Air pollution disproportionately impairs beneficial invertebrates: a meta-analysis.

Air pollution has the potential to disrupt ecologically- and economically-beneficial services provided by invertebrates, including pollination and natural pest regulation. To effectively predict and mitigate this disruption requires an understanding of how the impacts of air pollution vary between invertebrate groups. Here we conduct a global meta-analysis of 120 publications comparing the performance of different invertebrate functional groups in unpolluted and polluted atmospheres. We focus on the pollutants ozone, nitrogen oxides, sulfur dioxide and particulate matter. We show that beneficial invertebrate performance is reduced by air pollution, whereas the performance of plant pest invertebrates is not significantly affected. Ozone pollution has the most detrimental impacts, and these occur at concentrations below national and international air quality standards. Changes in invertebrate performance are not dependent on air pollutant concentrations, indicating that even low levels of pollution are damaging. Predicted increases in tropospheric ozone could result in unintended consequences to global invertebrate populations and their valuable ecological services.

CLEC11A methylation is correlated to AML subtypes and cytogenetic risk factors but not patient demographics.

Acute myeloid leukemia (AML) is an aggressive and lethal cancer of the blood, which leads to the death of over 11,000 patients in the United States each year. Research on identifying, characterizing, and treating AML is crucial in the fight against this deadly disease. Recent studies have examined the role of CLEC11A in cancer, including AML. However, there have been conflicting reports related to tumor progression and survival. Because survival is based on a variety of factors, including classification of the tumor, genetic risk factors, and demographics, it is imperative that we determine what role CLEC11A may have in cancer survival. Therefore, utilizing data from the Genomic Data Commons, we analyzed CLEC11A methylation in 108 AML patients compared to FAB classification, cytogenetic risk factors, age, race, and gender. Our results show statistically significant correlations between methylation of CLEC11A and FAB classification as well as poor genetic risk factors. However, no difference was observed in CLEC11A methylation when compared to demographic data. Our results, matched with a known biological function of CLEC11A in early hematopoiesis, indicate that CLEC11A may be an important marker for AML diagnosis and prognosis and provide relevant data in the ongoing search for novel therapeutics to improve AML survival.

'Raising the Pulse': The environmental, nutritional and health benefits of pulse-enhanced foods.

Diet is a key modulator of non-communicable diseases, and food production represents a major cause of environmental degradation and greenhouse gas emissions. Yet, 'nudging' people to make better food choices is challenging, as factors including affordability, convenience and taste often take priority over the achievement of health and environmental benefits. The overall 'Raising the Pulse' project aim is to bring about a step change in the nutritional value of the UK consumers' diet, and to do so in a way that leads to improved health and greater sustainability within the UK food system. To achieve our objectives, UK-specific faba bean production systems that optimise both end users' diets and environmental and economic sustainability of production will be implemented in collaboration with key stakeholders (including industry, the retail sector and government). Palatable faba bean flours will be produced and used to develop 'Raising the Pulse' food products with improved nutritional profile and environmental value. Consumer focus groups and workshops will establish attitudes, preferences, drivers of and barriers to increased consumption of such products. They will inform the co-creation of sensory testing and University-wide intervention studies to evaluate the effects of pulses and 'Raising the Pulse' foods on diet quality, self-reported satiety, nutritional knowledge, consumer acceptance and market potential. Nutrient bioavailability and satiety will be evaluated in a randomised-controlled postprandial human study. Finally, a system model will be developed that predicts changes to land use, environment, business viability, nutrition and human health after substitution of existing less nutritionally beneficial and environmentally sustainable ingredients with pulses. Government health and sustainability priorities will be addressed, helping to define policy-relevant solutions with significant beneficial supply chain economic impacts and transformed sustainable food systems to improve consumer diet quality, health and the environment.

Animal pollination increases stability of crop yield across spatial scales.

The benefits of animal pollination to crop yield are well known. In contrast, the effects of animal pollination on the spatial or temporal stability (the opposite of variability) of crop yield remain poorly understood. We use meta-analysis to combine variability information from 215 experimental comparisons between animal-pollinated and wind- or self-pollinated control plants in apple, oilseed rape and faba bean. Animal pollination increased yield stability (by an average of 32% per unit of yield) at between-flower, -plant, -plot and -field scales. Evidence suggests this occurs because yield benefits of animal pollination become progressively constrained closer to the maximum potential yield in a given context, causing clustering. The increase in yield stability with animal pollination is greatest when yield benefits of animal pollination are greatest, indicating that managing crop pollination to increase yield also increases yield stability. These additional pollination benefits have not yet been included in economic assessments but provide further justification for policies to protect pollinators.

Adverse weather conditions for UK wheat production under climate change.

Winter wheat is an important crop in the UK, suited to the typical weather conditions in the current climate. In a changing climate the increased frequency and severity of adverse weather events, which are often localised, are considered a major threat to wheat production. In the present study we assessed a range of adverse weather conditions, which can significantly affect yield, under current and future climates based on adverse weather indices. We analysed changes in the frequency, magnitude and spatial patterns of 10 adverse weather indices, at 25 sites across the UK, using climate scenarios from the CMIP5 ensemble of global climate models (GCMs) and two greenhouse gas emissions (RCP4.5 and RCP8.5). The future UK climate is expected to remain favourable for wheat production, with most adverse weather indicators reducing in magnitude by the mid-21st century. Hotter and drier summers would improve sowing and harvesting conditions and reduce the risk of lodging. The probability of late frosts and heat stress during reproductive and grain filling periods would likely remain small in 2050. Wetter winter and spring could cause issues with waterlogging. The severity of drought stress during reproduction would generally be lower in 2050, however localised differences suggest it is important to examine drought at a small spatial scale. Prolonged water stress does not increase considerably in the UK, as may be expected in other parts of Europe. Climate projections based on the CMIP5 ensemble reveal considerable uncertainty in the magnitude of adverse weather conditions including waterlogging, drought and water stress. The variation in adverse weather conditions due to GCMs was generally greater than between emissions scenarios. Accordingly, CMIP5 ensembles should be used in the assessment of adverse weather conditions for crop production to indicate the full range of possible impacts, which a limited number of GCMs may not provide.

Elevated temperature drives a shift from selfing to outcrossing in the insect-pollinated legume, faba bean (Vicia faba).

Climate change can threaten the reproductive success of plants, both directly, through physiological damage during increasingly extreme weather events, and indirectly, through disruption of plant-pollinator interactions. To explore how plant-pollinator interactions are modified by extreme weather, we exposed faba bean (Vicia faba) plants to elevated temperature for 5 d during flowering, simulating a heatwave. We then moved the plants to flight cages with either bumblebees or no pollinators, or to two field sites, where plants were enclosed in mesh bags or pollinated by wild insect communities. We used a morphological marker to quantify pollen movement between experimental plants. There was a substantial increase in the level of outcrossing by insect pollinators following heat stress. Proportion outcrossed seed increased from 17 % at control temperature, to 33 % following heat stress in the flight cages, and from 31 % to 80 % at one field site, but not at the other (33 % to 32 %). Abiotic stress can dramatically shift the relative contributions of cross- and self-pollination to reproduction in an insect pollinated plant. The resulting increases in gene flow have broad implications for genetic diversity and functioning of ecosystems, and may increase resilience by accelerating the selection of more stress-tolerant genotypes.

Insect pollination reduces yield loss following heat stress in faba bean (Vicia faba L.).

Global food security, particularly crop fertilization and yield production, is threatened by heat waves that are projected to increase in frequency and magnitude with climate change. Effects of heat stress on the fertilization of insect-pollinated plants are not well understood, but experiments conducted primarily in self-pollinated crops, such as wheat, show that transfer of fertile pollen may recover yield following stress. We hypothesized that in the partially pollinator-dependent crop, faba bean (Vicia faba L.), insect pollination would elicit similar yield recovery following heat stress. We exposed potted faba bean plants to heat stress for 5 days during floral development and anthesis. Temperature treatments were representative of heat waves projected in the UK for the period 2021-2050 and onwards. Following temperature treatments, plants were distributed in flight cages and either pollinated by domesticated Bombus terrestris colonies or received no insect pollination. Yield loss due to heat stress at 30 °C was greater in plants excluded from pollinators (15%) compared to those with bumblebee pollination (2.5%). Thus, the pollinator dependency of faba bean yield was 16% at control temperatures (18-26 °C) and extreme stress (34 °C), but was 53% following intermediate heat stress at 30 °C. These findings provide the first evidence that the pollinator dependency of crops can be modified by heat stress, and suggest that insect pollination may become more important in crop production as the probability of heat waves increases.

Collapsing factors in multitrait-multimethod models: examining consequences of a mismatch between measurement design and model.

Models of confirmatory factor analysis (CFA) are frequently applied to examine the convergent validity of scores obtained from multiple raters or methods in so-called multitrait-multimethod (MTMM) investigations. Many applications of CFA-MTMM and similarly structured models result in solutions in which at least one method (or specific) factor shows non-significant loading or variance estimates. Eid et al. (2008) distinguished between MTMM measurement designs with interchangeable (randomly selected) vs. structurally different (fixed) methods and showed that each type of measurement design implies specific CFA-MTMM measurement models. In the current study, we hypothesized that some of the problems that are commonly seen in applications of CFA-MTMM models may be due to a mismatch between the underlying measurement design and fitted models. Using simulations, we found that models with M method factors (where M is the total number of methods) and unconstrained loadings led to a higher proportion of solutions in which at least one method factor became empirically unstable when these models were fit to data generated from structurally different methods. The simulations also revealed that commonly used model goodness-of-fit criteria frequently failed to identify incorrectly specified CFA-MTMM models. We discuss implications of these findings for other complex CFA models in which similar issues occur, including nested (bifactor) and latent state-trait models.

Analyzing person, situation and person × situation interaction effects: Latent state-trait models for the combination of random and fixed situations.

Latent state-trait (LST) models (Steyer, Ferring, & Schmitt, 1992) allow separating person-specific (trait) effects from (1) effects of the situation and person × situation interactions, and (2) random measurement error in purely observational studies. Typical LST applications use measurement designs in which all situations are sampled randomly and do not have to be known for any individual. Limitations of conventional LST models for only random situations are that traits are implicitly assumed to generalize perfectly across situations, and that main effects of situations are inseparable from person × situation interaction effects because both are measured by the same latent variable. In this article, we show how these limitations can be overcome by using measurement designs in which two or more random situations are nested within two or more fixed situations that are known for each individual. We present extended LST models for the combination of random and fixed situations (LST-RF approach) and show that the extensions allow (1) examining the extent to which traits are situation-specific and (2) isolating person × situation interactions from situation main effects. We demonstrate that the LST-RF approach can be applied with both homogenous and heterogeneous indicators in either the single- or multilevel structural equation modeling frameworks. Advantages and limitations of the new models as well as their relation to other approaches for studying person × situation interactions are discussed.

Modeling latent growth with multiple indicators: a comparison of three approaches.

Latent growth curve models (LGCMs) are widely used methods for analyzing change in psychology and the social sciences. To date, most applications use first-order (single-indicator) LGCMs. These models have several limitations that can be overcome with multiple-indicator LGCMs. Currently, almost all multiple-indicator applications use the so-called second-order growth model (SGM; McArdle, 1988). In this article, we review the SGM and discuss 2 alternative, but less well-known, multiple-indicator LGCMs that overcome some of the limitations of the SGM: the generalized second-order growth model (GSGM) and the indicator-specific growth model (ISGM). In contrast to the SGM, the GSGM does not involve a proportionality constraint on the ratio of general to specific variance. The ISGM allows researchers to model indicator-specific growth. Both of these alternative models allow testing measurement invariance across time for state-variability components. We also present an empirical application regarding changes in self-reported levels of anxiety and discuss implications of the differences between the 3 models for applied research.

Analyzing latent state-trait and multiple-indicator latent growth curve models as multilevel structural equation models.

Latent state-trait (LST) and latent growth curve (LGC) models are frequently used in the analysis of longitudinal data. Although it is well-known that standard single-indicator LGC models can be analyzed within either the structural equation modeling (SEM) or multilevel (ML; hierarchical linear modeling) frameworks, few researchers realize that LST and multivariate LGC models, which use multiple indicators at each time point, can also be specified as ML models. In the present paper, we demonstrate that using the ML-SEM rather than the SL-SEM framework to estimate the parameters of these models can be practical when the study involves (1) a large number of time points, (2) individually-varying times of observation, (3) unequally spaced time intervals, and/or (4) incomplete data. Despite the practical advantages of the ML-SEM approach under these circumstances, there are also some limitations that researchers should consider. We present an application to an ecological momentary assessment study (N = 158 youths with an average of 23.49 observations of positive mood per person) using the software Mplus (Muthén and Muthén, 1998-2012) and discuss advantages and disadvantages of using the ML-SEM approach to estimate the parameters of LST and multiple-indicator LGC models.