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Climatic extremes have adverse concurrent and lagged effects on terrestrial carbon cycles. Here, a concurrent effect refers to the occurrence of a latent impact during climate extremes, and a lagged effect appears sometime thereafter. Nevertheless, the uncertainties of these extreme drought effects on net carbon uptake and the recovery processes of vegetation in different Tibetan Plateau (TP) ecosystems are poorly understood. In this study, we calculated the Standardised Precipitation–Evapotranspiration Index (SPEI) based on meteorological datasets with an improved spatial resolution, and we adopted the Carnegie–Ames–Stanford approach model to develop a net primary production (NPP) dataset based on multiple datasets across the TP during 1982–2015. On this basis, we quantised the net reduction in vegetation carbon uptake (NRVCU) on the TP, investigated the spatiotemporal variability of the NPP, NRVCU and SPEI, and analysed the NRVCUs that are caused by the concurrent and lagged effects of extreme drought and the recovery times in different ecosystems. According to our results, the Qaidam Basin and most forest regions possessed a significant trend towards drought during 1982–2015 (with Slope of SPEI < 0, P < 0.05), and the highest frequency of extreme drought events was principally distributed in the Qaidam Basin, with three to six events. The annual total net reduction in vegetation carbon uptake on the TP experienced a significant downward trend from 1982 to 2015 (−0.0018 ± 0.0002 PgC year−1, P < 0.001), which was negatively correlated with annual total precipitation and annual mean temperature (P < 0.05). In spatial scale, the NRVCU decrement was widely spread (approximately 55% of grids) with 17.86% of the area displaying significant declining trends (P < 0.05), and the sharpest declining trend (Slope ≤ −2) was mainly concentrated in southeastern TP. For the alpine steppe and alpine meadow ecosystems, the concurrent and lagged effects of extreme drought induced a significant difference in NRVCU (P < 0.05), while forests presented the opposite results. The recovery time comparisons from extreme drought suggest that forests require more time (27.62% of grids ≥ 6 years) to recover their net carbon uptakes compared to grasslands. Therefore, our results emphasise that extreme drought events have stronger lagged effects on forests than on grasslands on the TP. The improved resilience of forests in coping with extreme drought should also be considered in future research.

Climate change is projected to increase the frequency of extreme drought events, which can have dramatic consequences for ecosystems. Extreme drought may interact with other stressors such as invasion by non‐native species, yet little research has explored these dynamics. Here, we examine the physical mechanisms and temporal scale underlying a dieback of an invasive non‐native plant, Lepidium latifolium, in tidal salt marshes of the San Francisco Bay, California, USA, during an extreme, multi‐year drought occurring from 2012 to 2015. Using generalized additive mixed models (GAMMs), we explored the relationship between eight years of estuarine salinity data and five years of L. latifolium density data from three marshes spanning a gradient of salinity across the San Francisco Bay. We found a significant time‐lagged (3 yr) effect of estuarine salinity on L. latifolium density, with high salinities preceding reductions in L. latifolium densities and low salinities preceding increases. The most dramatic change in stem density, a 54% reduction in 2015, was preceded by a salinity increase of 43% from 2011 to 2012. We found the L. latifolium decline was driven by impacts on mature, rather than young, plants. Additionally, we tested the importance of local precipitation in driving L. latifolium densities in a one‐season rain exclusion experiment. We found 100% exclusion of precipitation during one rainy season (January–mid‐May) did not have a significant impact on densities of mature stands of L. latifolium. Our finding that estuarine salinity was a key driver of L. latifolium invasion dynamics suggests sea level rise, like extreme drought, may hinder L. latifolium invasion, as it will also raise estuarine salinities. Further, our study highlights the importance of temporal lags in understanding climate change impacts on biological invasions, which has received very little study to date.

1. Rainfall is a key determinant of production and composition in arid and semi-arid systems. Long-term studies relating composition and water availability primarily focus on current-year precipitation patterns, though mounting evidence highlights the importance of previous-year rainfall particularly in grasslands dominated by perennial species. The extent to which lagged precipitation effects occur in annual grasslands, however, remains largely unexplored. 2. We pair a long-term study with two manipulative experiments to identify patterns and mechanisms of lagged precipitation effects in annual grasslands. The long-term study captured variation in functional group (exotic annual forbs and grasses) abundance and precipitation across 8 years at three northern California grassland sites. We then tested whether lagged rainfall effects were created through seed production and litter (residual dry matter, RDM) by manipulating rainfall and litter, respectively. 3. Rainfall from the previous-year growing season (both seasonal and total rainfall) shifted functional group abundance. High lagged rainfall was associated with increased grass and decreased forb abundance the following year. Current-year seasonal rainfall also influenced species composition, with winter rain increasing forb and decreasing grass abundance. Lagged precipitation effects were generally stronger for forbs than for grasses. Our experimental studies provided evidence for two mechanisms that contributed to lagged effects in annual grasslands. Higher rainfall increased seed production for grasses, which translated to more germinable seed the following year. Higher rainfall also increased biomass production and RDM, which benefited grasses and reduced forb abundance. 4. Synthesis. Our results highlight the importance of previous-year precipitation in structuring annual community composition and suggest two important biotic pathways, seed rain and RDM, that regulate lagged community responses to rainfall. Incorporating lagged effects into models of grassland diversity and productivity could improve predictions of climate change impacts in annual grasslands.

Background It is true that Chronic obstructive pulmonary disease (COPD) will increase social burden, especially in developing countries. Urban-rural differences in the lagged effects of PM2.5 and PM10 on COPD mortality remain unclear, in Chongqing, China. Methods In this study, a distributed lag non-linear model (DLNMs) was established to describe the urban-rural differences in the lagged effects of PM2.5, PM10 and COPD mortality in Chongqing, using 312,917 deaths between 2015 and 2020. Results According to the DLNMs results, COPD mortality in Chongqing increases with increasing PM2.5 and PM10 concentrations, and the relative risk (RR) of the overall 7-day cumulative effect is higher in rural areas than in urban areas. High values of RR in urban areas occurred at the beginning of exposure (Lag 0 ~ Lag 1). High values of RR in rural areas occur mainly during Lag 1 to Lag 2 and Lag 6 to Lag 7. Conclusion Exposure to PM2.5 and PM10 is associated with an increased risk of COPD mortality in Chongqing, China. COPD mortality in urban areas has a high risk of increase in the initial phase of PM2.5 and PM10 exposure. There is a stronger lagging effect at high concentrations of PM2.5 and PM10 exposure in rural areas, which may further exacerbate inequalities in levels of health and urbanization.

Background Dengue fever represents a significant and escalating public health challenge in tropical megacities such as Bangkok, Thailand. This study aims to examine the associations between temperature, humidity, rainfall, and wind speed with dengue incidence across multiple lag periods and to explore spatial heterogeneity in these climate-dengue relationships across Bangkok’s urban zones. Methodology Analysis of 105,890 confirmed dengue cases reported in Bangkok between 2015 and 2024, stratified across six urban zones. Distributed Lag Non-Linear Models (DLNM) were employed to quantify the effects of four key meteorological variables: temperature, humidity, rainfall, and wind speed. Results Significant non-linear relationships and heterogeneity in climate-driven dengue risk were found across both space and time. Spatially, the effects were most pronounced for humidity and wind speed. For instance, over a 0–2 months lag, higher humidity was associated with a substantial increase in risk in North Krung Thon (RR=1.477, 95% CI: 1.290–1.690) but a significant protective effect in South and Middle Bangkok. Similarly, wind speed was associated with a significant risk increase in South Bangkok (RR=1.405, 95% CI: 1.293–1.526) but a protective effect in East Bangkok (RR=0.749, 95% CI: 0.710–0.789). Elevated minimum temperature also exhibited a spatially varied impact, peaking in Middle Bangkok (RR=1.350, 95% CI: 1.288–1.414). The analysis confirmed distinct patterns over time, with climate impacts manifesting as immediate risks in some zones and as pronounced delayed risks (8–12 months) in others. Conclusion The associations between climatic variables and dengue incidence in Bangkok are highly complex, non-linear, and characterized by significant spatial and temporal heterogeneity. The varied timing of climate impacts, from immediate to delayed, suggests that public health responses must be adapted to the unique temporal risk structures of each urban zone, providing a framework for more precise interventions in complex urban environments.

Hydro‐sedimentary responses critically influence water and ecological security, yet their nonlinear complexity challenges mechanistic understanding and reliable modeling, a gap that conventional association‐based methods hardly address. Emerging data‐driven causal inference offers promise in elucidating directional relationships and filtering spurious correlations, but its applicability in hydro‐sediment systems remains underexplored due to unclear system assumptions and intricate driver–sediment linkages. This study fills this gap by developing a generalizable causal framework integrating representative causal methods, Granger causality (GC), transfer entropy (TE), and convergent cross mapping (CCM), rigorously validated with 41‐year real‐world data from seven subtropical watersheds in China. Specifically, the framework evaluates method applicability in detecting precipitation–streamflow–sediment causality (direction/strength/time‐delay), analyzes environmental controls, and identifies key predictors to refine causal insights in machine learning (ML)‐based sediment predictions. Key findings indicate: (a) CCM excels in causality detection despite high variability, GC is flexible but insensitive in feature selection, and TE only suits confirmatory studies—collectively highlighting the dual deterministic‐stochastic nature of watershed hydro‐sediment systems. (b) Precipitation exerts similar causal effects on sediment and streamflow dynamics. Over 41 years, precipitation–sediment causality strength weakens, with reduced time‐delays and seasonal variations. (c) Precipitation–streamflow causality is shaped by precipitation and short‐term drought, while precipitation–sediment causality by land‐use and long‐term drought, with all causalities impacted by soil moisture and economic factors. (d) ML‐based predictions can partially reflect causality with precipitation/land‐use predictors but deteriorate with excessive antecedent hydroclimate predictors. This causality‐driven framework complements mechanistic understanding of hydro‐sedimentary responses, urging a shift from correlative to causal modeling in earth surface processes.

Cardiovascular and cerebrovascular diseases are critical public health challenges influenced by environmental and meteorological factors. Understanding the association between these factors and disease incidence can provide valuable insights for disease prevention and control.This study analyzed data from Anshun City, western Guizhou, collected between January 2018 and December 2022. A Distributed Lag Non-linear Model (DLNM) was employed to evaluate the lagged and non-linear effects of meteorological variables (e.g., temperature, precipitation, wind speed) and air pollutants (e.g., PM2.5, SO2) on the incidence of cardiovascular and cerebrovascular diseases. Covariates such as seasonality and time trends were included to adjust for confounding effects.The results revealed significant associations between meteorological factors, air pollution, and disease incidence. Increased precipitation and SO2 concentrations significantly elevated the risk of cardiovascular and cerebrovascular diseases, particularly at a lag of 25–30 days (e.g., RR for SO2 = 1.19, 95% CI: 1.10–1.28). Conversely, higher average and maximum wind speeds demonstrated a protective effect (e.g., RR for maximum wind speed = 0.70, 95% CI: 0.62–0.78). Seasonal patterns and temperature variations further influenced disease incidence.These findings highlight the complex interactions between meteorological factors and air pollution in influencing cardiovascular and cerebrovascular disease risk. The study provides evidence for targeted public health interventions and emphasizes the importance of incorporating meteorological and environmental data into disease prevention strategies.

Bidirectional relations have long been of interest in psychology and other social behavioral sciences. In recent years, the widespread use of intensive longitudinal data has provided new opportunities to examine dynamic bidirectional relations between variables. However, most previous studies have focused on the effect of one variable on the other (i.e., cross-lagged effects) rather than the overall effect representing the dynamic interplay between two variables (i.e., feedback effects), which we believe may be due to a lack of relevant methodological guidance. To quantify bidirectional relations as a whole, this study attempted to provide guidance for the estimation and interpretation of feedback effects based on dynamic structural equation models. First, we illustrated the estimation procedure for the average and person-specific feedback effects. Then, to facilitate the interpretation of feedback effects, we established an empirical benchmark by quantitatively synthesizing the results of relevant empirical studies. Finally, we used a set of empirical data to demonstrate how feedback effects can help (a) test theories based on bidirectional relations and (b) reveal correlates of individual differences in bidirectional relations. We also discussed the broad application prospects of feedback effects from a dynamic systems perspective. This study provides guidance for applied researchers interested in further examining feedback effects in bidirectional relations, and the shift from focusing on cross-lagged effects only to a comprehensive consideration of feedback effects may provide new insights into the study of bidirectional relations.

Recommendations for promoting mental health during the COVID-19 pandemic include maintaining social contact, through virtual rather than physical contact, moderating substance/alcohol use, and limiting news and media exposure. We seek to understand if these pandemic-related behaviors impact subsequent mental health. Daily online survey data were collected on adults during May/June 2020. Measures were of daily physical and virtual (online) contact with others; substance and media use; and indices of psychological striving, struggling and COVID-related worry. Using random-intercept cross-lagged panel analysis, dynamic within-person cross-lagged effects were separated from more static individual differences. In total, 1148 participants completed daily surveys [657 (57.2%) females, 484 (42.1%) males; mean age 40.6 (s.d. 12.4) years]. Daily increases in news consumed increased COVID-related worrying the next day [cross-lagged estimate = 0.034 (95% CI 0.018-0.049), FDR-adjusted = 0.00005] and [0.03 (0.012-0.048), FDR-adjusted = 0.0017]. Increased media consumption also exacerbated subsequent psychological struggling [0.064 (0.03-0.098), FDR-adjusted = 0.0005]. There were no significant cross-lagged effects of daily changes in social distancing or virtual contact on later mental health. We delineate a cycle wherein a daily increase in media consumption results in a subsequent increase in COVID-related worries, which in turn increases daily media consumption. Moreover, the adverse impact of news extended to broader measures of psychological struggling. A similar dynamic did not unfold between the daily amount of physical or virtual contact and subsequent mental health. Findings are consistent with current recommendations to moderate news and media consumption in order to promote mental health.

Vocational indecision has been found to be either a predictor or a consequence of academic motivation, but no study has examined whether the two processes share reciprocal links. Three academic motivation orientations have been found to contribute to these two processes: autonomous (i.e., going to school for the pleasure to learn or for the personally valued reasons), controlled (i.e., going to school to alleviate internal or external pressures) and amotivation (i.e., going to school without purpose). The goal of the present study was to test reciprocal and longitudinal links between each of these three motivation orientations and vocational indecision. This longitudinal study used a sample of 584 secondary school students (55% girls) surveyed annually over a 4-year period, where the effect of vocational indecision on each academic motivation orientation and the effect of each academic motivation on vocational indecision were estimated simultaneously. These links were tested both at interindividual (i.e., students are compared with each other) and intraindividual levels (i.e., students are compared to themselves). Results of cross-lagged models indicate that vocational indecision was negatively predicted by autonomous academic motivation but not vice versa, and that this link appeared only at the intraindividual level. Also, vocational indecision simultaneously predicted and was predicted by controlled academic motivation and academic amotivation at both levels. These results suggest that guidance counsellors could support the emergence and maintenance of autonomous motivation in students, to help them make a vocational decision. Also, the scope of their action could extend to school retention, as actions taken to support vocational decision-making could affect students’ motivation to engage or stay in school.