Mark Harmon Oral History Interview, November 11, 2020

SARA KHATIB: Hi, my name is Sara Khatib and this research is for my master's thesis. The research involves history of different traditions of science at the Andrews Forest and the philosophical perceptions of nature that underlie these different traditions. I would like to ask you a few open-ended questions regarding these topics. I expect the duration of the interview to take up to one hour to complete. Would you like to participate in this interview?

MARK HARMON: Yes. Glad to do so.

SK: Thank you. We can end the interview at any point you wish to do so. Please inform me right away if you no longer want to participate and the interview will immediately end. Do I have permission to record this interview?

MH: Yes, you do.

SK: Lastly, this interview will be deposited with the Oregon State University Library Special Collections and Archive Research Center for preservation and 00:01:00access. SCARC's commitment to open access includes the release of contextualized interview content online. Are you willing for this interview and its derivatives, including a transcript and an interview abstract to be made available online?

MH: Yes, I am.

SK: Okay, sounds good. We'll now get moving forward with the interview. We are here today with Dr. Mark Harmon who received his Ph.D. in 1986 at Oregon State University, Corvallis. Mark Harmon's research interests include ecosystem succession processes, decomposition, nutrient cycling, carbon dynamics and landscape dynamics and he installed the well-known and monumental 200-year log decomposition experiment. Thank you for being here with us, Dr. Mark Harmon.

MH: You're welcome.

SK: If you're ready to begin, the first section of this interview is going to include a set of questions that inquire about your perception of an ecosystem 00:02:00and your personal philosophy and take on scientific research. So, how the way you perceive nature influences your scientific approach. My first question I would like to ask you is how would you define an ecosystem, personally?

MH: The first thing, ecosystems involve both the biotic and abiotic environment, and how they interact with each other in a very general sense. That's because you can actually study the biology on its own or the abiotic environment, like the climate, without thinking about how they affect each other. That's the first thing. The second thing is in my mind it's really a set of processes, kinds of processes that are being investigated at the ecosystem level, an ecosystem science. What do I mean by that? Well, ecosystem science involves studying how 00:03:00matter and energy are stored and flow. That's all it is. There are a lot of things that influence that, including humans. It doesn't mean it doesn't involve humans. That's the basic subject matter. Where, say, is carbon stored in the forest? How does it move from one pool to another pool? How does it leave the ecosystem and go to the atmosphere and vice versa? That's what ecosystems are to me. I realized they're also used in a very, very general sense. Some people might think of them as the community of organisms, but I think that's community science. To me, I've got kind of a very, I think, standard ecosystem science definition of an ecosystem.

SK: From the perspective of an ecosystem scientist, is that correct?

MH: Yeah, to me an ecosystem is what an ecosystem scientist studies.

SK: How would you describe the nature of an ecosystem? Is it static? Is it dynamic? Orderly? Chaotic?

MH: Well, here's an interesting thing about ecosystems and ecological science in general. The answer to that question would really depend on the level that you looked at it. There isn't one answer to it. What do I mean by that? Well, and this relates to a landscape. It could be that a particular area, an ecosystem, is say affected by a disturbance. Another one is recovering from a disturbance.

If you looked at it at the small spatial scale, you'd see, well, one seems to be 00:05:00changing slowly and the other very quickly, must be a very dynamic system. But, it's entirely possible for that to happen at the landscape level for nothing to be going on, just changing places where it's going on. That's an interesting facet of ecological science in general. Answers like that depend on scale. Something seen at the level of an individual person could seem totally chaotic, but at the level of the population of people be completely predictable. That's why I don't think it's one or the other. I don't think that they are ever completely static, because, even at that higher level where things seem to be 00:06:00cancelling out, it never does it perfectly. The system is changing. It's just a matter of degree.

SK: Would you say the system's always changing?

MH: Well, it's always changing to some degree. It's whether we can measure it or not or can we observe it. In theory, there are always subtle changings. Nothing is exactly staying on, say, one parameter, staying on one number forever. That never happens in nature. It does have a high degree of variability compared to, say, a strictly physical system. Nonetheless, there are cases where it appears to change very, very slowly, if at all, or have very little variation and there 00:07:00are cases in which it seems to change radically, say, with the recent wildfires we just had near the Andrews.

SK: As an example.

MH: As an example.

SK: Within the Andrews Forest itself would you say that there are times and spaces or cases when it changes radically and times when it changes much more subtly, just within the Andrews Forest alone?

MH: Absolutely, absolutely. Usually the change is largest after a big event that disturbs things that kind of rearranges the system and then it will reorganize itself, or seem to anyway, and then when you get to older forests that had been a long time since they'd been disturbed, they seem to change more slowly, at 00:08:00least at a big scale. But if you analyze something like mortality, death of trees, at the level of trees, we wrote a paper on this, it appears like most trees continue to live, but a few die. It seems like, well, if you're looking at those few trees that died [that's] big change. If you look at the rest of the trees, not. It really depends on scale. I can't emphasize that too much. One of the problems of describing them, they're complexity is this fact that at different scales or levels of organization you see different things. It's all the same thing. It's just manifested in different ways. I hope that makes sense.

SK: No, it does. Thank you.

MH: That's what makes ecological science really fascinating.

SK: The immense amount of variability along spatial and temporal scales.

MH: Depends on how you look at it. That's tough to communicate for managers. I mean, just look at the reaction to the fires. It's like the world is ending. I realize people lost homes and all that. Don't let me make it seem like that wasn't important, but in the forest, you know, this sort of thing, it happens. It's happened historically. It's not really that surprising. Anyways, yes it definitely occurs in the Andrews at some levels and not others. Depends on what happens, too.

One of the things we found is that no matter what level you look at ecosystems, 00:10:00there's always some surprise event. Just when you think it's all behaving normally, there's always something that comes in that surprises people and we wrote a paper on this and you'd think at some level forests of the world mortality would be very predictable, because you'd have all the disturbances and all the human things going on. Then there's asteroids, which occur more frequently than people ever imagine, and they affect huge areas of the globe, of the forest. There's always something else that will come along to upset what you think is a very predictable, well-behaved system.

SK: And open the door to a whole new path of learning and discovering.

MH: Yes, that's right.

SK: I think that's characterized a lot of the stories that have come out of the Andrews Forest, right, throughout the history of surprises that sort of shake the community?

MH: Yeah, I think that's probably true.

SK: What is your theoretical or philosophical background and how is that applied to your scientific process?

MH: Well, I sort of believe in triangulation. I know there are people who are more observational, where you might call a founding scientist of the Andrews, like Jerry Franklin, are very observational in their nature. They've discovered a lot about forests. There are some who are very, very oriented towards experiments, experiments are the only way to learn things. I've done enough 00:12:00experiments to realize there's things like confounding factors. They tend to reduce the system down to what it isn't so you can figure out what it is, which can be misleading. In other words, you take out all these factors that you know will influence the result to figure out what's controlling the system, but then you've left out all those things that you knew where going to affect the system. So, what does that mean? Then, I also do a lot of modeling using theoretical constructs to understand the system. Between those three things I try to figure out what might be going on. It depends on where I am on a problem which approach 00:13:00I use. You could discover things from models that you'd never get from observations just because they force you to be rigorous about relationships. You can find things out experimentally, but, if you do not bring those back into a more synthetic, holistic science to figure out how they fit, you can be misled.

That's another thing I like to do. I like to move between scales and levels to see how consistent a finding is. That's probably coming from the modeling. I want to know how this would apply into a bigger system. I think I may be kind of unusual in that sense. Some people are very comfortable being more reductionist 00:14:00and other people are more comfortable being more holistic, sort of big, hand-wavy relationships. I think they both have strengths, so I like to move between them to see how it all fits together.

SK: Makes sense. Incorporating almost moving back and forth between the two.

MH: I'm not sure they're great words for these, but a reductionist is someone who's breaking it into smaller and smaller parts to find out what's really going on, but it can also become finding out more and more about less and less. I don't like that idea. I'm not opposed to reductionism, but I always want to go 00:15:00more in a synthetic direction, if I can, as well to figure out what we've missed out, what have we failed to learn. It's not just ecology where you find these things. There can be things that are true at one level, but either cancelled or counteracted by something else at a higher level. "A" is true, but when you put it in a higher level, it's like, well, it's just one of many possible outcomes. It's not the only outcome. We can't just simply add up everything and expect to know what's going on. It's not an additive system. Ecosystems and ecology that's another fascinating thing. They're not additive systems. If you approach it that way, you're not going to learn very much.

SK: That's an interesting way to describe it. So, does this relate then to the concept of emergent properties as well?

MH: Yes, exactly, exactly. There are things-it's not like they mysteriously appear. It's just that without these higher-level interactions you can't understand what's going on. Sometimes the things are incredibly simple that lead to really complicated behaviors. The fact like in mortality it acts in a complicated way because there's always some event outside of your view that you're not considering that can appear suddenly as this chaotic element. Then it's like, oh, well, it's just chaos. No, not really. It is probabilistic, which gives it a complexity. It's the fact that there's always something outside 00:17:00that's eventually going to race in and change the system on you in a way that you hadn't been anticipating.

SK: This understanding, would you say that it's only recently emerged within the field of ecology? I know at one point in time people thought of ecosystems as very static, right?

MH: Well, I suppose. There was some formal development of emergent properties. There were a lot of discussions of that in the '60s and '70s when ecosystems were first studied, because was it just elaborate chemistry or something like that? Well, yeah chemistry is involved, but more is going on than just pure chemistry. There are interactions that lead to behaviors like lags and things or 00:18:00a resistance to change, at least within certain limits. But I think the formal analysis was under the idea of biocomplexity and I think that was more like the early 2000s of biocomplexity and how things would emerge. I was influenced very much by that thinking.

SK: Who are some of the figures you can say that sort of led that?

MH: Well, I think Wolfram was in general someone who pushed that idea in science in general, but certainly Bob O'Neill from the Oak Ridge National Laboratory, Bob Gardner, those folks pushed those ideas quite a bit.

SK: Did they influence your scientific, philosophical development as well would you say?

MH: Well, to some degree. I wouldn't say that Bob O'Neill says this and, therefore, it is, and I must do this, but it made me realize that some of the explanations of why ecosystems behaved one way at one level and another way at another higher level. I was influenced by that a lot. I realized it explained some behaviors. One example I like to show people is [that if] they're convinced that when trees die, they can't store carbon, because they're decomposing. That's true if you observe a tree dies over time it's going to decompose and lose mass.

But if you look at a population of trees dying, and they die at different times, 00:20:00some of the newly dead trees offset the losses of the old trees and actually that pool can actually gain carbon over time. It can store. It can build up. It can increase, even though the individuals are losing, the population can increase. To me, that's an emergent behavior. It's fairly simple, but it's still hard for people to understand. I've given people some puzzles, you know. Suppose you have a function that goes like this [tilts forearm at an angle]. It's going down, and a function that goes like that [tilts forearm to opposite direction]. Well if you average them over the whole period, what's the average store of each? Assuming that the high point was one? It's a half. For both of them. It's completely independent of the direction. There are a lot of areas where I've 00:21:00tried to apply my science that I have to explain some of these counterintuitive findings, because they really influence how people think about systems. Sometimes they're making completely the wrong conclusion.

SK: It seems like really a relative finding too to current environmental problems with climate change.

MH: Yeah. I think so. Maybe in the sense that people realize that there's big changes over the seasons, so, therefore, how could there be any systematic change? But slight increases in each part or slight warming in each part of the year can lead to an overall warming trend quite easily, even though snowballs 00:22:00can be formed in parking lots, which is sort of referencing [Senator] Inhofe's famous snowball in the senate.

SK: You've already touched on this, but just to reiterate-my next question is how would you describe your scientific style? You used the term triangulation, right, to navigate between modeling, observational, and experimental and not get stuck in one area over the other, is that correct?

MH: No, it depends on where the science is. The scientific method starts with, you know, observations often. What is happening? Let's just observe what is happening. So, an example for us, me, where I recently made a discovery after 30 00:23:00years plus of working on it, was tree mortality because when we started measuring it it seemed completely chaotic. Some plots had trees die, some didn't. Sometimes there were huge amounts that died, sometimes small amounts. We were convinced, if we had long-term measurements, we would eventually get rid of all that noise-that we would finally get enough numbers in a big enough area and a long enough period, we'd figure out the average and then we could do our science. Well, as we gathered those numbers what started to occur to me was, yeah, we were getting a better average, but the variation never really went down. Variability was the key thing about mortality. That was the key property. It's variable. It's variable at every scale you look at it. By gathering that 00:24:00information, making those observations we could then develop a hypothesis about what mortality was really like. Then we demonstrated that by using that information to make models to just show, we can only observe limited places for a limited time. But if you take that information and apply it into a model what would it look like if you could look at a big area or a long period of time? It showed that we'd been thinking about mortality really completely in the wrong way. At least I think we have.

Now, that's a hypothesis which then can be tested probably with more observation in the sense that some ecosystem phenomena are like astrophysics. We can't move 00:25:00stars around, and we can't do experiments, but I would argue that astrophysicists are probably some of the most rigorous scientists you'll find on the planet. The fact that they can't do a manipulative experiment doesn't mean that they haven't learned anything or they're making it all up. Anyways, that'd be an example where we went from observations and then developed models and now, we have really the model is like I view as a very sophisticated hypothesis and now we can try to test that maybe in a different place than the Andrews, see if it applies somewhere else or not.

SK: How have your disciplinary roots and your personal training in science 00:26:00influenced the development of this approach?

MH: I think I'm a generalist. I have worked a lot in decomposition. I know a lot about that, done a lot of studies on that, known for doing that, but I'm really interested in communities and populations, in ecosystems, in landscapes. I'm interested in a whole series of different scales and relationships. For years I had to deal with colleagues who believed, for example, that species, which is more of a community level, could never influence ecosystem behavior. I just thought that was ridiculous, because the properties of species are different 00:27:00from each other. Therefore, if the properties are different for the different species and you change the mixture of species the properties change. I mean, how could they not change? I've always had in my carbon models a community-based model that allows the species to change. When I do experiments, I usually look at it over different species to see if there are differences between them. Some people are very comfortable, they just do ecosystem work. That's all they do. They don't think species are relevant. There are some people who do community work. That's all fine. But I've found that I've worked at multiple levels and I'm very comfortable doing that. Some people are very uncomfortable when they get outside of their narrow specialty. I'm not that way at all. I may not know 00:28:00what I'm doing, but I'm not afraid to think about things at different levels and scales and trying to integrate all the processes together to see how they work.

SK: With that perspective that differs maybe from other people within the Andrews community or even forest ecologists in general, how do you think that being a generalist maybe has changed your perspective on ecosystems? Or has given you insight that may not have been there, if you were more of a specialist, so to speak?

MH: Yeah, I think it's really because I'm willing to cross boundaries. It allows me to see how things actually do cross boundaries and how they influence behaviors. I think that's what's the key for me has been able to do that. I 00:29:00think the other thing is I like to synthesize. I like to put things together and not just do that, but do things like assess uncertainty as we put things together. A lot of folks are very happy and there's nothing wrong with it, but I'm not happy to just learn more and more about less and less, and that doesn't turn me on in the slightest.

SK: This might be an assumption, but I'm just curious-is moving across different scales as a generalist do you think that's made you realize the dynamic nature of ecosystems or the uncertainties more or has that sort of help understand patterns more? I don't know if that made sense.

MH: I think it's helped me understand everything more.

SK: Everything, okay.

MH: I still have a lot to learn. It's filled out the whole thing. You know there are some people [who] like just experiments, they won't accept any science that's not experimental based. I think that's like looking at an object from one direction only. I just question how you can understand what the object is unless you look at it at least several ways, and I likely look at it at least three ways and maybe, if you think about the scale aspects, it's not only three sides, but looking up and looking down. I like to look at it from every direction I can to see if I can get any idea what's going on. I just think it gives you a much stronger view and you can find inconsistencies when you do that that you'd never 00:31:00find otherwise, I don't think. I think that comes from being more of a generalist, using a wide [view], multiple tools, looking at different processes. Even though I'm really interested in ecosystems, I've always wondered how populations, well how these other things affect that ecosystem.

SK: What is your strategy in developing a research question? You a little bit touched on this already, but if you can maybe describe or answer explicitly how you go about doing that.

MH: Well, the first thing would be to assess the state of the science. What does it need to improve? Does it need observations? Does it need-so if there's not much information, I think you just have to start with making observations. What 00:32:00does the system do? How does it act? What seems to be influencing it? But, in some cases, we know all that. In fact, we know an awful lot, so I might start with a model that has all the knowledge that we use. I know you had a question about modeling, but much of ecological modeling has become statistical modeling. I'm not talking about it, something we derive from data per se. I'm talking about something we derive from knowledge. We know that these things happen. They happen there must be a way that they relate to each other. Okay, can we put that into a model and then predict? I recently sent out a manuscript into a journal. They asked could I give a perspective on dead trees and their biochemical 00:33:00function. I said, sure I'll do that. I thought about a whole series of processes and I created little models all on spreadsheets. What were the relationships here? One of the reviewers wrote back and said I was a little disappointed you didn't have any cartoons showing what was going on. What I did is I created mathematical models of what was going on, which I thought was a lot more rigorous, but some people they just don't do that. The want cartoons. If I can make a mathematical relationship, I do because it's very specific and explicit. I have to decide what's in and what's out and what's the relationship and all that. If I don't know all the parameter values, well, I'll just use a range to see, if it changes, how the system will act and that leads to questions about 00:34:00okay, well, then you know maybe we need to look at this now. Maybe that's-now we need these sets of observations. That's how it works for me. Maybe I need observations. Maybe I kind of know what's going on. I can make a model. Then I make predictions. Some of them are qualitative, but they could be, you could do experiments or make observations that would then confirm that it's at least on the right track. That's just how-and maybe something needs an experiment. I'm a little less enthralled with experiments in ecosystems because we often fail to control for some key variable, and it messes up the results or things don't act the way that we thought they would. It's not that it couldn't act that way. We 00:35:00just didn't believe it would and then it did, and our experiment is like, meh, didn't pan out. There's a lot of experiments that don't pan out in ecosystems.

SK: It's less applicable.

MH: Well, you know, an example-there are sites that have done a lot of experiments in LTER, so you can. But our forests are really large trees. There was a proposal to do climate alternations. We were supposed to build these 80-meter-tall by 100 meter by 100-meter greenhouses. Then it was pointed out that all we had to have was one tree which probably weighs 50 tons fall into the greenhouse and the experiment is over. How on earth can you, you know [shrugs], it's almost an uncontrollable system that way. But there's things you can do, 00:36:00experiments. It's just that a lot of times experiments are hard to carry out in our system or they introduce changes that we weren't anticipating that totally screw up the interpretation. So, [shrugs] experiment as you can, but just be aware that that's not a silver bullet necessarily.

SK: For my next question, what are your thoughts in regards to objectivity in scientific research?

MH: Oh, well, I think the objectivity is limited. It's not-let me put it this way - there's a scientific ideal of total objectivity. If it was carried out by robots, that might be achievable, but it's carried out by people. They have 00:37:00biases. Some of them are severe and ecologists, scientists have biases, too. If they believe they don't have any biases, then I think they're deluded, to put it in a word. Now we want to recognize when we have a bias, we want to counter it in some way, but I've noticed in talking with people and reviewing articles, manuscripts, and proposals for research that there has been some real bias. I just reviewed a manuscript in which they looked at how changing forests management would affect carbon stores. That's an area I work a lot in. Well, their model, very sophisticated, only included the live trees and only the 00:38:00above-ground part. Well, there are roots and soil and dead stuff and then, when you harvest a forest, you create products that store carbon. They had none of that. They analyzed the question and they made these conclusions and they didn't even qualify that, oh, well, we had these other pools that we ignored. They just said it was this way and it won't be the way they describe and then in their discussion they went on and on about how they could improve their model of this little teeny fraction of the system they decided was the whole thing. It was like-they were so biased; it was incredible, and they couldn't see it, because they were being super rigorous about their little part of the world and they could make it even better, but the idea that you have to back off and see that 00:39:00there's more to it. For example, they said, if we just grew the forest for a longer period of time, we'd store more carbon in the forest. Well, that's true. But we'd store less in products and the atmosphere perceives what's going on on the products versus the ecosystem. They both store. If one goes up and the other goes down and vice versa, it doesn't mean that there's more storage. But that's what they concluded. Because in my mind they're just so into their tunnel vision it's creating all these biases. I would say, yeah, it's full of it. Same thing with-I worked a lot on dead trees. You wouldn't believe how many people still can't, scientists can't envision that there are dead trees in a forest.

When I wrote this review article, this commentary review article, I had to point 00:40:00out you know trees have been around 340 million years and when the first tree died there were dead trees in the world. Only 340 million years ago and you're saying that we're just discovering this? Does that make any sense? I do it to make people laugh because it's just logically insane how do intelligent people continue to do this? You can't explain it through bias and lack of objectivity. It's something we have to be aware of-anything done by people is going to have bias and lack objectivity. We're just trying to maximize the objectivity and science to the degree that our human nature allows us to do it.

SK: Would you say then that recognizing it can help us to sort of overcome it by 00:41:00being conscious of it?

MH: Absolutely. I think that's how humans deal with things. When we realize that we-what our nature is, we have to sit and say now wait a minute before rushing to a conclusion I should reconsider that. I think that's true with human relationships, and, say, diversity. You may have a reaction to something, but you should then think, well, that's just me being stupid here. Okay, let's actually think of what's going on versus what we instantaneously conclude because of our biases. Scientists have a problem, because we believe we're objective we've created a barrier because we've basically said, "Well, I 00:42:00couldn't be biased." How could I be subjective? But no, we are.

SK: I'm sure, too, we've been talking on the individual level, but when you also talk on the social and political level with funding availability that also plays a huge role, I assume.

MH: Absolutely, because people want to fund what they do, what they work on, then they recognize the value of it. Sometimes there's a competition, let's not publish that, we have that idea too or something. That sort of pettiness comes out. But some of it is just their enthusiasm about what they recognize is much higher than the stuff that they don't recognize. That leads to this sort of creation of silos in tracks that just get deeper and deeper and deeper without stepping back and saying look, you know, there's 8 tracks here we have to pursue 00:43:00to get at integrated knowledge and we're on one of them or two of them. That's why I think it helps to look and synthesize because you realize what's missing. What have we missed here in our pursuit of knowledge? That's largely driven from the fact that the science tends to, there's a hot idea or an area where people are trained or they're familiar with and they just keep plowing in that direction as long as you'll let them go. I think that's just a human tendency.

SK: As a researcher, how do you use the scientific method to find truth?

MH: Well, first of all, you have to be very skeptical, even of your own 00:44:00conclusions, if you want the truth, because of this issue of you can start to confirm things that you've seen, but not try to falsify them. So that's one method. You're always trying to falsify the idea, not just confirm that you can see it somewhere else, but you know is this real? Can we find evidence that counters this? Are there lots of exceptions that just indicate that did happen, but it's just one of many possible outcomes? That's one approach.

Skepticism is really important and not only about others' ideas, but even your 00:45:00own ideas. You're constantly looking and searching and checking to see what are the logical and factual inconsistencies that we're seeing. Unfortunately, because ecosystems are somewhat idiosyncratic, it's really hard to come up with general relationships or conclusions. I wouldn't say relationships. We know plants grow everywhere and they die everywhere, and they decompose everywhere, and they do this and that. We just know that. But whether that system is going to gain a lot of carbon or lose a lot of carbon or whatever, we might not know that.

SK: I guess, to reiterate, remaining skeptical and always looking for the ways in which one might be wrong, right?

MH: Yeah, being open to that and always searching. Looking for relationships that are truly general. I think they exist, but you know some of them seem very mundane and so that's another bias that scientists have. We're much happier with a very exciting and new fresh result that seems to turn over the conventional wisdom, cutting edge. There's all kinds of stuff like that. I'm very skeptical whenever I hear any of that language. I think a lot of that just reflects our 00:47:00society. We're into novelty. We're into technology. We're into various things. You can see it in the science. Sometimes you don't need technology to solve a question. Sometimes you can't answer it without it. It really depends on the context.

SK: Are there laws of nature and if so, can you define what a law of nature means?

MH: Well, I think there's a scientific definition of a law and that's kind of what I would go towards which would be a phenomenon that is very well understood and documented, and it always behaves the way that is predicted. There aren't very many laws in science, real laws. I mean, there's the laws of thermodynamics 00:48:00but even some of those, like the conservation of mass law, which I use a lot in my science, has some exceptions, you know, with radioactive decay and that sort of thing where mass can be turned into energy. Well, it's not strictly observed in all situations, but there are lots of situations in which mass is conserved. I'd say that's a law. I will use those, like in my carbon science. It's one of the fundamental things I always start from and what's really weird is I find results and predictions that don't square with the conservation of mass law. That really bothers me. Ecology, when you get down to ecology, I'm not sure there are many laws. There's been a great attempt to create them, to discover them, not much success there, but it does conform to certain laws, like 00:49:00conservation of mass. To me, it's sort of almost like the highest level of scientific knowledge and predictability. That's a law and a theory is below that, like a theory of evolution by natural selection. It's not some wild-ass guess. It's not the common place thought of a theory. A theory means that there's a lot of observation, experimental evidence. There are mathematical models. All kinds of lines all lined up predicting the same thing.

SK: As a scientist, what are your overall motivations? Is yours curiosity driven? Is it for finding real-world solutions? How would you describe the purpose or motivations behind your work as a scientist?

MH: Well, it's a combination. I mean, I am really interested in discovery and thinking about how we discover things. I have about a year maybe year and a half discussion with a friend who's a musician. We talk about every week and one of our primary things that we talk about is discovery and how does he discover things and how do I discover things and how do we just go about discovering things? That's part of what motivates me is discovering how do I discover things? Come up with new ideas. To go back a little bit, we talked about, well, 00:51:00where do you start on a problem? One area is not only what is known, but what are the clichés about what's going on, how it works and then reexamine those clichés to see what are the holes in those things? What have they missed out?

Anyway, to me discovery's a big motivation. I do like to learn things. I do this in music and in woodworking, fishing. I go fishing. I'm always trying to figure out how to be better and never formulaic, just trying to figure out what's-that motivates me a lot. For the carbon work, I've tried to always think about how it could be applied and tried to explain it to people who might become practitioners.

I've never been an applied scientist, as someone who's said, "This is a problem I have to come up with this applied solution." That's never motivated me at all, but I have wanted to have my stuff applied. Now, I'm not sure it's really worked out. I've had some real questions about my carbon work in part because it's gotten really political and it's been really hard to actually have the truth come out or at least the basic principles. It's just so warped by what people want that I've become kind of discouraged about doing that work. I do still some, but it's sort of like oh my word. It's one of those fields where people 00:53:00have already decided what they want to do and then they justify it under carbon. I've given talks on this where people developing these carbon bombs, they're assembling them and dropping them on each other to show how they're management is just a travesty. If they don't do it my way, we won't get all this carbon stored. I've tried to point out that, if everybody's right, then we can do anything to forests we want to and they'll store carbon if we just accept what everyone says, so clearly the forest would be irrelevant. Is that what's going on? Or somebody is really telling tales, and that's not going to help the field either, because who would trust a field in which a major group has mislead so many for so long? I don't know. We just had an election where that may have 00:54:00occurred. I don't know.

SK: If we can go back for a minute, because I'm interested in your reflections on talking with your friend who is a musician and what that's-the conversations back and forth. Do you mind describing a little bit more about what that's taught you in terms of discovery and learning things in a little bit more detail?

MH: This sort of came from work with-Fred Swanson has encouraged us to work with the humanities and artists and I did a project where basically the artists did all the work, but I tried to encourage them to think about dead trees or what they might mean or how they'd represent them because they realized that just telling people scientific facts may not convince people to rethink what they're thinking, change how they view the world.

Anyways, it's kind of along those lines. How to get out of the track of, you 00:55:00know, activity A is this and this and this and this-it's only that. That's like, well that's a replication of something and maybe you do really well at replicating it or not, but how do you do something that hasn't been replicated? That's just new? How do you actually come up with a new thought? At that level, music and science-it's just human thought, really. That's what I've tried to generalize it to. It's kind of under the idea that humans, many humans anyway, or certainly enough, what really drives them is discovery. They want to find out something new. That's true for someone who's painting or making music or doing 00:56:00science or writing a novel or thinking about philosophy. That's just for a certain number of people that is really the motivator. What's behind all that? He's a little less sure of himself scientifically, so a lot of our experiments involve music where he's very sure of himself and I'm a little less, but it enables us to think about that. How do we go at that? Here's an idea I guess I didn't describe earlier: when I'm thinking about something often, I just lie down and envision it in my head. I don't think "I'm a scientist. I'm rigorous." I just lay back and [think], what is happening? How does it happen? Can I 00:57:00envision what's going on or what must be going on? I try to, sometimes I just brute force I'm going to figure this out, but a lot of times it's like, "Let's just relax and just let the brain cook in the background and let's not get uptight about discovering something. Something can appear."

The same thing about music, say a rhythm. We have certain conventions of rhythms, but if we just relax them out, well, this is an example of 4/4 time. Well, why can't we have 5/4 time? 7/4 time? 11/4 time? I mean, we do not have to have 3 or 4/4 time. That's an example I guess musically of just he'll often sit 00:58:00at the piano and he'll just hit random notes and actually it sounds great. If you transcribed it, it would be like what the hell is that? But he plays it with such confidence and when he finds a note at the end that he just really, boom, that note, and it's like wow, that sounded great. What was that? It's been real fun discussion. I think that's what motivates me is this discovery.

SK: That's great. It goes to show that I think maybe a public's view misses out a lot on is the creativity that can go on behind science as well.

MH: Yeah, sometimes it's just putting standard things together and you get oh yeah, that's a result. But sometimes it's like you might have all-so, an analogy with music would be you know you might know many of the chords and the theory 00:59:00and the rhythms, but you've learned all that, you've practiced it but then you just let it go and that guides you, but it doesn't hold you back. Science would be like you learn all the physics and chemistry and this and that, you learn all these tools, that's fine. But then you have to get to the space where you just let it rip. You grab what you need. That's kind of what I've described. I take what I need to make the thing work, or at least start to make it work.

SK: Awesome. That concludes our first section. However, we are at the hour.

MH: Well, that's fine. We can keep going.

SK: Would you like to take just a couple minute break?

MH: I don't need a break. If you need one, I'm happy to take one.

SK: Okay, I might take a couple minutes if that's okay and then we can-

MH: That's fine.

SK: I'll pause the recording.

MH: Okay.

SK: Moving on to the next section, I'll inquire about different traditions and approaches of science on a community level and what role these different approaches contribute to the Andrews science community as a whole. My first question is what role does natural history play in the Andrews Experimental Forest?

MH: I think it plays a very important role. I have a colleague who pointed out that, well, let me tell this story-I went to Amherst College and I applied as a natural history major. When I arrived, they'd done away with it. The reason I applied, was because I wanted to become an ecologist and that was really what 01:01:00ecology was, natural history. But they didn't think it was a rigorous science, so they stuck me in physics and chemistry and finally biology. The first year or two were just miserable. It's not what I wanted. Natural history is really important because it is the basis of ecology, ecological science. It's kind of where it began. It's kind of where it's grounded. It's not that it's just telling stories but there's a strong relationship to making observations of the natural system and telling people. But the other thing that is important, I guess now that I've said telling stories, is one of the things that has made the HJ Andrews work with so many disciplines was the ability to tell each other 01:02:00stories and I'm not sure, and I haven't been involved too much in the science in the last years with retirement, but that's always been a key part and in telling those stories you need to make things more general and cross disciplinary. I think it's helped both because that's the basis of the science generally but also because it's helped bridge these communities where you can tell each other stories. Now some people enjoy the different stories and some people don't. That's fine. But if you're willing to listen and enjoy, learn from them, you can learn an awful lot about the system through that kind of communication mode. For example, the people who work in the aquatic system would describe what they're 01:03:00observing in that system and then the people in the uplands would listen and they would learn and sometimes they copied things, but it really was a major way of trying to get a bigger community working versus really narrow, we're just working on this little problem and everybody talks the same way about the same thing.

SK: In this community you'd hear stories from the water people and stories from the soil people, right?

MH: And the climate, yeah. Now the humanities and philosophy and social sciences and sometimes you know it doesn't always make sense or whatever, but that's where this realization, oh just because I don't study it doesn't mean it isn't important or they don't have something to add here, so I should check myself 01:04:00when I start having those thoughts. Sometimes they're valid, but sometimes they're not. They're just, okay that's what I-I really like certain kinds of food and if that isn't served to me, you know, I'm upset, but it doesn't mean that what's served to me isn't good.

SK: What role do observational approaches play at the Andrews Experimental Forest?

MH: It's huge in several ways. One is because the system is hard to manipulate-you can do it, but we're kind of limited. We observe the system and see how it changes in response to various things that are kind of thrown at it. 01:05:00The other thing is that a lot of the underlying processes, you can't measure for a year or two and conclude what's going on. I mean you have to keep measuring the climate. You have to keep measuring, you know, how the trees are growing and dying and all that. We just had two watersheds that had fires go through them. Because we have all those pre-measurements of what was going on [and then] something comes in, now we can assess what that has done. How has that changed the system if we-typically what happens is people just, when something happens, they go and measure it, but you don't know what happened before, so how did it change? It's hard to determine that question. A lot of that comes from just 01:06:00making observations of key things and continuing them, observing and assessing, but then opportunities will appear that changes the system in some way and then you can start learning what's controlling the system. I think they're really key. They're not the only thing one should do but probably at least half, three-quarters of the Andrews work is maintaining long-term measurements and some people gripe about that because there should be more work on experiments and sexy new things. That's true. There's no reason not to do that, but having some of these records has turned out to be super important and led to new discoveries.

SK: That was one of my follow-up questions, I think, is an underlying philosophy of Andrews Forest community is long-term frameworks, and so that heavily 01:07:00correlates with observational approaches, right?

MH: Well, it's that, but also, you know, at least the LTER has been oriented around a question. The question is unanswerable, which for scientists is like what? But Phil Sollins years and years ago said you know what we should do is organize our work around a question that would take us 50 to 100 years to answer or maybe we would never answer it, but it would motivate us and focus us for the long-term period versus like LTER6 [sixth funding cycle, which come in 6 year increments] would answer this question. Then LTER, whatever, LTER7 [seventh funding cycle] will answer this question. Instead, it's been like well, we have this set of questions. It's really general, and what we'll do in each phase is 01:08:00we'll grab a different part and we'll try to answer that, and we'll now put that in our bag of knowledge, and then we'll go on to the next part.

Landscape dynamics was an emphasis at one point because we hadn't really thought about how the landscape works. We spent six years just thinking, how does it work? Now when we talk about our science we often talk about the landscape. The climate work now is very landscape-based versus what's happening at a weather station. The response of birds is really a landscape-level examination of what's going on and how they're moving from one part to another or whatever. That influenced the thinking. We have these questions that are designed to be 01:09:00long-term that actually keep things together, provide a focal point, even if it's kind of vague. It's still a focal point. It's been a real strength in almost every time the Andrews LTER get reviewed, they get high marks because of that, I think. It's just this underlying strength in commitment to long-term, not only measurements, but discovery.

SK: I'm interested, too-the general sense that I get when I read about the history of ecology and, correct me if I'm wrong, is it seems like natural history, or at least I've heard some people express that natural history has been pushed to the backburner a little bit. But that doesn't seem to be the case 01:10:00within the Andrews Forest community. Is that right? It still carries a lot of importance?

MH: Yeah, maybe that's so. I mean what happened is that, because there was such a descriptive nature, natural history basis of it, there was a real challenge of how do we make ecology more quantitative? There were several ways that his could happen, one was through simulation models, which I kind of like because I look at them as really synthesis tools and knowledge enhancement tools. But, that's kind of still a little vague, so what's happened in a lot of ecology, not only has it narrowed its focus into narrower and narrower tracks, but without a lot of synthesis, but it's tended to go to statistical analysis and modeling, really 01:11:00detailed statistics of data sets to discover what's going on. Well, there's nothing wrong with that. I worked as a community ecologist and that's what you do. You'd gather all this data, analyze it to figure out the patterns of the communities. It was really a fishing expedition, but didn't mean that it didn't result in anything. But it's become-modeling has become statistical modeling only. I used to teach a course and we did modeling, and the students would always say, "Well, where's the data that I model from?" I had a lecture specifically on I now give you permission to use knowledge. You may now use knowledge and apply it. You have permission. You don't need any data. You just need knowledge of relationships and maybe data will be useful to test your model 01:12:00or to run your model, whatever. We're not doing a gigantic regression of behavior. We actually know what's happening, how things relate to each other. Put it together and see how it behaves. But ecology has really become super statistical and super empirical to the degree where you can read manuscripts, and I review a lot of manuscripts, and I'm damned if I can figure out if there's a story. There's no story. It's a bunch of statistics. It's like, I think what's been lost is that humans in general relate to stories.

Jerry Franklin used to tell me that when you write a paper you've got to decide what your story is because humans want to hear a story. Now it doesn't have to 01:13:00be total B.S., made up stuff. But inherently that's what it is. For some people the story really is I had some data, I did a lot of statistical analysis, I found out a bunch of significant things. To me, it's like-oh, wow. Okay. Whatever. What is it that you found? I just still don't know what you found. Or you have this dilemma where you know people are trying to explain the last 5% of something, but they forgot about what controls the other 95%. It's like, "Okay. That's great. What about the rest? Do you ever think about that?" I tend to be a little skeptical about how we've gotten to be more quantitative, at the expense of telling stories and actually the natural history. I don't know if other 01:14:00scientists at the Andrews feel that way. That's just how I feel.

SK: How did your students respond in the lecture when you give them permission to just use knowledge?

MH: "Oh, yeah. I guess so." Because I pointed out like, you know, if you have all these studies where you find all these facts, do you mean you always have to keep finding different facts or whatever? You just pile up on the facts pile and we never look at the pile to figure out which ones could we use? That doesn't make sense, does it? But it's the only course they often got at OSU where they were given that kind of permission.

SK: What about hypothesis-driven experimental research? How does that contribute to the Andrews Experimental Forest?

MH: I think probably, compared to many LTERs, it's more limited. I think some of 01:15:00it has been just the ability to really manipulate at the ecosystems level. Now, studying sub-processes, little things, you can probably fare better at doing that. I wouldn't dismiss it. But it's been a real challenge because of the scale of the system. If you look at a lot of experiments at the Andrews, there've been things like clearcutting watersheds or partial cuts. You know there haven't been a lot of big, manipulative experiments because they're hard to pull off. We did try to do some. We proposed that we would burn up a watershed. There was a Phoenix Project we were going to light a fire at the base of a watershed and burn up the forest the best we could. See what would happen. But it was like, well, nobody was really interested in actually doing that. The managers were, 01:16:00they were game, but they did point out that, if this thing gets away this would be a disaster. So, it collapsed.

We haven't had as many big system experiments recently that would be formal experiments. Hypotheses are really important. They're always important, but the trouble is a lot of hypotheses are just, you know, I think this is going to happen. To me, that's not a hypothesis. There are a lot of pretty lousy hypotheses. I think first of all, they have to be falsifiable. Sometimes people have hypotheses that they know can't be wrong, because they don't want to be wrong. It has to be falsifiable. It has to be mechanistic, to me, to be a real-so someone once told me, if a hypothesis doesn't have "if", then, 01:17:00because-if we do this, then the following should happen because of this mechanism. If it doesn't have those three things in it, it's really a crap hypothesis.

A lot of hypotheses I've reviewed through the years, they're just crap. Sorry. They don't have any of those elements. You have to have---anyways, the other problem is the hypotheses have to be in the traditional sense very strict, narrow things. It's not bad, but it limits them. I've made the argument that these mechanistic models that we create, they are kind of very elaborate hypotheses that have all these relationships, not just one, but multiples that are integrated. A good process model is really a complex, higher-level 01:18:00hypothesis. Even if it predicts things, that doesn't mean it's right. It's just making a more elaborate, you know, complex prediction than a simple hypothesis would give. But most people are still back at the let's just deal with limited hypotheses. But if you try to look at a system like the Andrews at the biggest level, those tend to break down. They become overly simplistic and they just will not work, in my opinion, where you have to have a much more complicated set of relationships. I think it's key. It's a key tool in science, hypothesis, and testing them any way you can is important. It may not be an experiment, but it's 01:19:00important to test them and keep poking holes at them.

SK: What about ecosystem modeling and modeling in general within the Andrews Forest community?

MH: Well, most of it's statistical modeling because that's what ecologists do. I mean, that's how they have it and I've tried to push people into more process, mechanistic modeling but with rather limited success. A lot of people are just not comfortable doing that. They feel very comfortable tied to data. I understand that. When people say, I have a professor said you know, screw the data. Just create a model of relationships. You say, oh, but it's not tied to data.

Well, it's tied to knowledge. If the point is generating knowledge, then why 01:20:00can't we use the knowledge? But I think a lot of people are comfortable with empirical models. I've pushed them for years to come up with climate models which actually had, you know, like temperature models that had movement of air masses and that kind of thing, real mechanisms of it. They've kind of gone in that direction, but it's still kind of correlational-based models based on data. That's what they feel comfortable doing and they're getting a lot from it. The nice thing about processes is you can look at novel things. Things that are outside of data. I think that's what my professor was trying to tell me years ago is there's the data you have and then there's the data you don't have. Models can sometimes predict the data you don't have, whereas an empirical 01:21:00relationship, it'll only predict the data that you have, unless the data that you don't have is just like the data you have. The odds of that in ecology are pretty low.

SK: Are there other people that have taken that route outside of the Andrews Forest community?

MH: Of, you mean modeling?

SK: With processing?

MH: Oh yeah, but there's very few in the field. There was a time in ecosystem science that a lot of people were modeling and working in models. I mean a lot of it started through modeling. Then what happened was there are big questions about processes that were in those models and so what's happened I think was scientists became more focused on studying the subprocesses and they lost view of the synthesis. This is how they got into these more narrow tracks-hey, I'll 01:22:00become this. I'll look at all the details of this process, but the mechanisms to bring it back to the general level have largely been lost. There are people who work on global change, for example, who tend to be modelers because that's just something you have to approach through modeling, but once you get below that level there aren't a lot of process modelers anymore. Actually, people work in ecosystem science, a lot of them aren't even ecosystem scientists. They're sub-process ecosystem scientists. That's all they are. They're doing great science, but to me it's like what does it add up to? I always have that question: what does that add up to?

SK: And so, we've talked about natural history, observational, 01:23:00hypothesis-driven, experimental, and modeling-are there other approaches to ecological research other than those that are sort of in the cracks or in between that we haven't talked about?

MH: Well, I guess in this sense. I think those are the basic things with strict, traditional ecosystem science would be. But if we truly want to understand what's going on in ecosystems, we have to figure out what humans are doing. For a long time, ecosystem science just worked in systems where humans didn't influence it. Although, let's face, it humans have influenced about everything. But there are places where that influence was very minimal. I started that way, too. Let's go into old-growth forests or natural areas. It's fine. It's interesting. But then, when I got into the carbon work, I had to work with, 01:24:00well, people are harvesting these forests and this and that before, so I can't analyze how a forest behaves without thinking about what people are doing. Even that was kind of like, well, I would determine what people would do based on what I decided people wanted to do. I still didn't predict what people would do to the ecosystem. You really have to move into the social sciences and humanities to figure out what people are thinking and why they want to do things. That might involve things that aren't on that list.

Surveys, you know, just in social science, surveys-we don't, I guess we survey foresters and inventory them, but we're not asking their opinion of things. I 01:25:00guess in that sense it's different. That's important to figure out. How do human systems actually behave and get controlled? If ecologists want to figure out not just have this thing that comes outside to their box, that just comes as a result, but they want to figure out actually how it comes or how it varies, they have got to go beyond, involve disciplines in which they're not ecosystem-oriented at all, which are much more geared towards what people are doing.

I suppose you have hypotheses and observations and experiments you could do with people, so maybe it's not something new. But it's a really different perspective on science. Then we have to realize that not everything is-there's a lot about 01:26:00how people feel about things that determines how they react to things. That could involve the humanities, particularly if these things are spiritual or artistic, whatever, this could be a motivator for what they want to have happen that's not even captured by social science.

I guess what I'm arguing for, if an ecological scientist wants mechanistic [understanding] and really wanted to know the mechanisms of what's controlling or what's happening in a forest ecosystem and people are involved, then they have to go into the science, or someone has to get into the science of people. Because, otherwise, you're just declaring what people do and that's not mechanistic. It simply isn't. I've tried to make this argument to my colleagues. 01:27:00I think they've been receptive that, you know, if you're going to really go down the mechanism route for ecosystems, why won't you go down the mechanism for humans? Otherwise you're just saying humans are some black box that they just do A, B, or C, but you have no understanding of why, so how is that mechanistic? It isn't. I'm not sure that's exactly what you're looking for, but it's certainly-to the degree that those disciplines might involve different levels of evidence or approaches, you have to accept those methodologies to learn about that part of the system.

SK: It makes me think, too, of what you said earlier, just even within science, 01:28:00there are these diverse approaches and, if you want to understand an object, you can't just look at it from one direction and I think that extends as well to the social sciences and humanities as well, right?

MH: That's right. That's right. The irony is I listened to colleagues-we're rigorous, mechanistic, process-based, humans just do this. How is that any of what you've just described? It isn't.

SK: Right. Thank you for that. I just have a couple of concluding questions left to wrap our interview up. What significant ideas have emerged from the Andrews science community and how did the science of these ideas evolve in terms of questions and methods?

MH: Well, I think we're known for a couple of things. One is the importance of older, old-growth systems and behaviors of landscapes and ecosystems. That 01:29:00really came from looking-people knew what old growth systems were-biological deserts, unproductive, had to get rid of them. Jerry Franklin and others said, "Well, I don't know about that. Let's see what they actually are." They wrote a very important General Technical Report that was in the gray literature (Franklin et al, 1981, US Forest Service Gen. Tech. Rpt. PNW-118. https://andrewsforest.oregonstate.edu/sites/default/files/lter/pubs/pdf/pub125.pdf), but it was basically lessons of old-growth systems, essentially. That's been super important germ for other people thinking about forests and their management, and it's also had very applied effects of changing perspectives, even in places like Germany where it was production forest-driven and now 01:30:00they've got major areas that they're setting aside to turn into old forests. Now, I don't think it directly came from Andrews, but a lot of those ideas at their heart came from the Andrews. They've had world-wide impacts on forest management, what people study and think about and debate in the science and in the application.

Another one I got involved in was dead trees, the role that dead trees play in ecosystems. That I think has had a huge impact. We wrote a major review article. It's been cited, I don't know, three thousand times or more. It just keeps going up. At least three thousand times, I think. It's created a whole field of ecological science. Some of it is I think silly, because dead trees have been around. But nonetheless, that really came from the Andrews. I remember a series 01:31:00of proposals that we wrote and the first was rejected because dead trees only occurred on the Andrews Experimental Forest. It was a very interesting study, but they only occur in the Andrews. That's because it had been published there. Then the next review where we revised, the next series of reviews, well, it's all very interesting science, but it doesn't apply anywhere except for the Pacific Northwest. It just went through this series of ever-expanding areas where it applied. As I pointed out, dead trees have existed for 340 million years on planet earth. They resulted in major coal deposits. They changed the form of rivers. You know I just don't get it. But I think that was another big area.

I think two more, very quickly, one was trying to, and this was because of Fred Swanson and Steve Eubanks and others sort of management of scientific collaboration-how can we create an ecologically based forestry? I think that has been huge, particularly on the applied front. Riparian management-that's another big one. Huge impacts globally. I think the last one (I said, two), but the third one I think Fred and others through their work between the humanities and science that has been a huge contribution. It hasn't been solely the Andrews, 01:33:00but Fred Swanson has been a leader in that, and he really pushed that through the network, and I think that has been huge.

You know I go back to this idea of discovery. Ecological scientists do not have a monopoly on discovery. Scientists do not have a monopoly on discovery about our world. Other disciplines can discover things about our world. They might be subjective, but that's fine. It's still a discovery. I think he's pushed that idea that we do not have to have these separate silos. They certainly should communicate and the more that we can agree that we're all trying to find out 01:34:00what's going on and we can learn from each other, the more we'll learn and the faster we'll learn.

SK: That's good. Then with the arts and humanities and the interface specifically, is that relatively unique to Andrews Forest compared to the rest of the LTER community?

MH: No. They didn't all get involved that I know of, but a number of them got involved in really interesting ways. There's an LTER that's North Temperate Lakes [U. Wisconsin-Madison] and they got involved and our primary involvement was to writing, creative writing. They did more visual arts and so they had a fantastic- Well, it was a visual arts project, but it basically showed the replacement of an iconic fish, the walleye, by an invasive species of fish. It 01:35:00was really quite well done, but also just summarized what have we done to these places. Anyways, each site has had a different take on it, but I'm not sure the degree to which NSF is still supporting that. You'd have to talk to Fred, but even this project here. The fact that you're putting this into an archive so historians can look at it. That's another area of trying to, well provide fodder actually for the humanities. I think it's been really fruitful, and I think Fred had a huge influence and still has a huge influence in pushing that forward-history, written word. Some of it's been really quite received and very 01:36:00profound. I tried to one, a woman's poem, I can't remember her name, but I just couldn't help but sob. I mean, just thinking about it chokes me up. It was actually based on something she observed or thought about when she was at one of the log decomposition sites. I think this was something made of-I can't remember. Anyways, it was about how we're going to pass and what the role that death plays, basically, in life. It was pretty amazing stuff.

SK: Is it in the-there's the book that they put together.

MH: Heartwood, I think, or something like that. [the book was Forest Under Story] It may be. Yeah, and then there was the, another guy wrote an essay, 01:37:00[Robert Michael] Pyle I think his name was, about being in it for the long-term [2004. The Long Haul. Orion]. Really influential stuff, very powerful. It's not all that way, but I got to say some of the science is not very powerful either. Some of it is. Some of it isn't.

SK: For my concluding question-what do you see are the principles and motivating forces that drive the Andrews as a community?

MH: Well, probably there's several. One is, you know, the fact that there is a community and you can have these kinds of discussions. That attracts some people. Now, as the PI, I would give certain people money in trying to encourage them to be part of it, and they were really uncomfortable being a part of a 01:38:00community that diverse. That did not interest them. They took the money. They did good work, but they weren't motivated. But I think there are people who this is really attractive to. That's part of it. It creates opportunities to recruit a sort of person who want, who feel like the want to contribute to something greater than their narrow field. Not everybody's like that and it's fine. Sometimes they do not want to contribute to the community, but they still contribute and that's fine, too. I think the other thing that keeps it going is the fact that it exists, that there's a tradition. You start to create traditions of behavior and thinking that take on a life of their own. I suppose 01:39:00someone could come along and really do it in, or a group of people could come in and do it in. We see that. I don't want to get political, but it's hard to not observe today's politics and realize that a certain way of treating each other and working together is just, it's like evaporated. Hopefully, we get it back so that everyone isn't necessarily an enemy. I don't want to sound like a political candidate, but I differ with you but there are things that we think we have to work on and let's figure out a way to make it happen. You know, it's taken a long time to destroy that and hopefully it's not been destroyed.

I'm not saying it could go on forever without maintenance and some love and 01:40:00care, but I think it's probably reached a point where someone would really have to go after it hard and vigorously to snuff it out. It's too embedded now in too many people's thinking and it's an expectation that people have of how the system will work. It's taken a long time, but it started like in the '70s, '60s and '70s, with this idea of let's sit down and tell each other stories and learn things and let's also try to put stuff together and it's grown from there. It just keeps expanding out in terms of what it's including.

SK: I'm sure there's challenges along the way, but that's a natural, maybe productive part of it.

MH: Well, yeah there's challenges. You know keeping a long-term, say measurements program together is really hard because you're always asking for 01:41:00the same thing and it's like well, don't you ask for anything new? When will your study be done? What's fascinating about long-term data is ... everybody wants it, no one wants to pay for it. I've seen this over and over again. But as long as we keep discovering things, I suppose, you know, there will be a motivation to fund it. That's a tough thing. It's been hard to convince NSF to fund the humanities, social sciences, you know, are siloed, too, because the social sciences doesn't view that this cutting-edge social science work done at the LTERs, they're not interested. But what you find, if you study science, is that you can take two relatively well-known things and put them together and 01:42:00create something new. NSF's view is each thing has to be new to discover something new. But that's not how it works. You can take two knowns and discover new, in my mind, but they've struggled as an institution to recognize that. Keeping the diversity, there are scientists that are like, well, why should we fund anything on the humanities? It's not rigorous. It's subjective. But that doesn't mean it's not important or influential. Like I said, if you can read a piece of paper and start weeping, my God, how is that not influential? Give me a break.

SK: Or relative?

MH: I mean you know I'm an old football player. I like to smash people's heads. I'm reading something and weeping? I mean, maybe my brain's softening. Maybe 01:43:00I've been banged too many times in the head, I guess.

SK: Possibly [chuckles].

MH: More than likely [smiles].

SK: Well, is there any final words that you want to contribute to the interview, to the conversations we've had?

MH: No, no this has been fun. I'm glad we went through the whole thing. I enjoy this sort of thing. It's good to think about things in general terms and get out of your ruts and all the little paths you're always going on. I appreciate the opportunity.

SK: Of course. It's fun. My pleasure. I appreciate you taking the time today.

MH: Okay.

SK: Well, enjoy the rest of your evening.

MH: You too. Bye, now.

SK: Bye.