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Sabrina Demers-Thibeault

sabrina.demers-thibeault@mila.quebec
Independent visiting researcher - Université de Montréal
Supervisor
Étienne Laliberté
Research Topics
Biodiversity
Remote Sensing
Vegetation

Publications

Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis
Anna L. Crofts
Christine I. B. Wallis
Sabine St‐Jean
Sabrina Demers‐Thibeault
Deep Inamdar
J. Pablo Arroyo‐Mora
Margaret Kalácska
Étienne Laliberté
Mark Vellend
Imaging spectroscopy is emerging as a leading remote sensing method for quantifying plant biodiversity. The spectral variation hypothesis pr… (see more)edicts that variation in plant hyperspectral reflectance is related to variation in taxonomic and functional identity. While most studies report some correlation between spectral and field‐based (i.e., taxonomic and functional) expressions of biodiversity, the observed strength of association is highly variable, and the utility in applying spectral community properties to examine environmental drivers of communities remains unknown. We linked hyperspectral data acquired by airborne imaging spectrometers with precisely geolocated field plots to examine the spectral variation hypothesis along a temperate‐to‐boreal forest gradient in southern Québec, Canada. First, we examine the degree of association between spectral and field‐based dimensions of canopy tree composition and diversity. Second, we ask whether the relationships between field‐based community properties and the environment are reproduced when using spectral community properties. We found support for the spectral variation hypothesis with the strength of association generally greater for the functional than taxonomic dimension, but the strength of relationships was highly variable and dependent on the choice of method or metric used to quantify spectral and field‐based community properties. Using a multivariate approach (comparisons of separate ordinations), spectral composition was moderately well correlated with field‐based composition; however, the degree of association increased when univariately relating the main axes of compositional variation. Spectral diversity was most tightly associated with functional diversity metrics that quantify functional richness and divergence. For predicting canopy tree composition and diversity using environmental variables, the same qualitative conclusions emerge when hyperspectral or field‐based data are used. Spatial patterns of canopy tree community properties were strongly related to the turnover from temperate‐to‐boreal communities, with most variation explained by elevation. Spectral composition and diversity provide a straightforward way to quantify plant biodiversity across large spatial extents without the need for a priori field observations. While commonly framed as a potential tool for biodiversity monitoring, we show that spectral community properties can be applied more widely to assess the environmental drivers of biodiversity, thereby helping to advance our understanding of the drivers of biogeographical patterns of plant communities.
2024-05-05
Ecological Monographs (published)
doi.org
Evolutionary history explains foliar spectral differences between arbuscular and ectomycorrhizal plant species
Johanna R. Jantzen
Étienne Laliberté
Alexis Carteron
Rosalie Beauchamp‐Rioux
Florence Blanchard
Anna L. Crofts
Alizée Girard
Paul W. Hacker
Juliana Pardo
Anna K. Schweiger
Sabrina Demers‐Thibeault
Nicholas C. Coops
Margaret Kalácska
Mark Vellend
Anne Bruneau
Leaf spectra are integrated foliar phenotypes that capture a range of traits and can provide insight into ecological processes. Leaf traits,… (see more) and therefore leaf spectra, may reflect belowground processes such as mycorrhizal associations. However, evidence for the relationship between leaf traits and mycorrhizal association is mixed, and few studies account for shared evolutionary history. We conduct partial least squares discriminant analysis to assess the ability of spectra to predict mycorrhizal type. We model the evolution of leaf spectra for 92 vascular plant species and use phylogenetic comparative methods to assess differences in spectral properties between arbuscular mycorrhizal and ectomycorrhizal plant species. Partial least squares discriminant analysis classified spectra by mycorrhizal type with 90% (arbuscular) and 85% (ectomycorrhizal) accuracy. Univariate models of principal components identified multiple spectral optima corresponding with mycorrhizal type due to the close relationship between mycorrhizal type and phylogeny. Importantly, we found that spectra of arbuscular mycorrhizal and ectomycorrhizal species do not statistically differ from each other after accounting for phylogeny. While mycorrhizal type can be predicted from spectra, enabling the use of spectra to identify belowground traits using remote sensing, this is due to evolutionary history and not because of fundamental differences in leaf spectra due to mycorrhizal type.
2023-03-23
New Phytologist (published)
doi.org