Shan Kothari, Anna Schweiger

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.