Soil organic matter (SOM) is the largest terrestrial pool of organic carbon (C), and potential carbon-climate feedbacks involving SOM decomposition could exacerbate anthropogenic climate change. Despite the importance of SOM in the global C cycle, our understanding of the controls on SOM stabilization and decomposition is still developing, and as such, SOM dynamics are a source of major uncertainty in current Earth system models (ESMs), which reduces the effectiveness of these models in predicting the efficacy of climate change mitigation strategies. To improve our understanding of controls on SOM decomposition at scales relevant to such modeling efforts, A and upper B horizon soil samples from 22 National Ecological Observatory Network (NEON) sites spanning the conterminous U.S. were incubated for 52 weeks under conditions representing site-specific mean summer temperature and horizon-specific field capacity (-33 kPa) water potential. Cumulative CO2 respired was periodically measured and normalized by soil organic C content to obtain cumulative specific respiration (CSR). A two-pool decomposition model was fitted to the CSR data to calculate decomposition rates of fast- (kfast) and slow-cycling pools (kslow). Post-LASSO best subsets multiple linear regression was used to construct horizon-specific models of significant predictors for CSR, kfast, and kslow. Significant predictors for all three response variables consisted mostly of proximal factors related to clay-sized fraction mineralogy and SOM composition. Non-crystalline minerals and lower SOM lability negatively affected CSR for both A and B horizons. Significant predictors for decomposition rates varied by horizon and pool. B horizon decomposition rates were positively influenced by nitrogen (N) availability, while an index of pyrogenic C had a negative effect on kfast in both horizons. These results reinforce the recognized need to explicitly represent SOM stabilization via interactions with non-crystalline minerals in ESMs, and they also suggest that increased N inputs could enhance SOM decomposition in the subsoil, highlighting another mechanism beyond shifts in temperature and precipitation regimes that could alter SOM decomposition rates. / Master of Science / Soils contain a large amount of carbon (C) in the form of soil organic matter (SOM), and there is the potential for the increased decomposition of SOM due to warmer temperatures to cause climate change to become worse through the release of additional CO₂ into the atmosphere. However, we still do not know exactly what is most important for predicting how vulnerable SOM is to decomposition at continental scales, and this results in a substantial amount of uncertainty in Earth system models used to predict climate change. To address this question, the proportion of organic C decomposed in soil samples from the topsoil and subsoil from 22 sites across the conterminous U.S. was monitored over the course of a year under optimal moisture conditions and at site-specific summer temperature. Additionally, a mathematical model was fitted to the proportion of organic C decomposed over time to estimate decomposition rates of a quickly decomposing pool of SOM and a slowly decomposing pool of SOM. The proportion of organic C decomposed and decomposition rates were related to soil and site properties using multiple linear regression to find which soil and site properties were most important for predicting these response variables. The type of clay-sized mineral and SOM chemical composition were found to be important predictors of the proportion of organic C decomposed for both topsoil and subsoil samples. The important predictors for decomposition rates varied by pool and by topsoil vs. subsoil. For subsoil decomposition rates, it was found that a greater availability of nitrogen (N) increased decomposition rates, and in the quickly decomposing pool, it was found that fire-derived organic matter slowed decomposition rates. The results of this study showed the general importance of local factors for controlling SOM decomposition. Specifically, it showed that the type of clay-sized mineral present at a site needs to be considered as well as the fact that N might increase SOM decomposition in the subsoil.

<p>Forest canopies are a critical component of forest ecosystems as they influence many important functions. Specifically, the structure of forest canopies is a driver of the magnitude and rate of these functions. Therefore, being able to accurately measure canopy structure is crucial to ensure ecological models and forest management plans are as robust and efficient as possible. However, canopies are complex and dynamic entities and thus their structure can be challenging to accurately measure. Here we study the feasibility of using lidar to measure forest canopy structure across large spatial extents by investigating the compatibility of aerial and terrestrial lidar systems. Building on known structure-function relationships measured with terrestrial lidar, we establish grounds for scaling these relationships to the aerial scale. This would enable accurate measures of canopy structural complexity to be acquired at landscape and regional scales without the time and labor requirements of terrestrial data collection. Our results illustrate the potential for measures of canopy height, vegetation area, horizontal cover, and canopy roughness to be upscaled. Furthermore, we highlight the benefit of utilizing multivariate measures of canopy structure, and the capacity of lidar to identify forest structural types. Moving forward, lidar is a tool to be utilized in tandem with other technologies to best understand the spatial and temporal dynamics of forests and the influence of physical ecosystem structure. </p>

Water quality of a stream or river is influenced by the surrounding landscape and vegetation. The Normalized Difference Vegetation Index (NDVI) is commonly used to characterize landcover and vegetation density. Benthic macroinvertebrates are ubiquitous in freshwater streams and are excellent indicators of the quality of freshwater habitats. Data from one NDVI remote sensing flight and one macroinvertebrate sampling event for eight wadeable stream study sites in the National Ecological Observatory Network (NEON) were acquired. Proportions of high, moderate, and sparse vegetation were calculated for each stream watershed using ArcGIS. Functional feeding groups and tolerance values were assigned to macroinvertebrate taxa. The Fourth-corner and RLQ methods of analysis, available in the ade4 package for R software, were used to evaluate the relationships of macroinvertebrate traits with environmental variables. Hypothesis testing using Model 6 in the ade4 package resulted in p-values of 0.066 and 0.057 for global (overall) significance. Mean NDVI values of moderately vegetated areas and proportion of sparse vegetation were found to be significant to percent shredders at alpha ≤ 0.05. Results of these methods of analysis, when combined with traditional macroinvertebrate sampling metrics, show that NDVI can be a useful, additional tool to characterize a watershed and its effects on macroinvertebrate community composition and structure.