Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Ecological Observatory Network’s (NEON’s) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high‐resolution trait mapping. We tested the accuracy of readily available data products of NEON’s AOP, such as Leaf Area Index (LAI), Total Biomass, Ecosystem Structure (Canopy height model [CHM]), and Canopy Nitrogen, by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground‐measured LAI (
Disturbances can cause fluctuations in resource availability that influence plant performance. In systems with such dynamics, inter‐specific differences in resource capture may promote co‐existence by partitioning competition between periods of high or low resource availability. Such differences in resource use strategy have been described with the Plant Economics Spectrum, which hypothesizes that functions related to resource use and processing should co‐vary and can be predicted from plant traits. In pyrogenic systems, fires are associated with short‐term increases in soil nitrogen availability (“pulses”), and thus contribute to a fluctuating resource supply. In this study, we sought to understand whether plants differed in their capacity to capture a nitrogen pulse, and to what extent that ability influenced biomass recovery.
In two consecutive greenhouse experiments, we tested whether two functions — nitrogen assimilation (Experiment 1) and biomass regrowth after disturbance (Experiment 2) — co‐varied, and how each function corresponded to leaf and root functional traits.
In Experiment 1, four co‐occurring shrubs differed in their temporal patterns of nitrogen uptake, and nitrogen uptake was positively correlated with resource‐acquisitive leaf traits (leaf percent nitrogen). In Experiment 2, the biomass regrowth of a resource acquisitive and a resource conservative species was the same regardless of competitive environment (i.e., when grown in pots of mixed‐species or same‐species pairs). Rather than being associated with the capture of new nitrogen, biomass resprouting of both species was associated with the size of below‐ground resource stores and specific root length.
Our work suggests that resource acquisition and processing may be decoupled from each other after disturbance, and also highlights the need for explicit tests of the relationships between root traits and above‐ground plant function.
- NSF-PAR ID:
- 10461602
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Journal of Vegetation Science
- Volume:
- 30
- Issue:
- 1
- ISSN:
- 1100-9233
- Page Range / eLocation ID:
- p. 65-74
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Elizabeth Borer (Ed.)Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Ecological Observatory Network’s (NEON’s) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mapping. We tested the accuracy of readily available data products of NEON’s AOP, such as Leaf Area Index (LAI), Total Biomass, Ecosystem Structure (Canopy height model [CHM]), and Canopy Nitrogen, by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground-measured LAI (r = 0.32) and AOP Total Biomass and ground-measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380–2,500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression (PLSR) models. Among all the eight traits examined, only Nitrogen had a validation of more than 0.25. For all vegetation traits, validation ranged from 0.08 to 0.29 and the range of the root mean square error of prediction (RMSEP) was 14–64%. Our results suggest that currently available AOP-derived data products should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field-based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogeneous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time. But the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in-situ field measurements across a diversity of sites.more » « less
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