185
prairie systems that are frequently burned. We employed a parallel application of
spectroscopic imagery to assess above- and belowground diversity and functioning at
the grassland biodiversity experiment located at Cedar Creek Ecosystem Science
Reserve (Tilman et al. 2001). Rather than a monospecific forest canopy, the grassland
experiment consisted of replicated diversity treatments ranging from 1 to 16 perennial grassland species in 9 m × 9 m plots. This work had more technical challenges
associated with it compared to the aspen forest project due to the inherent complexity
of a mixed species system and the small spatial scale of the experimental plots.
The relationship between plant diversity and aboveground biomass in the Cedar
Creek BioDIV experiment is well documented (Tilman et al. 2001, 2006). Schweiger
et al. (2018) further demonstrate that both plant diversity and function are measurable via remotely sensed spectra within the experiment and that spectral diversity
predicted productivity. Wang et al. (2019) used AVIRIS imagery to map functional
traits across the experiment. Remotely sensed productivity and functional trait composition can thus be tested for linkages with belowground processes. In this system,
the quantity of inputs had a large impact on fungal composition and diversity (Cline
et al. 2018). Productivity, measured as annual aboveground biomass, given that it is
annually burned, can be accurately detected as remotely sensed vegetation cover
(Fig. 8.4a; Wang et al. 2019, following the method of Serbin et al. 2015). Remotely
sensed vegetation cover, in turn, predicted fungal diversity, measured as operational
taxonomic unit (OTU) richness (Fig. 8.4b), and cumulative soil respiration
(Fig. 8.4c). In addition to the total organic matter inputs to the soil, chemical composition also influenced belowground microbial communities. For example,
remotely sensed %N (Wang et al. 2019) was positively correlated with soil microbial biomass (Cavender-Bares et al., unpublished manuscript).
8.4.4 Challenges and Future Directions
Employing plant spectra to predict belowground processes has both caveats and
advantages over traditional belowground sampling. One important caveat is that any
prediction of belowground processes requires a solid understanding of the linkages
between above- and belowground processes in any given system. Examples in the
literature that link remotely sensed attributes of aboveground systems with belowground systems remain scarce, in part, because of the historic separation of the two
disciplines. It is unclear how well remotely sensed plant attributes will predict
microbial and soil processes across ecological systems. In the above forest example,
aspen forests were generally uniform in canopy coverage. It was also a singlespecies system where leaf structure remained consistent across the study area,
despite the large spatial sampling scheme. Consequently, most of the variation in
aspen spectral signal was likely due to variation in canopy chemistry and biomass
rather than leaf structure. Lastly, in this temperate forest system, leaf litter accounts
for a large fraction of inputs into belowground systems, compared to systems such
as Cedar Creek that are burned frequently and where fine-root turnover dominates
belowground inputs.
8 Linking Foliar Traits to Belowground Processes
prairie systems that are frequently burned. We employed a parallel application of
spectroscopic imagery to assess above- and belowground diversity and functioning at
the grassland biodiversity experiment located at Cedar Creek Ecosystem Science
Reserve (Tilman et al. 2001). Rather than a monospecific forest canopy, the grassland
experiment consisted of replicated diversity treatments ranging from 1 to 16 perennial grassland species in 9 m × 9 m plots. This work had more technical challenges
associated with it compared to the aspen forest project due to the inherent complexity
of a mixed species system and the small spatial scale of the experimental plots.
The relationship between plant diversity and aboveground biomass in the Cedar
Creek BioDIV experiment is well documented (Tilman et al. 2001, 2006). Schweiger
et al. (2018) further demonstrate that both plant diversity and function are measurable via remotely sensed spectra within the experiment and that spectral diversity
predicted productivity. Wang et al. (2019) used AVIRIS imagery to map functional
traits across the experiment. Remotely sensed productivity and functional trait composition can thus be tested for linkages with belowground processes. In this system,
the quantity of inputs had a large impact on fungal composition and diversity (Cline
et al. 2018). Productivity, measured as annual aboveground biomass, given that it is
annually burned, can be accurately detected as remotely sensed vegetation cover
(Fig. 8.4a; Wang et al. 2019, following the method of Serbin et al. 2015). Remotely
sensed vegetation cover, in turn, predicted fungal diversity, measured as operational
taxonomic unit (OTU) richness (Fig. 8.4b), and cumulative soil respiration
(Fig. 8.4c). In addition to the total organic matter inputs to the soil, chemical composition also influenced belowground microbial communities. For example,
remotely sensed %N (Wang et al. 2019) was positively correlated with soil microbial biomass (Cavender-Bares et al., unpublished manuscript).
8.4.4 Challenges and Future Directions
Employing plant spectra to predict belowground processes has both caveats and
advantages over traditional belowground sampling. One important caveat is that any
prediction of belowground processes requires a solid understanding of the linkages
between above- and belowground processes in any given system. Examples in the
literature that link remotely sensed attributes of aboveground systems with belowground systems remain scarce, in part, because of the historic separation of the two
disciplines. It is unclear how well remotely sensed plant attributes will predict
microbial and soil processes across ecological systems. In the above forest example,
aspen forests were generally uniform in canopy coverage. It was also a singlespecies system where leaf structure remained consistent across the study area,
despite the large spatial sampling scheme. Consequently, most of the variation in
aspen spectral signal was likely due to variation in canopy chemistry and biomass
rather than leaf structure. Lastly, in this temperate forest system, leaf litter accounts
for a large fraction of inputs into belowground systems, compared to systems such
as Cedar Creek that are burned frequently and where fine-root turnover dominates
belowground inputs.
8 Linking Foliar Traits to Belowground Processes