1. Remote Sensing Tools for Assessing Large Scale Habitat Quality for Ungulates Brad Griffith USGS, Alsaska Cooperative Fish and Wildlife Research Unit Institute of Arctic Biology, University of Alaska Fairbanks

2. Ungulates don’t respect boundaries Annual cycles encompass several jurisdictions Monitoring of annual ranges necessary to understand performance Remote sensing provides tools for large scale habitat monitoring

3. Purposes today List some available tools for habitat assessment Give two examples of research results with monitoring applications –One migratory species –One non-migratory species

4. Tools AVHRR – –1, 4, 8km resolution –High frequency of overpasses –Greenness, snow cover, surface temperature TM, MSS, SPOT –10-100m resolution –Low frequency overpasses –Greenness, land-cover classification and change Passive Microwave –25km resolution –Intermediate frequency overpasses –Snow water equivalent

5. Changes in lake levels between 1986 and 1998 - Old Crow Flats Green = No change Blue = Increased Water levels Yellow = Decreased Water levels Courtesy of Jim Hawking and Elizabeth Malta, Canadian Wildlife Service

6. AVHRR CH4 - THERMAL

11. Summer Warming: 1-2 o C Winter Warming: 3-4 o C

13. r 2 = 0.50, P = 0.0023

14. Western Arctic Porcupine Bathurs t Qamanirjuaq No Trends

21. Net Climate Effects +Earlier green-up Increased calf survival –Reduced forage quality in fall –Delayed age of first reproduction? (Cook et al. 2001, 2004) –Reduced body condition entering winter? (Cook et al. 2001, 2004) –Reduced winter survival? (Cook et al. 2001, 2004) –Increased icing on spring ranges –Reduced access to forage? –Increased travel costs? –Increased predation risk? =Warming induced population decline for Porcupine herd?  Substantial spatial and temporal heterogeneity in climate effects on caribou ranges and on population dynamics.  Do no expect same scenario for Western Arctic

22. Habitat Suitability for Dall’s Sheep (Ovis dalli) in Wrangell - St. Elias National Park & Preserve Miranda Terwilliger a.k.a. What constitutes Natural and Healthy?

23. Objectives Estimate population characteristics of sheep in Wrangell-St. Elias Inventory survey units for habitat characteristics Estimate relationships between population and habitat characteristics (JR Manes) (WRST NP/P)

25. POPULATION CHARACTERISTICS - RESULTS: ADULT DENSITY (6-11 sheep/km 2 ) (<3 aerial surveys)

26. POPULATION CHARACTERISTICS - RESULTS: ADULT DENSITY TRENDS

27. Habitat Characteristics - Inventory “Escape” Terrain –>60% slope with 150m buffer >40% (McKinney et al. 2004) Terrain Ruggedness –Surface to planar area ratio (Hobson 1972) Aspect –% south & west Relative Greenness –NDVI (Tucker et al. 1984) –Relative amount of plant biomass

28. Summary 56% of density was explained by: 56% of density was explained by: – (+) relative greenness (forage quantity) – (+) terrain ruggedness 64% of harvest was explained by: 64% of harvest was explained by: – (+) proportion of south facing slopes – (+) mean adult density 42% of horn length was explained by: 42% of horn length was explained by: – (+) trends in adult density – (+) perimeter to area ratio of escape terrain

29. Test predictive power in other areasTest predictive power in other areas Consider additional sources of variationConsider additional sources of variation Snow coverSnow cover Wind scouringWind scouring ClimateClimate PredationPredation Future Directions

30. What have we learned? Temporal resolution more important than spatial resolution for seasonally breeding ungulates (except for those damned sheep) Capture the seasonal dynamics –Static views may not be relevant to life history stages of interest Forget forested areas –Nothing eats the tops of trees

31. Monitoring Considerations Large scale problem for ungulates –Don’t look at your feet Document the background trends –Climate, physical environment, habitats Only way to understand system performance –Don’t assume “global” trends apply everywhere Match the scale of questions and data –Smaller grain increases variance Match the resolution of data and animal performance Don’t over-analyze

32. Habitat Characteristics - Vegetation MEDIAN NDVI % Alpine Tundra y = 1.33x + 0.50 r 2 = 0.78 Landcover Map: didn’t cover study area Unsupervised expansion: unsuccessful NDVI allowed consideration of all survey units

34. Model Results – Adult Density MODEL r2r2 AICc∆i∆i WiWi median NDVITerrain Ruggedness0.5665.620.000.82 median NDVI0.4271.065.440.05 median NDVI% south facing slopes0.4571.926.290.04 median NDVI% west facing slopes0.4572.356.720.03 median NDVIadult density trends0.4472.566.930.03 median NDVIPerimeter: Area Escape Terrain0.4373.217.580.02 median NDVI% Escape Terrain0.4273.537.900.02 Perimeter: Area Escape Terrain% south facing slopes0.3477.7412.120.00 Perimeter: Area Escape TerrainTerrain Ruggedness0.2979.6414.010.00 Perimeter: Area Escape Terrain 0.2180.4514.820.00

37. Monitoring Issues “Canned” vs. “Created” datasets –Reduced vs. enhanced resolution Time and money

42. How’s my time?

43. Effect not present on ranges of 3 Alaskan herds that continued to increase. FALL (21 Sept. - 30 Oct.) SPRING (21 Mar. - 30 Apr.) Frequency of spring and fall icing:

44. Alaska National Parkssheep/mi2sheep/km2 Yukon-Charley0.5 Wrangell-St. Elias*1.7-2.84.4-7.3 Wrangell-St. Elias – this study2.25.7 Gates of the Arctic*1.1-2.12.8-5.4 Denali*2.05.2 Lake Clark*0.82.1 * Singer 1984

45. Brad Griffith