Habitat Availability for Amur Tiger and Amur Leopard under

Changing Climate and Disturbance Regimes

PI: Hank Shugart (UVA)Co-Is: Tatiana Loboda (UMD), Guoqing

Sun (UMD), Dale Miquelle (WCS) Collaborators: Nancy Sherman (UVA), Mark Hebblewhite (UM), Zhiyu Zhang

(UMD)

Introduction

The Russian Far East (RFE)

WaterTree dominatedShrub dominatedHerbaceous cover dominatedHuman dominatedBarren and sparsely vegetated

Aggregated classes of the MODIS land cover product (MOD12Q1)

2000 0 2000 4000 km

Russia

Mongolia

China

Japan

N. Korea

S. Korea

RFE – biodiversity hotspotNon-forested landscapes:Shrub GrassWetlandHuman dominatedWater

Forests:Spruce/fir/pineLarchOak/elmDwarf pineMixed

Spruce/fir/pine forest Larch forest

Oak/elm forest Mixed forest

RFE – home to critically endangered large carnivores

Amur tiger (Panthera tigris altaica)

Amur leopard (Panthera pardus orientalis)

Photo: Wildlife Conservation Society (WCS)

Climate Change Impact

Climate change

Human activity Natural processes

Disturbance Vegetation

Wildland fire

Project Components

Habitat Availability and Quality

VegetationVegetationUVA team

DisturbanceDisturbanceUMD team

Habitat SuitabilityHabitat SuitabilityWCS team

New results and developments

Modeling

Units: probability * 1000

Cumulative Annual Fire Occurrence Probability in the Amur Tiger Habitat in the Russian Far East

Improved landscape-level fire representation within the FAR EAST vegetation model

• Input fire data (2001-2008):• MODIS active fire detections (MOD/MYD14)• MODIS-based regional burned area product (Loboda et al., 2007)

• Methodology• Regression tree based “forest” of monthly trees

• Products (gridded 1 km)• Monthly mean fire probability• Cumulative annual fire probability

Mean Monthly Fire Occurrence Probability in the Russian Far East

April July October

Units: probability * 1000

Spring and fall fire occurrence:• linked to human activity or presence• reaches nearly 20% in areas of high population density or agricultural land use

Summer fire occurrence:• linked to distribution of dark coniferous forests and previously disturbed sites• on average lower probability of fire occurrence

Disturbance: reference dataset

Landsat-based high/moderate resolution (30m) database of disturbances in the RFE:

• covers 1972 – 2002

• time stamps (in broad categories)

• type of disturbance (logging, burn)

Disturbance: historical mappingMapping previous disturbances from present day distribution of land cover types:

• 46 MODIS-based metrics for decision tree (min, max, mean JJA, mean JF) from 2008:

• BRDF corrected surface reflectance (7 bands)• LST (day and night)• VI (NDVI, NBR)

• Masked out Human dominated landscapes (cropland,

cropland mosaic, urban) using MCD12Q1

Observed classes

mature tree

disturbance total %

total (pix)

Omission %

Predicted classes

mature tree 90.96 4.49 12.73 1837 9.04

disturbance 9.04 95.71 87.27 12592 4.29

total 100 100 100

total (pix) 1405 13024 14429

Commission % 30.43 1.01

Overall Accuracy = 95.2457%

Kappa Coefficient = 0.7622

Accuracy Assessment for Aggregated Classes

Accuracy Assessment for Full Classification

Class Omission % Commission %

ENF 6.5 31.0

DNF 27.1 47.9

MF 7.0 11.2

DBF 11.4 27.1

burn70 100.0 0.0

burn80 44.4 31.8

burn90 10.3 22.6

burn00 31.1 23.7

log80 78.6 53.9

log90 100.0 0.0

log00 100.0 0.0

Overall Accuracy = (10767/14427) 74.6309%

Kappa Coefficient = 0.6021

Habitat Suitability• Input data:

– snow track surveys for tiger and prey species collected in February-March 2005 (Stephens, 2006)

– GIS and RS data sources• SRTM DEM

• MODIS NPP (MOD17A2)

• MODIS snow cover (MOD10A2)

• Vegetation communities (GIS map)

• Proximity to various features (roads, protected areas, settlements)

• Etc

• Methodology: applied Resource Selection Functions (Boyce and McDonald 1999, Manly et al. 2002) to develop spatial predictions of the probability of use for the different prey and tigers using a used-unused sampling design.

• Tiger habitat rank also includes prey use of the habitat

Habitat Suitability: prey

Probability of species presence

0 1

Boar Moose Musk deer

Red deer

Roe deer

Sika deer

Habitat Suitability: tiger &

leopard

Amur Tiger

Amur Leopard

Probability of species presence

0

1 Probability of species presence

0

1

Integration of the components for final

assessment

Fire Danger ∑(ROI, PFB, FWI)

Fire Intensity∑(PFB, FWI)

Post Fire HR

ROI12 months

PFB3 seasons

FWIdaily

Pre FireHR

vegetation recovery

HR deer

HR boar

HR moose

HR tiger

11 succession stages

fullHR

mean

Habitat Fragmentation

Habitat ConversionPost Fire HR – Pre Fire HR

matrix

Post Fire Habitat Potential

3 X 3window

Stage HR

Tiger Risk5 seasons

VegetationStages

matrixFire Threat

UMD

UVAWCS

Climate Change Scenarios

Next steps

• Select a representative GCM– Resolution– Consistency of projections– Extreme events

• Integrated runs of all model components within the Fire Threat Modeling framework– Present – Future under A2 and B1 SRES projections

Acknowledgements

• NASA Earth System Science Fellowship

• NASA Interdisciplinary Science Program grant 06-IDS06-93: “Evaluation of Habitat Availability for Large Carnivores under a Changing Climate and Disturbance Regime: The Amur Tiger and Amur Leopard Case Study”