Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

Fine-scale biodiversity is increasingly recognized as important to ecosystem-level processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.

[1]  A. Klute,et al.  Methods of soil analysis , 2015, American Potato Journal.

[2]  Clinton N. Jenkins,et al.  Achieving the Convention on Biological Diversity’s Goals for Plant Conservation , 2013, Science.

[3]  R. Ryel,et al.  Continental‐scale assessment of genetic diversity and population structure in quaking aspen (Populus tremuloides) , 2013 .

[4]  John A. Gamon,et al.  Effects of irradiance and photosynthetic downregulation on the photochemical reflectance index in Douglas-fir and ponderosa pine , 2013 .

[5]  A. Hamann,et al.  Recent declines of Populus tremuloides in North America linked to climate , 2013 .

[6]  J. Bailey,et al.  Belowground interactions shift the relative importance of direct and indirect genetic effects , 2013, Ecology and evolution.

[7]  Gregory Asner,et al.  Estimating Vegetation Beta Diversity from Airborne Imaging Spectroscopy and Unsupervised Clustering , 2013, Remote. Sens..

[8]  Gregory P. Asner,et al.  Observing Changing Ecological Diversity in the Anthropocene , 2013 .

[9]  Philip A. Townsend,et al.  Disentangling the contribution of biological and physical properties of leaves and canopies in imaging spectroscopy data , 2013, Proceedings of the National Academy of Sciences.

[10]  Katherine N. Suding,et al.  Plant–soil feedbacks: the past, the present and future challenges , 2013 .

[11]  D. Undersander,et al.  Rapid phytochemical analysis of birch (Betula) and poplar (Populus) foliage by near-infrared reflectance spectroscopy , 2013, Analytical and Bioanalytical Chemistry.

[12]  Richard S. Gardner,et al.  Widespread Triploidy in Western North American Aspen (Populus tremuloides) , 2012, PloS one.

[13]  S D Allison,et al.  A trait-based approach for modelling microbial litter decomposition. , 2012, Ecology letters.

[14]  S. Naeem,et al.  The Functions of Biological Diversity in an Age of Extinction , 2012, Science.

[15]  G. Daily,et al.  Biodiversity loss and its impact on humanity , 2012, Nature.

[16]  L. J. Lamit,et al.  Community specificity: life and afterlife effects of genes. , 2012, Trends in plant science.

[17]  R. Lindroth,et al.  Soil microbial communities adapt to genetic variation in leaf litter inputs , 2011 .

[18]  Kelly A. Carscadden,et al.  Beyond species: functional diversity and the maintenance of ecological processes and services , 2011 .

[19]  Shahid Naeem,et al.  Functional and phylogenetic diversity as predictors of biodiversity--ecosystem-function relationships. , 2011, Ecology.

[20]  M. Cadotte,et al.  Phylogenetically diverse grasslands are associated with pairwise interspecific processes that increase biomass. , 2011, Ecology.

[21]  K. Hambright,et al.  Variation in resource consumption across a gradient of increasing intra- and interspecific richness. , 2011, Ecology.

[22]  R. Hall,et al.  Massive mortality of aspen following severe drought along the southern edge of the Canadian boreal forest , 2011, Global Change Biology.

[23]  Gregory P Asner,et al.  Canopy phylogenetic, chemical and spectral assembly in a lowland Amazonian forest. , 2011, The New phytologist.

[24]  S. Hättenschwiler,et al.  Leaf traits and decomposition in tropical rainforests: revisiting some commonly held views and towards a new hypothesis. , 2011, The New phytologist.

[25]  Andrew Gonzalez,et al.  The functional role of producer diversity in ecosystems. , 2011, American journal of botany.

[26]  Peter P. Wolter,et al.  Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota , 2011 .

[27]  H. D. Cooper,et al.  Scenarios for Global Biodiversity in the 21st Century , 2010, Science.

[28]  A. Lowe,et al.  Building evolutionary resilience for conserving biodiversity under climate change , 2010, Evolutionary applications.

[29]  Markus Neteler,et al.  Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges , 2010, Ecol. Informatics.

[30]  Susan L Ustin,et al.  Remote sensing of plant functional types. , 2010, The New phytologist.

[31]  Michael E. Schaepman,et al.  Retrieval of foliar information about plant pigment systems from high resolution spectroscopy , 2009 .

[32]  R. Kokaly,et al.  Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies , 2009 .

[33]  J. Koricheva,et al.  From genes to ecosystems: a synthesis of the effects of plant genetic factors across levels of organization , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[34]  Roberta E. Martin,et al.  Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests , 2009 .

[35]  T. Umezawa,et al.  High-throughput determination of thioglycolic acid lignin from rice , 2009 .

[36]  Todd H. Oakley,et al.  Using Phylogenetic, Functional and Trait Diversity to Understand Patterns of Plant Community Productivity , 2009, PloS one.

[37]  Samantha L. Greene,et al.  Genetic mosaics of ecosystem functioning across aspen-dominated landscapes , 2009, Oecologia.

[38]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[39]  M. Hooten,et al.  Clonal dynamics in western North American aspen (Populus tremuloides) , 2008, Molecular ecology.

[40]  Roberta E. Martin,et al.  Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels , 2008 .

[41]  Clayton C. Kingdon,et al.  Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario , 2008 .

[42]  Sandra Díaz,et al.  Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. , 2008, Ecology letters.

[43]  S. Ollinger,et al.  A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems , 2008 .

[44]  C. LeRoy,et al.  From Genes to Ecosystems: The Genetic Basis of Condensed Tannins and Their Role in Nutrient Regulation in a Populus Model System , 2008, Ecosystems.

[45]  V. Hipkins,et al.  “Pando” Lives: Molecular Genetic Evidence of a Giant Aspen Clone in Central Utah , 2008 .

[46]  Mark Vellend,et al.  Ecological consequences of genetic diversity. , 2008, Ecology letters.

[47]  R. Ryel,et al.  Quantitative-Genetic Variation in Morphological and Physiological Traits Within a Quaking Aspen (Populus tremuloides) population , 2008 .

[48]  Roberta E. Martin,et al.  Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR. , 2008 .

[49]  G. Foody,et al.  Measuring and modelling biodiversity from space , 2008 .

[50]  W. Shepperd,et al.  Rapid mortality of Populus tremuloides in southwestern Colorado, USA , 2008 .

[51]  John Gamon,et al.  Tropical Remote Sensing‚ÄîOpportunities and Challenges , 2008 .

[52]  R. G. Davies,et al.  Methods to account for spatial autocorrelation in the analysis of species distributional data : a review , 2007 .

[53]  Anne-Béatrice Dufour,et al.  The ade4 Package: Implementing the Duality Diagram for Ecologists , 2007 .

[54]  H. Haberl,et al.  Quantifying and mapping the human appropriation of net primary production in earth's terrestrial ecosystems , 2007, Proceedings of the National Academy of Sciences.

[55]  Philip Lewis,et al.  Spectral invariants and scattering across multiple scales from within-leaf to canopy , 2007 .

[56]  Roberta E. Martin,et al.  Hyperspectral Remote Sensing of Canopy Biodiversity in Hawaiian Lowland Rainforests , 2007, Ecosystems.

[57]  R. Lindroth,et al.  Canopy herbivory can mediate the influence of plant genotype on soil processes through frass deposition , 2007 .

[58]  R. Lindroth,et al.  Genetics, environment, and their interaction determine efficacy of chemical defense in trembling aspen. , 2007, Ecology.

[59]  M. Gribskov,et al.  The Genome of Black Cottonwood, Populus trichocarpa (Torr. & Gray) , 2006, Science.

[60]  R. Lindroth,et al.  Genetic Identity of Populus tremuloides Litter Influences Decomposition and Nutrient Release in a Mixed Forest Stand , 2006, Ecosystems.

[61]  Alfred Stein,et al.  A bootstrap procedure to select hyperspectral wavebands related to tannin content , 2006 .

[62]  R. Lindroth,et al.  Genotype and environment determine allocation to and costs of resistance in quaking aspen , 2006, Oecologia.

[63]  B. Caldwell Enzyme activities as a component of soil biodiversity: A review , 2005 .

[64]  S. Hart,et al.  Nonadditive effects of mixing cottonwood genotypes on litter decomposition and nutrient dynamics , 2005 .

[65]  Craig A. Coburn,et al.  SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[66]  D. Roberts,et al.  Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales , 2005 .

[67]  Marcos J. Montes,et al.  The effects of atmospheric correction schemes on the hyperspectral imaging of littoral environments , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[68]  Brent R. Frey,et al.  Predicting landscape patterns of aspen dieback: mechanisms and knowledge gaps , 2004 .

[69]  H. Schulenburg,et al.  A simple method for the calculation of microsatellite genotype distances irrespective of ploidy level , 2004, Molecular ecology.

[70]  D. Wardle,et al.  Ecological Linkages Between Aboveground and Belowground Biota , 2004, Science.

[71]  D. Roberts,et al.  Using Imaging Spectroscopy to Study Ecosystem Processes and Properties , 2004 .

[72]  P. Keim,et al.  Genetically based trait in a dominant tree affects ecosystem processes , 2004 .

[73]  R. Dahlgren,et al.  Tannins in nutrient dynamics of forest ecosystems - a review , 2003, Plant and Soil.

[74]  Marie-Louise Smith,et al.  Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor , 2003, IEEE Trans. Geosci. Remote. Sens..

[75]  Jane R. Foster,et al.  Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS , 2003, IEEE Trans. Geosci. Remote. Sens..

[76]  M. Fladeland,et al.  Remote sensing for biodiversity science and conservation , 2003 .

[77]  M. Barker,et al.  Partial least squares for discrimination , 2003 .

[78]  M. Tenenhaus,et al.  Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach , 2003, Human Genetics.

[79]  Christina Gloeckner,et al.  Modern Applied Statistics With S , 2003 .

[80]  W. Horwath,et al.  Spectrophotometric Determination of Nitrate with a Single Reagent , 2003 .

[81]  Thomas M. Lillesand,et al.  Statewide land cover derived from multiseasonal Landsat TM data: A retrospective of the WISCLAND project , 2002 .

[82]  M. Hunter,et al.  PHENOTYPIC DIVERSITY INFLUENCES ECOSYSTEM FUNCTIONING IN AN OAK SANDHILLS COMMUNITY , 2002 .

[83]  S. Ollinger,et al.  Regional variation in foliar chemistry and n cycling among forests of diverse history and composition , 2002 .

[84]  Stephen J. Walsh,et al.  Remote sensing of forested wetlands: application of multitemporal and multispectral satellite imagery to determine plant community composition and structure in southeastern USA , 2001, Plant Ecology.

[85]  J. Merilä,et al.  Comparison of genetic differentiation at marker loci and quantitative traits , 2001 .

[86]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[87]  M. Smulders,et al.  Trinucleotide repeat microsatellite markers for black poplar ( Populus nigra L.) , 2001 .

[88]  H. Reynolds,et al.  Rapid assay for amidohydrolase (urease) activity in environmental samples , 2000 .

[89]  P. Vitousek,et al.  The role of polyphenols in terrestrial ecosystem nutrient cycling. , 2000, Trends in ecology & evolution.

[90]  Alan H. Strahler,et al.  An algorithm for the retrieval of albedo from space using semiempirical BRDF models , 2000, IEEE Trans. Geosci. Remote. Sens..

[91]  Mary E. Martin,et al.  Determining Forest Species Composition Using High Spectral Resolution Remote Sensing Data , 1998 .

[92]  J. Gamon,et al.  The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels , 1997, Oecologia.

[93]  H. Mooney,et al.  Human Domination of Earth’s Ecosystems , 1997, Renewable Energy.

[94]  Mary E. Martin,et al.  HIGH SPECTRAL RESOLUTION REMOTE SENSING OF FOREST CANOPY LIGNIN, NITROGEN, AND ECOSYSTEM PROCESSES , 1997 .

[95]  Shaw‐Yhi Hwang,et al.  Clonal variation in foliar chemistry of quaking aspen (Populus tremuloides Michx.) , 1996 .

[96]  D. Moorhead,et al.  Resource allocation to extracellular enzyme production: A model for nitrogen and phosphorus control of litter decomposition , 1994 .

[97]  R. Bennett,et al.  Secondary metabolites in plant defence mechanisms. , 1994, The New phytologist.

[98]  Christopher B. Field,et al.  Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves☆ , 1994 .

[99]  John E. Estes,et al.  A remote sensing research agenda for mapping and monitoring biodiversity , 1993 .

[100]  J. Roujean,et al.  A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data , 1992 .

[101]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[102]  C. Elvidge Visible and near infrared reflectance characteristics of dry plant materials , 1990 .

[103]  P. Curran Remote sensing of foliar chemistry , 1989 .

[104]  R. Bruce,et al.  Elicitation of lignin biosynthesis and isoperoxidase activity by pectic fragments in suspension cultures of castor bean. , 1989, Plant physiology.

[105]  L. Butler,et al.  Choosing appropriate methods and standards for assaying tannin , 1989, Journal of Chemical Ecology.

[106]  M. Fortin,et al.  Spatial pattern and ecological analysis , 1989, Vegetatio.

[107]  C. Braak Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis , 1986 .

[108]  B. G. Chan,et al.  The conversion of procyanidins and prodelphinidins to cyanidin and delphinidin , 1985 .

[109]  S. Wold,et al.  The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses , 1984 .

[110]  J. Bockheim,et al.  Deterioration of trembling aspen clones in the Great Lakes region , 1981 .

[111]  N. Mantel The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.

[112]  B. V. Barnes The clonal growth habit of American Aspens. , 1966 .

[113]  S. Ollinger Sources of variability in canopy reflectance and the convergent properties of plants. , 2011, The New phytologist.

[114]  Michael A. Schmidt,et al.  Qualitative Variation in Proanthocyanidin Composition of Populus Species and Hybrids: Genetics is the Key , 2010, Journal of Chemical Ecology.

[115]  S. Hart,et al.  Forest gene diversity is correlated with the composition and function of soil microbial communities , 2010, Population Ecology.

[116]  G. Bending,et al.  Sequestration of soil nitrogen as tannin-protein complexes may improve the competitive ability of sheep laurel (Kalmia angustifolia) relative to black spruce (Picea mariana). , 2009, The New phytologist.

[117]  R. L. Sinsabaugha,et al.  The effects of long term nitrogen deposition on extracellular enzyme activity in an Acer saccharum forest soil , 2002 .

[118]  Mary Ann Fajvan,et al.  A Comparison of Multispectral and Multitemporal Information in High Spatial Resolution Imagery for Classification of Individual Tree Species in a Temperate Hardwood Forest , 2001 .

[119]  M. C. Grant,et al.  Genetic variation and the natural history of quaking aspen , 1996 .

[120]  D. Sparks,et al.  Methods of soil analysis. Part 3 - chemical methods. , 1996 .

[121]  Peter T. Wolter,et al.  Improved forest classification in the northern Lake States using multi-temporal Landsat imagery , 1995 .

[122]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .