Wisconsin Public Television University Place Wednesday Nite at the Lab  Community Turnover Across Space and Time Jessica Blois  Aug.8,2012 UP757 cc >> Welcome, everyone, to Wednesday Nite at the Lab. I'm Tom Zinnen. I work here at the UW Madison Biotechnology Center. I also work for UW Extension Cooperative Extension, and on behalf of those folks and our other sponsors, Wisconsin Public Television, the Wisconsin Alumni Association, and the UW Madison Science Alliance, thanks for coming to Wednesday Nite at the Lab. We do this every Wednesday night, 50 times a year. Tonight it's my pleasure to introduce to you Jessica Blois. She's a postdoctoral research associate in the Center for Climatic Research and in the Department of Geography here at UW Madison. Jessica received her bachelor's of science in 2000 in ecology, behavior, and evolution from the University of California, San Diego. We have a lake; they have an ocean. If you've never been to the San Diego campus, I highly recommend it. 

I don't know how anybody ever leaves it. Then she worked as a biological technician for the US Forest Service in Oregon and in California. After earning her master's from Humboldt State University in 2005, she attended Stanford University where she received her PhD in biology in 2009. Her research examines the ecological and evolutionary responses that mammals and plants have shown to paleoclimate change over the last 21,000 years. She's starting a faculty position in the School of Natural Sciences at the new University of California, Merced in January 2013. Please join me in welcoming Jessica to Wednesday Nite at the Lab. [APPLAUSE] >> Thank you. Can you guys hear me in the back? Yep? Okay, well, thanks, Tom, for that introduction. And I'm going to repeat a little bit of it because I wanted to give you all a sense of who I am and where I'm coming from, what I'm bringing to this talk here, and what my background is, what led me to UW Madison, what led me to becoming a paleoecologist, which is how I describe myself to most people. As Tom said, I'm fully a product of California, and most of my relatives in California wonder what the heck I'm doing living in Wisconsin. 

But I started out in southern California, went to undergrad at UC San Diego. Then I moved, as Tom said, I worked for a little while for the forest service in southern Oregon and then up in Humboldt County. Then I started my master's degree at Humboldt County, or Humboldt State. And after Humboldt State, I went directly on to Stanford for my PhD. And my interests have always been in understanding ecological interactions, understanding evolutionary history of species and communities, and trying to understand different ways that humans and different drivers impact those ecological and evolutionary responses. So at UC San Diego my major was ecology, behavior, and evolution, and I really thought I would become a marine biologist, as most ecologists at UC San Diego do I think, but my job for the Forest Service after college really moved me firmly into the terrestrial realm, and I started doing wildlife surveys and tree climbing to look at this endangered or sensitive vole that lived in trees, and that led me down to look more closely into mammals and into their evolutionary history and then later into ecological history. And so, as I said, I've generally been very interested in mammals. And so here's some pictures of some of the ones that I am interested in. This is the tree vole that I looked at for when I worked for the Forest Service and my master's, and they said they live in trees so you have to tree climb to get up to their nests. They don't come to the ground. 

You have to climb up to capture them. And I put this picture in for Tom, who his little son who's very interested or loves to climb himself. So you can actually do this for a living. A hope to five-year-olds everywhere. And while I was at Humboldt, I was really sampling these tree voles to get samples of their DNA. And then I was using samples of their DNA to infer their evolutionary history which was really interesting, but I became more interested in seeing if there was some way we could actually, instead of making inferences back in time, can we get more direct measures of our communities and our species back in time. And that is really what led me into paleoecology is I was really interested in the fossil record and using the fossil record as a way to get more direct information about species and communities in the past. And so at Stanford I, again, focused on mammals but was really trying to look at the fossil record of mammals to understand ecological and evolutionary history of many different mammal species. Throughout my life, from a young age to this picture is from just a few years ago, I've really loved to play in the dirt. I think my father was in construction so I think going to job sites with him on the weekend and lots of dirt around led to that, led to my fascination with dirt. 

And so at Stanford I was really able to combine all of these different things, mammals, fossils, paleoecology, and do it in caves. And so I excavated a fossil deposit in cave, Samwel Cave, and then I dug out these small mammal fossils and looked at a variety of different things with them. And so my background has really been firmly rooted in going out into the field, collecting primary data, and bringing those primary data back into the lab and doing a number of analyses with them.  Then I moved to the Midwest for my postdoc, and I would say that my field site here has been more aptly described as my desk and my computer. I consider this my, I can take the field site anywhere with me, which is a good thing and a bad thing. And I've been focusing not on collecting my own primary data but, rather, looking at a database of sites. So looking at data that other people have collected. And so this is the diagram of the structure of the database that I use. And trying to see what kinds of patterns we can draw out when we aggregate a bunch of data from across space and through time. What does this tell us that we can't get from looking at a single site? And so it's this work that I'm really going to be talking about here tonight. 

And so, just to recap or to repeat my title, I'm really going to be looking at how we can understand differences or turnover in communities across pretty large spatial extents across eastern North America and across pretty broad time ranges, or relatively broad time ranges, over the past 21,000 years.  Now, this is a map that probably you've seen variations of this map before. It's just mapping the distribution of the number of species in the western hemisphere. So this particular map is for mammals, but there's syntheses of all species on Earth, of birds, of plants, you name it, showing that areas in the tropics tend to have higher diversity, they tend to have more species, and as you go towards the poles, you tend to have lower numbers of species. And so just going to a place and counting up the number of species is one way that we can characterize the biodiversity on Earth. Now, a map that you might not be as familiar with is a map of turnover. So, again, this is for mammals in the western hemisphere. But what exactly is turnover? Well, turnover is a measure of the dissimilarity or the differences between communities. So if you're at one location and you go to another location on Earth, you can look at the community composition at those two locations and see how are they similar and how are they different. And so turnover measures how different those communities are. 

You can also do this through time. You can stand at one place and you can look at how a community changes through time. And so this concept is very similar to, or very familiar to most of us. It's, in ecological timescales, it's called succession. And so we see succession, for example, from an old field, an abandoned agricultural field to a forested landscape over the past hundred years across many sites in eastern North America. And so I'm looking at this same concept succession but playing out over much, much, much longer timescales. But, fundamentally, whether you're looking at across space or whether you're looking through time, you're measuring this same thing which is how different communities are from one another. And so these two measures are very complementary ways to understand the patterns of biodiversity across the Earth's landscape. And so you can see that there's some similarities in these maps, but there's also a lot of differences. So, for example, if you look at southern South America, richness, or the number of species, is very low in Chile and Argentina, but turnover, in red there on the right, is very high. 

And this makes sense. The Andes are there. You have steep gradients, steep elevational and topographic and climatic gradients as you go from the ocean up to the top of the Andes. And so while the number of species isn't as high as in, say, the Amazon, as you move across the landscape, each community you get to is very different. And then, alternatively, in the upper Amazon basin, you have a very high number of species but turnover or, differences between communities across space, is very low. So in this talk I'm really going to focus on this turnover component of biodiversity. And one key question, I would say the big question that structures almost all of my research is, how does climate influence community turnover across space and through time?  We have a general idea that climate is one of the main determinants of community turnover. So this is looking at a map of the globe. This is the similar map to what you just saw for bird communities. Red areas are areas, as you go across space, the communities are very different from one another. 

Blue areas are very homogeneous. And these researchers have mapped climatic dissimilarities. Same concept. How dissimilar climates are. And they found a general correspondence between the amount of heterogeneity in climates and the amount of heterogeneity or dissimilarity in communities. >> I have a question. >> Yes. >> By climate, do you mean temperature and/or rainfall? >> Usually, so the question was, by climate, what do I mean by that? By temperature and/or rainfall? And I would say I'm using it to mean all of it. Usually researchers reduce climate, as a concept, down to two or three or a small number of different variables. And so you could look at this in terms of the heterogeneity in temperature, but most people look at it as a combined metric of overall climatic dissimilarity. 

So they combine multiple different variables. >> Give examples of the different variables. >> Can you hold that thought for a minute? >> Yes. >> And I will. I'll tell you exactly what the variables are that I'm looking at. >> Good. >> Okay. Now, this picture is across space, and it shows a relationship between climate dissimilarity and community dissimilarity. But we also have some idea that they're related to one another through time. So here plant community dissimilarity is on the top, and that's how dissimilar a plant community is at one location to the community that at some point in time to the present day community. 

And so time, as you'll notice, is going from 20,000 years before present to the left on up to the present, 0 thousand years before present on the right. And so you can see that the peaks in community dissimilarity in eastern North America in the black and Alaska in blue occurred around 14,000 to 11,000 years before present. Our plant communities were the most different than they've been from today's communities 14,000 to 11,000 years before present. And this corresponds generally with times that our climate was very dissimilar from the present. So across space and across time we have this sense that climate, the dissimilarity in climates is one of the main factors that drives or that influences dissimilarity in communities, but it would be nice to be a little bit more precise about that. What climatic variables are most influential?  Are all variables equally influential? And just really dig into this relationship a bit more. And so that's what I'm going to do here today. And so I'm going to take you through a series of four questions or three different sections that all look at how climate influences community turnover across space. And so first I'm just going to look at this broadly and ask, in this particular system, does climate influence dissimilarity or turnover in communities? But then I'm going to use this perspective of time and of the past 21,000 years and ask whether those relationships and that relationship between climate and community is the same as you go through time or does it change? How stable is that relationship? Then I'm going to actually take, assuming that climate does influence community dissimilarity to some extent, can we use that to build a model and make predictions at different times? And if so, what are the limits to making those predictions? And I think this is a big question facing scientists and citizens today because we're making a lot of predictions about future biodiversity with future climate change. How well, how confident can we be of these models and of these predictions? And so this third question speaks to that. 

At least looking into the past. And then, finally, one of the newest parts of my research is trying to see whether space and time are substitutable. So, we generally think or we often make an assumption that if you look at a pattern across space that you should see that pattern recapitulated through time. And, oftentimes, that's a very necessary assumption, but how well does it hold? Okay, so the system that I'm working in is vegetation from eastern North America over the past 21,000 years. And the question comes up, how do we actually make inferences about vegetation from 20,000 years ago. And so I'm using fossil pollen from lake sediments. And I'm going to get into how we actually do this in just a minute. But before we start, I wanted to provide you with some terms and some concept of time. Generally, my research focuses on the quaternary. So quaternary is the last two and a half million years of time, and it's broken up into two time periods called the Pleistocene and the Holocene. 

So the Pleistocene goes from 2.5 million years ago to 11,700 years before present, and then the Holocene is everything from 11,700 years to the present. Now, the Pleistocene is marked, in particular, by these interglacial cycles, and probably many of you are familiar with that, or if you've seen many of the Ice Age movies, they take place in the latest Pleistocene. And the Holocene period that we're in is an interglacial period. And so, actually, this is a wonderful time to look at the influence of climate on species and communities because there's been quite a bit of climate change over the past 21,000 years. So the last glacial maximum, the maximum extent of the ice sheets in North America and throughout the globe, the coldest times, really over the past several million years, occurred 21,000 years ago. Following that, ice sheets started retreating. We enter into a long-term warming phase. And around 15,000, 14,500 years before present, we have rapid warming. And this is called the Bolling-Allerod. You don't need to know that, but then we go into the Younger Dryas period which is a return or a partial return to these cold glacial conditions. 

And then 11,700 years ago we have rapid warming again, and then climates have been relatively stable throughout much of the past 11,000 years. So we have a long-term warming trend, and then superimposed on that, we have some really abrupt climate changes, both warming and cooling. And so I think that the past 21,000 years ago is a pretty nice system for studying how our species and communities respond to that climate. It's not going to be a perfect analog for the future, and sometimes it's not even going to be a good analog for the future, but it does give us a really long perspective on how responses of species play out over time. Okay, so what's so special about lakes? I think a few weeks ago you had a student from the Center for Limnology talking about his research on global lakes. And so, as you know, there's a lot of lakes around the globe, there's a lot of lakes here in the upper Midwest, and these lakes or data from these lakes have been added to a database called the Neotoma Paleoecology Database. And so you can see I'm focusing particularly on eastern North America, and there's a lot of red dots across eastern North America. All of those red dots are places where we have data on some aspect of past vegetation.  So how do we actually understand past vegetation? It's not so simple as just going out and grabbing a leaf from a tree. Well, we use pollen. 

And pollen are really nice because they're preserved in lake sediments and they capture a fairly diverse plant community. Now, pollen is a biased sample of a plant community. It's primarily sampling species that produce a lot of pollen and who have wind dispersed pollen. So what I'm reconstructing really is tree diversity of eastern North America. But for simplicity, I'm going to call it plant diversity. And there are pollen from other species that get in there, but a lot of it is these tree taxa like spruce and pine and oak. That's what we get. We get a lot of their pollen in lake sediments. So, how does pollen make its way to sediments in the bottom of the lake, and how do researchers get it out of the lake? Well, if you can imagine a small lake somewhere here in Wisconsin, for example Devil's Lake is a classic site in my field. I was very excited to come here and actually go hiking around Devil's Lake. 

So if you imagine Devil's Lake, Devil's Lake is a lake in a basin surrounded by forest, and all those trees are broadcasting pollen out, and that pollen is picked up by the winds and blown across the landscape. And some of that sort of adheres to the surface of the lake and then settles down through the water and gets incorporated into sediments. This happens to pollen, it happens to other materials as well. Things might drop seeds or cones into the edge of the lake. Sometimes you get small amounts of pollen that are washed in to the side of the lake. But the pollen eventually settles down into the sediments, and, in some cases, it can settle down in these annual layers. Not in many cases, but it is a very nice environment in the bottom of a lake for preserving fossil specimens. There's not a lot of mixing, and so this sediment builds up through time and is essentially capturing a snapshot or a continuous snapshot of the community surrounding the lake as that sediment builds up. So, we have sediment in the bottom of the lake. It has something we want: pollen. 

How do we get it out of there? Well, researchers go out and take a sediment core from the bottom of the lake. And so to do this, I went with the Williams lab and one of the grad students in the Williams lab to a lake outside of St. Louis, Missouri, earlier this summer. So I thought I'd show you some pictures from that trip. But some people go out in the middle of winter. Sara Hotchkiss in the botany department here is someone who does that. Other people think they're crazy for going out to lakes in the middle of winter and trying to get mud out of them. Our lab cores in the summer, and so we essentially have this coring platform which is pieces of wood set on inflatable canoes or kayaks. You can see that there's a hole in the middle of that platform. So we motor or we pull this setup into the middle of the lake or wherever we want to get sediments from. There's a hole in the center, and we have, essentially, a big steel tube or aluminum tube that we push down through the hole, and then we push into the sediment as far as we can using rods. 

And so that's what we're doing here. We pull that out. We extrude the sediment into a piece of PVC that's just cut in half. And then in that sediment, you have a variety of things you can see, like this is actually a fragment of a pretty big shell that was probably 14 feet down into the sediment. And then there's a lot of things that you can't see, like the pollen. So this is all stuff that happens in the field. It's very messy work, but it's also pretty fun as long as the coring is going well. And we bring these tubes of mud pack to the lab, and then we turn those into data. So, if you image this tube of mud is sort of a snapshot of a schematic here, the sediments from the bottom, presumably, are much older than the sediments from the top. And so, if you take samples at various points along the way, you just extract a little bit of mud, you process that mud and you pull out the pollen, the pollen from different tree species looks different from one another. 

Actually, not so much individual species but different genera. So pines look different from spruce which look different from oak, etc. So now you have pollen, a sample of pollen, and you count up the number of grains from each different species or genus, and you make pretty plots or turn that into data, something like this where here you have the relative abundance or changes in spruce, in pine, and in oak. And you're going to maybe get some samples and take some radiocarbon dates and figure out how old those sediments are at the very bottom and how old they are at the very top. So I've gone out and done some of this work mainly as an assistant, but I work with data at this stage. I work with those counts that other people have already accumulated and sent into the database. Okay. So, many researchers for many decades have been doing this, have been going out to lakes across eastern North America, getting pollen, and submitting their data to this Neotoma Database. And so, 14,000 years ago there's actually quite a few sites that have, we have data for quite a few sites. So, this is North America, to orient you, this is the big ice sheet that covered northern North America. 

By 8,000 years ago the ice sheet has retreated quite a bit. There's even more sites that we have data for. And then in the present, the only ice sheet we have is up in Greenland, and we have the distribution of sites is quite extensive. So, we have a varying number of sites, and I'm going to use these to look at community turnover across space and community turnover through time. And then this is getting to your question about how are we describing climate. We're pairing these fossil pollen data with simulated paleoclimate. So, this is actually work done by a professor here at the AOS department, Zhengyu Liu. And what he did is he took a global circulation model, the same that they're using to make projections into the future, but instead of starting it today or 10 years ago and running it into the future, he started it 22,000 years ago and ran it to the present. And so, this model, this simulation actually does a very good job of simulating the global climate changes that we saw earlier. It shows glacial conditions, that rapid warming, return to Younger Dryas and on into the interglacial period. 

And so, I'm taking this very rich data set of climate and for every thousand years from 21,000 to the present, I'm looking at six different variables. Summer temperature, which is shown in the middle row here, summer precipitation, winter precipitation, which is shown on the bottom, winter temperature, and then the seasonality of temperature and the seasonality of precipitation. And then we have one other factor that's going to go into this, and that's geographic distance. And that's because if you were to just look at two samples in space, samples that are closer to one another physically, might be more similar just because they're closer to each other physically. So it might not have anything to do with climate. It might have to do with the process of dispersal and the sort of ecological history at that site, and so we're going to sort of control and try to tease apart how much is really truly due to climate and how much is due to these spatial factors, spatial autocorrelation. Now, I'm not going to go, so let's look at the first two questions here. Does climate influence community turnover across space, and are those relationships stable? And so what I'm doing here is I'm only modeling community turnover across space. So I'm going to take a snapshot of time, say 14,000 years ago, and I'm going to build a model that relates turnover in communities, or dissimilarity amongst locations, with dissimilarity in their climates. And then I'm going to do that but this is across space, and then I'm going to do that at 13,000 years ago. 

And then I'm going to do that at 12,000 years ago and 10,000 years and so on for every thousand years up until the present. So I'm only looking at spatial dissimilarity, but I'm doing so with these snapshots of time. Now, I use time and space a lot, so feel free, and it's very confusing even to me sometimes, so feel free to interrupt with questions if you're unsure about whether I'm talking about space or time. So if we just look at the model, this is how much variation in communities as explained by these climatic and geographic factors. And it's about 50% for much of the past 6,000 years going back to a low of around 30% 11,000 years before present. And this is actually pretty good for the kind of model that we're using. Most modern systems get about 40%-50%. So we were pretty happy with this. We're looking at pretty abstract data, fossil pollen communities from 12,000 years ago, and we're able to explain 40% of the variation just by these modeled or simulated paleoclimate variables. But remember, some of that variation could just be due to autocorrelation, could just be because location one is close to location two and it's not close to location three. 

So how much of that variation of this 30%-50% of the variation is really truly explained by climate? And is the influence of climate variable over time? And so if we look at that here, the black bars, everything sums to one, and the black bar is the proportion of that variation that's explained by climate. And you can see that recently a lot, 40% of that variation is explained by climate, but back in the latest Pleistocene, 14,000-11,000 years ago, much less of that variation was explained by climate. And this time, actually, is really interesting. That's that time period when climate was changing very, very rapidly. It shifts to the warm Bolling-Allerod period to the cool Younger Dryas period. And the gray is the amount of variation that's really just explained by distance, by how similar or how close two sites are to one another. And then the white is shared. They couldn't really attribute that variation to one cause over the other. But you can see that the effect of geographic distance is much greater in that latest Pleistocene period, and it might be that climate is just not that important at that time. We still have to tease that apart, and we'll get into that a little bit more. 

But what this indicates is that the influence of climate isn't even across time. That climate is having a differential influence on communities over the past 14,000-15,000 years. Now, let's look a little bit more deeply into the different climate variables. So we have six different variables plus geographic distance. Are all of them important or are only some of them important? And so here to orient you to this figure, we have 15,000 years ago to the present, and then we have each row is a different variable listed here on the right. And so if a box appears at the intersection of a time and a variable, it means that that variable was present in the final model. It was a significant predictor of differences in plant communities. And so I've put some dashed lines to sort of indicate my grouping of these time periods. So 14,000-11,000 years ago, really only two variables were important and only one climate variable, and that's summer temperature. Let me find my mouse. 

It keeps disappearing. That's here. So summer temperature is important, plus there's that geographic distance we saw in the previous graph that's also important. 10,000 to 7,000 years ago you get the addition of winter temperature and summer precipitation sometimes, as well as precipitation in seasonality. And then more recently, those two factors drop off and you're just left with precipitation in seasonality, summer temperature, and geography. And so, even just looking at this, while climate isn't evenly influential through time, even more than that, different climate variables are important across time. So some are important at all times. Summer temperature is always a dominate variable, but the other ones sort of come and go, blink in and blink out. But we can go one step further with this kind of model, and we can say what's the relative influence? Is summer temperature equally as important as precipitation seasonality? Is it equally as important as geographic distance? And so if you do this, here the darker gray or black indicates more important variables, and the light gray indicates very low importance variables. So they're still significant in the final model, but they're really not doing that much. 

And so you can see that in the latest Pleistocene from 14,000 to 11,000 years ago, you really have an even contribution of spatial autocorrelation, of geographic distance, and summer temperature. And throughout the Holocene, it's been one dominate variable, which is summer temperature. And the other ones are fairly minor contributors. And so, not just the amount of variation explained by climate has changed over time, not just the presence and absence of different variables have changed over time, but the relative importance has changed over time as well. So, I think the take-home message, as I've just repeated one more time, is that, yes, climate does explain a lot of community dissimilarity in plant communities, but that that explanation and the influence of climate is variable through time, and it's not just a simple one-to-one relationship even. It's that different variables are important at different times. Now, why is this? What's going on here? One possibility is that the climate model, we're using simulated paleoclimate, and so those climate models could be incorrect. And to some extent every model is incorrect. It's just a model. I don't think that's the major factor here. 

I think, we reran all of this with a second, different climate model, and we got the same results, more or less. I think that one possibility is that communities are in an equilibrium with climate. So 14,000 to 11,000 years ago, climate is changing very rapidly. It's going warm then cold then warm. Species don't change or may not be able to change quite as rapidly. They might not be able to keep up. And if they do, if they can't keep up, then we're not really modeling that fundamental relationship between species and climate or we're modeling it incorrectly. So that is likely part of the answer. But another part of the answer is that there might just be something really different about the Pleistocene. There might be something, some factor that we didn't include in our models. 

It could be a climate factor such as growing degree days. We know that's really important in plant communities. It could also be something else, like the influence of mega-fauna. So we know that mega-mammals, mega-herbivores are present in the latest Pleistocene, and went extinct by the end of the Pleistocene. Those animals were having large influences on vegetation. So maybe there was some other factor that we need to include in those models in the Pleistocene that climate might not be the whole story then. And I don't have an answer for you on which is more important, but this is an area that I'm really interested in that we're continuing to look at in the Williams lab. Now, it's all well and good that these models change through time, but the key issue for conservation biology and for the future, I think, is how well can we make predictions. A lot of the conservation community nowadays is building models of species distributions or of communities with climate, and then using climate change to forecast where species will be in the future, what the distribution of our biodiversity will be like in the future. How confident can we be in these models given that we're facing a significant amount of climate change in the future? Well, I can't answer that right now, but what we can do is we can look and see what the relationship is between model reliability and climate change in the past. 

And so that's what we're going to look at here. And so in this case, I'm taking all of these models, I'm taking the model of how climate predicts differences in communities for different time periods, from 14,000 years ago, 13,000 years ago, etc, and I'm predicting community dissimilarity at the present, at zero thousand years before present. So, if you look at those orange dots, I think maybe they're orange on your screen, these are all, brown, these are all models built from some time in the late Holocene. So, say, 6,000 years ago we're building a model and we're predicting dissimilarity at zero, at today, and then we're looking at how well that predicted dissimilarity matches observed dissimilarity. And that's this correlation value on the Y axis. And so correlation values were around .7. That's pretty good. We're pretty happy with that. And the X axis is the amount of climate change that's happened sort of averaged across all variables over that time period. And so there's low-ish amounts of climate change over the past 6,000 years. 

If we add on models from the early Holocene, so now we're back 10,000 years before present, you can see that the correlation, the fit of those models, the predictiveness or predictability of those models is decreasing and we're getting more climate change. And then in the latest Pleistocene, the models don't work as well again, and we have even more climate change. And so, overall, there's a significant relationship. As you have more climate change, models don't work very well. This is not brain surgery. It's very, very intuitive result, but I think that what's interesting about this is that with the past we had the ability to actually look at this through time. We're making a lot of predictions about the future, but the future is unrealized. We don't know how well our models are doing until it happens. And so we're able to look at this, and one thing I haven't done and one thing I'd like to do is see how much, using these same calculations, how much climate difference are we expecting for the future and where on this line does it fall. And I should say, I'm showing you just one plot to the present, but it doesn't matter if you're taking the past and predicting to the future or predicting to the present or if you're taking the present and predicting to the past you get this same relationship, more climate change, lower predictability of models. 

And so I think that this, to me as a paleoecologist but also sort of a conservation paleoecologist, I'm very concerned about this message. How much faith can we have in our models for the future? And this would argue that there's limits to the predictions we can make. Okay, so all of this is speaking to this question of are space and time substitutable because any time we take a model, most of the times when we work today, we're taking models built across space and we're predicting them to some future time point. So, essentially, we have this implicit assumption that space and time are substitutable. That the relationship between species and communities across space is the same as the relationship between species and communities through time. And all of the work that I've done so far and that I've shown you is hinting at that, but it's not really directly testing that. I'm only ever building models across space, and I'm not directly modeling community dissimilarity through time. I'm taking this time slice approach. So, can we actually do a formal rigorous test of this, see if space and time are substitutable? So to do this, I constructed these two data sets, and let me take you through these data sets. So this spatial dissimilarity data set is, essentially, the same thing I've done throughout this talk where you have sites arranged across space, say today, and you're looking at how dissimilar communities are at two sites and how dissimilar their climates are at those two sites. 

And I'm doing that for all pairwise comparisons for the present, and then I'm adding on all of those spatial pairwise comparisons for every time back to 21,000 years ago. So I'm essentially expanding the range of possible community and climate comparisons that I can make, but always across space. Then I'm going to do the same across time. So we have those same sites and those are based on cores. They have some temporal component. So I'm going to take pairwise comparisons of communities at one site through time, all pairwise comparisons possible, all pairwise comparisons of climate, and then I'm going to stack them together with all of the sites in the data set. Again, just expanding the potential range of dissimilarities and seeing whether that relationship between climate and communities is the same across space and across time. So, same as before, I'm going to fit climate to the spatial data set, I'm going to fit climate to the temporal data set, and I'm going to look at those predictors, the presence and absence of them, and then as well their relative magnitude. And here I'm actually going to show you essentially what was under all of those black boxes and gray boxes. So here we have these variables. 

Winter precipitation, winter temperature, summer precipitation, summer temperature, precipitation seasonality, temperature seasonality, and I'm also adding carbon dioxide concentration. Carbon dioxide has varied through time. It's expected to vary into the future. It's generally invariant across space. So that's one big difference between what we think a spatial model and temporal model might capture. So, in this spatial model we have four variables that are important. Winter precipitation and winter temperature are very minor predictors. Summer temperature, same as before, is a major predictor of community dissimilarity. And then in this combined data set we get this additional influence of precipitation seasonality. So this is what the model of space looks like. 

And then I should explain a little bit. The X axis is the gradient of that variable. So this is summer temperatures across the entire 20,000 years goes somewhere from minus 10 to 30 degrees Celsius at various points. And then the line indicates community turnover along that gradient. And so the higher you get on the Y axis, the more important that variable is in explaining overall patterns of community dissimilarity. Let's look at time. This is the model for time. It's quite different. One, there's no single variable that pops out at you like summer temperature. All of them are there and all of them are moderately important. 

And there's four factors that are pretty different. So if we focus on the ones that are similar, winter precipitation and winter temperature are similar in the spatial model and in the temporal model. They're similar both in sort of the magnitude, the height on the Y axis they get, as well as in their shape along that gradient. Summer precipitation is important in time but not in space, temperature seasonality is important in time but not in space, and CO2 is important in time but not in space. These two variables are important in both time and space, but you can see that the influence of them is very different. So in both cases they're much more important in space and only moderately important in time.  So, the big question, though, is we know these models are different but how well do they make predictions? That's what I really want to know is how well a model across space will make predictions to, say, a data set through time, or how well a data set through time will make predictions to a data set across space. I think that's the big conservation question for me. So, let's take one model, say the spatial model, and try to predict dissimilarity in communities through time. Again, we're going to just compare predicted dissimilarity based in the spatial data set and predict it, and then we're going to take that temporal data set and predict essentially itself. 

We're going to predict temporal differences just to see what we can expect from a model. So if we do this, model dissimilarity is predicted fairly well. So on the left what we're trying to model are differences across space. On the right what we're trying to model are differences through time. In both cases, the predictions come from time on the X axis and space on the Y axis. You can see that there's a lot of points there, thousands of pairwise comparisons, but there's a strong and positive relationship that, despite the differences in the spatial model and the temporal model, when you compare the predictions they're making to the same data set, they actually do pretty well. There's a big but here though. And the but is that this is model dissimilarity. One key test, so this is what, to some extent, not to knock modelers, I guess I am a modeler, but to some extent what modelers work with, this is the sort of true test of the models. But what we also want to know is how well does it predict observations. 

And it doesn't do so well there. So here, this is patterns of observed spatial dissimilarity predicted from a spatial model on top and a temporal model on bottom. So even its own model predicting its own data set or a similar data set is only able to predict observations with correlation of .64 which is okay. But when you use a temporal model to predict spatial dissimilarity, you're basically flipping the coin. For temporal dissimilarity, it's a similar picture. Where you have the temporal model predicting itself, predicting temporal dissimilarity, observed temporal dissimilarity, and it correlates better than a spatial model, but when you do use the spatial model to predict dissimilarity through time, it doesn't work so well. So for this, I would say there's mixed take-home messages. So the spatial and the temporal models are different. They make similar predictions, but they don't actually make very good predictions for actual observations. So, to wrap everything up and see what all of this says, I think that all of these little pieces of the puzzle indicate that climate does play a large role in explaining the differences between communities both across space and through time. 

But that role is different across space at different times. So that's taking this snapshot approach. And even when you aggregate all space and compare it with all time, the relationship between communities and climates is different across space and through time. And so the final little piece showed that space for time substitution isn't perfect, but it may work to some extent. And so what I'm doing now is actually trying to take subsets of the data. Does it work for the Pleistocene but not the Holocene? Does it work for some sites, say the upper Midwest but not the southeast? What are the limits of these models, and can we come up and constrain these models to get these predictions to improve? And so, overall, I think that in terms of the future and sort of a conservation biology message, it's just reemphasizing that care is needed when we're making predictions of future community change and when we're making these assumptions about space and time. And we need to take even more care when the magnitude of climate change increases. And that's sort of a sobering message given that we're expecting pretty substantial climate change within a pretty short time window in the near future. And so, with that, thanks to all the different data contributors to the database, the Williams lab member, and my funding sources. And I think I'll take questions. 

[APPLAUSE]