1
00:00:13,193 --> 00:00:15,715
We all know that we receive
benefits from nature,

2
00:00:15,715 --> 00:00:18,054
but have you ever tried
to list them out?

3
00:00:18,054 --> 00:00:20,611
To identify them,
assign values to them

4
00:00:20,611 --> 00:00:24,191
or actually trace them back, to particular
landscapes that give rise to them.

5
00:00:24,191 --> 00:00:26,482
Most of us probably don't go
through this exercise

6
00:00:26,482 --> 00:00:28,258
on a regular basis if ever.

7
00:00:28,258 --> 00:00:32,106
But the answer to these questions
is fundamental to our ability

8
00:00:32,106 --> 00:00:34,607
to manage our landscapes,
for both sustainability

9
00:00:34,607 --> 00:00:36,888
and for improved quality of life.

10
00:00:36,965 --> 00:00:41,187
But to answer these questions,
I need to know,

11
00:00:41,525 --> 00:00:44,314
what values you assign

12
00:00:44,314 --> 00:00:45,524
to wetlands,

13
00:00:45,524 --> 00:00:48,310
streams, forests, fields?

14
00:00:48,031 --> 00:00:50,491
And this question isn't particularly
easy to answer,

15
00:00:50,491 --> 00:00:52,030
if you think about it.

16
00:00:52,030 --> 00:00:55,412
We all have familiarity with assigning
a value to a pint of maple syrup

17
00:00:55,412 --> 00:00:57,102
or a glass of water.

18
00:00:57,102 --> 00:01:00,448
But, what's the value of the maple trees
that produced that syrup,


19
00:01:00,448 --> 00:01:03,051
or the forest
where maple trees grow?

20
00:01:03,082 --> 00:01:08,136
Is the value of the forest equal
to the value of the maple syrup?

21
00:01:08,152 --> 00:01:10,241
Probably not.

22
00:01:10,272 --> 00:01:12,052
Forests produce
a lot of other services,

23
00:01:12,052 --> 00:01:13,775
and we could sit and think
about them for a minute.

24
00:01:13,775 --> 00:01:16,771
We can think whether it is –
board field of lumber it produces,

25
00:01:16,771 --> 00:01:21,640
or they generate other food,
fuel-fiber type resources, firewood –

26
00:01:21,640 --> 00:01:24,172
These all have market values,

27
00:01:24,203 --> 00:01:26,863
so, again, it's relatively easy
to look up at the values


28
00:01:26,863 --> 00:01:28,687
or think about them,
or think about trading them.

29
00:01:28,687 --> 00:01:30,769
But what about the elements,



30
00:01:30,769 --> 00:01:32,853
the services that we get
from this ecosystems

31
00:01:32,853 --> 00:01:35,798
that aren't necessarily material,

32
00:01:35,798 --> 00:01:37,765
that aren't part or the structure,

33
00:01:37,765 --> 00:01:41,120
but rather functions

34
00:01:41,120 --> 00:01:44,013
of the greater structural complexity
of these systems?

35
00:01:44,013 --> 00:01:46,379
That is – what is the value
of a forest as a forest

36
00:01:46,379 --> 00:01:49,319
as opposed to the value
as a piece of lumber?

37
00:01:49,319 --> 00:01:51,549
So, that's an important question
to think about.

38
00:01:51,549 --> 00:01:53,319
So if we can think about things like,

39
00:01:53,319 --> 00:01:55,816
forests absorb carbon dioxide
from the air,

40
00:01:55,816 --> 00:01:59,304
thereby medicating greenhouse
gas emissions and climate change –

41
00:01:59,304 --> 00:02:01,414
they produce oxygen
that we can breathe,

42
00:02:01,414 --> 00:02:04,789
they retain nutrients,
like phosphorus and nitrogen,

43
00:02:04,789 --> 00:02:06,845
as well as sediment,
keeping them out from water ways

44
00:02:06,845 --> 00:02:08,502
to keep them clear.

45
00:02:08,502 --> 00:02:10,911
They provide habitat for biodiversity

46
00:02:10,911 --> 00:02:13,781
and they provide endless recreation
opportunities for us.

47
00:02:13,796 --> 00:02:15,729
We can think of all
these kinds of things.

48
00:02:15,729 --> 00:02:17,956
So, maybe we can get
at a lower bound for value

49
00:02:17,956 --> 00:02:20,523
for a given forest,
if we try to add up


50
00:02:20,523 --> 00:02:24,237
the individual contributions
of each of these different elements

51
00:02:24,237 --> 00:02:25,838
to our well being.

52
00:02:25,838 --> 00:02:28,737
So we can try and do that.

53
00:02:28,737 --> 00:02:30,987
But now we are still faced
with a more fundamental problem,

54
00:02:30,987 --> 00:02:33,749
which is that – we're talking
about questions of value,

55
00:02:33,780 --> 00:02:35,370
and we're talking about value,

56
00:02:35,370 --> 00:02:37,771
we're talking about
people's perceptions of worth,

57
00:02:37,771 --> 00:02:40,957
which, been held subjectively,
vary widely across populations,

58
00:02:40,957 --> 00:02:45,075
culture, generation, ethnicity,
any number of things,

59
00:02:45,075 --> 00:02:47,138
we can think about these axes.

60
00:02:47,138 --> 00:02:49,353
So, that means extremely,
extremely difficult to assign

61
00:02:49,353 --> 00:02:53,183
blanket values, generalized values
to a given landscape,

62
00:02:53,183 --> 00:02:55,367
because the services
that they generate are valued

63
00:02:55,367 --> 00:02:57,870
by different people in different places
at different times.

64
00:02:57,870 --> 00:03:02,833
So, that's the problem space
that we want to play with here.

65
00:03:03,156 --> 00:03:06,323
So, if we're thinking
about this localization of the problem,

66
00:03:06,323 --> 00:03:09,716
maybe a more important question,
or a different way to phrase this,

67
00:03:09,716 --> 00:03:13,453
is not to ask ourselves,
or is not to try and say,

68
00:03:13,453 --> 00:03:16,422
"The value of a service
from forest is x,"

69
00:03:16,422 --> 00:03:18,162
but rather to say,

70
00:03:18,162 --> 00:03:22,256
"The value of this service from this forest
is x to these people,"

71
00:03:22,271 --> 00:03:25,067
and to get specific.

72
00:03:25,144 --> 00:03:27,748
So, in that spirit,
for the last few decades,

73
00:03:27,748 --> 00:03:30,105
our researchers
in ecosystems services area

74
00:03:30,305 --> 00:03:31,927
have been traveling
around the world

75
00:03:31,927 --> 00:03:35,109
and surveying people
about the values


76
00:03:35,109 --> 00:03:37,546
they assign to the services of nature.

77
00:03:37,546 --> 00:03:39,626
But, obviously these things
are time consuming

78
00:03:39,626 --> 00:03:41,706
and they are expensive.

79
00:03:41,706 --> 00:03:43,786
So it's extremely difficult
to get very much data here.

80
00:03:43,786 --> 00:03:45,765
There are databases built up
on these things,

81
00:03:45,765 --> 00:03:47,744
specially in the last decade –

82
00:03:47,744 --> 00:03:49,725
we've started to see some
databases emerging that you can query

83
00:03:49,725 --> 00:03:52,840
and try to get an idea
of what the literature says


84
00:03:52,840 --> 00:03:54,930
about some of these
different kinds of values –

85
00:03:54,930 --> 00:03:58,032
these, again, socio-economic values
that we're playing with.

86
00:03:58,032 --> 00:04:01,900
But, we don't think even
remotely close to global coverage,

87
00:04:01,900 --> 00:04:04,680
nowhere near.
And at the same time,

88
00:04:04,680 --> 00:04:08,843
especially in the last five to six years,
we've seen a major upswell

89
00:04:08,908 --> 00:04:11,485
in institutions of both
the public and the private sector,

90
00:04:11,516 --> 00:04:15,258
begging for global coverage
of ecosystems service information

91
00:04:15,258 --> 00:04:18,880
that they can use
for their land management decisions,

92
00:04:18,880 --> 00:04:21,326
and run scenarios against.

93
00:04:21,341 --> 00:04:25,579
So, as we've already seen, we do have
a lot of [due] a spatial data now.

94
00:04:25,579 --> 00:04:28,981
That's kind of a new big fun thing
in ecosystems services world –

95
00:04:28,981 --> 00:04:32,224
we're not just limited to doing
these one off surveys,

96
00:04:32,224 --> 00:04:35,026
because we can actually do
these secondary

97
00:04:35,026 --> 00:04:37,288
meta-level evaluations of the data.

98
00:04:37,298 --> 00:04:40,630
And what we get to do with this –
we get all this geospatial data,

99
00:04:40,630 --> 00:04:43,093
we put it together, and now
what we can do in filling these gaps,

100
00:04:43,093 --> 00:04:45,475
is we can actually try
and create functions

101
00:04:45,475 --> 00:04:48,848
that go in and study the structure
in the data

102
00:04:48,848 --> 00:04:53,450
of the landscapes and the people,
the cities, the community centers,


103
00:04:53,450 --> 00:04:55,120
the roads,
all these kinds of structures,

104
00:04:55,120 --> 00:04:58,122
and try to pull out
with these signature functions,

105
00:04:58,122 --> 00:05:01,186
where services are likely
to be produced,

106
00:05:01,186 --> 00:05:03,173
and where there's probably
demand for them.

107
00:05:03,173 --> 00:05:05,639
But, once you've applied
these kinds of functions,

108
00:05:05,655 --> 00:05:07,760
you still don't necessarily know –

109
00:05:07,760 --> 00:05:09,868
if you know where
the supply might be,

110
00:05:09,868 --> 00:05:11,362
and you know where
the demand might be

111
00:05:11,362 --> 00:05:13,425
in any given landscape,
once you've run these functions –

112
00:05:13,425 --> 00:05:15,575
you still don't know
if any service is being delivered.

113
00:05:15,575 --> 00:05:17,546
So, what we have to do there –

114
00:05:17,546 --> 00:05:21,347
is we take the landscapes,
we project this information

115
00:05:21,489 --> 00:05:25,196
about likelihood of supply and demand
up on to a network,

116
00:05:25,196 --> 00:05:27,875
and then we start flowing around,
we simulate in our computers

117
00:05:27,875 --> 00:05:30,577
across all this geospatial data.

118
00:05:30,592 --> 00:05:33,840
We simulate the flow of,
what we call "service carriers",

119
00:05:33,840 --> 00:05:36,315
so things like bees
for pollination services,

120
00:05:36,315 --> 00:05:39,426
or carbon dioxide moving around,
water moving for flooding

121
00:05:39,426 --> 00:05:43,669
and wild-fire, water supply,
water quality, any number of things.

122
00:05:43,669 --> 00:05:46,014
You move it across the landscape
and you try actually see –

123
00:05:46,014 --> 00:05:49,244
given any particular
topographic variables,

124
00:05:49,244 --> 00:05:52,648
what is the service flow topology,
any given area,

125
00:05:52,648 --> 00:05:56,577
and thereby, you can finally answer
the question:

126
00:05:56,577 --> 00:05:59,792
Who receives services from where
in any given landscape?

127
00:05:59,792 --> 00:06:03,981
And that's extremely powerful,
if you have that kind of information.

128
00:06:03,981 --> 00:06:08,985
So, the kinds of things
you can answer with that, now –

129
00:06:09,584 --> 00:06:11,078

– that's not too bad –

130
00:06:11,078 --> 00:06:13,919
So, for example, you can finally
show maps like this,


131
00:06:13,919 --> 00:06:16,318
where the green areas here are like

132
00:06:16,318 --> 00:06:19,608
[repeated sound of] on the top,
and over here you have a mountaineer,

133
00:06:19,608 --> 00:06:22,502
and we're looking at scenic views –

134
00:06:22,502 --> 00:06:24,591
So the impact of scenic views
on different properties –


135
00:06:24,607 --> 00:06:27,265
for the red, on the top there,
is the city of Kent.

136
00:06:27,265 --> 00:06:28,747
And so you can try and see:


137
00:06:28,747 --> 00:06:30,924
Who receives services from where,
and to what degree?

138
00:06:30,924 --> 00:06:32,707
And the yellow stuff is visual blight –

139
00:06:32,707 --> 00:06:37,386
You can actually look at the degree
to which individual properties

140
00:06:37,386 --> 00:06:39,911
are being impacted in terms
of their service,

141
00:06:39,911 --> 00:06:41,587
because of the way
landscapes configure.

142
00:06:41,587 --> 00:06:43,389
And you can run scenarios against this


143
00:06:43,389 --> 00:06:45,191
to try and actually, really answer
questions about

144
00:06:45,191 --> 00:06:47,885
who wins and who looses
on different management scenarios?


145
00:06:47,885 --> 00:06:50,834
So you say,
on a development scenario one,

146
00:06:50,834 --> 00:06:53,357
this group of people gained something,

147
00:06:53,357 --> 00:06:55,605
a different group of people gains
a little bit more,

148
00:06:55,605 --> 00:06:57,408
and this third group of people
gets hurt.

149
00:06:57,423 --> 00:07:00,499
Whereas under development scenario two,
if I develop in this area,


150
00:07:00,515 --> 00:07:02,572
well, it turns out that everybody
gets hurt a little bit,

151
00:07:02,572 --> 00:07:05,743
but if I develop in the third area,
everyone benefits.

152
00:07:09,218 --> 00:07:12,580
This is really becoming
very interesting

153
00:07:13,204 --> 00:07:16,653
in the US, in particular,
the EPA has this entire research divisions

154
00:07:16,653 --> 00:07:19,352
entirely turned around
to ecosystem service research these days;


155
00:07:19,352 --> 00:07:21,386
the US GS has a very
big program in that;

156
00:07:21,386 --> 00:07:23,996
the US GA has an office
of ecosystem services and market –

157
00:07:23,996 --> 00:07:26,102
just started off a few years ago
under this administration,

158
00:07:26,102 --> 00:07:27,925
and so on and so forth.

159
00:07:27,925 --> 00:07:29,631
So, our government's all into it.

160
00:07:29,631 --> 00:07:31,587
We're seeing a lot of
ecosystem service work,

161
00:07:31,587 --> 00:07:35,891
starting to find its way
into public policy in the EU.

162
00:07:35,891 --> 00:07:38,293
And I'm participating in some projects
in Africa as well,

163
00:07:38,293 --> 00:07:40,222
for the Gund Institute right now,

164
00:07:40,237 --> 00:07:42,263
where this stuff is also
coming into play.

165
00:07:42,263 --> 00:07:45,080
So, we're hoping that taking this kind
of technology


166
00:07:45,080 --> 00:07:48,319
to finally connect people
to the landscapes,

167
00:07:48,319 --> 00:07:51,061
the actual landscapes,
which generate their services,

168
00:07:51,061 --> 00:07:53,496
will really help us to better inform,

169
00:07:53,496 --> 00:07:55,931
better land management
in the future for all of us.

170
00:07:55,931 --> 00:07:57,767
Thank you.

171
00:07:57,767 --> 00:07:59,059
(Applause)