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Activity Number:
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69
- Statistical Methods in Ecology
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Type:
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Contributed
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Date/Time:
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Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #322237
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Title:
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Spatial Functional Data Modeling of Plant Reflectances
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Author(s):
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Philip White* and Henry Frye and Michael Christensen and Alan E. Gelfand and John A Silander, Jr.
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Companies:
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BYU and University of Connecticut and Duke University and Duke University and University of Connecticut
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Keywords:
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environment;
functional data analysis;
hierarchical model;
kernel weighting;
reflectance;
spatial confounding
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Abstract:
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Plant reflectance spectra supply the spectral signature for a species at a spatial location to enable estimation of functional and taxonomic diversity for plants. We consider leaf spectra as "responses" to be explained spatially. These spectra/reflectances are functions over a wavelength band that respond to the environment. Our motivating data are gathered for several families from the Cape Floristic Region (CFR) in South Africa and lead us to develop rich novel spatial models that can explain spectra for genera within families. Wavelength responses for an individual leaf are viewed as a function of wavelength, leading to functional data modeling. Local environmental features become covariates. We introduce wavelength-covariate interaction since the response to environmental regressors may vary with wavelength, so may variance. Formal spatial modeling enables prediction of reflectances for genera at unobserved locations with known environmental features. We incorporate spatial dependence, wavelength dependence, and space-wavelength interaction (in the spirit of space-time interaction). We then supply interpretation of the results.
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Authors who are presenting talks have a * after their name.
