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

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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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ObservationsSPECIES

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SRTM_HeightValue

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ObservationsSPECIES

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Value

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CLIMATE ONLY

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ObservationsSPECIES

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Non_ModifiedValue

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CLIMATE & REMOTE-SENSING

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ObservationsSPECIES

! Accurate

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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

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1.0

BIOCLIM DOMAIN GAMS MAXENT

Rel

ativ

e ra

nge

size

-1.2

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PastCurrentFuture

Variation in range size and location predicted by models

BIOCLIM DOMAIN GAMS MAXENT

Fal

se n

egat

ive

rate

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BIOCLIM DOMAIN GAMS MAXENT

Fal

se p

ositi

ve r

ate

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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