1 00:00:03,080 --> 00:00:07,080 Speaker 1: Welcome to Stuff to Blow Your Mind production of iHeartRadio. 2 00:00:13,560 --> 00:00:16,320 Speaker 2: Hey, welcome to Stuff to Blow Your Mind. My name 3 00:00:16,360 --> 00:00:20,080 Speaker 2: is Robert Lamb. Today I have an interview episode for you. 4 00:00:20,480 --> 00:00:23,840 Speaker 2: I recently spoke with Lee Alan Dugatkin, a professor of 5 00:00:23,880 --> 00:00:27,720 Speaker 2: evolution and behavior at the University of Louisville in Kentucky. 6 00:00:28,360 --> 00:00:31,240 Speaker 2: He is the author of the new book The Well 7 00:00:31,280 --> 00:00:37,159 Speaker 2: Connected Animal Social Networks and the Wondrous Complexity of Animal Societies. 8 00:00:37,600 --> 00:00:41,400 Speaker 2: This book comes out this Thursday, so wherever you get 9 00:00:41,400 --> 00:00:44,280 Speaker 2: your books, in whatever formats, you can probably go ahead 10 00:00:44,320 --> 00:00:47,200 Speaker 2: and pre order it. If not, grab it on Thursday. 11 00:00:47,560 --> 00:00:51,000 Speaker 2: It's a delightful read. And this was a delightful chat. 12 00:00:51,320 --> 00:00:54,400 Speaker 2: I really enjoyed talking with Lee. Let's go ahead and 13 00:00:54,520 --> 00:00:56,080 Speaker 2: jump right into the interview. 14 00:00:59,240 --> 00:00:59,320 Speaker 3: Hi. 15 00:00:59,480 --> 00:01:00,440 Speaker 2: Lee, welcome to the show. 16 00:01:00,760 --> 00:01:02,920 Speaker 3: Thank you for having me. I've been looking forward to it. 17 00:01:03,160 --> 00:01:07,000 Speaker 2: So you're an evolutionary biologist, an historian of science, and 18 00:01:07,040 --> 00:01:11,399 Speaker 2: an animal behaviorist. How do these classifications triangulate on your 19 00:01:11,440 --> 00:01:13,319 Speaker 2: work and how did you get involved in your field? 20 00:01:13,720 --> 00:01:18,600 Speaker 3: Yeah, so I got involved in a sort of circuitous way. 21 00:01:19,440 --> 00:01:22,880 Speaker 3: I really didn't know what I wanted to do, even 22 00:01:22,920 --> 00:01:26,640 Speaker 3: when I was in college. I happened upon the study 23 00:01:26,720 --> 00:01:30,360 Speaker 3: of animal behavior and evolution, and a friend of mine 24 00:01:30,760 --> 00:01:33,679 Speaker 3: mentioned a book he was reading, and before I knew it, 25 00:01:33,760 --> 00:01:37,920 Speaker 3: I was in graduate school studying evolution and behavior and 26 00:01:38,000 --> 00:01:40,440 Speaker 3: non humans because I just fell in love with the 27 00:01:40,480 --> 00:01:42,640 Speaker 3: topic and I thought it was sort of the ultimate 28 00:01:43,280 --> 00:01:47,000 Speaker 3: kinds of questions one could ask about life on the planet. 29 00:01:47,400 --> 00:01:50,240 Speaker 3: And so early on a lot of my work was 30 00:01:50,800 --> 00:01:53,800 Speaker 3: experimental work with animals. I looked at the evolution of 31 00:01:53,840 --> 00:01:58,600 Speaker 3: cooperation and the evolution of aggression and so on for 32 00:01:58,680 --> 00:02:03,760 Speaker 3: many many years in my laboratory. And then about fifteen 33 00:02:03,840 --> 00:02:06,320 Speaker 3: years ago or so, after I had been doing this 34 00:02:06,440 --> 00:02:09,919 Speaker 3: for a couple of decades, I started getting much more 35 00:02:09,919 --> 00:02:13,520 Speaker 3: interested in the history of the subjects that I was 36 00:02:13,960 --> 00:02:16,880 Speaker 3: working on. So it turns out that all of the 37 00:02:16,919 --> 00:02:20,200 Speaker 3: people early on who were studying the evolution of cooperation 38 00:02:20,440 --> 00:02:24,639 Speaker 3: were really fascinating characters in and of themselves, how they 39 00:02:24,680 --> 00:02:28,320 Speaker 3: came about it, the environment, and the kind of social 40 00:02:28,360 --> 00:02:32,560 Speaker 3: atmosphere in which they developed their ideas. And I began 41 00:02:32,840 --> 00:02:36,440 Speaker 3: to become much more interested in that aspect of the work. 42 00:02:36,639 --> 00:02:37,560 Speaker 2: And so. 43 00:02:39,200 --> 00:02:43,000 Speaker 3: All these areas dovetail in the sense that I still 44 00:02:43,080 --> 00:02:47,120 Speaker 3: do work on studying evolution and animal behavior. But I 45 00:02:47,200 --> 00:02:51,000 Speaker 3: also have had this component of it's worth stopping for 46 00:02:51,040 --> 00:02:53,880 Speaker 3: a minute and realizing that, you know, in the late 47 00:02:53,919 --> 00:02:58,040 Speaker 3: eighteen hundreds or something like that, here's how these ideas 48 00:02:58,080 --> 00:03:00,200 Speaker 3: came about. Here are the people who did it, and 49 00:03:00,240 --> 00:03:03,080 Speaker 3: here's the kind of social environment in which it happened. 50 00:03:03,080 --> 00:03:06,519 Speaker 3: And I think it makes the study of science itself 51 00:03:06,639 --> 00:03:08,239 Speaker 3: much richer to do it that way. 52 00:03:08,840 --> 00:03:11,960 Speaker 2: The new book is The Well Connected Animals, Social Networks 53 00:03:12,040 --> 00:03:15,560 Speaker 2: and the Wondrous Complexity of Animal Societies. Could you walk 54 00:03:15,600 --> 00:03:18,840 Speaker 2: us through what a social network is and say sociology 55 00:03:18,840 --> 00:03:22,760 Speaker 2: and anthropology and how and when the concept enters into 56 00:03:22,840 --> 00:03:25,079 Speaker 2: contemplation of non human animals. 57 00:03:25,840 --> 00:03:29,560 Speaker 3: Sure, And you know, I should say that there are 58 00:03:29,600 --> 00:03:35,280 Speaker 3: many many different definitions of social networks, depending on exactly 59 00:03:35,320 --> 00:03:39,280 Speaker 3: what discipline you're interested in and how mathematical you want 60 00:03:39,320 --> 00:03:42,360 Speaker 3: to get for me, and I think from I think 61 00:03:42,360 --> 00:03:46,240 Speaker 3: this is a reasonable general definition. Is a social network 62 00:03:46,320 --> 00:03:51,320 Speaker 3: is just a group of individuals through which information travels, 63 00:03:52,120 --> 00:03:55,640 Speaker 3: And it may travel directly and it may travel indirectly. 64 00:03:56,200 --> 00:03:59,760 Speaker 3: And it's also a group of individuals who somehow are 65 00:04:00,040 --> 00:04:03,840 Speaker 3: other affect one another in a real way. And again 66 00:04:04,600 --> 00:04:10,000 Speaker 3: that could happen either directly by your interaction with Steve, 67 00:04:10,320 --> 00:04:13,480 Speaker 3: or it could happen indirectly because you interact with Steve, 68 00:04:13,520 --> 00:04:16,920 Speaker 3: and Steve interacts with Nancy, and so Nancy is affected 69 00:04:16,960 --> 00:04:21,440 Speaker 3: by what you have done to Steve. And so generally 70 00:04:21,480 --> 00:04:28,000 Speaker 3: it's this information flow and effect on others in your network. 71 00:04:28,080 --> 00:04:33,360 Speaker 3: That's that for me, is the key to social networks. 72 00:04:34,200 --> 00:04:36,720 Speaker 3: You know, humans are in all sorts of social networks, 73 00:04:36,760 --> 00:04:40,080 Speaker 3: Facebook and that sort of thing, but we're also embedded 74 00:04:40,080 --> 00:04:43,440 Speaker 3: in them in almost everything we do in life. Are 75 00:04:43,600 --> 00:04:46,960 Speaker 3: our families or one kind of social network? Are the 76 00:04:47,040 --> 00:04:49,800 Speaker 3: people we work with or another, our friends or another, 77 00:04:50,839 --> 00:04:55,400 Speaker 3: and so on, and they all overlap. Now, in terms 78 00:04:55,440 --> 00:05:01,000 Speaker 3: of the history of the subject, I mean, anthropologists and 79 00:05:01,080 --> 00:05:07,080 Speaker 3: particularly sociologists have been interested in this in humans for 80 00:05:07,160 --> 00:05:11,280 Speaker 3: quite some time. It was, you know, in the nineteen 81 00:05:11,320 --> 00:05:15,479 Speaker 3: forties and nineteen fifties where people first began to get 82 00:05:15,520 --> 00:05:21,520 Speaker 3: serious about trying to understand the details of what we 83 00:05:21,640 --> 00:05:25,960 Speaker 3: now would refer to as social networks how they work 84 00:05:27,040 --> 00:05:33,000 Speaker 3: in humans. And that involved a lot of mathematics in 85 00:05:33,080 --> 00:05:37,640 Speaker 3: terms of understanding what it means if I interact with you, 86 00:05:37,720 --> 00:05:40,479 Speaker 3: and you interact with somebody else and that somebody else 87 00:05:40,600 --> 00:05:44,039 Speaker 3: is affected by me, and who is Who are the 88 00:05:44,120 --> 00:05:47,039 Speaker 3: key individuals in the social network, the ones that have 89 00:05:47,160 --> 00:05:52,040 Speaker 3: the most impact, and are there cliques where certain individuals 90 00:05:52,480 --> 00:05:56,680 Speaker 3: tend to interact with each other more? And that really 91 00:05:56,720 --> 00:05:59,800 Speaker 3: began to flourish, I would say, in the nineteen forties 92 00:05:59,800 --> 00:06:05,599 Speaker 3: and fifties in anthropology and sociology what we now refer 93 00:06:05,720 --> 00:06:10,680 Speaker 3: to as a social network. I think that language became 94 00:06:10,800 --> 00:06:16,880 Speaker 3: much more common in the late nineteen hundreds and into 95 00:06:16,920 --> 00:06:21,680 Speaker 3: the early two thousands. Now when it comes to studying 96 00:06:21,720 --> 00:06:27,120 Speaker 3: social networks and non humans, we can trace it back 97 00:06:27,279 --> 00:06:32,359 Speaker 3: quite far in the sense that even in Darwindnesday and 98 00:06:32,440 --> 00:06:36,679 Speaker 3: a little bit later, so the late eighteen hundreds, people 99 00:06:36,800 --> 00:06:40,800 Speaker 3: were beginning to think that the same kind of social 100 00:06:40,920 --> 00:06:44,560 Speaker 3: tools that can be used to study human behavior can 101 00:06:44,600 --> 00:06:47,920 Speaker 3: be used to study animal behavior. And so there are 102 00:06:47,960 --> 00:06:53,240 Speaker 3: people who were basically arguing that animal societies were structured 103 00:06:53,400 --> 00:06:57,120 Speaker 3: very much like human societies. And even though the word 104 00:06:57,279 --> 00:07:01,080 Speaker 3: social network wasn't around, the idea idea that we could 105 00:07:01,160 --> 00:07:05,000 Speaker 3: study non humans like we study humans was there. It 106 00:07:05,080 --> 00:07:09,360 Speaker 3: really began to flourish more again around the same time 107 00:07:09,440 --> 00:07:13,480 Speaker 3: as early work in humans did. That is, in this 108 00:07:13,560 --> 00:07:17,960 Speaker 3: case maybe the nineteen fifties and nineteen sixties when primatologists 109 00:07:18,000 --> 00:07:24,080 Speaker 3: people who study monkeys and apes, began to think in 110 00:07:24,200 --> 00:07:29,440 Speaker 3: terms of social networks. They began to realize that information 111 00:07:29,680 --> 00:07:35,600 Speaker 3: was flowing through these non human groups, again mostly primates, 112 00:07:36,960 --> 00:07:38,640 Speaker 3: in a way that was similar to the way it 113 00:07:38,680 --> 00:07:42,240 Speaker 3: flows through human groups. And it really mattered in a 114 00:07:42,280 --> 00:07:46,480 Speaker 3: group of chimpanzees, if one chimpanzee groomed the other and 115 00:07:46,520 --> 00:07:50,239 Speaker 3: got bugs and parasites off it back, it really mattered 116 00:07:50,320 --> 00:07:54,840 Speaker 3: who they groomed and who that individual groomed, and how 117 00:07:54,880 --> 00:07:59,640 Speaker 3: that kind of grooming social network developed. So it first 118 00:07:59,680 --> 00:08:03,960 Speaker 3: started with non human primates, which kind of makes sense 119 00:08:04,080 --> 00:08:07,840 Speaker 3: because there are closest evolutionary relatives, and then it began 120 00:08:07,960 --> 00:08:12,560 Speaker 3: to branch out in the nineteen eighties and nineteen nineties 121 00:08:12,560 --> 00:08:15,760 Speaker 3: when people began to study it in all sorts of organisms. 122 00:08:15,800 --> 00:08:19,520 Speaker 3: In fact, the two classic studies early on were done 123 00:08:19,520 --> 00:08:26,200 Speaker 3: in dolphins and in birds. So it's been around in 124 00:08:26,240 --> 00:08:29,920 Speaker 3: a serious way in the animal behavior literature for on 125 00:08:30,000 --> 00:08:32,000 Speaker 3: the order of twenty to thirty years. Now. 126 00:08:32,400 --> 00:08:35,280 Speaker 2: Okay, so the roots go back a bit deeper, but 127 00:08:35,320 --> 00:08:38,160 Speaker 2: it's really taken off in recent decades. 128 00:08:38,559 --> 00:08:41,640 Speaker 3: Absolutely, absolutely in fact, right now, the study of social 129 00:08:41,679 --> 00:08:45,160 Speaker 3: networks in non humans is probably is one of the 130 00:08:45,160 --> 00:08:48,680 Speaker 3: most active areas in the entire field. I mean, anytime 131 00:08:48,679 --> 00:08:51,200 Speaker 3: you open up a journal on behavior and non humans, 132 00:08:51,280 --> 00:08:55,560 Speaker 3: you're likely to find an article about social networks. And 133 00:08:55,640 --> 00:08:59,679 Speaker 3: those networks are important in almost every context for non humans. 134 00:09:00,160 --> 00:09:03,840 Speaker 3: There are networks about feeding, there are mating networks, there 135 00:09:03,840 --> 00:09:08,319 Speaker 3: are traveling networks, there are cooperation networks, there are power networks. 136 00:09:08,400 --> 00:09:12,040 Speaker 3: Everywhere you look. Now that we've started to actually explore 137 00:09:12,040 --> 00:09:14,319 Speaker 3: it in depth, we're finding it. And this is sort 138 00:09:14,360 --> 00:09:17,560 Speaker 3: of what happens in animal behavior a lot. We tend 139 00:09:17,600 --> 00:09:22,480 Speaker 3: to think of things initially as strictly human, and then 140 00:09:22,520 --> 00:09:26,400 Speaker 3: when we begin to probe deeper, we see similar sorts 141 00:09:26,400 --> 00:09:29,200 Speaker 3: of things in non humans, and once we begin to look, 142 00:09:29,240 --> 00:09:30,640 Speaker 3: we begin to see it everywhere. 143 00:09:30,880 --> 00:09:34,200 Speaker 2: And to be clear, with humans, we're talking about multiple 144 00:09:34,240 --> 00:09:37,480 Speaker 2: social networks being in place for a given individual or 145 00:09:37,559 --> 00:09:40,000 Speaker 2: a given group. But the same is true of these 146 00:09:40,080 --> 00:09:42,680 Speaker 2: various animal examples as well. Right, We're not talking about 147 00:09:42,720 --> 00:09:45,720 Speaker 2: just like the social network of the macaque. We're talking 148 00:09:45,760 --> 00:09:50,160 Speaker 2: about like the multiple interconnected social networks. 149 00:09:49,800 --> 00:09:56,840 Speaker 3: Right, absolutely, So it might be that you're studying networks 150 00:09:57,520 --> 00:10:00,240 Speaker 3: in the sense of who's going around feeding with who, 151 00:10:00,240 --> 00:10:03,119 Speaker 3: and who's sharing food with who. But in that same species, 152 00:10:03,160 --> 00:10:08,000 Speaker 3: you might be looking at who's being aggressive towards who, 153 00:10:08,040 --> 00:10:11,200 Speaker 3: and who's traveling together, and who's being nice to who, 154 00:10:11,240 --> 00:10:14,000 Speaker 3: and all of those are overlapping networks in the same 155 00:10:14,080 --> 00:10:15,400 Speaker 3: sense as in humans. 156 00:10:16,120 --> 00:10:19,000 Speaker 2: Now, is there still any kind of like pushback from 157 00:10:19,280 --> 00:10:23,679 Speaker 2: I guess human exceptionalists that argue that animals can't non 158 00:10:23,760 --> 00:10:27,560 Speaker 2: human animals can't or don't demonstrate this kind of complexity. 159 00:10:28,040 --> 00:10:32,880 Speaker 3: Well, not within I would say the scientific community. There 160 00:10:33,240 --> 00:10:36,640 Speaker 3: really isn't much of that pushback anymore. Initially there was 161 00:10:36,720 --> 00:10:40,240 Speaker 3: when people first began studying social networks and non humans. 162 00:10:41,320 --> 00:10:47,400 Speaker 3: Many people in who were animal behaviorists were leery that 163 00:10:47,800 --> 00:10:51,360 Speaker 3: the notion was that animals are complex and their societies 164 00:10:51,400 --> 00:10:56,600 Speaker 3: are complex. They're but they're not social network complex. That was, 165 00:10:56,800 --> 00:11:00,360 Speaker 3: you know, even in the early two thousands, They're There 166 00:11:00,480 --> 00:11:04,200 Speaker 3: was still some of that. I interviewed a bunch of 167 00:11:04,240 --> 00:11:08,160 Speaker 3: people for this book, and I had stories of people 168 00:11:08,280 --> 00:11:11,760 Speaker 3: telling me that in the early two thousands, when they 169 00:11:11,800 --> 00:11:15,360 Speaker 3: would put in grant applications to study social networks and 170 00:11:15,440 --> 00:11:19,440 Speaker 3: non humans, they would get this kind of pushback that was, 171 00:11:19,520 --> 00:11:22,880 Speaker 3: you know, it can't be this complex. You're you're, you're, 172 00:11:22,920 --> 00:11:25,040 Speaker 3: you're making it more complex than it needs to be, 173 00:11:25,080 --> 00:11:27,720 Speaker 3: and we're not ready to kind of give you money 174 00:11:27,720 --> 00:11:30,080 Speaker 3: to do this. But the more that people were able 175 00:11:30,120 --> 00:11:33,320 Speaker 3: to do it on their own, the more that it 176 00:11:33,400 --> 00:11:36,560 Speaker 3: became part and parcel of what people were looking for, 177 00:11:36,600 --> 00:11:39,800 Speaker 3: the less less the pushback. I mean, you're going to 178 00:11:39,880 --> 00:11:45,000 Speaker 3: get pushback from outside the scientific community, from people who 179 00:11:45,040 --> 00:11:48,280 Speaker 3: who just want to live in a world of human 180 00:11:48,360 --> 00:11:53,800 Speaker 3: exceptionalism that tends to fall apart eventually. I mean, you know, 181 00:11:53,880 --> 00:11:56,320 Speaker 3: we used to think that was true about tool use 182 00:11:56,400 --> 00:11:59,760 Speaker 3: in in in in animals, that you know, only humans 183 00:11:59,760 --> 00:12:02,480 Speaker 3: could use tools. Then we find that animals can use 184 00:12:02,559 --> 00:12:05,640 Speaker 3: tools only humans have culture. Well no, it turns out 185 00:12:05,679 --> 00:12:09,680 Speaker 3: animals have culture, only humans have social networks. No, in fact, 186 00:12:09,960 --> 00:12:13,959 Speaker 3: non humans have social networks. So the pushback is nowadays 187 00:12:14,080 --> 00:12:15,400 Speaker 3: not as bad as it used to be. 188 00:12:15,960 --> 00:12:18,600 Speaker 2: I like that you mentioned the tool use. You know, 189 00:12:18,640 --> 00:12:20,400 Speaker 2: this is one of those areas where when we think 190 00:12:20,400 --> 00:12:22,160 Speaker 2: of toolse do some in animals that a lot of 191 00:12:22,200 --> 00:12:25,480 Speaker 2: people instantly think of. Examples involving say like chimpanzees or 192 00:12:26,559 --> 00:12:32,200 Speaker 2: or certain you know, birds come to mind. Especially when 193 00:12:32,240 --> 00:12:34,319 Speaker 2: we think of culturally transmitted tool use, we may think 194 00:12:34,320 --> 00:12:38,439 Speaker 2: of the chimpanzee example, but you discuss examples involving dolphins 195 00:12:38,480 --> 00:12:42,160 Speaker 2: and whales, can you describe the tools they seem to 196 00:12:42,200 --> 00:12:42,600 Speaker 2: be using. 197 00:12:43,640 --> 00:12:48,520 Speaker 3: Sure. So there's this wonderful study on bottomnosed dolphins. It's 198 00:12:48,559 --> 00:12:52,640 Speaker 3: been going on in Australia in a place called Shark 199 00:12:52,760 --> 00:12:55,640 Speaker 3: Bay for the last thirty or forty years, and so 200 00:12:56,400 --> 00:13:01,000 Speaker 3: they know literally thousands of dolphins that are swimming around 201 00:13:01,000 --> 00:13:05,840 Speaker 3: in Shark Bay individually, and we've learned a tremendous amount 202 00:13:05,960 --> 00:13:09,160 Speaker 3: about how sort of culture, how complex they are socially. 203 00:13:09,240 --> 00:13:14,680 Speaker 3: But it wasn't until the last couple of decades that 204 00:13:14,840 --> 00:13:19,720 Speaker 3: this idea that tool use in dolphins was going on. 205 00:13:20,000 --> 00:13:26,000 Speaker 3: And in dolphins the tool is actually another living organism. 206 00:13:26,240 --> 00:13:31,120 Speaker 3: So basically what they do is they put a sponge 207 00:13:31,880 --> 00:13:37,280 Speaker 3: on the end of their their rostrum, their their their 208 00:13:37,320 --> 00:13:40,440 Speaker 3: face where where where their mouth is. They they put 209 00:13:40,480 --> 00:13:43,880 Speaker 3: this sponge over that when they're looking for food. And 210 00:13:43,920 --> 00:13:48,240 Speaker 3: the reason that they do that is that oftentimes they're 211 00:13:48,280 --> 00:13:51,520 Speaker 3: probing the bottom of the bay that's very rocky and 212 00:13:52,160 --> 00:13:54,839 Speaker 3: gravelly and it and it hurts to pound down on there. 213 00:13:54,880 --> 00:13:56,679 Speaker 3: And what they're doing is they're trying to find fish 214 00:13:56,679 --> 00:13:59,480 Speaker 3: that are hiding under the gravel and sand. So if 215 00:13:59,480 --> 00:14:04,440 Speaker 3: you stick soft sponge on top of your mouth, then 216 00:14:04,520 --> 00:14:10,000 Speaker 3: probing down there allows you much more flexibility and a 217 00:14:10,040 --> 00:14:15,080 Speaker 3: lot less pain. And it turns out that dolphins are 218 00:14:15,160 --> 00:14:18,480 Speaker 3: very particular about the sponges that they use. Once they 219 00:14:18,520 --> 00:14:22,280 Speaker 3: find one that works, they keep it for a long time. 220 00:14:22,360 --> 00:14:24,800 Speaker 3: So they'll go around probing on the bottom for food 221 00:14:24,880 --> 00:14:27,200 Speaker 3: and if they find a fish that's hidden, they drop 222 00:14:27,240 --> 00:14:29,840 Speaker 3: the sponge and they go and they catch that fish. 223 00:14:30,360 --> 00:14:34,960 Speaker 3: Then they come back and they look for the sponge 224 00:14:35,000 --> 00:14:37,360 Speaker 3: they had before it because if it worked once, it's 225 00:14:37,480 --> 00:14:40,360 Speaker 3: likely to work again. They pick it up, they put 226 00:14:40,440 --> 00:14:44,000 Speaker 3: it back well, they put it back onto their face, 227 00:14:44,680 --> 00:14:50,640 Speaker 3: and then they go looking for more things at sort 228 00:14:50,680 --> 00:14:52,680 Speaker 3: of hiding in the bottom of the sand. And they 229 00:14:52,800 --> 00:14:58,040 Speaker 3: learn this. They learn how to sponge from their mothers. 230 00:14:58,560 --> 00:15:02,520 Speaker 3: So if you watch calves, dolphin calves, they're basically learning 231 00:15:02,560 --> 00:15:05,480 Speaker 3: the technique of how to find a good sponge, put 232 00:15:05,480 --> 00:15:09,480 Speaker 3: it on, and hunt with it. And so the actual 233 00:15:10,280 --> 00:15:18,040 Speaker 3: learning is through mother child interaction. It's not genetic, but 234 00:15:18,120 --> 00:15:22,200 Speaker 3: it's rather they learn from their parents. Now in terms 235 00:15:22,240 --> 00:15:28,600 Speaker 3: of the networking. It's actually complex. So basically, when they 236 00:15:28,600 --> 00:15:33,560 Speaker 3: have these sponges on, they're hunting alone, and so you 237 00:15:33,600 --> 00:15:37,120 Speaker 3: wouldn't think that this sponging had anything to do with 238 00:15:37,200 --> 00:15:41,960 Speaker 3: social networks, but in fact it does. And the reason 239 00:15:42,000 --> 00:15:47,120 Speaker 3: that it does is dolphins often carry these sponges around 240 00:15:47,680 --> 00:15:50,200 Speaker 3: when they're not hunting because, as I say, if they 241 00:15:50,200 --> 00:15:54,160 Speaker 3: find a good sponge, they like it. Dolphins know who 242 00:15:54,280 --> 00:15:58,080 Speaker 3: else are spongers. Not everyone's a sponger, only a small 243 00:15:58,120 --> 00:16:01,360 Speaker 3: subset of them actually use these two tools. And so 244 00:16:01,600 --> 00:16:06,200 Speaker 3: what the researchers did was they started trying to understand 245 00:16:06,280 --> 00:16:10,920 Speaker 3: whether or not individuals who did use these sponge tools 246 00:16:11,240 --> 00:16:14,360 Speaker 3: hung out with each other when they actually weren't going 247 00:16:14,400 --> 00:16:17,680 Speaker 3: around looking for food, and it turns out they were. 248 00:16:17,760 --> 00:16:21,840 Speaker 3: There are these cliques of dolphins that use the sponge 249 00:16:21,880 --> 00:16:24,480 Speaker 3: tools that hang out with each other when they're not 250 00:16:24,720 --> 00:16:29,240 Speaker 3: actually down on the bottom looking for food. And the 251 00:16:29,320 --> 00:16:32,520 Speaker 3: reason we think they do this is if you hang 252 00:16:32,560 --> 00:16:36,880 Speaker 3: out with other spongers, you're likely to get information about 253 00:16:36,920 --> 00:16:42,160 Speaker 3: where the good places to sponge are, and so you 254 00:16:42,320 --> 00:16:46,000 Speaker 3: network with them so that you can take these sponges 255 00:16:46,160 --> 00:16:51,080 Speaker 3: and use them in the best possible locations, and the 256 00:16:51,080 --> 00:16:55,120 Speaker 3: sponges are really great tools. The ganet man who ran 257 00:16:55,200 --> 00:16:58,240 Speaker 3: this study, she and her colleagues actually thought, you know, 258 00:16:59,160 --> 00:17:03,560 Speaker 3: we should see if sponging really works. It kind of 259 00:17:03,600 --> 00:17:06,440 Speaker 3: looks like it works when you watch the dolphins. They 260 00:17:06,480 --> 00:17:09,920 Speaker 3: put sponges on their hands, and they went around under 261 00:17:09,960 --> 00:17:13,640 Speaker 3: the water using the sponges on their hands the way 262 00:17:13,680 --> 00:17:17,400 Speaker 3: dolphins use them on their snouts. And it turns out 263 00:17:17,480 --> 00:17:21,120 Speaker 3: you really do kick up a lot of prey items 264 00:17:21,160 --> 00:17:24,679 Speaker 3: things to eat when you use these sponges. And so 265 00:17:24,880 --> 00:17:30,840 Speaker 3: networking allowed us to kind of understand this cultural tool 266 00:17:30,920 --> 00:17:33,919 Speaker 3: use in a much more general way, not just this 267 00:17:34,040 --> 00:17:36,760 Speaker 3: dolphin does it or that dolphin does it, but that 268 00:17:36,840 --> 00:17:39,800 Speaker 3: they hang around together if they do it, and they 269 00:17:39,920 --> 00:17:42,360 Speaker 3: get all sorts of information from each other when they 270 00:17:42,359 --> 00:17:42,679 Speaker 3: do this. 271 00:17:43,000 --> 00:17:43,960 Speaker 2: Wow, that's incredible. 272 00:17:44,119 --> 00:17:49,640 Speaker 3: Yeah, no, it's a wonderful long term study of an 273 00:17:49,680 --> 00:17:55,240 Speaker 3: incredibly complex organism. I mean, dolphins. Everybody loves dolphins. Their 274 00:17:55,280 --> 00:17:59,320 Speaker 3: sociality is over the top in terms of the sorts 275 00:17:59,359 --> 00:18:01,840 Speaker 3: of things we typically see in nature. 276 00:18:10,640 --> 00:18:12,639 Speaker 2: Now, in the book, you divide the chapters up by 277 00:18:12,640 --> 00:18:16,720 Speaker 2: specific networks and needs. You know, you alluded to this earlier, 278 00:18:16,840 --> 00:18:21,680 Speaker 2: you know, food reproduction, power, safety, travel, communication, health, and culture. 279 00:18:22,160 --> 00:18:25,239 Speaker 2: What areas were you either most surprised about or do 280 00:18:25,280 --> 00:18:28,680 Speaker 2: you think will be most surprising to readers in these chapters? 281 00:18:28,720 --> 00:18:30,760 Speaker 2: You know, because I feel like for a lot of 282 00:18:30,800 --> 00:18:33,600 Speaker 2: general readers, you know, we can easily think of non 283 00:18:33,680 --> 00:18:37,160 Speaker 2: human animals engaging in some level of like food reproduction 284 00:18:37,240 --> 00:18:40,399 Speaker 2: or power dynamics. You know, we've seen enough documentaries or 285 00:18:40,440 --> 00:18:44,000 Speaker 2: engage with enough animal content. But communication and health don't 286 00:18:44,080 --> 00:18:46,879 Speaker 2: always instantly come to mind, I imagine, not with like 287 00:18:47,560 --> 00:18:50,280 Speaker 2: the number of species that are covered in the book. 288 00:18:51,680 --> 00:18:58,120 Speaker 3: Sure, yeah, so communication is a great one in terms 289 00:18:58,240 --> 00:19:02,240 Speaker 3: of the kinds of things that might surprise readers. There 290 00:19:02,320 --> 00:19:09,440 Speaker 3: is wonderful work that's been done on communication in chimpanzees, 291 00:19:09,480 --> 00:19:15,120 Speaker 3: for example. So in chimpanzees, when they're communicating with each other, 292 00:19:15,880 --> 00:19:20,919 Speaker 3: they can communicate in all sorts of different ways, and 293 00:19:21,000 --> 00:19:24,600 Speaker 3: some of those ways are much more visual, and some 294 00:19:24,720 --> 00:19:29,000 Speaker 3: of them are much more physical and tactile that involve touching. 295 00:19:29,640 --> 00:19:34,879 Speaker 3: And it turns out that again, social network thinking allows 296 00:19:34,960 --> 00:19:40,880 Speaker 3: us to probe really deep into these communication networks that 297 00:19:42,160 --> 00:19:48,920 Speaker 3: exist in chimpanzees. And so, for example, there's this population 298 00:19:49,040 --> 00:19:52,600 Speaker 3: of chimpanzees called the sun so population that's been studied 299 00:19:52,640 --> 00:19:55,720 Speaker 3: for a very long time, and it turns out that 300 00:19:55,800 --> 00:20:00,159 Speaker 3: when you look at the kind of gestural communication it 301 00:20:00,240 --> 00:20:04,760 Speaker 3: goes on in these chimpanzees, you see something very different 302 00:20:05,160 --> 00:20:09,679 Speaker 3: when individuals are interacting with friends in their social networks 303 00:20:10,240 --> 00:20:14,280 Speaker 3: rather than with others who they don't know quite as well. 304 00:20:14,920 --> 00:20:21,560 Speaker 3: So if you look at communication between chimpanzees who know 305 00:20:21,720 --> 00:20:25,800 Speaker 3: each other well and who have done things like cooperative 306 00:20:25,880 --> 00:20:33,480 Speaker 3: hunting together, they tend to use visual communication. And this 307 00:20:33,680 --> 00:20:38,760 Speaker 3: kind of visual communication. Other people have found that this 308 00:20:38,880 --> 00:20:43,359 Speaker 3: kind of really lowers their heart rates and reduces the 309 00:20:43,400 --> 00:20:48,840 Speaker 3: amount of stress that the chimpanzee itself feels. The individual 310 00:20:48,880 --> 00:20:53,040 Speaker 3: who's the recipient of the communication calms down, they have 311 00:20:53,080 --> 00:20:57,000 Speaker 3: a lower heart rate. This is when communication goes on 312 00:20:57,119 --> 00:21:01,200 Speaker 3: between friends. But when communication is going on between individuals 313 00:21:01,200 --> 00:21:04,040 Speaker 3: who don't know each other as well, maybe haven't interacted 314 00:21:04,080 --> 00:21:06,640 Speaker 3: as much in the past, then you tend to see 315 00:21:06,640 --> 00:21:09,399 Speaker 3: a different kind of communication. You tend to see it 316 00:21:09,440 --> 00:21:13,200 Speaker 3: being very physical and tactile, with them touching each other. 317 00:21:13,800 --> 00:21:17,160 Speaker 3: And the researchers who did this work, Anna and Sam 318 00:21:17,240 --> 00:21:22,280 Speaker 3: Roberts have argued that visual communication works perfectly well between 319 00:21:22,320 --> 00:21:25,320 Speaker 3: friends because you know each other and there's a large 320 00:21:25,400 --> 00:21:28,199 Speaker 3: level of trust that's already in place. But when you 321 00:21:28,320 --> 00:21:33,120 Speaker 3: don't know each other very well, visual communication doesn't work 322 00:21:33,119 --> 00:21:35,520 Speaker 3: as well because it's not as clear, it's. 323 00:21:35,359 --> 00:21:42,000 Speaker 4: Not as as salient as auditory and tactile communication, where 324 00:21:42,000 --> 00:21:45,159 Speaker 4: you're really up front, right in the face of the 325 00:21:45,200 --> 00:21:46,640 Speaker 4: individual touching them. 326 00:21:47,080 --> 00:21:49,800 Speaker 3: Then they can really understand what it is that you're 327 00:21:49,800 --> 00:21:55,639 Speaker 3: trying to communicate. And that's important because chimpanzee networks break 328 00:21:55,720 --> 00:21:58,000 Speaker 3: up and they come back together, and so when you're 329 00:21:58,320 --> 00:22:02,040 Speaker 3: interacting with individuals that you don't know very well, you 330 00:22:02,160 --> 00:22:05,840 Speaker 3: really want to make sure that your communication is clear. 331 00:22:06,240 --> 00:22:11,200 Speaker 3: And these kind of auditory and physical communication gestures are 332 00:22:11,359 --> 00:22:13,399 Speaker 3: much better at that. And so you see it among 333 00:22:13,400 --> 00:22:15,800 Speaker 3: individuals who don't know each other very well. 334 00:22:16,160 --> 00:22:16,800 Speaker 2: Fascinating. 335 00:22:17,240 --> 00:22:20,640 Speaker 3: But here's the thing, you know, so chimpanzees are our 336 00:22:20,680 --> 00:22:25,720 Speaker 3: closest living relatives and they have very large brains, right, 337 00:22:25,880 --> 00:22:29,919 Speaker 3: But you can go and find communication networks and honeybees 338 00:22:30,840 --> 00:22:34,640 Speaker 3: and a former student of mine has been studying that 339 00:22:35,040 --> 00:22:38,600 Speaker 3: in a research field station outside of London where they 340 00:22:38,640 --> 00:22:45,600 Speaker 3: have these experimental honeybee hives, and so in honeybees, the 341 00:22:45,640 --> 00:22:50,200 Speaker 3: primary way that they communicate is actually by something known 342 00:22:50,200 --> 00:22:53,240 Speaker 3: as the waggle dance. So here the bees are trying 343 00:22:53,280 --> 00:22:56,679 Speaker 3: to communicate to one another where a new food source is. 344 00:22:57,280 --> 00:22:59,719 Speaker 3: And the way that they typically do this is, if 345 00:22:59,760 --> 00:23:02,359 Speaker 3: you've a new food source and you go back to 346 00:23:02,400 --> 00:23:06,600 Speaker 3: the honeybee hive, you do this particular kind of dance 347 00:23:06,720 --> 00:23:10,960 Speaker 3: known as the waggle dance, and the waggle dance basically 348 00:23:11,600 --> 00:23:16,600 Speaker 3: involves moving very very quickly and shaking your abdomen as 349 00:23:16,640 --> 00:23:19,960 Speaker 3: you're moving through the hive. And we know from prior 350 00:23:20,040 --> 00:23:24,560 Speaker 3: work that this gives various types of information to others 351 00:23:24,600 --> 00:23:27,920 Speaker 3: in the hive about where the food source is. For example, 352 00:23:28,040 --> 00:23:32,520 Speaker 3: how long you dance gives information about how far away 353 00:23:32,520 --> 00:23:36,159 Speaker 3: the hive is, and there's a really nice translation. Every 354 00:23:36,400 --> 00:23:39,480 Speaker 3: tenth of a second of dancing translates into the food 355 00:23:39,520 --> 00:23:43,159 Speaker 3: being this many feet away from the hive. They also 356 00:23:43,320 --> 00:23:46,600 Speaker 3: dance at a certain angle, and the angle they dance at, 357 00:23:46,640 --> 00:23:50,040 Speaker 3: believe it or not, is the angle between the sun, 358 00:23:50,480 --> 00:23:54,280 Speaker 3: the hive and where the food is. So they're communicating 359 00:23:54,480 --> 00:23:59,440 Speaker 3: all of this information through the dance. Now, in terms 360 00:23:59,520 --> 00:24:03,440 Speaker 3: of working, it turns out that there were other ways 361 00:24:03,440 --> 00:24:08,280 Speaker 3: to communicate information about new food sources. They can communicate 362 00:24:08,320 --> 00:24:15,199 Speaker 3: this information when they transfer food from themselves to another 363 00:24:15,240 --> 00:24:19,800 Speaker 3: individual in the hive. They can also communicate information about 364 00:24:19,840 --> 00:24:24,560 Speaker 3: the food when they basically take their antennas and connect 365 00:24:24,560 --> 00:24:26,919 Speaker 3: to the antennas of another bee in the hive. This 366 00:24:27,080 --> 00:24:31,720 Speaker 3: also gives them information. So what has been done in 367 00:24:31,840 --> 00:24:35,240 Speaker 3: terms of the networking is people have asked, well, they 368 00:24:35,240 --> 00:24:39,560 Speaker 3: can get this information in all these different ways, how 369 00:24:39,600 --> 00:24:41,360 Speaker 3: do they do it? When do they decide if they're 370 00:24:41,400 --> 00:24:46,320 Speaker 3: going to transfer the information by dancing or moving food 371 00:24:46,359 --> 00:24:52,000 Speaker 3: from one mouth to another or touching antenna. So what 372 00:24:52,119 --> 00:24:55,160 Speaker 3: researchers did with they set up this experiment where they 373 00:24:55,200 --> 00:24:58,159 Speaker 3: had these hives that were placed out in a field 374 00:24:58,400 --> 00:25:01,160 Speaker 3: and they knew where the hives were, and they controlled 375 00:25:01,200 --> 00:25:05,640 Speaker 3: how much food was going into the hives. And what 376 00:25:05,800 --> 00:25:08,600 Speaker 3: they found was and so I should say, they marked 377 00:25:08,600 --> 00:25:11,200 Speaker 3: all of their bees, thousands of them, so they knew 378 00:25:11,240 --> 00:25:14,439 Speaker 3: who these bees were. And it turns out when you 379 00:25:14,480 --> 00:25:19,080 Speaker 3: look at communication networks and the bees, you find this 380 00:25:19,680 --> 00:25:24,560 Speaker 3: wonderfully complex system in place. If the sun is out there, 381 00:25:24,920 --> 00:25:29,280 Speaker 3: they use dancing to communicate because they can transfer information 382 00:25:30,040 --> 00:25:33,840 Speaker 3: about food using the angle of the sun compared to 383 00:25:33,880 --> 00:25:37,880 Speaker 3: the hive and the food source. But in London there 384 00:25:37,920 --> 00:25:41,240 Speaker 3: are many many days when it's not sunny, and when 385 00:25:41,240 --> 00:25:46,720 Speaker 3: it's not sunny, the communication network focuses on the transfer 386 00:25:46,760 --> 00:25:50,520 Speaker 3: of food or the touching of antenna. So they have 387 00:25:50,680 --> 00:25:55,439 Speaker 3: these kind of multiple communication networks and if one of 388 00:25:55,480 --> 00:25:58,400 Speaker 3: them is down because the sun isn't out, then they 389 00:25:58,440 --> 00:26:02,679 Speaker 3: move to a second kind of communication network. And so 390 00:26:03,480 --> 00:26:06,560 Speaker 3: what this tells us is that you know, you can 391 00:26:06,640 --> 00:26:09,399 Speaker 3: have a brain the size of a honeybee or the 392 00:26:09,440 --> 00:26:12,200 Speaker 3: brain the size of a chimpanzee, and you can still 393 00:26:12,200 --> 00:26:18,480 Speaker 3: get complex communication networks in non humans. You were also 394 00:26:18,560 --> 00:26:23,360 Speaker 3: asking about disease. I think for me this was one 395 00:26:23,359 --> 00:26:27,240 Speaker 3: of the more surprising components of social networks and animals, 396 00:26:27,640 --> 00:26:33,479 Speaker 3: the way that they relate to disease transmission. And here 397 00:26:34,160 --> 00:26:37,000 Speaker 3: what's kind of cool is that in almost all of 398 00:26:37,000 --> 00:26:41,400 Speaker 3: the other behaviors that we're talking about, feeding and communication 399 00:26:41,760 --> 00:26:48,119 Speaker 3: and cooperation, really it's individuals, animals and the network that 400 00:26:48,160 --> 00:26:51,440 Speaker 3: are the key. But when cut when it comes to disease, 401 00:26:51,640 --> 00:26:56,800 Speaker 3: what's basically going on is the disease is hitchhiking on 402 00:26:56,920 --> 00:27:00,199 Speaker 3: the social networks that the animals have in place. So 403 00:27:00,240 --> 00:27:05,440 Speaker 3: the animals don't want to transfer disease from one individual 404 00:27:05,720 --> 00:27:09,040 Speaker 3: to another, but the social networks are in place in 405 00:27:09,160 --> 00:27:14,360 Speaker 3: terms of, for example, aggression and power struggles. What that 406 00:27:14,440 --> 00:27:18,560 Speaker 3: means is diseases can use those networks to move from 407 00:27:18,640 --> 00:27:22,720 Speaker 3: one individual to another. And I think my favorite example 408 00:27:22,760 --> 00:27:27,280 Speaker 3: of this is this bizarre disease that has been studied 409 00:27:27,359 --> 00:27:33,160 Speaker 3: in Tasmanian devils. And if you can't picture a Tasmanian 410 00:27:33,280 --> 00:27:37,600 Speaker 3: devil in your head, think back to the Bugs Bunny cartoons, 411 00:27:37,680 --> 00:27:41,840 Speaker 3: because there was this wonderful caricature of a Tasmanian devil 412 00:27:42,119 --> 00:27:46,280 Speaker 3: in there. And in Tasmanian devils they have this thing 413 00:27:46,440 --> 00:27:51,280 Speaker 3: called facial tumor disease. And this is one of the 414 00:27:51,440 --> 00:27:55,720 Speaker 3: very rare instances that we know of in which cancer 415 00:27:57,119 --> 00:28:01,520 Speaker 3: is transmitted from one individual to another. The sort of 416 00:28:02,040 --> 00:28:05,440 Speaker 3: worst case scenario when we think about human cancers would 417 00:28:05,440 --> 00:28:08,679 Speaker 3: be if we could actually transmit cancer from one individual 418 00:28:08,800 --> 00:28:12,359 Speaker 3: to another. It doesn't happen, but in certain species, like 419 00:28:12,440 --> 00:28:16,639 Speaker 3: Tasmanian devils, it does. And what happens is if they 420 00:28:16,720 --> 00:28:23,399 Speaker 3: are infected with this cancer and they bite somebody really hard, right, 421 00:28:24,160 --> 00:28:30,600 Speaker 3: they can transmit that cancer to the other individual. So 422 00:28:30,760 --> 00:28:35,160 Speaker 3: here's where social networks come into play. Tasmanian Devils fight, 423 00:28:35,560 --> 00:28:39,320 Speaker 3: and they fight a lot, particularly during mating season. Males 424 00:28:39,360 --> 00:28:45,160 Speaker 3: will fight intensely to gain access to mates. Sometimes when 425 00:28:45,200 --> 00:28:48,640 Speaker 3: they fight, if you have facial tumor disease and you 426 00:28:48,720 --> 00:28:54,520 Speaker 3: bite somebody else, you can transfer that disease to that individual. Again, 427 00:28:54,600 --> 00:29:00,720 Speaker 3: the tumor is actually hitchhiking on the power show network 428 00:29:00,760 --> 00:29:05,280 Speaker 3: structure that's in place in the Tasmanian Devils. And so 429 00:29:05,440 --> 00:29:10,960 Speaker 3: it's not surprising, for example, that males transmit this disease 430 00:29:11,120 --> 00:29:14,520 Speaker 3: much more often to one another than females. And that's 431 00:29:14,520 --> 00:29:19,120 Speaker 3: because males fight a lot during mating season, and it's 432 00:29:19,160 --> 00:29:23,880 Speaker 3: only through bites that this disease can be transmitted. It's 433 00:29:23,880 --> 00:29:29,720 Speaker 3: more complex, and what we're discovering are strange things like 434 00:29:30,320 --> 00:29:33,040 Speaker 3: even though males are more likely to transmit the disease, 435 00:29:33,680 --> 00:29:38,080 Speaker 3: females also get facial tumor disease, and they tend to 436 00:29:38,120 --> 00:29:42,600 Speaker 3: get it from males who are defending them. But in 437 00:29:42,640 --> 00:29:45,720 Speaker 3: the context of courtships, sometimes it gets a little too 438 00:29:45,840 --> 00:29:48,960 Speaker 3: rough and you get some biting and females can get 439 00:29:49,000 --> 00:29:54,120 Speaker 3: infected with this disease. Now, what's weird is if you 440 00:29:54,200 --> 00:30:00,440 Speaker 3: look at female Tasmanian Devils. Usually it's individuals are in 441 00:30:00,520 --> 00:30:04,840 Speaker 3: the weakest health state that are most susceptible to getting disease. 442 00:30:05,920 --> 00:30:10,280 Speaker 3: But in the Tasmanian Devils, it's the healthiest females who 443 00:30:10,360 --> 00:30:14,400 Speaker 3: end up getting this cancerous disease by being bitten by males. 444 00:30:15,200 --> 00:30:19,160 Speaker 3: Why well, if you do a social network analysis, what 445 00:30:19,240 --> 00:30:23,360 Speaker 3: you find is the healthiest females are the ones that 446 00:30:23,400 --> 00:30:27,760 Speaker 3: the males prefer as mates. That means they're the ones 447 00:30:28,280 --> 00:30:33,360 Speaker 3: that are most likely to get bitten even unintentionally by 448 00:30:33,400 --> 00:30:38,200 Speaker 3: males during courtship. And so they the healthiest ones, end 449 00:30:38,280 --> 00:30:41,920 Speaker 3: up being most likely to get the cancer, which is 450 00:30:42,040 --> 00:30:45,640 Speaker 3: something that we can possibly have really understood without a 451 00:30:45,720 --> 00:30:47,400 Speaker 3: social network analysis. 452 00:30:47,880 --> 00:30:49,840 Speaker 2: Wow, that's incredible. Yeah, I was going to ask about 453 00:30:49,840 --> 00:30:53,400 Speaker 2: the Tasmanian devil example. That one definitely stood out to 454 00:30:53,440 --> 00:30:56,360 Speaker 2: me when I was reading. Another one was the vampire bats. 455 00:30:56,720 --> 00:31:04,880 Speaker 3: Oh. Yes, vampire bats are wonderful. They're a textbook example 456 00:31:05,520 --> 00:31:11,760 Speaker 3: of social behavior and in this case, cooperation. So let 457 00:31:11,800 --> 00:31:16,960 Speaker 3: me just tell you a little bit about what goes 458 00:31:17,000 --> 00:31:21,400 Speaker 3: on in the world of vampires. So, vampire bats do 459 00:31:21,520 --> 00:31:24,160 Speaker 3: in fact suck blood, right, but they don't suck human blood. 460 00:31:24,640 --> 00:31:29,480 Speaker 3: They suck the blood of cows and other kind of 461 00:31:29,600 --> 00:31:34,000 Speaker 3: domesticated species. And it turns out that if you're a 462 00:31:34,080 --> 00:31:36,600 Speaker 3: vampire bat, or if you're just a bat in general, 463 00:31:36,920 --> 00:31:40,520 Speaker 3: it's a really expensive way of living for a mammal. Right, 464 00:31:40,680 --> 00:31:44,360 Speaker 3: so bats are mammals, and most mammals don't fly, but 465 00:31:44,480 --> 00:31:49,680 Speaker 3: if you fly, it's energetically extremely expensive. And what people 466 00:31:49,680 --> 00:31:53,040 Speaker 3: have found is if a vampire bat doesn't get a 467 00:31:53,120 --> 00:31:59,360 Speaker 3: blood meal about every two days, it can starve to death. Now, 468 00:32:00,080 --> 00:32:04,400 Speaker 3: what the vampire bats do is they have this social 469 00:32:04,440 --> 00:32:10,960 Speaker 3: network in place where they transfer blood from one back 470 00:32:11,000 --> 00:32:13,960 Speaker 3: to the other. So if you take a camera and 471 00:32:14,000 --> 00:32:17,920 Speaker 3: you go inside a vampire bat roost, what you will 472 00:32:17,960 --> 00:32:21,320 Speaker 3: find sometimes is that one bat will come up to 473 00:32:21,360 --> 00:32:24,760 Speaker 3: another bat and it'll start licking its face in a 474 00:32:24,920 --> 00:32:29,120 Speaker 3: very kind of stereotypical manner. What that bat is doing 475 00:32:29,280 --> 00:32:32,080 Speaker 3: is it's trying to get the other bat to regurgitate 476 00:32:32,640 --> 00:32:34,760 Speaker 3: some of the blood that it has in its gut 477 00:32:35,280 --> 00:32:38,680 Speaker 3: so that the solicitor, the one that's licking the face, 478 00:32:39,160 --> 00:32:41,800 Speaker 3: can actually survive long enough to go out and get 479 00:32:41,840 --> 00:32:44,280 Speaker 3: its own blood meal. So it's hungry, it's starving. It 480 00:32:44,320 --> 00:32:47,480 Speaker 3: goes up to a bat that has a really distended 481 00:32:47,680 --> 00:32:50,480 Speaker 3: big gut, which means it has lots of blood. It 482 00:32:50,560 --> 00:32:53,480 Speaker 3: licks its face trying to get that bat to regurgitate 483 00:32:53,520 --> 00:32:55,760 Speaker 3: some of the blood to it. They will do that 484 00:32:56,320 --> 00:33:01,520 Speaker 3: for one another, but only when they know that it's 485 00:33:01,560 --> 00:33:05,480 Speaker 3: a trustworthy partner. So if one bat goes up to 486 00:33:05,520 --> 00:33:09,480 Speaker 3: another and tries to get blood regurgitated, the only way 487 00:33:09,520 --> 00:33:13,720 Speaker 3: it'll work is if in the past that blood that 488 00:33:13,760 --> 00:33:17,760 Speaker 3: bat who's hungry who needs the blood, has helped and 489 00:33:17,840 --> 00:33:21,040 Speaker 3: given blood to the bat that it's now asking for 490 00:33:21,120 --> 00:33:24,520 Speaker 3: blood from. So they're basically keeping track of who gave 491 00:33:24,560 --> 00:33:27,160 Speaker 3: them blood in the past when they were hungry, and 492 00:33:27,200 --> 00:33:29,920 Speaker 3: if that bat now comes up and tries to get 493 00:33:30,040 --> 00:33:33,720 Speaker 3: some blood you to recurgitate some blood, then you're much 494 00:33:33,760 --> 00:33:37,320 Speaker 3: more likely to do it. So that's the basic structure 495 00:33:38,000 --> 00:33:42,800 Speaker 3: that's in place in these vampire bat roots. Networking comes 496 00:33:42,800 --> 00:33:47,480 Speaker 3: into place because it turns out again that it really 497 00:33:47,560 --> 00:33:50,840 Speaker 3: matters not just sort of who gave you blood when 498 00:33:50,920 --> 00:33:55,880 Speaker 3: you were hungry, but also who you go at with 499 00:33:56,520 --> 00:34:00,840 Speaker 3: and look for blood when there's an nice night out 500 00:34:00,880 --> 00:34:03,680 Speaker 3: there to go out and fly and look for an 501 00:34:03,760 --> 00:34:09,040 Speaker 3: unsuspecting cow. So what researchers have done is they have 502 00:34:09,200 --> 00:34:15,560 Speaker 3: placed little what are known as pit tags on these bats. 503 00:34:15,600 --> 00:34:19,320 Speaker 3: And so basically all a pit tag is is this little, tiny, 504 00:34:19,920 --> 00:34:22,600 Speaker 3: one ounce less than one ounce device if they put 505 00:34:22,640 --> 00:34:25,800 Speaker 3: on a bat, and it allows them to track every 506 00:34:25,840 --> 00:34:28,000 Speaker 3: bat in the roost when the bat goes out and 507 00:34:28,040 --> 00:34:30,879 Speaker 3: looks for food. And the way it works is they 508 00:34:30,920 --> 00:34:33,759 Speaker 3: have all of these receivers that have been placed out 509 00:34:33,760 --> 00:34:36,399 Speaker 3: in the fields with the cows, and so you can 510 00:34:36,480 --> 00:34:39,080 Speaker 3: know where the bats are because the little things they 511 00:34:39,120 --> 00:34:43,360 Speaker 3: have on their bat are transmit are transmitting information to 512 00:34:44,239 --> 00:34:46,520 Speaker 3: these receivers that are in the field, so you know 513 00:34:46,640 --> 00:34:51,080 Speaker 3: everything about what the bats are doing. What's more, these 514 00:34:51,120 --> 00:34:54,640 Speaker 3: little pit tags that the bats have on their back 515 00:34:55,160 --> 00:34:59,320 Speaker 3: talk to each other. That is, if two vampire bats 516 00:34:59,400 --> 00:35:03,400 Speaker 3: come close enough to one another, these little pit tags 517 00:35:03,520 --> 00:35:06,680 Speaker 3: light up and you know that vampire Bat one and 518 00:35:06,800 --> 00:35:09,160 Speaker 3: vampire Bat two are on the back of a cow 519 00:35:09,200 --> 00:35:13,840 Speaker 3: together sucking blood. Now, these things are not easy to 520 00:35:13,920 --> 00:35:17,080 Speaker 3: set up, but after you have this system in place, 521 00:35:17,719 --> 00:35:21,839 Speaker 3: you can test all sorts of incredible things, like is 522 00:35:21,880 --> 00:35:25,720 Speaker 3: it the case that this kind of network of blood 523 00:35:25,760 --> 00:35:30,160 Speaker 3: sharing that goes on inside the roost translates into who's 524 00:35:30,239 --> 00:35:33,120 Speaker 3: hanging out with who? When they're actually going out and 525 00:35:33,160 --> 00:35:35,920 Speaker 3: looking for blood. That's a really hard thing to do 526 00:35:36,280 --> 00:35:38,200 Speaker 3: because you never know when bats are going to leave 527 00:35:38,239 --> 00:35:40,840 Speaker 3: their roost and they fly and they're really hard to follow. 528 00:35:40,880 --> 00:35:44,160 Speaker 3: But when you have these tags and these transmitters and 529 00:35:44,200 --> 00:35:46,920 Speaker 3: responders in the field, you can do this and lo 530 00:35:47,120 --> 00:35:51,040 Speaker 3: and behold. Basically, the network of blood sharing that goes 531 00:35:51,080 --> 00:35:56,759 Speaker 3: on inside the roost is a very good predictor of 532 00:35:56,840 --> 00:35:59,080 Speaker 3: who will be found on the back of the same 533 00:35:59,200 --> 00:36:02,520 Speaker 3: cow sucking blood when they go out. They go out, 534 00:36:02,719 --> 00:36:05,920 Speaker 3: The bats go out and start flying independently, but they 535 00:36:05,960 --> 00:36:09,520 Speaker 3: find their favorite partners one way or another, and they 536 00:36:09,640 --> 00:36:13,600 Speaker 3: end up tending to be found on the back of 537 00:36:13,640 --> 00:36:18,480 Speaker 3: some unsuspecting count in the field. And we can only 538 00:36:18,760 --> 00:36:22,000 Speaker 3: know how complex all of this is when we realize 539 00:36:22,000 --> 00:36:26,040 Speaker 3: that it's all embedded in these social networks that the 540 00:36:26,080 --> 00:36:27,920 Speaker 3: bats have built around blood sharing. 541 00:36:28,640 --> 00:36:32,080 Speaker 2: Wow, that's incredible. And the technology that goes into these studies. 542 00:36:32,239 --> 00:36:35,520 Speaker 2: I was just floored by numerous examples in the book. 543 00:36:35,760 --> 00:36:38,719 Speaker 3: Yeah, I mean, so the study of social networks and 544 00:36:38,760 --> 00:36:43,919 Speaker 3: animals has just absolutely exploded in part because of these 545 00:36:43,960 --> 00:36:49,200 Speaker 3: technological advances. So you have these talking sensors like I 546 00:36:49,360 --> 00:36:52,759 Speaker 3: just mentioned, to you where when the animals come close enough, 547 00:36:52,920 --> 00:36:55,360 Speaker 3: they both sensors light up and you know they're to 548 00:36:55,360 --> 00:37:00,400 Speaker 3: gather wherever they are. That's one thing. Then basically in 549 00:37:00,480 --> 00:37:03,440 Speaker 3: other studies, what they do, what individuals do, is they 550 00:37:03,960 --> 00:37:08,440 Speaker 3: attach the equivalent of kind of a GPS device to 551 00:37:08,560 --> 00:37:12,359 Speaker 3: the back of an animal and they can track it 552 00:37:12,440 --> 00:37:17,400 Speaker 3: then over really long distances. And my favorite example here 553 00:37:18,120 --> 00:37:24,160 Speaker 3: is white stork behavior. So white storks are these beautiful animals, right, 554 00:37:24,600 --> 00:37:30,040 Speaker 3: but they migrate thousands of miles in the winter, and 555 00:37:30,440 --> 00:37:34,520 Speaker 3: we know almost nothing about the details of their migration 556 00:37:34,600 --> 00:37:37,520 Speaker 3: because it's really hard to follow birds that are flying 557 00:37:38,080 --> 00:37:42,280 Speaker 3: a thousand feet above the ground and traveling thousands of miles. 558 00:37:42,400 --> 00:37:46,680 Speaker 3: But if you put GPS transponders on their back, then 559 00:37:47,080 --> 00:37:51,000 Speaker 3: you have the possibility of doing this. So people who 560 00:37:51,000 --> 00:37:55,239 Speaker 3: have worked with these white storks, Andrea Flack is a 561 00:37:55,280 --> 00:37:58,080 Speaker 3: researcher at the Max Planck Institute, So she put all 562 00:37:58,120 --> 00:38:02,400 Speaker 3: of she put these GPS trackers on the backs of 563 00:38:02,480 --> 00:38:08,840 Speaker 3: these white storks, and she basically would follow them as 564 00:38:08,880 --> 00:38:11,600 Speaker 3: they were making them migration. As I'm making them migration, 565 00:38:11,680 --> 00:38:15,160 Speaker 3: they kind of come down every night to rest, and 566 00:38:15,239 --> 00:38:18,400 Speaker 3: she would kind of try and track them for hundreds 567 00:38:18,400 --> 00:38:21,960 Speaker 3: of miles if she could on her own, and so 568 00:38:22,080 --> 00:38:24,920 Speaker 3: she was trying to follow them, but at the same time, 569 00:38:25,520 --> 00:38:28,200 Speaker 3: she could know what the birds were doing when they 570 00:38:28,200 --> 00:38:32,239 Speaker 3: were a thousand feet above the ground because the GPS 571 00:38:32,280 --> 00:38:37,080 Speaker 3: devices were so sensitive that she could know where a 572 00:38:37,160 --> 00:38:40,759 Speaker 3: bird was in the flock as they were flying these 573 00:38:40,800 --> 00:38:44,360 Speaker 3: thousands of miles. And it turns out that what that 574 00:38:44,520 --> 00:38:48,399 Speaker 3: allowed her to do was built a social network of 575 00:38:48,440 --> 00:38:52,800 Speaker 3: the flock as it was flying these south thousands of miles. 576 00:38:53,440 --> 00:38:57,160 Speaker 3: And what she found was that in this network, this 577 00:38:58,000 --> 00:39:02,840 Speaker 3: travel network, there were certain white storks that were the leaders. 578 00:39:03,320 --> 00:39:07,720 Speaker 3: They were the ones that were determining when the flock 579 00:39:07,760 --> 00:39:10,360 Speaker 3: would move this way or that way. So if you 580 00:39:10,360 --> 00:39:13,400 Speaker 3: look at a bird of flocks, a flock of birds 581 00:39:13,440 --> 00:39:16,440 Speaker 3: like geese, you can tell there are certain leaders that 582 00:39:16,560 --> 00:39:19,720 Speaker 3: are the ones that are determining the movement of the flock. 583 00:39:20,000 --> 00:39:22,080 Speaker 3: But you can't do that with white storks when they're 584 00:39:22,080 --> 00:39:26,000 Speaker 3: that far above the ground unless you have these GPS devices. 585 00:39:26,400 --> 00:39:30,680 Speaker 3: So there are these leaders and followers in the white 586 00:39:30,719 --> 00:39:35,399 Speaker 3: stalk travel networks, and it really matters. The leaders are 587 00:39:35,560 --> 00:39:39,799 Speaker 3: very very good at finding up drafts. So when you're 588 00:39:39,840 --> 00:39:42,719 Speaker 3: flying as a bird, what you want is to expend 589 00:39:42,840 --> 00:39:47,640 Speaker 3: as little energy as you can, and there are these updrafts, 590 00:39:47,719 --> 00:39:50,359 Speaker 3: these winds that come that allow you to fly at 591 00:39:50,440 --> 00:39:54,480 Speaker 3: very low energy levels. The leaders are tremendously good at 592 00:39:54,640 --> 00:39:59,120 Speaker 3: finding these, and in fact, what they were able to 593 00:39:59,120 --> 00:40:01,839 Speaker 3: do with the researcher were able to do was demonstrate 594 00:40:02,000 --> 00:40:05,320 Speaker 3: not just that there were leaders and followers in these flocks, 595 00:40:05,880 --> 00:40:09,360 Speaker 3: but that the leaders were actually able to migrate further 596 00:40:09,560 --> 00:40:13,200 Speaker 3: than the followers because they were so good at finding 597 00:40:13,239 --> 00:40:17,560 Speaker 3: these updrafts. So it really mattered who was a leader 598 00:40:17,920 --> 00:40:21,000 Speaker 3: and a follower in these networks. And this kind of 599 00:40:21,080 --> 00:40:25,759 Speaker 3: study would have been unthinkable twenty years ago. I mean, 600 00:40:25,920 --> 00:40:31,640 Speaker 3: without the technology, you simply cannot do this sort of thing. 601 00:40:32,480 --> 00:40:36,640 Speaker 3: You can move from flying one thousand meters above the 602 00:40:36,719 --> 00:40:41,719 Speaker 3: ground to swimming many many feet below the water and 603 00:40:41,840 --> 00:40:45,760 Speaker 3: find the same thing. And there's this wonderful study done 604 00:40:45,840 --> 00:40:49,759 Speaker 3: on manta rays in the South Pacific where they've done 605 00:40:49,800 --> 00:40:53,680 Speaker 3: exactly this. They've tagged them so they can study what 606 00:40:53,719 --> 00:40:57,080 Speaker 3: the manta rays are doing. And in addition to that, 607 00:40:57,960 --> 00:41:02,640 Speaker 3: they fly these drones over groups of mantar rays that 608 00:41:02,680 --> 00:41:06,160 Speaker 3: are swimming close to the water surface. The drones then 609 00:41:06,640 --> 00:41:11,839 Speaker 3: videotape the interactions between the manta rays. And the manta 610 00:41:11,960 --> 00:41:17,840 Speaker 3: rays have these sort of very unique color and patterns 611 00:41:18,239 --> 00:41:21,239 Speaker 3: that allow the researchers to know who's who. So you 612 00:41:21,280 --> 00:41:25,120 Speaker 3: can study them by the drones watching them from above, 613 00:41:25,640 --> 00:41:30,440 Speaker 3: and the little pit tags and various other GPS like 614 00:41:30,480 --> 00:41:33,279 Speaker 3: devices under the water. They'll let you know where they 615 00:41:33,280 --> 00:41:35,600 Speaker 3: are all the time, which means you can build social 616 00:41:35,640 --> 00:41:40,000 Speaker 3: networks you can look at In the case of the 617 00:41:40,040 --> 00:41:44,560 Speaker 3: manta rays, they look at who's a key hub in 618 00:41:44,600 --> 00:41:47,719 Speaker 3: a social network. So a key hub is an individual 619 00:41:48,719 --> 00:41:52,160 Speaker 3: through which a lot of information travels. So if you 620 00:41:52,200 --> 00:41:57,320 Speaker 3: think about Facebook, for example, key hubs are like public 621 00:41:57,360 --> 00:42:00,759 Speaker 3: figures where if you want to sort of track the 622 00:42:00,760 --> 00:42:05,160 Speaker 3: way that information flows, these individuals really matter. Well, it 623 00:42:05,239 --> 00:42:08,279 Speaker 3: turns out in manta rays there are key hubs as well, 624 00:42:08,320 --> 00:42:12,960 Speaker 3: individuals through which information travels. In this case, it turns 625 00:42:13,000 --> 00:42:17,280 Speaker 3: out that juvenile manta rays are the hubs in social 626 00:42:17,360 --> 00:42:21,719 Speaker 3: networks associated with feeding. They're the ones that are the 627 00:42:21,800 --> 00:42:28,800 Speaker 3: key to understanding how individuals swim around looking for food underwater. Again, 628 00:42:29,000 --> 00:42:32,759 Speaker 3: without the technology, these are just pipe dreams, but now 629 00:42:32,920 --> 00:42:35,080 Speaker 3: they are going on. All these kind of studies are 630 00:42:35,080 --> 00:42:37,600 Speaker 3: going on all the time in non humens. 631 00:42:38,040 --> 00:42:40,680 Speaker 2: It's also fascinating. And again we've only been able to 632 00:42:40,719 --> 00:42:43,239 Speaker 2: really touch on some of the examples from the book. 633 00:42:43,239 --> 00:42:47,000 Speaker 2: The book is loaded with excellent examples and fascinating tibots 634 00:42:47,000 --> 00:42:59,000 Speaker 2: about the methodology and technology that goes into studying them. 635 00:42:59,239 --> 00:43:02,360 Speaker 2: In the word of the book, you write that quote, 636 00:43:02,400 --> 00:43:04,839 Speaker 2: it's time to scratch off another item from the what 637 00:43:05,000 --> 00:43:09,320 Speaker 2: makes humans unique? List? So I thought I was just wondering. 638 00:43:09,360 --> 00:43:11,600 Speaker 2: You know, obviously the book is primarily about what we're 639 00:43:11,640 --> 00:43:14,280 Speaker 2: learning about these non human animals and their social networks. 640 00:43:14,320 --> 00:43:17,080 Speaker 2: But do you think to any degree you were able 641 00:43:17,120 --> 00:43:19,800 Speaker 2: to like turn some of these revelations around to better 642 00:43:19,880 --> 00:43:24,760 Speaker 2: understand like what human social networks are? You have only 643 00:43:24,840 --> 00:43:26,800 Speaker 2: just to remind us that we're not special. 644 00:43:27,320 --> 00:43:30,600 Speaker 3: Yeah, so I think at the most general level, it 645 00:43:30,600 --> 00:43:34,799 Speaker 3: does remind us that we're not special. And yet another way, 646 00:43:34,880 --> 00:43:38,080 Speaker 3: so we talked about tools and culture were things that 647 00:43:38,120 --> 00:43:40,359 Speaker 3: we used to think we were unique, and we're not. 648 00:43:40,560 --> 00:43:42,920 Speaker 3: We're not unique in social networks. But in terms of 649 00:43:42,960 --> 00:43:46,480 Speaker 3: like perhaps the lessons that we can learn by studying 650 00:43:46,680 --> 00:43:49,680 Speaker 3: social networks and non humans, I think there is some 651 00:43:49,840 --> 00:43:51,560 Speaker 3: you know, you always have to be careful when you 652 00:43:51,600 --> 00:43:54,759 Speaker 3: do this sort of thing. But let me give you 653 00:43:56,800 --> 00:44:00,000 Speaker 3: just one example that I think helps in terms of 654 00:44:00,200 --> 00:44:04,759 Speaker 3: what we might learn. So there's this great study that 655 00:44:04,840 --> 00:44:09,239 Speaker 3: was done on Reese's macaque monkeys and their social networks 656 00:44:09,239 --> 00:44:12,160 Speaker 3: on this little island in Puerto Rico. And I won't 657 00:44:12,160 --> 00:44:15,759 Speaker 3: get into the details of the social network study per se, 658 00:44:16,239 --> 00:44:21,239 Speaker 3: but basically the thing about this study was that they 659 00:44:21,280 --> 00:44:24,480 Speaker 3: had been studying these macaques on the island for a 660 00:44:24,560 --> 00:44:27,239 Speaker 3: very long time. They knew a lot about their social networks. 661 00:44:27,600 --> 00:44:32,680 Speaker 3: Then Hurricane Maria came through and absolutely devastated the island 662 00:44:33,440 --> 00:44:38,320 Speaker 3: where these mecacus lived. And obviously it also decimated Puerto 663 00:44:38,400 --> 00:44:41,360 Speaker 3: Rico and had all sorts of impacts on our own species, 664 00:44:41,360 --> 00:44:45,799 Speaker 3: but it basically destroyed the island that they lived on, 665 00:44:46,760 --> 00:44:50,120 Speaker 3: the mecacs lived on, and what that allowed the researchers 666 00:44:50,160 --> 00:44:57,160 Speaker 3: to do was study how social networks respond to disasters. 667 00:44:57,960 --> 00:45:01,200 Speaker 3: This kind of study was also done in in mice, 668 00:45:02,000 --> 00:45:06,880 Speaker 3: how they respond to catastrophic events, social networks respond to 669 00:45:06,920 --> 00:45:11,680 Speaker 3: catastrophic events. I think that the more information we get 670 00:45:11,719 --> 00:45:15,200 Speaker 3: that on that from non humans, the more we might 671 00:45:15,239 --> 00:45:20,120 Speaker 3: be able to understand ways that we might expect social 672 00:45:20,200 --> 00:45:24,960 Speaker 3: networks to respond to natural disasters in our own species. 673 00:45:25,400 --> 00:45:29,600 Speaker 3: One thing that I think studies and animal behavior allow 674 00:45:29,719 --> 00:45:35,160 Speaker 3: us to do is this, If really your primary interest, 675 00:45:35,239 --> 00:45:39,120 Speaker 3: and it's perfectly reasonable, would be social networks and non humans, 676 00:45:39,320 --> 00:45:42,480 Speaker 3: and you're not all that interested in social networks and 677 00:45:42,520 --> 00:45:45,239 Speaker 3: non humans, here's a reason you might want to rethink that. 678 00:45:45,800 --> 00:45:50,200 Speaker 3: If you think about this at the species level, we 679 00:45:50,280 --> 00:45:54,799 Speaker 3: have a species count of one if we only focus 680 00:45:55,000 --> 00:46:00,600 Speaker 3: on humans. But if these social networks are as complex 681 00:46:01,040 --> 00:46:04,560 Speaker 3: as they appear to be in non humans, that means 682 00:46:04,800 --> 00:46:09,359 Speaker 3: that we have this treasure chest of information about how 683 00:46:09,480 --> 00:46:13,279 Speaker 3: social networks work. How does a what does it mean 684 00:46:13,320 --> 00:46:15,839 Speaker 3: to be a hub in a social network? How much 685 00:46:15,880 --> 00:46:19,200 Speaker 3: does it matter what your friends do versus what your 686 00:46:19,280 --> 00:46:22,680 Speaker 3: friends of friends do. It turns out friends of friends 687 00:46:22,760 --> 00:46:26,640 Speaker 3: really matter not in non human social networks. How do 688 00:46:26,760 --> 00:46:30,719 Speaker 3: these things work? At a most general level, we're if 689 00:46:30,760 --> 00:46:33,799 Speaker 3: we only think about humans, our sample sizes one species. 690 00:46:33,800 --> 00:46:36,320 Speaker 3: But if we think about it in dolphins and humpback 691 00:46:36,360 --> 00:46:41,439 Speaker 3: whales and and manter rays and honey bees and chimpanzees 692 00:46:41,480 --> 00:46:43,880 Speaker 3: and so many other species, then all of a sudden, 693 00:46:44,280 --> 00:46:48,640 Speaker 3: maybe we can pick up some patterns about social networks 694 00:46:48,640 --> 00:46:52,120 Speaker 3: that we wouldn't have picked up otherwise, and so I 695 00:46:52,160 --> 00:46:55,680 Speaker 3: think that that's potentially a very powerful implication of studying 696 00:46:55,680 --> 00:46:56,600 Speaker 3: this in non humans. 697 00:46:57,320 --> 00:47:02,360 Speaker 2: Yeah, absolutely fascinating. I have one last question that relates 698 00:47:02,400 --> 00:47:06,360 Speaker 2: to an earlier book that you wrote was two thousand 699 00:47:06,360 --> 00:47:10,000 Speaker 2: and nine, Mister Jefferson and the Giant Moose, Natural History 700 00:47:10,040 --> 00:47:13,240 Speaker 2: in Early America. I was not familiar with the history 701 00:47:13,320 --> 00:47:16,160 Speaker 2: referenced in the title, as if nothing else, is just 702 00:47:16,160 --> 00:47:19,719 Speaker 2: a teaser for listeners who might be interested, Could you 703 00:47:19,880 --> 00:47:23,840 Speaker 2: just briefly tell us why Thomas Jefferson was obsessed with 704 00:47:24,239 --> 00:47:24,960 Speaker 2: a giant moose? 705 00:47:25,400 --> 00:47:30,600 Speaker 3: Sure? What had happened was the world's leading naturalists in France, 706 00:47:30,840 --> 00:47:33,560 Speaker 3: a guy by the name of Count Bufon, had written 707 00:47:33,640 --> 00:47:37,160 Speaker 3: this giant encyclopedia of natural history, and one of the 708 00:47:37,200 --> 00:47:41,600 Speaker 3: things that he did in an encyclopedia was promote this 709 00:47:41,680 --> 00:47:48,120 Speaker 3: idea called the degeneration hypothesis. And what this natural historian 710 00:47:48,239 --> 00:47:52,120 Speaker 3: said was that all life in the New World, particularly 711 00:47:52,280 --> 00:47:57,520 Speaker 3: in America, was degenerate compared to life other places, that 712 00:47:57,640 --> 00:48:02,279 Speaker 3: all the animals in in the United States or even 713 00:48:02,280 --> 00:48:07,680 Speaker 3: before that, the colonies were weak, feeble, and diminished compared 714 00:48:07,920 --> 00:48:12,160 Speaker 3: to species in other places in the world, and Jefferson 715 00:48:12,280 --> 00:48:18,240 Speaker 3: became obsessed in demonstrating to Count Buffon why he was wrong. 716 00:48:18,680 --> 00:48:21,799 Speaker 3: In fact, the longest chapter in the book, the only 717 00:48:21,800 --> 00:48:24,040 Speaker 3: book that Jefferson ever wrote, The Notes on the State 718 00:48:24,080 --> 00:48:29,279 Speaker 3: of Virginia, is all about proving how wrong this degeneracy 719 00:48:29,360 --> 00:48:32,719 Speaker 3: hypothesis is. And one of the key pieces of information 720 00:48:33,880 --> 00:48:37,839 Speaker 3: that he wanted Buffon to see was our moose, which 721 00:48:38,000 --> 00:48:42,480 Speaker 3: was it's massive right. So Jefferson wanted to send Buffon 722 00:48:42,680 --> 00:48:46,000 Speaker 3: a giant moose to show him how wrong he was 723 00:48:46,120 --> 00:48:50,880 Speaker 3: about this theory of American degeneracy. And so he spent 724 00:48:50,960 --> 00:48:54,120 Speaker 3: an extraordinary amount of time and effort getting this moose 725 00:48:54,239 --> 00:48:56,680 Speaker 3: and then shipping it over to Buffon. And so that's 726 00:48:56,680 --> 00:48:59,120 Speaker 3: where the moose in the title comes from. 727 00:48:59,120 --> 00:49:00,799 Speaker 2: Fascinating. I'm going to I'm gonna have to put this 728 00:49:00,880 --> 00:49:03,840 Speaker 2: on my to read list. I was instantly fascinated. 729 00:49:04,040 --> 00:49:06,040 Speaker 3: Oh, thank you, Yeah, I mean, it was a tremendously 730 00:49:06,080 --> 00:49:07,000 Speaker 3: fun project. 731 00:49:07,320 --> 00:49:09,520 Speaker 2: Well, Lee, thanks again for coming on the show. The 732 00:49:09,600 --> 00:49:13,160 Speaker 2: new book, The Well Connected Animals, Social Networks and the 733 00:49:13,200 --> 00:49:17,680 Speaker 2: Wondrous Complexity of Animal Societies is out this week, so 734 00:49:17,920 --> 00:49:20,560 Speaker 2: encourage all of our listeners to go check that out. 735 00:49:20,960 --> 00:49:22,719 Speaker 3: Thank you so much for having me. I enjoyed you 736 00:49:22,800 --> 00:49:23,200 Speaker 3: very much. 737 00:49:24,200 --> 00:49:29,520 Speaker 2: Thanks once more to Lee Alan Dugatkin for taking time 738 00:49:29,520 --> 00:49:31,120 Speaker 2: out of his day to chat with me about the 739 00:49:31,160 --> 00:49:34,600 Speaker 2: new book. That new book, again is The Well Connected 740 00:49:34,640 --> 00:49:38,919 Speaker 2: Animal Social Networks and the Wondrous Complexity of Animal Societies. 741 00:49:39,160 --> 00:49:43,040 Speaker 2: It is out this Thursday, so go grab yourself a copy. 742 00:49:43,520 --> 00:49:47,440 Speaker 2: Thanks as always to the excellent JJ Possway for producing 743 00:49:47,480 --> 00:49:49,319 Speaker 2: this episode and if you would like to get in 744 00:49:49,360 --> 00:49:52,920 Speaker 2: touch with Joe or myself, if you have suggestions for 745 00:49:53,000 --> 00:49:55,560 Speaker 2: future episodes of Stuff to Blow Your Mind, If you 746 00:49:55,640 --> 00:50:00,080 Speaker 2: have interview suggestions you'd like us to consider, write and 747 00:50:00,200 --> 00:50:02,040 Speaker 2: we would love to hear from you. You can email us 748 00:50:02,080 --> 00:50:12,480 Speaker 2: at contact at stuff to blow your Mind dot com. 749 00:50:12,480 --> 00:50:15,440 Speaker 1: Stuff to Blow Your Mind is production of iHeartRadio. For 750 00:50:15,520 --> 00:50:18,320 Speaker 1: more podcasts from my heart Radio, visit the iHeartRadio app, 751 00:50:18,480 --> 00:50:34,840 Speaker 1: Apple Podcasts, or wherever you listen to your favorite shows.