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Assessing Amur Tiger and Amur Leopard Habitat Availability

This project aims to evaluate the habitat availability for Amur Tigers and Amur Leopards in the Russian Far East under changing climate and disturbance regimes. The study includes modeling fire occurrence probability, mapping disturbances, and assessing habitat suitability using data on prey species and proximity to various features. The results will help understand the impact of climate change on these endangered carnivores.

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Assessing Amur Tiger and Amur Leopard Habitat Availability

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  1. Habitat Availability for Amur Tiger and Amur Leopard underChanging 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. Russia Aggregated classes of the MODIS land cover product (MOD12Q1) Water Tree dominated Shrub dominated Herbaceous cover dominated Human dominated Barren and sparsely vegetated Mongolia Japan China N. Korea S. Korea 2000 0 2000 4000 km The Russian Far East (RFE)

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

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

  6. Climate Change Impact Climate change Natural processes Human activity Disturbance Vegetation Wildland fire

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

  8. New results and developments

  9. Cumulative Annual Fire Occurrence Probability in the Amur Tiger Habitat in the Russian Far East Units: probability * 1000 Modeling • Improved landscape-level fire representation within the FAR EAST vegetation model • Input fire data (2001-2008): • MODIS active fire detections (MOD/MYD14) • MODIS-basedregional 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. July October April Units: probability * 1000 Mean Monthly Fire Occurrence Probability in the Russian Far East • Summer fire occurrence: • linked to distribution of dark coniferous forests and previously disturbed sites • on average lower probability of fire occurrence • Spring and fall fire occurrence: • linked to human activity or presence • reaches nearly 20% in areas of high population density or agricultural land use

  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. Accuracy Assessment for Aggregated Classes

  14. Accuracy Assessment for Full Classification

  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 Boar Moose Musk deer Red deer Roe deer Sika deer Probability of species presence 0 1

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

  18. Integration of the components for final assessment

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

  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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