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In response to the impacts of climate change and the intensity of human activities in the alpine meadow region, there is an urgent need to determine the ecological quality and its drivers in alpine meadow areas. In this paper, Shangri-La was adopted as an example, the spatial and temporal evolution patterns of the ecological quality in Shangri-La were determined in both natural and social dimensions, and the contributions of various driving factors were analyzed. The conclusions are as follows: (1) the natural status index of Shangri-La from 2000 to 2020 generally showed a spatial distribution pattern that decreased from the central townships toward the north and south, and the social pressure index was irregularly distributed in high-value areas and continuously distributed in low-value areas. (2) From 2000 to 2020, the areas with high values of the ecological quality index were mainly distributed in central Shangri-La, with a maximum value of 0.91, while the low values were largely distributed in some townships in the north and south, with a minimum value of 0.26. (3) In the driving factors, the influences of the normalized difference vegetation index (NDVI) and net primary productivity (NPP) were greater than those of the other factors, among which the NDVI attained the largest mean value of 0.452, while the relative humidity (RHU) attained the lowest value of 0.036. (4) In terms of relative contributions, evapotranspiration (EVP) and precipitation (TEM) shifted from a positive drive to a negative drive from south to north. The contribution of the temperature to the ecological quality was the highest, at 64%. The spatial heterogeneity in the contributions of human disturbance activity factors to the ecological quality varied significantly, with the largest negative driving contribution of the NPP, at − 42.36%. The results could provide a basis for regional ecological quality protection and restoration.

Vegetation plays a key role in maintaining ecosystem stability, promoting biodiversity conservation, serving as windbreaks, and facilitating sand fixation in deserts. Based on the Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index (MODIS NDVI) and climate data, a Theil—Sen median trend analysis combined with the Mann—Kendall test and partial correlation and residual analyses were employed to explore spatiotemporal patterns of vegetation dynamics and key drivers in the Badain Jaran and Tengger deserts and Mu Us Sandy Land. Data were collected during the growing season between 2001 and 2020. Further analyses quantified the relative contribution of climate variation and anthropogenic activities to NDVI changes. Results revealed a predominantly increasing trend for average NDVI. The spread of average annual NDVI and growth trends of the vegetation were determined to be influenced by spatial differences. The area with improved vegetation was greater than that of the degraded region. Climate variability and human activities were driving forces controlling vegetation cover changes, and their effects on vegetation dynamics varied by region. The response of vegetation dynamics was stronger for precipitation than temperature, indicating that precipitation was the main climate variable influencing the NDVI changes. The relative role of human activities was responsible for > 70% of the changes, demonstrating that human activities were the main driving factor of the NDVI changes. The implementation of ecological engineering is a key driver of increased vegetation coverage and has improved regional environmental quality. These results enhance our knowledge regarding NDVI change affected by climate variation and human activities and can provide future theoretical guidance for ecological restoration in arid areas.

Mechanisms such as conspecific negative density dependence (CNDD) and niche partitioning have been proposed to explain species coexistence and community diversity. However, as a potentially important axis of niche partitioning, the role of interannual climate variability in driving local community dynamics remains largely unknown. Here we used a 15-year monitoring data set of more than 53,000 seedlings in a temperate forest to examine (1) what are the relative effects of interannual climate variability, biotic interactions, and habitat conditions on seedling survival; (2) how the effects of biotic interactions change with interannual climate variability, and habitat conditions; and (3) whether the impacts of interannual climate variability, biotic interactions, and habitat conditions differ with plant traits. Interannual climate variability accounted for the most variation in seedling survival at the community level, followed by biotic interactions, and habitat conditions. Increased snowpack and decreased minimum temperature during the nongrowing season had positive effects on seedling survival. Effects of conspecific neighbor density were weakened in higher snowpack, effective accumulated temperature, elevation, and soil-resource gradient, but were intensified with increased ultraviolet radiation, maximum precipitation, minimum temperature, and soil moisture. In addition, the relative importance of interannual climate variability versus biotic interactions differed depending on species-trait groups. Specifically, biotic interactions for gravity-dispersed species had a larger effect size in affecting seedling survival than other trait groups. Also, gravity-dispersed species experienced a stronger CNDD than wind-dispersed, probably because wind-dispersed seedlings rarely had adult conspecifics nearby. We found that interannual climate variability was most strongly associated with seedling survival, but the magnitude of climatic effects varied among species-trait groups. Interannual climate variability may act as an inhibitor or accelerator to density-dependent interactions and should be accounted for in future studies, as both a potential direct and indirect factor in understanding the diversity of forest communities.

The relative contributions of ocean modes to the JJA and DJF land precipitation variabilities during 1934–2015 are investigated using a variety of statistical and dynamical system methods, i.e., singular value decomposition (SVD), multivariate linear regression, and information flow analysis. Through SVD analysis for the tropical land precipitation and sea surface temperature (SST), three ocean modes are found to most affect the trend and interdecadal variation of the land precipitation. They are the global warming (GW) mode, Atlantic Multidecadal Oscillation (AMO) and Interdecadal Pacific Oscillation (IPO). GW contributes dominantly to the tropical land rainfall variability in both the JJA and DJF seasons. In JJA (DJF), AMO (IPO) plays a role only secondary to GW. Locally, within the thin latitude bands 10° S–10° N, 50° N–60° N and 40° S–50° S, GW, AMO and IPO are of equal importance in JJA; outside these bands, in the same season the first two dominate. In the band 10° N–40° N, IPO is the primary contributor in DJF, but outside it, GW dominates. Also, these contributions differ geographically from continent to continent. These results have been substantiated in the application of information flow analysis, a recently developed method in physics for the inference of causality between dynamical events. In terms of information flow, we have presented the regions of sensitivity to the three modes. Also presented are a number of ECHAM model experiments, which, besides verifying the above results, show for the first time that the Indian Ocean is pivotal in having AMO and IPO in effect in causing the precipitation variabilities.

During El Niño decaying spring, Tropical North Atlantic (TNA) region displays a significant warm SST anomaly (SSTA). The relative role of El Niño-induced extratropical and tropical forcing in generating TNA SSTA is investigated through both an observational analysis and idealized numerical model simulation. The maximum warming tendency in TNA occurred in El Niño mature winter and was mainly caused by enhanced surface evaporation, which was primarily affected by reduced trade wind. A partial regression analysis was employed to reveal the relative roles of El Niño induced extratropical forcing through Pacific North American pattern (PNA) and tropical forcing via remote Gill response. It is found that the extratropical forcing contributes around 62–66% in inducing a southwesterly anomaly over TNA region while the tropical forcing contributes approximately 34–38%. Idealized numerical model (ECHAM4) experiments were further carried out. This model could successfully reproduce PNA pattern and tropical Gill response by adding an El Niño forcing. Then two sensitivity experiments were designed to assess the relative contribution of the extratropical and tropical forcing. (1) An experiment was designed in which a zone (15°–26°N, 90°E–85°W) was given a strong Newtonian-type damping to prevent the Rossby wave propagating to the mid-latitudes, which greatly suppresses PNA induced mid-latitude forcing effect. (2) Tropical forcing is attempted to be suppressed in the model by adding a positive diabatic heating rate anomaly over the tropical Atlantic region where the El Niño forcing tends to induce negative heating anomaly. The numerical model results show that the southwesterly anomaly over TNA region is attributed to extratropical forcing and tropical forcing with 58–68% and 32–42% contribution respectively, which is consistent with the observational partial regression analysis.

BackgroundLittle is known about the relative contribution of comorbidities in predicting the health-related quality of life (HRQoL) of people with Multiple Sclerosis (PwMS).ObjectiveTo determine the associations between the number of and individual comorbidities and HRQoL and estimate the relative contribution of different comorbidities on HRQoL.MethodsCross-sectional analysis of data on self-reported presence of 30 comorbidities and HRQoL from the Australian MS Longitudinal Study (AMSLS) participants (n = 902). HRQoL was measured using the Assessment of Quality of Life-8 Dimensions (AQoL-8D). Linear regression and general dominance analysis were used.ResultsHigher number of comorbidities was associated with lower HRQoL (p trend p < 0.01). Comorbidities accounted for 18.1% of the variance in HRQoL. Mental health and musculoskeletal disorders were the strongest contributors to lower HRQoL. Of individual comorbidities, systemic lupus erythematosus (SLE) [β = − 0.16 (− 0.27, − 0.05)] and depression [β = − 0.15(− 0.18, − 0.13)] were most strongly associated with overall HRQoL, depression [β = − 0.14(− 0.16, − 0.11)] and anxiety [β = − 0.10 (− 0.13, − 0.07)] with psychosocial HRQoL, and SLE [β = − 0.18 (− 0.29, − 0.07)], rheumatoid arthritis [β = − 0.11 (− 0.19, − 0.02)] and hyperthyroidism [β = − 0.11 (− 0.19, − 0.03)) with physical HRQoL.ConclusionComorbidities potentially make important contributions to HRQoL in PwMS. Our findings highlight groups of and individual comorbidities that could provide the largest benefits for the HRQoL of PwMS if they were targeted for prevention, early detection, and optimal treatment.

Purpose Little is known about the impact of co-morbidities on health-related quality of life (HRQoL) for people with idiopathic pulmonary fibrosis (IPF). We aimed to investigate the relative contribution of co-morbidities to HRQoL of people with IPF. Methods N  = 157 participants were recruited from the Australian IPF Registry (AIPFR). Health state utilities (HSUs), and the super-dimensions of physical and psychosocial scores were measured using the Assessment of Quality of Life-8-Dimensions (AQoL-8D). The impact of co-morbidities on HRQoL was investigated using linear regression and general dominance analyses. Results A higher number of co-morbidities was associated with lower HSUs ( p trend = 0.002). Co-morbidities explained 9.1% of the variance of HSUs, 16.0% of physical super-dimensional scores, and 4.2% of psychosocial super-dimensional scores. Arthritis was associated with a significant reduction on HSUs ( β  =  − 0.09, 95% confidence interval [CI] − 0.16 to − 0.02), largely driven by reduced scores on the physical super-dimension ( β  =  − 0.13, 95% CI − 0.20 to − 0.06). Heart diseases were associated with a significant reduction on HSUs ( β  =  − 0.09, 95% CI − 0.16 to − 0.02), driven by reduced scores on physical ( β  =  − 0.09, 95% CI − 0.16 to − 0.02) and psychosocial ( β  = -0.10, 95% CI − 0.17 to − 0.02) super-dimensions. Conclusions Co-morbidities significantly impact HRQoL of people with IPF, with markedly negative impacts on their HSUs and physical health. A more holistic approach to the care of people with IPF is important as better management of these co-morbidities could lead to improved HRQoL in people with IPF.

Genetic and exposomal factors contribute to the development of human aging. For example, genetic polymorphisms and exposure to environmental factors (air pollution, tobacco smoke, etc.) influence lung and skin aging traits. For prevention purposes it is highly desirable to know the extent to which each category of the exposome and genetic factors contribute to their development. Estimating such extents, however, is methodologically challenging, mainly because the predictors are often highly correlated. Tackling this challenge, this article proposes to use weighted risk scores to assess combined effects of categories of such predictors, and a measure of relative importance to quantify their relative contribution. The risk score weights are determined via regularized regression and the relative contributions are estimated by the proportion of explained variance in linear regression. This approach is applied to data from a cohort of elderly Caucasian women investigated in 2007–2010 by estimating the relative contribution of genetic and exposomal factors to skin and lung aging. Overall, the models explain 17% (95% CI: [9%, 28%]) of the outcome’s variance for skin aging and 23% ([11%, 34%]) for lung function parameters. For both aging traits, genetic factors make up the largest contribution. The proposed approach enables us to quantify and rank contributions of categories of exposomal and genetic factors to human aging traits and facilitates risk assessment related to common human diseases in general. Obtained rankings can aid political decision making, for example, by prioritizing protective measures such as limit values for certain exposures.

A decline in atmospheric oxygen concentration is projected in the 21st century given the background of global warming. The Qinghai-Tibetan Plateau is located at a high altitude, and thus, it faces a hypoxia challenge; however, knowledge of the factors contributing to its atmospheric oxygen concentration is still lacking. Here, we conducted joint observations of ecosystem oxygen production and carbon sinks and near-surface atmospheric oxygen concentrations on the Qinghai-Tibetan Plateau and meteorological elements at Beijing Fangshan Station. Using seasonal differences and statistical methods, we calculated the relative contribution rates of vegetation to changes in atmospheric oxygen concentration. Our results indicate that solar radiation, atmospheric humidity, and ecosystem oxygen consumption and production have a significant impact on the atmospheric oxygen concentration, and the impact shows temporal and spatial differences. Vegetation significantly impacts the oxygen concentration, with a contribution rate of 16.7%–24.5%, which is underestimated in existing research. Our findings provide important insights into the factors that influence atmospheric oxygen concentration and highlight the contribution of vegetation. To better understand the oxygen dynamics of the Qinghai-Tibetan Plateau, we recommend further field observations of soil respiration and vegetation photosynthesis to clarify the contributions of carbon storage, carbon sinks and other factors to the near-surface atmospheric oxygen concentration.

Central Asia is a drought-prone region that is sensitive to global climate change. The increased actual evapotranspiration intensifies the drought impacts in this area. However, little is known about the similarities and differences between various types of drought in Central Asia, as well as the relative importance of water income and consumption processes during drought events. Therefore, this study evaluates the trends and characteristics of meteorological, agricultural, and hydrological droughts in Central Asia using precipitation, soil moisture, and terrestrial water storage as indicators; explores the temporal correlation of and spatial similarity between various types of drought; and quantitatively assesses the contribution of water balance variables to drought intensity. The results indicate that drought has intensified in Central Asia, and the trends of precipitation, soil moisture, and terrestrial water storage in this region were −0.75 mm·yr−1 (p = 0.36), −0.0003 m3·m−3 yr−1 (p < 0.01), and −0.3742 cm·yr−1 (p < 0.001), respectively. Severe droughts are typically short in duration and high in intensity. Three various types of drought have low temporal correlation and spatial similarity. Furthermore, agricultural and hydrological droughts were primarily driven by actual evapotranspiration, accounting for relative contributions of 64.38% and 51.04% to these drought types, respectively. Moreover, the extent of increased actual evapotranspiration expanded to cover 49.88% of the region, exacerbating agricultural and hydrological droughts in 23.88% and 35.14% of the total study area, respectively. The study findings demonstrate that actual evapotranspiration plays a critical role in causing droughts. This study establishes a theoretical foundation to carry out drought assessment, the construction of multivariate drought indices, and water resource management in Central Asia.