Evolutionary relatedness underlies patterns of functional diversity in the natural world. Hyperspectral remote sensing has the potential to detect these patterns in plants through inherited patterns of leaf reflectance spectra. We collected leaf reflectance data across the California flora from plants grown in a common garden. Regions of the reflectance spectra vary in the depth and strength of phylogenetic signal. We also show that these differences are much greater than variation due to the geographic origin of the plant. At the phylogenetic extent of the California flora, spectral variation explained by the combination of ecotypic variation (divergent evolution) and convergent evolution of disparate lineages was minimal (3%–7%) but statistically significant. Interestingly, at the extent of a single genus (
Leaf reflectance spectroscopy is emerging as an effective tool for assessing plant diversity and function. However, the ability of leaf spectra to detect fine‐scale plant evolutionary diversity in complicated biological scenarios is not well understood. We test if reflectance spectra (400–2400 nm) can distinguish species and detect fine‐scale population structure and phylogenetic divergence – estimated from genomic data – in two co‐occurring, hybridizing, ecotypically differentiated species of Classification models based on leaf spectra identified two species of Reflectance spectra captured genetic variation and accurately distinguished fine‐scale population structure and hybrids of morphologically similar, closely related species growing in their home environment. Our findings suggest that fine‐scale evolutionary diversity is captured by reflectance spectra and should be considered as spectrally‐based biodiversity assessments become more prevalent.
- Award ID(s):
- 2021898
- NSF-PAR ID:
- 10446955
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 232
- Issue:
- 6
- ISSN:
- 0028-646X
- Page Range / eLocation ID:
- p. 2283-2294
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Time period All data were collected between 1978–2014.
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