John S. King’s research while affiliated with North Carolina State University and other places

Wetlands are the largest and most climate‐sensitive natural sources of methane. Accurately estimating wetland methane emissions involves reconciling inversion (“top‐down”) and process‐based (“bottom‐up”) models within the global methane budget. However, estimates from these two model types are inherently interdependent and often reveal substantial discrepancies. To enhance the reliability of both approaches, we need a comprehensive understanding of wetland methane emissions and an independent high‐resolution long‐term flux data set. Here, we employed a data‐driven random forest approach to identify key variables influencing methane emissions from subtropical freshwater wetlands in the Southeastern United States. The model‐estimated monthly mean methane fluxes fit well with measured methane fluxes (R² = 0.67) at four representative FLUXNET‐CH4 wetland sites across the region. Variable importance analysis highlighted the sensitivity of subtropical freshwater wetland methane emissions to variations in both temperature and water levels. High temperatures facilitate methanogenesis by enhancing microbial activities, while elevated water levels maintain anaerobic conditions necessary for methane production. Notably, the response of methane emissions to water level fluctuations is contingent on temperature conditions, and vice versa. Moreover, we constructed the first high‐spatial‐resolution (∼1 km × 1 km) and long‐term (1982–2010) gridded regional wetland methane flux product for the Southeastern United States, estimating annual methane emissions from subtropical freshwater wetlands in the region at 4.93 ± 0.11 Tg CH4 yr⁻¹ for 1982–2010. This new benchmark product holds promise for validating and parameterizing uncertain wetland methane emission processes in bottom‐up models and provides improved prior information for top‐down models.

Bottomland hardwood wetland forests along the Atlantic Coast of the United States have been changing over time; this change has been exceptionally apparent in the last two decades. Tree mortality is one of the most visually striking changes occurring in these coastal forests today. Using 2009–2019 tree mortality data from a bottomland hardwood forest monitored for long-term flux studies in North Carolina, we evaluated species composition and tree mortality trends and partitioned variance among hydrologic (e.g., sea level rise (SLR), groundwater table depth), biological (leaf area index (LAI)), and climatic (solar radiation and air temperature) variables affecting tree mortality. Results showed that the tree mortality rate rose from 1.64% in 2009 to 45.82% over 10 years. Tree mortality was primarily explained by a structural equation model (SEM) with R² estimates indicating the importance of hydrologic (R² = 0.65), biological (R² = 0.37), and climatic (R² = 0.10) variables. Prolonged inundation, SLR, and other stressors drove the early stages of ‘ghost forest’ formation in a formerly healthy forested wetland relatively far inland from the nearest coastline. This study contributes to a growing understanding of widespread coastal ecosystem transition as the continental margin adjusts to rising sea levels, which needs to be accounted for in ecosystem modeling frameworks.

Aim: To quantify the intra-community variability of leaf-out (ICVLo) among dominant trees in temperate deciduous forests, assess its links with specific and phylogenetic diversity, identify its environmental drivers and deduce its ecological consequences with regard to radiation received and exposure to late frost. Location: Eastern North America (ENA) and Europe (EUR). Time Period: 2009-2022. Major Taxa Studied: Temperate deciduous forest trees. Methods: We developed an approach to quantify ICVLo through the analysis of RGB images taken from phenological cameras. We related ICVLo to species richness, phylogenetic diversity and environmental conditions. We quantified the intra-community variability of the amount of radiation received and of exposure to late frost. Results: Leaf-out occurred over a longer time interval in ENA than in EUR. The sensitivity of leaf-out to temperature was identical in both regions (−3.4 days per °C). The distributions of ICVLo were similar in EUR and ENA forests, despite the latter being more species-rich and phylogenetically diverse. In both regions, cooler conditions and an earlier occurrence of leaf-out resulted in higher ICVLo. ICVLo resulted in ca. 8% difference of radiation received from leaf-out to September among individual trees. Forest communities in ENA had shorter safety margins as regards the exposure to late frosts, and were actually more frequently exposed to late frosts. Main Conclusions: We conducted the first intercontinental analysis of the variability of leaf-out at the scale of tree communities. North American and European forests showed similar ICVLo, in spite of their differences in terms of species richness and phylogenetic diversity, highlighting the relevance of environmental controls on ICVLo. We quantified two ecological implications of ICVLo (difference in terms of radiation received and exposure to late frost), which should be explored in the context of ongoing climate change, which affects trees differently according to their phenological niche.

The short-rotation coppice (SRC) culture of trees provides a sustainable form of renewable biomass energy, while simultaneously sequestering carbon and contributing to the regional carbon feedstock balance. To understand the role of SRC in carbon feedstock balances, field inventories with selective destructive tree sampling are commonly used to estimate aboveground biomass (AGB) and canopy structure dynamics. However, these methods are resource intensive and spatially limited. To address these constraints, we examined the utility of publicly available airborne Light Detection and Ranging (LiDAR) data and easily accessible imagery from Unmanned Aerial Systems (UASs) to estimate the AGB and canopy structure of an American sycamore SRC in the piedmont region of North Carolina, USA. We compared LiDAR-derived AGB estimates to field estimates from 2015, and UAS-derived AGB estimates to field estimates from 2022 across four planting densities (10,000, 5000, 2500, and 1250 trees per hectare (tph)). The results showed significant effects of planting density treatments on LIDAR- and UAS-derived canopy metrics and significant relationships between these canopy metrics and AGB. In the 10,000 tph, the field-estimated AGB in 2015 (7.00 ± 1.56 Mg ha⁻¹) and LiDAR-derived AGB (7.19 ± 0.13 Mg ha⁻¹) were comparable. On the other hand, the UAS-derived AGB was overestimated in the 10,000 tph planting density and underestimated in the 1250 tph compared to the 2022 field-estimated AGB. This study demonstrates that the remote sensing-derived estimates are within an acceptable level of error for biomass estimation when compared to precise field estimates, thereby showing the potential for increasing the use of accessible remote-sensing technology to estimate AGB of SRC plantations.

The growth performance of short-rotation woody coppice (SRWC) is strongly influenced by successful establishment in the initial months after planting. Future climates, expected to be warmer due to elevated atmospheric CO 2 (eCO 2), may bring about more frequent soil droughts alongside increased vapour pressure deficit (eVPD). Hence, this growth chamber experiment aimed to explore the interactive effects of eVPD, eCO 2 , and soil drought on growth and physiology traits of juvenile hybrid poplars under warmer climates. Our findings with juvenile hybrid poplar J-105 revealed that eVPD resulted in reductions in leaf area (-21%), root (-20%) and stem biomass (-9%), as well as in net assimilation (-15%), stomatal conductance (-26%), and transpiration (-13%). However, these decreases were relatively minor compared to the compensating effect of eCO 2 , which generally exerted a stronger influence than eVPD. While soil drought emerged as the primary growth-limiting factor in our study, elevated VPD is not expected to pose a significant additional threat to central European SRWC plantations of juvenile hybrid poplars under future conditions of ongoing climate change.

Evapotranspiration (ET) links water, energy, and carbon balances, and its magnitude and patterns are changing due to climate and land use change in the southeastern U.S. Quantifying the environmental controls on ET is essential for developing reliable ecohydrological models for water resources management. Here, we synthesized eddy covariance data from 24 AmeriFlux sites distributed across the southeastern U.S., comprising 162 site-years of flux data representing six representative ecosystems including cropland vegetation mosaic (CVM), deciduous broadleaf forests (DBF), evergreen needle-leaf forests (ENF), grasslands (GRA), savannas (SAV), and wetlands (WET). Our objectives were to assess the daily, seasonal, and annual variability in ET and to develop practical predictive models for regional applications in ecosystem service analysis. We evaluated the response of ET to climatic and biotic forcings including potential evapotranspiration (PET), precipitation (P), and leaf area index (LAI), and compared the performance of these empirical ET models based and those developed using machine learning algorithms. Our results showed that the mean daily ET varied significantly, ranging from 1.36 mm d − 1 in GRA to 2.30 mm d − 1 in SAV, with a numerical order : GRA < DBF < ENF < WET < CVM < SAV. In this humid region, mean annual PET exceeded P in 16 out of the 24 flux sites. Using the Budyko framework, we showed that ENF had the highest evaporative efficiency (ET/P). PET and leaf area index (LAI) emerged as the most influential factors explaining ET variability. Artificial neural networks (ANN) and random forest (RF) models demonstrated superior capabilities in predicting monthly ET across sites over generalized additive modeling (GAM) and multiple linear regression (MLR) methods. The present study confirmed that the Southeast region is generally 'energy limited', implying that atmospheric demand along with vegetation information can be used to reliably estimate monthly and annual ET. Our study provides valuable insights into how ET of specific ecosystems is controlled by climatic and land surface drivers, enabling the development of reliable predictive models for regional extrapolation of flux measurements in water resource management in the humid southeastern U.S. region.

Aim. To quantify the intra-community variability of leaf-out (ICVLo) among dominant trees in temperate deciduous forests, assess its links with specific and phylogenetic diversity, identify its environmental drivers, and deduce its ecological consequences with regard to radiation received and exposure to late frost. Location. Eastern North America (ENA) and Europe (EUR). Time period. 2009-2022 Major taxa studied. Temperate deciduous forest trees. Methods. We developed an approach to quantify ICVLo through the analysis of RGB images taken from phenological cameras. We related ICVLo to species richness, phylogenetic diversity and environmental conditions. We quantified the intra-community variability of the amount of radiation received and of exposure to late frost. Results. Leaf-out occurred over a longer time interval in ENA than in EUR. The sensitivity of leaf-out to temperature was identical in both regions (-3.4 days per°C). The distributions of ICVLo were similar in EUR and ENA forests, despite the latter being more species-rich and phylogenetically diverse. In both regions, cooler conditions and an earlier occurrence of leaf-out resulted in higher ICVLo. ICVLo resulted in a ca. 8% difference of radiation absorption over spring among individual trees. Forest communities in ENA had shorter safety margins as regards the exposure to late frosts, and were actually more frequently exposed to late frosts. Main conclusions. We conducted the first intercontinental analysis of the variability of leaf-out at the scale of tree communities. North American and European forests showed similar ICVLo, in spite of their differences in terms of species richness and phylogenetic diversity, highlighting the relevance of environmental controls on ICVLo. We quantified two ecological implications of ICVLo

Two micrometeorological methods utilizing high-frequency sampled air temperature were tested against eddy covariance (EC) sensible heat flux (H) measurements at three sites representing agricultural, agro-forestry, and forestry systems. The two methods cover conventional and newly proposed forms of the flux-variance (FV) and surface renewal (SR) schemes of differing complexities. The sites represent measurements in surface, roughness, and roughness to surface transitional layers. Regression analyzes against EC show that the most reliable FV and SR forms estimate H with slopes within ±10% from unity and coefficient of determination R2>0.9 across all the three sites. The best performance of both FV and SR was found at the agricultural site with measurements well within the surface layer, while the worst was found for the tall forest with measurements within the roughness sublayer where its thickness needed to be additionally estimated. The main variable driving H in FV is the temperature variance, whereas in SR, it is the geometry of ramp-like structures. Since these structures are also responsible for most of the temperature variance, a novel FV-SR approach emerging from combining the methods is proposed and evaluated against EC measurements and conventional FV and SR schemes. The proposed FV-SR approach requiring only a single fast response thermocouple is potentially independent of calibration and ameliorates some of the theoretical objections that arise when combining ramp statistics with similarity arguments. The combination of methods also provides new insights into the contribution of coherent structures to the temperature variance and its dependence on atmospheric stratification. Other potential utility of the new method is to include it in multi-tool assessments of surface energy fluxes, since a convergence or divergence of the results has a high diagnostic value.