Quantification of methane and carbon dioxide surface emissions from a metropolitan landfill based on quasi-continuous eddy covariance measurement.

The Sudokwon landfill (SL) in the Seoul metropolitan area, South Korea, is among the world's largest landfills, striving to curtail landfill gas (LFG) emissions and achieve carbon neutrality by 2050. Since 2005, the SL Management Corporation (SLC) has measured LFG emissions (i.e., methane (CH4) and carbon dioxide (CO2)) using a dynamic flux chamber proposed by the US EPA. However, uncertainty prevails in validating the reduction of LFG emissions due to the limited spatiotemporal data coverage. In 2020, an eddy covariance (EC) system was installed to enhance measurements, revealing highly fluctuating LFG emissions driven by waste layer LFG production, LFG collection, and atmospheric pressure changes. During the study period, the annual CH4 emission increased slightly from 465.0 ± 4.2 to 485.5 ± 6.4 g C m-2, while that of CO2 decreased by 2/3 (from 408.7 ± 16.5 to 270.6 ± 18.8 g C m-2), primarily due to the doubled CO2 uptake by the vegetated topsoil. Our first long-term (March 2020 to February 2022) quasi-continuous monitoring using EC (with a gap-filling and partitioning technique based on Random Forest) emphasizes the difficulty of temporal upscaling of discontinuously observed surface emissions to quantify the LFG inventory and the need for continuous observations or suitable proxies (e.g., atmospheric CH4 concentration).

Reliability of variability and complexity measures for task and task-free BOLD fMRI.

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.

Spatiotemporal variations and source on black carbon over Chongqing, China: Long-term changes and observational experiments.

Black carbon (BC), as a critical light-absorbing constituent within aerosols, exerts profound effects on atmospheric radiation balance, climate, air quality and human health, etc. And it is also a long-standing focus in rapidly developing megacities. So, this study primarily focuses on investigating the variation characteristics and underlying causes of BC in Chongqing (31,914,300 population), which is one of the municipalities directly under the central government of China, serving as a pivotal economic hub in southwest China. Utilizing MERRA-2 reanalysis data, we examined the long-term changes of atmospheric BC over Chongqing 20 years (from 2002 to 2021). Moreover, BC mass concentration observations were conducted using an Aethalometer (AE-33) from March 15 to June 14, 2021 in Liangping District, Chongqing. The statistical analysis over the last 20 years reveals an annual mean BC concentration in Chongqing of 3.42 ± 0.20 µg/m3, exhibiting growth from 2002 to 2008, followed by a decline from 2008 to 2021. Monthly concentration displays a "U-shaped" trend, with the lowest values occurring in summer and the highest in winter. Due to topographical and meteorological influences, local emissions primarily contribute to BC pollution, characterized by a spatial distribution pattern of high in the west and low in the east. Ground observation indicates a distinct dual-peaked pattern in the diurnal variation of BC, with peak concentrations aligning with periods of high traffic emissions. The variation in BC is significantly influenced by meteorological conditions (wind, temperature, atmospheric boundary layer) and local pollution sources (predominantly traffic). Furthermore, extreme events analysis suggests that local emissions and regional transport (with higher contributions from Chongqing and the Sichuan Basin) predominantly contributed to BC pollution. This study effectively makes up for the deficiency in analyzing the distribution and sources of BC pollution in Chongqing, providing valuable scientific insights for the atmospheric environment of megacities.

The dominant influencing factors of desertification and ecological risk changes in Qinghai Area of Qilian Mountains National Park: Climate change or human activity?

Transitional features of desert environments partially determine the risks associated with ecosystems. Influenced by climate change and human activities, the variability and uncertainty of desertification levels and ecological risks in the Qinghai Area of Qilian Mountain National Park (QMNPQA) has become increasingly prominent. As a critical ecological barrier in northwest China, monitoring desertification dynamics and ecological risks is crucial for maintaining ecosystem stability. This study identifies the optimal monitoring model from four constructed desertification monitoring models and analyzes spatiotemporal changes in desertification. The spatial and temporal changes in ecological risks and their primary driving factors were analyzed using methods such as raster overlay calculation, geographic detector, cloud model, and trend analysis. The main conclusions are as follows: The desertification feature spatial model based on GNDVI-Albedo demonstrates better applicability in the study area, with an inversion accuracy of 81.24%. The levels of desertification and ecological risks in QMNPQA exhibit significant spatial heterogeneity, with a gradual decrease observed from northwest to southeast. From 2000 to 2020, there is an overall decreasing trend in desertification levels and ecological risks, with the decreasing trend area accounting for 89.82% and 85.71% respectively, mainly concentrated in the southeastern and northwestern parts of the study area. The proportion of areas with increasing trends is 4.49% and 7.05% respectively, scattered in patches in the central and southern edge areas. Surface temperature (ST), Digital Elevation Map (DEM), and Green normalized difference vegetation index (GNDVI) are the most influential factors determining the spatial distribution of ecological risks in QMNPQA. The effects of management and climatic factors on ecological risks demonstrate a significant antagonistic effect, highlighting the positive contributions of human activities in mitigating the driving effects of climate change on ecological risks. The research results can provide reference for desertification prevention and ecological quality improvement in QMNPQA.

Climate change fluctuations can increase population abundance and range size.

Climate change threatens many species by a poleward/upward movement of their thermal niche. While we know that faster movement has stronger impacts, little is known on how fluctuations of niche movement affect population outcomes. Environmental fluctuations often affect populations negatively, but theory and experiments have revealed some positive effects. We study how fluctuations around the average speed of the niche impact a species' persistence, abundance and realized niche width under climate change. We find that the outcome depends on how fluctuations manifest and what the relative time scale of population growth and climate fluctuations are. When populations are close to extinction with the average speed, fluctuations around this average accelerate population decline. However, populations not yet close to extinction can increase in abundance and/or realized niche width from such fluctuations. Long-lived species increase more when their niche size remains constant, short-lived species increase more when their niche size varies.

Identification of environmental and methodological factors driving variability of Pepper Mild Mottle Virus (PMMoV) across three wastewater treatment plants in the City of Toronto.

PMMoV has been widely used to normalize the concentration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA, influenza, and respiratory syncytial virus (RSV) to account for variations in the fecal content of wastewater. PMMoV is also used as an internal RNA recovery control for wastewater-based epidemiology (WBE) tests. While potentially useful for the interpretation of WBE data, previous studies have suggested that PMMoV concentration can be affected by various physico-chemical characteristics of wastewater. There is also the possibility that laboratory methods, particularly the variability in centrifugation steps to remove supernatant from pellets can cause PMMoV variability. The goal of this study is to improve our understanding of the main drivers of PMMoV variability by assessing the relationship between PMMoV concentration, the physico-chemical characteristics of wastewater, and the methodological approach for concentrating wastewater samples. We analyzed 24-hour composite wastewater samples collected from the influent stream of three wastewater treatment plants (WWTPs) located in the City of Toronto, Ontario, Canada. Samples were collected 3 to 5 times per week starting from the beginning of March 2021 to mid-July 2023. The influent flow rate was used to partition the data into wet and dry weather conditions. Physico-chemical characteristics (e.g., total suspended solids (TSS), biological oxygen demand (BOD), alkalinity, electrical conductivity (EC), and ammonia (NH3)) of the raw wastewater were measured, and PMMoV was quantified. Spatial and temporal variability of PMMoV was observed throughout the study period. PMMoV concentration was significantly higher during dry weather conditions. Multiple linear regression analysis demonstrates that the number and type of physico-chemical parameters that drive PMMoV variability are site-specific, but overall BOD and alkalinity were the most important predictors. Differences in PMMoV concentration for a single WWTP between two different laboratory methods, along with a weak correlation between pellet mass and TSS using one method may indicate that differences in sample concentration and subjective subsampling bias could alter viral recovery and introduce variability to the data.

Daylight driven vertical migration of mesozooplankton in the Arabian sea and the Bay of Bengal.

Diurnal vertical migration (DVM) of mesozooplankton in the Deep Scattering Layer (DSL) of the Indian seas is poorly studied. This cyclical vertical migration substantially controls the carbon sequestration in the ocean. The present research is a comprehensive examination to analyse the factors affecting the DVM pattern of the zooplankton community in the Arabian Sea (AS) and the Bay of Bengal (BoB). Echo sounder profiling was conducted at shallow depths (∼10-400m) of the AS (January 2023) and BoB (March 2023) with a period of 24 h to monitor the DVM pattern of the DSL. Vertical migration in both basins showcased the notable influence of the spatio-temporal contrast in the occurrence of daybreak, with the day (descend) and night (ascend) cycle of the DSL. Delayed descent was observed in the AS contrary to BoB, owing to the delayed day break in the AS relative to BoB. Intensity and temporal pattern of the incoming solar radiation were correlated with the DVM whereas diurnal variation of sea surface temperature was observed to be contrasting. The preliminary analysis is indicative of the diversified community structure of the zooplankton community in these basins resulting from the vertical migration. Furthermore, it is conclusive that the surface residence time of the zooplankton is distinct and is affirmed based on daybreak and light intensity particular for each basin. Since daybreak vary with the geolocation, sole dependence on a particular time for migration study can be erroneous, which is highlighted in the present study.

Heavy metals in centralized drinking water sources of the Yangtze River: A comprehensive study from a basin-wide perspective.

Water quality in the Yangtze River Basin (YRB) has received considerable attention because it supplies water to 400 million people. However, the trends, sources, and risks associated with heavy metals (HMs) in water of centralized drinking water sources (CDWSs) in the YRB region are not well understood due to the lack of high-frequency, large-scale monitoring data. Moreover, research on the factors affecting the transportation of HMs in natural water are limited, all of which significantly reduce the effectiveness of CDWSs management. Therefore, this study utilized data on 11 HMs and water quality from 114 CDWSs, covering 71 prefecture-level cities (PLC) in 15 provinces (cities), to map unprecedented geospatial distribution of HMs in the YRB region and examine their concentrations in relation to water chemistry parameters. The findings revealed that the frequency of detection (FOD) of 11 HMs ranged from 28.59% (Hg) to 99.64% (Ba). The mean concentrations are ranked as follows: Ba (40.775 µg/L) > B (21.866 µg/L) > Zn (5.133 µg/L) > V (2.668 µg/L) > Cu (2.049 µg/L) > As (1.989 µg/L) > Mo (1.505 µg/L) > Ni (1.108 µg/L) > Sb (0.613 µg/L) > Pb (0.553 µg/L) > Hg (0.002 µg/L). Concentrations of Zn, As, Hg, Pb, Mo, Sb, Ni, and Ba exhibited decreasing trends from 2018 to 2022. Human activities, including industrial and agricultural production, have led to higher pollution levels in the midstream and downstream of the river than in its upstream. Additionally, the high concentrations of Ba and B are influenced by natural geological factors. Anion concentrations and nutrient levels, play a significant role in the transport of HMs in water. Probabilistic health risk assessment indicates that As, Ba, and Sb pose a potential carcinogenic risk. Additionally, non-carcinogenic risk to children under extreme conditions should also be considered.

Variability of ambient air ammonia in urban Europe (Finland, France, Italy, Spain, and the UK).

This study addressed the scarcity of NH3 measurements in urban Europe and the diverse monitoring protocols, hindering direct data comparison. Sixty-nine datasets from Finland, France, Italy, Spain, and the UK across various site types, including industrial (IND, 8), traffic (TR, 12), urban (UB, 22), suburban (SUB, 12), and regional background (RB, 15), are analyzed to this study. Among these, 26 sites provided 5, or more, years of data for time series analysis. Despite varied protocols, necessitating future harmonization, the average NH3 concentration across sites reached 8.0 ± 8.9 µg/m3. Excluding farming/agricultural hotspots (FAHs), IND and TR sites had the highest concentrations (4.7 ± 3.2 and 4.5 ± 1.0 µg/m3), followed by UB, SUB, and RB sites (3.3 ± 1.5, 2.7 ± 1.3, and 1.0 ± 0.3 µg/m3, respectively) indicating that industrial, traffic, and other urban sources were primary contributors to NH3 outside FAH regions. When referring exclusively to the FAHs, concentrations ranged from 10.0 ± 2.3 to 15.6 ± 17.2 µg/m3, with the highest concentrations being reached in RB sites close to the farming and agricultural sources, and that, on average for FAHs there is a decreasing NH3 concentration gradient towards the city. Time trends showed that over half of the sites (18/26) observed statistically significant trends. Approximately 50 % of UB and TR sites showed a decreasing trend, while 30 % an increasing one. Meta-analysis revealed a small insignificant decreasing trend for non-FAH RB sites. In FAHs, there was a significant upward trend at a rate of 3.51[0.45,6.57]%/yr. Seasonal patterns of NH3 concentrations varied, with urban areas experiencing fluctuations influenced by surrounding emissions, particularly in FAHs. Diel variation showed differing patterns at urban monitoring sites, all with higher daytime concentrations, but with variations in peak times depending on major emission sources and meteorological patterns. These results offer valuable insights into the spatio-temporal patterns of gas-phase NH3 concentrations in urban Europe, contributing to future efforts in benchmarking NH3 pollution control in urban areas.

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability.

Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing -5-6 µg·m-3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes.

Temporal gut microbiota variability and association with dietary patterns: From the one-year observational Diet, Cancer, and Health - Next Generations MAX study.

BACKGROUND: Knowledge about the variability of gut microbiota within an individual over time is important to allow meaningful investigations of the gut microbiota in relation to diet and health outcomes in observational studies. Plant-based dietary patterns have been associated with a lower risk of morbidity and mortality and may alter gut microbiota in a favorable direction. OBJECTIVES: To assess the gut microbiota variability during one year and investigate the association between adherence to diet indexes and the gut microbiota in a Danish population. METHODS: Four hundred forty-four participants were included in the Diet, Cancer, and Health - Next Generations MAX study (DCH-NG MAX). Stool samples collected up to three times during a year were analyzed by 16S ribosomal ribonucleic acid gene sequencing. Diet was obtained by 24-hour dietary recalls. Intraclass correlation coefficient (ICC) was calculated to assess temporal microbial variability based on 214 individuals. Diet indexes (Nordic, Mediterranean, and plant-based diets) and food groups thereof were associated with gut microbiota using linear regression analyses. RESULTS: We found that 91 out of 234 genera had an ICC >0.5. We identified three subgroups dominated by Bacteroides, Prevotella 9, and Ruminococcaceae and adherence to diet indexes differed between subgroups. Higher adherence to diet indexes was associated with the relative abundance of 22 genera. Across diet indexes, higher intakes of fruit, vegetables, whole grains/cereals, and nuts were most frequently associated with these genera. CONCLUSIONS: In the DCH-NG MAX study, 39% of the genera had an ICC >0.5 over one year, suggesting that these genera could be studied with health outcomes in prospective analyses with acceptable precision. Adherence to the Nordic, Mediterranean, and plant-based diets differed between bacterial subgroups and was associated with a higher abundance of genera with fiber-degrading properties. Fruits, vegetables, whole grains/cereals, and nuts were frequently associated with these genera.

Dynamics of Gut Bacteria Across Different Zooplankton Genera in the Baltic Sea.

In aquatic ecosystems, zooplankton-associated bacteria potentially have a great impact on the structure of ecosystems and trophic networks by providing various metabolic pathways and altering the ecological niche of host species. To understand the composition and drivers of zooplankton gut microbiota, we investigated the associated microbial communities of four zooplankton genera from different seasons in the Baltic Sea using the 16S rRNA gene. Among the 143 ASVs (amplified sequence variants) observed belonging to heterotrophic bacteria, 28 ASVs were shared across all zooplankton hosts over the season, and these shared core ASVs represented more than 25% and up to 60% of relative abundance in zooplankton hosts but were present at low relative abundance in the filtered water. Zooplankton host identity had stronger effects on bacterial composition than seasonal variation, with the composition of gut bacterial communities showing host-specific clustering patterns. Although bacterial compositions and dominating core bacteria were different between zooplankton hosts, higher gut bacteria diversity and more bacteria contributing to the temporal variation were found in Temora and Pseudocalanus, compared to Acartia and Synchaeta. Diet diatom and filamentous cyanobacteria negatively correlated with gut bacteria diversity, but the difference in diet composition did not explain the dissimilarity of gut bacteria composition, suggesting a general effect of diet on the inner conditions in the zooplankton gut. Synchaeta maintained high stability of gut bacterial communities with unexpectedly low bacteria-bacteria interactions as compared to the copepods, indicating host-specific regulation traits. Our results suggest that the patterns of gut bacteria dynamics are host-specific and the variability of gut bacteria is not only related to host taxonomy but also related to host behavior and life history traits.

The thermal journey of macroalgae: Four decades of temperature-induced changes in the southeastern Bay of Biscay.

Global warming is triggering significant shifts in temperate macroalgal communities worldwide, favoring small, warm-affinity species over large canopy-forming, cold-affinity species. The Cantabrian Sea, a region acutely impacted by climate change, is also witnessing this shift. This study delved into the impacts of increasing sea surface temperature on the subtidal macroalgal communities in the southeastern Bay of Biscay over the last four decades, by using data from the years 1982, 2007, 2014, and 2020. We found that temperature has shaped the community structure, with warm-affinity species steadily displacing their cold-affinity counterparts. Notably, new communities exhibited a profusion of smaller algal species, explaining the observed increased biodiversity within the area. In the last period investigated (2014-2020), we observed a partial recovery of the communities, coinciding with cooler sea surface temperatures. Shallow algal communities were more reactive to temperature variations than deeper communities, possibly associated with higher exposure to increased temperatures. Our study offered insights into the intricate relationship between the changes in ocean temperature and algal species in the southeastern Bay of Biscay, shedding light on the ongoing ecological shifts in this region.

EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit.

Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.

Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: A resting-state fMRI study.

BACKGROUND: Epilepsy is a chronic neurologic disorder characterized by abnormal functioning of brain networks, making it a complex research topic. Recent advancements in neuroimaging technology offer an effective approach to unraveling the intricacies of the human brain. Within different types of epilepsy, there is growing recognition regarding ongoing changes in the default mode network (DMN). However, little is known about the shared and distinct alterations of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in DMN among epileptic subtypes, especially in children with epilepsy. METHODS: Here, 110 children with epilepsy at a single center, including idiopathic generalized epilepsy (IGE), frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and parietal lobe epilepsy (PLE), as well as 84 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (fMRI) scan. We investigated both sFC and dFC between groups of the DMN. RESULTS: Decreased static and dynamic connectivity within the DMN subsystem were shared by all subtypes. In each epilepsy subtype, children with epilepsy displayed significant and distinct patterns of DMN connectivity compared to the control group: the IGE group showed reduced interhemispheric connectivity, the FLE group consistently demonstrated disturbances in frontal region connectivity, the TLE group exhibited significant disruptions in hippocampal connectivity, and the PLE group displayed a notable decrease in parietal-temporal connectivity within the DMN. Some state-specific FC disruptions (decreased dFC) were observed in each epilepsy subtype that cannot detect by sFC. To determine their uniqueness within specific subtypes, bootstrapping methods were employed and found the significant results (IGE: between PCC and bilateral precuneus, FLE: between right middle frontal gyrus and bilateral middle temporal gyrus, TLE: between left Hippocampus and right fusiform, PLE: between left angular and cingulate cortex). Furthermore, only children with IGE exhibited dynamic features associated with clinical variables. CONCLUSIONS: Our findings highlight both shared and distinct FC alterations within the DMN in children with different types of epilepsy. Furthermore, our work provides a novel perspective on the functional alterations in the DMN of pediatric patients, suggesting that combined sFC and dFC analysis can provide valuable insights for deepening our understanding of the neuronal mechanism underlying epilepsy in children.

Plantar intrinsic foot muscle activity and its relationship with postural sway during tiptoe standing in ballet dancers and non-dancers.

BACKGROUND: Minimizing postural sway during tiptoe standing is essential for ballet dancers. Investigation of the activity of the plantar intrinsic foot muscles (PIFMs) may provide insight into postural sway in dancers. Herein, we compared PIFM activity during tiptoe standing between dancers and non-dancers and examined its relationship with postural sway. METHODS: We enrolled 14 female ballet dancers and 13 female non-dancers. Electromyography (EMG) amplitudes of 64 channels of PIFMs and center of pressure (COP) data were recorded during bipedal tiptoe standing tasks performed with ankle plantarflexion angles of 20°, 40°, and 60° (dancers only). The EMG amplitudes were normalized to those during the maximum voluntary contraction, and the muscle activity level and its coefficient of variation over time (EMG-CVtime) during the task were assessed. Standard deviations in the anteroposterior and mediolateral directions, velocity, and area were calculated from the COP data. RESULTS: Most COP and EMG variables were significantly lower in dancers than in non-dancers in both the 20° and 40° tasks (p < 0.05). Significant correlations were found between most combinations of the COP and EMG variables in both the 20° and 40° tasks in the whole cohort (r = 0.468-0.807, p ≤ 0.014). In the 60° task in dancers, COP velocity was strongly correlated with EMG-CVtime (r = 0.700, p = 0.005). CONCLUSION: These results provide novel evidence that the PIFMs do not require high activity, but rather that its low, steady activity is the key, to achieve less postural sway during bipedal tiptoe standing in dancers.

Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems.

A tenet of ecology is that temporal variability in ecological structure and processes tends to decrease with increasing spatial scales (from locales to regions) and levels of biological organization (from populations to communities). However, patterns in temporal variability across trophic levels and the mechanisms that produce them remain poorly understood. Here we analyzed the abundance time series of spatially structured communities (i.e., metacommunities) spanning basal resources to top predators from 355 freshwater sites across three continents. Specifically, we used a hierarchical partitioning method to disentangle the propagation of temporal variability in abundance across spatial scales and trophic levels. We then used structural equation modeling to determine if the strength and direction of relationships between temporal variability, synchrony, biodiversity, and environmental and spatial settings depended on trophic level and spatial scale. We found that temporal variability in abundance decreased from producers to tertiary consumers but did so mainly at the local scale. Species population synchrony within sites increased with trophic level, whereas synchrony among communities decreased. At the local scale, temporal variability in precipitation and species diversity were associated with population variability (linear partial coefficient, ß = 0.23) and population synchrony (ß = -0.39) similarly across trophic levels, respectively. At the regional scale, community synchrony was not related to climatic or spatial predictors, but the strength of relationships between metacommunity variability and community synchrony decreased systematically from top predators (ß = 0.73) to secondary consumers (ß = 0.54), to primary consumers (ß = 0.30) to producers (ß = 0). Our results suggest that mobile predators may often stabilize metacommunities by buffering variability that originates at the base of food webs. This finding illustrates that the trophic structure of metacommunities, which integrates variation in organismal body size and its correlates, should be considered when investigating ecological stability in natural systems. More broadly, our work advances the notion that temporal stability is an emergent property of ecosystems that may be threatened in complex ways by biodiversity loss and habitat fragmentation.

Methane ebullition fluxes and temperature sensitivity in a shallow lake.

Inland waters are important sources of atmospheric methane (CH4), with a major contribution from the CH4 ebullition pathway. However, there is still a lack of CH4 ebullition flux (eFCH4) and their temperature sensitivity (Q10) in shallow lakes, which might lead to large uncertainties in CH4 emission response from aquatic to climate and environmental change. Herein, the magnitude and regulatory of two CH4 pathways (ebullition and diffusion) were studied in subtropical Lake Chaohu, China, using the real-time portable greenhouse gas (GHG) analyzer-floating chamber method at 18 sites over four seasons. eFCH4 (12.06 ± 4.10 nmol m-2 s-1) was the dominant contributing pathway (73.0 %) to the two CH4 emission pathways in Lake Chaohu. The whole-lake mass balance calculation demonstrated that 56.6 % of the CH4 emitted from the sediment escaped through the ebullition pathway. eFCH4 was significantly higher in the western (WL: 16.54 ± 22.22 nmol m-2 s-1) and eastern lake zones (EL: 11.89 ± 15.43 nmol m-2 s-1) than in the middle lake zone (ML: 8.86 ± 13.78 nmol m-2 s-1; p < 0.05) and were significantly higher in the nearshore lake zone (NL: 15.94 ± 19.58 nmol m-2 s-1) than in the pelagic lake zone (PL: 6.64 ± 12.37 nmol m-2 s-1; p < 0.05). eFCH4 was significantly higher in summer (32.12 ± 13.82 nmol m-2 s-1) than in other seasons (p < 0.05). eFCH4 had a strong temperature dependence. Sediment total organic carbon (STOC) is an important ecosystem level Q10 driver of eFCH4. The meta-analysis also verified that across ecosystems the ecosystem-level Q10 of eFCH4 was significantly positively correlated with STOC and latitude (p < 0.05). This study suggests that eFCH4 will become increasingly crucial in shallow lake ecosystems as climate change and human activities increase. The potential increase in ebullition fluxes in high-latitude lakes is of great importance.

Recent greening may curb urban warming in Latin American cities of better economic conditions.

Rising temperatures have profound impacts on the well-being of urban residents. However, factors explaining the temporal variability of urban thermal environment, or urban warming, remain insufficiently understood, especially in the Global South. Addressing this gap, we studied the relationship between city-level economic conditions and urban warming, and how urban green space mediated this relationship, focusing on 359 major Latin American cities between 2001 and 2022. While effect sizes varied by economic and temperature measures used, we found that better economic conditions were associated with lower baseline greenness in 2011, which contributed to faster warming. There was modest evidence that this faster warming associated with lower baseline greenness and improved economic conditions was partially offset by cooling from recent greening (2001-2022) in cities of better economic conditions. This offset was more evident in arid cities. Together, these findings provide insights into the urban warming mechanism manifested through the effect of economic conditions on urban green space, for Latin American cities and other high-density cities transforming in a similar context.

Can datasets from long-term biomonitoring programs detect climate change effects on stream benthos?

We analyzed datasets from a long-term monitoring program of stream ecosystems in British Columbia, Canada, to determine whether or not it could detect climate change effects. In the Fraser River Basin (monitoring timespan 1994-2019), there was a marked (∼50%) increase in alpha diversity in reference streams, while BC North Coast (2004-2021) streams showed a modest trend of decreasing diversity and Columbia River Basin (2003-2018) and Vancouver Island (2001-2019) streams showed modestly increasing diversity. In all four regions, diversity across all sites in a specific period was primarily a function of sampling effort during this period rather than a temporal trend. Across all the regions, only three of 21 groups of faunally similar sites defined by Reference Condition Approach predictive modeling showed a suggestion of a directional change in community structure over time. Only 1 of 15 reference sites that were repeatedly sampled over several years showed a pattern that may indicate a response to changing climate. Three, not mutually exclusive, reasons why we did not see a clear effect of climate change on BC stream ecosystems were: 1) Little or no effect of climate change relative to other, potentially interacting biotic and abiotic factors, 2) The timespan of monitoring was too short to detect cumulative effects of climate change, and, most importantly, 3) The sampling design and protocol were unable to detect climate change effects. To better detect and characterize the effects of climate change on streams in monitoring programs, we recommend annual re-sampling of a few reference sites and detailed analysis of the natural and human environment of the sites along with better characterization of the benthic community (e.g. with eDNA) at all monitored sites.