Biology subjects

Gamon, J. A.

Publications and source records attributed to Gamon, J. A..

4 recordsLinked to original sources

bioRxiv2025

Prairie management practices influence biodiversity, productivity and surface-atmosphere feedbacks

Restoration efforts aim to reestablish grassland cover and maintain ecosystem services. However, there is a lack of systematic evaluation of the effects of grassland restoration options on integrated biodiversity, carbon, ecosystem function, and climate feedbacks. Through a multi-year grassland restoration experiment in a tallgrass prairie site in Nebraska, USA, we investigated how different management practices affected biodiversity, productivity and the surface-atmosphere feedbacks using a combination of in situ measurements and airborne hyperspectral and thermal remote sensing. Our findings suggested that certain management treatments altered species diversity, productivity and energy balance. Higher diversity plots had higher vegetation cover, albedo, canopy water content and lower surface temperature, indicating clear effects of management treatments on grassland ecosystem processes influencing surface-atmosphere feedbacks of mass and energy. The coherent responses of multiple airborne remote sensing indices illustrate clear co-benefits of grassland restoration practices that enhance ecosystem productivity and biodiversity and mitigate climate change through surface atmosphere feedbacks, offering a new strategy to address the global challenges of biodiversity loss and climate change in prairie ecosystems.

ecology↗

bioRxiv2021

Remotely detected plant function in two midwestern prairie grassland experiments reveals belowground processes

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 grassland systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling, providing a potential link between remote sensing and belowground processes. 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 characterize 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. Specifically, 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 and 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. In contrast, at Wood River, where plant diversity and productivity were consistently higher, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates, while aboveground biomass did not. The strong, contrasting associations between the quantity and chemistry of aboveground inputs with belowground soil processes and properties 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.

ecology↗

bioRxiv2020

Spectral niches reveal taxonomic identity and complementarity in plant communities

Reflectance spectra provide integrative measures of plant phenotypes by capturing chemical, morphological, anatomical and architectural trait information. Here we investigate the linkages between plant spectral variation, spectral and resource-use complementarity that contribute to ecosystem productivity. In both a prairie grassland and a forest diversity experiment, we delineated N-dimensional hypervolumes using either wavelength-bands of reflectance spectra or foliar traits. First, we compared the hypervolume fraction unique to each species in either spectral or trait space with increasing dimensionality. Then, we investigated the association between the spectral space occupied by individual plants and their growth, as well as the spectral space occupied by plant communities and ecosystem productivity. We show that species are better distinguished in spectral space than in trait space, providing a conceptual basis for identifying plant taxa spectrally. In addition, the spectral space occupied by individuals increased with plant growth, and the spectral space occupied by plant communities increased with ecosystem productivity. Furthermore, ecosystem productivity was better explained by inter-individual spectral complementarity than by the large spectral space occupied by productive individuals. Our results indicate that spectral hypervolumes of plants can reflect ecological strategies that shape community composition and ecosystem function, and that spectral complementarity can reveal resource-use complementarity.

ecology↗

bioRxiv2017

Five years of land surface phenology in a large-scale flooding and draining manipulation in a coastal Arctic ecosystem

This study was motivated by the knowledge gap for observing the complex interplay between surface hydrology and plant phenology in arctic landscapes and was conducted as part of a large scale, multi investigator flooding and draining experiment near Barrow, Alaska (71{degrees}1701\" N, 156{degrees}3548\" W) during 2005 - 2009. Hyperspectral reflectance data were collected in the visible to near IR region of the spectrum using a robotic tram system that operated along a 300m transects during the snow free growing period between June and August, 2005-09. Interannual patterns of land-surface phenology (NDVI) unexpectedly lacked marked differences under experimental conditions. Measurement of NDVI was, however, compromised for presence of surface water. Land-surface phenology and surface water was negatively correlated, which held when scaled to a 2km by 2km MODIS subset of the study area. This result suggested that published findings of greening of the Arctic may relate to a drying of the Arctic i.e. reduced surface water in vegetated high-latitude landscapes where surface water is close to ground level.

ecology↗