Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems.

Climate warming is expected to increase global methane (CH4 ) emissions from wetland ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can potentially detect CH4 flux changes, most EC systems have only a few years of data collected, so temporal trends in CH4 remain uncertain. Here, we use established drivers to hindcast changes in CH4 fluxes (FCH4 ) since the early 1980s. We trained a machine learning (ML) model on CH4 flux measurements from 22 [methane-producing sites] in wetland, upland, and lake sites of the FLUXNET-CH4 database with at least two full years of measurements across temperate and boreal biomes. The gradient boosting decision tree ML model then hindcasted daily FCH4 over 1981-2018 using meteorological reanalysis data. We found that, mainly driven by rising temperature, half of the sites (n = 11) showed significant increases in annual, seasonal, and extreme FCH4 , with increases in FCH4 of ca. 10% or higher found in the fall from 1981-1989 to 2010-2018. The annual trends were driven by increases during summer and fall, particularly at high-CH4 -emitting fen sites dominated by aerenchymatous plants. We also found that the distribution of days of extremely high FCH4 (defined according to the 95th percentile of the daily FCH4 values over a reference period) have become more frequent during the last four decades and currently account for 10-40% of the total seasonal fluxes. The share of extreme FCH4 days in the total seasonal fluxes was greatest in winter for boreal/taiga sites and in spring for temperate sites, which highlights the increasing importance of the non-growing seasons in annual budgets. Our results shed light on the effects of climate warming on wetlands, which appears to be extending the CH4 emission seasons and boosting extreme emissions.

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.

Practical Guide to Measuring Wetland Carbon Pools and Fluxes.

Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We first define each of the major C pools and fluxes and provide rationale for their importance to wetland C dynamics. For each approach, we clarify what component of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such as where and when an approach is typically used, who can conduct the measurements (expertise, training requirements), and how approaches are conducted, including considerations on equipment complexity and costs. Finally, we review key covariates and ancillary measurements that enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13157-023-01722-2.

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.

Where old meets new: An ecosystem study of methanogenesis in a reflooded agricultural peatland.

Reflooding formerly drained peatlands has been proposed as a means to reduce losses of organic matter and sequester soil carbon for climate change mitigation, but a renewal of high methane emissions has been reported for these ecosystems, offsetting mitigation potential. Our ability to interpret observed methane fluxes in reflooded peatlands and make predictions about future flux trends is limited due to a lack of detailed studies of methanogenic processes. In this study we investigate methanogenesis in a reflooded agricultural peatland in the Sacramento Delta, California. We use the stable-and radio-carbon isotopic signatures of wetland sediment methane, ecosystem-scale eddy covariance flux observations, and laboratory incubation experiments, to identify which carbon sources and methanogenic production pathways fuel methanogenesis and how these processes are affected by vegetation and seasonality. We found that the old peat contribution to annual methane emissions was large (~30%) compared to intact wetlands, indicating a biogeochemical legacy of drainage. However, fresh carbon and the acetoclastic pathway still accounted for the majority of methanogenesis throughout the year. Although temperature sensitivities for bulk peat methanogenesis were similar between open-water (Q10  = 2.1) and vegetated (Q10  = 2.3) soils, methane production from both fresh and old carbon sources showed pronounced seasonality in vegetated zones. We conclude that high methane emissions in restored wetlands constitute a biogeochemical trade-off with contemporary carbon uptake, given that methane efflux is fueled primarily by fresh carbon inputs.

Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis.

Methane flux (FCH4 ) measurements using the eddy covariance technique have increased over the past decade. FCH4 measurements commonly include data gaps, as is the case with CO2 and energy fluxes. However, gap-filling FCH4 data are more challenging than other fluxes due to its unique characteristics including multidriver dependency, variabilities across multiple timescales, nonstationarity, spatial heterogeneity of flux footprints, and lagged influence of biophysical drivers. Some researchers have applied a marginal distribution sampling (MDS) algorithm, a standard gap-filling method for other fluxes, to FCH4 datasets, and others have applied artificial neural networks (ANN) to resolve the challenging characteristics of FCH4 . However, there is still no consensus regarding FCH4 gap-filling methods due to limited comparative research. We are not aware of the applications of machine learning (ML) algorithms beyond ANN to FCH4 datasets. Here, we compare the performance of MDS and three ML algorithms (ANN, random forest [RF], and support vector machine [SVM]) using multiple combinations of ancillary variables. In addition, we applied principal component analysis (PCA) as an input to the algorithms to address multidriver dependency of FCH4 and reduce the internal complexity of the algorithmic structures. We applied this approach to five benchmark FCH4 datasets from both natural and managed systems located in temperate and tropical wetlands and rice paddies. Results indicate that PCA improved the performance of MDS compared to traditional inputs. ML algorithms performed better when using all available biophysical variables compared to using PCA-derived inputs. Overall, RF was found to outperform other techniques for all sites. We found gap-filling uncertainty is much larger than measurement uncertainty in accumulated CH4 budget. Therefore, the approach used for FCH4 gap filling can have important implications for characterizing annual ecosystem-scale methane budgets, the accuracy of which is important for evaluating natural and managed systems and their interactions with global change processes.

Function and Caregiver Support Associated With Readmissions During Home Health for Individuals With Dementia.

OBJECTIVE: The purpose of this study was to determine the association between mobility, self-care, cognition, and caregiver support and 30-day potentially preventable readmissions (PPR) for individuals with dementia. DESIGN: This retrospective study derived data from 100% national Centers for Medicare and Medicaid Services data files from July 1, 2013, through June 1, 2015. PARTICIPANTS: Criteria from the Home Health Claims-Based Rehospitalization Measure and the Potentially Preventable 30-Day Post Discharge Readmission Measure for the Home Health Quality Reporting Program were used to identify a cohort of 118,171 Medicare beneficiaries. MAIN OUTCOME MEASURE: The 30-day PPR rates with associated 95% CIs were calculated for each patient characteristic. Multilevel logistic regression was used to study the relationship between mobility, self-care, caregiver support, and cognition domains and 30-day PPR during home health, adjusting for patient demographics and clinical characteristics. RESULTS: The overall rate of 30-day PPR was 7.6%. In the fully adjusted models, patients who were most dependent in mobility (odds ratio [OR], 1.59; 95% CI, 1.47-1.71) and self-care (OR, 1.73; 95% CI, 1.61-1.87) had higher odds for 30-day PPR. Patients with unmet caregiving needs had 1.11 (95% CI, 1.05-1.17) higher odds for 30-day PPR than patients whose caregiving needs were met. Patients with cognitive impairment had 1.23 (95% CI, 1.16-1.30) higher odds of readmission than those with minimal to no cognitive impairment. CONCLUSIONS: Decreased independence in mobility and self-care tasks, unmet caregiver needs, and impaired cognitive processing at admission to home health are associated with risk of 30-day PPR during home health for individuals with dementia. Our findings indicate that deficits in mobility and self-care tasks have the greatest effect on the risk for PPR.

Pneumonia Readmissions in Older Adults With Dementia.

BACKGROUND/OBJECTIVES: Pneumonia readmissions have significant quality of care and policy implications for patients and health care providers. Research indicates that initiatives to decrease readmissions should target high-risk subgroups. Older adults with dementia have an increased risk of pneumonia and subsequent hospitalizations, suggesting that they may be at high-risk of pneumonia readmissions. The purpose of this study was to determine if associations between patient factors and readmission rates differ for older adults with and without dementia who were hospitalized for pneumonia. DESIGN: This was a retrospective study of secondary data. PARTICIPANTS: A nationally representative sample of 389,198 discharge records was extracted from the 2013 Nationwide Readmission Database. MEASURES: Differences between groups were analyzed using χ and t tests. A generalized linear model was utilized to examine associations between patient factors and pneumonia readmissions. RESULTS: Significant differences were found (P<0.001) when comparing patient characteristics of older adults with and without dementia who were readmitted. Older adults with dementia had a readmission rate of 23.5% and were 2.9 times more likely to be readmitted (odds ratio; 95% confidence interval, 1.93, 4.40) than older adults without dementia. Associations were calculated using a generalized linear model with dementia included as an interactive effect. Dementia significantly modified (P<0.05) the relationship between pneumonia readmissions and 4 factors; (a) discharge disposition, (b) chronic conditions, (c) risk of mortality, and (d) median household income. CONCLUSIONS: Classifying older adults with dementia as a high-risk subgroup for pneumonia readmissions is supported by the findings of this study. Development of strategies to reduce pneumonia readmissions that are tailored to individuals with dementia should be considered.

Functional Status Is Associated With 30-Day Potentially Preventable Readmissions Following Home Health Care.

BACKGROUND: Beginning in 2019, home health agencies' rates of potentially preventable hospital readmissions over the 30 days following discharge will be publicly reported. OBJECTIVES: Our primary objective was to determine the association between patients' functional status at discharge from home health care and 30-day potentially preventable readmissions. A secondary objective was to identify the most common conditions resulting in potentially preventable readmissions. DESIGN: This was a retrospective cohort study. PARTICIPANTS: A total of 1,510,297 Medicare fee-for-service beneficiaries discharged from home health care in 2013-2015. Average age was 75.9 (SD, 10.9) years, 60.0% were female, and 84.2% non-Hispanic white. MEASUREMENTS: Thirty-day potentially preventable readmissions following home health discharge. Functional status measures included mobility, self-care, and impaired cognition. RESULTS: The overall rate of 30-day potentially preventable readmissions was 2.6% (N=39,452), which accounted for 40% of all 30-day readmissions. After adjusting for sociodemographic and clinical characteristics, the odds ratios for the most dependent score quartile versus the most independent was 1.58 [95% confidence interval (CI), 1.53-1.63] for mobility and 1.65 (95% CI, 1.59-1.69) for self-care. The odds ratios for impaired versus intact cognition was 1.21 (95% CI, 1.18-1.24). The 5 most common conditions resulting in a potentially preventable readmission were congestive heart failure (23.6%), septicemia (16.7%), bacterial pneumonia (9.8%), chronic obstructive pulmonary disease (9.4%), and renal failure (7.5%). CONCLUSIONS: Functional limitations at discharge from home health are associated with increased risk for potentially preventable readmissions. Future research is needed to determine whether improving functional independence decreases the risk for potentially preventable readmissions following home health care.

Effects of seasonality, transport pathway, and spatial structure on greenhouse gas fluxes in a restored wetland.

Wetlands can influence global climate via greenhouse gas (GHG) exchange of carbon dioxide (CO2 ), methane (CH4 ), and nitrous oxide (N2 O). Few studies have quantified the full GHG budget of wetlands due to the high spatial and temporal variability of fluxes. We report annual open-water diffusion and ebullition fluxes of CO2 , CH4 , and N2 O from a restored emergent marsh ecosystem. We combined these data with concurrent eddy-covariance measurements of whole-ecosystem CO2 and CH4 exchange to estimate GHG fluxes and associated radiative forcing effects for the whole wetland, and separately for open-water and vegetated cover types. Annual open-water CO2 , CH4 , and N2 O emissions were 915 ± 95 g C-CO2  m-2  yr-1 , 2.9 ± 0.5 g C-CH4  m-2  yr-1 , and 62 ± 17 mg N-N2 O m-2  yr-1 , respectively. Diffusion dominated open-water GHG transport, accounting for >99% of CO2 and N2 O emissions, and ~71% of CH4 emissions. Seasonality was minor for CO2 emissions, whereas CH4 and N2 O fluxes displayed strong and asynchronous seasonal dynamics. Notably, the overall radiative forcing of open-water fluxes (3.5 ± 0.3 kg CO2 -eq m-2  yr-1 ) exceeded that of vegetated zones (1.4 ± 0.4 kg CO2 -eq m-2  yr-1 ) due to high ecosystem respiration. After scaling results to the entire wetland using object-based cover classification of remote sensing imagery, net uptake of CO2 (-1.4 ± 0.6 kt CO2 -eq yr-1 ) did not offset CH4 emission (3.7 ± 0.03 kt CO2 -eq yr-1 ), producing an overall positive radiative forcing effect of 2.4 ± 0.3 kt CO2 -eq yr-1 . These results demonstrate clear effects of seasonality, spatial structure, and transport pathway on the magnitude and composition of wetland GHG emissions, and the efficacy of multiscale flux measurement to overcome challenges of wetland heterogeneity.

Agricultural peatland restoration: effects of land-use change on greenhouse gas (CO2 and CH4) fluxes in the Sacramento-San Joaquin Delta.

Agricultural drainage of organic soils has resulted in vast soil subsidence and contributed to increased atmospheric carbon dioxide (CO2) concentrations. The Sacramento-San Joaquin Delta in California was drained over a century ago for agriculture and human settlement and has since experienced subsidence rates that are among the highest in the world. It is recognized that drained agriculture in the Delta is unsustainable in the long-term, and to help reverse subsidence and capture carbon (C) there is an interest in restoring drained agricultural land-use types to flooded conditions. However, flooding may increase methane (CH4) emissions. We conducted a full year of simultaneous eddy covariance measurements at two conventional drained agricultural peatlands (a pasture and a corn field) and three flooded land-use types (a rice paddy and two restored wetlands) to assess the impact of drained to flooded land-use change on CO2 and CH4 fluxes in the Delta. We found that the drained sites were net C and greenhouse gas (GHG) sources, releasing up to 341 g C m(-2) yr(-1) as CO2 and 11.4 g C m(-2) yr(-1) as CH4. Conversely, the restored wetlands were net sinks of atmospheric CO2, sequestering up to 397 g C m(-2) yr(-1). However, they were large sources of CH4, with emissions ranging from 39 to 53 g C m(-2) yr(-1). In terms of the full GHG budget, the restored wetlands could be either GHG sources or sinks. Although the rice paddy was a small atmospheric CO2 sink, when considering harvest and CH4 emissions, it acted as both a C and GHG source. Annual photosynthesis was similar between sites, but flooding at the restored sites inhibited ecosystem respiration, making them net CO2 sinks. This study suggests that converting drained agricultural peat soils to flooded land-use types can help reduce or reverse soil subsidence and reduce GHG emissions.

Successful Discharge to Community From Home Health Less Likely for People in Late Stages of Dementia.

BACKGROUND AND PURPOSE: Several studies have established the efficacy of home health in meeting the health care needs of people with Alzheimer disease and related dementias (ADRD) and helping them to remain at home. However, transitioning to the community after discharge from home health presents challenges to patient safety and quality of life. The severity of an individual's functional impairments, cognitive limitations, and behavioral and psychological symptoms may compound these challenges. The purpose of this study was to examine the association between dementia severity and successful discharge to community (DTC) from home health. METHODS: This was a retrospective study of 142 376 Medicare beneficiaries with ADRD. Successful DTC was defined as having no unplanned hospitalization or death within 30 days of DTC from home health. Successful DTC rates were calculated, and multilevel logistic regression was used to estimate the relative risk (RR) of successful DTC, by dementia severity category, adjusted for patient and clinical characteristics. Six dementia severity categories were identified using a crosswalk between items on the Outcome and Assessment Information Set and the Functional Assessment Staging Tool. RESULTS AND DISCUSSION: Successful DTC occurred in 71.2% of beneficiaries. Beneficiaries in the 2 most severe dementia categories had significantly lower risk of successful DTC (category 6: RR = 0.90, 95% CI = 0.889-0.910; category 7: RR = 0.737, 95% CI = 0.704-0.770) than those in the least severe dementia category. The RR of successful DTC for people with ADRD decreased as the level of independence with oral medication management decreased and when there was an overall greater need for caregiver assistance. CONCLUSIONS: Patient status at the time of admission to home health is associated with outcomes after discharge from home health. Early identification of people in advanced stages of ADRD provides an opportunity to implement strategies to facilitate successful DTC while people are still receiving home care services. The severity of ADRD and availability of caregiver assistance should be key considerations in planning for successful DTC for people with ADRD.

The Role of Neighborhood Socioeconomic Status in Institutionalization of Home Health Care Patients With and Without Alzheimer's Disease and Related Dementias.

OBJECTIVES: To assess whether neighborhood socioeconomic status (SES) moderates the association between Alzheimer's disease and related dementias (ADRD) and successful discharge to the community. In addition, to explore whether the role of neighborhood SES on successful discharge for patients with ADRD varies by the severity of ADRD. DESIGN: This is a retrospective cohort study. SETTING AND PARTICIPANTS: Medicare Fee-for-service beneficiaries, aged 65 or older, who received home health care in 2019. METHODS: We used linear probability regression models with successful discharge to the community as the main outcome, and neighborhood SES and ADRD as independent variables. Also, we modified the Functional Assessment Staging Tool (FAST) to measure ADRD severity. RESULTS: Our study results show ADRD and residing in neighborhoods with lower socioeconomic conditions were independently associated with lower probabilities of successful discharge to the community. We also found that the differences in probabilities of remaining at home between patients with and without ADRD were larger among those in neighborhoods with lower SES (ADRD∗less disadvantaged neighborhood, coeff: -0.01, P < .001; ADRD∗more disadvantaged neighborhood, coeff: -0.02, P < .001; ADRD∗most disadvantaged neighborhood, coeff: 0.032, P < .001). Among patients with ADRD, patients with the most advanced ADRD were less likely to remain in their homes and community when living in neighborhoods with lower SES. CONCLUSIONS AND IMPLICATIONS: Our study results show that when patients with ADRD receiving home health care live in neighborhoods with lower SES, they face further challenges to remaining in their homes and community. Public health officials and community planners should consider using area-level interventions to improve care and health outcomes for patients with ADRD. Also, further research aimed at identifying the specific factors and resources influencing lower care quality and poorer health outcomes in socioeconomically disadvantaged neighborhoods, particularly for patients with ADRD, can provide valuable insights for the development and implementation of targeted interventions.

Boreal-Arctic wetland methane emissions modulated by warming and vegetation activity.

Wetland methane (CH4) emissions over the Boreal-Arctic region are vulnerable to climate change and linked to climate feedbacks, yet understanding of their long-term dynamics remains uncertain. Here, we upscaled and analysed two decades (2002-2021) of Boreal-Arctic wetland CH4 emissions, representing an unprecedented compilation of eddy covariance and chamber observations. We found a robust increasing trend of CH4 emissions (+8.9%) with strong inter-annual variability. The majority of emission increases occurred in early summer (June and July) and were mainly driven by warming (52.3%) and ecosystem productivity (40.7%). Moreover, a 2 °C temperature anomaly in 2016 led to the highest recorded annual CH4 emissions (22.3 Tg CH4 yr-1) over this region, driven primarily by high emissions over Western Siberian lowlands. However, current-generation models from the Global Carbon Project failed to capture the emission magnitude and trend, and may bias the estimates in future wetland CH4 emission driven by amplified Boreal-Arctic warming and greening.

Salinity causes widespread restriction of methane emissions from small inland waters.

Inland waters are one of the largest natural sources of methane (CH4), a potent greenhouse gas, but emissions models and estimates were developed for solute-poor ecosystems and may not apply to salt-rich inland waters. Here we combine field surveys and eddy covariance measurements to show that salinity constrains microbial CH4 cycling through complex mechanisms, restricting aquatic emissions from one of the largest global hardwater regions (the Canadian Prairies). Existing models overestimated CH4 emissions from ponds and wetlands by up to several orders of magnitude, with discrepancies linked to salinity. While not significant for rivers and larger lakes, salinity interacted with organic matter availability to shape CH4 patterns in small lentic habitats. We estimate that excluding salinity leads to overestimation of emissions from small Canadian Prairie waterbodies by at least 81% ( ~ 1 Tg yr-1 CO2 equivalent), a quantity comparable to other major national emissions sources. Our findings are consistent with patterns in other hardwater landscapes, likely leading to an overestimation of global lentic CH4 emissions. Widespread salinization of inland waters may impact CH4 cycling and should be considered in future projections of aquatic emissions.

Association of Dementia Severity and Caregiver Support With Early Discharge From Home Health.

INTRODUCTION: The objective of this study was to examine the relationship between dementia severity and early discharge from home health. METHODS: This was a retrospective study of 100% national Medicare home health da ta files (2016-2017). Multilevel logistic regression was used to study the relationship of dementia severity, caregiver support, and medication assistance with early discharge from home health. RESULTS: The final cohort consisted of 91 302 Medicare beneficiaries with an ADRD diagnosis. A pattern of early discharge rates across dementia severity levels was not demonstrated. The relative risk for early discharge was lower for individuals who needed assistance with medication and for those with unmet caregiver needs. DISCUSSION: The findings of this study do not support the hypothesis that dementia severity contributes to early discharge from home health. Further research is needed to fully understand key factors contributing to early discharge from home health.

Home health utilization association with discharge to community for people with dementia.

Introduction: The objective of this study was to identify home health utilization factors associated with successful discharge to community after home health care for patients with and without Alzheimer's disease and related dementias (ADRD). Methods: This was a retrospective study of 100% national Medicare home health data files (2016 to 2017). Multilevel logistic regression was used to study the relationship of home health utilization with a modified definition of successful discharge to community (M-SDC) after home health (no readmission or discharge within 30 days). Significant interactions were identified using backward selection. The associations between domains were examined in a model stratified by ADRD, with and without controlling for mobility, self-care, and caregiver assistance. Results: The cohort consisted of 535,691 patients, 18.0% with ADRD. The overall M-SDC rate was 92.1%. The likelihood of M-SDC was increased when physical therapy services were provided, episodes of care were longer than 15 days, and the total number of therapy visits was greater than 10. The likelihood of M-SDC decreased when speech therapy, nursing, and home health aide services were provided and when patients were discharged early. When controlling for mobility, self-care, and caregiver support, length of home health episode was the only characteristic that showed a significant interaction with ADRD. Discussion: The results of this study indicate that the provision of physical therapy services and moderate lengths of care and volume of visits are associated with increased likelihood of M-SDC. A decreased likelihood of M-SDC when speech therapy, nursing, and home health aide services are delivered may be a proxy indicator of patient acuity and disease severity and needs to be further investigated. An important next step in understanding home health access and outcomes for people with ADRD is to examine the impact of the Patient-Driven Groupings Model on home health utilization characteristics, especially length of episodes. Highlights: Most people remain in the community after discharge from home health.Likelihood of modified successful discharge to community (M-SDC) increased with physical therapy, longer episodes, and more than 10 visits.Likelihood of M-SDC decreased with speech therapy, skilled nursing, home health aide visits, and early discharge.Longer home health episodes increased likelihood of M-SDC for people with Alzheimer's disease and related dementias.

Validation of the Suicide Counseling Skills Inventory.

Background: The self-report measures used in evaluations of the Applied Suicide Intervention Skills Training (ASIST) program have tended not to detect an improvement in a broad range of suicide counseling skills from pre- to posttraining or among trainees with better skills at pretraining. Aims: The purpose of this study was to develop and validate the Suicide Counseling Skills Inventory (SCSI), which included ten brief counselor-client scenarios and three counselor responses to each scenario. Method: Data were collected from several samples to develop and evaluate the SCSI. Trainee scores were subtracted from criterion expert scores to create discrepancy scores. Results: The SCSI detected an improvement in skills from pre- to posttraining across samples, including among trainees with better skills at pretraining. Internal consistency and test-retest reliability were good. Limitations: The results may not generalize across different training models. Conclusion: Trainee scores were more like expert scores at posttraining. The SCSI may be useful in evaluating suicide counseling competency.