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Understanding the sources and sinks of methane (CH4) is critical to both predicting and mitigating future climate change. There are large uncertainties in the global budget of atmospheric CH4, but natural emissions are estimated to be of a similar magnitude to anthropogenic emissions. To understand CH4 flux from biogenic sources in the United States (US) of America, a multi-scale CH4 observation network focused on CH4 flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and land cover. To determine the gaps in physical infrastructure for developing this network, we need to understand the landscape representativeness of the current infrastructure. We focus here on eddy covariance (EC) flux towers because they are essential for a bottom-up framework that bridges the gap between point-based chamber measurements and airborne or satellite platforms that inform policy decisions and global climate agreements. Using dissimilarity, multidimensional scaling, and cluster analysis, the US was divided into 10 clusters distributed across temperature and precipitation gradients. We evaluated dissimilarity within each cluster for research sites with active CH4 EC towers to identify gaps in existing infrastructure that limit our ability to constrain the contribution of US biogenic CH4 emissions to the global budget. Through our analysis using climate, land cover, and location variables, we identified priority areas for research infrastructure to provide a more complete understanding of the CH4 flux potential of ecosystem types across the US. Clusters corresponding to Alaska and the Rocky Mountains, which are inherently difficult to capture, are the most poorly represented, and all clusters require a greater representation of vegetation types.

Tropical cyclones drive coastal ecosystem dynamics, and their frequency, intensity, and spatial distribution are predicted to shift with climate change. Patterns of resistance and resilience were synthesized for 4138 ecosystem time series from n = 26 storms occurring between 1985 and 2018 in the Northern Hemisphere to predict how coastal ecosystems will respond to future disturbance regimes. Data were grouped by ecosystems (fresh water, salt water, terrestrial, and wetland) and response categories (biogeochemistry, hydrography, mobile biota, sedentary fauna, and vascular plants). We observed a repeated pattern of trade-offs between resistance and resilience across analyses. These patterns are likely the outcomes of evolutionary adaptation, they conform to disturbance theories, and they indicate that consistent rules may govern ecosystem susceptibility to tropical cyclones.

Longleaf pine (Pinus palustris Mill.) savannas and woodlands are known for providing numerous ecosystem services such as promoting biodiversity, reducing risk of wildfire and insect outbreaks, and increasing water yields. In these open pine systems, there is also interest in managing carbon (C) in ways that do not diminish other ecosystem services. Additionally, there may be management strategies for accomplishing these same objectives in plantations and degraded stands that developed from natural regeneration. For example, C accumulation in live trees and C storage in harvested wood products could be increased by extending rotations and converting plantations to multi-aged stands. Belowground C storage could be enhanced by incorporating pyrogenic C into the mineral soil before planting longleaf pines in clearcut areas, but this may be contrary to findings that indicate that minimizing soil disturbance is important for long-term soil C storage. We suggest examining approaches to reduce total ecosystem C emissions that include using targeted browsing or grazing with domesticated livestock to supplement prescribed burning, thereby reducing C emissions from burning. The mastication of woody vegetation followed by a program of frequent prescribed burning could be used to reduce the risk of substantial C emissions from wildfires and to restore function to savannas and woodlands with hardwood encroachment and altered fire regimes. Many of these approaches need to be validated with field studies or model simulations. There is also a need to improve the estimates of dead wood C stocks and C storage in harvested wood products. Finally, eddy covariance techniques have improved our understanding of how disturbances influence longleaf pine C dynamics over multiple time scales. However, there is a need to determine the degree to which different silvicultural approaches, especially those for adapting ecosystems to climate change, influence C accumulation. Overall, our review suggests that there are numerous opportunities for research on C dynamics in longleaf pine ecosystems, and these systems are likely well-positioned to accomplish C objectives while offering other ecosystem services.

Ecosystem process models can be used to predict forest response to disturbances at a range of scales. Selection of the spatial class of model should depend on the scale of the research or management question, and model type should depend on the ecosystem attributes of interest. In some cases, multiple classes of models could be used to address a single research question, with evaluations at each scale having potential benefits and drawbacks. This study examines two classes of models relative to how fire return intervals impact carbon and species dynamics in a southeastern U.S. pineland. A model that can be run as a global class model (ED) and a landscape class model (LANDIS‐II) were parameterized with species inventory data from an experimental Pinus Palustris (longleaf pine) forest in southwest Georgia, and simulations were calibrated with literature values, then validated with eddy‐covariance data from the study site. A variety of fire scenarios that included prescribed fire with a 2‐yr return interval, fire exclusion, and three wildfire scenarios (20‐, 50‐, and 100‐yr return intervals) were used for model runs. Results were compared and evaluated with regard to ecosystem carbon and species dynamics. Both models illustrated that prescribed fire provided the greatest carbon sequestration potential and most stable aboveground biomass through time when compared to the wildfire scenarios. The fire exclusion scenario for LANDIS‐II was the only scenario where prescribed fire did not provide the greatest carbon sequestration potential. However, fire exclusion on the order of centuries was a condition of this outcome and the occurrence of such long fire‐free periods is considered unrealistic in this fire‐prone landscape. Differences between models were primarily the result of the underlying characteristics of each model class, namely the spatial resolution and number of species included. In the end, two vastly different scale models supported the conclusion that high frequency prescribed fire in southeastern U.S. pinelands stabilizes carbon and maintains species composition in an ecosystem that is a known ecological hotspot.

As awareness of climate change increases, the need for carbon neutral fuel sources is growing. Lignocellulosic biofuel derived from pine trees has been suggested as one potential energy source; however, it requires more research before its efficacy for climate change mitigation can be determined. Due to the large share of forest carbon held in soils and the extensive area of pine plantations in the southeast U.S., a better understanding of plantation soil carbon dynamics is critical for biofuel carbon accounting. This study evaluated the effects of canopy development and productivity on soil CO2 efflux, a proxy for soil respiration (Rs), in an intensively managed loblolly pine (Pinus taeda) stand over a period from May 2015 to December 2019. We found that leaf area index (LAI) and gross ecosystem production (GEP), as well as meteorological variables, had significant effects on Rs, but that both overall Rs and soil carbon pools did not increase over the course of the study. We thus hypothesize that GEP and LAI had intra-annual effects on Rs, and that the lack of change in Rs is the result of an increase in autotrophic respiration (Ra) that offset a decrease in decomposition of the previous stand’s organic matter.

Understanding the sources and sinks of CH4 is critical to both predicting and mitigating future climate change. There are large uncertainties in the global budget of atmospheric CH4, but natural emissions are estimated to be of a similar magnitude to total anthropogenic emissions. The largest sources of uncertainty in scaling bottom-up CH4 estimates stem from limited ground-based measurements and the misalignment between drivers of CH4 fluxes and current land use classifications. To understand the CH4 flux potential of natural ecosystems and agricultural lands in the United States (US) of America, a multi-scale CH4 observation network focused on CH4 flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and landcover. To determine the gaps in physical infrastructure for developing this network, we need to understand the representativeness of current measurements. We focus here on eddy covariance (EC) flux towers because they are essential for a bottom-up framework that bridges the gap between point-based chamber measurements and airborne or satellite platforms, informing the remote sensing and modelling communities and policy decisions, all the way to IPCC reports. Using multidimensional scaling and a cluster analysis, the US was divided into 10 clusters that were distributed across temperature and wetness gradients. We evaluated the distance to the medoid condition within each cluster for research sites with EC tower infrastructure to identify the gaps in existing infrastructure that limit our ability to constrain the contribution of US biogenic CH4 emissions to the global budget. These gaps occurred across all EC flux tower networks and independently managed sites as well as in some environmental clusters. Through our analysis using climate, land cover, and location variables, we have identified priority areas to target for research infrastructure to provide a more complete understanding of the CH4 flux potential of ecosystem types across the US.

PREMISE Wetland plants regularly experience physiological stresses resulting from inundation; however, plant responses to the interacting effects of water level and inundation duration are not fully understood. METHODS We conducted a mesocosm experiment on two wetland species, sawgrass (Cladium jamaicense) and muhly grass (Muhlenbergia filipes), that co-dominate many freshwater wetlands in the Florida Everglades. We tracked photosynthesis, respiration, and growth at water levels of -10 (control), 10 (shallow), and 35 cm (deep) with reference to soil surface over 6 months. RESULTS The response of photosynthesis to inundation was nonlinear. Specifically, photosynthetic capacity (Amax ) declined by 25% in sawgrass and by 70% in muhly grass after 1-2 months of inundation. After 4 months, Amax of muhly grass in the deep-water treatment declined to near zero. Inundated sawgrass maintained similar leaf respiration and growth rates as the control, whereas inundated muhly grass suppressed both respiration and growth. At the end of the experiment, sawgrass had similar nonstructural carbohydrate pools in all treatments. By contrast, muhly grass in the deep-water treatment had largely depleted sugar reserves but maintained a similar starch pool as the control, which is critical for post-stress recovery. CONCLUSIONS Overall, the two species exhibited nonlinear and contrasting patterns of carbon uptake and use under inundation stress, which ultimately defines their strategies of surviving regularly flooded habitats. The results suggest that a future scenario with more intensive inundation, due to the water management and climate change, may weaken the dominance of muhly grass in many freshwater wetlands of the Everglades.

Climate change has altered global precipitation patterns and has led to greater variation in hydrological conditions. Wetlands are important globally for their soil carbon storage. Given that wetland carbon processes are primarily driven by hydrology, a comprehensive understanding of the effect of inundation is needed. In this study, we evaluated the effect of water level (WL) and inundation duration (ID) on carbon dioxide (CO2 ) fluxes by analysing a 10-year (2008-2017) eddy covariance dataset from a seasonally inundated freshwater marl prairie in the Everglades National Park. Both gross primary production (GPP) and ecosystem respiration (ER) rates showed declines under inundation. While GPP rates decreased almost linearly as WL and ID increased, ER rates were less responsive to WL increase beyond 30 cm and extended inundation periods. The unequal responses between GPP and ER caused a weaker net ecosystem CO2 sink strength as inundation intensity increased. Eventually, the ecosystem tended to become a net CO2 source on a daily basis when either WL exceeded 46 cm or inundation lasted longer than 7 months. Particularly, with an extended period of high-WLs in 2016 (i.e., WL remained >40 cm for >9 months), the ecosystem became a CO2 source, as opposed to being a sink or neutral for CO2 in other years. Furthermore, the extreme inundation in 2016 was followed by a 4-month postinundation period with lower net ecosystem CO2 uptake compared to other years. Given that inundation plays a key role in controlling ecosystem CO2 balance, we suggest that a future with more intensive inundation caused by climate change or water management activities can weaken the CO2 sink strength of the Everglades freshwater marl prairies and similar wetlands globally, creating a positive feedback to climate change.

Fine dead fuel moisture has a major influence on wildland fire behavior yet the dynamics driving water exchange of fuel particles in forested environments remain poorly understood. Most fire behavior models rely on simple, stand-level fuel moisture estimates, ignoring potentially important variation occurring within fuelbeds that could influence fire behavior. This is especially true in surface fire regimes where variation in fine-scale fuel properties drive fire behavior and subsequent fire effects. Saw palmetto [Serenoa repens (Bartr.) Small] dominated fuelbeds in the pine forests of the southeastern United States have high within stand variation in one of the most fire prone habitats in the world. Pine needles and palmetto fronds dominate the biomass of fine dead fuel types that produce extreme fire behavior. To assess predictors of fine dead fuel moisture, we analyzed fuel moisture dynamics of these two fine dead fuel types over a two-year period in conjunction with under- and overstory forest meteorological data. Using multiple models and time lag analysis of within-stand moisture dynamics, the results indicate that saw palmetto and pine dramatically differ in drying regimes, primarily resulting from different responses to cumulative rainfall, net radiation, and antecedent atmospheric moisture content. Despite being responsive to changes in relative humidity, saw palmetto was significantly dryer than pine under nearly all meteorological conditions, and it was capable of maintaining extremely low fuel moisture despite high relative humidity or rainfall. Our results point to the need to capture additional drivers of microclimatic variation to aid fire managers in accurately predicting within-stand fuel moisture and subsequent fire behavior. Improving the scientific community’s understanding of variation in complex fuel beds is critical for effectively managing risk in fire prone ecosystems.

How aquatic primary productivity influences the carbon (C) sequestering capacity of wetlands is uncertain. We evaluated the magnitude and variability in aquatic C dynamics and compared them to net ecosystem CO2 exchange (NEE) and ecosystem respiration (Reco) rates within calcareous freshwater wetlands in Everglades National Park. We continuously recorded 30-min measurements of dissolved oxygen (DO), water level, water temperature (Twater), and photosynthetically active radiation (PAR). These measurements were coupled with ecosystem CO2 fluxes over 5 years (2012–2016) in a long-hydroperiod peat-rich, freshwater marsh and a short-hydroperiod, freshwater marl prairie. Daily net aquatic primary productivity (NAPP) rates indicated both wetlands were generally net heterotrophic. Gross aquatic primary productivity (GAPP) ranged from 0 to − 6.3 g C m−2 day−1 and aquatic respiration (RAq) from 0 to 6.13 g C m−2 day−1. Nonlinear interactions between water level, Twater, and GAPP and RAq resulted in high variability in NAPP that contributed to NEE. Net aquatic primary productivity accounted for 4–5% of the deviance explained in NEE rates. With respect to the flux magnitude, daily NAPP was a greater proportion of daily NEE at the long-hydroperiod site (mean = 95%) compared to the short-hydroperiod site (mean = 64%). Although we have confirmed the significant contribution of NAPP to NEE in both long- and short-hydroperiod freshwater wetlands, the decoupling of the aquatic and ecosystem fluxes could largely depend on emergent vegetation, the carbonate cycle, and the lateral C flux.

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.

Understanding plant phenological change is of great concern in the context of global climate change. Phenological models can aid in understanding and predicting growing season changes and can be parameterized with gross primary production (GPP) estimated using the eddy covariance (EC) technique. This study used nine years of EC-derived GPP data from three mature subtropical longleaf pine forests in the southeastern United States with differing soil water holding capacity in combination with site-specific micrometeorological data to parameterize a photosynthesis-based phenological model. We evaluated how weather conditions and prescribed fire led to variation in the ecosystem phenological processes. The results suggest that soil water availability had an effect on phenology, and greater soil water availability was associated with a longer growing season (LOS). We also observed that prescribed fire, a common forest management activity in the region, had a limited impact on phenological processes. Dormant season fire had no significant effect on phenological processes by site, but we observed differences in the start of the growing season (SOS) between fire and non-fire years. Fire delayed SOS by 10 d &plusmn, 5 d (SE), and this effect was greater with higher soil water availability, extending SOS by 18 d on average. Fire was also associated with increased sensitivity of spring phenology to radiation and air temperature. We found that interannual climate change and periodic weather anomalies (flood, short-term drought, and long-term drought), controlled annual ecosystem phenological processes more than prescribed fire. When water availability increased following short-term summer drought, the growing season was extended. With future climate change, subtropical areas of the Southeastern US are expected to experience more frequent short-term droughts, which could shorten the region&rsquo, s growing season and lead to a reduction in the longleaf pine ecosystem&rsquo, s carbon sequestration capacity.

The eddy covariance (EC) technique has been widely used to measure ecosystem surface energy fluxes. However, with a relatively small representative area (footprint), errors are often introduced when upscaling EC data based on variables derived from large-footprint satellite products. Furthermore, EC measurements often fail to close the energy balance in ecosystems such as wetlands. As an alternative to EC, large aperture scintillometers (LAS) have been used to measure sensible heat fluxes (H) over representative areas comparable to the resolution of large-scale satellite images. However, because LAS measurements are based on semi-empirical simulations, their accuracy needs further evaluation across ecosystems. Changes in hydrology on the land surface significantly alter measurement conditions as well as the processes of energy transfer, and thus, may affect the accuracy of energy measurements from LAS. To address this issue, we compared the LAS-measured H (HLAS) to EC-derived H (HEC) in a seasonally inundated wetland within Everglades National Park. Overall, the HLAS was 7.1 ± 0.3 (SE) % greater than HEC, and the ratio of HLAS to HEC remained similar between the wet season, when the ecosystem was inundated, and the dry season. However, inundation, as a thermal buffer in the system, reduced the magnitude of H in the wet season and resulted in a smaller difference of seasonal H budget estimates between LAS and EC measurements. Because of the homogenous land surface at our site, the discrepancies between HLAS and HEC could not be explained by footprint mismatch. Compared to the energy balance closure calculated with HEC, the closure calculated with HLAS was improved by 6% during the dry season but remained similar during the wet season. Overall, we conclude that LAS can be a reliable approach to measure H in wetland ecosystems and its measurements were stable with seasonal hydrological changes.

The Everglades short-hydroperiod freshwater prairies exhibit strong reductions in CO2 uptake that coincide with inundation, but the underlying basis is not fully understood. To address one of the processes potentially underlying this decline, we measured photosynthetic capacity of the dominant species, sawgrass (Cladium jamaicense) and muhly grass (Muhlenbergia filipes), during wet and dry seasons (2009–2012 and 2016–2017). The measurements in the seasonally inundated prairie were compared to those taken on a nearby rarely inundated levee situated 29 cm above the prairie with similar species composition. During the dry seasons, muhly grass exhibited a much higher photosynthetic capacity (28.5 μmol m−2 s−1) than sawgrass (14.2 μmol m−2 s−1), and no differences were found between the prairie and levee for either species. During the wet seasons when the prairie was inundated, photosynthetic capacity declined substantially (67%) in the prairie for muhly grass while it remained similar across seasons for sawgrass. Analyses of leaf reflectance, chlorophyll fluorescence and leaf nutrient indicated photosystem impairment was the main driver of photosynthetic capacity reduction for muhly grass when the ecosystem is inundated. Our study suggests that declines in photosynthesis of macrophytes, particularly muhly grass, contribute to low wet season productivity in Everglades short-hydroperiod freshwater prairies.

Globally, soil CO2 efflux rates (Fs) have been linked to changes in soil water content (SWC), rainfall and temperature and/or productivity. However, within an ecosystem, Fs can vary based on site structure and function, which can be affected by a combination of abiotic and biotic factors. This becomes particularly important when an ecosystem is faced with disturbances, such as drought or fire. Site-specific compensatory responses to disturbances may therefore alter C mineralization, as well as root respiration. Hence, single location Fs estimates may not be a representative for ecosystems across their distributional ranges. We conducted a 6-year study along an edaphic moisture gradient of longleaf pine ecosystems that were maintained with prescribed fire, using eddy covariance and soil respiration measurements to address how Fs varies with changes in ecosystem structure and function, as well as disturbances. Lower air temperatures (Tair) decreased Fs at all sites, but that response was also affected by productivity and SWC. Productivity significantly altered Fs rates at all sites, especially when we accounted for changes in temperature and SWC. Plant regrowth post-fire temporarily increased Fs (10–40%), whereas drought reduced Fs at all sites. Our results show that site productivity, Fs and the degree to which ecosystems adapt to climate variations and disturbance can be site specific. Hence, model forecasting of carbon dynamics would strongly benefit from multi-location measurements of Fs across the distributional range of an ecosystem.

We thank the principal investigators of the AmeriFlux network, and the U.S. Department of Energy’s Office of Science for funding the AmeriFlux data resources. We thank the U.S. Department of Energy Lawrence Berkeley Lab Ameriflux Network Management Project for core site support. This research was supported through the National Science Foundation award EF1137306/MIT subaward 5710003122 to the University of California Davis; and other government, industry and foundation sponsors of the MIT Joint Program on the Science and Policy of Global Change. For a complete list of sponsors and U.S. government funding sources, please visit http://globalchange.mit.edu/sponsors/all.

Wetlands are highly productive ecosystems that can sequester large quantities of carbon. However, variation in physiological potential can alter their capacity to sequester carbon. To examine variation in ecosystem physiological capacity, we established three coastal marsh sites in Mississippi and Alabama spanning a productivity gradient. Over 1 year, we measured ecophysiological activity and spectral indices in two vegetation zones within each marsh to develop a better understanding of variation in ecosystem responses and health. Gross ecosystem exchange of carbon and ecosystem respiration rates (Reco) differed significantly among sites, with the highest activity at Grand Bay, Mississippi and Point Aux Pines, Alabama and lower ecophysiological activity at Dauphin Island, Alabama. Net ecosystem exchange was similar for all three study areas because greater carbon assimilation was negated by higher levels of respiration. Spectral indices and leaf area were significantly different by marsh vegetation zone, suggesting that alterations in species composition and plant productivity can have important implications for carbon sequestration. While limited to 1 year, this study establishes a foundation by which to evaluate future research conducted over greater temporal and spatial scales, thereby enhancing our understanding of marsh physiological activity.

Technological advances have allowed in situ monitoring of soil water content in an automated manner. These advances, along with an increase in large-scale networks monitoring soil water content, stress the need for a robust calibration framework that ensures that soil water content measurements are accurate and reliable. We have developed an approach to make consistent and comparable soil water content sensor calibrations across a continental-scale network in a production framework that incorporates a thorough accounting of uncertainties. More than 150 soil blocks of varying characteristics from 33 locations across the United States were used to generate soil-specific calibration coefficients for a capacitance sensor. We found that the manufacturer’s nominal calibration coefficients poorly fit the data for nearly all soil types. This resulted in negative (91% of samples) and positive (5% of samples) biases and a mean root mean square error (RMSE) of 0.123 cm cm (1σ) relative to reference standard measurements. We derived soil-specific coefficients, and when used with the manufacturer’s nominal function, the biases were corrected and the mean RMSE dropped to ±0.017 cm cm (±1σ). A logistic calibration function further reduced the mean RMSE to ±0.016 cm cm (±1σ) and increased the range of soil moistures to which the calibration applied by 18% compared with the manufacturer’s function. However, the uncertainty of the reference standard was notable (±0.022 cm cm), and when propagated in quadrature with RMSE estimates, the combined uncertainty of the calibrated volumetric soil water content values increased to ±0.028 cm cm regardless of the calibration function used.

Wetlands play a critical role in the global carbon (C) budget by sequestering carbon dioxide (CO2) and storing significant amounts of C, but climate change is altering C dynamics in these ecosystems. Therefore, predicting and mapping net ecosystem exchange (NEE) of CO2 in wetlands has emerged as a focus of research to better understand where, how, and when biogeochemical cycles will be modified by human and natural disturbances like climate change. To address this, we developed an object-based machine learning ensemble approach to upscale eddy covariance (EC) CO2 flux measurements across two major wetland ecosystems (cypress swamp and freshwater marshes) in the Greater Everglades of south Florida. We linked 30-m Landsat, water depth, and air temperature data with EC flux measurements using the temporally composite concept for model development, and mapped fluxes within Big Cypress National Preserve (BCNP) and Everglades National Park (ENP) to characterize two distinct seasonal flux patterns. Using an ensemble analysis of three machine learning model outputs (Artificial Neural Network, Support Vector Machine, and Random Forest), our models of NEE at BCNP had a high predictive power with a R2 greater than 0.8, while NEE was predicted at ENP with a R2 greater than 0.5. We also generated an uncertainty map to quantify the prediction diversity caused by application of multiple models in upscaling. This uncertainty map identifies regions that are either easy or challenging to predict from model based analyses. We conclude that integration of Landsat, water depth, and air temperature data is valuable for quantifying CO2 exchange between the atmosphere and Everglades ecosystems, and the developed paradigm is promising for upscaling EC flux measurements in wetlands to study and mitigate disturbances to wetland ecosystem services such as hurricanes, fires, water management and/or climate change.

Measurements from a dense network of GPS receivers have been used to clarify the relationship between substorm auroras and GPS signal corruption as manifested by loss of lock on the received signal. A network of nine receivers was deployed along roadways near the Poker Flat Research Range in central Alaska, with receiver spacing between 15 and 30 km. Instances of large-amplitude phase fluctuations and signal loss of lock were registered in space and time with auroral forms associated with a sequence of westward traveling surges associated with a substorm onset over central Canada. The following conclusions were obtained: (1) The signal corruption originated in the ionospheric E region, between 100 and 150 km altitude, and (2) the GPS links suffering loss of lock were confined to a narrow band (

Recent warming in the Arctic, which has been amplified during the winter1–3, greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO2)4. However, the amount of CO2 released in winter is not known and has not been well represented by ecosystem models or empirically based estimates5,6. Here we synthesize regional in situ observations of CO2 flux from Arctic and boreal soils to assess current and future winter carbon losses from the northern permafrost domain. We estimate a contemporary loss of 1,662 TgC per year from the permafrost region during the winter season (October–April). This loss is greater than the average growing season carbon uptake for this region estimated from process models (−1,032 TgC per year). Extending model predictions to warmer conditions up to 2100 indicates that winter CO2 emissions will increase 17% under a moderate mitigation scenario—Representative Concentration Pathway 4.5—and 41% under business-as-usual emissions scenario—Representative Concentration Pathway 8.5. Our results provide a baseline for winter CO2 emissions from northern terrestrial regions and indicate that enhanced soil CO2 loss due to winter warming may offset growing season carbon uptake under future climatic conditions. Winter warming in the Arctic will increase the CO2 flux from soils. A pan-Arctic analysis shows a current loss of 1,662 TgC per year over the winter, exceeding estimated carbon uptake in the growing season; projections suggest a 17% increase under RCP 4.5 and a 41% increase under RCP 8.5 by 2100.

Hurricanes affect the structure and function of forests by removing leaf area, reducing biomass, and causing plant mortality. Quantifying the effects of hurricanes on the phenological processes of forests can help to develop a better understanding of the responses of these systems to natural disasters. On October 10, 2018 Hurricane Michael made landfall in the northern Gulf of Mexico causing extensive damage to forests within its path. Using a phenology model, we evaluated the short-term response and recovery of phenological processes of two subtropical forests that were affected by the storm. Our results suggest that the hurricane accelerated senescence in autumn following the storm, leading to a shorter growing season. The response was dependent on the structure of the forest prior to the storm and the degree of damage; the forest with a taller canopy had greater damage, and the recovery period was prolonged compared to the forest with a shorter canopy. In the summer of the first year following the hurricane, ecosystem physiological function began to return to pre-hurricane levels which corresponded to a recovery in growing season length. The functional diversity in the understory may have aided recovery of post-hurricane spring phenology. While summer phenology was synchronized with the rate of vegetation coverage and mainly driven by increase in canopy leaf area, these forests have not completely recovered during the study. As extreme weather events and disasters induced by global climate change may become more frequent, our research can provide a reference for post-disaster forest management practices which can be adapted to local conditions and contribute to restoration efforts.

The southeastern US has experienced an increase in the number of extreme heat events since the 1970s, due in part to global change. Despite rising temperatures, greater variability in weather has also led to more freeze events across parts of the Southeast, particularly during El Nino winters. Structural variation in forest stands and plant functional diversity can lead to localized micrometeorological differences that may alter the recovery of different forests from these temperature-induced disturbances. This variation can lead to differences in energy and entropy dynamics, which drive the metabolic response of these systems. Using thermodynamic metrics, we quantified metabolic energy and entropy production in response to extreme heat and freeze events at three longleaf pine savanna sites spanning an edaphic moisture gradient (i.e., xeric, intermediate, mesic). The sites also differed in anthropogenic legacy, with soil tillage occurring at the intermediate site, resulting in greater woody species abundance in the understory and overstory. We found that energy reserves in this ecosystem were built during low precipitation periods and when temperatures were below 20°C. The mesic site, which had the highest plant functional diversity, exhibited an adaptive capacity to temperature extremes by maintaining low metabolic activity throughout temperature disturbances, while the intermediate and xeric sites started with high metabolic activity that gradually declined by ~15% with prolonged temperature extremes. Response to these temperature extremes was a function of hydrological drivers, as lower water availability reduced energy reserves during cold periods and photosynthetic activity during heatwaves – especially at the intermediate and xeric sites as a result of the high energy demand of oaks in the overstory. As climate change continues to alter weather patterns across the globe, it becomes increasingly important to assess metabolic resilience to greater weather variability as a function of ecosystem structure.

The high utility of eddy covariance (EC) data has made it the cornerstone of carbon dynamics research for more than two decades. However, a substantial number of measurements from EC data can be missing for various reasons. Robust gap-filling methods are required to estimate carbon budgets from net ecosystem exchange measurements of CO 2 (NEE) with high precision and accuracy. While the gap-filled methods used have provided unbiased estimates of annual NEE, little research has been done on preserving the variance structures associated with gap-filled flux data. In this project, we used EC data from a longleaf pine ecosystem located in the southeast US to investigate variance preservation in gap-filling methods. We used three non-linear regression approaches to impute artificially created gaps of different sizes via light and temperature response curves: 1) “traditional” fixed monthly window, 2) moving window, and 3) moving window with parameter prediction using physiological drivers. The results of gap-filling simulations showed that the variability of NEE estimates made with moving window and parameter prediction methods were closer to that of observed NEE, whereas the traditional method had overall lower variability. The average root mean square errors (RMSE) of predictions was lower for moving window and parameter prediction (3.38 and 3.22, respectively), versus that of the traditional method (3.42) over one year, including both daytime and nighttime data across all gap sizes. The variances associated with moving window and parameter prediction methods were 52% and 57%, respectively, of the observed variance, versus that of the traditional (51%), while the average of first-order autocorrelation coefficients was 0.76 for each method compared to 0.58 for observed. The results showed that the moving window approaches provided better estimates (lower RMSEs and more similar variance) at annual scales, yet underestimated the observed variance. These results contribute toward the development of a framework of standardized gap-filling approaches which maintain variation inherent in EC data. Moreover, these results call for further research on potential environmental drivers and their interactions for inclusion in gap-filling models, as well as exploration of sampling size of estimation windows and averaging time (half hour) of flux data to promote variance maintenance and decrease the autocorrelation of predictions.

Urban forest research and management requires improved methods for quantifying ecosystem structure and function. Regional equations for urban tree crown width and height can accordingly improve predictions of urban tree structure. Using a large regional dataset with 12 locations in the southeastern US, we developed diameter-based equations for 97 urban tree species. Whereas previously published urban equations have almost exclusively been developed with one location on public or commercial land, our data included both public and private land uses. For 5 widespread, common urban tree species (Acer rubrum, Cornus florida, Pinus taeda, Quercus nigra and Lagerstroemia spp.), we also assessed the inclusion of additional variables such as crown light exposure, land cover, basal area, and location. Overall, height and crown width models were improved when including additional predictors, although competition and location effects varied by species. Study city was a significant predictor of tree height in all species except C. florida, and a significant predictor of crown width for all species except C. florida and Q. nigra. This indicates that anthropogenically-influenced variation among cities can lead to significant differences in both tree form and structure and that future model development should utilize data encompassing multiple cities. Our predictive equations for urban tree crown characteristics provide an improved method for planning, management, and estimating the provision of ecosystem services to improve quality of life in cities. © 2016 Elsevier GmbH