Variation in Leaf Reflectance Spectra Across the California Flora Partitioned by Evolutionary History, Geographic Origin, and Deep Time

Abstract

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 (Arctostaphylos) no unique variation could be attributed to geographic origin. However, up to 18% of the spectral variation among Arctostaphylos individuals was shared between phylogeny and intraspecific variation stemming from ecotypic differences (i.e., geographic origin). Future studies could conduct more structured experiments (e.g., transplants or observations along environmental gradients) to disentangle these sources of variation and include other intraspecific variation (e.g., plasticity). We constrain broad‐scale spectral variability due to ecotypic sources, providing further support for the idea that phylogenetic clusters of species might be detectable through remote sensing. Phylogenetic clusters could represent a valuable dimension of biodiversity monitoring and detection.

Capturing patterns of evolutionary relatedness with reflectance spectra to model and monitor biodiversity

“…Past research on remote sensing of evolutionary history with spectral information relied primarily on field or leaf spectroscopy ( 11 – 14 , 60 ). Field-collected spectra represent nearly optimal data, are collected to represent pure spectral signatures of a target, and do not have positional error, or atmospheric interference.…”

“…Numerous studies have indicated that VSWIR (visible-to-short wavelength infrared; 400-2,500 nm) reflectance properties of vegetation capture evolutionarily conserved biochemical, structural, and other functional attributes of plant species (e.g., refs. ( 11 – 14 )). Yet, phylogenetic turnover and diversity have not been fully explored as alternative methods for assessing biodiversity from space.…”

Grass Evolutionary Lineages Can Be Identified Using Hyperspectral Leaf Reflectance

“…Spectral diversity or spectral dissimilarity from experimental plots also increases with evolutionary divergence time and was linked to variation in productivity (Schweiger et al., 2018). Phylogeny‐based approaches have enabled the partitioning of sources of spectral variability in regional floras (Griffith, Byrd, Anderegg, et al., 2023) and the mapping of dominant plant lineages (Griffith et al., 2023a, 2023b). Most studies showing phylogenetic conservatism in leaf spectra, however, are limited to single sites and few sampling periods despite recognition that leaf traits are highly plastic within a given species and change under varying resource limitations (Bachle & Nippert, 2022).…”

“…A major goal for imaging spectroscopy is to remotely monitor dimensions of biodiversity (Cawse-Nicholson et al, 2021;Griffith et al, 2023aGriffith et al, , 2023bRocchini et al, 2022). To that end, numerous studies have sought to relate spectral diversity to taxonomic, functional, and phylogenetic diversity across scales (e.g., Cavender-Bares et al, 2017;Schweiger et al, 2018).…”

Evolutionary lineage explains trait variation among 75 coexisting grass species

“…Grouping traits by lineage can also explain trait variation, as seen in Edwards et al's (2007) study, where traits of Echinochloa appeared to be outliers in its C 4 PFT group, suggesting that the genus may have traits unique to its independent C 4 lineage. Furthermore, LFT approaches have the potential to be broadly applicable in other ecosystem types and for remote sensing and scaling applications (Anderegg et al, 2022;Griffith et al, 2023;R. Slapikas et al, unpublished).…”