The dynamics of sociality and glucocorticoids in wild male Assamese macaques.

For males of gregarious species, dominance status and the strength of affiliative relationships can have major fitness consequences. Social dynamics also impose costs by affecting glucocorticoids, mediators of homeostasis and indicators of the physiological response to challenges and within-group competition. We investigated the relationships between dominance, social bonds, seasonal challenges, and faecal glucocorticoid metabolite (fGC) measures in wild Assamese macaques (Macaca assamensis) at Phu Khieo Wildlife Sanctuary, Thailand, combining behavioural data with 4129 samples from 62 adult males over 15 years. Our previous work on this population suggested that increased competition during the mating season was associated with elevated fGC levels and that, unusually for male primates, lower rank position correlated with higher fGC levels. With a much larger dataset and dynamic measures of sociality, we re-examined these relationships and additionally tested the potentially fGC-attenuating effect of social support. Contrary to our previous study, yet consistent with the majority of work on male primates, dominance rank had a positive relationship with fGC levels, as high status correlated with elevated glucocorticoid measures. fGC levels were increased at the onset of the mating season. We demonstrated an fGC-reducing effect of supportive relationships in males and showed that dynamics in affiliation can correlate with dynamics in physiological responses. Our results suggest that in a system with intermediate contest potential, high dominance status can impose physiological costs on males that may potentially be moderated by social relationships. We highlight the need to consider the dynamics of sociality and competition that influence hormonal processes.

Understanding Factorial Designs, Main Effects, and Interaction Effects: Simply Explained with a Worked Example.

A factorial design examines the effects of two independent variables on a single, continuous dependent variable. The statistical test employed to analyze the data is a two-way analysis of variance (ANOVA). This test yields three results: a main effect for each of the independent variables and an interaction effect between the two independent variables. This article explains factorial designs and two-way ANOVA with the help of a worked example using hypothetical data in a spreadsheet provided as a supplementary file. The main effects and interaction effects are explained and illustrated using tables and figures. A short discussion provides general notes about the concepts explained in this article, along with brief notes on repeated measures ANOVA and higher order ANOVAs. Many additional examples, with figures and explanations, are provided in the supplementary materials, which the reader is strongly encouraged to view.

Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets.

Simulation frameworks are useful to stress-test predictive models when data is scarce, or to assert model sensitivity to specific data distributions. Such frameworks often need to recapitulate several layers of data complexity, including emergent properties that arise implicitly from the interaction between simulation components. Antibody-antigen binding is a complex mechanism by which an antibody sequence wraps itself around an antigen with high affinity. In this study, we use a synthetic simulation framework for antibody-antigen folding and binding on a 3D lattice that include full details on the spatial conformation of both molecules. We investigate how emergent properties arise in this framework, in particular the physical proximity of amino acids, their presence on the binding interface, or the binding status of a sequence, and relate that to the individual and pairwise contributions of amino acids in statistical models for binding prediction. We show that weights learnt from a simple logistic regression model align with some but not all features of amino acids involved in the binding, and that predictive sequence binding patterns can be enriched. In particular, main effects correlated with the capacity of a sequence to bind any antigen, while statistical interactions were related to sequence specificity.

Genotype-by-Environment Interaction Analysis for Quantity and Quality Traits in Faba Beans Using AMMI, GGE Models, and Stability Indices.

Faba beans are considered one of the most important crops for animal feed. The genotype × environment interaction (GEI) has a considerable effect on faba bean seed production. The objectives of this study included assessing multiple locations and genotypes to understand how various ecosystems and faba bean genotypes relate to one another, and suggesting the ideal climatic conditions, crop management system, and genotypes so that they are carefully chosen for their stability. A 2-year experiment was conducted in order to define the stability across four environments based on stability indices for certain characteristics: moisture (%), ash content (%), crude protein content (%), crude fat (%), total starch (%), and crude fiber content (%). Statistically significant differences indicated that GEIs were present. The heritability was generally high for qualitative traits in comparison with quantitative traits. The crude protein content, plant height, and thousand-seed weight were all positively correlated with the seed yield; however, the other qualitative variables were adversely correlated. The crude protein content of the cultivar Tanagra displayed a high stability index, followed by Ste1. Under conventional management, Tanagra demonstrated high values for the seed yield in Giannitsa and Florina. Ste1 and Ste2 are particularly promising genetic materials that showed high values under low-input conditions. The best genotypes to use and the most favorable environments/types of cultivation were the Tanagra cultivar, followed by the Ste2 genotype, according to the additive main effects and multiplicative interaction (AMMI) and genotype plus genotype-by-environment (GGE) biplot models. Earliness showed significant heritability values and very high stability indices, again indicating qualitative behavior according to genetic parameters. With the exception of the number of pods per plant, which demonstrated low heritability while having excellent index values, traits like seed yield showed relatively low-stability-based heritability values. Global efforts aimed at improving the genetics of faba beans might benefit from genotypes that exhibit consistent yields in various conditions.

A Comparison and Analysis of Three Methods of Aluminum Crown Forgings in Processing Optimization.

In this study, three parameter optimization methods and two designs of experiments (DOE) were used for the optimization of three major design parameters ((bill diameter (D), billet length (L), and barrier wall design (BWD)) in crown forging to improve the formability of aluminum workpiece for shock absorbers. The first optimization method is the response surface method (RSM) combined with Box-Behnken's experimental design to establish fifteen (15) sets of parameter combinations for research. The second one is the main effects plot method (MEP). The third one is the multiobjective optimization method combined with Taguchi's experimental design method, which designed nine (9) parameter combinations and conducted research and analysis through grey relational analysis (GRA). Initially, a new type of forging die and billet in the controlled deformation zone (CDZ) was established by CAD (computer-aided design) modeling and the finite element method (FEM) for model simulation. Then, this investigation showed that the optimal parameter conditions obtained by these three optimization approaches (RSM, MEP, and multiobjective optimization) are consistent, with the same results. The best optimization parameters are the dimension of the billet ((D: 40 mm, the length of the billet (L): 205 mm, and the design of the barrier wall (BWD): 22 mm)). The results indicate that the optimization methods used in this research all have a high degree of accuracy. According to the research results of grey relational analysis (GRA), the size of the barrier wall design (BWD) in the controllable deformation zone (CDZ) has the greatest influence on the improvement of the preforming die, indicating that it is an important factor to increase the filling rate of aluminum crown forgings. At the end, the optimized parameters are verified by FEM simulation analysis and actual production validation as well as grain streamline distribution, processing map, and microstructure analysis on crown forgings. The novelty of this work is that it provides a novel preforming die through the mutual verification of different optimization methods to solve a typical problem such as material underfill.

Cerebro-cerebellar Dysconnectivity in Children and Adolescents With Attention-Deficit/Hyperactivity Disorder.

OBJECTIVE: Abnormal cerebellar development has been implicated in attention-deficit/hyperactivity disorder (ADHD), although cerebro-cerebellar functional connectivity (FC) has yet to be examined in ADHD. Our objective is to investigate the disturbed cerebro-cerebellar FC in children and adolescents with ADHD. METHOD: We analyzed a dataset of 106 individuals with ADHD (68 children, 38 adolescents) and 62 healthy comparison individuals (34 children, 28 adolescents) from the publicly available ADHD-200 dataset. We identified 7 cerebellar subregions based on cerebro-cerebellar FC and subsequently obtained the FC maps of cerebro-cerebellar networks. The main effects of ADHD and age and their interaction were examined using 2-way analysis of variance. RESULTS: Compared to comparisons, ADHD showed higher cerebro-cerebellar FC in the superior temporal gyrus within the somatomotor network. Interactions of diagnosis and age were identified in the supplementary motor area and postcentral gyrus within the somatomotor network and middle temporal gyrus within the ventral attention network. Follow-up Pearson correlation analysis revealed decreased cerebro-cerebellar FC in these regions with increasing age in comparisons, whereas the opposite pattern of increased cerebro-cerebellar FC occurred in ADHD. CONCLUSION: Increased cerebro-cerebellar FC in the superior temporal gyrus within the somatomotor network could underlie impairments in cognitive control and somatic motor function in ADHD. In addition, increasing cerebro-cerebellar FC in older participants with ADHD suggests that enhanced cerebellar involvement may compensate for dysfunctions of the cerebral cortex in ADHD.

Screening of stable resistant accessions and identification of resistance loci to Barley yellow mosaic virus disease.

Background: The disease caused by Barley yellow mosaic virus (BaYMV) infection is a serious threat to autumn-sown barley (Hordeum vulgare L.) production in Europe, East Asia and Iran. Due to the rapid diversification of BaYMV strains, it is urgent to discover novel germplasm and genes to assist breeding new varieties with resistance to different BaYMV strains, thus minimizing the effect of BaYMV disease on barley cropping. Methods: A natural population consisting of 181 barley accessions with different levels of resistance to BaYMV disease was selected for field resistance identification in two separate locations (Yangzhou and Yancheng, Jiangsu Province, China). Additive main effects and multiplicative interaction (AMMI) analysis was used to identify accessions with stable resistance. Genome-wide association study (GWAS) of BaYMV disease resistance was broadly performed by combining both single nucleotide polymorphisms (SNPs) and specific molecular markers associated with the reported BaYMV disease resistance genes. Furthermore, the viral protein genome linked (VPg) sequences of the virus were amplified and analyzed to assess the differences between the BaYMV strains sourced from the different experimental sites. Results: Seven barley accessions with lower standardized Area Under the Disease Progress Steps (sAUDPS) index in every environment were identified and shown to have stable resistance to BaYMV disease in each assessed location. Apart from the reported BaYMV disease resistance genes rym4 and rym5, one novel resistance locus explaining 24.21% of the phenotypic variation was identified at the Yangzhou testing site, while two other novel resistance loci that contributed 19.23% and 19.79% of the phenotypic variation were identified at the Yancheng testing site, respectively. Further analysis regarding the difference in the VPg sequence of the predominant strain of BaYMV collected from these two testing sites may explain the difference of resistance loci differentially identified under geographically distinct regions. Our research provides novel genetic resources and resistance loci for breeding barley varieties for BaMYV disease resistance.

Repeated Measures ANOVA with Latent Variables to Analyze Interindividual Differences in Contrasts.

Repeated measures analysis of variance (RM-ANOVA) is a broadly used statistical method to analyze data from experimental designs. RM-ANOVA aims at investigating effects of experimental conditions (i.e., factors) and predictors that affect the outcome of interest. It mainly considers contrasts that test standard main and interaction effects, even though more complex contrasts can in principle be used. Analyses, however, only focus on drawing conclusions about average effects and do not take into consideration interindividual differences in these effects. We propose an alternative approach to RM-ANOVA for analyzing repeated measures data, termed latent repeated measures analysis of variance (L-RM-ANOVA). The new approach is based on structural equation modeling and extends the latent growth components approach. L-RM-ANOVA enables the researcher to not only consider mean differences between different experimental conditions (i.e., average effects), but also to investigate interindividual differences in effects. Such interindividual differences are considered with regard to standard main and interactions effects and also with regard to customized contrasts that allow for testing specific hypotheses of interest. Furthermore, L-RM-ANOVA can include a measurement model for latent variables and can be used for the analysis of complex multi-factorial repeated measures designs. We conclude the presentation by demonstrating L-RM-ANOVA using a minimal repeated measures example.

Are some cultures more mind-minded in their moral judgements than others?

Cross-cultural research on moral reasoning has brought to the fore the question of whether moral judgements always turn on inferences about the mental states of others. Formal legal systems for assigning blame and punishment typically make fine-grained distinctions about mental states, as illustrated by the concept of mens rea, and experimental studies in the USA and elsewhere suggest everyday moral judgements also make use of such distinctions. On the other hand, anthropologists have suggested that some societies have a morality that is disregarding of mental states, and have marshalled ethnographic and experimental evidence in support of this claim. Here, we argue against the claim that some societies are simply less 'mind-minded' than others about morality. In place of this cultural main effects hypothesis about the role of mindreading in morality, we propose a contextual variability view in which the role of mental states in moral judgement depends on the context and the reasons for judgement. On this view, which mental states are or are not relevant for a judgement is context-specific, and what appear to be cultural main effects are better explained by culture-by-context interactions. This article is part of the theme issue 'The language of cooperation: reputation and honest signalling'.

Understanding the Effects of Growing Seasons, Genotypes, and Their Interactions on the Anthesis Date of Wheat Sown in North China.

Quantitative studies on the effects of growing season, genotype (including photoperiod genes and vernalization genes), and their interaction (GGI) on the anthesis date of winter wheat (Triticum aestivum L.) are helpful to provide a scientific reference for selecting or developing adaptive varieties in target environments. In this study, we collected 100 winter wheat varieties with ecological adaptability in North China and identified the anthesis date under field conditions for three consecutive years from 2016 to 2019 with mapped photoperiod and vernalization alleles. Our results showed that the number of the photoperiod-insensitive Ppd-D1a allele increased with variety replacement, while the haplotype Ppd-A1b + Ppd-D1b + vrn-D1 (A4B2) decreased from the 1940s to 2000s. The anthesis date of A4B2 was significantly delayed due to the photoperiod-insensitive alleles Ppd-A1b and Ppd-D1b. The additive main effect and multiplicative interaction (AMMI) model and GGI biplot analysis were used for data analysis. A large portion of the total variation was explained by growing seasons (66.3%), while genotypes and GGIs explained 21.9% and 10.1% of the anthesis dates, respectively. The varieties from the 1940s and before had a great influence on the anthesis date, suggesting these germplasms tend to avoid premature anthesis and could facilitate the development of phenological resilient varieties.

Powering and Structuring Intersectionality: Beyond Main and Interactive Associations.

It is exciting to watch intersectionality travel from its roots in Black feminist activism and critical legal studies to increasingly more mainstream research domains such as psychology and psychopathology. We commend Mennies et al. (Journal of Abnormal Child Psychology, 2020) for their application of the intersectionality framework to the study of psychopathology and treatment utilization in youth in the ABCD study. We argue, however, that this application falls short of its intersectional promise. We discuss some conceptual and methodological/analytical issues that evidence the focal article's lack of alignment with intersectionality's core tenets, particularly regarding the central role of power and social-structural factors as drivers of inequities across intersectional positions. Specifically, we discuss our concerns with the testing and flattening of intersectionality, the selection and operationalization of intersectional positions, and the use of conventional regression models as quantitative analytical approach. We end by suggesting ways that intersectionality can help reduce the inequities in psychopathology and treatment utilization identified by Mennies et al. (Journal of Abnormal Child Psychology, 2020).

Genotype by Environment Interaction on Resistance to Cassava Green Mite Associated Traits and Effects on Yield Performance of Cassava Genotypes in Nigeria.

Cassava is the main source of carbohydrate for over 70% of the people in Nigeria, the world's largest producer and consumer of the crop. The yields of cassava are, however, relatively low in Nigeria largely due to pests and disease infections that significantly lead to inconsistencies in productivity of cassava genotypes in various environments. Fifty-eight F1 hybrid cassava genotypes plus their two parents which served as check varieties were evaluated in three locations for two years (that is six environments). The objectives of the study were to evaluate genotype by environment interactions (GEI) on resistance to cassava green mite [CGM, Mononychellus tanajoa (Bondar)] associated traits and effects on yield performance of cassava genotypes in Nigeria and to identify superior genotypes that exhibit high stability which combine CGM resistance and high fresh root yield with general and specific environmental adaptation using additive main effects and multiplicative interaction (AMMI) and genotype stability index (GSI). The combined analysis of variance based on AMMI revealed significant genotype, environment, and genotype by environment interactions (GEI) for all traits. The percentage variation due to environment was higher than the percentage variation due to genotype for cassava green mite severity (CGMS), leaf retention (LR), root dry matter content (RDMC), and fresh root yield (FRY) indicating that environment greatly influenced the expression of these traits. The percentage variation due to GEI accounted for higher percentage variation than that of genotype and environment separately for all traits, indicating the influence of genotype by environment interaction on expression of the traits. These findings reveal that screening/evaluating for these traits needs multi-environment trials. According to GSI ranking, genotypes G31 (IBA131794), G19 (IBA131762), the check variety G52 (TMEB778), and G11 (IBA131748) were identified as the most stable and most resistant to CGM which also combine high FRY and other useful agronomic traits, implying that these traits in cassava can even be incorporated as preferred by farmers. These genotypes can be tested in more environments to determine their adaptability and potential recommendation for release to farmers for growing.

Genetic Progress of Seed Yield and Nitrogen Use Efficiency of Brazilian carioca Common Bean Cultivars Using Bayesian Approaches.

Common bean (Phaseolus vulgaris L.) is one of the most important crops worldwide and is considered an essential source of proteins, fibers, and minerals in the daily diet of several countries. Nitrogen (N) is considered the most important nutrient for common bean crop. On the other hand, the reduction of chemical fertilizers is a global challenge, and the development of cultivars with more N use efficiency (NUsE) is considered one of the main strategies to reduce the amount of N fertilizers. Genetic progress of NUsE has been reported in several crops; however, there was still no quantity in common bean. In this study, our goal was to analyze the genetic progress of seed yield (SY) and NUsE-related traits of 40 carioca common bean cultivars release from 1970 to 2017 in eight environments under low (zero) or high N (40 kg ha-1) in top-dressing. Genetic progress, principal component analysis, correlations among traits, and cultivar stability were analyzed using Bayesian approaches. The lowest values of the deviance information criterion (DIC) for the full model tested indicated the presence of the genotype × N × environment interaction for all evaluated traits. Nitrogen utilization efficiency (NUtE) and nitrogen uptake efficiency (NUpE) were the traits that most contributed to discriminate cultivars. The genetic progress of SY under high N (0.53% year-1, 95% HPD = 0.39; 0.65% year-1) was similar to that obtained in low N conditions (0.48% year-1, 95% HPD = 0.31; 0.64% year-1). These results indicate that modern cultivars do not demand more N fertilizers to be more productive. In addition, we observed a high genetic variability for NUsE-related traits, but there was no genetic progress for these variables. SY showed negative correlation with seed protein content (Prot) in both N conditions, and there was no reduction in Prot in modern cultivars. Both modern and old cultivars showed adaptability and stability under contrasting N conditions. Our study contributed to improve our knowledge about the genetic progress of common bean breeding program in Brazil in the last 47 years, and our data will help researchers to face the challenge of increase NUsE and Prot in the next few years.

ANOVA simultaneous component analysis: A tutorial review.

When analyzing experimental chemical data, it is often necessary to incorporate the structure of the study design into the chemometric/statistical models to effectively address the research questions of interest. ANOVA-Simultaneous Component Analysis (ASCA) is one of the most prominent methods to include such information in the quantitative analysis of multivariate data, especially when the number of variables is large. This tutorial review intends to explain in a simple way how ASCA works, how it is operated and how to correctly interpret ASCA results, with approachable mathematical and visual descriptions. Two examples are given: the first, a simulated chemical reaction, serves to illustrate the ASCA steps and the second, from a real chemical ecology data set, the interpretation of results. An overview of methods closely related to ASCA is also provided, pointing out their differences and scope, to give a wide-ranging picture of the available options to build multivariate models that take experimental design into account.

DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reduction.

Detecting epistatic interaction is a typical way of identifying the genetic susceptibility of complex diseases. Multifactor dimensionality reduction (MDR) is a decent solution for epistasis detection. Existing MDR-based methods still suffer from high computational costs or poor performance. In this paper, we propose a new solution that integrates a dual screening strategy with MDR, termed as DualWMDR. Particularly, the first screening employs an adaptive clustering algorithm with part mutual information (PMI) to group single nucleotide polymorphisms (SNPs) and exclude noisy SNPs; the second screening takes into account both the single-locus effect and interaction effect to select dominant SNPs, which effectively alleviates the negative impact of main effects and provides a much smaller but accurate candidate set for MDR. After that, MDR uses the weighted classification evaluation to improve its performance in epistasis identification on the candidate set. The results on diverse simulation datasets show that DualWMDR outperforms existing competitive methods, and the results on three real genome-wide datasets: the age-related macular degeneration (AMD) dataset, breast cancer (BC), and celiac disease (CD) datasets from the Wellcome Trust Case Control Consortium, again corroborate the effectiveness of DualWMDR.

Multienvironment Testing for Trait Stability and G × E Interaction on N2 Fixation, Plant Development, and Water-Use Efficiency of 21 Elite Groundnut (Arachis hypogaea L.) Genotypes in the Guinea Savanna.

Groundnut production constitutes an integral part of the livelihoods of the people in the Guinea savanna of West Africa. This region accounts for over 70% of the total groundnut production in Ghana, 90% in Nigeria, and 100% in Mali and Burkina Faso. However, harsh environmental conditions often result in drastic yield reductions. In this study, we identified groundnut genotypes with superior symbiotic efficiency, greater pod yield, and plant water-use efficiency from 21 advanced groundnut breeding lines from ICRISAT after testing them at three locations in the Guinea savanna of Ghana over two consecutive years. Average N contribution by the groundnut genotypes ranged from 48 to 108 kg N ha-1, and mean pod yield from 580 to 2,100 kg ha-1. Genotype 17 (ICGV-IS 08837) produced about 2.5-fold more pods than genotype 1 (Chinese), which was the most widely cultivated variety by farmers. Of the 21 genotypes studied, genotype 16 (ICGV 99247) recorded the highest shoot δ13C value and was superior in water-use efficiency, which was consistent with stability estimates and mean performance. We also measured the effects of G × E on pod yield, N2 fixation, shoot δ13C, and mega-environments for testing groundnut in the Guinea savanna, and these were all significant, although the effect was minimal on shoot δ13C values. Of the locations studied, Nyankpala and Damongo were more discriminating, and each constituted a mega-environment for conducting future groundnut trials in the Guinea savanna. Genotype 3 (ICG 6222) emerged as the best cultivar for the Damongo mega-environment, while genotype 17 was the best genotype for the Nyankpala mega-environment. The genotypes exhibiting the highest sensitivity of N2 fixation in the environment included genotype 3 (ICG 6222), genotype 4 (ICGV 00068), and genotype 10 (ICGV 03315) (bi > 1.3), while Pi estimates ranked genotypes 3, 10, and 17 as the best groundnut cultivars in terms of symbiotic N contribution. Based on the results of this study, genotype 17 (ICGV-IS 08837), genotype 3 (ICG 6222), genotype 10 (ICGV 03315), and genotype 4 (ICGV 00068), which were the most outstanding in terms of the overall pod yield, shoot biomass production, and amount of N-fixed, were the most suitable candidates to recommend for use in developing new varieties for the Guinea savanna of Ghana. Genotype 17 (ICGV-IS 08837) has already been released as a commercial variety for the Guinea savanna of Ghana since October 2018.

Analysis of Variance Models with Stochastic Group Weights.

The analysis of variance (ANOVA) is still one of the most widely used statistical methods in the social sciences. This article is about stochastic group weights in ANOVA models - a neglected aspect in the literature. Stochastic group weights are present whenever the experimenter does not determine the exact group sizes before conducting the experiment. We show that classic ANOVA tests based on estimated marginal means can have an inflated type I error rate when stochastic group weights are not taken into account, even in randomized experiments. We propose two new ways to incorporate stochastic group weights in the tests of average effects - one based on the general linear model and one based on multigroup structural equation models (SEMs). We show in simulation studies that our methods have nominal type I error rates in experiments with stochastic group weights while classic approaches show an inflated type I error rate. The SEM approach can additionally deal with heteroscedastic residual variances and latent variables. An easy-to-use software package with graphical user interface is provided.

Comparing the Efficacy of Static and Dynamic Graph Types in Communicating Complex Statistical Relationships.

Graphs are useful tools to communicate meaningful patterns in data, but their efficacy varies considerably based on the figure's construction and presentation medium. Specifically, a digital format figure can be dynamic, allowing the reader to manipulate it and little is known about the efficacy of dynamic figures. This present study compared how effectively static and dynamic graphical formats convey relationship information, and in particular variable interactions. Undergraduates (N = 128, 56% female, M age = 18.9) were given a brief tutorial on main effects and interactions in data and then answered 48 multiple-choice questions about specific graphs. Each question involved one of four figure types and one of four relationship types (main effect only, interaction only, main effect and interaction, or no relationship), with relationship types and graphical formats fully crossed. Multilevel logistic regression analysis revealed that participants were fairly accurate at detecting main effects and null relationships but struggled with interaction effects. Additionally, the static 3D graph lowered performance for detecting main effects, although this negative effect disappeared when participants were allowed to rotate the 3D graph. These results suggest that dynamic figures in digital publications are a potential tool to effectively communicate data, but they are not a panacea. Undergraduates continued to struggle with more complicated relationships (e.g., interactions) regardless of graph type. Future studies will need to examine more experienced populations and additional dynamic graph formats, especially ones tailored for demonstrating interactions (e.g., profiler plots).

Interacting Temperature, Nutrients and Zooplankton Grazing Control Phytoplankton Size-Abundance Relationships in Eight Swiss Lakes.

Biomass distribution among size classes follows a power law where the Log-abundance of taxa scales to Log-size with a slope that responds to environmental abiotic and biotic conditions. The interactions between ecological mechanisms controlling the slope of locally realized size-abundance relationships (SAR) are however not well understood. Here we tested how warming, nutrient levels, and grazing affect the slope of phytoplankton community SARs in decadal time-series from eight Swiss lakes of the peri-alpine region, which underwent environmental forcing due to climate change and oligotrophication. We expected rising temperature to have a negative effect on slope (favoring small phytoplankton), and increasing nutrient levels and grazing pressure to have a positive effect (benefiting large phytoplankton). Using a random forest approach to extract robust patterns from the noisy data, we found that the effects of temperature (direct and indirect through water column stability), nutrient availability (phosphorus and total biomass), and large herbivore (copepods and daphnids) grazing and selectivity on slope were non-linear and interactive. Increasing water temperature or total grazing pressure, and decreasing phosphorus levels, had a positive effect on slope (favoring large phytoplankton, which are predominantly mixotrophic in the lake dataset). Our results therefore showed patterns that were opposite to the expected long-term effects of temperature and nutrient levels, and support a paradigm in which (i) small phototrophic phytoplankton appear to be favored under high nutrients levels, low temperature and low grazing, and (ii) large mixotrophic algae are favored under oligotrophic conditions when temperature and grazing pressure are high. The effects of temperature were stronger under nutrient limitation, and the effects of nutrients and grazing were stronger at high temperature. Our study shows that the phytoplankton local SARs in lakes respond to both the independent and the interactive effects of resources, grazing and water temperature in a complex, unexpected way, and observations from long-term studies can deviate significantly from general theoretical expectations.

The Role of Social Support in Motivating Reductions in Alcohol Use: A Test of Three Models of Social Support in Alcohol-Impaired Drivers.

BACKGROUND: Social support has been linked to many therapeutic benefits (e.g., treatment retention, reduced posttreatment relapse) for individuals with alcohol use disorder. However, the positive impacts of social support have not been well understood in the context of alcohol-impaired driving. This article examines the role of social support in motivating those with histories of driving while intoxicated (DWI) arrest to reduce alcohol use by testing 3 major models of social support: the Main-Effects model, the Buffering model, and the Optimal Matching model. METHODS: One hundred and nineteen participants with histories of DWI arrest were recruited from a correctional treatment facility (n = 59) and the local community (n = 60). Participants completed interviews to assess alcohol consumption, psychiatric/physical conditions, and psychosocial factors associated with drinking behavior (e.g., social support, alcohol-related problems, and motivation to change). Hierarchical regression analyses were conducted to test the 3 models. Additionally, the relative magnitude of the effects of general and recovery-specific social support was compared based on the approach of statistical inference of confidence intervals. RESULTS: Overall social support was positively associated with some motivation to change (i.e., importance of change, confidence in change) among alcohol-impaired drivers, supporting the Main-Effects model. However, the impact of overall social support on motivation to change was not moderated by alcohol-related problems of individuals arrested for DWI, which did not confirm the Buffering model. Last, recovery-specific social support, rather than general social support, contributed to increasing motivation to reduce alcohol use, which supported the Optimal Matching model. CONCLUSIONS: These findings highlight the benefits of social support (i.e., increased motivation to change alcohol use) for alcohol-impaired drivers. Regardless of the severity of alcohol-related problems of alcohol-impaired drivers, social support had direct positive impacts on motivation to change. In particular, the results underscore that social support can be more effective when it is matched to the recovery effort of individuals, which is consistent with the Optimal Matching model.