Plant spectra as integrative measures of plant phenotypes

Abstract

1. Spectroscopy at the leaf or canopy scales is becoming one of the core tools of plant functional ecology. Remotely sensed reflectance spectra can allow ecologists to infer plant traits and strategies-and the community-or ecosystem-level processes they correlate with-continuously over unprecedented spatial scales.2. Because of the complex entanglement of structural and chemical factors that generate spectra, it can be tricky to understand exactly what phenotypic information they contain. We discuss common approaches to estimating plant traits from spectra-radiative transfer models and multivariate empirical models-and elaborate on their strengths and limitations in terms of the causal influences of various traits on the spectrum. Many chemical traits have broad, shallow, and overlapping absorption features, and we suggest that covariance among traits may have an important role in giving empirical models the flexibility to estimate such traits.3. While trait estimates from reflectance spectra have been used to test ecological hypotheses over the past 20 years, we review a growing body of research that uses spectra directly, without estimating specific traits. By treating positions of species in multidimensional spectral space as analogous to trait space, researchers can infer processes that structure plant communities using the information content of the full spectrum, which may be greater than any standard set of traits. We illustrate this power by showing that co-occurring grassland species are more separable in spectral space than in trait space and that the intrinsic dimensionality of spectral data is comparable to fairly comprehensive trait data sets. Nevertheless, using spectra this way may make it harder to interpret patterns in terms of specific biological processes. 4. Synthesis. Plant spectra integrate many aspects of plant function. The information in the spectrum can be distilled into estimates of specific traits, or the spectrum can be used in its own right. These two approaches may be complementary-the former being most useful when specific traits of interest are known in advance and reliable models exist to estimate them, and the latter being most useful in taking advantage of the information in the full spectrum under uncertainty about which aspects of function matter most.

Predicting leaf traits across functional groups using reflectance spectroscopy

“…We built PLSR models to predict widely used foliar traits from reflectance spectra across nearly 2000 samples of 104 species, including several functional groups and ecosystems. Our findings underscore that leaf spectra integrate many aspects of leaf phenotypes in a single measurement (Cavender-Bares et al 2017; Kothari & Schweiger 2022) and help explain why spectral variation can serve as a surrogate for phenotypic variation (Schweiger et al 2018; Frye et al 2021).…”

“…The model could have incorporated any optical features that distinguish A. flexuosa from other specimens, whether or not those features truly result from Na concentration. This kind of constellation effect may underlie the spectroscopic estimation of many elements (Nunes et al 2017; Kothari & Schweiger 2022).…”

“…Understanding leaf spectral variation is essential for scaling up to the canopy level (Asner et al 2011), and leaf-level spectral estimates of traits are part of many workflows for canopy trait mapping (Wang et al 2020). These uses of leaf spectra make it important to understand their functional drivers (Kothari & Schweiger 2022).…”

“…This situation may occur most often when the target trait lacks major, well-defined absorption features, like concentrations of many elements (Nunes et al 2017). If trait covariance patterns vary among datasets, overfitting to the training data could cause such models to produce inaccurate trait estimates from new spectral data (Kothari & Schweiger 2022). Empirical models may be especially fallible beyond the range of data used to train them (Schweiger 2020;Burnett et al 2021).…”

Drivers of plant diversity, community composition, functional traits and soil processes along an alpine gradient in the central Chilean Andes

“…The copyright holder for this preprint this version posted January 15, 2023. ; https://doi.org/10.1101/2023.01.13.523936 doi: bioRxiv preprint contribute to inferences on whether adaptations to environmental niches are clustered within particular lineages or the extent to which lineages have diversified along environmental gradients (Cavender-Bares, Kozak, Fine, & Kembel, 2009;Tofts & Silvertown, 2000;Webb, 2000). Spectral diversity integrates chemical, structural, and morphological diversity with light reflectance across a range of wavelengths, providing a high-dimensional, high throughput quantification of plant phenotypes (Kothari & Schweiger, 2022). Functional, phylogenetic, and spectral diversity are closely related in prairie and forest systems (Cavender-Bares et al, 2017a;E.…”