Coupling spectral and resource-use complementarity in experimental grassland and forest communities

Abstract

24• Plants' spectra provide integrative measures of their chemical, morphological, anatomical, and 25 architectural traits. We posit that the degree to which plants differentiate in n-dimensional 26 spectral space is a measure of niche differentiation and reveals functional complementarity. 27• In both experimentally and naturally assembled communities, we quantified plant niches using 28 hypervolumes delineated by either plant spectra or 10 functional traits. We compared the niche 29 fraction unique to each species in spectral and trait spaces with increasing dimensionality, and 30 investigated the association between the spectral space occupied, plant growth and community 31 productivity. 32• We show that spectral niches differentiated species better than their functional trait niches. The 33 amount of spectral space occupied by individuals and plant communities increased with plant 34 growth and community productivity, respectively. Further, community productivity was better 35 explained by inter-individual spectral complementarity than by productive individuals 36 occupying large spectral niches. 37 • The degree of differentiation in spectral space provides the conceptual basis for identifying 38 plant taxa spectrally. Moreover, our results indicate that the size and position of plant spectral 39 niches reflect ecological strategies that shape community composition and ecosystem function, 40 with the potential to reveal insight in niche partitioning over large areas with spectroscopy. 41 42 Keywords 43 complementarity, functional traits, ecosystem function, Hutchinsonian niche, plant species 44 identification, spectroscopy, spectral space, remote sensing 45 3

Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves

“…Among the samples misidentified, most were mistaken for congenerics (Fig. 5), particularly for pressed and ground specimens, which implies that related species are more spectrally similar (Schweiger et al 2018;Meireles et al 2020a;Schweiger et al 2021). Hence, discriminating more closely related species might pose a greater challenge and lead to a higher error rate.…”

“…Reflectance spectra often show phylogenetic signal, at least in particular wavelength ranges, because of phylogenetic conservatism in the evolution of their underlying traits (McManus et al 2016;Diniz et al 2020;Meireles et al 2020a;Schweiger et al 2021). This phylogenetic signal is what often makes it possible to classify species or higher-level taxa from fresh-leaf spectra (Cavender-Bares et al 2016;Meireles et al 2020b), but it may or may not be retained in pressed-leaf spectra.…”

Predicting species distributions and community composition using satellite remote sensing predictors

“…An unparalleled alternative is the direct detection of species using imaging spectroscopy 57,58 . For example, leaf-spectra variation among individual plants obtained using imaging spectroscopy provide su cient information for the correct assignment of populations to species to clades 59,60 and airborne imagery accurately assigns vegetation canopies to species 61 .…”

Predicting species distributions and community composition using remote sensing products

“…An unparalleled alternative is the direct detection of species using imaging spectroscopy (Cavender-Bares et al, 2017;Schweiger et al, 2020). For example, leaf-spectra variation among individual plants obtained using imaging spectroscopy provide sufficient information for the correct assignment of populations to species to clades (Cavender-Bares et al, 2016;Meireles et al, 2020;Schweiger et al, 2020) and airborne imagery accurately assigns vegetation canopies to species (Foster and Townsend, 2004).…”

“…An unparalleled alternative is the direct detection of species using imaging spectroscopy (Cavender-Bares et al, 2017;Schweiger et al, 2020). For example, leaf-spectra variation among individual plants obtained using imaging spectroscopy provide sufficient information for the correct assignment of populations to species to clades (Cavender-Bares et al, 2016;Meireles et al, 2020;Schweiger et al, 2020) and airborne imagery accurately assigns vegetation canopies to species (Foster and Townsend, 2004). Current and forthcoming hyperspectral images from DESIS sensors and forthcoming SBG and CHIMES sensors, among others (Alonso et al, 2019;Stavros et al, 2017;Turner, 2014) will capture information from the Earth at fine spectral resolution, allowing the estimation of plant traits, plant nutrient content, biophysical variables (e.g., leaf area index, biomass), that can be used for direct detection of functional and perhaps community diversity from space (Jetz et al, 2016).…”