Sustainability Videos & Lecture Series

[Mindy Pieper:] Now, to get started, I’d like to introduce Matt Chew, who is the assistant research professor in the School of Life Sciences, and he will introduce our guest speaker today.

[Matt:] That’s enough. Okay, I do have a cheat sheet. As a person from the program in biology, and society, I am interested in any sort of intersections between those two kinds of studies and Oliver is one of the people who does that sort of thing. Oliver Pergams is a professor in the Department of Natural Sciences at Olive-Harvey City College in Chicago. He also holds research appointments at the University of Illinois at Chicago and the Field Museum. He’s the founding director of the Red Rock Institute in Pennsylvania, which has as its mission examining all aspects of people’s changing relationship with nature through the use of both the life and the social sciences.

That’s a pretty big mission and it’s a dangerous one, because it—as it includes it also alienates, so it’s interesting to find the way people react to you when you try and cross disciplinary lines like this. He says, as a conservation and evolutionary biologist, he views conservation as a problem-focused discipline and has used diverse methods, as we would imagine, to solve various conservation problems. He received his Ph.D. in biological sciences from the University of Illinois at Chicago and is published in journals such as Nature, PNAS and the Public Library of Science. He has authored several book chapters and encyclopedia entries.

His awards include an EPA STAR fellowship, a David H. Smith conservation research fellowship, the U.S. Department of State Fulbright Senior of Lecturing Research Award, and he has testified as an expert witness before the U.S. Congress, and if that’s not fun, I don’t know what is. Please welcome Oliver Pergams to ASU.

[Oliver:] That was great. Thank you very much. It was fun in front of Congress, by the way. I’m going to piece together various aspects of my work, much of it with Patty Zaradic, the other founder of Red Rock Institute. In general, what happens as a conservation biologist—are there any other conservation biologists in the room besides—anybody? No? As a conservation biologist, your job is to try to save at-risk species and habitats.

As soon as you get one problem, you fix it if you’re lucky, and then there’s 50 more, 100 more, 1,000 more behind it, and you get pretty disillusioned. You get pretty—it seems to be a hopeless task. You realize that unless you start changing the way that people—at least understand the way people behave and possibly change the way people behave in relation to nature, you don’t have a chance and you know the way this thing is gonna end.

I had the luxury, starting 2002, 2004, to switch over some of my research to this intersection of people and nature. I did so and we’re continuing to this day. I was originally a—I have to add, though, all of biology is a second career for me. My first career was quite different and I hope it leant some perspective to what I do now. This is Greed, Videogames and Nature, and Section 1 is Greed. This first section is called: When the Stock Market Crashes, Forget About Money for Conservation.

This first career, I was a trader; a foreign exchange trader at the Bank of New York was my last job. I was a Japanese yen trader. Then, I was in charge of the options division of a fed dealer, and then I started my own company called Chicago Options. I sold my half of it to my business partner in ’93. Then, I went back. I stayed home a few years with the kids and then I went back and did what I always wanted to do in the first place, which was be a biologist. I think these two different careers I’ve had—these very different careers, right, almost in opposition to each other in many ways. I guess in many ways I’m probably paying penance for my first career.

I think what they have done is helped me think outside the box a little bit. Also, I’ve been relatively unafraid. Some people will tell me I should be much more afraid of working in areas where I’ve had relatively little formal training, and these areas have been at the intersection of people and nature. The methodology I’ve used has been very simple. Very simple analyses applied in what I think are common sense ways, but they’ve been in fields where often there hasn’t been much work done before.

The first work I did in this was combining my old career a little bit and my new career. I became convinced on a, I guess, an anecdotal basis, that what I heard from other conservation biologists that everybody was really interested in conservation. You can get lots of money for conservation. People would fund you no problem. I was convinced that was not the case. I was convinced that, in economic terms, conservation was a luxury good, a marginal utility, and that as soon as funds would dry up in the economy in general, as soon as excess capital left, conservation would be the first up against the wall and the first to leave. That was just my opinion. That was me talking to my friends.

The first thing I did is I tried to do an analysis. What I wanted to do was simply compare times series. I wanted to compare times series of economic variables and times series of conservation variables, and then if there significant correlations, to perform linear regressions. By the way, is there any problem if I ask people to feel free to ask questions during the talk?

[Female Voice:] No problem.

[Oliver:] Would you please do that if you feel like it? Thank you. Are there so far? No. There’s no trouble finding economic indicators. They’re all over the place. I used some wealth indicators and some income indicators, so the DOW and Standard and Poor’s, the GDP, personal income were the big four that were easy to use. They were all inflation adjusted and chained to—I think these were 2001 fed dollars. Here’s a chart of the four economic indicators I used, the DOW, GDP, personal income and S&P; S&P multiplied by ten to match the scale of the others. The period was, I think, 1928 to 2001.

Now, everything else was tough though. Remember, I’m comparing economic indicators to conservation indicators, so I had to come up with some conservation indicators. I had to figure out what to use. One thing that was pretty obvious was how much money people gave to conservation NGOs, conservation organizations, like, World Wildlife Fund, Sierra Club, Environmental Defense and the Nature Conservancy. These are total contributions, total revenues for these big NGOs over that period. These weren’t public knowledge. I had to use various means to get the information—all of them legal.

They were mostly talking to the chief financial officers of these NGOs and convincing them that they had a vested self-interest in having this analysis done, and so to give me the information. Some of it was easy, some of it was hard, but I ended up getting these four. Nature Conservancy on this is divided by 100, because, as some of you may know, Nature Conservancy is huge compared to everything else. Sorry, this one was between 22 and 45 years of data, depending upon which organization.

Another one I came up with, and I just did the best I could to come up with conservation variables, times series, right. This one was the number of university conservation biology programs that existed each year that were published or advertised to exist each year. There were some gaps in the data; 29 years of data total. I figured that what percentage of people visited U.S. national parks might be at least an environmental indicator, if not a conservation indicator, so an indicator of environmental consciousness. This is the number of people visiting national parks each year divided by the U.S. population.

The interesting thing about this chart, and perhaps you could keep this in mind for later, is that you see how this chart is basically up since 1939 and it’s down since about 1987? This has continued lower today. This is something that really—this has two big moves up and down. It’s something that bothered me. I kept it my mind later I wanted to examine. Reaching for yet another variable, membership in conservation or professional organizations, the Society for Conservation Biology and the Natural Areas Association. You may be familiar with Society for Conservation Biology, that’s for the scientists, the biologists. The Natural Areas Association is the one for the practitioners.

Total national park acreage, so that’s been increasing over the years. Congress allocates money to buy new pieces of land here and there, and it varies from year-to-year. There were some data gaps. There was also one huge anomaly, which was Alaska National Land Act, 55 million acres at one swoop, and that was taken out during the analysis, so 71 years of data there. The correlations were—everything was really highly correlated. You’re surprised at that, I’m sure, because all these things were in trends, right? If you’re comparing two things that are both trending, they’re gonna correlate.

The contributions to conservation NGOs correlated with the economic indicators 97 – 98 percent, conservation biology programs, 70 to 96 percent, et cetera. In all the cases, GDP and personal income were better correlated than the stock market indices. Now, it does make a difference that they correlated, but it also can be confounding. The fact that, for instance, both contributions to NGOs were constantly going up and the stock market in general was going up means it’s going to be correlated. That helps you for long-term planning, but it doesn’t do anything for you for short-term planning.

You need to see if these values were correlated on a short-term basis, and to do that I incorporated what economists call a difference model. Instead of using the actual values, you use the change from year-to-year, and then you convert it to a percentage. For instance, if you had the DOW 1,000 in 1980 and 1,100 in 1981, the number you would use there would be 10 percent; plus 10 percent. This gets rid of all the trend effects. Put simply, otherwise, if you don’t do that, the fact that the red points are down years is just completely overwhelmed by the overriding long-term uptrend.

Using this difference model, these percent year-to-year changes, there were still some correlations, still some meaningful ones. I started lagging years as well. I started lagging. In other words, I compared not only same-year numbers, but compared same-year, one year with one-year later, two years later, three years later, four years later, five years later under the—thinking of the possibility that it would take a while for the effects of, say, the stock market to go to be able to buy land or contributions might be lagged. In other words, there might be some delay between the GDP or the personal income and the effect on conservation. In fact, there was in one and not in another.

Total contributions and gross domestic product were highly correlated with a P-value of .005, under .005, and both gross domestic product and personal income were correlated with how much land was bought for national parks three years later. Whatever number it was for GDP, whatever that difference was—GDP went up 1 percent for 1970. Three years later, 1973, there was a correlation with how much land was purchased in national parks. I don’t think I did a very good job on that. Can you ask questions to help me out here? Really? Okay.

It makes sense that there’s an immediate impact of GDP on personal income or total conservation contributions. Personal income is largely representative of individuals. GDP is mostly representative of corporations. At least they have a disproportionate influence on the number. In both numbers, for tax reasons, the more money people have, the more they give away right away. It makes sense it’s the same year contribution. It also makes some sense that there’s a three-year lag between GDP and land bought for national parks, because that three-year period is, on average, about the time it takes Congress to allocate funds, to act and to buy the land, so it did make a little sense.

Using those percent year-to-year changes, we just performed simple linear progressions. We used the ones that were the most correlated, so it was total contributions to NGOs against gross domestic product same year, gross domestic product against national park acreage three years later, and the results were—you almost can’t get any simpler than that, can you, as a linear regression? Total contributions equals .793 times gross domestic product plus a little bit more for the same year, with the P-value you see here.

Except, you can get a little simper. Essentially, gross domestic product simply multiplied by one-third equals the change in the amount of land bought three years later. These are very, very simple models. Very simple relationships. What they show is that at least these conservation indicators—yes, please?

[Male Voice:] Just to clarify, why did you use gross domestic product?

[Oliver:] As opposed to?

[Male Voice:] For national parks as opposed to, say, the tax [inaudible] or government budget? You know, look at that as the government budget.

[Oliver:] I just started out using four widely used indicators and stuck with them. I didn’t refine it afterwards. I think that would be a good refinement, but I didn’t do it. Was there anybody else? Yes, please.

[Male Voice:] What effect or was there any negligible effecting terms of the raise in prices for going to the national park? Was that any problem at all?

[Oliver:] No. Actually, we’ll go into that in the next—shortly, yeah. That’s exactly the next topic. Thank you. What this, to me, showed was that there were, with these four broad indicators, they were indeed correlated with conservation. We could predict, at least in terms of contributions to NGOs and in terms of purchases of national park lands, we could predict fairly precisely what was going to happen based on GDP and personal income. The real issue on this was that the fact that they were so closely related did indeed mean that conservation monies, at least for these things, only went out when it was fiscally convenient to do so. The hypothesis that it was a luxury good seems to be corroborated. Yes, please?

[Female Voice:] You just did this up through 2000 yes and 2001?

[Oliver:] No, sorry. Some of the stuff—well, this I have not, period, but some of the stuff coming up goes up to 2006 or ’08. Acknowledgements for this part, a lot of these people are data acknowledgements, right. I had to get that data from different individuals and different NGOs.

[Female Voice:] [Fading voice] before you move on?

[Oliver:] Sure.

[Female Voice:] I was wondering about the donations to NGOs over this period of time. Is there any way to get a handle on the remainder of what the NGOs thought their effectiveness was at doing their work?

[Oliver:] Oh, kind of like a Charity Navigator number or something?

[Female Voice:] Well, I just thought that as, should we say, opposition got more organized, the problem was happening like you said. They maybe win one battle over a long period of time and then there were 50 others cropping, so to go at it one at a time—

[Oliver:] It’s not something I looked at. A lot of this work I think is crude. I mean, I really do think it’s crude. On the other hand, when you go onto something new that other people have looked at, it’s valid, I think to start crude. It’s valid to use correlation analyses and to see if there are basic relationships, and then either to go from there and refine it, whether it’s yourself or other people. No, I will be the first to agree that this work is just kind of a crude first swoop at the data.

[Female Voice:] Maybe in the interviews with Sierra Club and stuff [inaudible] it’s gotten tougher [inaudible].

[Oliver:] These weren’t interviews. These are receipts of either mounds of paper or spreadsheets that had numbers on them, which were contributions and revenues. This was not social science research, this was economics essentially. Then, the next part concerns Part 2, Videogames. Remember, Greed, Videogames and Nature, so now we’re at videogames. That chart that we were thinking about, the national park, so the per capita national park visits that—I’m in your way, sorry about that. The percentage of people that visit national parks, the population weighted number, how it went up from 1938 to 1987 and then started coming down.

That bothered me, right, and it bothered Patty, my partner at Red Rock. We just wanted to see if we could come up with some potential proximate cause and do another cruise swoop for the data. We basically went out and looked for times series, the same methodology as the last time. It was opportunistic. We found existing sets and we didn’t find existing sets. We didn’t make any. They were either out there or not. The criteria we used to look for data was that there was a plausible link to the drop in business. In other words, that they were at least a plausible proximate cause and that the data went back at least to 1988, because the thing turned around in 1987. The data had to at least exist for the entirety of the downturn, the drop.

Whatever variables we found we compared to that chart you saw, the national park visits chart. Much of the data was not normally distributed, so we used Spearman correlations. Many of our correlations were, again, using this difference model, this percent year-to-year change model, and then we did the same thing again. We took the variables and did linear regression, except this time, multiple linear regressions. Here are the variables we came up with. Again, these were opportunistic. What did we think might be affecting national park visits and we could find data for it, so one thing was funding. What were the Congressional line items to support national parks?

Now, this number, it didn’t end up being significant, but that one I think still needs work. These line items, that was the kinda data we were able to get. They’re very gross. You have no idea how much of the money actually makes it to the ground, makes it to the practitioners. The park service can be building a big office building somewhere and that’s in that number. Oil prices, the thinking there was that most people—remember National Lampoon Family Vacation with Chevy Chase? They mostly drive to the national parks and gas costs money. The more gas costs, the less you go was our hypothesis.

Vacation days, if vacation days are changing over time, if people have less time to go on vacation, maybe that’s the reason. Income, if they have less money to go, maybe that’s the reason. If they’re doing other things—and this one is just a bare scratching of what you could do. This Appalachian Trail number, we thought, well—you’re familiar with the Appalachian Trail. We’ve have congressmen talking about it a lot. Governor, right? Sorry. The number of people completing the Appalachian Trail was one indicator. We thought maybe people are doing more extreme forms of activity outside. Maybe they’re doing long hikes instead of driving through national parks.

Foreign travel, maybe they’re doing safaris in Africa or cruising the Amazon instead of visiting national parks. I’m just trying to give you an idea of what we were thinking of these potential causes. Then, because both Patty and I are parents—my kids are mostly grown. Did you see a picture on the board when you came in? There’s mostly grown, but Patty’s are still pretty little. Who here has—well, you got a whole age spread here. This is going to be an interesting question and I didn’t think about doing this. Who here is disturbed by the amount of time people spend in front of video screens?

[Male Voice:] Absolutely, yeah.

[Oliver:] You are self-selected by being here, aren’t you? Who is not disturbed? Peer pressure. I expected an age distribution, but because I think the self-selection overwhelmed it. Because we’re parents, especially Patty, concerned about our kids, how much time they spend on screens, we were able to get these data. These were a number of average hours per person spent watching TV, movies, home movies, in the theater, videogames and on the Internet. Yes, please, I’m sorry.

[Male Voice:] Can I ask where you get those numbers and who collects that data?

[Oliver:] Yeah, these were—we bought them. We bought them from MediaMark. That’s one of these big polling services. A lot of other stuff came from statistical abstracts, which in certain—have you ever used that, statistical abstracts? A lot of other stuff came too, but of course that came from other places originally, too.

[Male Voice:] I was more interested in the theater hours, and game hours and Internet hours.

[Oliver:] Yeah, that was from—

[Male Voice:] That’s very personal stuff. That’s very hard to accumulate that kinda data.

[Oliver:] It is. The reason they have it is because they actually gather it for the manufacturers, the different corporations that do it. They conduct these polls and they sell it to you as proprietary data. Patty and I got a grant to do this and they said they wanted $50,000 for this data. Patty and I said, “We’ll give you $5,000 and an acknowledgement,” and they said, “Okay.” It cost $5,000, yeah. To tell you the truth, Patty’s the one that talked to them. She’s better at those kinds of things. Then, of course, we compared everything to national park visits. Any other questions so far? Yes, please.

[Female Voice:] Did you look average personal income or household income [cross talk]?

[Oliver:] Median family income.

[Female Voice:] Okay. I couldn’t quite see the bottom corner. Was that what it was?

[Oliver:] Yeah, median family income. Well, I think I might have mentioned I think this is a massively incomplete list, but it’s what we found at the time. We did look, but after this came out, all kinds of other possibilities came out that people wanted to look at that were very valid. Guess what? Visits to national parks were negatively correlated with just about everything. Actually, almost everything. These are the raw numbers though, right. Not percent year-to-year change, but just the raw times series. All the video stuff was also negatively correlated with the Appalachian Trail hikers, income, oil prices, foreign travel. Now, which of these make sense to you, which doesn’t make sense?

These make sense, right, the time swap, the more time on screens, the less time visiting. What about Appalachian Trail hikers?

[Female Voice:] That’s a very special class.

[Oliver:] Yeah, I know. It was supposed to be a proxy for extreme recreation, but it’s not much of a proxy, just the best we could find. Income, now, isn’t it kinda interesting the more money you made, the less you went to national parks?

[Female Voice:] They went to foreign travel.

[Oliver:] There you go, yeah, that’s what they did, but you knew right away. Oil prices, the oil prices make sense. Oil prices lower, visits higher. Gas lower, visits higher. Foreign travel is as you’d expect. More visits to national parks, less safaris, right?

[Male Voice:] That’s because [inaudible].

[Oliver:] That’s right, yeah. It’s corroborated here. Income was significantly positively correlated with foreign travel, which is your hypothesis now, and negatively correlated with national park visit. No significant correlation with vacation days, so that was ruled out. The park capacity, did anybody think of that as a possibility? Have you ever gone to a national park, like, Yellowstone, and there’s this long line of cars and stuff? Some people, they just turn around, right. We did an analysis of—oh, it’s not here. We did an analysis of park capacity, whether that could be a contributing factor. It ended up it couldn’t be, so it wasn’t park capacity.

Federal funding actually increased the whole time, so since it increased the whole time, it couldn’t be a factor either. In other words, there were no down years in federal funding. Going back to the—we did a multi-linear regression and this is what we came up with. We came up with some pretty incredible P values. A multi-linear regression consisting of the video things—it’s not TV though. TV was excluded, but home movies, Internet, videogame and movie theaters, as well as oil prices—and look at those individual P values—explained as an adjusted multiple r squared of .925 and with a P value of—right?

You can see that obviously in this blow-up. You have that same old chart you’ve seen twice before already and then you blow up this part here, you blow it up here, the black line is national park visitation, the same as here. This red line is the multi-linear regression. That’s close, right? That red line is only video variables and oil prices. Does it correlate with what we know? Obviously, correlation is not causation, but what does it match to? The average person in the U.S. went from 0 hours on the Internet in ’87 to 174 hours in 2003. I don’t even know what it is now; I think it’s 400 or something.

0 hours a year playing videogames in 1987 to 90 hours a year in 2003, that one is more than quadrupled. I don’t know the exact numbers, since 2003 I mean, in the last ten years. Importantly, we also should note that both of these activities essentially came into existence about the time this drop began. I think that may have some relevance. All together, the average person in the U.S. spent 327 more hours on this stuff in 2003 than they did in 1987, so that’s a bunch of time.

What does it all mean? Well, it means that as people—if this is a predictive model, if this time swap is actually taking place as we suspect it is, we can predict further declines in outdoor nature recreation, national park visits specifically. What does it mean for childhood development? What does it mean for the future of conservation? How general is the decline in nature recreation? We wrote a review paper on child development and I’m not going to go over that one today, because it was a review. It wasn’t original work.

What was the reaction to this paper, this videophilia paper? A lot of press. Congressional hearings; they had the first set of Congressional hearings on national park attendance. We were asked to be keynote speakers at the first Children and Nature summit. I got to speak right after Dirk Kemthorne. Remember him? Bush’s Secretary of the Interior, yeah. I got to talk to him right after in between the two of us talking. He told me that what he thought we should do is put a whole lot of web cameras in the national parks. If we put—I’m not kidding you.

He told me that you should put millions of webcams—this is his words. He said, “Millions of webcams in national parks. That way, it would be great, because everybody could go to the national parks. There wouldn’t be any wear and tear on the roads. You wouldn’t have to hire so many rangers and everybody would be happy.”

[Female Voice:] Oh, my God.

[Female Voice:] Not everybody.

[Laughter]

[Oliver:] You ever have a situation where you don’t even know what to say, where you start and stuff?

[Male Voice:] He’s on drugs.

[Oliver:] After all this—was there a question back there? I’m sorry. No? I’ve got to move it along here. After the—people came up with other ideas what could be proximate causes. Admission fees; didn’t you say that, sir?

[Male Voice:] Yeah.

[Oliver:] Yeah, and decaying infrastructure, reduced interpretive staff, other natural areas taking away market share and increased outdoor adventure goods sales. The hypothesis there was that, because a lot of people wear Timberland boots, so it couldn’t be true, right? A lot of people own SUVs, so it couldn’t be true. We came up with this definition of—you all know Wilson’s and Kellert’s definition of biophilia, right? We came up with the term videophilia, which we defined as the relatively new human tendency to focus on sedentary activities involving video screens.

I think it’s gonna make it into the next web series, actually, which is sad. We think, essentially, that this time swap is what’s taking place. How pervasive is the decline? We listened to people giving all these other reasons, possible proximate causes for what was going on. Then we said, “Okay, let’s go back to the drawing board. Let’s go find a whole bunch of other variables, both in the U.S. and in other countries, and see if the same thing’s going on,” so that’s what we did.

We found a total of 16—this is a paper that was in PNAS in 2008. We found a total of 16 times series. One of them was the good old U.S. national parks, that’s here, but we had the Bureau of Land Management sites, national forests, state parks, national parks in Japan and Spain. We sent requests out to a whole bunch of countries that were in similar economic straits to the U.S., so that nature recreation could be viewed in a similar fashion, but we only got those two back. Dirk did it for it, so you would think they would respond, but they didn’t.

We used number of game licenses issued, hunting, duck stamps, fishing. Time spent camping, three different times series. Backpacking and hiking, another three series, as well as the old Appalachian Trail stuff. The way it looked—and this is a pie chart. It shouldn’t probably be a pie chart, because it’s not part of the same pie. What this is supposed to show is what the relative per capita participation was of all these different variables.

What did people do the most? What was the highest population wave of participation? National park visits in Japan was the highest. What was the next? That was state park visits in the U.S. After that, national park visits here, that old number that we used already. After that, camping, and so on and so on. The numbers that were really small on that risk, and this is why I put this thing together this way, was they were hiking numbers. The hiking numbers showed very small participation, but that’s what they were.

Here’s Japanese national parks, here’s U.S. national parks, these are state parks in the U.S., here’s Spain. They showed all of these times series were in downtrends. They show losses of 1 percent to 3 percent a year in participation ever year. The longest and most complete times series, the declines all began—they all declined. They all started declining between 1981 and 1991. They are also proceeding rates of around 1 or a little more percent a year, and total to date, but this date is 2008, remember, is 18 to 25 percent. Those were duck licenses, fishing licenses, national park visits, Japanese national park visits and Appalachian Trail.

Now, back to these difference models, we did the same thing again, percent year-to-year changes. We found all kinds of significant stuff. I won’t go through this, but the point of it is, is that all this stuff was also correlated internally as well, so that corroborated the analyses. There were significant correlations both within and between public lands use, game licenses and camping, which doesn’t surprise anybody, right? There was a minor counter trend though, which was hiking. Hiking actually is the only thing that went up during that his period, backpacking and hiking. The problem with that is it’s just a tiny piece of the pie, but that tiny piece of the pie got to be a slightly bigger piece of the pie, and that was the only thing out of all the nature recreation variables that increased.

Conclusion, all of the major lines of evidence pointed to an ongoing and fundamental shift away from nature-based recreation. What was the reaction? It was pretty much a media storm. Let’s see, we gave a bunch of interviews, I think maybe 200. I gave, like, 100-some and Patty gave some. This is one. I’ll just play a short piece here. This was on NBC. [Video playing in background.] They had it timed perfectly, so don’t really feel it’s worth turning the sound down, you know what I mean? Why didn’t that work? What did I do wrong? Oh, so what do I do now?

[Male Voice:] Yeah, you’re right there.

[Oliver:] Hit this one?

[Male Voice:] Hit play on there.

[Oliver:] Hit play again and just let it go?

[cross talk]

[Oliver:] 30 seconds, not 15, sorry. Thank you. Oh, I gotta hit play again, right?

[Male Voice:] Yeah.

[Oliver:] Sorry. Can I hit big, get bigger? Oh, here, okay. [Video playing.] She was chairman of the board of Nature Conservancy at the time. [Video playing.]

[Female Voice:] What year was this?

[Oliver:] 2008. You know, what else, you know, so a lot of talk shows and I got to testify before Congress again. This is me here, see? I’m the guy that looks like a Secret Service agent. There. I looked a lot more official without the beard. Why did people get so excited? I guess because—I’m pretty sure it’s because they saw what their kids were doing. It was so unlike what they did when they were kids. Anybody else feel that way?

[Male Voice:] Oh, yeah. You can’t communicate with them unless you’ve got your iPod or you’re texting. They don’t know how to talk anymore.

[Oliver:] Yes, sir.

[Male Voice:] I think a great example of your videophilia and sedentary practices, as well as being outside, is when my wife and I took our grandson to the playground to play. Several parents were there. All the parents were sitting there looking at their—

[Oliver:] Their phones.

[Male Voice:] Their phones, their smart phones or they had their laptops. They weren’t even watching their kids. We’re out there playing with our grandson and the other kids are looking around looking at the parents, hoping to get some feedback. They got nothing. This is not unusual, from what I’ve seen.

[Oliver:] It’s really not. Anybody else have stories like this? Yeah, so I think that’s what it is. People are just—they’re really uncomfortable with it that their kids are so different from what they did when they were kids. I think this provided validation to some extent. Now, I’m gonna say something I don’t—this is off the cuff. You probably are not gonna agree with this, but I think half of science, maybe three-quarters of science is putting numbers on what you already know.

[Female Voice:] Exactly.

[Oliver:] You don’t disagree?

[Female Voice:] I agree.

[Oliver:] Okay. I think this is one of those cases. It’s just we all could see this happening and the parents could see this happening, but they felt validated when there were numbers put on top of it. Peter wrote in an essay, Peter Kareiva, wrote an essay, the following. This was in PNAS. He was writing an essay about this article.

“Pergams and Zaradic show a trend in human behavior that ultimately may be far more foreboding for the environment than even declining tropical forest cover, increasing greenhouse gas emissions, widespread declines in nature-based recreation. If people never experience nature and have negligible understanding of the services that nature provides, it is unlikely people will choose a sustainable future. If Pergams and Zaradic are right, then the pervasive decline in nature recreation may well be the world’s greatest environmental threat.” What does he—did you—

[Male Voice:] Oh, no. I didn’t mean to interrupt.

[Oliver:] No, please.

[Male Voice:] I’m just gonna say environmental threat, but also a psychological threat, and I’m wondering if the American Psychological Association is looking at this as either a deficit in getting out and enjoying nature or an excess in terms of the electronic media controlling.

[Oliver:] For a while there, we were talking to Richard Louv a lot. He wrote that Last Child in the Woods book. He told me that they were—this I’m not sure I’m really that much on top of, but that they were gonna put nature deficit disorder in the next physician’s diagnoses manual.

[Male Voice:] Yeah, DSM-5.

[Oliver:] Right, right, right.

[Female Voice:] I actually [cross talk] actually read a study that says that the more you use Facebook [inaudible 49:34] likely to be depressed.

[Oliver:] It should be the opposite; you’ve got all these friends there.

[Laughter]

[Female Voice:] You can’t touch them.

[Male Voice:] They’re not always friends.

[Oliver:] Right, friends that you—new definition of friend or something. People you never met and never talked to in life. What Peter was talking about here—and by the way, Peter Kareiva is Chief Scientist at Nature Conservancy and he’s head of the—what is that U.N. Capital?

[Female Voice:] In London someplace?

[Oliver:] No. What’s that thing over at Stanford? He’s the Capital—

[Female Voice:] He’s been long-time associated with [inaudible].

[Oliver:] Okay. Do you remember what the name of that project is at Stanford that he does with—

[Female Voice:] No, I don’t.

[Oliver:] Okay, sorry. Anyway, what he’s talking about is as long as people care, you’ve got a chance. I mean, if you care about some conservation environmental issue and it has a chance of getting fixed, you can throw money at it. You can work at it. What happens if people stop caring completely? That’s why he thinks, and I think, it may be a huge problem. The last thing I have to talk about is much shorter. Again, a lot of additional saying thank you for data and funding. The last part is Nature, Part 3, Nature. What we’re gonna to talk about now is we’re going to talk about—I know I’m running a little late, but I think this will be another five minutes or so.

We decided to use the same blunt tool and methodology to see if we could figure out how experience in nature was affecting how people acted toward nature in the future. We just reversed the data, essentially. We switched the times series around. This was exactly the same data that you saw before, the contributions to conservation NGOs, how much money they got every year in revenues. Then, these are the same nature participation variables. Nothing’s different, but we just turned them around and we did a couple other things that are a little bit different.

In this thing, well, again, we did Spearman’s again, because they were nonparametric. We wrote a program in R that would move the spreadsheet, essentially, for all possible time lags. In other words, if you had national park visits that was 20 years and you had—I’m sorry. NGO contribution was a 20-year time series and you had national park visits that was a 50-year time series, it would do every possible time year lag that could exist. It would try all possible combinations using all possible lags in time. Did that make sense? You sure? Okay.

Then we put together this—Patty actually did this. She put together this random—you have to correct for multiple tests in some way, shape or form, right, because you’re doing a whole lot of tests here. She put together a randomization filter, a big randomization model that accomplished that. Okay, so here’s what we found. We found that there was a negative correlation between state park visitation, national park visitation, national park forest camping, Bureau of Land Management, national park visit data fishing—I’m sorry. You’ll remember all of these are being compared to contributions to nature conservancy, contributions to environmental defense, contributes to World Wildlife Fund.

There was a negative correlation between all these public land use variables and contributions. The more people visited national parks, the less money they gave to nature conservancy in different time lag periods. Four years later, seven years later, fifteen years later, seventeen years later. Does that make sense to anybody here? There was a positive correlation between the money that these conservation organizations got and only these three variables: the backpacking and hiking stuff. The only positive correlations with contributions were how much you hiked or backpacked 11 or 12 years earlier, and that’s it.

All this other stuff was somewhat negative with relatively low P values. All this stuff was positive with very high P values. What we took this part to mean—and this is just blatant hand-waving. If you go to national parks a lot, if you go fishing, if you go to national forests, why do you feel the need to give a lot of money to nature conservancy? They buy private lands. They either restore them or they just flip them over to government agencies. Very rarely, hardly ever are they lands you would get to use for hunting or fishing, or camping. They’re just out there. They’re not public use lands.

The hiking and backpacking data was the opposite though. What was going on there? To graphically represent that, and this is a little tough, this red one here is those NGO contributions, dollars contributions to those four agencies. The green is Appalachian Trail hiking. The blue is backpacking; one backpacking, the square blue one. The round blue one is the other backpacking. What we did though with these three numbers here is we moved them. We moved them back so that they—we changed them, so that, in fact, the contributions to NGOs, this number to this number was 11 years later.

This number to this number was 11 years later, so we just slid all those backpacking and hiking numbers back in time, so we could see how they matched up. In fact, what was predicted was if you backpacked or hiked more now, you would give more money to these conservational organizations 11 or 12 years later. Does anybody have any reason why that—a possible explanation for that? Please.

[Male Voice:] Well, you tend to do backpacking and hiking when you’re younger and more energetic, but you generally haven’t yet much money.

[Oliver:] Yep.

[Female Voice:] Yes.

[Male Voice:] You’re using that money for your hiking and backpacking. As you get older, you get less able to do, but you have more money. You have more money available, usually you start to give more money as you earn more money, as you get older, so you start giving more.

[Oliver:] I wish you would have written our conclusions. I mean, I couldn’t—I mean, that’s what we thought, too. The problem is, though, remember that backpacking/hiking was really a small piece of the pie. It’s .054. That equals the average American goes backpacking once every eighteen and a half years. What numbers this translated to, the average American who went hiking or backpacking 11 or 12 years earlier contributes $200 or $300 annually to conservation NGOs. This was a big deal for nature conservancies, this data, because now they had a predictive model for how much money they were gonna be getting in revenues.

It seemed to be based on, essentially, old white guys were—yeah, I mean, because who is a backpacker? Who is the hikers? Exactly as you said, sir, most backpackers are younger, right. Add in the 11 to 12 years, that would put them into middle age, probably into their income prime. The demographics of backpackers and hikers are overwhelmingly European-American, college-educated, higher income and over 35. What does this mean for the base of—if this is all not just insanity, if it means something, what does it mean for conservation contributions to these organizations?

It means it’s based on old white guys who backpacked 11 or 12 years earlier and, as the demographics in the U.S. change, they better do something about that. Given the enormous shift in demographics that’s ongoing, organization, economic and cultural changes, they absolutely have to diversify their outreach if this is true. The general conclusion for my talk—I’m finally done, thank you for your patience—is, ultimately, the fate of biodiversity and intact ecosystems may depend less on rates of habitat loss, rates of invasive species, and on public perception whether conservation should be supported at all.

Solution? Go out. These are—there’s an internship program that Patty and I did a long-term evaluation for, Leaders in Environmental Action for the Future that’s run by Nature Conservancy in New York in the high schools. This is from our long-term evaluation: “Alumni of this program volunteer at twice the national average, 52 percent versus 22, and for environmental organizations, more than 10 times the national average.” Hike more, camp more, I mean, it’s nice to see some successes, right? I have some concerns though whether these little efforts, whether they reach enough children and enough of disadvantaged children though.

I think this is absolutely the last slide, future research. I’m not gonna talk about this. Maybe I’ll just leave it. Future research, this is from—this thing is supposed to come out this month. This is, like, Encyclopedia of Biodiversity. We put down the kind of directions and part of this article, this chapter that we want to go in the future, this is kinda how we view the world in terms of our research. Conservation, nature, the economy, videophilia, nature exposure and the interactions between them, the data lines here are simply places where we wanna fill in what we perceive as the gaps. It’s just a conceptual map of our world view. Thank you.

[Announcer:] This presentation is brought to you by Arizona State University’s Global Institute of Sustainability for educational and noncommercial use only.

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