CO2 fertilization of terrestrial photosynthesis inferred from site to global scales.

SignificanceThe magnitude of the CO2 fertilization effect on terrestrial photosynthesis is uncertain because it is not directly observed and is subject to confounding effects of climatic variability. We apply three well-established eco-evolutionary optimality theories of gas exchange and photosynthesis, constraining the main processes of CO2 fertilization using measurable variables. Using this framework, we provide robust observationally inferred evidence that a strong CO2 fertilization effect is detectable in globally distributed eddy covariance networks. Applying our method to upscale photosynthesis globally, we find that the magnitude of the CO2 fertilization effect is comparable to its in situ counterpart but highlight the potential for substantial underestimation of this effect in tropical forests for many reflectance-based satellite photosynthesis products.

Plant responses to elevated CO2 under competing hypotheses of nitrogen and phosphorus limitations.

The future ecosystem carbon cycle has important implications for biosphere-climate feedback. The magnitude of future plant growth and carbon accumulation depends on plant strategies for nutrient uptake under the stresses of nitrogen (N) versus phosphorus (P) limitations. Two archetypal theories have been widely acknowledged in the literature to represent N and P limitations on ecosystem processes: Liebig's Law of the Minimum (LLM) and the Multiple Element Limitation (MEL) approach. LLM states that the more limiting nutrient controls plant growth, and commonly leads to predictions of dramatically dampened ecosystem carbon accumulation over the 21st century. Conversely, the MEL approach recognizes that plants possess multiple pathways to coordinate N and P availability and invest resources to alleviate N or P limitation. We implemented these two contrasting approaches in the E3SM model, and compiled 98 in situ forest N or P fertilization experiments to evaluate how terrestrial ecosystems will respond to N and P limitations. We find that MEL better captured the observed plant responses to nutrient perturbations globally, compared with LLM. Furthermore, LLM and MEL diverged dramatically in responses to elevated CO2 concentrations, leading to a two-fold difference in CO2 fertilization effects on Net Primary Productivity by the end of the 21st century. The larger CO2 fertilization effects indicated by MEL mainly resulted from plant mediation on N and P resource supplies through N2 fixation and phosphatase activities. This analysis provides quantitative evidence of how different N and P limitation strategies can diversely affect future carbon and nutrient dynamics.

Evolutionary lineage explains trait variation among 75 coexisting grass species.

Evolutionary history plays a key role driving patterns of trait variation across plant species. For scaling and modeling purposes, grass species are typically organized into C3 vs C4 plant functional types (PFTs). Plant functional type groupings may obscure important functional differences among species. Rather, grouping grasses by evolutionary lineage may better represent grass functional diversity. We measured 11 structural and physiological traits in situ from 75 grass species within the North American tallgrass prairie. We tested whether traits differed significantly among photosynthetic pathways or lineages (tribe) in annual and perennial grass species. Critically, we found evidence that grass traits varied among lineages, including independent origins of C4 photosynthesis. Using a rigorous model selection approach, tribe was included in the top models for five of nine traits for perennial species. Tribes were separable in a multivariate and phylogenetically controlled analysis of traits, owing to coordination of important structural and ecophysiological characteristics. Our findings suggest grouping grass species by photosynthetic pathway overlooks variation in several functional traits, particularly for C4 species. These results indicate that further assessment of lineage-based differences at other sites and across other grass species distributions may improve representation of C4 species in trait comparison analyses and modeling investigations.

Vegetation clumping modulates global photosynthesis through adjusting canopy light environment.

The spatial dispersion of photoelements within a vegetation canopy, quantified by the clumping index (CI), directly regulates the within-canopy light environment and photosynthesis rate, but is not commonly implemented in terrestrial biosphere models to estimate the ecosystem carbon cycle. A few global CI products have been developed recently with remote sensing measurements, making it possible to examine the global impacts of CI. This study deployed CI in the radiative transfer scheme of the Community Land Model version 5 (CLM5) and used the revised CLM5 to quantitatively evaluate the extent to which CI can affect canopy absorbed radiation and gross primary production (GPP), and for the first time, considering the uncertainty and seasonal variation of CI with multiple remote sensing products. Compared to the results without considering the CI impact, the revised CLM5 estimated that sunlit canopy absorbed up to 9%-15% and 23%-34% less direct and diffuse radiation, respectively, while shaded canopy absorbed 3%-18% more diffuse radiation across different biome types. The CI impacts on canopy light conditions included changes in canopy light absorption, and sunlit-shaded leaf area fraction related to nitrogen distribution and thus the maximum rate of Rubisco carboxylase activity (Vcmax ), which together decreased photosynthesis in sunlit canopy by 5.9-7.2 PgC year-1 while enhanced photosynthesis by 6.9-8.2 PgC year-1 in shaded canopy. With higher light use efficiency of shaded leaves, shaded canopy increased photosynthesis compensated and exceeded the lost photosynthesis in sunlit canopy, resulting in 1.0 ± 0.12 PgC year-1 net increase in GPP. The uncertainty of GPP due to the different input CI datasets was much larger than that caused by CI seasonal variations, and was up to 50% of the magnitude of GPP interannual variations in the tropical regions. This study highlights the necessity of considering the impacts of CI and its uncertainty in terrestrial biosphere models.

Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions.

Wetlands are the largest natural source of methane (CH4 ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH4 , but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model-iPEACE-reasonably reproduced CH4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH4 production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH4 emissions. Based on a sensitivity analysis, CH4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH4 production and CH4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH4 production, plant-mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH4 emissions across biomes. These processes and associated parameters for CH4 emissions among and within the wetlands provide useful insights for interpreting observed net CH4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH4 fluxes.

Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates.

The recent rise in atmospheric methane (CH4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year-1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Representing plant diversity in land models: An evolutionary approach to make "Functional Types" more functional.

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet, Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of "Plant Functional Types" (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, that is, to determine what new PFTs to add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages ("Lineage Functional Types") will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.

Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland.

Permafrost thaw is a major potential feedback source to climate change as it can drive the increased release of greenhouse gases carbon dioxide (CO2 ) and methane (CH4 ). This carbon release from the decomposition of thawing soil organic material can be mitigated by increased net primary productivity (NPP) caused by warming, increasing atmospheric CO2 , and plant community transition. However, the net effect on C storage also depends on how these plant community changes alter plant litter quantity, quality, and decomposition rates. Predicting decomposition rates based on litter quality remains challenging, but a promising new way forward is to incorporate measures of the energetic favorability to soil microbes of plant biomass decomposition. We asked how the variation in one such measure, the nominal oxidation state of carbon (NOSC), interacts with changing quantities of plant material inputs to influence the net C balance of a thawing permafrost peatland. We found: (1) Plant productivity (NPP) increased post-thaw, but instead of contributing to increased standing biomass, it increased plant biomass turnover via increased litter inputs to soil; (2) Plant litter thermodynamic favorability (NOSC) and decomposition rate both increased post-thaw, despite limited changes in bulk C:N ratios; (3) these increases caused the higher NPP to cycle more rapidly through both plants and soil, contributing to higher CO2 and CH4  fluxes from decomposition. Thus, the increased C-storage expected from higher productivity was limited and the high global warming potential of CH4 contributed a net positive warming effect. Although post-thaw peatlands are currently C sinks due to high NPP offsetting high CO2 release, this status is very sensitive to the plant community's litter input rate and quality. Integration of novel bioavailability metrics based on litter chemistry, including NOSC, into studies of ecosystem dynamics, is needed to improve the understanding of controls on arctic C stocks under continued ecosystem transition.

Regional trends and drivers of the global methane budget.

The ongoing development of the Global Carbon Project (GCP) global methane (CH4 ) budget shows a continuation of increasing CH4 emissions and CH4 accumulation in the atmosphere during 2000-2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top-down (TD) (atmospheric and transport model-based) and bottom-up (BU) (inventory- and process model-based) CH4 emission estimates demonstrates robust temporal trends with CH4 emissions increasing in 16 of the 19 regions. Five regions-China, Southeast Asia, USA, South Asia, and Brazil-account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270 Tg CH4  yr-1 in 2008-2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH4 emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH4 burden and the greatest opportunities for greenhouse gas abatement.

Predicting how environmental conditions and smolt body length when entering the marine environment impact individual Atlantic salmon Salmo salar adult return rates.

Populations of Atlantic salmon Salmo salar have experienced precipitous declines in abundance since the 1970s. This decline has been associated with reduced numbers of adult salmon returning to fresh water from their marine migration, i.e., their marine return rates (MRR). Thus, understanding the factors that affect MRR is of crucial conservation importance. The authors used a state-space model with a 13-year time series of individually tagged salmon mark-recapture histories on the River Frome, southern England, to test the effect of smolt body length on their MRR. In addition to smolt length, the model tested for the influence of environmental covariates that were representative of the conditions experienced by the smolts in the early stages of their seaward migration, i.e., from the lower river to the estuary exit. The model indicated that, even when accounting for environmental covariates, smolt body length was an important predictor of MRR. Although larger smolts have a higher probability of returning to their natal river as adults than smaller smolts, and one-sea-winter salmon have a survival rate twice as high as multi-sea-winter salmon, the actual biological mechanisms underpinning this phenomenon remain uncertain. These results have important applications for salmon conservation, as efforts to bolster salmon populations in the freshwater environment should consider methods to improve smolt quality (i.e., body size) as well as smolt quantity.

Scale matters in understanding the complexity of Amazon fires: A response to the Editor.

The Amazon Basin is experiencing climate change, altered hydrological cycles, and forest loss. The processes causing increased fires are complex, and therefore cannot be attributed to climate change or human-induced deforestation alone. Here, we show why the Amazon fires must be understood across spatial scales within the regional coupled system.

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales.

While wetlands are the largest natural source of methane (CH4 ) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.

Public Policy Impact of the COVID-19 Pandemic on Blood Supply in the United States.

The COVID-19 pandemic has precipitated an acute blood shortage for medical transfusions, exacerbating an already tenuous blood supply system in the United States, contributing to the public health crisis, and raising deeper questions regarding emergency preparedness planning for ensuring blood availability. However, these issues around blood availability during the pandemic are related primarily to the decline in supply caused by reduced donations during the pandemic rather than increased demand for transfusion of patients with COVID-19.The challenges to ensure a safe blood supply during the pandemic will continue until a vaccine is developed, effective treatments are available, or the virus goes away. If this virus or a similar virus were capable of transmission through blood, it would have a catastrophic impact on the health care system, causing a future public health emergency that would jeopardize the national blood supply.In this article, we identify the impact of the COVID-19 pandemic on blood supply adequacy, discuss the public health implications, propose recovery strategies, and present recommendations for preparing for the next disruption in blood supply driven by a public health emergency.

Finding Liebig's law of the minimum.

Liebig's law of the minimum (LLM) is often used to interpret empirical biological growth data and model multiple substrates co-limited growth. However, its mechanistic foundation is rarely discussed, even though its validity has been questioned since its introduction in the 1820s. Here we first show that LLM is a crude approximation of the law of mass action, the state of art theory of biochemical reactions, and the LLM model is less accurate than two other approximations of the law of mass action: the synthesizing unit model and the additive model. We corroborate this conclusion using empirical data sets of algae and plants grown under two co-limiting substrates. Based on our analysis, we show that when growth is modeled directly as a function of substrate uptake, the LLM model improperly restricts the organism to be of fixed elemental stoichiometry, making it incapable of consistently resolving biological adaptation, ecological evolution, and community assembly. When growth is modeled as a function of the cellular nutrient quota, the LLM model may obtain good results at the risk of incorrect model parameters as compared to those inferred from the more accurate synthesizing unit model. However, biogeochemical models that implement these three formulations are needed to evaluate which formulation is acceptably accurate and their impacts on predicted long-term ecosystem dynamics. In particular, studies are needed that explore the extent to which parameter calibration can rescue model performance when the mechanistic representation of a biogeochemical process is known to be deficient.

Can the Minimum Protamine Dose to Neutralize Heparin at the Completion of Cardiopulmonary Bypass be Significantly Lower than the Conventional Practice?

Systemic anticoagulation with heparin during cardiopulmonary bypass (CPB) should be neutralized by protamine administration to restore normal hemostasis. However, protamine has potentially serious side effects and excessive protamine can cause increased postoperative bleeding. Thus, our goal is to appropriately dose protamine at the completion of CPB to neutralize heparin so that neither residual heparin nor excessive protamine is present. We performed a retrospective study of 216 patients who underwent cardiac surgery to search for a safe minimum protamine dose (PD) when measuring heparin concentration (HC). In addition, we developed a formula to determine PD using total heparin dose (THD) and CPB time without measuring HC. When protamine-to-heparin ratio (P-to-H) is set at 1 mg protamine to 100 international unit (IU) heparin in HMS Plus Hemostasis Management System (HMS), we determined that 75% of the calculated total PD is a safe minimum PD to sufficiently neutralize circulating heparin after CPB. On average, this translates into either .37 mg protamine/100 IU heparin of THD or .54 mg/100 IU of the first heparin bolus. The formula we developed to calculate PD without measuring HC can provide a PD that strongly agrees with the safe minimum PD when measuring HC. The safe minimum PD to neutralize circulating heparin after CPB can be significantly lower than conventional dosing practices. Reduction of PD may decrease the risk of postoperative bleeding and protamine-related adverse events. Based on our data, we decreased P-to-H in HMS to examine whether it is possible to reduce PD further than the safe minimum PD determined in this study.

Factors associated with errors in the heparin dose response test: recommendations to improve individualized heparin management in cardiopulmonary bypass.

BACKGROUND: A critical aspect of cardiopulmonary bypass (CPB) is to achieve full anticoagulation to prevent thrombosis and consumptive coagulation without using excessive amount of heparin. This can be achieved with heparin dose response (HDR) test in vitro to calculate an individualized heparin bolus to reach a target activated clotting time (ACT) and heparin concentration. However, we often observe that the measured ACT (mACT) with the calculated heparin bolus gives significant errors, both positive (mACT is higher than expected) and negative (mACT is lower), from expected ACT (eACT). METHODS: We performed a retrospective study of 250 patients who underwent cardiac surgery to attain an error distribution of the mACT from eACT with calculated heparin bolus. In addition, it is aimed to identify possible patterns of baseline ACT (bACT), calculated heparin concentration (CHC) and HDR slope that are associated with the significant positive and negative errors. RESULTS: We found that individualized heparin bolus by HDR test is consistently underestimated while it gave a significant number of positive and negative errors. Further analysis indicates that significant negative errors correlate with high bACT and slope and low CHC while significant positive errors with low bACT and slope and high CHC. CONCLUSION: The mACT can be substantially different from eACT. The accuracy of the HDR test appears to be dependent upon bACT, slope, and CHC. Based on our analysis, we provide several recommendations and a flow chart to improve the quality of individualized heparin management on CPB.

Warm winters and cool springs negatively influence recruitment of Atlantic salmon (Salmo salar L.) in a southern England chalk stream.

Previous work suggests that juvenile salmon recruitment in rain-fed rivers is negatively influenced by warm and wet winters and cool springs. We tested whether this is generally applicable to a southern England chalk stream characterized by comparatively stable discharges and temperatures. We found that warm spawning and cool emergence temperatures negatively influenced juvenile recruitment between 2015 and 2020. Together these findings suggest an ability to predict juvenile productivity from water temperature records around spawning and fry emergence, thereby allowing time for management interventions in years of unfavourable temperatures.

Framework for Multisector Alignment Research.

CONTEXT: Public health collaboratives are effective platforms to develop interventions for improving population health. Most collaboratives are limited to the public health and health care delivery sectors; however, multisector collaboratives are becoming more recognized as a strategy for combining efforts from medical, public health, social services, and other sectors. PROGRAM: Based on a 4-year multisector collaborative project, we identify concepts for widening the lens to conduct multisector alignment research. The goal of the collaborative was to address the serious care fragmentation and conflicting financing systems for persons with behavioral health disorders. Our work with these 7 sectors provides insight for creating a framework to conduct multisector alignment research for investigating how alignment problems can be identified, investigated, and applied to achieve systems alignment. IMPLEMENTATION: The multisector collaborative was undertaken in Maricopa County, encompassing Phoenix, Arizona, and consisted of more than 50 organizations representing 7 sectors. EVALUATION: We develop a framework for systems alignment consisting of 4 dimensions (alignment problems, alignment mechanisms, alignment solutions, and goal attainment) and a vocabulary for implementing multisector alignment research. We then describe the interplay and reciprocity between the 4 dimensions. DISCUSSION: This framework can be used by multisector collaboratives to help identify strategies, implement programs, and develop metrics to assess impact on population health and equity.

Lineage-based functional types: characterising functional diversity to enhance the representation of ecological behaviour in Land Surface Models.

Process-based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass-dominated ecosystems are broadly distributed across all vegetated continents and harbour large functional diversity, yet most Land Surface Models (LSMs) summarise grasses into two generic PFTs based primarily on differences between temperate C3 grasses and (sub)tropical C4 grasses. Incorporation of species-level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance-related) of dominant lineages to improve LSM representations. Our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage-based functional types (LFTs), situated between species-level trait data and PFT-level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.