All language subtitles for Animal Espionage PBS NOVA 1080p EN SUB

af Afrikaans
sq Albanian
am Amharic
ar Arabic
hy Armenian
az Azerbaijani
eu Basque
be Belarusian
bn Bengali
bs Bosnian
bg Bulgarian
ca Catalan
ceb Cebuano
ny Chichewa
zh-CN Chinese (Simplified)
zh-TW Chinese (Traditional)
co Corsican
hr Croatian
cs Czech
da Danish
nl Dutch Download
en English
eo Esperanto
et Estonian
tl Filipino
fi Finnish
fr French
fy Frisian
gl Galician
ka Georgian
de German
el Greek
gu Gujarati
ht Haitian Creole
ha Hausa
haw Hawaiian
iw Hebrew
hi Hindi
hmn Hmong
hu Hungarian
is Icelandic
ig Igbo
id Indonesian
ga Irish
it Italian
ja Japanese
jw Javanese
kn Kannada
kk Kazakh
km Khmer
ko Korean
ku Kurdish (Kurmanji)
ky Kyrgyz
lo Lao
la Latin
lv Latvian
lt Lithuanian
lb Luxembourgish
mk Macedonian
mg Malagasy
ms Malay
ml Malayalam
mt Maltese
mi Maori
mr Marathi
mn Mongolian
my Myanmar (Burmese)
ne Nepali
no Norwegian
ps Pashto
fa Persian
pl Polish
pt Portuguese
pa Punjabi
ro Romanian
ru Russian
sm Samoan
gd Scots Gaelic
sr Serbian
st Sesotho
sn Shona
sd Sindhi
si Sinhala
sk Slovak
sl Slovenian
so Somali
es Spanish
su Sundanese
sw Swahili
sv Swedish
tg Tajik
ta Tamil
te Telugu
th Thai
tr Turkish Download
uk Ukrainian
ur Urdu
uz Uzbek
vi Vietnamese
cy Welsh
xh Xhosa
yi Yiddish
yo Yoruba
zu Zulu
or Odia (Oriya)
rw Kinyarwanda
tk Turkmen
tt Tatar
ug Uyghur
Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:01,600 --> 00:00:02,733 NARRATOR: From frigid oceans... 2 00:00:02,766 --> 00:00:04,933 Okay, there he is. 3 00:00:04,966 --> 00:00:06,866 NARRATOR: ...to distant jungles... 4 00:00:06,900 --> 00:00:08,266 ULLAS KARANTH: You can keep going a little bit more. 5 00:00:08,300 --> 00:00:09,933 NARRATOR: ...there's a hidden world 6 00:00:09,966 --> 00:00:11,266 of exotic creatures 7 00:00:11,300 --> 00:00:14,400 just out of view. 8 00:00:14,433 --> 00:00:16,333 ARNAUD DESBIEZ: Finding the animal is like looking 9 00:00:16,366 --> 00:00:17,733 for a needle in a haystack. 10 00:00:17,766 --> 00:00:18,966 It's really difficult. 11 00:00:19,000 --> 00:00:20,300 NARRATOR: Around the world, 12 00:00:20,333 --> 00:00:23,433 researchers are tracking the most vulnerable animals, 13 00:00:23,466 --> 00:00:26,800 trying to save them before they vanish forever. 14 00:00:26,833 --> 00:00:30,866 KARANTH: This powerful-looking animal is so fragile. 15 00:00:30,900 --> 00:00:32,933 The pieces of knowledge that are needed 16 00:00:32,966 --> 00:00:33,933 to make it survive 17 00:00:33,966 --> 00:00:34,900 are critical. 18 00:00:34,933 --> 00:00:37,833 NARRATOR: Now, new technology 19 00:00:37,866 --> 00:00:41,200 is revealing their secret lives. 20 00:00:41,233 --> 00:00:43,733 DESBIEZ: They're really silent little spies 21 00:00:43,766 --> 00:00:45,933 that make no noise, 22 00:00:45,966 --> 00:00:48,566 that can capture intimate moments of the animals. 23 00:00:48,600 --> 00:00:50,500 CRAIG PACKER: You get millions and millions of photographs, 24 00:00:50,533 --> 00:00:54,433 and you suddenly see things for the first time. 25 00:00:54,466 --> 00:00:56,000 NARRATOR: Frame by frame, 26 00:00:56,033 --> 00:00:59,333 the invisible world of animals is coming to life. 27 00:00:59,366 --> 00:01:01,933 DESBIEZ: I could not believe this species existed, 28 00:01:01,966 --> 00:01:04,566 that it was right here, around us. 29 00:01:04,600 --> 00:01:06,966 I had no idea they did that. 30 00:01:07,000 --> 00:01:12,600 NARRATOR: Their habits, fears, and most intimate moments. 31 00:01:12,633 --> 00:01:14,500 SARAH FORTUNE: It has completely revolutionized 32 00:01:14,533 --> 00:01:17,566 our ability to understand behaviors. 33 00:01:17,600 --> 00:01:20,500 NARRATOR: Rare footage from the animal kingdom 34 00:01:20,533 --> 00:01:22,400 is offering up new clues. 35 00:01:22,433 --> 00:01:26,033 Can we uncover the secrets to these animals' survival 36 00:01:26,066 --> 00:01:28,166 before it's too late? 37 00:01:28,200 --> 00:01:30,333 "Animal Espionage," 38 00:01:30,366 --> 00:01:34,433 right now, on "NOVA." 39 00:01:34,466 --> 00:01:37,766 ♪ 40 00:01:39,333 --> 00:01:43,100 Major funding for "NOVA" is provided by the following: 41 00:01:53,366 --> 00:01:56,300 ♪ 42 00:01:56,333 --> 00:02:00,533 NARRATOR: Our planet is teeming with millions of species, 43 00:02:00,566 --> 00:02:06,033 yet we've only been able to study a small fraction of them. 44 00:02:06,066 --> 00:02:10,800 In this hidden world, much goes unseen-- 45 00:02:10,833 --> 00:02:13,766 until now. 46 00:02:13,800 --> 00:02:16,266 Advances in camera technology 47 00:02:16,300 --> 00:02:19,933 are opening our eyes to the world around us. 48 00:02:19,966 --> 00:02:24,233 LIANA ZANETTE: The invaluable information that people will get 49 00:02:24,266 --> 00:02:26,633 from a simple, wee, little camera 50 00:02:26,666 --> 00:02:29,200 that anybody can buy off the shelf, 51 00:02:29,233 --> 00:02:32,166 it's unbelievable. 52 00:02:32,200 --> 00:02:37,366 NARRATOR: What can researchers learn by spying on animals? 53 00:02:37,400 --> 00:02:38,966 ART RODGERS: Why are they doing that? 54 00:02:39,000 --> 00:02:40,300 And why did they do that? 55 00:02:40,333 --> 00:02:41,666 And what are they going to do next? 56 00:02:41,700 --> 00:02:45,233 NARRATOR: Can a new wave of animal surveillance 57 00:02:45,266 --> 00:02:46,666 turn the tide 58 00:02:46,700 --> 00:02:50,266 and help preserve our planet's most vulnerable species 59 00:02:50,300 --> 00:02:54,033 before they disappear? 60 00:02:55,633 --> 00:02:57,300 ♪ 61 00:02:57,333 --> 00:03:00,233 Cumberland Sound, Canada, 62 00:03:00,266 --> 00:03:03,900 about 300 miles west of Greenland. 63 00:03:03,900 --> 00:03:03,933 about 300 miles west of Greenland. 64 00:03:03,933 --> 00:03:10,766 Beneath these frigid waters dwells a mysterious giant-- 65 00:03:10,800 --> 00:03:13,566 the bowhead whale. 66 00:03:13,600 --> 00:03:17,566 Little footage of these 100-ton creatures exists. 67 00:03:17,600 --> 00:03:21,466 They are the longest-living mammal on the planet. 68 00:03:21,500 --> 00:03:26,800 Some have reached the ripe old age of 200. 69 00:03:26,833 --> 00:03:30,400 But their survival isn't guaranteed. 70 00:03:30,433 --> 00:03:35,033 The species could be in trouble. 71 00:03:35,066 --> 00:03:37,933 FORTUNE: They're living in the Arctic, and this is a place 72 00:03:37,966 --> 00:03:42,166 where climate change could be threatening their, their future. 73 00:03:42,200 --> 00:03:46,833 NARRATOR: Marine biologist Sarah Fortune studies bowheads 74 00:03:46,866 --> 00:03:48,266 in Cumberland Sound, 75 00:03:48,300 --> 00:03:52,333 where the whales come to feed for months at a time. 76 00:03:52,366 --> 00:03:56,033 But rising temperatures and melting sea ice 77 00:03:56,066 --> 00:03:58,966 are affecting their primary food source-- 78 00:03:59,000 --> 00:04:02,500 tiny animals called zooplankton. 79 00:04:02,533 --> 00:04:06,200 Bowheads favor a nutrient-rich variety, 80 00:04:06,233 --> 00:04:08,600 and their numbers are dropping. 81 00:04:08,633 --> 00:04:11,033 This could be catastrophic for the whales: 82 00:04:11,066 --> 00:04:16,700 one bowhead needs to eat about 100 tons of food each year. 83 00:04:16,733 --> 00:04:20,766 FORTUNE: I need know what the whales are feeding on today 84 00:04:20,800 --> 00:04:24,533 and how energy-rich their current food resource is. 85 00:04:24,566 --> 00:04:28,266 NARRATOR: Monitoring their size and weight over time 86 00:04:28,300 --> 00:04:31,433 will tell Sarah if these whales are getting enough to eat. 87 00:04:31,466 --> 00:04:35,966 But tracking them is no simple task. 88 00:04:36,000 --> 00:04:39,233 FORTUNE: Bowheads are a little bit elusive. 89 00:04:39,266 --> 00:04:43,200 They dive for half an hour, an hour. 90 00:04:43,233 --> 00:04:46,533 You spend a lot of time waiting for them to come up again. 91 00:04:46,566 --> 00:04:48,700 And then it's also, can be really difficult 92 00:04:48,733 --> 00:04:51,100 to track where that whale has gone. 93 00:04:52,833 --> 00:04:54,300 Okay, there he is. 94 00:04:54,333 --> 00:04:56,233 NARRATOR: Even when they find a whale, 95 00:04:56,266 --> 00:04:59,166 it's hard to see its entire body. 96 00:04:59,200 --> 00:05:01,933 FORTUNE: We see what everyone else sees-- 97 00:05:01,966 --> 00:05:04,666 the top of the whale's head, their flukes, 98 00:05:04,700 --> 00:05:07,533 sort of, a really small proportion of the whale's body. 99 00:05:07,566 --> 00:05:14,133 And so that means that a lot of their behavior goes unknown. 100 00:05:14,166 --> 00:05:18,333 NARRATOR: Fortune and her colleague Bill Koski 101 00:05:18,366 --> 00:05:21,066 are monitoring a group of about 80 bowheads 102 00:05:21,100 --> 00:05:22,666 in Cumberland Sound. 103 00:05:22,700 --> 00:05:27,066 A bowhead expert, Bill is eager to get a new perspective 104 00:05:27,100 --> 00:05:29,700 on an animal he's been studying for decades. 105 00:05:29,733 --> 00:05:32,166 KOSKI: Most of the studies I've done, 106 00:05:32,200 --> 00:05:34,800 I've been flying in an airplane, 107 00:05:34,833 --> 00:05:37,333 and we're circling whales at a thousand feet or so, 108 00:05:37,366 --> 00:05:39,533 so that we won't affect their behavior. 109 00:05:39,566 --> 00:05:43,866 NARRATOR: Any closer, and a noisy plane spooks the whales, 110 00:05:43,900 --> 00:05:47,166 who will dive and disappear. 111 00:05:49,100 --> 00:05:50,133 Is he up now? 112 00:05:51,666 --> 00:05:54,400 NARRATOR: So Sarah is trying a new approach. 113 00:05:54,433 --> 00:05:55,700 She's going spy on the whales 114 00:05:55,733 --> 00:06:00,366 with a-state-of-the-art high-definition drone. 115 00:06:00,400 --> 00:06:03,466 All right, full power. Okay. 116 00:06:03,466 --> 00:06:03,500 All right, full power. Okay. 117 00:06:03,500 --> 00:06:06,033 FORTUNE: Awesome. 118 00:06:06,066 --> 00:06:08,366 ♪ 119 00:06:18,700 --> 00:06:22,533 (water sprays out blowhole) 120 00:06:24,433 --> 00:06:27,433 NARRATOR: The drone quietly hovers just above the whales. 121 00:06:27,466 --> 00:06:30,133 (buzzing softly) 122 00:06:30,166 --> 00:06:34,700 They seem oblivious to the flying camera following them. 123 00:06:34,733 --> 00:06:38,666 ♪ 124 00:06:38,700 --> 00:06:42,066 FORTUNE: It's exactly analogous to a bird. 125 00:06:42,100 --> 00:06:44,033 The same level of reaction 126 00:06:44,066 --> 00:06:47,133 that you would get from a bowhead having birds overhead 127 00:06:47,166 --> 00:06:52,533 is what you get with a drone being overhead. 128 00:06:52,566 --> 00:06:56,466 NARRATOR: Finally, they can see the whale in its entirety. 129 00:06:56,500 --> 00:07:02,366 Its body tells a story about day-to-day life in the Arctic. 130 00:07:02,400 --> 00:07:06,566 FORTUNE: They often need to break thick ice with their heads. 131 00:07:06,600 --> 00:07:09,533 And so, we'll see that they have white scars. 132 00:07:09,566 --> 00:07:13,200 NARRATOR: The scars are like fingerprints, 133 00:07:13,233 --> 00:07:18,433 allowing scientists to identify and track individual whales. 134 00:07:18,466 --> 00:07:21,600 The drone helps the team measure the whale 135 00:07:21,633 --> 00:07:25,033 by comparing its body to the length of the boat. 136 00:07:25,066 --> 00:07:30,733 FORTUNE: That gives us an idea of how fat or how skinny an individual is. 137 00:07:30,766 --> 00:07:34,666 And that's a way that we can assess their overall health. 138 00:07:34,700 --> 00:07:36,900 So, are these whales getting enough food to eat? 139 00:07:36,933 --> 00:07:40,166 Over time, we can monitor these animals 140 00:07:40,200 --> 00:07:41,833 to see how healthy they are 141 00:07:41,866 --> 00:07:45,166 in the face of a changing environment. 142 00:07:45,200 --> 00:07:49,000 NARRATOR: When the whale dives below the surface to feed, 143 00:07:49,033 --> 00:07:51,933 the drone keeps an eye on it. 144 00:07:51,966 --> 00:07:54,066 FORTUNE: Because the water is so clear here, 145 00:07:54,100 --> 00:07:57,100 it provides this really wonderful opportunity 146 00:07:57,133 --> 00:08:00,600 to observe their behaviors over long periods of time. 147 00:08:00,633 --> 00:08:03,500 Otherwise, we would just be sitting on the boat 148 00:08:03,533 --> 00:08:05,800 wondering where the whale had gone. 149 00:08:05,833 --> 00:08:07,800 ♪ 150 00:08:07,833 --> 00:08:09,733 NARRATOR: Now, clear water and a bird's-eye view 151 00:08:09,766 --> 00:08:13,966 reveal new insights into bowhead behavior. 152 00:08:14,000 --> 00:08:17,633 Biologists used to think that bowheads were solitary creatures 153 00:08:17,666 --> 00:08:22,833 that sometimes swam in pods, but rarely interacted. 154 00:08:22,866 --> 00:08:26,000 FORTUNE: The whales were constantly touching each other. 155 00:08:26,033 --> 00:08:27,600 And before, there was no way 156 00:08:27,633 --> 00:08:28,900 that we could have seen that, right? 157 00:08:28,933 --> 00:08:33,366 It was illuminating to see how these animals 158 00:08:33,400 --> 00:08:35,733 are more social than we could appreciate 159 00:08:35,766 --> 00:08:39,366 just by observing them at the surface. 160 00:08:39,400 --> 00:08:43,233 We're able to see how that whale is engaging with other animals, 161 00:08:43,266 --> 00:08:45,900 how it's engaging with the environment. 162 00:08:45,933 --> 00:08:50,200 So I think it has completely revolutionized our ability 163 00:08:50,233 --> 00:08:55,000 to understand bowhead whale behaviors. 164 00:08:55,033 --> 00:08:58,333 NARRATOR: For scientists like Sarah, the drone is a window 165 00:08:58,366 --> 00:09:02,000 into the lives of these mysterious creatures 166 00:09:02,000 --> 00:09:02,033 into the lives of these mysterious creatures 167 00:09:02,033 --> 00:09:07,433 and a way to gauge their survival in a changing climate. 168 00:09:07,466 --> 00:09:11,566 When the Inuit's ancestors first settled Baffin Island 169 00:09:11,600 --> 00:09:12,933 thousands of years ago, 170 00:09:12,966 --> 00:09:17,033 the surrounding waters were teeming with whales. 171 00:09:17,066 --> 00:09:19,600 By the late 19th century, 172 00:09:19,633 --> 00:09:23,533 the commercial whaling industry had nearly wiped them out. 173 00:09:23,566 --> 00:09:27,900 Today, the Inuit are among the few communities in the world 174 00:09:27,933 --> 00:09:30,966 permitted to sustainably hunt bowhead whales. 175 00:09:31,000 --> 00:09:35,166 The Inuit in this region take up to five whales per year. 176 00:09:35,200 --> 00:09:39,533 A single bowhead will feed hundreds of people. 177 00:09:39,566 --> 00:09:42,966 ♪ 178 00:09:43,000 --> 00:09:45,333 Sarah is sharing her research with the board 179 00:09:45,366 --> 00:09:47,366 of the Hunters and Trappers Association, 180 00:09:47,400 --> 00:09:48,733 which manages hunting. 181 00:09:48,766 --> 00:09:53,266 They're concerned about the fate of the 6,500 bowheads 182 00:09:53,300 --> 00:09:56,433 in this area of the Arctic. 183 00:09:56,466 --> 00:09:59,533 FORTUNE: If anyone has any suggestions or questions 184 00:09:59,566 --> 00:10:01,666 that you think we could answer 185 00:10:01,700 --> 00:10:03,066 with this technology, 186 00:10:03,100 --> 00:10:04,666 that would be really helpful to know. 187 00:10:04,700 --> 00:10:08,233 NARRATOR: The images yield new insights 188 00:10:08,266 --> 00:10:09,700 that intrigue even the locals, 189 00:10:09,733 --> 00:10:12,733 who have lived with these whales for decades. 190 00:10:12,766 --> 00:10:16,766 MAN: What part do you study in order to get the age? 191 00:10:16,800 --> 00:10:19,600 KOSKI: Based, based on our experience with the photographs, 192 00:10:19,633 --> 00:10:22,633 the amount of white just in front of the tail, 193 00:10:22,666 --> 00:10:25,866 it gets more and more white as they get older. 194 00:10:25,900 --> 00:10:27,433 So when you see one 195 00:10:27,466 --> 00:10:29,366 with lots of white on it, you know it's a very old whale, 196 00:10:29,400 --> 00:10:31,766 probably 150 years or so. 197 00:10:31,800 --> 00:10:36,200 NARRATOR: Knowing the size and age of the whales around here 198 00:10:36,233 --> 00:10:38,733 helps locals plan for hunts 199 00:10:38,766 --> 00:10:42,066 that leave enough whales in the ocean for future generations. 200 00:10:43,566 --> 00:10:44,733 There's one question 201 00:10:44,766 --> 00:10:48,300 that fascinates both locals and scientists. 202 00:10:48,333 --> 00:10:52,566 Year after year, the bowheads gravitate toward the shore 203 00:10:52,600 --> 00:10:55,400 and hang around the big rocks there. 204 00:10:55,433 --> 00:10:58,600 No one knows why. 205 00:10:58,633 --> 00:11:02,600 Sarah is hoping the drone will explain a mystery 206 00:11:02,633 --> 00:11:05,466 first recorded more than 170 years ago. 207 00:11:05,500 --> 00:11:09,633 Ricky Killabuck, an Inuit fisherman, 208 00:11:09,666 --> 00:11:11,200 brings them to the site. 209 00:11:11,233 --> 00:11:13,866 FORTUNE: So have you seen any whales in this bay this year? 210 00:11:13,900 --> 00:11:15,633 KILLABUCK: Oh, yeah, yeah. Yeah? Okay. 211 00:11:15,666 --> 00:11:20,433 FORTUNE: If you go back to the whaling records dating back to 1845, 212 00:11:20,466 --> 00:11:22,433 whalers had made note 213 00:11:22,466 --> 00:11:23,766 that these whales would go near shore, 214 00:11:23,800 --> 00:11:26,266 and they'd rest their heads, or their chins, 215 00:11:26,300 --> 00:11:27,766 upon these large rocks. 216 00:11:27,800 --> 00:11:30,533 Going along this coast, 217 00:11:30,566 --> 00:11:34,366 we've been seeing whales along the rocks 218 00:11:34,400 --> 00:11:36,100 in this area. Okay. 219 00:11:36,133 --> 00:11:39,366 Some people thought that they might be feeding. 220 00:11:39,400 --> 00:11:41,500 Others thought that they're resting. 221 00:11:41,533 --> 00:11:45,266 NARRATOR: Without a clear view, it was impossible to know. 222 00:11:45,300 --> 00:11:47,066 Around our 11:00. 223 00:11:47,100 --> 00:11:49,466 So we have a whale up ahead. 224 00:11:49,500 --> 00:11:50,800 We're heading towards it now. 225 00:11:50,833 --> 00:11:53,700 TOMMY: Set that camera out to the aft. 226 00:11:53,733 --> 00:11:56,900 Full power, go. 227 00:11:56,933 --> 00:11:58,766 So then, I think you're going to want to bring it 228 00:11:58,800 --> 00:12:01,000 to our 11:00 here, maybe to the bow. 229 00:12:01,033 --> 00:12:03,700 TOMMY: It's starting to come shallow. 230 00:12:03,700 --> 00:12:03,733 TOMMY: It's starting to come shallow. 231 00:12:03,733 --> 00:12:05,833 FORTUNE: Mm-hmm, it's coming. 232 00:12:05,866 --> 00:12:11,366 ♪ 233 00:12:15,766 --> 00:12:18,566 NARRATOR: This whale seems to be scratching his back 234 00:12:18,600 --> 00:12:22,166 against the rocks. 235 00:12:22,200 --> 00:12:24,866 FORTUNE: Now we know that the whales aren't just coming here 236 00:12:24,900 --> 00:12:26,400 for feeding purposes. 237 00:12:26,433 --> 00:12:29,700 They're also coming here for molting purposes, 238 00:12:29,733 --> 00:12:33,133 rubbing on these large boulders as exfoliation, 239 00:12:33,166 --> 00:12:35,766 so to help expedite the molting process. 240 00:12:35,800 --> 00:12:40,700 NARRATOR: The best guess is they're trying to keep their skin healthy 241 00:12:40,733 --> 00:12:42,766 and free of parasites. 242 00:12:42,800 --> 00:12:45,500 The drone reveals Cumberland Sound, 243 00:12:45,533 --> 00:12:48,500 with its shallow rocks and plentiful zooplankton, 244 00:12:48,533 --> 00:12:51,666 to be a critical bowhead habitat. 245 00:12:51,700 --> 00:12:56,133 Yet it's also a place destined to change. 246 00:12:56,166 --> 00:12:59,033 FORTUNE: These are whales that will be impacted 247 00:12:59,066 --> 00:13:01,433 in one way or another by environmental change. 248 00:13:01,466 --> 00:13:04,966 We don't know if it's going to be detrimental, 249 00:13:05,000 --> 00:13:09,233 we don't know if these whales will be very adaptable, 250 00:13:09,266 --> 00:13:11,100 but we know that things are going to change, 251 00:13:11,133 --> 00:13:12,800 just like they're going to change 252 00:13:12,833 --> 00:13:14,366 for the people in the North 253 00:13:14,400 --> 00:13:17,733 that are living in these communities. 254 00:13:17,766 --> 00:13:21,600 NARRATOR: For now, keeping a close eye on these giants of the Arctic 255 00:13:21,633 --> 00:13:23,733 is critical. 256 00:13:23,766 --> 00:13:26,400 FORTUNE: The really big win about drones 257 00:13:26,433 --> 00:13:31,566 is that we're able to collect a lot of data about the whales 258 00:13:31,600 --> 00:13:34,066 with zero impact to them. 259 00:13:34,100 --> 00:13:38,400 And so, I think this is a very positive step forwards. 260 00:13:38,433 --> 00:13:43,066 ♪ 261 00:13:43,100 --> 00:13:44,666 Great, awesome. 262 00:13:44,700 --> 00:13:47,300 Thanks so much, guys. 263 00:13:48,600 --> 00:13:51,000 NARRATOR: For more than 100 years, 264 00:13:51,033 --> 00:13:54,266 we've used cameras to try to capture the natural world 265 00:13:54,300 --> 00:13:58,200 as it truly is, away from human eyes. 266 00:13:58,233 --> 00:14:00,366 In the late 19th century, 267 00:14:00,400 --> 00:14:04,266 an ambitious young photographer named George Shiras III 268 00:14:04,300 --> 00:14:08,166 pioneered the field of spying on animals. 269 00:14:08,200 --> 00:14:11,100 ♪ 270 00:14:11,133 --> 00:14:13,900 Using crude trip wires and flashbulbs, 271 00:14:13,933 --> 00:14:19,800 he was the first to photograph a hidden world. 272 00:14:19,833 --> 00:14:22,200 He roamed North America, 273 00:14:22,233 --> 00:14:27,300 photographing predator and prey alike. 274 00:14:27,333 --> 00:14:30,866 Published in "National Geographic" in 1906, 275 00:14:30,900 --> 00:14:33,566 his images were the first of their kind 276 00:14:33,600 --> 00:14:36,166 ever printed in that magazine. 277 00:14:36,200 --> 00:14:40,066 The experience turned Shiras from a hunter and fisherman 278 00:14:40,100 --> 00:14:41,933 into a conservationist. 279 00:14:41,966 --> 00:14:46,500 He pushed for the creation of parks and policies 280 00:14:46,533 --> 00:14:50,566 to protect the wildlife he photographed. 281 00:14:50,600 --> 00:14:55,566 Years later, scientists like Arnaud Desbiez 282 00:14:55,600 --> 00:14:58,466 are perfecting Shiras's camera-trap system, 283 00:14:58,500 --> 00:15:00,933 trying to capture images of creatures 284 00:15:00,966 --> 00:15:03,700 that few people have ever laid eyes on. 285 00:15:03,700 --> 00:15:03,733 that few people have ever laid eyes on. 286 00:15:03,733 --> 00:15:07,166 The animals Arnaud seeks 287 00:15:07,200 --> 00:15:10,533 live in the Pantanal region of Brazil, 288 00:15:10,566 --> 00:15:13,633 far south of the Amazon River. 289 00:15:13,666 --> 00:15:16,566 At nearly 75,000 square miles, 290 00:15:16,600 --> 00:15:19,666 it is the world's largest wetland 291 00:15:19,700 --> 00:15:25,000 and home to some fascinating creatures. 292 00:15:25,033 --> 00:15:27,600 You could also say that the Pantanal is the land of giants. 293 00:15:27,633 --> 00:15:30,200 Here we have giant otters, 294 00:15:30,233 --> 00:15:31,933 giant anteaters, 295 00:15:31,966 --> 00:15:34,633 the largest jaguars. 296 00:15:34,666 --> 00:15:38,100 And of course, the giant armadillo. 297 00:15:40,200 --> 00:15:44,233 NARRATOR: The giant armadillo, which practically no one-- 298 00:15:44,266 --> 00:15:46,833 not even among the local population-- 299 00:15:46,866 --> 00:15:48,733 has ever seen. 300 00:15:48,766 --> 00:15:53,600 DESBIEZ: The giant armadillo is almost like a ghost species, 301 00:15:53,633 --> 00:15:56,833 the Holy Grail of, of animals. 302 00:15:56,866 --> 00:15:59,733 They occur at very, very low density, 303 00:15:59,766 --> 00:16:02,266 and they're very, very hard to find. 304 00:16:02,300 --> 00:16:03,833 They are a nocturnal species, 305 00:16:03,866 --> 00:16:07,733 so to follow them at night is almost impossible. 306 00:16:07,766 --> 00:16:11,533 NARRATOR: There are more than 20 different species of armadillo 307 00:16:11,566 --> 00:16:16,533 all across the Americas, some as far north as Nebraska. 308 00:16:16,566 --> 00:16:18,900 Like their anteater cousins, 309 00:16:18,933 --> 00:16:22,500 armadillos dine mostly on insects and grubs, 310 00:16:22,533 --> 00:16:25,900 which they dig for with powerful claws 311 00:16:25,933 --> 00:16:27,800 and lap up with sticky, long tongues. 312 00:16:27,833 --> 00:16:30,433 A shell of overlapping bony plates 313 00:16:30,466 --> 00:16:34,033 protects them from predators. 314 00:16:34,066 --> 00:16:35,400 The smallest of the species 315 00:16:35,433 --> 00:16:37,666 could fit in the palm of your hand, 316 00:16:37,700 --> 00:16:40,300 while giant armadillos can grow to be as big 317 00:16:40,333 --> 00:16:42,833 as a Labrador retriever. 318 00:16:42,866 --> 00:16:46,533 But so little is known about them. 319 00:16:46,566 --> 00:16:49,133 How many offspring do they have? 320 00:16:49,166 --> 00:16:50,966 How do they communicate? 321 00:16:51,000 --> 00:16:54,933 Are they thriving or doomed to extinction? 322 00:16:54,966 --> 00:16:58,633 Arnaud is hoping to find out by setting up cameras 323 00:16:58,666 --> 00:17:02,166 right outside their homes. 324 00:17:02,200 --> 00:17:03,700 DESBIEZ: Finding a giant armadillo burrow 325 00:17:03,733 --> 00:17:06,200 is like looking for a needle in a haystack. 326 00:17:06,233 --> 00:17:07,600 It's really, really difficult. 327 00:17:07,633 --> 00:17:11,700 NARRATOR: Individual giant armadillos are thinly scattered 328 00:17:11,733 --> 00:17:13,366 across the Pantanal, 329 00:17:13,400 --> 00:17:17,233 sometimes as few as seven in a 40-square-mile area. 330 00:17:17,266 --> 00:17:21,233 So Arnaud has placed thousands of camera traps like this one 331 00:17:21,266 --> 00:17:23,566 all over the wetlands. 332 00:17:23,600 --> 00:17:24,966 DESBIEZ: A camera trap is essentially 333 00:17:25,000 --> 00:17:29,100 a device that you can place anywhere. 334 00:17:29,133 --> 00:17:31,666 And when something passes in front of it, 335 00:17:31,700 --> 00:17:36,233 it will take a series of pictures and videos. 336 00:17:37,900 --> 00:17:39,433 This is the part I really want to get. 337 00:17:39,466 --> 00:17:42,933 So, I'm going to get the motion sensor to work. 338 00:17:42,966 --> 00:17:47,266 NARRATOR: Then, he waits. 339 00:17:47,300 --> 00:17:49,800 DESBIEZ: So, the camera traps are, for a field biologist, 340 00:17:49,833 --> 00:17:52,833 what a microscope is to a microbiologist. 341 00:17:52,866 --> 00:17:57,466 It helps us see things that we can't see with our own eyes. 342 00:17:57,500 --> 00:18:03,366 The camera traps are basically our eyes in the field. 343 00:18:03,366 --> 00:18:04,966 The camera traps are basically our eyes in the field. 344 00:18:04,966 --> 00:18:07,933 NARRATOR: Weeks later, Arnaud and his team review the footage. 345 00:18:07,966 --> 00:18:10,400 Frame by frame, 346 00:18:10,433 --> 00:18:14,333 the hidden world of the Pantanal comes to life. 347 00:18:14,366 --> 00:18:17,866 (scientists talking in background) 348 00:18:17,900 --> 00:18:20,700 ♪ 349 00:18:20,733 --> 00:18:23,600 NARRATOR: But no sign of the giant armadillo. 350 00:18:23,633 --> 00:18:26,933 After sifting through hours' worth of footage, 351 00:18:26,966 --> 00:18:30,533 the star finally appears. 352 00:18:30,566 --> 00:18:31,966 (scientists murmuring excitedly) 353 00:18:32,000 --> 00:18:33,466 (exclaims) 354 00:18:33,500 --> 00:18:34,800 (gasps) 355 00:18:36,133 --> 00:18:38,233 DESBIEZ: Do you remember when you were a child 356 00:18:38,266 --> 00:18:41,600 and you saw your first image of a dinosaur? 357 00:18:41,633 --> 00:18:43,733 That's how I felt the first time I saw an image 358 00:18:43,766 --> 00:18:47,366 of a giant armadillo from a camera trap. 359 00:18:47,400 --> 00:18:50,666 I could not believe that this species existed, 360 00:18:50,700 --> 00:18:53,700 that it was right here, around us. 361 00:18:53,733 --> 00:18:56,666 NARRATOR: Arnaud's fleet of camera traps 362 00:18:56,700 --> 00:19:00,133 has revealed much about this prehistoric creature. 363 00:19:00,166 --> 00:19:03,533 DESBIEZ: We were able to document the role of giant armadillos 364 00:19:03,566 --> 00:19:06,100 as ecosystem engineers. 365 00:19:06,133 --> 00:19:08,966 Giant-armadillo burrows are used by other species 366 00:19:09,000 --> 00:19:11,200 as a refuge against predators, 367 00:19:11,233 --> 00:19:17,200 as a refuge against extreme temperatures, 368 00:19:17,233 --> 00:19:19,133 as a place to forage. 369 00:19:19,166 --> 00:19:21,600 We suddenly were able to register 370 00:19:21,633 --> 00:19:25,400 a whole community of animals using giant-armadillo burrows. 371 00:19:25,433 --> 00:19:30,300 NARRATOR: And that giant sand mound outside their door? 372 00:19:30,333 --> 00:19:34,866 DESBIEZ: It's like their inbox, where they leave messages, 373 00:19:34,900 --> 00:19:36,533 because when they dig, they defecate and urinate. 374 00:19:36,566 --> 00:19:38,500 The giant armadillos, which are solitary creatures, 375 00:19:38,533 --> 00:19:40,166 will communicate and learn about each other 376 00:19:40,200 --> 00:19:41,400 in, from the sand mound. 377 00:19:41,433 --> 00:19:43,866 Leaving a camera trap in front of the sand mound, 378 00:19:43,900 --> 00:19:47,100 we can find out who's coming to visit. 379 00:19:48,566 --> 00:19:50,300 NARRATOR: And the camera traps caught something 380 00:19:50,333 --> 00:19:52,966 never before recorded on camera. 381 00:19:53,000 --> 00:19:55,300 (exclaiming softly) 382 00:19:55,333 --> 00:19:57,233 (scientists chuckling) 383 00:19:57,266 --> 00:20:00,266 NARRATOR: A baby giant armadillo. 384 00:20:00,300 --> 00:20:01,900 (yelps) 385 00:20:01,933 --> 00:20:04,766 (speaking foreign language) 386 00:20:04,800 --> 00:20:06,966 DESBIEZ: It was an incredible experience 387 00:20:07,000 --> 00:20:09,666 to be able to see this tiny little white shape. 388 00:20:09,700 --> 00:20:10,866 They have no coloring. 389 00:20:10,900 --> 00:20:13,200 You can tell that the shell is soft, 390 00:20:13,233 --> 00:20:17,700 and they're a little bit clumsy the way they move. 391 00:20:17,733 --> 00:20:19,766 NARRATOR: The scientists nicknamed the baby 392 00:20:19,800 --> 00:20:22,533 Alex. 393 00:20:22,566 --> 00:20:26,433 DESBIEZ: All of us got extremely attached to this little giant armadillo, 394 00:20:26,466 --> 00:20:30,400 with whom we actually had no physical contact. 395 00:20:30,433 --> 00:20:34,000 Our whole relationship was through these images. 396 00:20:34,033 --> 00:20:37,733 Every time we came to the field, it was an exciting moment. 397 00:20:37,766 --> 00:20:39,266 "What is Alex going to be doing now? 398 00:20:39,300 --> 00:20:41,766 How has he progressed?" 399 00:20:41,800 --> 00:20:43,366 NARRATOR: Thanks to Alex, 400 00:20:43,400 --> 00:20:46,966 scientists estimate that giant armadillos 401 00:20:47,000 --> 00:20:49,733 have just one offspring every three years. 402 00:20:49,766 --> 00:20:51,533 The babies nurse for a year 403 00:20:51,566 --> 00:20:54,400 and live with their mothers for 18 months. 404 00:20:54,433 --> 00:20:57,766 DESBIEZ: Parental care is much, much longer 405 00:20:57,800 --> 00:20:59,700 than we could ever have imagined. 406 00:20:59,733 --> 00:21:01,300 And so, we were able to follow that-- 407 00:21:01,333 --> 00:21:03,366 time spent inside the burrow, 408 00:21:03,366 --> 00:21:03,400 time spent inside the burrow, 409 00:21:03,400 --> 00:21:05,433 time spent outside the burrow. 410 00:21:05,466 --> 00:21:07,266 And so those measures of time, now, today, 411 00:21:07,300 --> 00:21:10,433 help us to estimate the age of a baby giant armadillo, 412 00:21:10,466 --> 00:21:14,033 because we related those to the age of Alex. 413 00:21:14,066 --> 00:21:16,466 (speaking Portuguese) 414 00:21:16,500 --> 00:21:18,500 (speaking Portuguese) 415 00:21:18,533 --> 00:21:22,733 NARRATOR: Arnaud shared Alex's story with the public. 416 00:21:22,766 --> 00:21:25,333 Soon, everyone was hooked on the day-to-day life 417 00:21:25,366 --> 00:21:28,266 of this vulnerable baby armadillo. 418 00:21:28,300 --> 00:21:30,166 DESBIEZ: I remember telling them 419 00:21:30,200 --> 00:21:31,400 when he predated his first termite mound. 420 00:21:31,433 --> 00:21:32,633 I remember when he dug his first burrows. 421 00:21:32,666 --> 00:21:34,900 We were almost like you'd celebrate 422 00:21:34,933 --> 00:21:36,533 a child's first achievements; 423 00:21:36,566 --> 00:21:39,033 we were doing that with Alex. 424 00:21:39,066 --> 00:21:42,033 NARRATOR: After a few months living on his own, 425 00:21:42,066 --> 00:21:45,900 Alex's story took a sad turn. 426 00:21:45,933 --> 00:21:48,066 DESBIEZ: One day, we saw 427 00:21:48,100 --> 00:21:50,766 that he had entered one of his mother's old burrows. 428 00:21:50,800 --> 00:21:53,266 So we set a camera trap in front of the burrow, 429 00:21:53,300 --> 00:21:55,766 but he didn't come out that night. 430 00:21:55,800 --> 00:21:57,733 And he didn't come out the night after. 431 00:21:57,766 --> 00:22:00,500 (vulture squawking) 432 00:22:00,533 --> 00:22:04,366 We saw a vulture land in front of the camera trap. 433 00:22:04,400 --> 00:22:07,033 I went and put my face against the burrow, 434 00:22:07,066 --> 00:22:12,000 and I smelled a rotting, nasty smell from the burrow. 435 00:22:12,033 --> 00:22:16,333 NARRATOR: A necropsy revealed a mortal wound in his shoulder. 436 00:22:16,366 --> 00:22:20,833 Only one animal in this area could inflict such damage: 437 00:22:20,866 --> 00:22:25,100 the puma. 438 00:22:25,133 --> 00:22:28,633 News of Alex's death hit hard. 439 00:22:28,666 --> 00:22:32,366 There was an outpouring of public sympathy. 440 00:22:32,400 --> 00:22:34,400 DESBIEZ: This little armadillo had actually become 441 00:22:34,433 --> 00:22:38,433 quite the ambassador for, for his species. 442 00:22:40,466 --> 00:22:44,333 People were able to understand how vulnerable this species is, 443 00:22:44,366 --> 00:22:47,200 and how easy it is to locally extinct 444 00:22:47,233 --> 00:22:49,000 a population of giant armadillos, 445 00:22:49,033 --> 00:22:51,466 because any threat-- whether it's habitat loss 446 00:22:51,500 --> 00:22:52,766 or hunting or roadkill-- 447 00:22:52,800 --> 00:22:55,300 will have a huge impact on the species. 448 00:22:55,333 --> 00:22:59,233 NARRATOR: That impact is already evident. 449 00:22:59,266 --> 00:23:02,500 In the past 25 years, the giant armadillo population 450 00:23:02,533 --> 00:23:06,300 has likely declined by at least 30%. 451 00:23:06,333 --> 00:23:09,400 In eight years, Arnaud's camera traps 452 00:23:09,433 --> 00:23:12,133 have captured just 50 giant armadillos. 453 00:23:12,166 --> 00:23:15,200 Each one needs monitoring. 454 00:23:15,233 --> 00:23:18,300 DESBIEZ (whispering): So now we just applied the anesthetic. 455 00:23:18,333 --> 00:23:19,700 We're going to wait a few minutes 456 00:23:19,733 --> 00:23:22,233 for the animal to fall asleep, 457 00:23:22,266 --> 00:23:24,533 and then we'll take him out for, to start the procedure. 458 00:23:24,566 --> 00:23:27,133 NARRATOR: Arnaud and his team will tag, track, 459 00:23:27,166 --> 00:23:31,833 and spy on this young armadillo, like they did with Alex. 460 00:23:31,866 --> 00:23:33,100 DESBIEZ: It's a highlight of our project. 461 00:23:33,133 --> 00:23:34,533 This is a moment we get to interact 462 00:23:34,566 --> 00:23:38,366 and get to meet the species we hardly spend any time with. 463 00:23:38,400 --> 00:23:40,000 We're actually like paparazzi, 464 00:23:40,033 --> 00:23:42,666 we're spying on the animal the whole time. 465 00:23:42,700 --> 00:23:44,700 So, for us, yes, it's like meeting a celebrity. 466 00:23:44,733 --> 00:23:46,233 It's a, this is a highlight for us. 467 00:23:46,266 --> 00:23:47,433 It's very, very exciting. 468 00:23:47,466 --> 00:23:50,500 NARRATOR: Today, state authorities in Brazil 469 00:23:50,533 --> 00:23:53,666 use the giant armadillo as a guide 470 00:23:53,700 --> 00:23:56,033 when planning new parks and protected areas. 471 00:23:56,066 --> 00:23:59,700 The goal is to keep this species' habitat intact. 472 00:23:59,733 --> 00:24:05,033 Arnaud's camera trap data is a key piece of those efforts. 473 00:24:05,033 --> 00:24:05,066 Arnaud's camera trap data is a key piece of those efforts. 474 00:24:05,066 --> 00:24:07,466 DESBIEZ: We will try to estimate densities 475 00:24:07,500 --> 00:24:09,500 and find out how many are still left, 476 00:24:09,533 --> 00:24:10,933 so that we can find out, 477 00:24:10,966 --> 00:24:14,700 are there enough giant armadillos for the future, 478 00:24:14,733 --> 00:24:18,333 or are these populations already ecologically extinct? 479 00:24:18,366 --> 00:24:21,733 And so we want to inform conservation measures, 480 00:24:21,766 --> 00:24:25,233 such as habitat protection, creation of corridors, 481 00:24:25,266 --> 00:24:27,166 so that we can protect giant armadillos 482 00:24:27,200 --> 00:24:28,333 for generations to come. 483 00:24:28,366 --> 00:24:31,333 ♪ 484 00:24:31,366 --> 00:24:33,233 NARRATOR: Remote cameras introduce us 485 00:24:33,266 --> 00:24:37,266 to species rarely seen by the human eye, 486 00:24:37,300 --> 00:24:42,533 and invite us to see the world from a different point of view. 487 00:24:42,566 --> 00:24:44,433 ♪ 488 00:24:44,466 --> 00:24:47,000 MAN (on radio): Location's coming up just over this next ridgeline. 489 00:24:47,033 --> 00:24:50,666 NARRATOR: Research scientist Art Rodgers is headed 490 00:24:50,700 --> 00:24:53,200 into Canada's boreal forest, 491 00:24:53,233 --> 00:24:56,900 a large swath of mostly coniferous trees and bogs 492 00:24:56,933 --> 00:24:59,533 stretching across the country. 493 00:24:59,566 --> 00:25:03,233 It's home to rare and endangered animals, 494 00:25:03,266 --> 00:25:05,800 including a subspecies of reindeer, 495 00:25:05,833 --> 00:25:08,433 the boreal woodland caribou. 496 00:25:08,466 --> 00:25:14,733 Caribou roam across Europe, Siberia, and North America. 497 00:25:16,266 --> 00:25:17,666 RODGERS: Where's the antenna? 498 00:25:17,700 --> 00:25:19,200 BLAKE: It's in my pack. 499 00:25:19,233 --> 00:25:22,100 NARRATOR: Here, in Ontario's boreal forest, 500 00:25:22,133 --> 00:25:24,833 there are just 5,000 boreal woodland caribou left-- 501 00:25:24,866 --> 00:25:28,766 and they are hard to find. 502 00:25:28,800 --> 00:25:33,200 RODGERS: These caribou generally don't occur in large numbers. 503 00:25:33,233 --> 00:25:35,233 They're fairly solitary animals, 504 00:25:35,266 --> 00:25:37,933 moving in relatively small groups 505 00:25:37,966 --> 00:25:40,633 of maybe five to ten. 506 00:25:40,666 --> 00:25:42,566 NARRATOR: Industrial development poses 507 00:25:42,600 --> 00:25:44,766 a serious threat to these caribou. 508 00:25:44,800 --> 00:25:48,966 They need vast areas of intact forest to survive, 509 00:25:49,000 --> 00:25:51,833 and that land is disappearing. 510 00:25:51,866 --> 00:25:55,266 Art wants to figure out which habitats need protecting 511 00:25:55,300 --> 00:25:59,200 to ensure the caribou don't go extinct. 512 00:25:59,233 --> 00:26:02,733 RODGERS: One of the key things we, we need to know about caribou 513 00:26:02,766 --> 00:26:04,200 is their food habits. 514 00:26:04,233 --> 00:26:07,866 We know that caribou are eating lichen through the wintertime. 515 00:26:07,900 --> 00:26:09,466 So, we wanted to find out 516 00:26:09,500 --> 00:26:11,700 what caribou were eating during the summertime. 517 00:26:11,733 --> 00:26:17,733 What kinds of habitats have the food that they really need? 518 00:26:17,766 --> 00:26:22,000 NARRATOR: These caribou roam across 100 square miles or more, 519 00:26:22,033 --> 00:26:23,600 and are hard to track. 520 00:26:23,633 --> 00:26:26,666 Camera traps are not an option. 521 00:26:26,700 --> 00:26:30,166 So, one of Art's colleagues came up with an idea: 522 00:26:30,200 --> 00:26:33,033 why not hitch a ride with the caribou 523 00:26:33,066 --> 00:26:34,833 and watch them eat? 524 00:26:34,866 --> 00:26:35,933 RODGERS: Huh, oh, there it is. 525 00:26:35,966 --> 00:26:36,933 BLAKE: We were close. 526 00:26:36,966 --> 00:26:37,966 RODGERS: Ah, good place for it. 527 00:26:38,000 --> 00:26:39,500 NARRATOR: This lightweight collar 528 00:26:39,533 --> 00:26:41,900 contains a small camera and GPS. 529 00:26:41,933 --> 00:26:44,133 The leather, the belting isn't chewed too much. 530 00:26:44,166 --> 00:26:46,766 NARRATOR: Six months ago, researchers placed it 531 00:26:46,800 --> 00:26:50,066 around the neck of a captured caribou. 532 00:26:50,100 --> 00:26:51,366 RODGERS: The camera is programmed 533 00:26:51,400 --> 00:26:55,666 to take a ten-second clip every ten minutes 534 00:26:55,700 --> 00:26:58,400 for two hours in the morning and two hours towards the evening, 535 00:26:58,433 --> 00:26:59,766 during the times of day 536 00:26:59,800 --> 00:27:02,800 when we know that caribou are likely to be feeding. 537 00:27:02,800 --> 00:27:02,833 when we know that caribou are likely to be feeding. 538 00:27:02,833 --> 00:27:05,000 Yeah, we got the collar. 539 00:27:05,033 --> 00:27:06,966 NARRATOR: Art is hoping the footage on this camera will reveal 540 00:27:07,000 --> 00:27:08,700 everything he wants to know 541 00:27:08,733 --> 00:27:12,433 about where and what this caribou ate. 542 00:27:12,466 --> 00:27:14,400 Oh, here we go, look at this. 543 00:27:14,433 --> 00:27:19,333 ♪ 544 00:27:22,666 --> 00:27:25,666 NARRATOR: Not Oscar-winning cinematography, 545 00:27:25,700 --> 00:27:29,800 but to Art, the footage is simply amazing. 546 00:27:29,833 --> 00:27:30,866 RODGERS: Wow, look. 547 00:27:30,900 --> 00:27:32,133 We can see this. 548 00:27:32,166 --> 00:27:33,400 We can actually see what they're doing. 549 00:27:33,433 --> 00:27:34,766 We can see what they're eating. 550 00:27:34,800 --> 00:27:37,800 It allows you to accompany the animal 551 00:27:37,833 --> 00:27:40,666 on its journey through life. 552 00:27:40,700 --> 00:27:45,000 NARRATOR: Finally, Art and his team can see what caribou are munching on 553 00:27:45,033 --> 00:27:47,233 during the summer. 554 00:27:47,266 --> 00:27:49,066 The result is surprising: 555 00:27:49,100 --> 00:27:52,733 more lichen. 556 00:27:52,766 --> 00:27:54,700 We thought, well, once, you know, the world turns green, 557 00:27:54,733 --> 00:27:58,000 and all the other plants and leafy vegetation comes up, 558 00:27:58,033 --> 00:28:00,366 that they would switch on to the, the easy stuff, 559 00:28:00,400 --> 00:28:01,633 relatively speaking. 560 00:28:01,666 --> 00:28:03,733 And relatively more nutritious. 561 00:28:05,000 --> 00:28:08,800 NARRATOR: But the way they eat it in the summer is unique. 562 00:28:08,833 --> 00:28:12,000 RODGERS: They graze along the top of the lichen mat, 563 00:28:12,033 --> 00:28:13,900 and maybe just take the top centimeter or two, 564 00:28:13,933 --> 00:28:18,000 a couple of centimeters, sort of the newest growth on the lichen. 565 00:28:18,033 --> 00:28:20,266 And in a sense, you can call that sort of farming the lichen. 566 00:28:20,300 --> 00:28:22,733 They're leaving some behind to grow back for another time. 567 00:28:23,900 --> 00:28:26,733 NARRATOR: And the cameras turn up more surprises. 568 00:28:26,766 --> 00:28:29,933 Caribou like mushrooms. 569 00:28:29,966 --> 00:28:31,400 RODGERS: It's quite amusing to watch 570 00:28:31,433 --> 00:28:33,433 a caribou walking through a forest, 571 00:28:33,466 --> 00:28:35,433 feeding on these large mushrooms 572 00:28:35,466 --> 00:28:38,466 and basically just picking them off. 573 00:28:38,500 --> 00:28:40,466 Oh, there goes another mushroom. 574 00:28:40,500 --> 00:28:41,433 And another one. 575 00:28:41,466 --> 00:28:44,000 RODGERS: There's just no other way 576 00:28:44,033 --> 00:28:45,733 we would have known that or seen that, 577 00:28:45,766 --> 00:28:48,966 and no one ever has, till we got these videos. 578 00:28:49,000 --> 00:28:52,500 NARRATOR: With fresh water scarce in the winter months, 579 00:28:52,533 --> 00:28:54,133 caribou wash their food down 580 00:28:54,166 --> 00:28:57,533 by mushing up snow and ice with their hooves, 581 00:28:57,566 --> 00:29:01,300 a behavior Art calls slushing. 582 00:29:01,333 --> 00:29:07,600 The cameras create caribou home movies of entire herds, 583 00:29:07,633 --> 00:29:12,033 including its newest members. 584 00:29:12,066 --> 00:29:14,933 RODGERS: One of the most exciting moments was 585 00:29:14,966 --> 00:29:18,500 the first time we saw a newborn calf in one of our video clips 586 00:29:18,533 --> 00:29:21,266 trying to stand up for the first time, 587 00:29:21,300 --> 00:29:23,666 and mom drying it off. 588 00:29:23,700 --> 00:29:26,100 It gave me the impression right away that, 589 00:29:26,133 --> 00:29:28,700 "Gosh, we're going to see all kinds of wonderful things 590 00:29:28,733 --> 00:29:32,400 "that we would never, ever, ever see any other way 591 00:29:32,433 --> 00:29:36,800 than without having these video cameras on the collars." 592 00:29:36,833 --> 00:29:38,800 NARRATOR: One key discovery: 593 00:29:38,833 --> 00:29:41,833 certain habitats are especially important 594 00:29:41,866 --> 00:29:44,633 for calf-bearing and -rearing. 595 00:29:44,666 --> 00:29:48,666 New mothers stick close to the forest's lakes and bogs, 596 00:29:48,700 --> 00:29:50,600 with nearby islands. 597 00:29:50,633 --> 00:29:57,100 If mom senses a predator, she can swim her calf to safety. 598 00:29:57,133 --> 00:29:58,466 Over the course of eight years, 599 00:29:58,500 --> 00:30:02,333 scientists have mounted cameras on dozens of caribou here. 600 00:30:02,333 --> 00:30:02,366 scientists have mounted cameras on dozens of caribou here. 601 00:30:02,366 --> 00:30:06,500 They can see the boreal forest as a caribou would 602 00:30:06,533 --> 00:30:09,400 and understand which areas it needs 603 00:30:09,433 --> 00:30:11,066 to survive. 604 00:30:11,100 --> 00:30:14,100 RODGERS: And when we know what those habitat types are, 605 00:30:14,133 --> 00:30:15,433 we can start planning for those, 606 00:30:15,466 --> 00:30:19,166 in terms of, of land-use planning and forest management 607 00:30:19,200 --> 00:30:21,633 and other industrial developments, 608 00:30:21,666 --> 00:30:23,600 and make sure that there is enough of that 609 00:30:23,633 --> 00:30:28,233 to conserve caribou on the landscape. 610 00:30:29,400 --> 00:30:31,700 NARRATOR: Camera technology is opening our eyes 611 00:30:31,733 --> 00:30:34,233 to the hidden lives of animals. 612 00:30:34,266 --> 00:30:38,566 But what can it tell us about not just one species, 613 00:30:38,600 --> 00:30:41,900 but an entire ecosystem? 614 00:30:41,933 --> 00:30:44,566 ♪ 615 00:30:44,600 --> 00:30:47,000 We need hundreds of cameras in this area if we can get it. 616 00:30:47,033 --> 00:30:52,100 NARRATOR: Biologist Craig Packer has traveled all over Africa, 617 00:30:52,133 --> 00:30:56,066 studying wildlife in the continent's parks and reserves. 618 00:30:56,100 --> 00:31:00,266 And it's clear to him the animals are in trouble. 619 00:31:00,300 --> 00:31:03,300 PACKER: A lot of the research all points to the same thing: 620 00:31:03,333 --> 00:31:06,800 that wildlife populations are declining quite rapidly. 621 00:31:06,833 --> 00:31:10,466 NARRATOR: In Africa, elephants, 622 00:31:10,500 --> 00:31:12,033 lions, 623 00:31:12,066 --> 00:31:14,500 wild dogs, 624 00:31:14,533 --> 00:31:15,633 and the black rhino 625 00:31:15,666 --> 00:31:17,400 are just a few of the species 626 00:31:17,433 --> 00:31:20,966 whose numbers have plummeted in the past 50 years. 627 00:31:21,000 --> 00:31:24,200 Habitat loss and poaching 628 00:31:24,233 --> 00:31:27,233 are the biggest threats to their existence. 629 00:31:27,266 --> 00:31:29,300 Different countries are tackling these problems 630 00:31:29,333 --> 00:31:30,966 with a variety of methods, 631 00:31:31,000 --> 00:31:34,333 in the hopes of saving their wildlife. 632 00:31:34,366 --> 00:31:36,266 But how can anyone know 633 00:31:36,300 --> 00:31:40,633 which conservation methods are actually working? 634 00:31:40,666 --> 00:31:43,166 What I know as a scientist is that we have to measure things. 635 00:31:43,200 --> 00:31:45,066 So we want to make it possible 636 00:31:45,100 --> 00:31:47,700 for people to have readily available to them 637 00:31:47,733 --> 00:31:51,633 reliable information on the abundance and the trends 638 00:31:51,666 --> 00:31:54,533 in all of the species within their reserves. 639 00:31:54,566 --> 00:31:55,900 ♪ 640 00:31:55,933 --> 00:31:58,666 NARRATOR: So Craig had an idea. 641 00:31:58,700 --> 00:31:59,900 What if you took a census 642 00:31:59,933 --> 00:32:03,733 of all the wildlife parks and reserves in Africa 643 00:32:03,766 --> 00:32:05,200 to get a clear picture 644 00:32:05,233 --> 00:32:09,166 of animal populations and conservation methods? 645 00:32:09,200 --> 00:32:12,633 PACKER: I'm aiming for this program to include camera grids 646 00:32:12,666 --> 00:32:14,300 from 50 different sites. 647 00:32:14,333 --> 00:32:17,500 This will be able to provide data 648 00:32:17,533 --> 00:32:20,600 that we can use to assess how things are going 649 00:32:20,633 --> 00:32:21,933 in terms of the conservation. 650 00:32:21,966 --> 00:32:24,633 We've got literally thousands of these cameras 651 00:32:24,666 --> 00:32:26,766 being set up all over Africa. 652 00:32:26,800 --> 00:32:30,533 Just have to make sure we know where we are and when. 653 00:32:30,566 --> 00:32:33,400 So we're in the Klaserie Reserve, 654 00:32:33,433 --> 00:32:36,066 this is camera K013, 655 00:32:36,100 --> 00:32:39,700 and this is the 22nd of July, I hope. 656 00:32:39,733 --> 00:32:42,233 This is the 23rd of July. 657 00:32:42,266 --> 00:32:45,866 So we'll have camera-trap grids in Kruger Park, 658 00:32:45,900 --> 00:32:48,166 Mountain Zebra National Park, 659 00:32:48,200 --> 00:32:49,800 Maasai Mara in Kenya. 660 00:32:49,833 --> 00:32:52,300 There's cameras in Ruaha in Tanzania. 661 00:32:52,333 --> 00:32:56,600 There are cameras in Niassa Reserve in Mozambique. 662 00:32:56,633 --> 00:32:58,833 NARRATOR: Thousands of motion-sensor cameras, 663 00:32:58,866 --> 00:33:03,100 powered on 24/7 for weeks at a time, 664 00:33:03,100 --> 00:33:03,133 powered on 24/7 for weeks at a time, 665 00:33:03,133 --> 00:33:06,600 watching everything. 666 00:33:06,633 --> 00:33:08,800 They will show that what may look 667 00:33:08,833 --> 00:33:11,033 like a tranquil savanna landscape 668 00:33:11,066 --> 00:33:15,100 is actually an ecosystem teeming with life. 669 00:33:15,133 --> 00:33:18,900 ♪ 670 00:33:18,933 --> 00:33:23,300 The cameras reveal where zebras gather... 671 00:33:25,733 --> 00:33:29,633 The gentle intimacy of elephants... 672 00:33:31,100 --> 00:33:34,933 And an antelope's curious nature. 673 00:33:37,466 --> 00:33:42,000 But the cameras were snapping photos nonstop. 674 00:33:42,033 --> 00:33:45,533 PACKER: The practicalities were daunting. 675 00:33:45,566 --> 00:33:47,866 We were generating millions of photographs. 676 00:33:47,900 --> 00:33:53,100 NARRATOR: How do you make scientific sense out of so many images? 677 00:33:53,133 --> 00:33:56,933 Then, Craig's graduate students came up with a solution: 678 00:33:56,966 --> 00:33:59,066 the internet. 679 00:33:59,100 --> 00:34:01,933 They would upload all their photos 680 00:34:01,966 --> 00:34:04,833 and ask the world for help. 681 00:34:04,866 --> 00:34:07,500 PACKER: And you could have volunteers from all over the world 682 00:34:07,533 --> 00:34:11,933 look at your data and then help classify it. 683 00:34:11,966 --> 00:34:16,200 NARRATOR: More than 140,000 people from all across the globe 684 00:34:16,233 --> 00:34:19,633 have participated in Craig's project 685 00:34:19,666 --> 00:34:21,333 as citizen scientists. 686 00:34:21,366 --> 00:34:23,466 PACKER: There is a real community 687 00:34:23,500 --> 00:34:25,833 around the camera-trap process 688 00:34:25,866 --> 00:34:28,533 that involves a broader segment of society 689 00:34:28,566 --> 00:34:30,000 than we ever could have otherwise. 690 00:34:30,033 --> 00:34:31,533 ♪ 691 00:34:31,566 --> 00:34:34,833 NARRATOR: So far, millions of pictures and over 50 species 692 00:34:34,866 --> 00:34:38,066 have been IDed and catalogued. 693 00:34:38,100 --> 00:34:42,033 The citizen scientists have helped discover behaviors 694 00:34:42,066 --> 00:34:43,766 that had been mysteries to biologists, 695 00:34:43,800 --> 00:34:48,433 like relationships between major predators. 696 00:34:48,466 --> 00:34:51,833 PACKER: After a very large number of observations of lions 697 00:34:51,866 --> 00:34:54,766 at these cameras, 698 00:34:54,800 --> 00:34:57,066 we never saw a cheetah show up at the same spot 699 00:34:57,100 --> 00:34:59,133 less than 12 hours afterwards. 700 00:34:59,166 --> 00:35:00,666 So they waited a good, safe time. 701 00:35:00,700 --> 00:35:01,966 And then they might come 702 00:35:02,000 --> 00:35:03,633 and actually sleep under the same tree, 703 00:35:03,666 --> 00:35:06,566 so they're, it's kind of a timeshare. 704 00:35:06,600 --> 00:35:08,600 And they're safe enough apart in time 705 00:35:08,633 --> 00:35:10,866 that there's no risk of an encounter. 706 00:35:12,100 --> 00:35:15,866 NARRATOR: The cameras capture some surprising moments. 707 00:35:15,900 --> 00:35:19,366 With the cameras, we know where everything goes at night. 708 00:35:19,400 --> 00:35:23,200 ♪ 709 00:35:23,233 --> 00:35:26,566 Birds that ordinarily roost in trees 710 00:35:26,600 --> 00:35:28,333 we've discovered like to roost 711 00:35:28,366 --> 00:35:30,600 in the crotch of a giraffe, for example. 712 00:35:30,633 --> 00:35:32,066 These are oxpeckers 713 00:35:32,100 --> 00:35:34,366 who've decided that that's a nice, warm place 714 00:35:34,400 --> 00:35:38,366 to spend the night, and I had no idea they did that. 715 00:35:38,400 --> 00:35:42,366 There's also interactions between other species 716 00:35:42,400 --> 00:35:44,200 that sometimes seem really amusing, 717 00:35:44,233 --> 00:35:47,866 like a warthog that looks like it's talking to a gazelle. 718 00:35:47,900 --> 00:35:51,300 So those kinds of things can just suddenly make you laugh. 719 00:35:51,333 --> 00:35:56,033 NARRATOR: And while some images might bring a smile, 720 00:35:56,066 --> 00:35:59,833 all are part of a long-term study 721 00:35:59,866 --> 00:36:02,966 trying to answer tough questions about wildlife management 722 00:36:02,966 --> 00:36:03,000 trying to answer tough questions about wildlife management 723 00:36:03,000 --> 00:36:06,200 in one of the wildest places on Earth. 724 00:36:06,233 --> 00:36:10,066 Can you save prey animals without destroying predators? 725 00:36:10,100 --> 00:36:12,966 Do fences help or hurt? 726 00:36:13,000 --> 00:36:15,466 What investments are most effective 727 00:36:15,500 --> 00:36:18,033 when you're managing a wildlife reserve? 728 00:36:18,066 --> 00:36:20,833 PACKER: An ideal outcome ten to 15 years from now is, 729 00:36:20,866 --> 00:36:22,233 we have a really good view of what's going on. 730 00:36:22,266 --> 00:36:25,700 I think the ultimate power of these cameras is 731 00:36:25,733 --> 00:36:29,533 that you've got hundreds of eyes out in the field 732 00:36:29,566 --> 00:36:31,900 that are collecting information. 733 00:36:31,933 --> 00:36:34,633 And you have literally hundreds of thousands of eyes 734 00:36:34,666 --> 00:36:36,200 looking at those photographs 735 00:36:36,233 --> 00:36:38,933 that are all part of the scientific program 736 00:36:38,966 --> 00:36:40,900 to say, "This is what's happening. 737 00:36:40,933 --> 00:36:42,700 This is how well this area is being conserved." 738 00:36:42,733 --> 00:36:45,366 ♪ 739 00:36:49,700 --> 00:36:52,300 NARRATOR: Sometimes, conventional camera traps 740 00:36:52,333 --> 00:36:56,000 can't capture all the data that scientists need. 741 00:36:56,033 --> 00:36:57,400 MIKE CLINCHY: So this is... 742 00:36:57,433 --> 00:36:59,400 ZANETTE: All right, we're at Den Four, this is ABR 15. 743 00:36:59,433 --> 00:37:00,566 Right. 744 00:37:00,600 --> 00:37:02,333 NARRATOR: Like Craig, 745 00:37:02,366 --> 00:37:06,133 biologists Liana Zanette and Mike Clinchy are spying 746 00:37:06,166 --> 00:37:08,966 on animals in South Africa. 747 00:37:09,000 --> 00:37:12,200 But their camera traps are very different. 748 00:37:12,233 --> 00:37:14,366 It's playing hoopoes. 749 00:37:14,400 --> 00:37:18,033 NARRATOR: This camera setup plays back sounds of predators 750 00:37:18,066 --> 00:37:20,633 in order to trigger a fear response. 751 00:37:20,666 --> 00:37:26,233 So lions at 11:42 on the 23rd of July. 752 00:37:26,266 --> 00:37:28,700 Make the terrible noise, there we are. 753 00:37:28,733 --> 00:37:30,266 (lions growling on recording) 754 00:37:30,300 --> 00:37:32,166 ZANETTE: When the animal walks by, 755 00:37:32,200 --> 00:37:34,700 the system will activate the speaker. 756 00:37:34,733 --> 00:37:36,933 It'll get that ten seconds of sound, 757 00:37:36,966 --> 00:37:38,766 so we can see what the animal was doing 758 00:37:38,800 --> 00:37:40,500 just before it heard the sound, 759 00:37:40,533 --> 00:37:42,933 what it does when it's hearing the sound, 760 00:37:42,966 --> 00:37:46,233 and also what it does after the sound stops. 761 00:37:46,266 --> 00:37:50,066 NARRATOR: This may sound like a mean practical joke. 762 00:37:50,100 --> 00:37:51,366 But Liana and Mike are trying 763 00:37:51,400 --> 00:37:55,033 to understand the role that fear plays in an ecosystem. 764 00:37:55,066 --> 00:37:59,200 What happens when animals aren't killed 765 00:37:59,233 --> 00:38:02,766 but just scared? 766 00:38:02,800 --> 00:38:05,933 ZANETTE: We are basically counting fear. 767 00:38:05,966 --> 00:38:08,266 So we're figuring out the degree 768 00:38:08,300 --> 00:38:09,666 to which fear affects everything. 769 00:38:09,700 --> 00:38:12,266 NARRATOR: Their work addresses a serious problem 770 00:38:12,300 --> 00:38:15,100 in ecosystems all over the world: 771 00:38:15,133 --> 00:38:21,000 the dwindling number of scary, but natural, predators. 772 00:38:21,033 --> 00:38:24,100 ZANETTE: Wherever large carnivores have been exterminated, 773 00:38:24,133 --> 00:38:26,833 there's often massive ecosystem problems. 774 00:38:26,866 --> 00:38:29,466 The prey have nothing to fear. 775 00:38:29,500 --> 00:38:31,933 And because they have nothing to fear, 776 00:38:31,966 --> 00:38:33,266 they can overgraze everything down to the ground. 777 00:38:33,300 --> 00:38:36,166 That's happened repeatedly all over the world, 778 00:38:36,200 --> 00:38:37,766 it continues to happen, 779 00:38:37,800 --> 00:38:40,866 and it's a real ecological problem. 780 00:38:40,900 --> 00:38:43,766 NARRATOR: Decades ago, 781 00:38:43,800 --> 00:38:46,800 Yellowstone National Park faced a crisis. 782 00:38:46,833 --> 00:38:50,233 With the native gray wolf locally extinct, 783 00:38:50,266 --> 00:38:52,600 the elk population exploded, 784 00:38:52,633 --> 00:38:56,633 gorging on plants and decimating the landscape. 785 00:38:56,666 --> 00:39:00,533 In 1995, the park service 786 00:39:00,566 --> 00:39:02,900 reintroduced the gray wolf to Yellowstone, 787 00:39:02,900 --> 00:39:02,933 reintroduced the gray wolf to Yellowstone, 788 00:39:02,933 --> 00:39:06,266 and the elk population dropped. 789 00:39:06,300 --> 00:39:08,833 Soon, parts of the ecosystem began to change. 790 00:39:08,866 --> 00:39:10,900 Vegetation flourished. 791 00:39:10,933 --> 00:39:12,900 Willow trees thrived, 792 00:39:12,933 --> 00:39:16,266 helping to stabilize the once-eroding river banks. 793 00:39:16,300 --> 00:39:19,833 Scavengers such as fox, black bear, 794 00:39:19,866 --> 00:39:20,866 and even birds 795 00:39:20,900 --> 00:39:22,933 benefited from the elk carcasses 796 00:39:22,966 --> 00:39:24,833 left by wolves. 797 00:39:24,866 --> 00:39:28,966 Exactly how the wolves changed Yellowstone's landscape 798 00:39:29,000 --> 00:39:30,633 is still being debated. 799 00:39:30,666 --> 00:39:32,866 But Liana and Mike say it's not just 800 00:39:32,900 --> 00:39:35,633 about the number of kills that predators make, 801 00:39:35,666 --> 00:39:38,500 it's how many prey they scare. 802 00:39:39,966 --> 00:39:43,400 ZANETTE: Predators will kill way fewer prey 803 00:39:43,433 --> 00:39:45,433 than they scare. 804 00:39:45,466 --> 00:39:47,833 Predators scare all of their prey, 805 00:39:47,866 --> 00:39:48,933 they kill a few of them. 806 00:39:48,966 --> 00:39:51,733 NARRATOR: To better understand 807 00:39:51,766 --> 00:39:53,133 how fear affects animals, 808 00:39:53,166 --> 00:39:56,766 Liana and Mike have spent days setting up dozens of cameras 809 00:39:56,800 --> 00:39:59,100 that record video and play sounds 810 00:39:59,133 --> 00:40:02,166 from three different predators here: 811 00:40:02,200 --> 00:40:07,533 lions, cheetahs, and wild dogs. 812 00:40:07,566 --> 00:40:09,166 ZANETTE: The cameras give us the ability 813 00:40:09,200 --> 00:40:11,633 to do a manipulation of this sort, 814 00:40:11,666 --> 00:40:12,866 which is very difficult. 815 00:40:12,900 --> 00:40:15,333 I mean, working out here is very difficult, right? 816 00:40:15,366 --> 00:40:18,133 These animals, we don't know where they're going to be. 817 00:40:18,166 --> 00:40:20,000 They're not radio-tagged or anything like that. 818 00:40:20,033 --> 00:40:21,433 I don't want to be out here at night, 819 00:40:21,466 --> 00:40:22,733 when all the lions and the cheetahs 820 00:40:22,766 --> 00:40:25,266 and the leopards are out. 821 00:40:25,300 --> 00:40:27,633 Thankfully, we have the cameras that can be out here. 822 00:40:27,666 --> 00:40:31,833 ♪ 823 00:40:34,933 --> 00:40:39,200 NARRATOR: A week later, they return. 824 00:40:39,233 --> 00:40:41,866 Grab the laptop. 825 00:40:41,900 --> 00:40:44,333 Okay, just double-check. 826 00:40:44,366 --> 00:40:45,800 NARRATOR: Looking through hours of footage, 827 00:40:45,833 --> 00:40:49,333 Liana and Mike analyze fear responses 828 00:40:49,366 --> 00:40:51,033 to the three predators. 829 00:40:51,066 --> 00:40:52,033 (recorded wild dogs barking) 830 00:40:52,066 --> 00:40:53,200 ZANETTE: This is... ooh! 831 00:40:53,233 --> 00:40:55,266 Ooh, didn't like the wild dogs. 832 00:40:55,300 --> 00:40:56,700 (chuckles) 833 00:40:56,733 --> 00:40:59,033 NARRATOR: Cheetahs startle some animals... 834 00:40:59,066 --> 00:41:01,066 (recorded cheetah moaning) 835 00:41:01,100 --> 00:41:05,200 But not others. 836 00:41:05,233 --> 00:41:08,033 Wild dogs are scary... 837 00:41:08,066 --> 00:41:09,600 (recorded wild dogs barking) 838 00:41:09,633 --> 00:41:11,566 ♪ 839 00:41:11,600 --> 00:41:15,666 Unless you're a rhino. 840 00:41:15,700 --> 00:41:17,566 (recorded lion growling) 841 00:41:17,600 --> 00:41:20,533 And lions make just about everybody run for the hills. 842 00:41:20,566 --> 00:41:21,766 (recorded lion roars) 843 00:41:21,800 --> 00:41:25,533 (recorded lion growling) 844 00:41:25,566 --> 00:41:26,566 ZANETTE: Camera 13. 845 00:41:26,600 --> 00:41:27,800 CLINCHY: Camera 13. 846 00:41:27,833 --> 00:41:29,800 NARRATOR: The next phase will be to see 847 00:41:29,833 --> 00:41:32,300 how fear affects these animals' reproduction rates 848 00:41:32,333 --> 00:41:34,333 and feeding times. 849 00:41:34,366 --> 00:41:37,100 Liana and Mike have conducted similar studies 850 00:41:37,133 --> 00:41:38,400 elsewhere in the world, 851 00:41:38,433 --> 00:41:40,966 and the results are startling. 852 00:41:41,000 --> 00:41:42,400 ZANETTE: What we've discovered over the years 853 00:41:42,433 --> 00:41:45,966 is that this has massive repercussions 854 00:41:46,000 --> 00:41:49,433 on a long timescale in terms of the number of offspring 855 00:41:49,466 --> 00:41:51,133 that animals are able to produce. 856 00:41:51,166 --> 00:41:52,766 (chirping) 857 00:41:52,800 --> 00:41:55,733 NARRATOR: In British Columbia, sparrows subjected 858 00:41:55,766 --> 00:41:57,366 to the sounds of a hawk 859 00:41:57,400 --> 00:42:00,866 produced 40% fewer offspring. 860 00:42:00,900 --> 00:42:03,966 Raccoons frightened by hearing large carnivores... 861 00:42:03,966 --> 00:42:04,000 Raccoons frightened by hearing large carnivores... 862 00:42:04,000 --> 00:42:06,033 (recorded animal growling) 863 00:42:06,066 --> 00:42:09,133 ...spent 66% less time feeding, 864 00:42:09,166 --> 00:42:12,333 leaving more crabs and fish in the oceans. 865 00:42:12,366 --> 00:42:18,233 And when cougars heard the sound of their predator-- humans-- 866 00:42:18,266 --> 00:42:22,933 their feeding times went down by half. 867 00:42:22,966 --> 00:42:24,966 ZANETTE: Just because they think that there's predators around, 868 00:42:25,000 --> 00:42:26,800 there's fewer offspring that are produced. 869 00:42:28,066 --> 00:42:30,100 The predators aren't killing the offspring. 870 00:42:30,133 --> 00:42:32,400 It's just thinking that there's predators around 871 00:42:32,433 --> 00:42:35,566 that is causing this massive reduction in population. 872 00:42:35,600 --> 00:42:39,866 NARRATOR: Their research is sounding an alarm to conservationists: 873 00:42:39,900 --> 00:42:43,066 Big, scary predators affect landscapes 874 00:42:43,100 --> 00:42:45,800 in ways that aren't always obvious. 875 00:42:45,833 --> 00:42:47,800 Failing to protect them 876 00:42:47,833 --> 00:42:51,200 could cause entire ecosystems to collapse. 877 00:42:51,233 --> 00:42:54,466 ZANETTE: By incorporating fear into the equation, 878 00:42:54,500 --> 00:42:56,900 we have a much better understanding 879 00:42:56,933 --> 00:43:00,466 of management plans that, that may work, 880 00:43:00,500 --> 00:43:02,466 management plans that will not work. 881 00:43:02,500 --> 00:43:06,700 It's just the beginning of a whole new understanding 882 00:43:06,733 --> 00:43:11,033 of how the fear of predators can shape everything. 883 00:43:11,066 --> 00:43:13,600 It's unbelievable. 884 00:43:13,633 --> 00:43:16,233 ♪ 885 00:43:16,266 --> 00:43:18,133 NARRATOR: On another continent, 886 00:43:18,166 --> 00:43:20,933 a predator at the apex of the food chain 887 00:43:20,966 --> 00:43:24,400 is struggling to survive: 888 00:43:24,433 --> 00:43:26,533 the wild tiger. 889 00:43:26,566 --> 00:43:28,966 The largest member of the cat family, 890 00:43:29,000 --> 00:43:32,366 tigers can weigh 500 pounds or more. 891 00:43:32,400 --> 00:43:36,100 They roam solo, and hunt often; 892 00:43:36,133 --> 00:43:40,100 an adult tiger needs one large prey animal per week 893 00:43:40,133 --> 00:43:42,566 to survive. 894 00:43:42,600 --> 00:43:44,666 KARANTH: You can keep going a little bit more. 895 00:43:44,700 --> 00:43:49,433 NARRATOR: Ullas Karanth is a tiger expert and conservationist 896 00:43:49,466 --> 00:43:51,633 working in Karnataka state in India, 897 00:43:51,666 --> 00:43:54,666 where most of the world's tigers live. 898 00:43:54,700 --> 00:43:57,333 He has dedicated his life 899 00:43:57,366 --> 00:44:01,433 to preserving these elusive predators. 900 00:44:01,466 --> 00:44:04,066 I grew up in a small village. 901 00:44:04,100 --> 00:44:09,666 The local culture had tiger deeply infused in it. 902 00:44:09,700 --> 00:44:15,633 People used to wear tiger masks and dance during festivals. 903 00:44:15,666 --> 00:44:18,000 Yet ironically, 904 00:44:18,033 --> 00:44:20,666 last of the wild tigers were being hunted out 905 00:44:20,700 --> 00:44:23,100 by people around me. 906 00:44:23,133 --> 00:44:25,466 NARRATOR: 100 years ago, 907 00:44:25,500 --> 00:44:28,600 there were close to 100,000 tigers in Asia. 908 00:44:28,633 --> 00:44:33,166 Today, only about 3,500 remain. 909 00:44:33,200 --> 00:44:35,533 Most of them are in India, 910 00:44:35,566 --> 00:44:38,600 where conservation campaigns and a hunting ban 911 00:44:38,633 --> 00:44:41,433 saved the species from local extinction. 912 00:44:41,466 --> 00:44:43,000 But even here, 913 00:44:43,033 --> 00:44:46,733 this iconic predator is far from safe. 914 00:44:46,766 --> 00:44:48,166 (rifle fires) 915 00:44:48,200 --> 00:44:50,033 Poaching is still a problem. 916 00:44:50,066 --> 00:44:53,066 And as India develops at a rapid clip, 917 00:44:53,100 --> 00:44:55,633 tiger habitats get carved up. 918 00:44:55,666 --> 00:44:58,966 In some areas, tigers are running out of prey, 919 00:44:59,000 --> 00:45:01,866 such as deer and wild cattle. 920 00:45:01,866 --> 00:45:01,900 such as deer and wild cattle. 921 00:45:01,900 --> 00:45:03,633 KARANTH: Often tigers disappear 922 00:45:03,666 --> 00:45:06,300 not because they have been hunted, 923 00:45:06,333 --> 00:45:07,633 but because their food has been taken away, 924 00:45:07,666 --> 00:45:11,033 their prey have been hunted out by local people. 925 00:45:11,066 --> 00:45:12,333 NARRATOR: How do you protect 926 00:45:12,366 --> 00:45:14,933 one of the world's most vulnerable predators 927 00:45:14,966 --> 00:45:18,066 in one of the fastest-growing countries? 928 00:45:18,100 --> 00:45:21,100 KARANTH: Conservation is a difficult enterprise. 929 00:45:21,133 --> 00:45:23,966 That's where the role of counting tigers accurately, 930 00:45:24,000 --> 00:45:25,366 monitoring their populations, 931 00:45:25,400 --> 00:45:27,100 monitoring their distributions, comes. 932 00:45:27,133 --> 00:45:30,133 It's an audit of whether tiger conservation 933 00:45:30,166 --> 00:45:31,666 is succeeding or failing. 934 00:45:31,700 --> 00:45:35,433 NARRATOR: An audit that requires accuracy 935 00:45:35,466 --> 00:45:37,966 if we are to know how many tigers are left 936 00:45:38,000 --> 00:45:39,800 and where they are thriving-- 937 00:45:39,833 --> 00:45:42,666 not easy when counting one of the world's 938 00:45:42,700 --> 00:45:45,266 most dangerous and elusive predators. 939 00:45:45,300 --> 00:45:49,933 For years, conservationists kept a safe distance 940 00:45:49,966 --> 00:45:52,266 by counting tiger pawprints. 941 00:45:52,300 --> 00:45:57,100 But when a young Ullas Karanth began studying tigers in 1986, 942 00:45:57,133 --> 00:46:00,266 he spotted a serious flaw. 943 00:46:00,300 --> 00:46:04,500 It's almost impossible to identify each tiger individually 944 00:46:04,533 --> 00:46:06,000 from its track shape, 945 00:46:06,033 --> 00:46:08,833 because the speed at which the animal is walking, 946 00:46:08,866 --> 00:46:10,566 the soil on which it's walking-- 947 00:46:10,600 --> 00:46:12,666 all these make massive differences 948 00:46:12,700 --> 00:46:14,800 and distort the shape. 949 00:46:14,833 --> 00:46:17,566 It is impossible to wander 950 00:46:17,600 --> 00:46:20,366 across hundreds of square kilometers of tiger habitat 951 00:46:20,400 --> 00:46:21,733 in a couple of weeks, 952 00:46:21,766 --> 00:46:24,266 and find the tracks of every tiger, 953 00:46:24,300 --> 00:46:27,166 so it simply didn't work. 954 00:46:27,200 --> 00:46:29,200 NARRATOR: Ullas had a better idea. 955 00:46:29,233 --> 00:46:32,833 Tiger stripes are like fingerprints-- 956 00:46:32,866 --> 00:46:34,800 no two are alike. 957 00:46:34,833 --> 00:46:39,000 Why not count tigers by photographing them? 958 00:46:39,033 --> 00:46:43,366 KARANTH: What camera trapping allows you to do 959 00:46:43,400 --> 00:46:47,700 is to photographically capture a very large number of tigers 960 00:46:47,733 --> 00:46:49,666 over very vast landscapes, 961 00:46:49,700 --> 00:46:52,666 which you cannot do with any other technique. 962 00:46:52,700 --> 00:46:56,466 The stripes on two sides are very different, 963 00:46:56,500 --> 00:46:58,100 so you need two cameras 964 00:46:58,133 --> 00:46:59,600 so that you get both sides of the animal 965 00:46:59,633 --> 00:47:02,766 and identify it permanently. 966 00:47:02,800 --> 00:47:04,833 Once you have a permanent identification, 967 00:47:04,866 --> 00:47:06,633 any single-flank picture 968 00:47:06,666 --> 00:47:09,133 also can be pinned down to that tiger. 969 00:47:09,166 --> 00:47:13,433 NARRATOR: As the database grew, Ullas faced a new challenge. 970 00:47:13,466 --> 00:47:16,500 How do you compare each new tiger image 971 00:47:16,533 --> 00:47:18,966 to thousands of others? 972 00:47:19,000 --> 00:47:22,000 KARANTH: See, you have to compare the same side. 973 00:47:22,033 --> 00:47:23,666 NARRATOR: So, Ullas turned to scientists, 974 00:47:23,700 --> 00:47:28,666 who pioneered a new way to identify individual animals. 975 00:47:28,700 --> 00:47:32,133 This program examines each tiger-stripe pattern 976 00:47:32,166 --> 00:47:34,233 as a series of squares. 977 00:47:34,266 --> 00:47:37,666 In minutes, its algorithm compares this series 978 00:47:37,700 --> 00:47:42,166 to thousands of others, until it hits a match. 979 00:47:42,200 --> 00:47:43,700 KARANTH: Once the model is matched, 980 00:47:43,733 --> 00:47:45,433 then it's very easy to identify. 981 00:47:45,466 --> 00:47:49,933 NARRATOR: Ullas ran decades' worth of tiger photos 982 00:47:49,966 --> 00:47:51,000 through the software. 983 00:47:51,033 --> 00:47:53,666 What emerged were hundreds of matches 984 00:47:53,700 --> 00:47:56,866 for individual tigers. 985 00:47:56,900 --> 00:47:59,900 KARANTH: It adds up to a lot of knowledge about tigers, 986 00:47:59,933 --> 00:48:02,033 how they are spread across the land. 987 00:48:02,033 --> 00:48:02,066 how they are spread across the land. 988 00:48:02,066 --> 00:48:04,400 And using that data, we can know 989 00:48:04,433 --> 00:48:06,566 not only how many tigers there are, 990 00:48:06,600 --> 00:48:09,166 we can estimate how those numbers are changing. 991 00:48:09,200 --> 00:48:10,500 We can get to know 992 00:48:10,533 --> 00:48:12,833 what proportion of tigers are surviving, 993 00:48:12,866 --> 00:48:15,833 how many new tigers are getting to the population. 994 00:48:15,866 --> 00:48:17,433 All this adds up to knowledge 995 00:48:17,466 --> 00:48:20,100 that is critical for saving tigers. 996 00:48:20,133 --> 00:48:23,400 NARRATOR: The pictures have revealed how far a tiger can range 997 00:48:23,433 --> 00:48:27,833 from its birthplace-- up to 100 miles. 998 00:48:27,866 --> 00:48:30,166 In some instances, 999 00:48:30,200 --> 00:48:33,000 Ullas's data has been used to convict poachers. 1000 00:48:33,033 --> 00:48:38,266 Camera traps are now widely used for tracking tigers in India. 1001 00:48:38,300 --> 00:48:40,533 In Karnataka state alone, 1002 00:48:40,566 --> 00:48:43,933 Ullas has generated 25 years' worth of data, 1003 00:48:43,966 --> 00:48:48,700 information that could give conservationists a clearer idea 1004 00:48:48,733 --> 00:48:54,400 of where to focus their efforts, now and in the future. 1005 00:48:54,433 --> 00:48:58,133 KARANTH: This powerful-looking animal is so fragile ecologically. 1006 00:48:58,166 --> 00:48:59,533 It can disappear so fast. 1007 00:48:59,566 --> 00:49:02,633 The pieces of knowledge that are needed to make it survive 1008 00:49:02,666 --> 00:49:04,000 are critical. 1009 00:49:04,933 --> 00:49:06,333 NARRATOR: Today, 1010 00:49:06,366 --> 00:49:10,533 cameras are revealing more about our planet's wildlife 1011 00:49:10,566 --> 00:49:15,033 than we could ever see with the naked eye. 1012 00:49:15,066 --> 00:49:18,333 In the Pacific, off Vancouver Island, 1013 00:49:18,366 --> 00:49:21,400 unmanned cameras are 7,000 feet down, 1014 00:49:21,433 --> 00:49:26,600 filming fantastic creatures few people have ever heard of, 1015 00:49:26,633 --> 00:49:28,433 let alone seen. 1016 00:49:28,466 --> 00:49:31,200 At this bat cave, 1017 00:49:31,233 --> 00:49:32,966 high-speed thermal cameras shed light 1018 00:49:33,000 --> 00:49:35,733 on an otherwise pitch-black world. 1019 00:49:35,766 --> 00:49:40,266 Slowed down, the images allow scientists to track individuals, 1020 00:49:40,300 --> 00:49:43,566 count wing beats-- 1021 00:49:43,600 --> 00:49:46,266 even watch the bats interact. 1022 00:49:46,300 --> 00:49:47,966 (bats squeaking) 1023 00:49:48,000 --> 00:49:51,533 This 36-hour time lapse in the savanna 1024 00:49:51,566 --> 00:49:55,966 shows us just how many animals are fed by a single kill. 1025 00:49:56,000 --> 00:49:58,666 ♪ 1026 00:49:58,700 --> 00:50:01,966 Remote cameras can be left behind 1027 00:50:02,000 --> 00:50:04,133 in the coldest places on Earth, 1028 00:50:04,166 --> 00:50:06,933 like in Antarctica, 1029 00:50:06,966 --> 00:50:08,533 where Penguin Watch uses a network 1030 00:50:08,566 --> 00:50:13,000 of 75 weatherproof, solar-powered cameras 1031 00:50:13,033 --> 00:50:15,433 to record the secret lives of penguins 1032 00:50:15,466 --> 00:50:20,300 and the impact of climate change on their world. 1033 00:50:20,333 --> 00:50:23,066 Frame by frame, 1034 00:50:23,100 --> 00:50:25,433 cameras document a changing planet 1035 00:50:25,466 --> 00:50:29,200 and the risks facing its most vulnerable creatures. 1036 00:50:29,233 --> 00:50:32,200 FORTUNE: Someone whose daily life isn't really affected 1037 00:50:32,233 --> 00:50:33,933 by environmental change, 1038 00:50:33,966 --> 00:50:37,533 to be able to see imagery of the animals 1039 00:50:37,566 --> 00:50:40,933 that are reliant on their natural environment 1040 00:50:40,966 --> 00:50:42,600 is really powerful, 1041 00:50:42,633 --> 00:50:45,833 and I think that's one of the, the benefits of this technology. 1042 00:50:45,866 --> 00:50:48,100 ♪ 1043 00:50:48,133 --> 00:50:50,500 NARRATOR: Cameras are playing a major role in conservation, 1044 00:50:50,533 --> 00:50:54,366 from the Arctic Circle to deepest Africa. 1045 00:50:54,400 --> 00:50:58,100 Their data could help save species from extinction. 1046 00:50:58,133 --> 00:51:01,300 PACKER: Unless we can really say 1047 00:51:01,333 --> 00:51:03,700 that there are growing populations of wildebeests, 1048 00:51:03,700 --> 00:51:03,733 that there are growing populations of wildebeests, 1049 00:51:03,733 --> 00:51:05,800 zebra, impala, et cetera, 1050 00:51:05,833 --> 00:51:06,933 we can't really be sure 1051 00:51:06,966 --> 00:51:09,466 whether these places are truly succeeding. 1052 00:51:09,500 --> 00:51:12,766 RODGERS: You could spend all the time in the world 1053 00:51:12,800 --> 00:51:15,100 trying to track these animals on foot through the bush, 1054 00:51:15,133 --> 00:51:17,800 and never get close enough to, to observe these things. 1055 00:51:17,833 --> 00:51:20,100 (caribou grunting) 1056 00:51:20,133 --> 00:51:22,733 KARANTH: When so much is invested in tiger conservation-- 1057 00:51:22,766 --> 00:51:25,633 people even sacrificing their lives for tigers-- 1058 00:51:25,666 --> 00:51:27,533 we need to know accurately 1059 00:51:27,566 --> 00:51:29,400 whether what we are doing is working. 1060 00:51:29,433 --> 00:51:31,566 ♪ 1061 00:51:31,600 --> 00:51:36,166 NARRATOR: And with each new image, cameras give us another chance 1062 00:51:36,200 --> 00:51:40,066 to connect with the natural world. 1063 00:51:40,100 --> 00:51:41,433 DESBIEZ: These images help us 1064 00:51:41,466 --> 00:51:45,800 reach people's minds through their hearts. 1065 00:51:45,833 --> 00:51:50,333 ♪ 1066 00:51:54,400 --> 00:51:56,166 We can show people, "Look, 1067 00:51:56,200 --> 00:51:58,733 "here is this incredible species, 1068 00:51:58,766 --> 00:52:01,066 "and it's right here, right now, 1069 00:52:01,100 --> 00:52:03,933 and if we don't do something, we will lose it." 1070 00:52:03,966 --> 00:52:07,600 ♪ 1071 00:52:16,033 --> 00:52:19,233 Major funding for "NOVA" is provided by the following: 1072 00:52:29,933 --> 00:52:35,833 ♪ 1073 00:52:49,966 --> 00:52:52,500 To order this program on DVD, 1074 00:52:52,533 --> 00:52:57,433 visit ShopPBS or call 1-800-PLAY-PBS. 1075 00:52:57,466 --> 00:53:00,266 Episodes of "NOVA" are available with Passport. 1076 00:53:00,300 --> 00:53:03,566 "NOVA" is also available on Amazon Prime Video. 1077 00:53:03,600 --> 00:53:09,633 ♪ 84683

Can't find what you're looking for?
Get subtitles in any language from opensubtitles.com, and translate them here.