Phenotypic lags influence rapid evolution throughout a drought cycle

Climate anomalies are increasing and posing strong selection, which can lead to rapid evolution. This is occurring on a backdrop of interannual variability that might weaken or even reverse selection. However, the effect of interannual climatic variability on rapid evolution is rarely considered. We study the climatic differences that contribute to rapid evolution throughout a seven-year period encompassing a severe drought across 12 populations of Mimulus cardinalis (scarlet monkeyflower). Plants were grown in a common greenhouse environment under wet and dry treatments, where specific leaf area and date of flowering were measured. We examine the association between trait values and different climate metrics at different time-periods, including the collection year, prior years, and cumulative metrics across sequential years. Of the climatic variables we assessed, we find that anomalies in mean annual precipitation best describe trait differences over our study period. Past climates, of one- to two years prior, are often related to trait values in a conflicting direction to collection-year climate. Uncovering these complex climatic impacts on evolution is critical to better predict and interpret the impacts of climate change.

Using Instrumental Variables to Measure Causation over Time in Cross-Lagged Panel Models

Cross-lagged panel models (CLPMs) are commonly used to estimate causal influences between two variables with repeated assessments. The lagged effects in a CLPM depend on the time interval between assessments, eventually becoming undetectable at longer intervals. To address this limitation, we incorporate instrumental variables (IVs) into the CLPM with two study waves and two variables. Doing so enables estimation of both the lagged (i.e., "distal") effects and the bidirectional cross-sectional (i.e., "proximal") effects at each wave. The distal effects reflect Granger-causal influences across time, which decay with increasing time intervals. The proximal effects capture causal influences that accrue over time and can help infer causality when the distal effects become undetectable at longer intervals. Significant proximal effects, with a negligible distal effect, would imply that the time interval is too long to estimate a lagged effect at that time interval using the standard CLPM. Through simulations and an empirical application, we demonstrate the impact of time intervals on causal inference in the CLPM and present modeling strategies to detect causal influences regardless of the time interval in a study. Furthermore, to motivate empirical applications of the proposed model, we highlight the utility and limitations of using genetic variables as IVs in large-scale panel studies.

Prediction of Ross River Virus Incidence Using Mosquito Data in Three Cities of Queensland, Australia

Ross River virus (RRV) is the most common mosquito-borne disease in Australia, with Queensland recording high incidence rates (with an annual average incidence rate of 0.05% over the last 20 years). Accurate prediction of RRV incidence is critical for disease management and control. Many factors, including mosquito abundance, climate, weather, geographical factors, and socio-economic indices, can influence the RRV transmission cycle and thus have potential utility as predictors of RRV incidence. We collected mosquito data from the city councils of Brisbane, Redlands, and Mackay in Queensland, together with other meteorological and geographical data. Predictors were selected to build negative binomial generalised linear models for prediction. The models demonstrated excellent performance in Brisbane and Redlands but were less satisfactory in Mackay. Mosquito abundance was selected in the Brisbane model and can improve the predictive performance. Sufficient sample sizes of continuous mosquito data and RRV cases were essential for accurate and effective prediction, highlighting the importance of routine vector surveillance for disease management and control. Our results are consistent with variation in transmission cycles across different cities, and our study demonstrates the usefulness of mosquito surveillance data for predicting RRV incidence within small geographical areas.

Drought legacies and ecosystem responses to subsequent drought

Climate change is expected to increase the frequency and severity of droughts. These events, which can cause significant perturbations of terrestrial ecosystems and potentially long-term impacts on ecosystem structure and functioning after the drought has subsided are often called 'drought legacies'. While the immediate effects of drought on ecosystems have been comparatively well characterized, our broader understanding of drought legacies is just emerging. Drought legacies can relate to all aspects of ecosystem structure and functioning, involving changes at the species and the community scale as well as alterations of soil properties. This has consequences for ecosystem responses to subsequent drought. Here, we synthesize current knowledge on drought legacies and the underlying mechanisms. We highlight the relevance of legacy duration to different ecosystem processes using examples of carbon cycling and community composition. We present hypotheses characterizing how intrinsic (i.e. biotic and abiotic properties and processes) and extrinsic (i.e. drought timing, severity, and frequency) factors could alter resilience trajectories under scenarios of recurrent drought events. We propose ways for improving our understanding of drought legacies and their implications for subsequent drought events, needed to assess the longer-term consequences of droughts on ecosystem structure and functioning.

Lagged and dormant season climate better predict plant vital rates than climate during the growing season

Understanding the effects of climate on the vital rates (e.g., survival, development, reproduction) and dynamics of natural populations is a long-standing quest in ecology, with ever-increasing relevance in the face of climate change. However, linking climate drivers to demographic processes requires identifying the appropriate time windows during which climate influences vital rates. Researchers often do not have access to the long-term data required to test a large number of windows, and are thus forced to make a priori choices. In this study, we first synthesize the literature to assess current a priori choices employed in studies performed on 104 plant species that link climate drivers with demographic responses. Second, we use a sliding-window approach to investigate which combination of climate drivers and temporal window have the best predictive ability for vital rates of four perennial plant species that each have over a decade of demographic data (Helianthella quinquenervis, Frasera speciosa, Cylindriopuntia imbricata, and Cryptantha flava). Our literature review shows that most studies consider time windows in only the year preceding the measurement of the vital rate(s) of interest, and focus on annual or growing season temporal scales. In contrast, our sliding-window analysis shows that in only four out of 13 vital rates the selected climate drivers have time windows that align with, or are similar to, the growing season. For many vital rates, the best window lagged more than 1 year and up to 4 years before the measurement of the vital rate. Our results demonstrate that for the vital rates of these four species, climate drivers that are lagged or outside of the growing season are the norm. Our study suggests that considering climatic predictors that fall outside of the most recent growing season will improve our understanding of how climate affects population dynamics.

How the Lagged and Accumulated Effects of Stress, Coping, and Tasks Affect Mood and Fatigue during Nurses' Shifts

Nurses experience significant stress and emotional exhaustion, leading to burnout and fatigue. This study assessed how the nurses' mood and fatigue evolves during their shifts, and the temporal factors that influence these phenomena. Performing a two-level design with repeated measures with moments nested into a person level, a random sample of 96 nurses was recruited. The ecological momentary assessment of demand, control, effort, reward, coping, and nursing tasks were measured in order to predict mood and fatigue, studying their current, lagged, and accumulated effects. The results show that: (1) Mood appeared to be explained by effort, by the negative lagged effect of reward, and by the accumulated effort, each following a quadratic trend, and it was influenced by previously executing a direct care task. By contrast, fatigue was explained by the current and lagged effect of effort, by the lagged effect of reward, and by the accumulated effort, again following quadratic trends. (2) Mood was also explained by problem-focused and emotion-focused coping strategies, indicative of negative mood, and by support-seeking and refusal coping strategies. (3) Fatigue was also associated with direct care and the prior effect of documentation and communication tasks. We can conclude that mood and fatigue do not depend on a single factor, such as workload, but rather on the evolution and distribution of the nursing tasks, as well as on the stress during a shift and how it is handled. The evening and night shifts seem to provoke more fatigue than the other work shifts when approaching the last third of the shift. These data show the need to plan the tasks within a shift to avoid unfinished or delayed care during the shift, and to minimize accumulated negative effects.

Divergent concurrent and lagged effects of the reciprocal relation between work-nonwork interactions and sleep disturbance

STUDY OBJECTIVES

Work-nonwork interactions and sleep disturbances are found to be important predictors of well-being and job performance outcomes. However, little is known about the mutual interrelations of the interactions between life domains and disturbed sleep over short and long periods of time.

METHODS

In total, 4079 representative individuals of the Swedish working population completed three subsequent waves of surveys with a time interval of 2 years (i.e. longitudinal design).

RESULTS

Concurrent, cross-lagged, and reverse directionality effects were simultaneously examined using autoregressive longitudinal path analysis. Contemporarily, interference between work and nonwork increased sleep disturbances, whereas work-nonwork enhancement decreased sleep disturbances. From one time point to the other, work-nonwork interference negatively related to sleep disturbances, and work-nonwork enhancement was mostly no longer (or positively) related to sleep disturbances. Over time sleep disturbances, in turn, predicted more interference and less enhancement between both life domains.

CONCLUSIONS

The results highlight that problematic work-nonwork interactions (i.e. high work-nonwork interference and low work-nonwork enhancement) disturb an individual's sleep in the short term (i.e. cross-sectional). Furthermore, the results suggest that individuals adapt to negative work-nonwork interactions over time, but that sleep disturbances impair an individual's work-nonwork interactions 2 years later.

Coupled effects of wind-storms and drought on tree mortality across 115 forest stands from the Western Alps and the Jura mountains

Damage due to wind-storms and droughts is increasing in many temperate forests, yet little is known about the long-term roles of these key climatic factors in forest dynamics and in the carbon budget. The objective of this study was to estimate individual and coupled effects of droughts and wind-storms on adult tree mortality across a 31-year period in 115 managed, mixed coniferous forest stands from the Western Alps and the Jura mountains. For each stand, yearly mortality was inferred from management records, yearly drought from interpolated fields of monthly temperature, precipitation and soil water holding capacity, and wind-storms from interpolated fields of daily maximum wind speed. We performed a thorough model selection based on a leave-one-out cross-validation of the time series. We compared different critical wind speeds (CWSs) for damage, wind-storm, and stand variables and statistical models. We found that a model including stand characteristics, drought, and storm strength using a CWS of 25 ms^-1 performed the best across most stands. Using this best model, we found that drought increased damage risk only in the most southerly forests, and its effect is generally maintained for up to 2 years. Storm strength increased damage risk in all forests in a relatively uniform way. In some stands, we found positive interaction between drought and storm strength most likely because drought weakens trees, and they became more prone to stem breakage under wind-loading. In other stands, we found negative interaction between drought and storm strength, where excessive rain likely leads to soil water saturation making trees more susceptible to overturning in a wind-storm. Our results stress that temporal data are essential to make valid inferences about ecological impacts of disturbance events, and that making inferences about disturbance agents separately can be of limited validity. Under projected future climatic conditions, the direction and strength of these ecological interactions could also change.

Persistent and contemporaneous effects of job stressors on mental health: a study testing multiple analytic approaches across 13 waves of annually collected cohort data

OBJECTIVES

This study investigated the extent that psychosocial job stressors had lasting effects on a scaled measure of mental health. We applied econometric approaches to a longitudinal cohort to: (1) control for unmeasured individual effects; (2) assess the role of prior (lagged) exposures of job stressors on mental health and (3) the persistence of mental health.

METHODS

We used a panel study with 13 annual waves and applied fixed-effects, first-difference and fixed-effects Arellano-Bond models. The Short Form 36 (SF-36) Mental Health Component Summary score was the outcome variable and the key exposures included: job control, job demands, job insecurity and fairness of pay.

RESULTS

Results from the Arellano-Bond models suggest that greater fairness of pay (β-coefficient 0.34, 95% CI 0.23 to 0.45), job control (β-coefficient 0.15, 95% CI 0.10 to 0.20) and job security (β-coefficient 0.37, 95% CI 0.32 to 0.42) were contemporaneously associated with better mental health. Similar results were found for the fixed-effects and first-difference models. The Arellano-Bond model also showed persistent effects of individual mental health, whereby individuals' previous reports of mental health were related to their reporting in subsequent waves. The estimated long-run impact of job demands on mental health increased after accounting for time-related dynamics, while there were more minimal impacts for the other job stressor variables.

CONCLUSIONS

Our results showed that the majority of the effects of psychosocial job stressors on a scaled measure of mental health are contemporaneous except for job demands where accounting for the lagged dynamics was important.

Estimating Temperature-Mortality Exposure-Response Relationships and Optimum Ambient Temperature at the Multi-City Level of China

Few studies have explored temperature–mortality relationships in China, especially at the multi-large city level. This study was based on the data of seven typical, large Chinese cities to examine temperature-mortality relationships and optimum temperature of China. A generalized additive model (GAM) was applied to analyze the acute-effect of temperature on non-accidental mortality, and meta-analysis was used to merge data. Furthermore, the lagged effects of temperature up to 40 days on mortality and optimum temperature were analyzed using the distributed lag non-linear model (DLNM). We found that for all non-accidental mortality, high temperature could significantly increase the excess risk (ER) of death by 0.33% (95% confidence interval: 0.11%, 0.56%) with the temperature increase of 1 °C. Similar but non-significant ER of death was observed when temperature decreased. The lagged effect of temperature showed that the relative risk of non-accidental mortality was lowest at 21 °C. Our research suggests that high temperatures are more likely to cause an acute increase in mortality. There was a lagged effect of temperature on mortality, with an optimum temperature of 21 °C. Our results could provide a theoretical basis for climate-related public health policy.