1 00:00:06,200 --> 00:00:08,440 Speaker 1: Hey, welcome to Stuff to Blow Your Mind. This is 2 00:00:08,520 --> 00:00:10,960 Speaker 1: Robert Lamb. Today we have another vault episode for you. 3 00:00:11,039 --> 00:00:13,440 Speaker 1: This is going to be The Well Connected Animal with 4 00:00:13,520 --> 00:00:17,040 Speaker 1: Lee Alan Dugatkin. This is an interview episode that originally 5 00:00:17,079 --> 00:00:21,319 Speaker 1: published five fourteen, twenty twenty four. You'll learn all about 6 00:00:21,400 --> 00:00:25,560 Speaker 1: the complex social networks of vampire bats, dolphins, bees, and more. 7 00:00:25,840 --> 00:00:26,680 Speaker 1: Let's jump right in. 8 00:00:29,960 --> 00:00:33,720 Speaker 2: Welcome to Stuff to Blow Your Mind, a production of iHeartRadio. 9 00:00:40,440 --> 00:00:43,159 Speaker 1: Hey, welcome to Stuff to Blow Your Mind. My name 10 00:00:43,240 --> 00:00:46,960 Speaker 1: is Robert Lamb. Today I have an interview episode for you. 11 00:00:47,360 --> 00:00:50,680 Speaker 1: I recently spoke with Lee Alan Dugatkin, a professor of 12 00:00:50,720 --> 00:00:54,600 Speaker 1: evolution and behavior at the University of Louisville in Kentucky. 13 00:00:55,240 --> 00:00:58,120 Speaker 1: He is the author of the new book The Well 14 00:00:58,120 --> 00:01:04,040 Speaker 1: Connected Animal, Social Networks and the Wondrous Complexity of Animal Societies. 15 00:01:04,440 --> 00:01:08,240 Speaker 1: This book comes out this Thursday, so wherever you get 16 00:01:08,280 --> 00:01:11,160 Speaker 1: your books, in whatever formats, you can probably go ahead 17 00:01:11,160 --> 00:01:14,040 Speaker 1: and pre order it. If not, grab it on Thursday. 18 00:01:14,440 --> 00:01:17,839 Speaker 1: It's a delightful read, and this was a delightful chat. 19 00:01:18,200 --> 00:01:21,280 Speaker 1: I really enjoyed talking with Lee. Let's go ahead and 20 00:01:21,360 --> 00:01:27,280 Speaker 1: jump right into the interview. Hi, Lee, Welcome to the show. 21 00:01:27,600 --> 00:01:29,760 Speaker 3: Thank you for having me. I've been looking forward to it. 22 00:01:30,040 --> 00:01:33,880 Speaker 1: So you're an evolutionary biologist, a historian of science, and 23 00:01:33,920 --> 00:01:38,280 Speaker 1: an animal behaviorist. How do these classifications triangulate on your 24 00:01:38,319 --> 00:01:40,199 Speaker 1: work and how did you get involved in your field? 25 00:01:40,560 --> 00:01:45,479 Speaker 3: Yeah, so I got involved in a sort of securitous way. 26 00:01:46,319 --> 00:01:49,760 Speaker 3: I really didn't know what I wanted to do. Even 27 00:01:49,760 --> 00:01:53,520 Speaker 3: when I was in college. I happened upon the study 28 00:01:53,600 --> 00:01:57,160 Speaker 3: of animal behavior and evolution, and a friend of mine 29 00:01:57,640 --> 00:02:00,560 Speaker 3: mentioned a book he was reading, and before I knew it, 30 00:02:00,600 --> 00:02:04,800 Speaker 3: I was in graduate school studying evolution and behavior and 31 00:02:04,840 --> 00:02:07,320 Speaker 3: non humans because I just fell in love with the 32 00:02:07,360 --> 00:02:09,519 Speaker 3: topic and I thought it was sort of the ultimate 33 00:02:10,160 --> 00:02:13,800 Speaker 3: kinds of questions one could ask about life on the planet. 34 00:02:14,280 --> 00:02:17,080 Speaker 3: And so early on, a lot of my work was 35 00:02:17,639 --> 00:02:20,680 Speaker 3: experimental work with animals. I looked at the evolution of 36 00:02:20,720 --> 00:02:25,480 Speaker 3: cooperation and the evolution of aggression and so on for 37 00:02:25,560 --> 00:02:30,600 Speaker 3: many many years in my laboratory. And then about fifteen 38 00:02:30,720 --> 00:02:33,200 Speaker 3: years ago or so, after I had been doing this 39 00:02:33,320 --> 00:02:36,760 Speaker 3: for a couple of decades, I started getting much more 40 00:02:36,800 --> 00:02:40,360 Speaker 3: interested in the history of the subjects that I was 41 00:02:40,800 --> 00:02:43,760 Speaker 3: working on. So it turns out that all of the 42 00:02:43,800 --> 00:02:47,040 Speaker 3: people early on who were studying the evolution of cooperation 43 00:02:47,280 --> 00:02:51,520 Speaker 3: were really fascinating characters in and of themselves, how they 44 00:02:51,560 --> 00:02:55,160 Speaker 3: came about it, the environment and the kind of social 45 00:02:55,200 --> 00:02:59,440 Speaker 3: atmosphere in which they developed their ideas. And I began 46 00:02:59,760 --> 00:03:03,280 Speaker 3: because I'm much more interested in that aspect of the work. 47 00:03:03,480 --> 00:03:08,480 Speaker 3: And so all these areas dovetail in the sense that 48 00:03:09,320 --> 00:03:13,600 Speaker 3: I still do work on studying evolution and animal behavior, 49 00:03:13,680 --> 00:03:17,200 Speaker 3: but I also have added this component of it's worth 50 00:03:17,280 --> 00:03:20,320 Speaker 3: stopping for a minute and realizing that, you know, in 51 00:03:20,360 --> 00:03:24,120 Speaker 3: the late eighteen hundreds or something like that, here's how 52 00:03:24,160 --> 00:03:26,880 Speaker 3: these ideas came about. Here are the people who did it, 53 00:03:26,919 --> 00:03:29,920 Speaker 3: and here's the kind of social environment in which it happened. 54 00:03:29,960 --> 00:03:33,399 Speaker 3: And I think it makes the study of science itself 55 00:03:33,480 --> 00:03:35,120 Speaker 3: much richer to do it that way. 56 00:03:35,680 --> 00:03:38,840 Speaker 1: The new book is The Well Connected Animals, Social Networks 57 00:03:38,920 --> 00:03:42,400 Speaker 1: and the Wondrous Complexity of Animal Societies. Could you walk 58 00:03:42,440 --> 00:03:45,680 Speaker 1: us through what a social network is and say, sociology 59 00:03:45,680 --> 00:03:49,600 Speaker 1: and anthropology and how and when the concept enters into 60 00:03:49,720 --> 00:03:52,000 Speaker 1: contemplation of non human animals. 61 00:03:52,720 --> 00:03:56,400 Speaker 3: Sure, And you know, I should say that there are 62 00:03:56,440 --> 00:04:01,360 Speaker 3: many many different definitions of social network, uh, depending on 63 00:04:01,600 --> 00:04:05,960 Speaker 3: exactly what discipline you're interested in and how mathematical you 64 00:04:06,000 --> 00:04:09,040 Speaker 3: want to get for me, and I think from I 65 00:04:09,040 --> 00:04:12,600 Speaker 3: think this is a reasonable general definition. Is a social 66 00:04:12,640 --> 00:04:18,160 Speaker 3: network is just a group of individuals through which information travels, 67 00:04:18,960 --> 00:04:22,520 Speaker 3: and it may travel directly and it may travel indirectly. 68 00:04:23,080 --> 00:04:26,680 Speaker 3: And it's also a group of individuals who somehow or 69 00:04:26,720 --> 00:04:30,720 Speaker 3: another affect one another in a real way. And again 70 00:04:31,480 --> 00:04:36,880 Speaker 3: that could happen either directly by your interaction with Steve, 71 00:04:37,200 --> 00:04:40,320 Speaker 3: or it could happen indirectly because you interact with Steve, 72 00:04:40,360 --> 00:04:43,799 Speaker 3: and Steve interacts with Nancy, and so Nancy is affected 73 00:04:43,800 --> 00:04:48,320 Speaker 3: by what you have done to Steve. And so generally 74 00:04:48,360 --> 00:04:54,880 Speaker 3: it's this information flow and effect on others in your network. 75 00:04:54,920 --> 00:05:00,200 Speaker 3: That's that for me, is the key to social network. 76 00:05:01,040 --> 00:05:03,600 Speaker 3: You know, humans are in all sorts of social networks, 77 00:05:03,640 --> 00:05:06,880 Speaker 3: Facebook and that sort of thing. But we're also embedded 78 00:05:06,920 --> 00:05:10,280 Speaker 3: in them in almost everything we do in life. Our 79 00:05:10,680 --> 00:05:14,360 Speaker 3: families or one kind of social network, the people we 80 00:05:14,400 --> 00:05:18,080 Speaker 3: work with or another, our friends or another, and so on, 81 00:05:18,120 --> 00:05:24,479 Speaker 3: and they all overlap. Now in terms of the history 82 00:05:24,520 --> 00:05:29,560 Speaker 3: of the subject, I mean, anthropologists and particularly sociologists have 83 00:05:29,600 --> 00:05:35,000 Speaker 3: been interested in this in humans for quite some time. 84 00:05:35,560 --> 00:05:39,039 Speaker 3: It was you know, in the nineteen forties and nineteen 85 00:05:39,080 --> 00:05:43,760 Speaker 3: fifties where people first began to get serious about trying 86 00:05:43,800 --> 00:05:49,239 Speaker 3: to understand the details of what we now would refer 87 00:05:49,360 --> 00:05:54,640 Speaker 3: to as social networks, how they work in humans. And 88 00:05:55,360 --> 00:06:02,200 Speaker 3: that involved a lot of mathematics terms of understanding what 89 00:06:02,240 --> 00:06:05,320 Speaker 3: it means if I interact with you and you interact 90 00:06:05,320 --> 00:06:08,320 Speaker 3: with somebody else, and that somebody else is affected by me, 91 00:06:08,600 --> 00:06:12,279 Speaker 3: and who is who are the key individuals in the 92 00:06:12,320 --> 00:06:15,200 Speaker 3: social network, the ones that have the most impact, and 93 00:06:16,279 --> 00:06:20,480 Speaker 3: are there cliques where certain individuals tend to interact with 94 00:06:20,560 --> 00:06:25,039 Speaker 3: each other more? And that really began to flourish, I 95 00:06:25,040 --> 00:06:28,320 Speaker 3: would say in the nineteen forties and fifties in anthropology 96 00:06:28,600 --> 00:06:33,560 Speaker 3: and sociology what we now refer to as as social 97 00:06:33,640 --> 00:06:40,239 Speaker 3: network I think that language became much more common in 98 00:06:40,279 --> 00:06:46,200 Speaker 3: the late nineteen hundreds and into the early two thousands. 99 00:06:46,560 --> 00:06:50,800 Speaker 3: Now when it comes to studying social networks and non humans, 100 00:06:52,200 --> 00:06:55,680 Speaker 3: we can trace it back quite far in the sense 101 00:06:55,839 --> 00:07:00,520 Speaker 3: that even in Darwindnesday and a little bit late so 102 00:07:00,600 --> 00:07:05,880 Speaker 3: the late eighteen hundreds, people were beginning to think that 103 00:07:06,040 --> 00:07:09,080 Speaker 3: the same kind of social tools that can be used 104 00:07:09,120 --> 00:07:13,520 Speaker 3: to study human behavior can be used to study animal behavior, 105 00:07:14,000 --> 00:07:17,960 Speaker 3: and so there are people who were basically arguing that 106 00:07:18,040 --> 00:07:22,840 Speaker 3: animal societies were structured very much like human societies. And 107 00:07:22,920 --> 00:07:27,120 Speaker 3: even though the word social network wasn't around, the idea 108 00:07:27,360 --> 00:07:30,440 Speaker 3: that we could study non humans like we study humans 109 00:07:30,560 --> 00:07:35,480 Speaker 3: was there. It really began to flourish more again around 110 00:07:35,560 --> 00:07:39,240 Speaker 3: the same time as early work in humans did. That is, 111 00:07:40,120 --> 00:07:43,520 Speaker 3: in this case, maybe the nineteen fifties and nineteen sixties, 112 00:07:43,560 --> 00:07:49,960 Speaker 3: when primatologists, people who study monkeys and apes, began to 113 00:07:50,440 --> 00:07:54,520 Speaker 3: think in terms of social networks. They began to realize 114 00:07:54,560 --> 00:08:00,800 Speaker 3: that information was flowing through these non humans groups, again 115 00:08:00,920 --> 00:08:05,200 Speaker 3: mostly primates, in a way that was similar to the 116 00:08:05,240 --> 00:08:08,040 Speaker 3: way it flows through human groups. And it really mattered 117 00:08:08,280 --> 00:08:12,720 Speaker 3: in a group of chimpanzees, if one chimpanzee groomed the 118 00:08:12,800 --> 00:08:16,320 Speaker 3: other and got bugs and parasites off it back, it 119 00:08:16,400 --> 00:08:20,920 Speaker 3: really mattered who they groomed and who that individual groomed, 120 00:08:21,040 --> 00:08:25,840 Speaker 3: and how that kind of grooming social network developed. So 121 00:08:26,200 --> 00:08:30,280 Speaker 3: it first started with non human primates, which kind of 122 00:08:30,280 --> 00:08:34,079 Speaker 3: makes sense because there are closest evolutionary relatives, and then 123 00:08:34,240 --> 00:08:38,559 Speaker 3: it began to branch out in the nineteen eighties and 124 00:08:38,640 --> 00:08:41,480 Speaker 3: nineteen nineties when people began to study it in all 125 00:08:41,520 --> 00:08:45,559 Speaker 3: sorts of organisms. In fact, the two classic studies early 126 00:08:45,640 --> 00:08:52,040 Speaker 3: on were done in dolphins and in birds. So it's 127 00:08:52,080 --> 00:08:55,160 Speaker 3: been around in a serious way in the animal behavior 128 00:08:55,200 --> 00:08:57,880 Speaker 3: literature for you know, on the order of twenty to 129 00:08:57,920 --> 00:08:58,559 Speaker 3: thirty years. 130 00:08:58,600 --> 00:09:02,120 Speaker 1: Now, okay, the roots go back a bit deeper, but 131 00:09:02,200 --> 00:09:05,040 Speaker 1: it's really taken off in recent decades. 132 00:09:05,440 --> 00:09:08,520 Speaker 3: Absolutely, absolutely, In fact, right now, the study of social 133 00:09:08,559 --> 00:09:12,000 Speaker 3: networks and non humans is probably is one of the 134 00:09:12,040 --> 00:09:15,520 Speaker 3: most active areas in the entire field. I mean, anytime 135 00:09:15,559 --> 00:09:18,120 Speaker 3: you open up a journal on behavior and non humans, 136 00:09:18,120 --> 00:09:22,440 Speaker 3: you're likely to find an article about social networks. And 137 00:09:22,480 --> 00:09:26,560 Speaker 3: those networks are important in almost every context for non humans. 138 00:09:27,000 --> 00:09:30,719 Speaker 3: There are networks about feeding. There a mating networks, there 139 00:09:30,720 --> 00:09:35,200 Speaker 3: are traveling networks, there are cooperation networks, there are power networks. 140 00:09:35,240 --> 00:09:38,880 Speaker 3: Everywhere you look. Now that we've started to actually explore 141 00:09:38,920 --> 00:09:41,199 Speaker 3: it in depth, we're finding it. And this is sort 142 00:09:41,200 --> 00:09:44,360 Speaker 3: of what happens in animal behavior a lot. We tend 143 00:09:44,440 --> 00:09:49,319 Speaker 3: to think of things initially as strictly human, and then 144 00:09:49,360 --> 00:09:53,240 Speaker 3: when we begin to probe deeper, we see similar sorts 145 00:09:53,240 --> 00:09:56,040 Speaker 3: of things in non humans, and once we begin to look, 146 00:09:56,120 --> 00:09:57,479 Speaker 3: we begin to see it everywhere. 147 00:09:57,720 --> 00:10:00,520 Speaker 1: And to be clear, like with humans, we're talking about 148 00:10:00,679 --> 00:10:04,119 Speaker 1: multiple social networks being in place for a given individual 149 00:10:04,320 --> 00:10:06,679 Speaker 1: or a given group. But the same is true of 150 00:10:06,720 --> 00:10:09,199 Speaker 1: these various animal examples as well. Right, We're not talking 151 00:10:09,200 --> 00:10:12,320 Speaker 1: about just like the social network of the macaque. We're 152 00:10:12,320 --> 00:10:16,600 Speaker 1: talking about like the multiple interconnected social networks. 153 00:10:16,679 --> 00:10:16,839 Speaker 2: Right. 154 00:10:17,240 --> 00:10:24,520 Speaker 3: Absolutely. So it might be that you're studying networks in 155 00:10:24,559 --> 00:10:27,240 Speaker 3: the sense of who's going around feeding with who, and 156 00:10:27,280 --> 00:10:29,960 Speaker 3: who's sharing food with who, But in that same species, 157 00:10:30,000 --> 00:10:34,880 Speaker 3: you might be looking at who's being aggressive towards who, 158 00:10:34,920 --> 00:10:38,040 Speaker 3: and who's traveling together, and who's being nice to who, 159 00:10:38,120 --> 00:10:40,880 Speaker 3: and all of those are overlapping networks in the same 160 00:10:40,960 --> 00:10:42,320 Speaker 3: sense as in humans. 161 00:10:42,960 --> 00:10:45,920 Speaker 1: Now, is there still any kind of like pushback from 162 00:10:46,160 --> 00:10:50,520 Speaker 1: I guess human exceptionalists that argue that animals can't non 163 00:10:50,600 --> 00:10:55,040 Speaker 1: human animals can't or don't demonstrate this kind of complexity. 164 00:10:54,920 --> 00:11:00,400 Speaker 3: Well, not within I would say the scientific community really 165 00:11:00,440 --> 00:11:03,719 Speaker 3: isn't much of that pushback anymore. Initially there was when 166 00:11:03,760 --> 00:11:08,520 Speaker 3: people first began studying social networks and non humans. Many 167 00:11:08,679 --> 00:11:14,760 Speaker 3: people in who were animal behaviorists were leery that the 168 00:11:14,840 --> 00:11:18,800 Speaker 3: notion was that animals are complex and their societies are complex, 169 00:11:18,880 --> 00:11:23,480 Speaker 3: but they're but they're not social network complex that that was. 170 00:11:23,640 --> 00:11:27,200 Speaker 3: You know, even in the early two thousands, there there 171 00:11:27,240 --> 00:11:31,040 Speaker 3: were still some of that. I interviewed a bunch of 172 00:11:31,080 --> 00:11:35,040 Speaker 3: people for this book, and I had stories of people 173 00:11:35,120 --> 00:11:38,600 Speaker 3: telling me that in the early two thousands, when they 174 00:11:38,640 --> 00:11:42,240 Speaker 3: would put in grant applications to study social networks and 175 00:11:42,280 --> 00:11:46,320 Speaker 3: non humans, they would get this kind of pushback that was, 176 00:11:46,360 --> 00:11:50,280 Speaker 3: you know, it can't be this complex. You're you're making 177 00:11:50,280 --> 00:11:52,480 Speaker 3: it more complex than it needs to be, and we're 178 00:11:52,520 --> 00:11:55,000 Speaker 3: not ready to kind of give you money to do this. 179 00:11:55,200 --> 00:11:57,480 Speaker 3: But the more that people were able to do it 180 00:11:58,679 --> 00:12:01,400 Speaker 3: on their own, the more that it became part and 181 00:12:01,480 --> 00:12:04,440 Speaker 3: parcel of what people were looking for, the less less 182 00:12:04,480 --> 00:12:08,559 Speaker 3: the pushback. I mean, you're going to get pushback from 183 00:12:08,559 --> 00:12:13,280 Speaker 3: outside the scientific community, from people who who just want 184 00:12:13,320 --> 00:12:18,240 Speaker 3: to live in a world of human exceptionalism. That tends 185 00:12:18,280 --> 00:12:21,120 Speaker 3: to fall apart eventually. I mean, you know, we used 186 00:12:21,160 --> 00:12:24,040 Speaker 3: to think that was true about tool use in in 187 00:12:24,040 --> 00:12:27,560 Speaker 3: in in animals, that you know, only humans could use tools. 188 00:12:27,559 --> 00:12:30,840 Speaker 3: Then we find that animals can use tools only humans 189 00:12:30,880 --> 00:12:33,600 Speaker 3: have culture. Well, no, it turns out animals have culture, 190 00:12:34,360 --> 00:12:37,520 Speaker 3: only humans have social networks. No, in fact, non humans 191 00:12:37,559 --> 00:12:41,240 Speaker 3: have social networks. So the pushback is nowadays not as 192 00:12:41,320 --> 00:12:42,280 Speaker 3: bad as it used to be. 193 00:12:42,800 --> 00:12:45,480 Speaker 1: I like that you mentioned the tool use. You know 194 00:12:45,520 --> 00:12:47,080 Speaker 1: this This is one of those areas where when we 195 00:12:47,080 --> 00:12:49,040 Speaker 1: think of tool use use in animals, a lot of 196 00:12:49,040 --> 00:12:52,440 Speaker 1: people instantly think of examples involving say like chimpanzees, or 197 00:12:52,880 --> 00:12:56,040 Speaker 1: or or or or certain you know birds come to mind. 198 00:12:57,360 --> 00:13:00,760 Speaker 1: And especially when we think of culturally trans submitted tool use, 199 00:13:00,800 --> 00:13:03,600 Speaker 1: we may think of the chimpanzee example, But you discuss 200 00:13:03,720 --> 00:13:08,440 Speaker 1: examples involving dolphins and whales, can you describe the tools 201 00:13:08,480 --> 00:13:09,480 Speaker 1: they seem to be using. 202 00:13:10,480 --> 00:13:15,400 Speaker 3: Sure? So there's this wonderful study on bottomnosed dolphins. It's 203 00:13:15,400 --> 00:13:19,520 Speaker 3: been going on in Australia in a place called Shark 204 00:13:19,600 --> 00:13:22,520 Speaker 3: Bay for the last thirty or forty years, and so 205 00:13:23,280 --> 00:13:27,840 Speaker 3: they know literally thousands of dolphins that are swimming around 206 00:13:27,880 --> 00:13:32,760 Speaker 3: in Shark Bay individually, and we've learned a tremendous amount 207 00:13:32,800 --> 00:13:36,200 Speaker 3: about sort of culture, how complex they are socially. But 208 00:13:37,760 --> 00:13:41,880 Speaker 3: it wasn't until the last couple of decades that this 209 00:13:42,040 --> 00:13:47,000 Speaker 3: idea that tool use in dolphins was going on. And 210 00:13:47,600 --> 00:13:53,200 Speaker 3: in dolphins the tool is actually another living organism. So 211 00:13:53,440 --> 00:13:59,680 Speaker 3: basically what they do is they put a sponge on 212 00:13:59,800 --> 00:14:06,120 Speaker 3: the end of their rostrum, their their face, where their 213 00:14:06,160 --> 00:14:09,720 Speaker 3: mouth is. They put this sponge over that when they're 214 00:14:09,760 --> 00:14:12,160 Speaker 3: looking for food. And the reason that they do that 215 00:14:12,559 --> 00:14:16,920 Speaker 3: is that oftentimes they're probing the bottom of the bay 216 00:14:16,960 --> 00:14:21,080 Speaker 3: that's very rocky and gravelly, and it hurts to pound 217 00:14:21,120 --> 00:14:22,920 Speaker 3: down on there, and what they're doing is they're trying 218 00:14:22,920 --> 00:14:25,520 Speaker 3: to find fish that are hiding under the gravel and sand. 219 00:14:26,000 --> 00:14:30,080 Speaker 3: So if you stick this soft sponge on top of 220 00:14:30,120 --> 00:14:35,760 Speaker 3: your mouth, then probing down there allows you much more 221 00:14:35,800 --> 00:14:40,320 Speaker 3: flexibility and a lot less pain. And it turns out 222 00:14:40,920 --> 00:14:44,760 Speaker 3: that dolphins are very particular about the sponges that they use. 223 00:14:44,880 --> 00:14:48,480 Speaker 3: Once they find one that works, they keep it for 224 00:14:48,520 --> 00:14:50,720 Speaker 3: a long time. So they'll go around probing on the 225 00:14:50,760 --> 00:14:53,320 Speaker 3: bottom for food, and if they find a fish that's hidden, 226 00:14:53,640 --> 00:14:56,160 Speaker 3: they drop the sponge and they go and they catch 227 00:14:56,200 --> 00:15:00,320 Speaker 3: that fish. Then they come back and they look for 228 00:15:00,960 --> 00:15:03,920 Speaker 3: the sponge they had before, because if it worked once, 229 00:15:04,000 --> 00:15:07,040 Speaker 3: it's likely to work again. They pick it up, they 230 00:15:07,080 --> 00:15:10,880 Speaker 3: put it back well, they put it back onto their face, 231 00:15:11,520 --> 00:15:17,520 Speaker 3: and then they go looking for more things at sort 232 00:15:17,520 --> 00:15:19,560 Speaker 3: of hiding in the bottom of the sand. And they 233 00:15:19,680 --> 00:15:24,920 Speaker 3: learn this, they learn how to sponge from their mothers. 234 00:15:25,400 --> 00:15:29,360 Speaker 3: So if you watch calves, dolphin calves, they're basically learning 235 00:15:29,400 --> 00:15:32,320 Speaker 3: the technique of how to find a good sponge, put 236 00:15:32,360 --> 00:15:36,400 Speaker 3: it on, and hunt with it. And so the actual 237 00:15:37,120 --> 00:15:44,920 Speaker 3: learning is through mother child interaction. It's not genetic, but 238 00:15:45,000 --> 00:15:49,080 Speaker 3: it's rather they learn from their parents. Now, in terms 239 00:15:49,080 --> 00:15:55,440 Speaker 3: of the networking, it's actually complex. So basically when they 240 00:15:55,480 --> 00:16:00,240 Speaker 3: have these sponges on, they are hunting alone, and so 241 00:16:00,360 --> 00:16:03,800 Speaker 3: you wouldn't think that the sponging had anything to do 242 00:16:03,880 --> 00:16:08,440 Speaker 3: with social networks, but in fact it does. And the 243 00:16:08,480 --> 00:16:13,520 Speaker 3: reason that it does is dolphins often carry these sponges 244 00:16:13,560 --> 00:16:16,920 Speaker 3: around when they're not hunting because, as I say, if 245 00:16:16,920 --> 00:16:20,120 Speaker 3: they find a good sponge, they like it. Dolphins know 246 00:16:20,760 --> 00:16:24,640 Speaker 3: who else are spongers. Not everyone's a sponger, only a 247 00:16:24,640 --> 00:16:28,240 Speaker 3: small subset of them actually use these tools. And so 248 00:16:28,480 --> 00:16:33,040 Speaker 3: what the researchers did was they started trying to understand 249 00:16:33,120 --> 00:16:37,760 Speaker 3: whether or not individuals who did use these sponge tools 250 00:16:38,120 --> 00:16:41,240 Speaker 3: hung out with each other when they actually weren't going 251 00:16:41,280 --> 00:16:44,520 Speaker 3: around looking for food, and it turns out they were. 252 00:16:44,600 --> 00:16:48,720 Speaker 3: There are these clicks of dolphins that use the sponge 253 00:16:48,720 --> 00:16:51,320 Speaker 3: tools that hang out with each other when they're not 254 00:16:51,600 --> 00:16:56,080 Speaker 3: actually down on the bottom looking for food. And the 255 00:16:56,160 --> 00:16:59,360 Speaker 3: reason we think they do this is if you hang 256 00:16:59,400 --> 00:17:03,760 Speaker 3: out with others spongers, you're likely to get information about 257 00:17:03,760 --> 00:17:09,040 Speaker 3: where the good places to sponge are, and so you 258 00:17:09,160 --> 00:17:12,880 Speaker 3: network with them so that you can take these sponges 259 00:17:13,040 --> 00:17:17,919 Speaker 3: and use them in the best possible locations. And the 260 00:17:17,960 --> 00:17:22,000 Speaker 3: sponges are really great tools. The ganet man who ran 261 00:17:22,080 --> 00:17:25,080 Speaker 3: this study, she and her colleagues actually thought, you know, 262 00:17:26,040 --> 00:17:30,439 Speaker 3: we should see if sponging really works. It kind of 263 00:17:30,440 --> 00:17:33,320 Speaker 3: looks like it works when you watch the dolphins. They 264 00:17:33,359 --> 00:17:36,760 Speaker 3: put sponges on their hands and they went around under 265 00:17:36,800 --> 00:17:40,480 Speaker 3: the water using the sponges on their hands the way 266 00:17:40,520 --> 00:17:44,240 Speaker 3: dolphins used them on their snouts. And it turns out 267 00:17:44,320 --> 00:17:48,000 Speaker 3: you really do kick up a lot of prey items 268 00:17:48,040 --> 00:17:51,560 Speaker 3: things to eat when you use these sponges. And so 269 00:17:51,760 --> 00:17:57,680 Speaker 3: networking allowed us to kind of understand this cultural tool 270 00:17:57,800 --> 00:18:00,760 Speaker 3: use in a much more general way, not just this 271 00:18:00,880 --> 00:18:03,639 Speaker 3: dolphin does it or that dolphin does it, but that 272 00:18:03,680 --> 00:18:06,680 Speaker 3: they hang around together if they do it, and they 273 00:18:06,800 --> 00:18:09,199 Speaker 3: get all sorts of information from each other when they 274 00:18:09,240 --> 00:18:09,560 Speaker 3: do this. 275 00:18:09,880 --> 00:18:10,800 Speaker 1: Wow, that's incredible. 276 00:18:10,960 --> 00:18:16,520 Speaker 3: Yeah, No, it's a wonderful long term study of an 277 00:18:16,560 --> 00:18:22,120 Speaker 3: incredibly complex organism. I mean dolphins. Everybody loves dolphins. Their 278 00:18:22,160 --> 00:18:26,200 Speaker 3: sociality is over the top in terms of the sorts 279 00:18:26,240 --> 00:18:28,000 Speaker 3: of things we typically see in nature. 280 00:18:37,520 --> 00:18:39,479 Speaker 1: Now, in the book, you divide the chapters up by 281 00:18:39,480 --> 00:18:43,560 Speaker 1: specific networks and needs. You know you alluded to this earlier, 282 00:18:43,720 --> 00:18:48,560 Speaker 1: you know, food reproduction, power, safety, travel, communication, health, and culture. 283 00:18:49,040 --> 00:18:52,080 Speaker 1: What areas were you either most surprised about or do 284 00:18:52,119 --> 00:18:55,520 Speaker 1: you think will be most surprising to readers in these chapters? 285 00:18:55,560 --> 00:18:57,639 Speaker 1: You know, because I feel like for a lot of 286 00:18:57,680 --> 00:19:00,439 Speaker 1: general readers, you know, we can easily think of non 287 00:19:00,560 --> 00:19:04,159 Speaker 1: human animals engaging in some level of food reproduction or 288 00:19:04,200 --> 00:19:07,679 Speaker 1: power dynamics. You know, we've seen enough documentaries or engage 289 00:19:07,680 --> 00:19:11,400 Speaker 1: with enough animal content. But communication and health don't always 290 00:19:11,400 --> 00:19:14,480 Speaker 1: instantly come to mind, I imagine, not with like the 291 00:19:14,840 --> 00:19:17,160 Speaker 1: number of species that are covered in the book. 292 00:19:18,560 --> 00:19:24,959 Speaker 3: Sure, yeah, so communication is a great one in terms 293 00:19:25,080 --> 00:19:29,120 Speaker 3: of the kinds of things that might surprise readers. There 294 00:19:29,200 --> 00:19:36,280 Speaker 3: is wonderful work that's been done on communication in chimpanzees, 295 00:19:36,320 --> 00:19:41,960 Speaker 3: for example. So in chimpanzees, when they're communicating with each other, 296 00:19:42,720 --> 00:19:47,800 Speaker 3: they can communicate in all sorts of different ways, and 297 00:19:47,840 --> 00:19:51,480 Speaker 3: some of those ways are much more visual, and some 298 00:19:51,560 --> 00:19:55,880 Speaker 3: of them are much more physical and tactile that involve touching. 299 00:19:56,520 --> 00:20:01,040 Speaker 3: And it turns out that again, social netw work thinking 300 00:20:01,240 --> 00:20:06,959 Speaker 3: allows us to probe really deep into these communication networks 301 00:20:07,600 --> 00:20:15,119 Speaker 3: that exist in chimpanzees. And so, for example, there's this 302 00:20:15,240 --> 00:20:19,480 Speaker 3: population of chimpanzees called the Suns population that's been studied 303 00:20:19,480 --> 00:20:22,600 Speaker 3: for a very long time, and it turns out that 304 00:20:22,680 --> 00:20:26,959 Speaker 3: when you look at the kind of gestural communication that 305 00:20:27,040 --> 00:20:31,640 Speaker 3: goes on in these chimpanzees, you see something very different 306 00:20:32,040 --> 00:20:36,520 Speaker 3: when individuals are interacting with friends in their social networks 307 00:20:37,080 --> 00:20:41,119 Speaker 3: rather than with others who they don't know quite as well. 308 00:20:41,760 --> 00:20:48,439 Speaker 3: So if you look at communication between chimpanzees who know 309 00:20:48,560 --> 00:20:52,680 Speaker 3: each other well and who have done things like cooperative 310 00:20:52,760 --> 00:21:00,399 Speaker 3: hunting together, they tend to use visual communication. And this 311 00:21:00,520 --> 00:21:05,640 Speaker 3: kind of visual communication. Other people have found that this 312 00:21:05,720 --> 00:21:10,199 Speaker 3: kind of really lowers their heart rates and reduces the 313 00:21:10,240 --> 00:21:15,719 Speaker 3: amount of stress that the chimpanzee itself feels. The individual 314 00:21:15,760 --> 00:21:19,919 Speaker 3: who's the recipient of the communication calms down, they have 315 00:21:19,960 --> 00:21:23,840 Speaker 3: a lower heart rate. This is when communication goes on 316 00:21:24,000 --> 00:21:28,000 Speaker 3: between friends, But when communication is going on between individuals 317 00:21:28,040 --> 00:21:30,879 Speaker 3: who don't know each other as well, maybe haven't interacted 318 00:21:30,920 --> 00:21:33,520 Speaker 3: as much in the past. Then you tend to see 319 00:21:33,520 --> 00:21:36,240 Speaker 3: a different kind of communication. You tend to see it 320 00:21:36,320 --> 00:21:40,040 Speaker 3: being very physical and tactile, with them touching each other. 321 00:21:40,640 --> 00:21:44,600 Speaker 3: And the researchers who did this work, Anna and Sam Roberts, 322 00:21:44,960 --> 00:21:49,600 Speaker 3: have argued that visual communication works perfectly well between friends 323 00:21:49,640 --> 00:21:52,520 Speaker 3: because you know each other and there's a large level 324 00:21:52,560 --> 00:21:55,440 Speaker 3: of trust that's already in place. But when you don't 325 00:21:55,520 --> 00:22:00,400 Speaker 3: know each other very well, visual communication doesn't work well 326 00:22:00,440 --> 00:22:05,200 Speaker 3: because it's not as clear, it's not as as salient 327 00:22:05,760 --> 00:22:11,000 Speaker 3: as auditory and tactile communication, where you're really up front, 328 00:22:11,119 --> 00:22:14,160 Speaker 3: right in the face of the individual touching them, then 329 00:22:14,240 --> 00:22:16,960 Speaker 3: they can really understand what it is that you're trying 330 00:22:17,000 --> 00:22:22,719 Speaker 3: to communicate. And that's important because chimpanzee networks break up 331 00:22:22,760 --> 00:22:25,680 Speaker 3: and they come back together. And so when you're interacting 332 00:22:25,680 --> 00:22:29,320 Speaker 3: with individuals that you don't know very well, you really 333 00:22:29,359 --> 00:22:33,199 Speaker 3: want to make sure that your communication is clear. And 334 00:22:33,240 --> 00:22:38,480 Speaker 3: these kind of auditory and physical communication gestures are much 335 00:22:38,520 --> 00:22:40,800 Speaker 3: better at that. And so you see it among individuals 336 00:22:40,840 --> 00:22:42,639 Speaker 3: who don't know each other very well. 337 00:22:43,040 --> 00:22:43,680 Speaker 1: Fascinating. 338 00:22:44,119 --> 00:22:47,480 Speaker 3: But here's the thing, you know, So chimpanzees are our 339 00:22:47,520 --> 00:22:52,600 Speaker 3: closest living relatives and they have very large brains, right, 340 00:22:52,720 --> 00:22:56,840 Speaker 3: But you can go and find communication networks and honeybees, 341 00:22:57,680 --> 00:23:01,600 Speaker 3: and a former student of mine has been studying that 342 00:23:01,920 --> 00:23:05,480 Speaker 3: in a research field station outside of London where they 343 00:23:05,480 --> 00:23:12,440 Speaker 3: have these experimental honeybee hives, and so in honeybees, the 344 00:23:12,520 --> 00:23:17,040 Speaker 3: primary way that they communicate is actually by something known 345 00:23:17,080 --> 00:23:20,080 Speaker 3: as the waggle dance. So here the bees are trying 346 00:23:20,119 --> 00:23:23,520 Speaker 3: to communicate to one another where a new food source is. 347 00:23:24,119 --> 00:23:26,560 Speaker 3: And the way that they typically do this is if 348 00:23:26,640 --> 00:23:29,120 Speaker 3: you find a new food source and you go back 349 00:23:29,160 --> 00:23:33,000 Speaker 3: to the honeybee hive, you do this particular kind of 350 00:23:33,119 --> 00:23:36,800 Speaker 3: dance known as the waggle dance, and the waggle dance 351 00:23:37,200 --> 00:23:43,080 Speaker 3: basically involves moving very very quickly and shaking your abdomen 352 00:23:43,240 --> 00:23:46,440 Speaker 3: as you're moving through the hive. And we know from 353 00:23:46,520 --> 00:23:50,879 Speaker 3: prior work that this gives various types of information to 354 00:23:51,040 --> 00:23:53,679 Speaker 3: others in the hive about where the food source is. 355 00:23:54,200 --> 00:23:58,760 Speaker 3: For example, how long you dance gives information about how 356 00:23:58,840 --> 00:24:01,960 Speaker 3: far away the hive is, and there's a really nice translation. 357 00:24:02,160 --> 00:24:06,000 Speaker 3: Every tenth of a second of dancing translates into the 358 00:24:06,040 --> 00:24:09,640 Speaker 3: food being this many feet away from the hive. They 359 00:24:09,680 --> 00:24:12,840 Speaker 3: also dance at a certain angle, and the angle they 360 00:24:13,000 --> 00:24:15,720 Speaker 3: dance it, believe it or not, is the angle between 361 00:24:16,359 --> 00:24:20,040 Speaker 3: the sun, the hive and where the food is. So 362 00:24:20,080 --> 00:24:25,200 Speaker 3: they're communicating all of this information through the dance. Now, 363 00:24:25,800 --> 00:24:29,439 Speaker 3: in terms of networking, it turns out that there are 364 00:24:29,520 --> 00:24:34,320 Speaker 3: other ways to communicate information about new food sources. They 365 00:24:34,320 --> 00:24:41,119 Speaker 3: can communicate this information when they transfer food from themselves 366 00:24:41,520 --> 00:24:45,760 Speaker 3: to another individual in the hive. They can also communicate 367 00:24:45,800 --> 00:24:50,280 Speaker 3: information about the food when they basically take their antennas 368 00:24:50,520 --> 00:24:53,560 Speaker 3: and connect to the antennas of another bee in the hive. 369 00:24:53,680 --> 00:24:58,480 Speaker 3: This also gives them information. So what has been done 370 00:24:58,520 --> 00:25:01,560 Speaker 3: in terms of the networking is people have asked, well, 371 00:25:01,920 --> 00:25:05,240 Speaker 3: they can get this information in all these different ways, 372 00:25:06,280 --> 00:25:08,000 Speaker 3: how do they do it? When do they decide if 373 00:25:08,040 --> 00:25:12,800 Speaker 3: they're going to transfer the information by dancing or moving 374 00:25:12,880 --> 00:25:18,080 Speaker 3: food from one mouth to another or touching antenna. So 375 00:25:18,720 --> 00:25:21,840 Speaker 3: what researchers did was they set up this experiment where 376 00:25:21,880 --> 00:25:24,600 Speaker 3: they had these hives that were placed out in a 377 00:25:24,680 --> 00:25:27,480 Speaker 3: field and they knew where the hives were, and they 378 00:25:27,520 --> 00:25:31,320 Speaker 3: controlled how much food was going into the hives. And 379 00:25:32,240 --> 00:25:35,080 Speaker 3: what they found was and so I should say they 380 00:25:35,119 --> 00:25:37,679 Speaker 3: marked all of their bees thousands of them, so they 381 00:25:37,760 --> 00:25:41,160 Speaker 3: knew who these bees were. And it turns out when 382 00:25:41,200 --> 00:25:45,720 Speaker 3: you look at communication networks and the bees, you find 383 00:25:45,760 --> 00:25:50,879 Speaker 3: this wonderfully complex system in place. If the sun is 384 00:25:50,920 --> 00:25:54,720 Speaker 3: out there, they used dancing to communicate because they can 385 00:25:55,000 --> 00:25:59,960 Speaker 3: transfer information about food using the angle of the sun 386 00:26:00,119 --> 00:26:04,000 Speaker 3: compared to the hive and the food source. But in 387 00:26:04,040 --> 00:26:07,080 Speaker 3: London there are many many days when it's not sunny, 388 00:26:07,680 --> 00:26:12,560 Speaker 3: and when it's not sunny, the communication network focuses on 389 00:26:12,880 --> 00:26:16,640 Speaker 3: the transfer of food or the touching of antenna. So 390 00:26:17,080 --> 00:26:22,040 Speaker 3: they have these kind of multiple communication networks, and if 391 00:26:22,040 --> 00:26:24,480 Speaker 3: one of them is down because the sun isn't out, 392 00:26:24,880 --> 00:26:28,880 Speaker 3: then they move to a second kind of communication network. 393 00:26:29,280 --> 00:26:32,879 Speaker 3: And so what this tells us is that you know, 394 00:26:33,119 --> 00:26:35,959 Speaker 3: you can have a brain the size of a honeybee 395 00:26:36,080 --> 00:26:38,479 Speaker 3: or the brain the size of a chimpanzee, and you 396 00:26:38,520 --> 00:26:44,879 Speaker 3: can still get complex communication networks in non humans. You 397 00:26:44,960 --> 00:26:48,600 Speaker 3: were also asking about disease. I think for me this 398 00:26:48,840 --> 00:26:53,440 Speaker 3: was one of the more surprising components of social networks 399 00:26:53,440 --> 00:27:00,359 Speaker 3: and animals, the way that they relate to disease transmission. Here, 400 00:27:01,040 --> 00:27:03,840 Speaker 3: what's kind of cool is that in almost all of 401 00:27:03,880 --> 00:27:08,240 Speaker 3: the other behaviors that we're talking about feeding and communication 402 00:27:08,600 --> 00:27:15,000 Speaker 3: and cooperation. Really it's individuals, animals and the network that 403 00:27:15,040 --> 00:27:17,600 Speaker 3: are the key. But when you cut when it comes 404 00:27:17,640 --> 00:27:22,000 Speaker 3: to disease, what's basically going on is the disease is 405 00:27:22,160 --> 00:27:26,560 Speaker 3: hitchhiking on the social networks that the animals have in place. 406 00:27:26,960 --> 00:27:31,640 Speaker 3: So the animals don't want to transfer disease from one 407 00:27:31,680 --> 00:27:35,639 Speaker 3: individual to another, but the social networks are in place 408 00:27:35,760 --> 00:27:41,040 Speaker 3: in terms of for example, aggression and power struggles. What 409 00:27:41,080 --> 00:27:45,119 Speaker 3: that means is diseases can use those networks to move 410 00:27:45,200 --> 00:27:49,120 Speaker 3: from one individual to another. And I think my favorite 411 00:27:49,160 --> 00:27:53,640 Speaker 3: example of this is this bizarre disease that has been 412 00:27:53,680 --> 00:27:59,159 Speaker 3: studied in Tasmanian devils. And if you can't picture a 413 00:27:59,320 --> 00:28:03,080 Speaker 3: Tasmanian devil in your head, think back to the Bugs 414 00:28:03,119 --> 00:28:07,159 Speaker 3: Bunny cartoons, because there was this wonderful cariacture of a 415 00:28:07,240 --> 00:28:12,639 Speaker 3: Tasmanian devil in there. And in Tasmanian devils they have 416 00:28:12,720 --> 00:28:17,720 Speaker 3: this thing called facial tumor disease, and this is one 417 00:28:17,760 --> 00:28:21,520 Speaker 3: of the very rare instances that we know of in 418 00:28:21,560 --> 00:28:27,960 Speaker 3: which cancer is transmitted from one individual to another. The 419 00:28:28,040 --> 00:28:31,480 Speaker 3: sort of worst case scenario when we think about human 420 00:28:31,560 --> 00:28:34,640 Speaker 3: cancers would be if we could actually transmit cancer from 421 00:28:34,720 --> 00:28:38,440 Speaker 3: one individual to another. It doesn't happen, but in certain 422 00:28:38,480 --> 00:28:42,200 Speaker 3: species like Tasmanian devils, it does. And what happens is 423 00:28:43,000 --> 00:28:47,959 Speaker 3: if they are infected with this cancer and they bite 424 00:28:48,080 --> 00:28:55,200 Speaker 3: somebody really hard, right, they can transmit that cancer to 425 00:28:55,360 --> 00:28:59,920 Speaker 3: the other individual. So here's where social networks come into play. 426 00:29:00,520 --> 00:29:04,840 Speaker 3: Tasmanian devils fight, and they fight a lot, particularly during 427 00:29:04,880 --> 00:29:10,440 Speaker 3: mating season. Males will fight intensely to gain access to mates. 428 00:29:11,120 --> 00:29:15,080 Speaker 3: Sometimes when they fight, if you have facial tumor disease 429 00:29:15,200 --> 00:29:19,360 Speaker 3: and you bite somebody else, you can transfer that disease 430 00:29:19,840 --> 00:29:24,920 Speaker 3: to that individual. Again, the tumor is actually hitchhiking on 431 00:29:25,120 --> 00:29:29,200 Speaker 3: the power social network structure that's in place in the 432 00:29:29,240 --> 00:29:34,720 Speaker 3: Tasmanian devils. And so it's not surprising, for example, that 433 00:29:35,160 --> 00:29:40,120 Speaker 3: males transmit this disease much more often to one another 434 00:29:40,160 --> 00:29:44,080 Speaker 3: than females. And that's because males fight a lot during 435 00:29:44,520 --> 00:29:48,480 Speaker 3: mating season, and it's only through bites that this disease 436 00:29:48,560 --> 00:29:54,320 Speaker 3: can be transmitted. It's more complex, and what we're discovering 437 00:29:54,680 --> 00:29:58,640 Speaker 3: are strange things like even though males are more likely 438 00:29:58,680 --> 00:30:03,840 Speaker 3: to transmit the disease, females also get facial tumor disease, 439 00:30:04,040 --> 00:30:08,240 Speaker 3: and they tend to get it from males who are 440 00:30:08,280 --> 00:30:11,760 Speaker 3: defending them. But in the context of courtships, sometimes it 441 00:30:11,760 --> 00:30:14,560 Speaker 3: gets a little too rough and you get some biting 442 00:30:14,680 --> 00:30:19,360 Speaker 3: and females can get infected with this disease. Now what's 443 00:30:19,400 --> 00:30:25,000 Speaker 3: weird is if you look at female Tasmanian devils, usually 444 00:30:25,720 --> 00:30:29,520 Speaker 3: it's individuals who are in the weakest health state that 445 00:30:29,600 --> 00:30:34,800 Speaker 3: are most susceptible to getting disease. But in the Tasmanian devils, 446 00:30:34,880 --> 00:30:39,240 Speaker 3: it's the healthiest females who end up getting this cancerous 447 00:30:39,280 --> 00:30:44,000 Speaker 3: disease by being bitten by males. Why well, if you 448 00:30:44,040 --> 00:30:47,520 Speaker 3: do a social network analysis, what you find is the 449 00:30:47,640 --> 00:30:52,200 Speaker 3: healthiest females are the ones that the males prefer as mates. 450 00:30:53,120 --> 00:30:56,280 Speaker 3: That means they're the ones that are most likely to 451 00:30:56,320 --> 00:31:02,600 Speaker 3: get bitten, even unintentionally by males during courtship, and so 452 00:31:03,200 --> 00:31:06,400 Speaker 3: they the healthiest ones end up being most likely to 453 00:31:06,440 --> 00:31:10,800 Speaker 3: get the cancer, which is something that we can possibly 454 00:31:10,880 --> 00:31:14,280 Speaker 3: have really understood without a social network analysis. 455 00:31:14,720 --> 00:31:16,680 Speaker 1: Wow, that's incredible. Yeah, I was going to ask about 456 00:31:16,720 --> 00:31:20,280 Speaker 1: the Tasmanian devil example. That one definitely stood out to 457 00:31:20,280 --> 00:31:23,240 Speaker 1: me when I was reading another one was the vampire bats. 458 00:31:23,600 --> 00:31:31,719 Speaker 3: Oh, yes, vampire bats are wonderful. They're a textbook example 459 00:31:32,400 --> 00:31:38,640 Speaker 3: of social behavior and in this case, cooperation. So let 460 00:31:38,680 --> 00:31:43,840 Speaker 3: me just tell you a little bit about what goes 461 00:31:43,880 --> 00:31:48,239 Speaker 3: on in the world of vampires. So, vampire bats do, 462 00:31:48,400 --> 00:31:51,040 Speaker 3: in fact suck blood, right, but they don't suck human blood. 463 00:31:51,480 --> 00:31:56,320 Speaker 3: They suck the blood of cows and other kind of 464 00:31:56,480 --> 00:32:00,880 Speaker 3: domesticated species. And it turns out that if you're a 465 00:32:00,920 --> 00:32:03,480 Speaker 3: vampire bat, or if you're just a bat in general, 466 00:32:03,800 --> 00:32:06,840 Speaker 3: it's a really expensive way of living for a mammal. 467 00:32:07,560 --> 00:32:11,200 Speaker 3: So bats are mammals, and most mammals don't fly, but 468 00:32:11,320 --> 00:32:16,560 Speaker 3: if you fly, it's energetically extremely expensive. And what people 469 00:32:16,560 --> 00:32:19,920 Speaker 3: have found is if a vampire bat doesn't get a 470 00:32:19,960 --> 00:32:26,240 Speaker 3: blood meal about every two days, it can starve to death. Now, 471 00:32:26,760 --> 00:32:31,240 Speaker 3: what the vampire bats do is they have this social 472 00:32:31,320 --> 00:32:37,760 Speaker 3: network in place where they transfer blood from one back 473 00:32:37,880 --> 00:32:40,840 Speaker 3: to the other. So if you take a camera and 474 00:32:40,880 --> 00:32:44,760 Speaker 3: you go inside a vampire bat roost, what you will 475 00:32:44,800 --> 00:32:48,200 Speaker 3: find sometimes is that one bat will come up to 476 00:32:48,240 --> 00:32:51,640 Speaker 3: another bat and it'll start licking its face in a 477 00:32:51,760 --> 00:32:56,000 Speaker 3: very kind of stereotypical manner. What that bat is doing 478 00:32:56,160 --> 00:32:58,920 Speaker 3: is it's trying to get the other bat to regurgitate 479 00:32:59,480 --> 00:33:01,600 Speaker 3: some of the blood that it has in its gut 480 00:33:02,160 --> 00:33:05,520 Speaker 3: so that the solicitor, the one that's licking the face, 481 00:33:06,000 --> 00:33:08,680 Speaker 3: can actually survive long enough to go out and get 482 00:33:08,720 --> 00:33:11,120 Speaker 3: its own blood meal. So it's hungry, it's starving. It 483 00:33:11,160 --> 00:33:14,360 Speaker 3: goes up to a bat that has a really distended 484 00:33:14,560 --> 00:33:17,360 Speaker 3: big gut, which means it has lots of blood. It 485 00:33:17,400 --> 00:33:20,320 Speaker 3: licks its face trying to get that bat to regurgitate 486 00:33:20,360 --> 00:33:22,680 Speaker 3: some of the blood to it. They will do that 487 00:33:23,200 --> 00:33:28,400 Speaker 3: for one another, but only when they know that it's 488 00:33:28,440 --> 00:33:32,360 Speaker 3: a trustworthy partner. So if one bat goes up to 489 00:33:32,400 --> 00:33:36,320 Speaker 3: another and tries to get blood regurgitated, the only way 490 00:33:36,360 --> 00:33:40,560 Speaker 3: it'll work is if in the past that blood that 491 00:33:40,600 --> 00:33:44,640 Speaker 3: bat who's hungry who needs the blood, has helped and 492 00:33:44,720 --> 00:33:47,880 Speaker 3: given blood to the bat that it's now asking for 493 00:33:48,000 --> 00:33:51,360 Speaker 3: blood from. So they're basically keeping track of who gave 494 00:33:51,440 --> 00:33:54,000 Speaker 3: them blood in the past when they were hungry, and 495 00:33:54,080 --> 00:33:56,800 Speaker 3: if that bat now comes up and tries to get 496 00:33:56,920 --> 00:34:00,600 Speaker 3: some blood you to regurgitate some blood, then you're much 497 00:34:00,600 --> 00:34:04,160 Speaker 3: more likely to do it. So that's the basic structure 498 00:34:04,880 --> 00:34:09,640 Speaker 3: that's in place in these vampire bat roots. Networking comes 499 00:34:09,680 --> 00:34:14,360 Speaker 3: into place, because it turns out again that it really 500 00:34:14,440 --> 00:34:17,759 Speaker 3: matters not just sort of who gave you blood when 501 00:34:17,760 --> 00:34:22,759 Speaker 3: you were hungry, but also who you go out with 502 00:34:23,360 --> 00:34:27,719 Speaker 3: and look for blood when there's a nice night out 503 00:34:27,760 --> 00:34:30,560 Speaker 3: there to go out and fly and look for an 504 00:34:30,640 --> 00:34:35,920 Speaker 3: unsuspecting cow. So what researchers have done is they have 505 00:34:36,080 --> 00:34:42,400 Speaker 3: placed little what are known as pit tags on these bats. 506 00:34:42,440 --> 00:34:45,560 Speaker 3: And so basically all a pittag is is this little 507 00:34:45,680 --> 00:34:49,239 Speaker 3: tiny one ounce less than one ounce device if they 508 00:34:49,239 --> 00:34:52,240 Speaker 3: put on a bat, and it allows them to track 509 00:34:52,360 --> 00:34:54,719 Speaker 3: every bat in the roost when the bat goes out 510 00:34:54,760 --> 00:34:57,080 Speaker 3: and looks for food. And the way it works is 511 00:34:57,560 --> 00:35:00,400 Speaker 3: they have all of these receivers that have been placed 512 00:35:00,440 --> 00:35:03,000 Speaker 3: out in the fields with the cows, and so you 513 00:35:03,080 --> 00:35:05,800 Speaker 3: can know where the bats are because the little things 514 00:35:05,800 --> 00:35:09,320 Speaker 3: they have on their bat are transmit are transmitting information 515 00:35:10,160 --> 00:35:13,200 Speaker 3: to these receivers that are in the field, so you 516 00:35:13,280 --> 00:35:17,080 Speaker 3: know everything about what the bats are doing. What's more, 517 00:35:17,719 --> 00:35:21,160 Speaker 3: these little pit tags that the bats have on their 518 00:35:21,239 --> 00:35:25,759 Speaker 3: back talk to each other. That is, if two vampire 519 00:35:25,800 --> 00:35:29,799 Speaker 3: bats come close enough to one another, these little pit 520 00:35:29,880 --> 00:35:33,359 Speaker 3: tags light up and you know that vampire bat one 521 00:35:33,480 --> 00:35:35,759 Speaker 3: and vampire bat two are on the back of a 522 00:35:35,840 --> 00:35:40,600 Speaker 3: cow together sucking blood. Now, these things are not easy 523 00:35:40,640 --> 00:35:43,920 Speaker 3: to set up, but after you have this system in place, 524 00:35:44,560 --> 00:35:48,680 Speaker 3: you can test all sorts of incredible things, like is 525 00:35:48,719 --> 00:35:52,600 Speaker 3: it the case that this kind of network of blood 526 00:35:52,600 --> 00:35:57,040 Speaker 3: sharing that goes on inside the roost translates into who's 527 00:35:57,120 --> 00:36:00,040 Speaker 3: hanging out with who when they're actually going out and 528 00:36:00,120 --> 00:36:02,759 Speaker 3: looking for blood. That's a really hard thing to do 529 00:36:03,120 --> 00:36:05,080 Speaker 3: because you never know when bats are going to leave 530 00:36:05,120 --> 00:36:07,680 Speaker 3: their roost, and they fly and they're really hard to follow. 531 00:36:07,760 --> 00:36:11,000 Speaker 3: But when you have these tags and these transmitters and 532 00:36:11,040 --> 00:36:13,799 Speaker 3: responders in the field, you can do this and lo 533 00:36:13,960 --> 00:36:17,919 Speaker 3: and behold. Basically, the network of blood sharing that goes 534 00:36:17,960 --> 00:36:23,640 Speaker 3: on inside the roost is a very good predictor of 535 00:36:23,680 --> 00:36:25,960 Speaker 3: who will be found on the back of the same 536 00:36:26,080 --> 00:36:29,359 Speaker 3: cow sucking blood when they go out. They go out, 537 00:36:29,560 --> 00:36:32,800 Speaker 3: The bats go out and start flying independently, but they 538 00:36:32,840 --> 00:36:36,359 Speaker 3: find their favorite partners one way or another, and they 539 00:36:36,480 --> 00:36:40,480 Speaker 3: end up tending to be found on the back of 540 00:36:40,520 --> 00:36:45,360 Speaker 3: some unsuspecting cow in the field. And we can only 541 00:36:45,600 --> 00:36:48,960 Speaker 3: know how complex all this is when we realize that 542 00:36:49,040 --> 00:36:53,200 Speaker 3: it's all embedded in these social networks that the bats 543 00:36:53,239 --> 00:36:54,800 Speaker 3: have built around blood sharing. 544 00:36:55,480 --> 00:36:58,960 Speaker 1: Wow, that's incredible. And the technology that goes into these studies. 545 00:36:59,080 --> 00:37:02,360 Speaker 1: I was just ord by numerous examples in the book. 546 00:37:02,600 --> 00:37:05,560 Speaker 3: Yeah, I mean, so the study of social networks and 547 00:37:05,600 --> 00:37:10,759 Speaker 3: animals has just absolutely exploded in part because of these 548 00:37:10,800 --> 00:37:16,040 Speaker 3: technological advances. So you have these talking sensors like I 549 00:37:16,239 --> 00:37:19,640 Speaker 3: just mentioned to you, where when the animals come close enough, 550 00:37:19,800 --> 00:37:22,480 Speaker 3: they both sensors light up and you know they're together 551 00:37:22,600 --> 00:37:28,040 Speaker 3: wherever they are. That's one thing. Then basically in other studies, 552 00:37:28,040 --> 00:37:32,040 Speaker 3: what they do, what individuals do, is they attach the 553 00:37:32,120 --> 00:37:35,920 Speaker 3: equivalent of kind of a GPS device to the back 554 00:37:36,560 --> 00:37:40,239 Speaker 3: of an animal and they can track it then over 555 00:37:40,440 --> 00:37:46,440 Speaker 3: really long distances. And my favorite example here is white 556 00:37:46,560 --> 00:37:51,000 Speaker 3: stork behavior. So white storks are these beautiful animals, right, 557 00:37:51,440 --> 00:37:56,879 Speaker 3: but they migrate thousands of miles in the winter, and 558 00:37:57,320 --> 00:38:01,360 Speaker 3: we know almost nothing about the tells of their migration 559 00:38:01,440 --> 00:38:04,400 Speaker 3: because it's really hard to follow birds that are flying 560 00:38:04,920 --> 00:38:09,160 Speaker 3: a thousand feet above the ground and traveling thousands of miles. 561 00:38:09,280 --> 00:38:13,520 Speaker 3: But if you put GPS transponders on their back, then 562 00:38:13,920 --> 00:38:17,799 Speaker 3: you have the possibility of doing this. So people who 563 00:38:17,880 --> 00:38:22,080 Speaker 3: have worked with these white storks, Andrea Flack is a 564 00:38:22,120 --> 00:38:24,920 Speaker 3: researcher at the Max Plank Institute. So she put all 565 00:38:24,960 --> 00:38:29,080 Speaker 3: of she put these giant GPS trackers on the backs 566 00:38:29,200 --> 00:38:34,880 Speaker 3: of these white storks and she basically would follow them 567 00:38:35,400 --> 00:38:38,480 Speaker 3: as they were making them migration. As I'm making them migration, 568 00:38:38,560 --> 00:38:42,040 Speaker 3: they kind of come down every night to rest, and 569 00:38:42,080 --> 00:38:45,240 Speaker 3: she would kind of try and track them for hundreds 570 00:38:45,280 --> 00:38:48,839 Speaker 3: of miles if she could on her own. And so 571 00:38:48,920 --> 00:38:51,760 Speaker 3: she was trying to follow them, but at the same time, 572 00:38:52,400 --> 00:38:55,040 Speaker 3: she could know what the birds were doing when they 573 00:38:55,040 --> 00:38:59,120 Speaker 3: were a thousand feet above the ground because the GPS 574 00:38:59,120 --> 00:39:04,000 Speaker 3: devices were so sensitive that she could know where a 575 00:39:04,040 --> 00:39:07,600 Speaker 3: bird was in the flock as they were flying these 576 00:39:07,640 --> 00:39:11,239 Speaker 3: thousands of miles. And it turns out that what that 577 00:39:11,400 --> 00:39:15,279 Speaker 3: allowed her to do was built a social network of 578 00:39:15,320 --> 00:39:19,680 Speaker 3: the flock as it was flying these south thousands of miles. 579 00:39:20,320 --> 00:39:24,040 Speaker 3: And what she found was that in this network, this 580 00:39:24,880 --> 00:39:29,719 Speaker 3: travel network, there were certain white storks that were the leaders. 581 00:39:30,200 --> 00:39:34,600 Speaker 3: They were the ones that were determining when the flock 582 00:39:34,600 --> 00:39:37,200 Speaker 3: would move this way or that way. So if you 583 00:39:37,239 --> 00:39:40,240 Speaker 3: look at a bird of flocks, a flock of birds 584 00:39:40,280 --> 00:39:43,279 Speaker 3: like geese, you can tell there are certain leaders that 585 00:39:43,400 --> 00:39:46,560 Speaker 3: are the ones that are determining the movement of the flock. 586 00:39:46,880 --> 00:39:48,920 Speaker 3: But you can't do that with white storks when they're 587 00:39:48,960 --> 00:39:52,880 Speaker 3: that far above the ground unless you have these GPS devices. 588 00:39:53,280 --> 00:39:57,560 Speaker 3: So there are these leaders and followers in the white 589 00:39:57,560 --> 00:40:01,960 Speaker 3: stalk travel networks, and it it really matters. The leaders 590 00:40:02,040 --> 00:40:06,439 Speaker 3: are very very good at finding up drafts. So when 591 00:40:06,440 --> 00:40:09,080 Speaker 3: you're flying as a bird, what you want is to 592 00:40:09,120 --> 00:40:13,359 Speaker 3: expend as little energy as you can, and there are 593 00:40:13,400 --> 00:40:16,719 Speaker 3: these updrafts, these wins that come that allow you to 594 00:40:16,760 --> 00:40:20,760 Speaker 3: fly at very low energy levels. The leaders are tremendously 595 00:40:20,840 --> 00:40:25,480 Speaker 3: good at finding these, and in fact, what they were 596 00:40:25,520 --> 00:40:27,600 Speaker 3: able to do, what the researchers were able to do, 597 00:40:27,920 --> 00:40:31,279 Speaker 3: was demonstrate not just that there were leaders and followers 598 00:40:31,320 --> 00:40:34,880 Speaker 3: in these flocks, but that the leaders were actually able 599 00:40:34,960 --> 00:40:38,319 Speaker 3: to migrate further than the followers because they were so 600 00:40:38,600 --> 00:40:43,680 Speaker 3: good at finding these updrafts. So it really mattered who 601 00:40:43,800 --> 00:40:46,920 Speaker 3: was a leader and a follower in these networks. And 602 00:40:47,280 --> 00:40:52,279 Speaker 3: this kind of study would have been unthinkable twenty years ago. 603 00:40:52,360 --> 00:40:57,319 Speaker 3: I mean, without the technology, you simply cannot do this 604 00:40:57,840 --> 00:41:02,440 Speaker 3: sort of thing. You can move from flying a thousand 605 00:41:02,480 --> 00:41:07,440 Speaker 3: meters above the ground to swimming many many feet below 606 00:41:07,480 --> 00:41:11,000 Speaker 3: the water and find the same thing and there's this 607 00:41:11,120 --> 00:41:15,200 Speaker 3: wonderful study done on mant rays in the South Pacific 608 00:41:15,600 --> 00:41:19,520 Speaker 3: where they've done exactly this. They they've tagged them so 609 00:41:19,560 --> 00:41:22,960 Speaker 3: they can study what the manta rays are doing. And 610 00:41:23,040 --> 00:41:28,400 Speaker 3: in addition to that, they fly these drones over groups 611 00:41:28,480 --> 00:41:31,400 Speaker 3: of manta rays that are swimming close to the water surface. 612 00:41:31,920 --> 00:41:37,840 Speaker 3: The drones then videotape the interactions between the manta rays. 613 00:41:38,160 --> 00:41:42,960 Speaker 3: And the mantrays have these sort of very unique color 614 00:41:43,400 --> 00:41:47,399 Speaker 3: and patterns that allow the researchers to know who's who, 615 00:41:47,840 --> 00:41:51,399 Speaker 3: so you can study them by the drones watching them 616 00:41:51,400 --> 00:41:56,319 Speaker 3: from above, and the little pit tags and various other 617 00:41:56,520 --> 00:41:59,640 Speaker 3: GPS like devices under the water, they'll let you know 618 00:41:59,719 --> 00:42:01,799 Speaker 3: where they are all the time, which means you can 619 00:42:01,800 --> 00:42:06,520 Speaker 3: build social networks. You can look at in the case 620 00:42:06,560 --> 00:42:10,560 Speaker 3: of the manta rays, they look at who's a key 621 00:42:10,719 --> 00:42:13,719 Speaker 3: hub in a social network. So a key hub is 622 00:42:13,760 --> 00:42:18,680 Speaker 3: an individual through which a lot of information travels. So 623 00:42:18,760 --> 00:42:23,160 Speaker 3: if you think about Facebook, for example, key hubs are 624 00:42:23,280 --> 00:42:27,000 Speaker 3: like public figures where if you want to sort of 625 00:42:27,120 --> 00:42:31,920 Speaker 3: track the way that information flows, these individuals really matter. Well, 626 00:42:31,960 --> 00:42:34,760 Speaker 3: it turns out in manta rays there are key hubs 627 00:42:34,800 --> 00:42:39,240 Speaker 3: as well, individuals through which information travels. In this case, 628 00:42:39,360 --> 00:42:43,160 Speaker 3: it turns out that juvenile manta rays are the hubs 629 00:42:43,200 --> 00:42:48,120 Speaker 3: in social networks associated with feeding. They're the ones that 630 00:42:48,280 --> 00:42:53,560 Speaker 3: are the key to understanding how individuals swim around looking 631 00:42:53,600 --> 00:42:57,840 Speaker 3: for food underwater. Again, without the technology, these are just 632 00:42:57,920 --> 00:43:01,200 Speaker 3: pipe dreams. But now they're are going on. All these 633 00:43:01,239 --> 00:43:03,160 Speaker 3: kind of studies are going on all the time in 634 00:43:03,840 --> 00:43:04,480 Speaker 3: non humans. 635 00:43:04,880 --> 00:43:07,560 Speaker 1: It's also fascinating, and again we've only been able to 636 00:43:07,600 --> 00:43:10,080 Speaker 1: really touch on some of the examples from the book. 637 00:43:10,080 --> 00:43:13,880 Speaker 1: The book is loaded with excellent examples and fascinating timbits 638 00:43:13,880 --> 00:43:25,920 Speaker 1: about the methodology and technology that goes into studying them. 639 00:43:26,120 --> 00:43:28,240 Speaker 1: In the in the afterward of the book, you write 640 00:43:28,239 --> 00:43:31,239 Speaker 1: that quote, it's time to scratch off another item from 641 00:43:31,320 --> 00:43:35,560 Speaker 1: the what makes humans Unique? List? So I thought I 642 00:43:35,600 --> 00:43:37,840 Speaker 1: was just wondering, you know, obviously the book is primarily 643 00:43:37,880 --> 00:43:40,040 Speaker 1: about what we're learning about these non human animals and 644 00:43:40,080 --> 00:43:43,439 Speaker 1: their social networks. But do you think to any degree 645 00:43:43,480 --> 00:43:45,719 Speaker 1: you were able to like turn some of these revelations 646 00:43:45,760 --> 00:43:49,319 Speaker 1: around to better understand, like what human social networks are. 647 00:43:50,840 --> 00:43:53,680 Speaker 1: I have only just to remind us that we're not special. 648 00:43:54,200 --> 00:43:57,440 Speaker 3: Yeah, So I think at the most general level, it 649 00:43:57,480 --> 00:44:01,680 Speaker 3: does remind us that we're not special and yet another way, 650 00:44:01,719 --> 00:44:04,920 Speaker 3: so we talked about tools and culture were things that 651 00:44:04,960 --> 00:44:07,200 Speaker 3: we used to think we were unique, and we're not. 652 00:44:07,400 --> 00:44:09,759 Speaker 3: We're not unique in social networks. But in terms of 653 00:44:09,800 --> 00:44:13,360 Speaker 3: like perhaps the lessons that we could learn by studying 654 00:44:13,520 --> 00:44:16,560 Speaker 3: social networks and non humans, I think there are some. 655 00:44:16,680 --> 00:44:18,440 Speaker 3: You know, you always have to be careful when you 656 00:44:18,480 --> 00:44:21,600 Speaker 3: do this sort of thing, but let me give you 657 00:44:23,640 --> 00:44:27,040 Speaker 3: just one example that I think helps in terms of 658 00:44:27,080 --> 00:44:31,640 Speaker 3: what we might learn. So there's this great study that 659 00:44:31,719 --> 00:44:36,080 Speaker 3: was done on Reese's macaque monkeys and their social networks 660 00:44:36,120 --> 00:44:39,000 Speaker 3: on this little island in Puerto Rico. And I won't 661 00:44:39,000 --> 00:44:42,640 Speaker 3: get into the details of the social network study per se, 662 00:44:43,120 --> 00:44:48,080 Speaker 3: but basically the thing about this study was that they 663 00:44:48,120 --> 00:44:51,320 Speaker 3: had been studying these macaques on the island for a 664 00:44:51,400 --> 00:44:54,040 Speaker 3: very long time. They knew a lot about their social networks. 665 00:44:54,440 --> 00:44:59,560 Speaker 3: Then Hurricane Maria came through and absolutely devastated the island 666 00:45:00,320 --> 00:45:05,160 Speaker 3: where these macacus lived. And obviously it also decimated Puerto 667 00:45:05,239 --> 00:45:08,200 Speaker 3: Rico and had all sorts of impacts on our own species, 668 00:45:08,239 --> 00:45:12,640 Speaker 3: but it basically destroyed the island that they lived on, 669 00:45:13,640 --> 00:45:17,000 Speaker 3: the mechacs lived on, and what that allowed the researchers 670 00:45:17,040 --> 00:45:24,000 Speaker 3: to do was study how social networks respond to disasters. 671 00:45:24,840 --> 00:45:29,040 Speaker 3: This kind of study was also done in mice, how 672 00:45:29,040 --> 00:45:33,719 Speaker 3: they respond to catastrophic events, how social networks respond to 673 00:45:33,800 --> 00:45:38,560 Speaker 3: catastrophic events. I think that the more information we get 674 00:45:38,560 --> 00:45:42,080 Speaker 3: that on that from non humans, the more we might 675 00:45:42,120 --> 00:45:47,000 Speaker 3: be able to understand ways that we might expect social 676 00:45:47,040 --> 00:45:51,839 Speaker 3: networks to respond to natural disasters in our own species. 677 00:45:52,280 --> 00:45:56,440 Speaker 3: One thing that I think studies and animal behavior allow 678 00:45:56,560 --> 00:46:02,000 Speaker 3: us to do is this, If really your primary interest, 679 00:46:02,120 --> 00:46:05,960 Speaker 3: and it's perfectly reasonable, would be social networks and non humans, 680 00:46:06,160 --> 00:46:09,319 Speaker 3: and you're not all that interested in social networks and 681 00:46:09,400 --> 00:46:12,040 Speaker 3: non humans, here's a reason you might want to rethink that. 682 00:46:12,640 --> 00:46:17,080 Speaker 3: If you think about this at the species level, we 683 00:46:17,160 --> 00:46:21,520 Speaker 3: have a species count of one if we only focus 684 00:46:21,840 --> 00:46:27,440 Speaker 3: on humans. But if these social networks are as complex 685 00:46:27,920 --> 00:46:31,400 Speaker 3: as they appear to be in non humans, that means 686 00:46:31,680 --> 00:46:36,280 Speaker 3: that we have this treasure chest of information about how 687 00:46:36,320 --> 00:46:40,120 Speaker 3: social networks work. How does a what does it mean 688 00:46:40,200 --> 00:46:42,719 Speaker 3: to be a hub in a social network? How much 689 00:46:42,760 --> 00:46:46,040 Speaker 3: does it matter what your friends do versus what your 690 00:46:46,120 --> 00:46:49,560 Speaker 3: friends of friends do. It turns out friends of friends 691 00:46:49,640 --> 00:46:53,399 Speaker 3: really matter and not in non human social networks. How 692 00:46:53,440 --> 00:46:57,239 Speaker 3: do these things work at a most general level. We're 693 00:46:57,400 --> 00:47:00,640 Speaker 3: if we only think about humans, our sample sizes one species, 694 00:47:00,680 --> 00:47:03,200 Speaker 3: But if we think about it in dolphins and humpback 695 00:47:03,239 --> 00:47:08,799 Speaker 3: whales and manta rays and honeybees and chimpanzees and so 696 00:47:08,840 --> 00:47:11,719 Speaker 3: many other species, then all of a sudden, maybe we 697 00:47:11,760 --> 00:47:15,719 Speaker 3: can pick up some patterns about social networks that we 698 00:47:15,719 --> 00:47:19,759 Speaker 3: wouldn't have picked up otherwise. And so I think that's 699 00:47:19,840 --> 00:47:23,440 Speaker 3: potentially a very powerful implication of studying this in non humans. 700 00:47:24,160 --> 00:47:29,239 Speaker 1: Yeah, absolutely fascinating. I have one last question that relates 701 00:47:29,280 --> 00:47:32,879 Speaker 1: to an earlier book that you wrote. There was two 702 00:47:32,880 --> 00:47:36,440 Speaker 1: thousand and nine Mister Jefferson and the Giant Moose, Natural 703 00:47:36,520 --> 00:47:39,719 Speaker 1: History in Early America. I was not familiar with the 704 00:47:39,800 --> 00:47:42,880 Speaker 1: history referenced in the title. As if nothing else, is 705 00:47:42,920 --> 00:47:46,439 Speaker 1: just a teaser for listeners who might be interested, could 706 00:47:46,480 --> 00:47:50,640 Speaker 1: you just briefly tell us why Thomas Jefferson was obsessed 707 00:47:50,640 --> 00:47:51,800 Speaker 1: with a giant moose? 708 00:47:52,239 --> 00:47:57,440 Speaker 3: Sure? What had happened was the world's leading naturalists in France, 709 00:47:57,719 --> 00:48:00,680 Speaker 3: a guy by the name of Count Ufond written this 710 00:48:00,880 --> 00:48:04,320 Speaker 3: giant encyclopedia of natural history, and one of the things 711 00:48:04,360 --> 00:48:08,960 Speaker 3: that he did in the encyclopedia was promote this idea 712 00:48:09,200 --> 00:48:15,359 Speaker 3: called the degeneration hypothesis. And what this natural historian said 713 00:48:15,520 --> 00:48:19,840 Speaker 3: was that all life in the New World, particularly in America, 714 00:48:20,560 --> 00:48:24,840 Speaker 3: was degenerate compared to life other places, that all the 715 00:48:24,920 --> 00:48:29,680 Speaker 3: animals in the United States or even before that, the 716 00:48:29,760 --> 00:48:36,120 Speaker 3: colonies were weak, feeble, and diminished compared to species in 717 00:48:36,200 --> 00:48:40,440 Speaker 3: other places in the world. And Jefferson became obsessed in 718 00:48:40,560 --> 00:48:46,040 Speaker 3: demonstrating to Count Bufond why he was wrong. In fact, 719 00:48:46,600 --> 00:48:49,000 Speaker 3: the longest chapter in the book, the only book that 720 00:48:49,040 --> 00:48:51,560 Speaker 3: Jefferson ever wrote, the Notes on the State of Virginia, 721 00:48:51,800 --> 00:48:57,239 Speaker 3: is all about proving how wrong this degeneracy hypothesis is. 722 00:48:57,320 --> 00:49:01,000 Speaker 3: And one of the key pieces of information that he 723 00:49:01,120 --> 00:49:05,440 Speaker 3: wanted Buffon to see was our moose, which was you know, 724 00:49:05,440 --> 00:49:09,640 Speaker 3: it's massive, right, So Jefferson wanted to send Buffon a 725 00:49:09,719 --> 00:49:13,320 Speaker 3: giant moose to show him how wrong he was about 726 00:49:13,320 --> 00:49:17,960 Speaker 3: this theory of American degeneracy. And so he spent an 727 00:49:17,960 --> 00:49:21,200 Speaker 3: extraordinary amount of time and effort getting this moose and 728 00:49:21,239 --> 00:49:23,759 Speaker 3: then shipping it over to Bufon. And so that's where 729 00:49:23,800 --> 00:49:25,960 Speaker 3: the moose and the title comes from. 730 00:49:26,000 --> 00:49:28,200 Speaker 1: Fascinating. I'm gonna have to put this on on my 731 00:49:28,400 --> 00:49:30,719 Speaker 1: to read list. I was instantly fascinated. 732 00:49:30,920 --> 00:49:32,879 Speaker 3: Oh thank you, Yeah, I mean it was a tremendously 733 00:49:32,960 --> 00:49:33,799 Speaker 3: fun project. 734 00:49:34,200 --> 00:49:36,400 Speaker 1: Well, Lee, thanks again for coming on the show. The 735 00:49:36,480 --> 00:49:40,000 Speaker 1: new book, The Well Connected Animals, Social Networks and the 736 00:49:40,040 --> 00:49:44,520 Speaker 1: Wondrous Complexity of Animal Societies is out this week, so 737 00:49:44,800 --> 00:49:47,479 Speaker 1: encourage all of our listeners to go check that out. 738 00:49:47,800 --> 00:49:49,520 Speaker 3: Thank you so much for having me. I enjoyed it 739 00:49:49,640 --> 00:49:50,040 Speaker 3: very much. 740 00:49:53,200 --> 00:49:56,359 Speaker 1: Thanks once more to Lee Alan Dugatkin for taking time 741 00:49:56,400 --> 00:49:57,960 Speaker 1: out of his day to chat with me about the 742 00:49:58,040 --> 00:50:01,920 Speaker 1: new book. That new book, again is The Well Connected Animal, 743 00:50:02,200 --> 00:50:06,120 Speaker 1: Social Networks and the Wondrous Complexity of Animal Societies. It 744 00:50:06,200 --> 00:50:09,920 Speaker 1: is out this Thursday, so go grab yourself a copy. 745 00:50:10,360 --> 00:50:14,279 Speaker 1: Thanks as always to the excellent JJ Possway for producing 746 00:50:14,320 --> 00:50:16,200 Speaker 1: this episode. And if you would like to get in 747 00:50:16,239 --> 00:50:19,759 Speaker 1: touch with Joe or myself, if you have suggestions for 748 00:50:19,840 --> 00:50:22,440 Speaker 1: future episodes of Stuff to Blow Your Mind, If you 749 00:50:22,520 --> 00:50:26,920 Speaker 1: have interview suggestions you'd like us to consider, write in 750 00:50:27,040 --> 00:50:28,920 Speaker 1: we would love to hear from. You can email us 751 00:50:28,960 --> 00:50:39,239 Speaker 1: at contact a's Stuff to Blow your Mind dot com. 752 00:50:39,360 --> 00:50:42,319 Speaker 2: Stuff to Blow Your Mind is production of iHeartRadio. For 753 00:50:42,400 --> 00:50:45,200 Speaker 2: more podcasts from my Heart Radio, visit the iHeartRadio app 754 00:50:45,320 --> 00:51:06,000 Speaker 2: Apple podcasts or wherever you listen to your favorite shows. 755 00:51:00,400 --> 00:51:04,440 Speaker 3: A PO