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 phylogenetic‐functional 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.
Efforts to catalog global biodiversity have often focused on aboveground taxonomic diversity, with limited consideration of belowground communities. However, diversity aboveground may influence the diversity of belowground communities and vice versa. In addition to taxonomic diversity, the structural diversity of plant communities may be related to the diversity of soil bacterial and fungal communities, which drive important ecosystem processes but are difficult to characterize across broad spatial scales. In forests, canopy structural diversity may influence soil microorganisms through its effects on ecosystem productivity and root architecture, and via associations between canopy structure, stand age, and species richness. Given that structural diversity is one of the few types of diversity that can be readily measured remotely (e.g., using light detection and ranging—LiDAR), establishing links between structural and microbial diversity could facilitate the detection of belowground biodiversity hotspots. We investigated the potential for using remotely sensed information about forest structural diversity as a predictor of soil microbial community richness and composition. We calculated LiDAR‐derived metrics of structural diversity as well as a suite of stand and soil properties from 38 forested plots across the central hardwoods region of Indiana, USA, to test whether forest canopy structure is linked with the community richness and diversity of four key soil microbial groups: bacteria, fungi, arbuscular mycorrhizal (AM) fungi, and ectomycorrhizal (EM) fungi. We found that the density of canopy vegetation is positively associated with the taxonomic richness (alpha diversity) of EM fungi, independent of changes in plant taxonomic richness. Further, structural diversity metrics were significantly correlated with the overall community composition of bacteria, EM, and total fungal communities. However, soil properties were the strongest predictors of variation in the taxonomic richness and community composition of microbial communities in comparison with structural diversity and tree species diversity. As remote sensing tools and algorithms are rapidly advancing, these results may have important implications for the use of remote sensing of vegetation structural diversity for management and restoration practices aimed at preserving belowground biodiversity.
more » « less- Award ID(s):
- 2044406
- NSF-PAR ID:
- 10473868
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecosphere
- Volume:
- 14
- Issue:
- 11
- ISSN:
- 2150-8925
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Ectomycorrhizal (EM) associations can promote the dominance of tree species in otherwise diverse tropical forests. These EM associations between trees and their fungal mutualists have important consequences for soil organic matter cycling, yet the influence of these EM-associated effects on surrounding microbial communities is not well known, particularly in neotropical forests. We examined fungal and prokaryotic community composition in surface soil samples from mixed arbuscular mycorrhizal (AM) and ectomycorrhizal (EM) stands as well as stands dominated by EM-associated
Oreomunnea mexicana (Juglandaceae) in four watersheds differing in soil fertility in the Fortuna Forest Reserve, Panama. We hypothesized that EM-dominated stands would support distinct microbial community assemblages relative to the mixed AM-EM stands due to differences in carbon and nitrogen cycling associated with the dominance of EM trees. We expected that this microbiome selection in EM-dominated stands would lead to lower overall microbial community diversity and turnover, with tighter correspondence between general fungal and prokaryotic communities. We measured fungal and prokaryotic community composition via high-throughput Illumina sequencing of theITS2 (fungi) and16S rRNA (prokaryotic) gene regions. We analyzed differences in alpha and beta diversity between forest stands associated with different mycorrhizal types, as well as the relative abundance of fungal functional groups and various microbial taxa. We found that fungal and prokaryotic community composition differed based on stand mycorrhizal type. There was lower prokaryotic diversity and lower relative abundance of fungal saprotrophs and pathogens in EM-dominated than AM-EM mixed stands. However, contrary to our prediction, there was lower homogeneity for fungal communities in EM-dominated stands compared to mixed AM-EM stands. Overall, we demonstrate that EM-dominated tropical forest stands have distinct soil microbiomes relative to surrounding diverse forests, suggesting that EM fungi may filter microbial functional groups in ways that could potentially influence plant performance or ecosystem function. -
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Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).
Location Eastern USA.
Time period 2017–2018.
Taxa studied Trees.
Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.
Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.
Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.
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Habitat heterogeneity is a key driver of biodiversity of macroorganisms, yet how heterogeneity structures belowground microbial communities is not well understood. Importantly, belowground microbial communities may respond to any number of abiotic, biotic, and spatial drivers found in heterogeneous environments. Here, we examine potential drivers of prokaryotic and fungal communities in soils across the heterogenous landscape of the imperiled Florida scrub, a pyrogenic ecosystem where slight differences in elevation lead to large changes in water and nutrient availability and vegetation composition. We employ a comprehensive, large-scale sampling design to characterize the communities of prokaryotes and fungi associated with three habitat types and two soil depths (crust and subterranean) to evaluate (i) differences in microbial communities across these heterogeneous habitats, (ii) the relative roles of abiotic, biotic, and spatial drivers in shaping community structure, and (iii) the distribution of fungal guilds across these habitats. We sequenced soils from 40 complete replicates of habitat × soil depth combinations and sequenced the prokaryotic 16S and fungal internal transcribed spacer (ITS) regions using Illumina MiSeq. Habitat heterogeneity generated distinct communities of soil prokaryotes and fungi. Spatial distance played a role in structuring crust communities, whereas subterranean microbial communities were primarily structured by the shrub community, whose roots they presumably interacted with. This result helps to explain the unexpected transition we observed between arbuscular mycorrhiza–dominated soils at low-elevation habitats to ectomycorrhiza-dominated soils at high-elevation habitats. Our results challenge previous notions of environmental determinism of microbial communities and generate new hypotheses regarding symbiotic relationships across heterogeneous environments.more » « less
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Abstract Arctic regions are experiencing the greatest rates of climate warming on the planet and marked changes have already been observed in terrestrial arctic ecosystems. While most studies have focused on the effects of warming on arctic vegetation and nutrient cycling, little is known about how belowground communities, such as fungi root‐associated, respond to warming. Here, we investigate how long‐term summer warming affects ectomycorrhizal (ECM) fungal communities. We used Ion Torrent sequencing of the
rDNA internal transcribed spacer 2 (ITS2) region to compare ECM fungal communities in plots with and without long‐term experimental warming in both dry and moist tussock tundra.Cortinarius was the most OTU‐rich genus in the moist tundra, while the most diverse genus in the dry tundra wasTomentella . On the diversity level, in the moist tundra we found significant differences in community composition, and a sharp decrease in the richness of ECM fungi due to warming. On the functional level, our results indicate that warming induces shifts in the extramatrical properties of the communities, where the species with medium‐distance exploration type seem to be favored with potential implications for the mobilization of different nutrient pools in the soil. In the dry tundra, neither community richness nor community composition was significantly altered by warming, similar to what had been observed in ECM host plants. There was, however, a marginally significant increase in OTUs identified as ECM fungi with the medium‐distance exploration type in the warmed plots. Linking our findings of decreasing richness with previous results of increasing ECM fungal biomass suggests that certain ECM species are favored by warming and may become more abundant, while many other species may go locally extinct due to direct or indirect effects of warming. Such compositional shifts in the community might affect nutrient cycling and soil organic C storage.
