline in moose and a crash in wolves. And you can follow the cycles from there and read about it and think about correlation and causation. Another famous example, sorry for all the mammals eating other mammals but these are the things I like a lot. Here you have a very large cat called the lynx and a fairly big rabbit called the snowshoe hare. And they, the lynx depends quite a lot on these big hairs. They, this is, these are the hairs that change their pelage over the course of the season so they'll be more camouflaged in summer and also in winter. But that doesn't keep them, of course, from being consumed by the lynx. Oh no, yeah, no, it got away, I promise. On the ecological, at the ecological level here these numbers were tracked all the way back in time as a result of the very careful accounting that hunters did in identifying the number of pelts that they obtained through the trapping and hunting seasons. They had to report these numbers to the local authorities and apparently they did so with some faithfulness. And so ecologists could go back and mine those old, those old records to look at the numbers of individuals in these populations over time. And there appear to be these fantastic, this fantastic entrainment of numbers of the two populations in time. And yet the correlation exists but the causal relationships are very subtle and some people even speculate that they're related more to solar activity than directly to dynamics on the ground. As a result of changes in solar activity you could have changes in vegetation that propagate through the herbivore and that influence the predator. I've still got you for two more minutes you guys. Settle down for a little bit longer. So yeah, you can read further about that in your book as you like. Herbivory is really, can be considered in terms of its effects as a very similar to predation because it benefits one participant and harms the other. And herbivory can be anything from grazing buffalo or dugongs and manatees under water or caterpillars and leaves. It's the consumption of plants or algae by a consuming organism. You'll get a lot more into this in the botany section and plants have fantastic defenses against being eaten and you'll hear much more about that. Just know for my purposes some of the diversity of types of herbivory. Nectar consumption or the eating of fruit by fruit bats or the use of gums and saps are all types of plant consumption and herbivory. Mutualisms we can save and talk about next time. Thanks everybody.


So, this is of course what we're trying to build and data capture is, I'm assuming you've seen a number of these slides before, but data capture is really, you mean it was a jigsaw puzzle, it's now this. You don't want to say the data commons is a jigsaw puzzle. So I guess what I really want to talk about is what happens there with the research data and what happens there and I'm assuming Claire's going to talk about the two brown things in the right and top quadrants. You've seen this one? That's a jigsaw puzzle. That's another jigsaw puzzle. So what I'm hearing is you're deconstructing the things that I've been working on, but that's probably good. The point about this is that in the curation continuum, the data capture really kind of happens down here in what back then I called the private research domain. So it's the stuff that's coming out of instruments and laboratory information management systems and it's getting captured into there. And I want to structure the few minutes I've got this afternoon around the data sharing verbs. Is that a jigsaw? No. Excellent. Next time in March. It could be. So how many people here have heard of the ANS data sharing verbs? Wow. Okay. Excellent. I'm not going to talk to you about the data sharing verbs much, but they are one of the ways in which we think about the things that need to happen in order for data to be reused. And the names of some of our services are modeled on the verbs. So identify my data, register my data, describe my data and so on. Everything outside the verbs, these green things in the middle, are planning. I'm contractually required and Margaret is sitting on my right to talk about planning. So the planning has to happen in order for this to work. And down the end we've got the preserved verb which we haven't talked much about yet. We've been kind of taking the cop out which is oh we have to finish by the middle of next year. Haven't got time to worry about preservation. I don't think that's any longer a defensible argument so we are going to have to start worrying about preserving. So creating or capturing, because of course increasingly the way data is generated is by capturing it from something rather than creating it out of thin air. Ross and I in a paper we wrote for eScience a couple of years back made the argument, I don't think we're the first to make this, but we made the argument in this paper that the closer you can get to the point of capture for capturing the metadata the cheaper it is to do. So in other words pushing it out down the track makes it harder to do and more expensive. And so one of the things we've been trying to emphasise in the data capture program is focusing on putting in place infrastructure to make that possible and as you know we're investing $12 million directly with the institutions through the expression of interest and another $5 million that we're proposing to spend in a variety of thematic ways. Another way of thinking about it is this which I suspect is also not a dig still puzzle. Good, excellent. So this is the diagram that we worked up for that eScience paper and essentially it was looking at how you might go about reducing the cost associated with metadata. So you have an experiment, it generates some data, that data goes into a laboratory information management system, you add some experiment metadata, you perhaps migrate it across to a collaborative, so this is the shared domain in terms of that curation continuum, which makes some context explicit, you finally deposit it into some kind of public repository and you can now discover it. And so down the bottom here you're going to try and auto generate your technical metadata rather than asking the researcher for it. Here you might try and extract some experiment metadata from the laboratory information management system, here you might insert some context that you've already got and here you might provide some discovery metadata and you can see that idea that we were talking about earlier on this afternoon that part of that discovery metadata might be a link to the publication. In terms of the kinds of metadata that I'm going to talk about briefly in a little while and more tomorrow, this is what you might call information for reuse, the stuff that would let you reuse the data, that is information for determination of value, the rich context around the data and the discovery is obviously information for discovery. So we've got the create verb, store verb, we don't do storage but we care that it happens. We've got the advantage in Australia that we have the Australian Code for Responsible Conduct of Research which has obligations on researchers and on institutions and clearly we're interested here in working with institutional and national data stores and integrating with the new national data fabric, whatever that looks like. So Florian's talked about the data fabric environment that we have now, there's this extra 50 million that Ross talked about which is going towards a new national data storage infrastructure and Anne's clearly cares about how that's going to pan out. Once you've stored something you need to describe it, I am going to spend more time on metadata stores tomorrow so I won't say much about this now. These four kinds of information that I referred to before, discovery, determination of value, access and reuse and you'll note that some of those might apply most obviously at collection level and some might be both at collection and object level and again that's a theme I'm going to return to tomorrow. You also need to identify things and of course we have the persistent identifier services, the identify my data service that give you this level of indirection to assist with long term access so you provide a handle or a DOI, you can then repoint that as the object moves around. So the handle version of identify my data is available now, there's machine and human interfaces and Anne's thanks to the sterling efforts of Adrian Burton who is going to be spending tonight up late talking to the data site conference that's happening in Germany at the moment was a foundation member of the data site consortium which is looking to produce DOIs for data objects and obviously the fact that we're going to be able to produce DOIs for data is going to assist us with that linkage between publications and data that we talked about before. So DOIs for publications and DOIs for data are going to help with that.  I did want to talk a little bit about the data capture EOI process because I understand that there's been, we could have been perhaps possibly clearer about what the sum of the steps were for getting from the EOI to signed contract. So the letters went out in November, most of the institutions got a visit typically from Ross and me and sometimes some other people. The institutions then worked with their assigned ANSBA to refine the project description and that's an iterative process. So typically what's happened is there's been a first draft, it's been sent either to the ANSBA or to me or to some of my staff to comment. We've sent it back with some suggested changes so there's been a few goes around that loop. Andrew Williams in particular bears the scars as some of those goes around the loop. But the point is that when you fall out of that loop you now have a project description that's in reasonably good shape. I would like to think that, you know, I added a tiny amount of value, Andrew. At that point we send it off for independent assessment and the reason for that is that we need to be able to say to the auditors we weren't just handing money out willy nearly to our friends that there was an independent assessment process. So that's typically done by a couple of my staff who will independently look at it. One of them in particular is very, very good at, she describes herself as a hole puncher. She's good at finding problems with things which is a rare skill and she's very good at it and as a result what will happen is there will be an assessment which will come out which will in every case so far identify problems. There has not been one data capture project that has got through this phase unscathed. So if you get told, so I'll come to you in a minute Frankie. So if you get told that there are problems it's not we're picking on you, we're picking on everybody. I'm sure that makes you feel better, Andrew. Frankie. I have a quick question, because there's so many loops, how do you know if you're at the loop stage where you're still working with the ANSBA, if you're at the independent assessment stage? If you think, if you've got to the stage where whoever you're working with has said I think this is ready to go off to be assessed, that's how you would know I guess. There is a tracking spreadsheet that we maintain internally that says where we're up to on those. Can you follow that up and find out? Yeah. So you are in fact according to the current iteration of the process it has been through almost as many iterations as there's been. Possibly more. Possibly more. So you are supposed to get a letter from ANS, telling you that we're about to do the independent review. A formal letter? There's an email from ANS. Right. Okay. It's not a letter with process signature and letterhead, but it is supposed to be an email from the VA saying we think we've agreed, passing it on for an independent assessment. No problem. Well, I've come. And yes. But it may also not have come because it's a relatively recent step in the process because people having exactly that little confusion. So I was asked about, I don't know where I'm in the process and we were making people like Andrew's life difficult by not letting people know that that was happening. And because it was fun. Sorry. And because it was fun. Yeah. Well, you know, that's part of what the Director of ANS is all about making people's life difficult, but on the whole we try to do that only to our own staff. Yeah, that's true. So it goes through the assessment process. Sometimes, and the assessments then go to me, and I don't always agree with them. Sometimes the people who are doing the assessment will say, we can't fund this and I'll say, well, no, we can. So sometimes there's a loop there where I'll bounce it back to them or to here. Finally, what happens, what falls out of this is once the project description's been improved as a result of that and that, or hopefully improved as a result of that and that, I write an approval recommendation, sorry, the BA drafts an approval recommendation that I sign and send off to Ross. At least sometimes at that point it says no. And we have an instance at the moment where something's gone to Ross and he said, well, I'm not entirely happy about this, which is why there's a loop on the end. Once Ross signs off and says, yes, I'm prepared to approve the expenditure of $875,000 with this institution on these things or whatever the amount is, we then move into contract processing and finance processing. I can't give you times for any of those things with loops on them, obviously, because it depends how many times around the loop you go. Nor can I give you times for this or for this. The reason I can't give you a time for this is that people's ability to lose contracts is truly astonishing. The current record is a nameless institution that managed to lose their contract three times, which I think is, you know, they're just clearly trying too hard. So what was the, was it, was the mean time for this measured in, was it 90 days, 95 days? Like 90 days, because the back and forth between, it depends on how A, whether people don't lose their contract. And that does happen surprisingly often. The, whether they then manage to lose that within the institution. So if someone gets a copy, he says, yes, we've got an incentive to the lawyer or whoever needs to go in there that's lost there. And then depending on how much value the lawyer decides to add at the institution. Some of them are quite happy. Some of them decide they want to go right back to first principles and learn how to question the contract. We have had people quite a question. Monash's contract with Disar, which is the overarching contract under which all of these are funded. Yeah. We think you Monash shouldn't have signed this deal with the department that gave you $48 million. Well, okay, fine. Thanks for that. Probably not. So it is a slow process and we are finding that it takes a lot of probing at our end to actually get the contract back from whence they have gone. Now I say this not to depress you, but to simply point out that do not assume that once Ross signs the letter, the money will flow immediately. So I'm just managing expectations down. And is this my last money date? Yep, sure. Okay. So status update, second last slide. 23 institutions got data capture funding. There will not be a second EOI round. Let me just say that again. Any of you thinking that there will be a second EOI round? There won't be. There's some money that we haven't spent yet, but it's not going to be an EOI. Over 20 have started discussions about what they want to do. Over 10 have near-final proposals to, I think, have signed a contract. And as Ross has indicated already over the long term, the shift, and by the long term I mean a year out, data capture is going to shift from commissioning to adapting and adopting and deploying. So the kinds of things we're looking to do in data capture in this year are not what we're going to be doing in the two out years. So that's going to be an interesting kind of shift. And that's why we're going to be giving you, not giving you a grief, but pushing hard the idea of reusable software. So not something that's just specific to your particular case. And of course, this is why we're doing it all because we care about researchers. This is serendipitously stolen from today's first dog on the moon. I'm assured that the titles at the top are actual research, that the Department of Innovation is currently funding. Temperature-specific adaptations of Antarctic octopus venoms. But I'm assuming that the gloss on the bottom is all first dog on the moons and there's no relationship to reality. And I'll stop there.