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Remote-sensing and biodiversity in a changing
climate
Catherine GrahamSUNY-Stony Brook
Robert Hijmans, UC-BerkeleyLianrong Zhai, SUNY-Stony Brook
Sassan Saatchi, JPL/UCLATom Smith, UCLA
Research Program (NIP)
• Integrate remote-sensing data into species distributional modeling
• Determine remote-sensing correlates of species richness across multiple taxonomic groups and spatial scales
• Integrate remote-sensing data with patterns of evolutionary diversification
• Predict future species distributions• Train Latin America and US scientists and
conservationists
Research Program (NIP)
• Integrate remote-sensing data into species distributional modeling
• Determine remote-sensing correlates of species richness across multiple taxonomic groups and spatial scales
• Integrate remote-sensing data with patterns of evolutionary diversification
• Predict future species distributions• Train Latin America and US scientists and
conservationists
1) Extract environmental data for point localities;
Annual Temperature
Annual Rainfall
2) Make statistical model describing distribution in envirnomental space;
Species Distributional Models
3) Project this model in geographic space to create a map.
Possible environmental datasets
Remote sensing•Indirect measurements•High resolution •Global coverage•Recent coverage
Climate•Direct measurements•Low resolution•Extrapolations•Global coverage•Long term coverage
Remote-sensing data: issues for species
distributional modeling
• Age of point locality data • Spatial accuracy of point locality
data
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ObservationsSPECIES
! Accurate
! Museum
Modis TreeValue
0 - 10
10.1 - 20.2
20.2- 30.3
30.3 - 40.4
40.4 - 50.5
50.5 - 60.6
60.6 - 70.7
70.7 - 80.8
80.8 - 90.9
90.9 - 100
0 6 12 18 243Kilometers
mtree_n = 46*10*normal(x, 57, 20.6053)
-50 -30 -10 10 30 50 70 90
mtree_n
0%
4%
9%
13%
17%
22%
26%
30%
Per
cent
of
obs
bio16 = 46*100*normal(x, 876.8478, 310.297)
100 300 500 700 900 1100 1300 1500 1700
bio16
0%
4%
9%
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17%
22%
26%
Per
cent
of
obs
Remote-sensing data: issues for species distributional modeling
Climate
Remote-sensing
A solution• Use all point locality data with
climate surfaces (museum and accurate recent survey data)
• Use only “accurate” point locality data with remote-sensing layers (modis tree)
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<Double-click here to enter title>
ObservationsSPECIES
! Accurate
! Museum
SRTM_HeightValue
0 - 500
500 - 1,000
1,000 - 1,500
1,500 - 2,000
2,000- 2,500
2,500 - 3,000
3,000 - 3,500
3,500 - 4,000
4,000 - 4,500
4,500 - 5,000
0 400 800 1,200 1,600200Kilometers
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ObservationsSPECIES
! Accurate
! Museum
Value
High : 100
Low : 0
0 250 500 750 1,000125Kilometers
CLIMATE ONLY
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ObservationsSPECIES
! Accurate
! Museum
Non_ModifiedValue
High : 100
Low : 0
0 250 500 750 1,000125Kilometers
CLIMATE & REMOTE-SENSING
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ObservationsSPECIES
! Accurate
! Museum
With RSValue
High : 100
Low : 0
0 250 500 750 1,000125Kilometers
CLIMATE & REMOTE-SENSINGWITH ACCURATE POINTS
Research program
• Integrating remote-sensing data into species distributional modeling
• Determining remote-sensing correlates of species richness across multiple taxonomic groups and spatial scales
• Integrating remote-sensing data with patterns of evolutionary diversification
• Predicting future species distributions• Training programs in Latin America and the
US
Extinction Risk from Climate Change (Thomas et al. 2004; Nature)
• Predict 18 to 35% of species ’committed to extinction’ by 2050
• Global warming major threat to biodiversity
Potential Problem with Niche Modeling and Climate
Change
Future climates will not be completely analogous to current.
=> Will models predict lower probabilities (model artifact)?
=> Validity of models should be tested using experimental approaches, historical evidence, physiological models and internal consistency.
Environmental space and climate change
Current
Future
Species environmentalrequirements
Approach
Compare results from physiology-based models (mechanistic models) with species distribution models
Assume mechanistic models are “correct”
Currentclimate
Future climate
Physiological Model Niche Model
Extracted points
A
B
C D
E
Experimental Design
Compare
Species distribution (niche models) used
• BIOCLIM – envelop (boxcar) method
• DOMAIN – based on similarity statistics
• GAM – Non-linear regression
• MAXENT – machine learning/maximum entropy
BIOCLIM DOMAIN GAMS MAXENT
Ove
rlap
inde
x0.0
0.2
0.4
0.6
0.8
1.0
BIOCLIM DOMAIN GAMS MAXENT
Rel
ativ
e ra
nge
size
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
PastCurrentFuture
Variation in range size and location predicted by models
BIOCLIM DOMAIN GAMS MAXENT
Fal
se n
egat
ive
rate
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
BIOCLIM DOMAIN GAMS MAXENT
Fal
se p
ositi
ve r
ate
0.00
0.05
0.10
0.15
0.20
0.25
CurrentFuturePast
Errors
Environmental space and climate change
Current
Future
Species environmentalrequirements
BIOCLIM
GAMSMAXENT
DOMAIN
Modeling species distributions across climates
• Species distributional modeling can provide similar results to mechanistic models.
• Performance of species distributional models varies
• Next? Incorporate climate change with land use patterns to evaluate extinction risk for a suite of species