Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments

Abstract

Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling. We hypothesized that ecosystem productivity, and the chemical, structural and phylogeneticfunctional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments: the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. We tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy data were used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic-functional community composition of vegetation. We examined the relationships between the image-derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment-which has low overall diversity and productivity despite high variation in each-belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River-where plant diversity and productivity were consistently higher-belowground processes were driven mainly by variation in the quality of aboveground inputs to soils. Consequently, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates but aboveground biomass (or cover) did not. The contrasting associations between the quantity (productivity) and quality (composition) of aboveground inputs with belowground soil attributes provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly.

Representing plant diversity in land models: An evolutionary approach to make “Functional Types” more functional

“…One particularly exciting feature that may make Lineage Functional Types both easier to operationalize and useful for benchmarking longer term vegetation model dynamics is that they can potentially be remotely sensed by satellite. There is considerable evidence that the spectral properties of plant canopies are phylogenetically conserved, similar to physiological traits (Cavender- Bares et al, 2016Bares et al, , 2017Meireles et al, 2020;Schweiger et al, 2018) and that phylogenetic lineages can be remotely sensed (Cavender-Bares et al, 2021). Whether serendipitously or mechanistically linked to ecological niche conservatism, the phylogenetic conservatism of plant spectra (Meireles et al, 2020) could allow rapidly proliferating hyperspectral data to be used to map LFTs.…”

A review on effective soil health bio-indicators for ecosystem restoration and sustainability

“…Restoration ecologists and other conservation biologists agree that habitat is the most important locus of biodiversity protection. There has been a growing realization and appreciation in restoration ecology for the role of soil ecology in restoring and maintaining diverse biological communities both above- and belowground (Farrell et al, 2020 ; Cavender-Bares et al, 2021 ). Therefore, soil health restoration is important to consider while designing ecological restoration strategies.…”

Plant spectra as integrative measures of plant phenotypes

“…Applications range from modeling and predicting leaf (Asner et al 2014;Serbin et al 2014) and canopy traits (Asner et al 2017, Singh et al 2015, to detecting plant stress (Asner et al 2016) and natural enemies (Pontius et al 2005, Sapes et al 2022, to differentiating species and broader taxonomic clades (Féret & Asner 2012) from spectra. Indeed, maps of plant ) and plant community traits (Cavender-Bares et al 2022), species (Roth et al 2016) and functional group composition (Schmidtlein et al 2012;Schweiger et al 2017) are highly valuable for investigating a plethora of ecological questions beyond the scale of individual research plots.…”

“…Today's applications of spectroscopy range from modelling and predicting leaf (Asner et al, 2014; Serbin et al, 2014) and canopy traits (Asner et al, 2017; Singh et al, 2015), to detecting plant stress (Asner et al, 2016) and natural enemies (Pontius et al, 2005; Sapes et al, 2022 ), to differentiating species and broader taxonomic clades (Féret & Asner, 2013; Meireles et al, 2020; Sapes et al, 2022). Indeed, maps of species (Roth et al, 2015), functional group composition (Schmidtlein et al, 2012; Schweiger et al, 2017), and traits of individual plants (Asner & Martin, 2009) or plant communities (Cavender‐Bares et al, 2022) are highly valuable for investigating a plethora of ecological questions beyond the scale of individual research plots. In addition to trait and species mapping, plant spectroscopy over the past decade has also seen the growing use of spectra as integrated measures of plant phenotypes (Cavender‐Bares et al, 2017; Ustin & Gamon, 2010), including in biodiversity‐ecosystem function research (Schweiger et al, 2018, 2021; Williams et al, 2021) and as measures of plant diversity (Draper et al, 2019; Féret & Asner, 2014; Frye et al, 2021; Rocchini et al, 2010; Schweiger et al, 2018; Schweiger & Laliberté, 2022; Wang & Gamon, 2019).…”