Remote Sensing of Forest Genetic Diversity and Assessment of Below Ground Microbial Communities in Populus tremuloides Forests Mike Madritch - Appalachian State University Phil Townsend –University of Wisconsin Karen Mock – Utah State University Rick Lindroth – University of Wisconsin

2 Trembling aspen (Populus tremuloides) Populus tremuloides –Widespread –Genetically diverse –Chemically diverse –Clonal –Dominant canopy species –Economically important

3 Secondary metabolites affect decomposition Affect soil organisms Alter N availability through protein complexes Interact with other nutrients

4 Populus tremuloides Genotype “Dan1” Populus tremuloides Genotype “Wau1” After ~1 year decomposition:

5 Genotype Phenotype Nutrient Cycles Litter Chemistry Environment

6 Genotype Phenotype Nutrient Cycles Litter Chemistry Environment

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8 Genotype AGenotype B Litterfall Genotype B Nutrient cycling Genotype A Nutrient cycling

            

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Objectives 1. Estimate the genetic diversity of aspen stands across multiple ecoregions using remotely sensed data. 2. Build predictive models of genetically- mediated leaf chemistry using remotely sensed hyperspectral data. 3. Measure belowground microbial biodiversity and functional diversity that results from genetically determined variation in plant chemistry. 11

Hyperspectral data

13 Midwest remote sensing July 2009

AVIRIS Pre-processing: Steps Uncorrected image 1) Cloud, shadow, water mask 2) Cross-track correction 3) Remove redundant bands 4) Atmospheric correction 5) Terrain normalization Aditya Singh

AVIRIS Pre-processing: Mask development B22 550nm B43 750nm B nm B nm NDVI [B43 – B22]/[B43 + B22] NDII [B43 – B97]/[B43 + B97] Lee filter Water = NDVI < 0 Cloud shadow = NDII > 0.6 Clouds = fB164 > 500 Combined Water + Cloud + Cloud shadow mask 1.Mask generation: Water + Cloud + Cloud shadow Aditya Singh

AVIRIS Pre-processing: Mask development Uncorrect ed B nm NDIIMask Aditya Singh

AVIRIS Pre-processing: Cross-track illumination correction Get DN values DN i 2.Bilinear cross-track illumination correction: For each band… Get pixel locations (x, y) Mask for Water/Cloud/Shadow = DN im, x m, y m Regress DN to coordinates: Dn im → β0 + β1*x m + β2*y m + β3*x m *y m Estimate trend for entire scene: IL i ← β0 + β1*x + β2*y + β3*x*y Note: x, y, locations for full scene De-trend full scene using IL i ; add mean of masked scene DN corr = DN i – IL i + mean(DN im ) Aditya Singh

AVIRIS Pre-processing: Cross-track illumination correction Uncorrected Corrected Aditya Singh

3.Remove overlapping bands 4.Atmospheric correction ACORN5b (Atmospheric CORrection Now) Mode 1.5 Advanced atmospheric correction for hyperspectral data with spectral fitting for water vapor and vegetation liquid water 365nm 655nm 1253nm 1866nm 1263nm 1872nm 2496nm 667nm AVIRIS Pre-processing: Removing redundant bands, Atmos. Corr.

5.Terrain normalization: C-Factor (Teillet et al. 1982) AVIRIS Pre-processing: C-factor terrain normalization Where: θ p slope, θ z solar zenith angle, φ a solar azimuth, φ o aspect Note: IL (cosine-i) image is included with AVIRIS data product. Where: ρ H : Reflectance from horizontal surface; ρ H uncorrected reflectance b k and m k determined by regressing each band with the IL image Aditya Singh

AVIRIS Pre-processing: C-factor terrain normalization Cross-track, atmospheric and terrain corrected Uncorrected Aditya Singh

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Leaf and Soil analyses Leaf Carbon, nitrogen Condensed tannins, lignin Soil Nutrient: C, N, NH 4 +, NO 3 - Microbial: extracellular enzymes, t-RFLP 24

Microbial analyses Extracellular enzymes Rate limiting step in decomposition Functional description T-RFLP Terminal Restriction Fragment Length Polymorphism Widely applicable, molecular-based 25

Project Status Upper midwest sites collected July/ August 2009 Data and sample processing underway Good range of N, CT SSR markers confirmed t-RFLP primer sets developed 26

Project Status Western sites (UT an CO) planned for July

Mock et al Molecular Ecology

30 Acknowledgments NASA Peter Wolter Aditya Singh Timothy Whitby Peter Johnston Anthony Hatcher Mason Roberts Jacqui Bryant

31 Not all secondary metabolites are effective defense chemicals From Hemming and Lindroth 1995