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Habitat Availability for Amur Tiger and Amur Leopard under Changing Climate and Disturbance Regimes PI: Hank Shugart (UVA) Co-Is: Tatiana Loboda (UMD),

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Presentation on theme: "Habitat Availability for Amur Tiger and Amur Leopard under Changing Climate and Disturbance Regimes PI: Hank Shugart (UVA) Co-Is: Tatiana Loboda (UMD),"— Presentation transcript:

1 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)

2 Introduction

3 The Russian Far East (RFE) Water Tree dominated Shrub dominated Herbaceous cover dominated Human dominated Barren and sparsely vegetated Aggregated classes of the MODIS land cover product (MOD12Q1) 20000 4000 km Russia Mongolia China Japan N. Korea S. Korea

4 RFE – biodiversity hotspot Non-forested landscapes: Shrub Grass Wetland Human dominated Water Forests: Spruce/fir/pine Larch Oak/elm Dwarf pine Mixed Spruce/fir/pine forest Larch forest Oak/elm forest Mixed forest

5 RFE – home to critically endangered large carnivores Amur tiger (Panthera tigris altaica) Amur leopard (Panthera pardus orientalis) Photo: Wildlife Conservation Society (WCS)

6 Climate Change Impact Climate change Human activity Natural processes DisturbanceVegetation Wildland fire

7 Project Components Habitat Availability and Quality Vegetation UVA teamDisturbance UMD team Habitat Suitability WCS team

8 New results and developments

9 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

10 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

11 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)

12 Disturbance: historical mapping Mapping 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

13 Observed classes mature tree disturban cetotal % total (pix) Omission % Predicted classes mature tree90.964.4912.7318379.04 disturbance9.0495.7187.27125924.29 total100 total (pix)14051302414429 Commission %30.431.01 Overall Accuracy = 95.2457% Kappa Coefficient = 0.7622 Accuracy Assessment for Aggregated Classes

14 Accuracy Assessment for Full Classification ClassOmission %Commission % ENF6.531.0 DNF27.147.9 MF7.011.2 DBF11.427.1 burn70100.00.0 burn8044.431.8 burn9010.322.6 burn0031.123.7 log8078.653.9 log90100.00.0 log00100.00.0 Overall Accuracy = (10767/14427) 74.6309% Kappa Coefficient = 0.6021

15 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

16 Habitat Suitability: prey Probability of species presence 01 BoarMooseMusk deer Red deer Roe deer Sika deer

17 Habitat Suitability: tiger & leopard Amur Tiger Amur Leopard Probability of species presence 0 1 0 1

18 Integration of the components for final assessment

19 Fire Danger ∑(ROI, PFB, FWI) Fire Intensity ∑(PFB, FWI) Post Fire HR ROI 12 months PFB 3 seasons FWI daily Pre Fire HR vegetation recovery HR deer HR boar HR moose HR tiger 11 succession stages full HR mean Habitat Fragmentation Habitat Conversion Post Fire HR – Pre Fire HR matrix Post Fire Habitat Potential 3 X 3 window Stage HR Tiger Risk 5 seasons Vegetation Stages matrix Fire Threat UMD UVA WCS Climate Change Scenarios

20 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

21 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”


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