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These are the user uploaded subtitles that are being translated: 1 00:00:00,000 --> 00:00:03,167 (elegant piano music) 2 00:00:02,000 --> 00:00:07,000 Downloaded from YTS.MX 3 00:00:05,540 --> 00:00:06,700 - In a lot of ways you can think 4 00:00:06,700 --> 00:00:11,700 of cities as one of the largest unplanned experiments 5 00:00:08,000 --> 00:00:13,000 Official YIFY movies site: YTS.MX 6 00:00:11,700 --> 00:00:12,730 of all time. 7 00:00:12,730 --> 00:00:15,897 (elegant piano music) 8 00:00:20,667 --> 00:00:23,373 - There can be really small differences 9 00:00:23,373 --> 00:00:27,193 that have very large biological significance. 10 00:00:27,193 --> 00:00:30,360 (elegant piano music) 11 00:00:34,849 --> 00:00:38,400 - Cities are places, we call them extreme habitats really. 12 00:00:38,400 --> 00:00:41,620 They are places where there is a lot of opportunity. 13 00:00:41,620 --> 00:00:44,500 And at the same time, there's also challenges. 14 00:00:44,500 --> 00:00:48,260 - [Narrator] As our city spread, how will nature respond? 15 00:00:48,260 --> 00:00:50,400 Will plants and animals dwindle, 16 00:00:50,400 --> 00:00:52,870 or will they adapt to urban life? 17 00:00:52,870 --> 00:00:57,013 And what kind of new encounters will we see in the city? 18 00:01:11,660 --> 00:01:14,100 In the historic French town of Albi, 19 00:01:14,100 --> 00:01:18,610 biologist Frederic Santoul keeps an eye on his catfish. 20 00:01:18,610 --> 00:01:23,020 In 1983, fishermen released these Eastern European fish 21 00:01:23,020 --> 00:01:24,580 into the Riverton. 22 00:01:24,580 --> 00:01:27,156 Today, they're at the top of the river's food chain. 23 00:01:27,156 --> 00:01:28,730 (Frederic speaking in foreign language) 24 00:01:28,730 --> 00:01:30,500 - [Voiceover] This is a fascinating species 25 00:01:30,500 --> 00:01:32,830 because we know so little about them. 26 00:01:32,830 --> 00:01:35,150 There are many myths that people believe, 27 00:01:35,150 --> 00:01:36,970 even that they eat dogs. 28 00:01:36,970 --> 00:01:38,223 There are many stories. 29 00:01:42,510 --> 00:01:44,040 - [Narrator] The biologist is interested 30 00:01:44,040 --> 00:01:46,150 in the behavior of the large fish 31 00:01:46,150 --> 00:01:48,449 that circle the reservoir's basins. 32 00:01:48,449 --> 00:01:51,366 (mysterious music) 33 00:01:54,079 --> 00:01:55,230 (Frederic speaking in foreign language) 34 00:01:55,230 --> 00:01:57,920 - [Voiceover] We work with fishermen to tag the fish. 35 00:01:57,920 --> 00:02:01,720 They contacted us after observing very strange behavior 36 00:02:01,720 --> 00:02:03,453 in the fish here in Albi. 37 00:02:07,400 --> 00:02:08,790 - [Narrator] The man-made landscape 38 00:02:08,790 --> 00:02:12,693 of the city fosters new encounters of species. 39 00:02:14,505 --> 00:02:15,450 (Frederic speaking in foreign language) 40 00:02:15,450 --> 00:02:17,670 - [Voiceover] The pigeons have never had to face predators 41 00:02:17,670 --> 00:02:18,653 from the water. 42 00:02:19,590 --> 00:02:22,853 Instead, they scan the sky for birds of prey. 43 00:02:26,200 --> 00:02:27,800 - [Narrator] The pigeons approach the water 44 00:02:27,800 --> 00:02:29,103 to bathe and drink. 45 00:02:31,930 --> 00:02:35,370 Sometimes a bird misses the narrow strip of safe ground 46 00:02:36,490 --> 00:02:38,713 and touches down in open water. 47 00:02:43,556 --> 00:02:44,880 (Frederic speaking in foreign language) 48 00:02:44,880 --> 00:02:47,530 - [Voiceover] The catfish don't really see the pigeons, 49 00:02:47,530 --> 00:02:49,790 but once they sense the birds' movements 50 00:02:49,790 --> 00:02:52,983 in the water with their bobbles, then they strike. 51 00:02:54,760 --> 00:02:56,450 - [Narrator] This new hunting behavior 52 00:02:56,450 --> 00:02:59,750 of the catfish was observed by the biologists 53 00:02:59,750 --> 00:03:01,940 for the first time in 2010. 54 00:03:01,940 --> 00:03:04,773 (whimsical music) 55 00:03:24,210 --> 00:03:26,210 For some catfish here, 56 00:03:26,210 --> 00:03:29,983 pigeons now account for up to 40% of their prey. 57 00:03:33,920 --> 00:03:36,170 - There's suddenly this ecological interaction 58 00:03:36,170 --> 00:03:39,340 which allows for evolution to start 59 00:03:39,340 --> 00:03:42,460 to improve the bird-catching ability of the catfish 60 00:03:42,460 --> 00:03:46,150 and also to improve the escape ability of the pigeons. 61 00:03:46,150 --> 00:03:49,190 So you can expect that all these new interactions are 62 00:03:49,190 --> 00:03:52,263 also causing new evolutionary dynamic. 63 00:03:55,170 --> 00:03:57,150 - [Narrator] Dutch evolutionary biologist 64 00:03:57,150 --> 00:04:00,750 Menno Schilthuizen researches the adaptation 65 00:04:00,750 --> 00:04:02,613 of wildlife to the city. 66 00:04:04,290 --> 00:04:08,253 Darwin's theory, he believes, has gone urban. 67 00:04:12,930 --> 00:04:16,440 - Urban evolution is evolutionary change. 68 00:04:16,440 --> 00:04:19,250 So really genetic change in wild animals 69 00:04:19,250 --> 00:04:21,420 and plants in cities. 70 00:04:21,420 --> 00:04:26,300 It's all about understanding how species will be able 71 00:04:26,300 --> 00:04:29,893 to survive in this very human-dominated context. 72 00:04:32,150 --> 00:04:34,190 - [Narrator] Cities are homosapiens' 73 00:04:34,190 --> 00:04:37,290 most extreme intervention in nature. 74 00:04:37,290 --> 00:04:41,210 With concrete and steel, we create new landscapes 75 00:04:41,210 --> 00:04:43,820 and alter the face of the earth. 76 00:04:43,820 --> 00:04:46,290 Already, most people live in cities, 77 00:04:46,290 --> 00:04:47,953 rather than in the countryside. 78 00:04:49,940 --> 00:04:52,410 How does this influence evolution? 79 00:04:52,410 --> 00:04:55,000 The development of new species? 80 00:04:55,000 --> 00:04:59,020 What selection pressures does the city create? 81 00:04:59,020 --> 00:05:02,073 A summit evening in the Dutch capital of Amsterdam, 82 00:05:03,260 --> 00:05:06,090 in the Vondelpark in the center of the city, 83 00:05:06,090 --> 00:05:09,850 Biologist Menno Schilthuizen uses a light trap 84 00:05:09,850 --> 00:05:11,343 to catch insects. 85 00:05:14,030 --> 00:05:16,380 He's leading a citizen science project 86 00:05:16,380 --> 00:05:18,423 to explore urban nature. 87 00:05:21,770 --> 00:05:25,310 - For insects and for some smaller plants, 88 00:05:25,310 --> 00:05:29,330 the diversity today in cities seems to be higher 89 00:05:29,330 --> 00:05:32,993 than in intensively managed agricultural areas. 90 00:05:33,940 --> 00:05:38,000 Today, agricultural land is so intensively managed 91 00:05:38,000 --> 00:05:40,760 and every last bit of production is squeezed out 92 00:05:40,760 --> 00:05:43,530 of every square meter of surface area 93 00:05:43,530 --> 00:05:45,920 that there's no space for nature anymore in the countryside. 94 00:05:45,920 --> 00:05:49,640 And at the same time, cities get more, 95 00:05:49,640 --> 00:05:52,900 they get greener, people pay more attention to nature 96 00:05:52,900 --> 00:05:54,460 and to urban nature. 97 00:05:54,460 --> 00:05:56,570 So it's actually becoming a very rich environment 98 00:05:56,570 --> 00:05:59,658 with a higher biodiversity than outside of the city. 99 00:05:59,658 --> 00:06:02,241 (uneasy music) 100 00:06:03,740 --> 00:06:07,660 - [Narrator] But overall, we're rapidly losing biodiversity, 101 00:06:07,660 --> 00:06:11,150 both within and outside our cities. 102 00:06:11,150 --> 00:06:14,493 For insects, the declines are particularly severe. 103 00:06:16,850 --> 00:06:18,930 In the Swiss Alps near Zurich, 104 00:06:18,930 --> 00:06:23,063 Scientist Florian Altermatt has set up his light traps. 105 00:06:23,940 --> 00:06:27,010 Ever since humans began to light up the night, 106 00:06:27,010 --> 00:06:29,130 billions of nocturnal insects 107 00:06:29,130 --> 00:06:32,247 have been dying off every year. 108 00:06:32,247 --> 00:06:33,630 (Florian speaking in foreign language) 109 00:06:33,630 --> 00:06:35,600 - [Voiceover] For such a species it's problematic 110 00:06:35,600 --> 00:06:37,280 if it's attracted by light 111 00:06:37,280 --> 00:06:39,770 because then it cannot use the few short days it has 112 00:06:39,770 --> 00:06:41,233 as a moth to lay eggs. 113 00:06:43,460 --> 00:06:44,940 - [Narrator] Light pollution is one 114 00:06:44,940 --> 00:06:47,480 of the major threats to moths. 115 00:06:47,480 --> 00:06:51,103 Scientists are now even speaking of an insect apocalypse. 116 00:06:52,126 --> 00:06:53,890 (Florian speaking in foreign language) 117 00:06:53,890 --> 00:06:55,500 - [Voiceover] I think the declines we're now seeing 118 00:06:55,500 --> 00:06:57,920 are already quite worrying. 119 00:06:57,920 --> 00:07:01,550 Studies show a 60 to 80% decline in biomass, 120 00:07:01,550 --> 00:07:04,160 sometimes even in nature reserves. 121 00:07:04,160 --> 00:07:06,493 These are incredibly large numbers. 122 00:07:08,926 --> 00:07:12,780 In my childhood, I used to observe moths like these. 123 00:07:12,780 --> 00:07:15,290 I would set up this trap next to my parents' house 124 00:07:15,290 --> 00:07:18,881 and attract moths, actually, in quite large numbers. 125 00:07:18,881 --> 00:07:20,983 I don't think I'd find many today. 126 00:07:23,920 --> 00:07:25,790 - [Narrator] But might insects be capable 127 00:07:25,790 --> 00:07:29,600 of adapting to life in the perpetual light of our cities? 128 00:07:29,600 --> 00:07:32,930 That's what Florian Altermatt wanted to find out. 129 00:07:32,930 --> 00:07:37,090 His test subject, the spindle ermine moth, 130 00:07:37,090 --> 00:07:40,138 whose caterpillars develop on the European spindle tree. 131 00:07:40,138 --> 00:07:43,011 (bird singing) 132 00:07:43,011 --> 00:07:45,490 (Florian speaking in foreign language) 133 00:07:45,490 --> 00:07:47,520 - [Voiceover] Actually, it was a coincidence. 134 00:07:47,520 --> 00:07:49,970 While I was working on my PhD thesis, 135 00:07:49,970 --> 00:07:51,430 every day I walked through a park 136 00:07:51,430 --> 00:07:53,780 which had those Europeans spindle bushes. 137 00:07:53,780 --> 00:07:55,970 And I noticed that there were these caterpillars, 138 00:07:55,970 --> 00:07:58,570 these moths which must have lived there for years 139 00:07:58,570 --> 00:08:00,870 in a city park with permanent light pollution. 140 00:08:01,980 --> 00:08:04,910 And then I thought I could just collect them, raise them, 141 00:08:04,910 --> 00:08:07,993 and test how much the adult moths are attracted by light. 142 00:08:09,990 --> 00:08:12,670 - [Narrator] With his experiments in 2006, 143 00:08:12,670 --> 00:08:16,500 Altermatt pioneered research into urban evolution. 144 00:08:16,500 --> 00:08:18,990 He released the moths in a darkened room. 145 00:08:18,990 --> 00:08:22,120 The next morning, he counted how many had flown 146 00:08:22,120 --> 00:08:23,293 into the light trap. 147 00:08:24,162 --> 00:08:25,940 (Florian speaking in foreign language) 148 00:08:25,940 --> 00:08:28,800 - [Voiceover] The results showed a difference. 149 00:08:28,800 --> 00:08:32,393 About 20% fewer urban moths flew into the trap. 150 00:08:36,167 --> 00:08:37,920 I was very surprised. 151 00:08:37,920 --> 00:08:40,710 It was widely known that moths attracted by light, 152 00:08:40,710 --> 00:08:42,550 some more than others, 153 00:08:42,550 --> 00:08:44,560 but these differences have always been observed 154 00:08:44,560 --> 00:08:46,680 between different species. 155 00:08:46,680 --> 00:08:49,480 Seeing variations within a single species, 156 00:08:49,480 --> 00:08:51,313 that we'd never seen before. 157 00:08:53,200 --> 00:08:55,310 - [Narrator] The experiment clearly demonstrated 158 00:08:55,310 --> 00:08:58,800 a hereditary adaptation to life in the city, 159 00:08:58,800 --> 00:09:01,203 direct proof of urban evolution. 160 00:09:02,300 --> 00:09:04,930 For Dutch biologist Menno Schilthuizen, 161 00:09:04,930 --> 00:09:08,050 the findings confirm a larger picture. 162 00:09:08,050 --> 00:09:12,090 In Amsterdam, he and his group of citizen scientists debate 163 00:09:12,090 --> 00:09:14,740 whether we might soon observe even more 164 00:09:14,740 --> 00:09:19,740 and greater adaptations of animals and plants to this city. 165 00:09:20,160 --> 00:09:24,120 - We see that evolutionary processes are starting 166 00:09:24,120 --> 00:09:26,450 which will eventually 167 00:09:26,450 --> 00:09:29,490 or could eventually produce new species 168 00:09:29,490 --> 00:09:32,750 that are specialized on living in the city. 169 00:09:34,166 --> 00:09:38,550 - [Narrator] For Menno Schilthuizen, it's not if, but when. 170 00:09:38,550 --> 00:09:40,350 - Every organism that lives 171 00:09:40,350 --> 00:09:43,930 in the city will show this urban evolution. 172 00:09:43,930 --> 00:09:46,040 These rapid changes in their behavior, 173 00:09:46,040 --> 00:09:48,960 in their physiology, in their appearances 174 00:09:48,960 --> 00:09:52,233 to optimize their life in an urban environment. 175 00:09:53,258 --> 00:09:54,560 - [Narrator] But what elements 176 00:09:54,560 --> 00:09:58,283 of urban landscapes prompt wildlife to adapt? 177 00:10:00,120 --> 00:10:03,350 Evolutionary biologist Jason Munshi-South 178 00:10:03,350 --> 00:10:08,110 is an expert on animals found in the parks of New York City. 179 00:10:08,110 --> 00:10:12,453 For years, he's been studying how rodents adapt to the city. 180 00:10:13,710 --> 00:10:16,160 Along with human immigrants from Europe, 181 00:10:16,160 --> 00:10:19,150 rats also voyage to the New World. 182 00:10:19,150 --> 00:10:23,170 Today, they roam the city in subway tunnels. 183 00:10:23,170 --> 00:10:25,780 Most native rodent species, however, 184 00:10:25,780 --> 00:10:28,700 don't dare try their luck crossing town. 185 00:10:28,700 --> 00:10:32,230 This distinction sparked the scientist's interest. 186 00:10:32,230 --> 00:10:33,780 - I used to be a tropical biologist, 187 00:10:33,780 --> 00:10:35,030 but then I moved to New York City 188 00:10:35,030 --> 00:10:38,950 for my first academic job after graduate school. 189 00:10:38,950 --> 00:10:41,770 And I decided I wanted to do some local work 190 00:10:41,770 --> 00:10:43,870 that would be interesting to the people of New York City 191 00:10:43,870 --> 00:10:45,680 and to my myself. 192 00:10:45,680 --> 00:10:48,450 And I found out that there was these small mammals living 193 00:10:48,450 --> 00:10:50,420 in essentially islands of forest in the city, 194 00:10:50,420 --> 00:10:51,850 and I thought, you know, that's interesting. 195 00:10:51,850 --> 00:10:53,700 Nobody's really ever looked at these. 196 00:10:53,700 --> 00:10:55,460 Are they becoming genetically different 197 00:10:55,460 --> 00:10:56,780 from mice outside of the city? 198 00:10:56,780 --> 00:10:59,730 Are they adapting? And that's how it all started. 199 00:10:59,730 --> 00:11:03,860 - [Narrator] Central Park opened in 1873. 200 00:11:03,860 --> 00:11:06,300 It still hosts animal species 201 00:11:06,300 --> 00:11:08,833 that lived here long before the city was built. 202 00:11:10,240 --> 00:11:12,340 - Right now we're in the middle of Central Park. 203 00:11:12,340 --> 00:11:15,090 We're gonna be traveling to the north end of the park 204 00:11:15,090 --> 00:11:18,110 where there's a very nice forest called the North Woods. 205 00:11:18,110 --> 00:11:20,700 And there we'll be setting up traps hopefully 206 00:11:20,700 --> 00:11:22,150 to capture white-footed mice. 207 00:11:24,080 --> 00:11:25,920 One of the things that inspired me 208 00:11:25,920 --> 00:11:27,340 when I first started this work is 209 00:11:27,340 --> 00:11:29,610 if you look at a New York City subway map, 210 00:11:29,610 --> 00:11:31,120 you see the subway lines, 211 00:11:31,120 --> 00:11:33,120 but then there are these large green shapes, 212 00:11:33,120 --> 00:11:37,800 rectangles, and ovals, and so forth that are the parklands. 213 00:11:37,800 --> 00:11:40,070 And they put those on the map so you know where they are, 214 00:11:40,070 --> 00:11:42,260 but you also see that they are almost like a chain 215 00:11:42,260 --> 00:11:43,580 of islands that are scattered 216 00:11:43,580 --> 00:11:48,460 in this sea of concrete and roads and buildings and, 217 00:11:48,460 --> 00:11:50,990 you know, 8.5 million people. 218 00:11:50,990 --> 00:11:53,760 So in a sense, if it's a species like a mouse 219 00:11:53,760 --> 00:11:57,150 that can't leave the forest, cross, 220 00:11:57,150 --> 00:11:59,610 you know, neighborhoods and buildings and roads 221 00:11:59,610 --> 00:12:01,270 and make it to the other patch, 222 00:12:01,270 --> 00:12:04,060 it is essentially the same biologically 223 00:12:04,060 --> 00:12:06,010 as if they were on an island 224 00:12:06,010 --> 00:12:08,030 in terms of them not being able to move 225 00:12:08,030 --> 00:12:10,540 and spread their genes with the other patches. 226 00:12:10,540 --> 00:12:12,050 And these urban patches, 227 00:12:12,050 --> 00:12:13,890 once they become sufficiently isolated, 228 00:12:13,890 --> 00:12:16,170 operate like a mini Galapagos 229 00:12:17,640 --> 00:12:19,920 and may be driving the evolution of many species 230 00:12:19,920 --> 00:12:22,630 that are, you know, stuck there now. 231 00:12:22,630 --> 00:12:25,440 - [Narrator] The evolutionary biologist is investigating 232 00:12:25,440 --> 00:12:28,370 whether the white-footed mice actually develop 233 00:12:28,370 --> 00:12:32,085 in distinct ways in each of the various parks. 234 00:12:32,085 --> 00:12:33,387 - Yeah. 235 00:12:33,387 --> 00:12:36,860 So this would be a really nice spot for white-footed mice. 236 00:12:36,860 --> 00:12:38,400 They like to move next to logs 237 00:12:38,400 --> 00:12:40,210 so they're not completely out in the open. 238 00:12:40,210 --> 00:12:42,370 They might actually even be living inside this log 239 00:12:42,370 --> 00:12:45,650 where it's rotting or in holes underneath the log. 240 00:12:45,650 --> 00:12:47,550 So this is pretty much the ideal spot. 241 00:12:48,950 --> 00:12:51,803 - [Voiceover] This forest is encircled by the Big Apple. 242 00:12:52,780 --> 00:12:55,933 Have the mice already adapted to this unique environment? 243 00:12:56,980 --> 00:12:59,473 What traits do they need to survive here? 244 00:13:03,080 --> 00:13:05,513 - No shortage of good trapping spots. 245 00:13:07,220 --> 00:13:10,100 Later I'll be going to one of our more suburban, 246 00:13:10,100 --> 00:13:13,960 almost rural sites with the larger, more intact forests, 247 00:13:13,960 --> 00:13:16,940 lesser urbanization, and I'll be setting out, 248 00:13:16,940 --> 00:13:18,290 you know, an equal number of traps 249 00:13:18,290 --> 00:13:20,640 with the hope that we catch mice there as well. 250 00:13:22,360 --> 00:13:24,670 - [Narrator] Jason Munshi-South will search 251 00:13:24,670 --> 00:13:28,860 within the animal's genetic codes for the markers of life 252 00:13:28,860 --> 00:13:29,960 in the big city. 253 00:13:29,960 --> 00:13:32,377 (soft music) 254 00:13:34,900 --> 00:13:36,280 - I think what's been most interesting 255 00:13:36,280 --> 00:13:39,240 to me is thinking about how the things 256 00:13:39,240 --> 00:13:41,180 that we are all doing in our daily lives, 257 00:13:41,180 --> 00:13:42,890 where we put our garbage, 258 00:13:42,890 --> 00:13:46,470 what we're choosing to eat and what we generate as waste, 259 00:13:46,470 --> 00:13:48,250 where we choose to live, 260 00:13:48,250 --> 00:13:50,100 how we choose to go to work 261 00:13:50,100 --> 00:13:52,210 or out to a restaurant or something, 262 00:13:52,210 --> 00:13:53,580 all of these things we are doing 263 00:13:53,580 --> 00:13:55,830 are now influencing other species 264 00:13:55,830 --> 00:13:58,230 in a way that we're just starting to understand. 265 00:14:02,440 --> 00:14:04,270 - [Narrator] Though it's not only animals 266 00:14:04,270 --> 00:14:07,420 that adapt to human intervention in the natural world. 267 00:14:07,420 --> 00:14:08,553 So do plants. 268 00:14:09,560 --> 00:14:12,680 In Southern France, the yellow-flowered Crepis sancta 269 00:14:12,680 --> 00:14:15,720 is being studied by Biologist Pierre-Olivier Cheptou. 270 00:14:16,663 --> 00:14:19,000 - [Voiceover] Crepis sancta is a very common species 271 00:14:19,000 --> 00:14:20,540 in the Mediterranean region, 272 00:14:20,540 --> 00:14:24,600 a kind of Mediterranean dandelion from the same family. 273 00:14:24,600 --> 00:14:26,910 And its essential advantage as a model is 274 00:14:26,910 --> 00:14:29,160 that it produces two types of seeds, 275 00:14:29,160 --> 00:14:30,893 large ones and small ones. 276 00:14:32,900 --> 00:14:34,850 - [Narrator] The small wildflower produces 277 00:14:34,850 --> 00:14:38,900 both lighter seeds with parachutes allowing them to glide 278 00:14:38,900 --> 00:14:42,784 and heavier seeds that simply fall to the ground. 279 00:14:42,784 --> 00:14:43,710 (Pierre speaking in foreign language) 280 00:14:43,710 --> 00:14:46,010 - [Voiceover] I'm interested in the process of adaptation 281 00:14:46,010 --> 00:14:47,550 to an urban environment. 282 00:14:47,550 --> 00:14:49,330 And in particular, what happens 283 00:14:49,330 --> 00:14:52,110 when a species first arrives a city. 284 00:14:52,110 --> 00:14:55,640 It has recently colonized certain areas of Montpellier. 285 00:14:55,640 --> 00:14:59,220 And in my comparison between rural and urban populations, 286 00:14:59,220 --> 00:15:02,043 I focus on the traits related to seeds. 287 00:15:04,240 --> 00:15:06,620 - [Narrator] The idea of studying the adaptation 288 00:15:06,620 --> 00:15:08,960 of the modest plant of the city came 289 00:15:08,960 --> 00:15:11,470 to Cheptou to almost by chance. 290 00:15:11,470 --> 00:15:13,190 When he came back from abroad, 291 00:15:13,190 --> 00:15:15,930 he noticed the inconspicuousness flowers growing 292 00:15:15,930 --> 00:15:17,219 in the city. 293 00:15:17,219 --> 00:15:18,190 (Pierre speaking in foreign language) 294 00:15:18,190 --> 00:15:19,240 - [Voiceover] I had left Montreal 295 00:15:19,240 --> 00:15:20,830 in the middle of a blizzard. 296 00:15:20,830 --> 00:15:22,648 I got the plane to Paris. 297 00:15:22,648 --> 00:15:25,260 And when I took the bus to Downtown Montpellier, 298 00:15:25,260 --> 00:15:28,140 it was sunny with a pristine blue sky. 299 00:15:28,140 --> 00:15:30,950 And then I noticed there were Crepis sancta flowers 300 00:15:30,950 --> 00:15:33,900 everywhere in those tiny urban patches. 301 00:15:33,900 --> 00:15:36,070 It was then the intuition hit me. 302 00:15:36,070 --> 00:15:39,210 I knew there was something to discover here, 303 00:15:39,210 --> 00:15:40,513 something to uncover. 304 00:15:41,780 --> 00:15:45,330 - Was the plant already adapting to the city? 305 00:15:45,330 --> 00:15:48,393 Which aspects of the city would drive that change? 306 00:15:49,394 --> 00:15:50,580 (Pierre speaking in foreign language) 307 00:15:50,580 --> 00:15:52,360 - [Voiceover] The predominant mineral in cities, 308 00:15:52,360 --> 00:15:55,740 especially in European cities, is concrete. 309 00:15:55,740 --> 00:15:59,760 Concrete exerts a powerful fragmenting force on plants, 310 00:15:59,760 --> 00:16:03,600 driving them to ever-shrinking refuges in these habitats. 311 00:16:03,600 --> 00:16:05,640 - [Narrator] Sometimes the city's constraints 312 00:16:05,640 --> 00:16:08,720 on a plant's habitat can be extreme. 313 00:16:08,720 --> 00:16:10,873 How will evolution respond? 314 00:16:14,545 --> 00:16:15,580 (Pierre speaking in foreign language) 315 00:16:15,580 --> 00:16:16,413 - [Voiceover] I'm looking at 316 00:16:16,413 --> 00:16:18,800 how urban fragmentation will modify 317 00:16:18,800 --> 00:16:21,490 the dispersal traits of this species. 318 00:16:21,490 --> 00:16:23,570 I expect plants that produce more 319 00:16:23,570 --> 00:16:26,010 of the larger seeds will be more successful 320 00:16:26,010 --> 00:16:28,200 at reproduction in urban areas 321 00:16:28,200 --> 00:16:30,100 than they would be in the countryside. 322 00:16:31,390 --> 00:16:33,470 - [Narrator] The heavier seeds face less risk 323 00:16:33,470 --> 00:16:36,000 of being swept onto the asphalt. 324 00:16:36,000 --> 00:16:39,340 And indeed, the biologist discovered that far more plants 325 00:16:39,340 --> 00:16:41,650 in the city produced the heavier seeds 326 00:16:41,650 --> 00:16:44,020 and are thus better able to survive. 327 00:16:44,020 --> 00:16:46,420 A difference of 15%. 328 00:16:46,420 --> 00:16:50,143 But what most stands out is the speed of this adaptation. 329 00:16:51,455 --> 00:16:53,070 (Pierre speaking in foreign language) 330 00:16:53,070 --> 00:16:55,180 - [Voiceover] The evolution we have seen has taken 331 00:16:55,180 --> 00:16:57,320 about 15 years. 332 00:16:57,320 --> 00:16:59,620 This is extremely brief. 333 00:16:59,620 --> 00:17:01,360 It was the first demonstration 334 00:17:01,360 --> 00:17:04,880 of such a rapid evolution of seed traits for plants. 335 00:17:04,880 --> 00:17:08,100 And that's due to this highly fragmented composition 336 00:17:08,100 --> 00:17:09,353 of the urban environment. 337 00:17:12,860 --> 00:17:14,450 - [Narrator] Genetic changes occurring 338 00:17:14,450 --> 00:17:18,010 at such a rate have long been considered unlikely, 339 00:17:18,010 --> 00:17:20,073 even impossible by science. 340 00:17:23,540 --> 00:17:26,920 - I think Darwin would have been amazed by the fastness 341 00:17:26,920 --> 00:17:28,610 by which these changes take place. 342 00:17:28,610 --> 00:17:31,520 He was sort of underestimating the power 343 00:17:31,520 --> 00:17:33,610 of natural selection himself. 344 00:17:33,610 --> 00:17:36,420 He said that you could never see any 345 00:17:36,420 --> 00:17:38,450 of these changes in progress. 346 00:17:38,450 --> 00:17:39,860 You cannot actually observe them, 347 00:17:39,860 --> 00:17:42,760 you can only deduce them from the fossils, 348 00:17:42,760 --> 00:17:44,490 from the patterns that you see in nature. 349 00:17:44,490 --> 00:17:46,970 You said evolution is too slow 350 00:17:46,970 --> 00:17:49,280 to see it happening in real time. 351 00:17:49,280 --> 00:17:51,840 And the fact that now today, especially in cities, 352 00:17:51,840 --> 00:17:53,860 we see these changes taking place 353 00:17:53,860 --> 00:17:57,310 under our eyes in the streets where we live right around us, 354 00:17:57,310 --> 00:17:59,710 I think Darwin would have been thrilled by that. 355 00:18:02,240 --> 00:18:06,800 - In Los Angeles, the UCLA campus also nurtures species 356 00:18:06,800 --> 00:18:10,240 that have migrated from surrounding areas to the city. 357 00:18:10,240 --> 00:18:13,530 Evolutionary biologist Pamela Yeh studies 358 00:18:13,530 --> 00:18:15,093 the dark-eyed junco. 359 00:18:17,470 --> 00:18:19,460 The mountain bird has only been settled 360 00:18:19,460 --> 00:18:22,163 in California cities for a few decades. 361 00:18:23,240 --> 00:18:24,930 Pamela Yeh began her research 362 00:18:24,930 --> 00:18:27,823 in San Diego more than 20 years ago. 363 00:18:28,770 --> 00:18:33,190 Today, she's investigating how the once shy bird manages 364 00:18:33,190 --> 00:18:35,690 to survive in bustling Los Angeles. 365 00:18:40,710 --> 00:18:42,970 - So this is a junko nest. 366 00:18:42,970 --> 00:18:44,550 Typically in the mountains they make all 367 00:18:44,550 --> 00:18:46,430 of their nests on the ground, 368 00:18:46,430 --> 00:18:49,500 but up here, we have a good percentage of our nests 369 00:18:49,500 --> 00:18:51,910 that are up in the middle of, you know, 370 00:18:51,910 --> 00:18:54,570 branches of trees or up on the buildings, 371 00:18:54,570 --> 00:18:56,560 higher up off the ground. 372 00:18:56,560 --> 00:18:58,640 - [Narrator] Darwin himself was fascinated 373 00:18:58,640 --> 00:19:00,350 by the evolution of birds, 374 00:19:00,350 --> 00:19:04,100 but until now, biologists couldn't observe their adaptation 375 00:19:04,100 --> 00:19:06,370 to new habitats in real time. 376 00:19:06,370 --> 00:19:09,220 - They have this very famous study on Darwin's finches 377 00:19:09,220 --> 00:19:10,280 in the Galapagos, 378 00:19:10,280 --> 00:19:13,770 and they can show really large biological differences 379 00:19:13,770 --> 00:19:14,790 in terms of like mortality, 380 00:19:14,790 --> 00:19:16,270 like who's surviving and who's dying, 381 00:19:16,270 --> 00:19:19,180 by like a millimeter or less of a beak depth 382 00:19:19,180 --> 00:19:21,900 or a beak length or beak width, right? 383 00:19:21,900 --> 00:19:25,340 And so I think there can be really small differences 384 00:19:25,340 --> 00:19:29,760 that have very large biological significance. 385 00:19:29,760 --> 00:19:32,090 - [Narrator] To track such subtle changes. 386 00:19:32,090 --> 00:19:33,760 Pamela Yeh and her team led 387 00:19:33,760 --> 00:19:37,290 by PhD candidate Ellie Diamant first have 388 00:19:37,290 --> 00:19:39,310 to catch the birds. 389 00:19:39,310 --> 00:19:43,563 - So the nest is just over here. 390 00:19:45,940 --> 00:19:47,410 Can you see it in the grate? 391 00:19:47,410 --> 00:19:49,920 There are five chicks up there, nine days old, 392 00:19:49,920 --> 00:19:53,193 and the parents are going back and feeding them. 393 00:19:54,120 --> 00:19:56,840 - [Narrator] Might recorded birdsong jolt the bird 394 00:19:56,840 --> 00:19:58,450 into rash action? 395 00:19:58,450 --> 00:20:00,030 - So we're gonna play a playback, 396 00:20:00,030 --> 00:20:01,660 like a territorial junko song. 397 00:20:01,660 --> 00:20:04,940 And it's going to go to that speaker there. 398 00:20:04,940 --> 00:20:06,400 And hopefully the male 399 00:20:06,400 --> 00:20:08,270 who seems like he's feeding his chicks right now 400 00:20:08,270 --> 00:20:10,490 is gonna fly over, try to check it out, 401 00:20:10,490 --> 00:20:12,040 maybe go for the speaker, 402 00:20:12,040 --> 00:20:14,920 and hopefully get caught in this net or in this net. 403 00:20:14,920 --> 00:20:15,753 We'll see. 404 00:20:15,753 --> 00:20:16,586 Okay. 405 00:20:19,435 --> 00:20:22,610 (junko chirping) 406 00:20:22,610 --> 00:20:24,920 - [Narrator] The call of a rival bird. 407 00:20:24,920 --> 00:20:27,633 The male, intent on driving it off, rushes over. 408 00:20:29,680 --> 00:20:31,670 - [Ellie] Oh, there is a junco in the net. 409 00:20:31,670 --> 00:20:33,463 Guys, that was easy. 410 00:20:34,730 --> 00:20:37,290 - [Narrator] The team tags and measures the bird 411 00:20:37,290 --> 00:20:39,620 and collects DNA samples. 412 00:20:39,620 --> 00:20:42,100 Back in San Diego in 2003, 413 00:20:42,100 --> 00:20:44,120 Professor Yeh had discovered a change 414 00:20:44,120 --> 00:20:48,000 in the appearance of the city junco's tail feathers. 415 00:20:48,000 --> 00:20:49,450 Now her team wants to see 416 00:20:49,450 --> 00:20:51,763 if something similar is going on here. 417 00:20:53,280 --> 00:20:55,340 - [Ellie] Spreading his tail so you can see the white 418 00:20:55,340 --> 00:20:56,363 on his tail there. 419 00:20:58,740 --> 00:21:00,990 - One of the main differences that we found is 420 00:21:00,990 --> 00:21:04,150 that urban juncos had much less white in the tail feather 421 00:21:04,150 --> 00:21:06,530 than the populations in the local mountains, 422 00:21:06,530 --> 00:21:07,810 which had a lot of white. 423 00:21:07,810 --> 00:21:09,630 And what we wanted to know was then, well, 424 00:21:09,630 --> 00:21:12,260 why is it there's this huge difference 425 00:21:12,260 --> 00:21:13,680 that we're finding between the mountains 426 00:21:13,680 --> 00:21:14,903 and the city juncos? 427 00:21:16,050 --> 00:21:17,900 What's the reason for that? 428 00:21:17,900 --> 00:21:20,780 - [Narrator] Evidence suggests the white tail feathers serve 429 00:21:20,780 --> 00:21:23,930 an important signaling function in competition 430 00:21:23,930 --> 00:21:26,053 and partner selection by the birds. 431 00:21:28,307 --> 00:21:29,640 - Cool. Success. 432 00:21:29,640 --> 00:21:30,473 All right. 433 00:21:31,430 --> 00:21:34,130 - [Narrator] What new demands does life between motorways 434 00:21:34,130 --> 00:21:36,420 and buildings make on birds 435 00:21:36,420 --> 00:21:39,793 to change their mating and territorial preferences? 436 00:21:42,210 --> 00:21:44,550 The case of a species adapting its color 437 00:21:44,550 --> 00:21:47,410 to man-made environments was first observed 438 00:21:47,410 --> 00:21:50,920 by natural scientists during industrialization, 439 00:21:50,920 --> 00:21:53,720 even before Darwin put forward his theory 440 00:21:53,720 --> 00:21:55,483 on the origin of species. 441 00:21:58,350 --> 00:22:01,770 - The peppered moth is a species of moth 442 00:22:01,770 --> 00:22:03,920 that comes in two forms, 443 00:22:03,920 --> 00:22:06,450 a light-colored form with light wings 444 00:22:06,450 --> 00:22:08,190 and one with dark wings. 445 00:22:08,190 --> 00:22:11,000 And the dark-winged form was only first found 446 00:22:11,000 --> 00:22:13,700 in England in the Industrial Revolution. 447 00:22:13,700 --> 00:22:15,800 In the middle of the 19th century, 448 00:22:15,800 --> 00:22:18,510 it began to appear and suddenly it started to become more 449 00:22:18,510 --> 00:22:21,840 and more common over a relatively short period of time 450 00:22:21,840 --> 00:22:24,540 in areas where there was a lot of air pollution. 451 00:22:24,540 --> 00:22:26,270 - [Narrator] The smoke from the chimneys coated 452 00:22:26,270 --> 00:22:29,300 the white birch bark with dark soot. 453 00:22:29,300 --> 00:22:32,930 The white moths thus became easy prey for birds. 454 00:22:32,930 --> 00:22:36,770 The new darker moths enjoyed superior camouflage. 455 00:22:36,770 --> 00:22:38,720 - People didn't really understand what was going on. 456 00:22:38,720 --> 00:22:42,350 They thought that the female moths maybe saw 457 00:22:42,350 --> 00:22:44,200 that the environment was getting darker 458 00:22:44,200 --> 00:22:46,580 and somehow that became imprinted on their offspring. 459 00:22:46,580 --> 00:22:48,010 I mean, that's the way people thought 460 00:22:48,010 --> 00:22:51,490 about genetics and heredity in those days. 461 00:22:51,490 --> 00:22:54,150 - [Narrator] The conclusive evidence connecting the spread 462 00:22:54,150 --> 00:22:59,150 of the dark moths to coal soot was only found in the 1980s. 463 00:22:59,210 --> 00:23:01,210 The air was getting cleaner again, 464 00:23:01,210 --> 00:23:03,893 and the white moths returned. 465 00:23:05,740 --> 00:23:07,870 - Evolution can proceed fast 466 00:23:07,870 --> 00:23:10,980 if the environment changes very dramatically, 467 00:23:10,980 --> 00:23:13,350 which means that a lot of individuals will die 468 00:23:13,350 --> 00:23:14,500 and a few will survive, 469 00:23:14,500 --> 00:23:16,960 the ones that have some genetic characteristic 470 00:23:16,960 --> 00:23:18,440 that makes them survive better. 471 00:23:18,440 --> 00:23:21,090 And they will then produce the next generation. 472 00:23:21,090 --> 00:23:24,080 And they may also have some adaptations 473 00:23:24,080 --> 00:23:27,470 which then spread so that evolution can proceed faster. 474 00:23:27,470 --> 00:23:29,100 - [Narrator] The color change was a simple 475 00:23:29,100 --> 00:23:31,010 but effective adaptation. 476 00:23:31,010 --> 00:23:33,893 It only required a mutation of a single gene. 477 00:23:34,820 --> 00:23:38,060 But what if man-made pollutants substantially distort 478 00:23:38,060 --> 00:23:40,223 the biochemistry of organisms? 479 00:23:41,070 --> 00:23:44,740 In the 1970s, the water at New Bedford Harbor near Boston 480 00:23:44,740 --> 00:23:46,973 was severely polluted with PCBs. 481 00:23:48,080 --> 00:23:51,510 The US Environmental Protection Agency, EPA, 482 00:23:51,510 --> 00:23:54,603 wanted to know just how bad the pollution really was. 483 00:23:55,770 --> 00:23:59,700 - The original focus was on what must be wrong 484 00:23:59,700 --> 00:24:02,270 with all the fish that live in that harbor 485 00:24:02,270 --> 00:24:04,910 because of the toxic chemicals. 486 00:24:04,910 --> 00:24:06,910 Instead, we came here looking trying 487 00:24:06,910 --> 00:24:10,570 to understand what must be right about those fish 488 00:24:10,570 --> 00:24:11,963 that could survive here. 489 00:24:14,970 --> 00:24:17,920 So they've become a natural experiment for us 490 00:24:17,920 --> 00:24:22,140 to study how animals can adapt 491 00:24:22,140 --> 00:24:25,670 to toxic human-made pollutants. 492 00:24:25,670 --> 00:24:28,560 Terrific. Just what we're looking for. 493 00:24:28,560 --> 00:24:31,160 Let's get 'em into a net, bring 'em back to the lab. 494 00:24:33,140 --> 00:24:35,514 - [Narrator] Diane Nacci heads the EPA lab 495 00:24:35,514 --> 00:24:37,698 in Narragansett Rhode Island. 496 00:24:37,698 --> 00:24:40,615 (mysterious music) 497 00:24:45,310 --> 00:24:46,950 In the breeding facility, 498 00:24:46,950 --> 00:24:50,300 the scientists want to unravel the mechanism 499 00:24:50,300 --> 00:24:54,150 that allows this population of a killifish species 500 00:24:54,150 --> 00:24:58,363 to survive in the PCB-polluted water of New Bedford Harbor. 501 00:25:02,370 --> 00:25:04,970 - [Diane] So let's see if they left any eggs for us. 502 00:25:06,510 --> 00:25:08,400 - [Narrator] They plan to compare eggs 503 00:25:08,400 --> 00:25:10,540 from the New Bedford Harbor fish 504 00:25:10,540 --> 00:25:14,173 with those of a fish population from a cleaner site. 505 00:25:17,750 --> 00:25:19,370 - Let's start a test and see what 506 00:25:19,370 --> 00:25:22,103 they do when we expose 'em to chemicals. 507 00:25:23,660 --> 00:25:26,160 - This killifish species occurs all 508 00:25:26,160 --> 00:25:29,390 along the North American Atlantic coast. 509 00:25:29,390 --> 00:25:32,070 - The killifish has been a favorite 510 00:25:32,070 --> 00:25:35,090 of biology literally for centuries. 511 00:25:35,090 --> 00:25:38,390 That they are quite common, they are non-migratory, 512 00:25:38,390 --> 00:25:41,110 so they reflect their local environment, 513 00:25:41,110 --> 00:25:43,890 and each population is unique in 514 00:25:43,890 --> 00:25:45,980 that it is genetically different. 515 00:25:45,980 --> 00:25:48,340 It is adapted to its local environment. 516 00:25:48,340 --> 00:25:51,423 So it gives us opportunity for lots of studies. 517 00:25:53,090 --> 00:25:55,730 - [Narrator] The researchers need to observe the development 518 00:25:55,730 --> 00:26:00,730 of the fish embryos in the egg in order to understand 519 00:26:01,350 --> 00:26:05,510 at which stages the environmental toxin disrupts 520 00:26:05,510 --> 00:26:09,800 the animal's biochemistry or not. 521 00:26:09,800 --> 00:26:13,080 - So we'll look at the rate at which the embryo is developed 522 00:26:13,080 --> 00:26:17,130 and certain features that we know that PCBs fees can disturb 523 00:26:17,130 --> 00:26:20,060 like a proper development of the heart, 524 00:26:20,060 --> 00:26:23,400 evidence of proper development of the circulatory system, 525 00:26:23,400 --> 00:26:25,213 and proper body size. 526 00:26:28,230 --> 00:26:30,210 - [Narrator] Why are these particular fish able 527 00:26:30,210 --> 00:26:33,473 to resist deadly environmental toxins? 528 00:26:35,980 --> 00:26:38,570 - It's important to understand which species can 529 00:26:38,570 --> 00:26:41,290 and which cannot adapt because we want to forecast. 530 00:26:41,290 --> 00:26:44,410 We want to predict what the environment will look like 531 00:26:44,410 --> 00:26:47,200 in the future, whether the ecosystem surfaces 532 00:26:47,200 --> 00:26:49,980 that we rely on today like pollination, 533 00:26:49,980 --> 00:26:52,930 clean water, et cetera, can still be relied on. 534 00:26:52,930 --> 00:26:55,610 And for that, we're going to need adaptation 535 00:26:55,610 --> 00:26:57,770 of all kinds of animals and plants 536 00:26:57,770 --> 00:27:00,043 to this new world that we are creating. 537 00:27:01,230 --> 00:27:04,950 - [Narrator] In California, some city juncos have less white 538 00:27:04,950 --> 00:27:08,180 in their tail feathers than their mountain peers. 539 00:27:08,180 --> 00:27:11,540 Which selection pressures are driving this evolution? 540 00:27:11,540 --> 00:27:14,283 What is the function of this pigmentation? 541 00:27:17,980 --> 00:27:19,300 - And one of the main purposes appears 542 00:27:19,300 --> 00:27:21,920 to be in dominance interactions 543 00:27:21,920 --> 00:27:24,610 between individuals where typically the more white 544 00:27:24,610 --> 00:27:26,220 that you have, the more dominant 545 00:27:26,220 --> 00:27:27,970 and the more aggressive you are, 546 00:27:27,970 --> 00:27:30,270 and your successful at winning competitions. 547 00:27:30,270 --> 00:27:31,890 If you're fighting with another one, 548 00:27:31,890 --> 00:27:34,303 often the one that has more white wins. 549 00:27:35,830 --> 00:27:37,990 - So we're gonna do some aggression playbacks 550 00:27:37,990 --> 00:27:39,200 around Dickson Court. 551 00:27:39,200 --> 00:27:40,530 - All right. Good luck today. 552 00:27:40,530 --> 00:27:41,363 - All right. - Okay. Yeah. 553 00:27:41,363 --> 00:27:42,448 Thank you so much today, Pam. 554 00:27:42,448 --> 00:27:43,281 - Okay. See you soon. - We'll keep you up-to-date. 555 00:27:43,281 --> 00:27:44,870 - Okay. Bye-bye. 556 00:27:44,870 --> 00:27:46,720 - [Narrator] To learn more about the adaptation 557 00:27:46,720 --> 00:27:48,340 of the birds to the city, 558 00:27:48,340 --> 00:27:51,180 PhD student Ellie Diamant aims 559 00:27:51,180 --> 00:27:54,450 to scientifically measure their aggression levels here 560 00:27:54,450 --> 00:27:55,670 in Los Angeles. 561 00:27:55,670 --> 00:27:58,400 - So we're essentially setting up an arena 562 00:27:58,400 --> 00:27:59,510 to watch the bird. 563 00:27:59,510 --> 00:28:01,570 While they're in the car, it's fine. 564 00:28:01,570 --> 00:28:04,223 This is a Boris the fake junko, 565 00:28:05,210 --> 00:28:09,120 and it's the visual stimulus for the bird. 566 00:28:09,120 --> 00:28:10,380 He's just gonna sit here 567 00:28:11,230 --> 00:28:14,400 and there's gonna be a song playing from there, 568 00:28:14,400 --> 00:28:17,680 and the bird is going to think that this is a real junco 569 00:28:17,680 --> 00:28:20,190 and it's gonna hear the song that it's singing, 570 00:28:20,190 --> 00:28:21,990 and then hopefully come and think 571 00:28:21,990 --> 00:28:24,690 that there is an intruding bird on the territory. 572 00:28:24,690 --> 00:28:27,090 And we'll see how it reacts to that bird. 573 00:28:27,090 --> 00:28:29,680 - [Narrator] Juncos are territorial birds. 574 00:28:29,680 --> 00:28:32,940 from a male's response to a stranger in his territory, 575 00:28:32,940 --> 00:28:35,293 the researchers can gauge his aggressiveness. 576 00:28:36,210 --> 00:28:37,110 - Ready? 577 00:28:37,110 --> 00:28:38,359 - [Researcher] Yeah. All right. 578 00:28:38,359 --> 00:28:39,372 Starting now. 579 00:28:39,372 --> 00:28:40,205 - [Ellie] Okay. 580 00:28:40,205 --> 00:28:42,955 (junco chirping) 581 00:28:47,408 --> 00:28:48,991 I think I hear him. 582 00:28:49,907 --> 00:28:50,740 Oh. - Oh. 583 00:28:50,740 --> 00:28:52,373 - He's there. Yeah, that's him. 584 00:28:53,440 --> 00:28:54,671 He's in the tree. 585 00:28:54,671 --> 00:28:55,660 - This angle you can. 586 00:28:55,660 --> 00:28:56,910 - Yeah, let me try. He's still singing. 587 00:28:56,910 --> 00:28:58,163 - [Researcher] He turned around. 588 00:28:59,220 --> 00:29:00,053 - Where is he? Oh. 589 00:29:00,053 --> 00:29:01,193 - [Researcher] He's down. Oh. 590 00:29:01,193 --> 00:29:02,383 - He's entering. - He's entered. 591 00:29:04,400 --> 00:29:05,420 - [Narrator] Shortly before the end 592 00:29:05,420 --> 00:29:07,630 of the 15-minute trial period, 593 00:29:07,630 --> 00:29:10,303 the male stalks the intruder closely. 594 00:29:12,610 --> 00:29:14,210 - He's hiding behind some grass. 595 00:29:18,750 --> 00:29:20,050 Is he gonna knock it over? 596 00:29:22,440 --> 00:29:24,130 - [Narrator] The researchers stop the playback 597 00:29:24,130 --> 00:29:25,960 to end the experiment. 598 00:29:25,960 --> 00:29:29,163 The male displayed some aggression, but not a great deal. 599 00:29:31,010 --> 00:29:32,450 Why do the city birds seem 600 00:29:32,450 --> 00:29:34,633 to lose their drive for competition? 601 00:29:36,390 --> 00:29:38,090 - What we think is happening is 602 00:29:38,090 --> 00:29:41,870 that there's selection towards for less aggressive birds 603 00:29:41,870 --> 00:29:44,670 who are better mates, the better parents, 604 00:29:44,670 --> 00:29:46,390 because you can likely increase your fitness 605 00:29:46,390 --> 00:29:50,120 so much more by taking care of the chicks that you do have, 606 00:29:50,120 --> 00:29:52,620 rather than looking for extra pair copulation 607 00:29:52,620 --> 00:29:55,000 and extra pair fertilizations. 608 00:29:55,000 --> 00:29:56,970 - [Narrator] With the longest summers in the city 609 00:29:56,970 --> 00:29:59,830 and abundant food from watered greens, 610 00:29:59,830 --> 00:30:02,230 the birds can maintain more nests per year 611 00:30:02,230 --> 00:30:04,090 than in the mountains. 612 00:30:04,090 --> 00:30:05,980 Competition for additional mates 613 00:30:05,980 --> 00:30:08,833 and territories no longer seems worthwhile. 614 00:30:13,490 --> 00:30:15,010 - There's a lot of reasons why these birds 615 00:30:15,010 --> 00:30:17,640 shouldn't be successful in urban areas, 616 00:30:17,640 --> 00:30:18,730 and yet they are. 617 00:30:18,730 --> 00:30:20,140 So I think understanding why they 618 00:30:20,140 --> 00:30:22,280 are and understanding the mechanisms, 619 00:30:22,280 --> 00:30:25,100 the processes underlying that can tell us something 620 00:30:25,100 --> 00:30:27,240 about how do we encourage other species 621 00:30:27,240 --> 00:30:28,920 to also be successful. 622 00:30:28,920 --> 00:30:32,910 (elegant piano music) 623 00:30:32,910 --> 00:30:33,990 - [Narrator] What are the factors 624 00:30:33,990 --> 00:30:37,483 that allow individual species to adapt to the city? 625 00:30:38,560 --> 00:30:42,393 Are parallel developments taking place in cities worldwide? 626 00:30:43,930 --> 00:30:46,710 At the University of Toronto Mississauga, 627 00:30:46,710 --> 00:30:50,963 evolutionary biologist Marc Johnson pursues these questions. 628 00:30:52,790 --> 00:30:54,580 - In a lot of ways you can think of cities 629 00:30:54,580 --> 00:30:59,580 as one of the largest unplanned experiments of all time. 630 00:31:00,330 --> 00:31:02,320 The problem is, is there's very few organisms 631 00:31:02,320 --> 00:31:04,270 where you could study adaptation 632 00:31:04,270 --> 00:31:07,610 to urban environments on a global scale. 633 00:31:07,610 --> 00:31:10,840 And white clover is one of those very few organisms 634 00:31:10,840 --> 00:31:11,927 where you can actually do that. 635 00:31:11,927 --> 00:31:15,370 And so now this then becomes the model 636 00:31:15,370 --> 00:31:19,000 to understand whether organisms in general can adapt 637 00:31:19,000 --> 00:31:21,850 to the convergent environmental change associated 638 00:31:21,850 --> 00:31:24,390 with cities throughout the world. 639 00:31:24,390 --> 00:31:26,280 - [Narrator] Researchers across the globe 640 00:31:26,280 --> 00:31:28,460 are working together in this study. 641 00:31:28,460 --> 00:31:30,970 Evolutionary biologist Stephan Greiner 642 00:31:30,970 --> 00:31:34,850 and his team are collecting white clover in Berlin. 643 00:31:34,850 --> 00:31:38,460 In cities, the plant faces a different habitat. 644 00:31:38,460 --> 00:31:39,840 Temperatures are higher than 645 00:31:39,840 --> 00:31:41,763 in the suburbs and the countryside. 646 00:31:43,880 --> 00:31:45,668 (Stephan speaking in foreign language) 647 00:31:45,668 --> 00:31:46,880 - [Voiceover] What you can expect is that 648 00:31:46,880 --> 00:31:49,350 as humans create new environmental conditions, 649 00:31:49,350 --> 00:31:50,650 life will adapt. 650 00:31:50,650 --> 00:31:52,750 And to be able to show that on a global scale, 651 00:31:52,750 --> 00:31:54,880 that's a real scientific benefit. 652 00:31:54,880 --> 00:31:56,020 It has to be done. 653 00:31:56,020 --> 00:31:58,920 That's why we're dedicating our free time to this project. 654 00:32:01,670 --> 00:32:03,740 - [Narrator] As they proceed from the countryside 655 00:32:03,740 --> 00:32:05,360 to the city center of Berlin, 656 00:32:05,360 --> 00:32:10,360 Greiner and his team collect specimens at 30 locations. 657 00:32:10,400 --> 00:32:13,130 That means the data will be broad enough to compare 658 00:32:13,130 --> 00:32:15,303 with that from other global cities. 659 00:32:16,240 --> 00:32:18,290 In the shadow of the Television Tower, 660 00:32:18,290 --> 00:32:20,283 they find their final samples. 661 00:32:23,177 --> 00:32:25,143 - [Voiceover] That's it. All done. 662 00:32:27,880 --> 00:32:30,560 - In all, we have 168 cities right now, 663 00:32:30,560 --> 00:32:33,510 and over 250 collaborators all working 664 00:32:33,510 --> 00:32:35,070 on the same project together. 665 00:32:35,070 --> 00:32:39,550 There's never been a collaborative project 666 00:32:39,550 --> 00:32:42,910 on evolutionary biology of this scale. 667 00:32:42,910 --> 00:32:45,320 And so this is the largest collaborative project 668 00:32:45,320 --> 00:32:47,630 in evolutionary biology ever. 669 00:32:47,630 --> 00:32:50,290 - [Narrator] So is clover developing in the same way 670 00:32:50,290 --> 00:32:54,120 all over the world into a kind of global city clover? 671 00:32:54,120 --> 00:32:55,970 From the vast set of data, 672 00:32:55,970 --> 00:32:58,033 the researchers hope to find an answer. 673 00:33:00,690 --> 00:33:04,400 In the grounds of a research institute north of New York, 674 00:33:04,400 --> 00:33:07,140 geneticist Jason Munshi-South wants 675 00:33:07,140 --> 00:33:11,350 to catch white-footed mice to compare their DNA 676 00:33:11,350 --> 00:33:13,380 with that of those in the city, 677 00:33:13,380 --> 00:33:14,311 but it's not easy. - Go to the labs. 678 00:33:14,311 --> 00:33:15,500 - [Researcher] It's good. 679 00:33:15,500 --> 00:33:16,333 - Okay. 680 00:33:21,560 --> 00:33:23,080 So this is a trap that was open 681 00:33:23,080 --> 00:33:24,930 that didn't catch anything obviously. 682 00:33:27,970 --> 00:33:29,230 Wow, that's a toad. 683 00:33:29,230 --> 00:33:30,480 I thought it was a mouse. 684 00:33:31,370 --> 00:33:35,813 Nice day for other wildlife, I guess. 685 00:33:38,880 --> 00:33:39,800 We were really surprised 686 00:33:39,800 --> 00:33:42,260 that almost every park was different from every other park. 687 00:33:42,260 --> 00:33:44,030 It's almost to the point where you can take a mouse 688 00:33:44,030 --> 00:33:46,120 from one park, give it to our lab, 689 00:33:46,120 --> 00:33:48,860 and we could just look at a small segment of its genome 690 00:33:48,860 --> 00:33:50,560 and tell you where it came from. 691 00:33:50,560 --> 00:33:52,700 That's how much they had changed just randomly 692 00:33:52,700 --> 00:33:54,780 over time from being isolated. 693 00:33:54,780 --> 00:33:57,270 And that's when we started our current studies looking 694 00:33:57,270 --> 00:33:59,315 at over 20,000 genes to see what genes 695 00:33:59,315 --> 00:34:02,020 and potentially what functions change 696 00:34:02,020 --> 00:34:04,366 when they adapt to living in inside of New York City. 697 00:34:04,366 --> 00:34:07,616 (rich thrumming music) 698 00:34:08,941 --> 00:34:09,837 Oh, there's one. 699 00:34:10,702 --> 00:34:12,702 The first white-footed mouse of the day. 700 00:34:14,250 --> 00:34:15,230 - [Narrator] For the new study, 701 00:34:15,230 --> 00:34:18,670 Jason Munshi-South and his team have already caught more 702 00:34:18,670 --> 00:34:22,873 than 100 mice and analyzed their genetic compositions. 703 00:34:24,880 --> 00:34:28,380 They try to take their samples as gently as possible 704 00:34:28,380 --> 00:34:30,083 so they don't hurt the animals. 705 00:34:34,469 --> 00:34:36,210 - And we take a genetic sample. 706 00:34:36,210 --> 00:34:39,170 In this case, we will be using this small tool. 707 00:34:39,170 --> 00:34:42,750 It's like a paper punch, but for tissue. 708 00:34:42,750 --> 00:34:46,280 And we store that for a genetic analysis. 709 00:34:46,280 --> 00:34:47,550 And we want to be able to tie 710 00:34:47,550 --> 00:34:49,210 that tissue sample to a location 711 00:34:49,210 --> 00:34:50,670 because that's important for understanding 712 00:34:50,670 --> 00:34:51,780 how they vary when they're 713 00:34:51,780 --> 00:34:53,933 in a more urban or a less urban population. 714 00:34:57,070 --> 00:34:59,650 So now they're pretty immobile. 715 00:34:59,650 --> 00:35:00,583 This is a male. 716 00:35:02,310 --> 00:35:03,503 It's a young male. 717 00:35:04,750 --> 00:35:06,140 All right, why don't we take the ear punch 718 00:35:06,140 --> 00:35:07,177 and we'll start on the other one. 719 00:35:07,177 --> 00:35:10,190 (rich thrumming music) 720 00:35:10,190 --> 00:35:11,500 - [Narrator] Hair isn't suitable 721 00:35:11,500 --> 00:35:13,945 for comprehensive genetic analysis. 722 00:35:13,945 --> 00:35:16,862 (thoughtful music) 723 00:35:23,040 --> 00:35:25,960 After collecting the samples and some measurements, 724 00:35:25,960 --> 00:35:27,963 the scientists release the mice. 725 00:35:29,890 --> 00:35:33,580 Genetic analysis can reveal the evolutionary trajectories 726 00:35:33,580 --> 00:35:34,960 of the mice. 727 00:35:34,960 --> 00:35:37,020 They point to a variety of physical 728 00:35:37,020 --> 00:35:40,500 and behavioral changes spreading among the animals, 729 00:35:40,500 --> 00:35:43,260 each of them unique to the challenges 730 00:35:43,260 --> 00:35:45,417 of each city park environment. 731 00:35:45,417 --> 00:35:48,334 (thoughtful music) 732 00:35:54,780 --> 00:35:58,623 - So we're starting to fill in our gradient really nicely. 733 00:36:00,220 --> 00:36:02,430 So here are the mice we have today from the Calder Center, 734 00:36:02,430 --> 00:36:03,460 and you can see it's right 735 00:36:03,460 --> 00:36:06,780 in between highly urbanized New York City. 736 00:36:06,780 --> 00:36:09,760 And then all these sites we have up here and went out here. 737 00:36:09,760 --> 00:36:10,670 So Central Park seems 738 00:36:10,670 --> 00:36:12,780 to be our most distinct population 739 00:36:12,780 --> 00:36:13,730 to date. - We tested it. 740 00:36:13,730 --> 00:36:14,768 - It makes sense. Yeah. 741 00:36:14,768 --> 00:36:15,980 - Yeah. 742 00:36:15,980 --> 00:36:19,030 - Most urban, probably the most isolated. 743 00:36:19,030 --> 00:36:20,570 So if you took a mouse from Central Park, 744 00:36:20,570 --> 00:36:22,537 some of its genes will be different from a mouse outside 745 00:36:22,537 --> 00:36:24,450 in the countryside in a big park somewhere. 746 00:36:24,450 --> 00:36:25,870 Right, so for this one in particular- 747 00:36:25,870 --> 00:36:27,750 - [Narrator] The food supply in Central Park, 748 00:36:27,750 --> 00:36:29,986 much of it human food waste, 749 00:36:29,986 --> 00:36:32,160 might've triggered a genetic response. 750 00:36:32,160 --> 00:36:34,350 - Other places in the city near Central Park. 751 00:36:34,350 --> 00:36:35,183 And then once you get, 752 00:36:35,183 --> 00:36:37,880 so what we've learned so far is that one set of genes 753 00:36:37,880 --> 00:36:41,490 that are changing in the city have to do with metabolism. 754 00:36:41,490 --> 00:36:43,650 So these white-footed mice are eating things, 755 00:36:43,650 --> 00:36:46,520 then they have to digest them and assimilate the nutrients. 756 00:36:46,520 --> 00:36:48,590 And we know it's evolution because a heritable change 757 00:36:48,590 --> 00:36:50,700 in DNA sequence is evolution. 758 00:36:50,700 --> 00:36:52,030 - [Narrator] Central Park mice seem 759 00:36:52,030 --> 00:36:54,210 to have genetically altered their metabolism 760 00:36:54,210 --> 00:36:56,770 to better digest fast food. 761 00:36:56,770 --> 00:36:59,170 - It raises several like broader questions 762 00:36:59,170 --> 00:37:02,110 about what we are doing as a species. 763 00:37:02,110 --> 00:37:05,600 As we modify the Earth's habitat for our needs, 764 00:37:05,600 --> 00:37:08,550 how are we changing the future of other species? 765 00:37:08,550 --> 00:37:09,640 Not only are we affecting them, 766 00:37:09,640 --> 00:37:12,390 but we're changing what they will become in the future. 767 00:37:16,988 --> 00:37:19,160 - [Narrator] And Narragansett, Diane Nacci 768 00:37:19,160 --> 00:37:21,010 and her team are investigating 769 00:37:21,010 --> 00:37:23,780 how the different fish embryos exposed 770 00:37:23,780 --> 00:37:27,220 to the toxic PCBs have developed. 771 00:37:27,220 --> 00:37:28,387 - So, come in. 772 00:37:30,400 --> 00:37:31,820 Hi, how's it going? - Hi. 773 00:37:31,820 --> 00:37:32,653 Really well. 774 00:37:34,300 --> 00:37:38,000 So this is the study that's comparing Scorton Creek 775 00:37:38,000 --> 00:37:42,110 and New Bedford Harbor exposed to PCB 126. 776 00:37:42,110 --> 00:37:47,110 So this one is the group that was treated with PCBs, 777 00:37:48,290 --> 00:37:51,730 also from the clean site, Scorton Creek. 778 00:37:51,730 --> 00:37:52,817 And as you can see, 779 00:37:52,817 --> 00:37:55,870 the PCBs have had a pretty dramatic effect 780 00:37:55,870 --> 00:37:57,810 on the development, which is what we expect 781 00:37:57,810 --> 00:37:59,970 with these very toxic chemicals. 782 00:37:59,970 --> 00:38:00,803 - Yeah. 783 00:38:00,803 --> 00:38:02,730 And in my experience, when I see this, 784 00:38:02,730 --> 00:38:07,480 this constellation of anomalies, it's absolutely lethal. 785 00:38:07,480 --> 00:38:10,840 There's no way that an animal would even hatch, 786 00:38:10,840 --> 00:38:13,200 nevermind survive, after hatching 787 00:38:13,200 --> 00:38:14,890 if the heart is not functional 788 00:38:14,890 --> 00:38:17,830 and the blood is essentially not circulating 789 00:38:17,830 --> 00:38:18,810 around the body. 790 00:38:18,810 --> 00:38:22,360 So let's take a look at that biochemical endpoint 791 00:38:22,360 --> 00:38:27,360 to see if they are also responsive at the biochemical level. 792 00:38:27,800 --> 00:38:30,140 - [Narrator] Using a special contrast agent, 793 00:38:30,140 --> 00:38:32,860 the scientists can trace enzyme activity 794 00:38:32,860 --> 00:38:34,233 in the unhatched fish. 795 00:38:37,220 --> 00:38:41,580 - So you can see that the substrate is fluorescing 796 00:38:41,580 --> 00:38:44,760 in the bladder, showing that this enzyme system is working 797 00:38:44,760 --> 00:38:48,070 and we're getting the expected metabolites in the bladder. 798 00:38:48,070 --> 00:38:51,100 - [Diane] That's a very dramatic demonstration 799 00:38:51,100 --> 00:38:54,020 of enzyme activity in a living organism. 800 00:38:54,020 --> 00:38:55,510 - Mm-hmm. 801 00:38:55,510 --> 00:38:57,240 - [Narrator] The active enzymes reveal 802 00:38:57,240 --> 00:39:00,290 how the organism tries to break down the toxin, 803 00:39:00,290 --> 00:39:02,123 but parishes in the process. 804 00:39:03,290 --> 00:39:05,840 Then the team observes how the offspring of the fish 805 00:39:05,840 --> 00:39:08,033 from New Bedford Harbor have developed. 806 00:39:09,980 --> 00:39:13,850 - Okay, so these are fish from New Bedford Harbor 807 00:39:13,850 --> 00:39:16,380 that were exposed to the same level of PCB 808 00:39:16,380 --> 00:39:18,300 that we were just looking at. 809 00:39:18,300 --> 00:39:20,580 As you can see with this embryo, 810 00:39:20,580 --> 00:39:22,040 it doesn't seem to have any effect. 811 00:39:22,040 --> 00:39:25,280 The heart is still beating normally and healthy 812 00:39:25,280 --> 00:39:27,440 and it's developed really well. 813 00:39:27,440 --> 00:39:30,780 - That looks like an embryo that's about ready to hatch. 814 00:39:30,780 --> 00:39:33,730 - [Researcher] Some of them actually already hatched. 815 00:39:33,730 --> 00:39:35,440 - [Narrator] These fish should be dead, 816 00:39:35,440 --> 00:39:39,120 poisoned by one of the most lethal environmental toxins. 817 00:39:39,120 --> 00:39:41,683 But life, it seems, has found a way. 818 00:39:43,000 --> 00:39:46,140 - One thing we know about this class of chemicals is 819 00:39:46,140 --> 00:39:49,053 that in all vertebrates, including people, 820 00:39:49,940 --> 00:39:52,803 it turns on a certain enzyme pathway. 821 00:39:53,650 --> 00:39:56,990 So a normally responsive person, 822 00:39:56,990 --> 00:39:59,310 or in this case, of fish, 823 00:39:59,310 --> 00:40:02,610 should have that enzyme system turned on 824 00:40:02,610 --> 00:40:04,793 if they were exposed to PCBs. 825 00:40:05,920 --> 00:40:07,970 - [Narrator] The contrast-enhanced image shows 826 00:40:07,970 --> 00:40:10,270 how the enzymes that normally respond 827 00:40:10,270 --> 00:40:12,803 to the toxin remain silent. 828 00:40:14,860 --> 00:40:16,560 - So in this case, 829 00:40:16,560 --> 00:40:19,490 I see very little that's glowing brightly. 830 00:40:19,490 --> 00:40:23,050 It is a dramatic visual difference that suggests 831 00:40:23,050 --> 00:40:27,973 that that enzyme system is broken in the New Bedford fish. 832 00:40:29,220 --> 00:40:31,240 - [Narrator] The killifish from New Bedford Harbor 833 00:40:31,240 --> 00:40:33,370 have changed their metabolism. 834 00:40:33,370 --> 00:40:36,330 The poison can no longer harm them. 835 00:40:36,330 --> 00:40:38,197 But which genetic modifications lead 836 00:40:38,197 --> 00:40:40,773 to the fish's toxin resistance? 837 00:40:42,090 --> 00:40:43,910 That's what geneticist Mark Hahn 838 00:40:43,910 --> 00:40:46,463 of Woods Hole Institute wants to find out. 839 00:40:47,510 --> 00:40:49,880 Could this be a key to understanding 840 00:40:49,880 --> 00:40:53,620 how nature might resist human interference? 841 00:40:53,620 --> 00:40:57,243 In the laboratory, he uses the CRISPR-Cas method. 842 00:40:58,150 --> 00:40:59,650 - It's an incredibly powerful way 843 00:40:59,650 --> 00:41:03,870 to modify the genetics of an experimental fish like this, 844 00:41:03,870 --> 00:41:07,060 to ask questions about the roles of certain genes, 845 00:41:07,060 --> 00:41:09,980 and, in fact, the roles of even single amino acids 846 00:41:09,980 --> 00:41:11,445 in the protein can be investigated 847 00:41:11,445 --> 00:41:13,780 with this CRISPR-Cas method. 848 00:41:13,780 --> 00:41:15,240 - [Narrator] To test their assumptions 849 00:41:15,240 --> 00:41:17,310 about the resistant killifish, 850 00:41:17,310 --> 00:41:21,270 Mark Hahn and his team experiment with zebrafish. 851 00:41:21,270 --> 00:41:23,360 - I want to find out exactly what are the changes 852 00:41:23,360 --> 00:41:26,150 in those genes, and to be able to actually zero 853 00:41:26,150 --> 00:41:28,870 in on the specific molecular changes 854 00:41:28,870 --> 00:41:30,970 that are responsible for the resistance, 855 00:41:30,970 --> 00:41:33,790 and to be able to recreate that in the laboratory 856 00:41:33,790 --> 00:41:37,413 to actually prove that that's the mechanism of resistance. 857 00:41:38,310 --> 00:41:40,890 - [Narrator] They're inserting portions of DNA taken 858 00:41:40,890 --> 00:41:45,033 from the resistant killifish into embryos of zebrafish. 859 00:41:49,040 --> 00:41:49,873 - Hey, Mark. - Hey, Neil. 860 00:41:49,873 --> 00:41:51,090 - How are you? 861 00:41:51,090 --> 00:41:51,923 - Good. 862 00:41:52,800 --> 00:41:53,800 - [Mark] Which ones are these? 863 00:41:53,800 --> 00:41:57,490 - [Neil] I'm injecting AIP exon two. 864 00:41:57,490 --> 00:41:58,930 - [Narrator] If the zebrafish growing 865 00:41:58,930 --> 00:42:01,860 from these eggs exhibit the same resistance, 866 00:42:01,860 --> 00:42:05,030 they will have found the responsible genes. 867 00:42:05,030 --> 00:42:07,650 - Here we are interested to study a function 868 00:42:07,650 --> 00:42:10,160 of a gene known as AIP. 869 00:42:10,160 --> 00:42:11,780 So we are trying to delete this gene 870 00:42:11,780 --> 00:42:12,637 in this particular species 871 00:42:12,637 --> 00:42:15,800 and then try to study what's the function of this gene 872 00:42:15,800 --> 00:42:19,840 and however that will alter the resistance to PCBs. 873 00:42:22,640 --> 00:42:24,490 - [Narrator] With these experiments, 874 00:42:24,490 --> 00:42:29,060 science is venturing deep into the source code of creation. 875 00:42:29,060 --> 00:42:31,980 The scientists believe this research could yield 876 00:42:31,980 --> 00:42:34,740 the secret of life's ability to adapt 877 00:42:34,740 --> 00:42:37,760 to the most extreme conditions. 878 00:42:37,760 --> 00:42:40,850 And this knowledge could also help other creatures 879 00:42:40,850 --> 00:42:44,503 to adapt and survive in a rapidly changing world. 880 00:42:49,980 --> 00:42:52,620 - I think we will understand the extent 881 00:42:52,620 --> 00:42:55,360 to which we can extrapolate our knowledge 882 00:42:55,360 --> 00:42:58,240 from the killifish system out beyond 883 00:42:58,240 --> 00:43:01,290 to other fish and even other vertebrates. 884 00:43:01,290 --> 00:43:05,940 So a broader understanding of the toxicology of pollutants 885 00:43:05,940 --> 00:43:09,400 and how that will impact the natural world. 886 00:43:09,400 --> 00:43:11,070 How we can understand what will be 887 00:43:11,070 --> 00:43:12,618 the most vulnerable species. 888 00:43:12,618 --> 00:43:15,535 (mysterious music) 889 00:43:18,081 --> 00:43:20,790 - [Narrator] At the Max Planck Institute in Potsdam, 890 00:43:20,790 --> 00:43:24,510 the research team processes the clover samples from Berlin. 891 00:43:24,510 --> 00:43:27,420 Their goal, to find cyanide. 892 00:43:27,420 --> 00:43:29,337 Clover plants that produce cyanide 893 00:43:29,337 --> 00:43:31,920 are better protected against predators, 894 00:43:31,920 --> 00:43:34,990 but are less able to tolerate cold. 895 00:43:34,990 --> 00:43:37,000 It's warmer in city centers, 896 00:43:37,000 --> 00:43:39,693 so this clover might be more common there. 897 00:43:41,114 --> 00:43:42,270 (Stephan speaking in foreign language) 898 00:43:42,270 --> 00:43:44,160 - [Voiceover] This is a qualitative test. 899 00:43:44,160 --> 00:43:46,880 We use it to indirectly detect a specific gene 900 00:43:46,880 --> 00:43:48,303 that generates this cyanide. 901 00:43:53,210 --> 00:43:55,340 - [Voiceover] So these are both rural? 902 00:43:55,340 --> 00:43:57,333 - [Voiceover] Yeah, they're both still rural. 903 00:44:00,207 --> 00:44:02,590 - [Narrator] Greiner and his team send their results 904 00:44:02,590 --> 00:44:05,600 and other clover specimens to Marc Johnson 905 00:44:05,600 --> 00:44:07,053 in his Toronto lab. 906 00:44:10,840 --> 00:44:12,680 - [Marc] Hey gang, how's it going? 907 00:44:12,680 --> 00:44:13,800 - Good. 908 00:44:13,800 --> 00:44:14,836 - Bita, how did that extraction go yesterday? 909 00:44:14,836 --> 00:44:15,930 - It was good. Yeah. - Yeah. 910 00:44:15,930 --> 00:44:20,753 So remind me, this is Berlin and Buenos Aires. 911 00:44:21,870 --> 00:44:23,590 - [Narrator] The team prepares the clover 912 00:44:23,590 --> 00:44:25,360 for gene sequencing, 913 00:44:25,360 --> 00:44:27,590 but the cyanide values taken by the team 914 00:44:27,590 --> 00:44:29,430 in Germany should show 915 00:44:29,430 --> 00:44:32,880 whether the clover has adapted an urban existence already. 916 00:44:32,880 --> 00:44:36,790 - Okay, Bita. Did we get the data from Berlin? 917 00:44:36,790 --> 00:44:37,623 - [Bita] Yes, we did. 918 00:44:37,623 --> 00:44:39,470 And this screen here it is. 919 00:44:39,470 --> 00:44:40,998 So I think it's really good. - Great. 920 00:44:40,998 --> 00:44:41,831 Okay. 921 00:44:41,831 --> 00:44:43,500 James, have you had a chance to look 922 00:44:43,500 --> 00:44:45,350 at the data from Berlin 923 00:44:45,350 --> 00:44:47,450 so we can understand how the environment's changing 924 00:44:47,450 --> 00:44:50,160 from Downtown Berlin through the suburbs 925 00:44:50,160 --> 00:44:51,516 and into the rural areas? 926 00:44:51,516 --> 00:44:52,349 You got it. 927 00:44:52,349 --> 00:44:53,853 - All right. So let's take a look at. 928 00:44:55,160 --> 00:44:56,670 So Berlin is one of the cities 929 00:44:56,670 --> 00:45:01,030 where we see white clover adapting to urban rural gradients. 930 00:45:01,030 --> 00:45:01,863 - [James] Yeah. 931 00:45:01,863 --> 00:45:02,696 - [Marc] Nice. 932 00:45:02,696 --> 00:45:04,620 And so now we're at about 33% of cities 933 00:45:04,620 --> 00:45:06,690 where white clover adapts? 934 00:45:06,690 --> 00:45:08,106 - Maybe 33.29. 935 00:45:08,106 --> 00:45:09,952 (Marc laughs) 936 00:45:09,952 --> 00:45:10,960 But yeah. - Fair enough. 937 00:45:10,960 --> 00:45:11,793 - [James] It's about 33%. 938 00:45:11,793 --> 00:45:12,650 - Okay. 939 00:45:12,650 --> 00:45:13,930 So then next I think what we're gonna have 940 00:45:13,930 --> 00:45:15,940 to do is figure out what are the drivers, 941 00:45:15,940 --> 00:45:17,840 the environmental drivers of this adaptation. 942 00:45:17,840 --> 00:45:19,620 So that's really cool. 943 00:45:19,620 --> 00:45:22,450 - [Narrator] In Berlin, plants from downtown a more likely 944 00:45:22,450 --> 00:45:26,040 to produce cyanide, as is the case in a third 945 00:45:26,040 --> 00:45:28,560 of the city surveyed so far. 946 00:45:28,560 --> 00:45:31,113 An indication of parallel evolution? 947 00:45:35,640 --> 00:45:39,200 - Some of the preliminary insights are fascinating. 948 00:45:39,200 --> 00:45:40,800 So it really looks like, 949 00:45:40,800 --> 00:45:43,550 regardless of where you are in the world, 950 00:45:43,550 --> 00:45:47,300 whether you're in Europe, North America, Japan, 951 00:45:47,300 --> 00:45:50,210 China, Australia, New Zealand, 952 00:45:50,210 --> 00:45:54,160 we see the ability for this humble white clover 953 00:45:54,160 --> 00:45:56,073 to adapt to these cities. 954 00:45:57,110 --> 00:45:59,020 - [Narrator] In the warmth of the city, 955 00:45:59,020 --> 00:46:02,943 cyanide-producing clover stands a better chance of survival. 956 00:46:06,080 --> 00:46:08,280 But to survive in the city, 957 00:46:08,280 --> 00:46:11,930 all organisms must adapt to higher temperatures. 958 00:46:11,930 --> 00:46:14,567 What scientists call "heat islands." 959 00:46:18,810 --> 00:46:22,640 - In cities, humans and their machinery creates 960 00:46:22,640 --> 00:46:25,930 a lot of heat, and we have a bubble of hot air 961 00:46:25,930 --> 00:46:26,950 in large cities. 962 00:46:26,950 --> 00:46:29,870 A city of more than a million people can be seven, 963 00:46:29,870 --> 00:46:31,273 eight degrees Celsius hotter 964 00:46:32,200 --> 00:46:34,530 in the center of the city than outside of the city. 965 00:46:34,530 --> 00:46:37,430 - [Narrator] This, Menno Schilthuizen believes, 966 00:46:37,430 --> 00:46:41,333 also influences the evolution of the white-lipped snail. 967 00:46:42,220 --> 00:46:44,760 Their shells come in many shades 968 00:46:44,760 --> 00:46:47,030 from brown to pale yellow. 969 00:46:47,030 --> 00:46:49,323 A single gene determines the color. 970 00:46:50,320 --> 00:46:53,220 - So they basically carry their genes on their back. 971 00:46:53,220 --> 00:46:57,280 The shell color determines the internal temperature 972 00:46:57,280 --> 00:46:59,740 of the snail to some extent. 973 00:46:59,740 --> 00:47:04,030 The difference in temperature inside can be two degrees 974 00:47:04,030 --> 00:47:05,370 under the same conditions. 975 00:47:05,370 --> 00:47:07,390 And that could be just the difference 976 00:47:07,390 --> 00:47:08,450 between life or death. 977 00:47:08,450 --> 00:47:09,740 On a hot summer day, 978 00:47:09,740 --> 00:47:12,950 you know it was 40 degrees in Amsterdam a few weeks ago, 979 00:47:12,950 --> 00:47:16,420 it could be that some of these yellow snails survived, 980 00:47:16,420 --> 00:47:19,560 but many of the brown ones died because they got too hot, 981 00:47:19,560 --> 00:47:21,860 they overheated, and they died. 982 00:47:21,860 --> 00:47:25,071 - [Narrator] But will statistics confirm this hypothesis? 983 00:47:25,071 --> 00:47:30,071 - Snails are evolving towards more yellow shelling. 984 00:47:30,350 --> 00:47:34,590 So the plan is now to just add some data to the dataset. 985 00:47:34,590 --> 00:47:36,053 So let's go. 986 00:47:37,260 --> 00:47:39,210 - [Narrator] In order to collect and evaluate 987 00:47:39,210 --> 00:47:42,300 as many snails as possible throughout Europe, 988 00:47:42,300 --> 00:47:45,473 Menno Schilthuizen is helped by volunteers. 989 00:47:47,360 --> 00:47:49,160 - You know, you don't have to go to the Galapagos 990 00:47:49,160 --> 00:47:51,850 to study evolution or become a paleontologist. 991 00:47:51,850 --> 00:47:53,860 It's happening everywhere all the time. 992 00:47:53,860 --> 00:47:57,963 It's a continuous, very normal biological process. 993 00:47:59,030 --> 00:48:01,180 - [Narrator] This time the volunteers find only 994 00:48:01,180 --> 00:48:03,060 a few life snails, 995 00:48:03,060 --> 00:48:06,730 but empty snail shells can also provide data. 996 00:48:06,730 --> 00:48:08,980 The snail shells are photographed 997 00:48:08,980 --> 00:48:11,340 and added to the database with an app 998 00:48:11,340 --> 00:48:13,943 that anyone can install on their mobile phone. 999 00:48:17,800 --> 00:48:19,960 - If we're looking at the adaptations 1000 00:48:19,960 --> 00:48:23,650 of urban animals and plants to the urban heat islands, 1001 00:48:23,650 --> 00:48:25,300 which, of course, is happening, 1002 00:48:25,300 --> 00:48:27,790 has been happening more rapidly than global climate change, 1003 00:48:27,790 --> 00:48:31,470 we can probably predict what's going to happen globally 1004 00:48:31,470 --> 00:48:33,123 in response to climate change. 1005 00:48:34,027 --> 00:48:36,490 - [Narrator] With temperatures rising globally, 1006 00:48:36,490 --> 00:48:38,220 our cities become a window 1007 00:48:38,220 --> 00:48:41,103 into nature's future transformations. 1008 00:48:42,360 --> 00:48:45,670 Every October in Mexico's Sierra Nevada, 1009 00:48:45,670 --> 00:48:49,470 monarch butterflies gather in their millions. 1010 00:48:49,470 --> 00:48:52,980 They traverse the whole of the North American continent 1011 00:48:52,980 --> 00:48:54,763 to spend winter down south. 1012 00:48:56,570 --> 00:48:58,010 But our growing cities make 1013 00:48:58,010 --> 00:48:59,993 their journey ever more perilous. 1014 00:49:01,190 --> 00:49:04,413 - Monarch butterflies are these really great insects. 1015 00:49:05,320 --> 00:49:08,190 Unfortunately, right now they're in decline. 1016 00:49:08,190 --> 00:49:09,930 In the United States, 1017 00:49:09,930 --> 00:49:14,050 they've experienced 80% population declines. 1018 00:49:14,050 --> 00:49:14,883 There we go. 1019 00:49:14,883 --> 00:49:17,940 - In this industrial area of Toronto, 1020 00:49:17,940 --> 00:49:21,863 monarch butterflies take a rest stop before flying on. 1021 00:49:23,020 --> 00:49:26,523 They also take the opportunity to mate and reproduce. 1022 00:49:26,523 --> 00:49:30,600 - Ooh, it's a really big caterpillar. 1023 00:49:30,600 --> 00:49:34,090 This is the baby monarch. 1024 00:49:34,090 --> 00:49:36,600 This one's probably a day or two away 1025 00:49:36,600 --> 00:49:39,230 from going into its chrysalis 1026 00:49:39,230 --> 00:49:42,530 and then becoming a monarch butterfly. 1027 00:49:42,530 --> 00:49:44,650 - [Narrator] While other species have the ability 1028 00:49:44,650 --> 00:49:47,360 to switch to other food sources, 1029 00:49:47,360 --> 00:49:50,993 monarch butterflies remained dependent on a single plant. 1030 00:49:53,320 --> 00:49:54,670 - [Man] I got one. 1031 00:49:54,670 --> 00:49:58,183 - Let's check it out. You found a monarch. 1032 00:50:00,290 --> 00:50:03,620 - [Narrator] The butterflies lay their eggs on the milkweed. 1033 00:50:03,620 --> 00:50:07,230 Their caterpillars feed exclusively on this plant. 1034 00:50:07,230 --> 00:50:08,830 And in many cities, 1035 00:50:08,830 --> 00:50:12,453 the land on which milkweed can grow is disappearing. 1036 00:50:15,340 --> 00:50:18,460 - Unfortunately, a lot of the cities are providing 1037 00:50:18,460 --> 00:50:21,000 these barriers that just don't have the resources 1038 00:50:21,000 --> 00:50:22,250 that they need. 1039 00:50:22,250 --> 00:50:24,840 And so it would basically be 1040 00:50:24,840 --> 00:50:26,700 if you're driving along the road 1041 00:50:26,700 --> 00:50:29,110 and you have any fuel stations 1042 00:50:29,110 --> 00:50:31,863 and you run out of gas, you're stuck, 1043 00:50:32,710 --> 00:50:35,860 then that's what's happening with these butterflies. 1044 00:50:35,860 --> 00:50:38,520 - [Narrator] Not all species can adapt. 1045 00:50:38,520 --> 00:50:41,020 As our cities continue to expand, 1046 00:50:41,020 --> 00:50:43,683 accommodating wildlife might be crucial. 1047 00:50:44,640 --> 00:50:48,750 How we shape our cities in the future may prove decisive 1048 00:50:48,750 --> 00:50:51,700 for the course of life on earth. 1049 00:50:51,700 --> 00:50:54,950 - Biodiversity helps us with the food that we eat. 1050 00:50:54,950 --> 00:50:57,280 It helps us with the air that we breathe. 1051 00:50:57,280 --> 00:51:00,920 So if we continue along the path that we have, 1052 00:51:00,920 --> 00:51:04,830 many different populations, including human populations, 1053 00:51:04,830 --> 00:51:06,283 will start to crash. 1054 00:51:07,510 --> 00:51:11,950 - Urban evolution can help us design green cities 1055 00:51:11,950 --> 00:51:13,623 in a Darwinian way. 1056 00:51:15,080 --> 00:51:19,240 - As humans become more urban, we have the potential to, 1057 00:51:19,240 --> 00:51:20,960 you know, allow some species to live in the city 1058 00:51:20,960 --> 00:51:22,130 and adapt to our cities, 1059 00:51:22,130 --> 00:51:25,760 but then put less pressure on the other habitats, 1060 00:51:25,760 --> 00:51:26,620 which would allow, you know, 1061 00:51:26,620 --> 00:51:28,680 the species that can't survive in the city 1062 00:51:28,680 --> 00:51:29,873 to continue to thrive. 1063 00:51:31,860 --> 00:51:34,880 - We're going to see more and more the realization 1064 00:51:34,880 --> 00:51:37,500 that we are part of nature, 1065 00:51:37,500 --> 00:51:41,098 and that is actually probably going to help us survive. 1066 00:51:41,098 --> 00:51:43,765 (elegant music) 79735

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