Reading light: leaf spectra capture fine-scale diversity of closely related, hybridizing arctic shrubs.

Abstract

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 Dryas. We also analyze the correlation among taxonomically diagnostic leaf traits to understand the challenges hybrids pose to classification models based on leaf spectra. Classification models based on leaf spectra identified two species of Dryas with 99.7% overall accuracy and genetic populations with 98.9% overall accuracy. All regions of the spectrum carried significant phylogenetic signal. Hybrids were classified with an average overall accuracy of 80%, and our morphological analysis revealed weak trait correlations within hybrids compared to parent species. 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.

Plant phenotyping relevance

葉の反射スペクトルを用いて、近縁植物の種・集団構造・雑種を高精度に識別し、遺伝的多様性を推定する測定・解析手法が研究の中心であるため。

Code and data availability

The paper's leaf reflectance spectra (the core phenotyping measurements) are publicly deposited on figshare, and the authors' R code for spectral analysis is on GitHub, both stated in the Data availability section. The NCBI BioProject is genomic data and excluded.