Foliar Spectra and Traits of Bog Plants across Nitrogen Deposition Gradients

Bogs, as nutrient-poor ecosystems, are particularly sensitive to atmospheric nitrogen (N) deposition. Nitrogen deposition alters bog plant community composition and can limit their ability to sequester carbon (C). Spectroscopy is a promising approach for studying how N deposition affects bogs because of its ability to remotely determine changes in plant species composition in the long term as well as shorter-term changes in foliar chemistry. However, there is limited knowledge on the extent to which bog plants differ in their foliar spectral properties, how N deposition might affect those properties, and whether subtle inter- or intraspecific changes in foliar traits can be spectrally detected. The objective of the study was to assess the effect of N deposition on foliar traits and spectra. Using an integrating sphere fitted to a field spectrometer, we measured spectral properties of leaves from the four most common vascular plant species (Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum and Eriophorum vaginatum) in three bogs in southern Quebec and Ontario, Canada, exposed to different atmospheric N deposition levels, including one subjected to a 18-year N fertilization experiment. We also measured chemical and morphological properties of those leaves. We found detectable intraspecific changes in leaf structural traits and chemistry (namely chlorophyll b and N concentrations) with increasing N deposition and identified spectral regions that helped distinguish the site-specific populations within each species. Most of the variation in leaf spectral, chemical, and morphological properties was among species. As such, species had distinct spectral foliar signatures, allowing us to identify them with high accuracy with partial least squares discriminant analyses (PLSDA). Predictions of foliar traits from spectra using partial least squares regression (PLSR) were generally accurate, particularly for the concentrations of N and C, soluble C, leaf water, and dry matter content (<10% RMSEP). However, these multi-species PLSR models were not accurate within species, where the range of values was narrow. To improve the detection of short-term intraspecific changes in functional traits, models should be trained with more species-specific data. Our field study showing clear differences in foliar spectra and traits among species, and some within-species differences due to N deposition, suggest that spectroscopy is a promising approach for assessing long-term vegetation changes in bogs subject to atmospheric pollution.

[1]  E. Gorham Northern Peatlands: Role in the Carbon Cycle and Probable Responses to Climatic Warming. , 1991, Ecological applications : a publication of the Ecological Society of America.

[2]  Kelly M. McManus,et al.  Phylogenetic Structure of Foliar Spectral Traits in Tropical Forest Canopies , 2016, Remote. Sens..

[3]  Raymond J. Ritchie,et al.  Consistent Sets of Spectrophotometric Chlorophyll Equations for Acetone, Methanol and Ethanol Solvents , 2006, Photosynthesis Research.

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

[5]  Richard Lucas,et al.  Hyperspectral remote sensing of peatland floristic gradients , 2015 .

[6]  Sebastian Schmidtlein,et al.  Mapping the floristic continuum : Ordination space position estimated from imaging spectroscopy , 2007 .

[7]  S. Juutinen,et al.  Effects of nutrient addition on leaf chemistry, morphology, and photosynthetic capacity of three bog shrubs , 2011, Oecologia.

[8]  G. Asner,et al.  Nitrogen Cycles: Past, Present, and Future , 2004 .

[9]  P. Legendre,et al.  Partitioning plant spectral diversity into alpha and beta components , 2019, Ecology letters.

[10]  Roberta E. Martin,et al.  Brightness-normalized Partial Least Squares Regression for hyperspectral data , 2010 .

[11]  Margaret Kalacska,et al.  Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: Scaling from leaf to image , 2015 .

[12]  C. Warren,et al.  Rapid Measurement of Chlorophylls with a Microplate Reader , 2008 .

[13]  George Leblanc,et al.  Estimating Peatland Water Table Depth and Net Ecosystem Exchange: A Comparison between Satellite and Airborne Imagery , 2018, Remote Sensing.

[14]  N. Breemen,et al.  How Sphagnum bogs down other plants. , 1995 .

[15]  P. Treitz,et al.  Ordination and hyperspectral remote sensing approach to classify peatland biotopes along soil moisture and fertility gradients , 2012 .

[16]  T. Moore,et al.  Predicting peatland carbon fluxes from non-destructive plant traits , 2017 .

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

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

[19]  George Leblanc,et al.  Implementation of a UAV–Hyperspectral Pushbroom Imager for Ecological Monitoring , 2019, Drones.

[20]  Terry Caelli,et al.  Discrimination of lianas and trees with leaf-level hyperspectral data , 2004 .

[21]  E. Laliberté,et al.  Measuring leaf carbon fractions with the ANKOM2000 Fiber Analyzer v1 , 2019, protocols.io.

[22]  M. Govender,et al.  Review of commonly used remote sensing and ground-based technologies to measure plant water stress , 2009 .

[23]  M. Kalacska,et al.  Evaluation of phenospectral dynamics with Sentinel-2A using a bottom-up approach in a northern ombrotrophic peatland , 2018, Remote Sensing of Environment.

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

[25]  Benoit Rivard,et al.  Foliar spectral properties following leaf clipping and implications for handling techniques , 2006 .

[26]  Roberta E. Martin,et al.  Leaf reflectance spectra capture the evolutionary history of seed plants , 2020, The New phytologist.

[27]  M. Turetsky,et al.  Response of Sphagnum fuscum to Nitrogen Deposition: A Case Study of Ombrogenous Peatlands in Alberta, Canada , 2003 .

[28]  T. Moore,et al.  The effect of long-term fertilization on peat in an ombrotrophic bog , 2019, Geoderma.

[29]  T. Moore,et al.  Effects of long-term fertilization on peat stoichiometry and associated microbial enzyme activity in an ombrotrophic bog , 2016, Biogeochemistry.

[30]  Benoit Rivard,et al.  Variability in leaf optical properties of Mesoamerican trees and the potential for species classification. , 2006, American journal of botany.

[31]  Wen-Shin Lin,et al.  Classifying cultivars of rice (Oryza sativa L.) based on corrected canopy reflectance spectra data using the orthogonal projections to latent structures (O-PLS) method , 2012 .

[32]  D. Tilman,et al.  Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function , 2018, Nature Ecology & Evolution.

[33]  Clayton C. Kingdon,et al.  Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[34]  J. Gamon,et al.  Spectral niches reveal taxonomic identity and complementarity in plant communities , 2020 .

[35]  Tim R. Moore,et al.  Fine-scale vegetation distribution in a cool temperate peatland , 2006 .

[36]  J. Galloway,et al.  Transformation of the Nitrogen Cycle: Recent Trends, Questions, and Potential Solutions , 2008, Science.

[37]  N. Malmer,et al.  The dynamics of peat accumulation on bogs: mass balance of hummocks and hollows and its variation throughout a millennium , 1999 .

[38]  J. Kellner,et al.  The case for remote sensing of individual plants. , 2019, American journal of botany.

[39]  T. Ellis,et al.  Atmospheric nitrogen deposition promotes carbon loss from peat bogs , 2006, Proceedings of the National Academy of Sciences.

[40]  Aditya Singh,et al.  Associations of Leaf Spectra with Genetic and Phylogenetic Variation in Oaks: Prospects for Remote Detection of Biodiversity , 2016, Remote. Sens..

[41]  Jeannine Cavender-Bares,et al.  The spatial sensitivity of the spectral diversity-biodiversity relationship: an experimental test in a prairie grassland. , 2018, Ecological applications : a publication of the Ecological Society of America.

[42]  Roberta E. Martin,et al.  Functional and biological diversity of foliar spectra in tree canopies throughout the Andes to Amazon region. , 2014, The New phytologist.

[43]  Martin Evans,et al.  Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring , 2014, Remote. Sens..

[44]  Jean-Baptiste Féret,et al.  Spectroscopic classification of tropical forest species using radiative transfer modeling , 2011 .

[45]  K. Zhao,et al.  Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests. , 2016 .

[46]  Jukka Turunen,et al.  Nitrogen deposition and increased carbon accumulation in ombrotrophic peatlands in eastern Canada , 2004 .

[47]  Tim R. Moore,et al.  Effects of nutrient addition on vegetation and carbon cycling in an ombrotrophic bog , 2007 .

[48]  E. Robert,et al.  Développement D'une Vaste Tourbière Ombrotrophe Non Perturbée en Contexte Périurbain au Québec Méridional , 2012 .

[49]  Susan L. Ustin,et al.  Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem , 2008 .

[50]  S. Juutinen,et al.  Vegetation feedbacks of nutrient addition lead to a weaker carbon sink in an ombrotrophic bog , 2013, Global change biology.

[51]  Takeshi Motohka,et al.  Accurate measurement of optical properties of narrow leaves and conifer needles with a typical integrating sphere and spectroradiometer. , 2013, Plant, cell & environment.

[52]  Roberta E. Martin,et al.  Taxonomy and remote sensing of leaf mass per area (LMA) in humid tropical forests. , 2011, Ecological applications : a publication of the Ecological Society of America.

[53]  P. Treitz,et al.  Image classification of a northern peatland complex using spectral and plant community data , 2003 .