Interactive and Dynamic Effects of Rootstock and Rhizobiome on Scion Nutrition in Cacao Seedlings

Perennial agroecosystems often seek to optimize productivity by breeding nutrient-efficient, disease-resistant rootstocks. In cacao (Theobroma cacao L.), however, rootstock selection has traditionally relied on locally available open pollinated populations with limited data on performance. Furthermore, rootstock associations with the rhizobiome, or rhizosphere microbiome, have been neglected. Better understanding of rootstock and scion effects on cacao-specific traits, particularly those involved in root-microbe interactions and nutrient acquisition, could contribute to more efficient rootstock selection and breeding. A rootstock-scion interaction study was conducted using three scion genotypes and eight rootstock populations under greenhouse conditions to better understand the relationships among rootstock and scion identities, soil fertility, and rhizobiome composition and the impacts of these factors on plant uptake of macro- and micronutrients. We show that rootstock genotype has a stronger influence than scion on nutrient uptake, bacterial and fungal diversity, and rhizobiome composition, and that the relative contributions of rootstock and scion genotype to foliar nutrient status are dynamic over time. Correlation analysis and stepwise regression revealed complex relationships of soil physicochemical parameters and the rhizobiome to plant nutrition and emphasized strong impacts of microbial diversity and composition on specific nutrients. Linear discriminant analysis effect size estimation identified rootstock-responsive taxa potentially related to plant nutrition. This study highlights the importance of considering root-associated microbial communities as a factor in cacao rootstock breeding and the need for further investigation into mechanisms underlying nutrient acquisition and microbial interactions in grafted plants.

Large-Scale Societal Dynamics are Reflected in Human Mood and Brain

The stock market is a bellwether of socio-economic changes that may directly affect individual well-being. Using large-scale UK-biobank data generated over 14 years, we applied specification curve analysis to rigorously identify significant associations between the local stock market index (FTSE100) and 479,791 UK residents’ mood, as well as their alcohol intake and blood pressure adjusting the results for a large number of potential confounders, including age, sex, linear and non-linear effects of time, research site, other stock market indexes. Furthermore, we found similar associations between FTSE100 and volumetric measures of affective brain regions in a subsample (n = 39,755; measurements performed over 5.5 years), which were particularly strong around phase transitions characterized by maximum volatility in the market. The main findings did not depend on applied effect-size estimation criteria (linear methods or mutual information criterion) and were replicated in two independent US-based studies (Parkinson’s Progression Markers Initiative; n = 424; performed over 2,5 years and MyConnectome; n = 1; 81 measurements over 1,5 years). Our results suggest that phase transitions in the society, indexed by stock market, exhibit close relationships with human mood, health and the affective brain from an individual to population level.

New Statistics, Old Schools: An Overview of Current Introductory Undergraduate and Graduate Statistics Pedagogy Practices

Background A philosophical shift in statistics regarding emphasis on “New Statistics” (NS; Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7-29.) over conventional null hypothesis significance testing (NHST) raises the question of appropriate material coverage in undergraduate statistics courses. Objective We examined current practices in statistics pedagogy at the graduate and undergraduate levels for both NS and NHST. Method Using an online survey of a nationwide sample of current graduate students ( n = 452) and graduate faculty ( n = 162), we examined statistics pedagogy and perceptions of best approaches for teaching undergraduate statistics. Results In undergraduate statistics courses, coverage of NS material involves modest instruction in effect sizes and confidence intervals, while NHST remains dominant. Graduate courses have more balanced coverage. Effect size estimation was regarded as the most important NS knowledge for success in graduate school and the topic most in need of increased undergraduate coverage. Conclusion Undergraduate statistics courses could increase NS coverage, particularly effect size estimation, to better align with and prepare students for graduate work. Teaching Implications This research summarizes graduate program expectations and graduate student experiences regarding undergraduate statistics that current instructors can use to shape the content of their classes.

Using and Understanding Power in Psychological Research: A Survey Study

Statistical power is key to planning studies if understood and used correctly. Power is the probability of obtaining a statistically significant p-value, given a set alpha, sample size, and population effect size. The literature suggests that psychology studies are underpowered due to small sample sizes, and that researchers do not hold accurate intuitions about sensible sample sizes and associated levels of power. In this study, we surveyed 214 psychological researchers, and asked them about their experiences of using a priori power analysis, effect size estimation methods, post hoc power, and their understanding of what the term “power” actually means. Power analysis use was high, although participants reported difficulties with complex research designs, and effect size estimation. Participants also typically could not accurately define power. If psychological researchers are expected to compute a priori power analyses to plan their research, clearer educational material and guidelines should be made available.

Designing Studies and Evaluating Research Results: Type M and Type S Errors for Pearson Correlation Coefficient

It is widely appreciated that many studies in psychological science suffer from low statistical power. One of the consequences of analyzing underpowered studies with thresholds of statistical significance, is a high risk of finding exaggerated effect size estimates, in the right or in the wrong direction. These inferential risks can be directly quantified in terms of Type M (magnitude) error and Type S (sign) error, which directly communicate the consequences of design choices on effect size estimation. Given a study design, Type M error is the factor by which a statistically significant effect is on average exaggerated. Type S error is the probability to find a statistically significant result in the opposite direction to the plausible one. Ideally, these errors should be considered during a prospective design analysis in the design phase of a study to determine the appropriate sample size. However, they can also be considered when evaluating studies’ results in a retrospective design analysis. In the present contribution we aim to facilitate the considerations of these errors in the research practice in psychology. For this reason we illustrate how to consider Type M and Type S errors in a design analysis using one of the most common effect size measures in psychology: Pearson correlation coefficient. We provide various examples and make the R functions freely available to enable researchers to perform design analysis for their research projects.

SAMPLE SIZE PLANNING IN QUANTITATIVE L2 RESEARCH

Researchers are traditionally advised to plan for their required sample size such that achieving a sufficient level of statistical power is ensured (Cohen, 1988). While this method helps distinguishing statistically significant effects from the nonsignificant ones, it does not help achieving the higher goal of accurately estimating the actual size of those effects in an intended study. Adopting an open-science approach, this article presents an alternative approach, accuracy in effect size estimation (AESE), to sample size planning that ensures that researchers obtain adequately narrow confidence intervals (CI) for their effect sizes of interest thereby ensuring accuracy in estimating the actual size of those effects. Specifically, I (a) compare the underpinnings of power-analytic and AESE methods, (b) provide a practical definition of narrow CIs, (c) apply the AESE method to various research studies from L2 literature, and (d) offer several flexible R programs to implement the methods discussed in this article.