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NARRATOR:
From frigid oceans...
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Okay, there he is.
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NARRATOR:
...to distant jungles...
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ULLAS KARANTH:
You can keep going
a little bit more.
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NARRATOR:
...there's a hidden world
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of exotic creatures
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just out of view.
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ARNAUD DESBIEZ:
Finding the animal is
like looking
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for a needle in a haystack.
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It's really difficult.
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NARRATOR:
Around the world,
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researchers are tracking
the most vulnerable animals,
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trying to save them
before they vanish forever.
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KARANTH:
This powerful-looking animal
is so fragile.
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The pieces of knowledge
that are needed
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to make it survive
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are critical.
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NARRATOR:
Now, new technology
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is revealing their secret lives.
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DESBIEZ:
They're really
silent little spies
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that make no noise,
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that can capture
intimate moments of the animals.
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CRAIG PACKER:
You get millions and millions
of photographs,
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and you suddenly see things
for the first time.
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NARRATOR:
Frame by frame,
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the invisible world of animals
is coming to life.
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DESBIEZ:
I could not believe
this species existed,
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that it was right here,
around us.
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I had no idea they did that.
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NARRATOR:
Their habits, fears,
and most intimate moments.
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SARAH FORTUNE:
It has completely revolutionized
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our ability
to understand behaviors.
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NARRATOR:
Rare footage
from the animal kingdom
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is offering up new clues.
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Can we uncover the secrets
to these animals' survival
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before it's too late?
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"Animal Espionage,"
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right now, on "NOVA."
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♪
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Major funding for "NOVA"
is provided by the following:
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♪
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NARRATOR:
Our planet is teeming
with millions of species,
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yet we've only been able to
study a small fraction of them.
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In this hidden world,
much goes unseen--
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until now.
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Advances in camera technology
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are opening our eyes
to the world around us.
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LIANA ZANETTE:
The invaluable information
that people will get
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from a simple,
wee, little camera
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that anybody can buy
off the shelf,
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it's unbelievable.
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NARRATOR:
What can researchers learn
by spying on animals?
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ART RODGERS:
Why are they doing that?
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And why did they do that?
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And what are they going
to do next?
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NARRATOR:
Can a new wave
of animal surveillance
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turn the tide
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and help preserve our planet's
most vulnerable species
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before they disappear?
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♪
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Cumberland Sound, Canada,
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about 300 miles west
of Greenland.
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about 300 miles west
of Greenland.
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Beneath these frigid waters
dwells a mysterious giant--
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the bowhead whale.
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Little footage of
these 100-ton creatures exists.
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They are the longest-living
mammal on the planet.
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Some have reached
the ripe old age of 200.
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But their survival
isn't guaranteed.
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The species could be in trouble.
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FORTUNE:
They're living in the Arctic,
and this is a place
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where climate change could be
threatening their, their future.
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NARRATOR:
Marine biologist Sarah Fortune
studies bowheads
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in Cumberland Sound,
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where the whales come to feed
for months at a time.
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But rising temperatures
and melting sea ice
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are affecting
their primary food source--
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tiny animals called zooplankton.
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Bowheads favor
a nutrient-rich variety,
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and their numbers are dropping.
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This could be catastrophic
for the whales:
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one bowhead needs to eat about
100 tons of food each year.
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FORTUNE:
I need know what the whales
are feeding on today
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and how energy-rich
their current food resource is.
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NARRATOR:
Monitoring their size
and weight over time
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will tell Sarah if these whales
are getting enough to eat.
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But tracking them
is no simple task.
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FORTUNE:
Bowheads are
a little bit elusive.
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They dive for half an hour,
an hour.
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You spend a lot of time waiting
for them to come up again.
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And then it's also,
can be really difficult
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to track where that whale
has gone.
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Okay, there he is.
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NARRATOR:
Even when they find a whale,
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it's hard to see
its entire body.
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FORTUNE:
We see what everyone else sees--
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00:05:01,966 --> 00:05:04,666
the top of the whale's head,
their flukes,
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sort of, a really small
proportion of the whale's body.
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00:05:07,566 --> 00:05:14,133
And so that means that a lot of
their behavior goes unknown.
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NARRATOR:
Fortune and
her colleague Bill Koski
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are monitoring a group
of about 80 bowheads
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in Cumberland Sound.
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A bowhead expert, Bill is eager
to get a new perspective
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on an animal
he's been studying for decades.
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KOSKI:
Most of the studies I've done,
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I've been flying in an airplane,
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and we're circling whales
at a thousand feet or so,
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so that we won't affect
their behavior.
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NARRATOR:
Any closer, and a noisy plane
spooks the whales,
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who will dive and disappear.
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Is he up now?
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NARRATOR:
So Sarah is trying
a new approach.
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She's going spy on the whales
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with a-state-of-the-art
high-definition drone.
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All right, full power.
Okay.
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All right, full power.
Okay.
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FORTUNE:
Awesome.
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♪
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(water sprays out blowhole)
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NARRATOR:
The drone quietly hovers
just above the whales.
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(buzzing softly)
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They seem oblivious to the
flying camera following them.
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♪
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FORTUNE:
It's exactly analogous
to a bird.
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The same level of reaction
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that you would get from
a bowhead having birds overhead
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is what you get
with a drone being overhead.
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NARRATOR:
Finally, they can see the whale
in its entirety.
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Its body tells a story about
day-to-day life in the Arctic.
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00:07:02,400 --> 00:07:06,566
FORTUNE:
They often need to break
thick ice with their heads.
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00:07:06,600 --> 00:07:09,533
And so, we'll see
that they have white scars.
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NARRATOR:
The scars are like fingerprints,
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allowing scientists to identify
and track individual whales.
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The drone helps
the team measure the whale
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00:07:21,633 --> 00:07:25,033
by comparing its body
to the length of the boat.
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FORTUNE:
That gives us an idea of how fat
or how skinny an individual is.
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And that's a way that we can
assess their overall health.
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So, are these whales getting
enough food to eat?
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Over time,
we can monitor these animals
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to see how healthy they are
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in the face
of a changing environment.
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NARRATOR:
When the whale dives
below the surface to feed,
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the drone keeps an eye on it.
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FORTUNE:
Because the water is
so clear here,
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it provides this really
wonderful opportunity
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to observe their behaviors
over long periods of time.
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Otherwise, we would just be
sitting on the boat
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wondering
where the whale had gone.
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♪
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NARRATOR:
Now, clear water
and a bird's-eye view
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00:08:09,766 --> 00:08:13,966
reveal new insights
into bowhead behavior.
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Biologists used to think that
bowheads were solitary creatures
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that sometimes swam in pods,
but rarely interacted.
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FORTUNE:
The whales were constantly
touching each other.
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And before, there was no way
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that we could have seen that,
right?
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It was illuminating
to see how these animals
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are more social
than we could appreciate
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just by observing them
at the surface.
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We're able to see how that whale
is engaging with other animals,
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how it's engaging
with the environment.
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So I think it has completely
revolutionized our ability
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to understand
bowhead whale behaviors.
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NARRATOR:
For scientists like Sarah,
the drone is a window
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into the lives of these
mysterious creatures
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00:09:02,000 --> 00:09:02,033
into the lives of these
mysterious creatures
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and a way to gauge their
survival in a changing climate.
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When the Inuit's ancestors
first settled Baffin Island
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thousands of years ago,
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the surrounding waters
were teeming with whales.
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By the late 19th century,
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the commercial whaling industry
had nearly wiped them out.
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Today, the Inuit are among the
few communities in the world
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permitted to sustainably hunt
bowhead whales.
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The Inuit in this region take
up to five whales per year.
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A single bowhead will feed
hundreds of people.
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♪
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Sarah is sharing her research
with the board
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of the Hunters and Trappers
Association,
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which manages hunting.
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They're concerned about the fate
of the 6,500 bowheads
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in this area of the Arctic.
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FORTUNE:
If anyone has any
suggestions or questions
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that you think
we could answer
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with this technology,
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00:10:03,100 --> 00:10:04,666
that would be really helpful
to know.
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NARRATOR:
The images yield new insights
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that intrigue even the locals,
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00:10:09,733 --> 00:10:12,733
who have lived with these whales
for decades.
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MAN:
What part do you study
in order to get the age?
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KOSKI:
Based, based on our experience
with the photographs,
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00:10:19,633 --> 00:10:22,633
the amount of white
just in front of the tail,
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it gets more and more white
as they get older.
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00:10:25,900 --> 00:10:27,433
So when you see one
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00:10:27,466 --> 00:10:29,366
with lots of white on it,
you know it's a very old whale,
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probably 150 years or so.
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NARRATOR:
Knowing the size and age
of the whales around here
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helps locals plan for hunts
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that leave enough whales in the
ocean for future generations.
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00:10:43,566 --> 00:10:44,733
There's one question
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00:10:44,766 --> 00:10:48,300
that fascinates both locals
and scientists.
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Year after year, the bowheads
gravitate toward the shore
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and hang around
the big rocks there.
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No one knows why.
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Sarah is hoping the drone
will explain a mystery
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00:11:02,633 --> 00:11:05,466
first recorded
more than 170 years ago.
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00:11:05,500 --> 00:11:09,633
Ricky Killabuck,
an Inuit fisherman,
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00:11:09,666 --> 00:11:11,200
brings them to the site.
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FORTUNE:
So have you seen any whales
in this bay this year?
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00:11:13,900 --> 00:11:15,633
KILLABUCK:
Oh, yeah, yeah.
Yeah? Okay.
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FORTUNE:
If you go back to the whaling
records dating back to 1845,
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00:11:20,466 --> 00:11:22,433
whalers had made note
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00:11:22,466 --> 00:11:23,766
that these whales
would go near shore,
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00:11:23,800 --> 00:11:26,266
and they'd rest their heads,
or their chins,
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upon these large rocks.
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00:11:27,800 --> 00:11:30,533
Going along this coast,
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00:11:30,566 --> 00:11:34,366
we've been seeing whales
along the rocks
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00:11:34,400 --> 00:11:36,100
in this area.
Okay.
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00:11:36,133 --> 00:11:39,366
Some people thought
that they might be feeding.
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00:11:39,400 --> 00:11:41,500
Others thought
that they're resting.
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NARRATOR:
Without a clear view,
it was impossible to know.
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00:11:45,300 --> 00:11:47,066
Around our 11:00.
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00:11:47,100 --> 00:11:49,466
So we have a whale
up ahead.
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We're heading towards it
now.
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00:11:50,833 --> 00:11:53,700
TOMMY:
Set that camera out
to the aft.
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00:11:53,733 --> 00:11:56,900
Full power, go.
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00:11:56,933 --> 00:11:58,766
So then, I think you're going
to want to bring it
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00:11:58,800 --> 00:12:01,000
to our 11:00 here,
maybe to the bow.
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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.
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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
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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.
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00:12:35,800 --> 00:12:40,700
NARRATOR:
The best guess is they're trying
to keep their skin healthy
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00:12:40,733 --> 00:12:42,766
and free of parasites.
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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
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