Vineyards are an appropriate model for testing the filtering effect of management practices on weed communities, as a wide range of practices (tillage, herbicides and mowing) is implemented. The aim of this study is to highlight which trait values are selected by each practice in different environmental conditions, with special references to Grime's CSR strategies. A combination of a multivariate analysis (RLQ) and the fourth-corner analysis was used to analyse 400 floristic samples belonging to 100 vineyards in the wine-growing region of Bordeaux (France). The main structure of vineyard weed communities was shaped by the opposition between mowing, favouring hemicryptophytes with a competitive strategy, and soil tillage, favouring therophytes and nutrient-demanding species with a ruderal strategy. Secondly, the vineyard weed communities differed according to the trophic status of the soil.Vineyards on acidic, sandy soils with low organic matter were characterised by smallseeded annuals with a stress-tolerant strategy whereas vineyards with clayey, calcareous soils rich in organic matter, harboured larger perennial nitrophilous species with large seeds and a competitive strategy. Our study is the first to show that weed species responded consistently to two independent gradients with specific traits associated with disturbance (life cycle and SLA) and soil resources (plant height and seed mass) gradients. Based on knowledge of the soil characteristics, it becomes possible to predict which type of weeds will develop according to the combination of practices applied on the vineyard rows and inter-rows.

There is today ample evidence that academic achievement depends on individual disparities in socioeconomic status (SES), working memory (WM) and academic self-concept (ASC). However, because these factors were investigated intensively but in separate fields of research in the past four to six decades, their relationships remain largely unknown. The present study investigated whether SES, WM and ASC interact with each other or represent independent contributions to academic achievement in 2379 adolescents in middle and high schools. The findings confirmed previous results showing that students with lower SES, lower WM, and lower ASC perform less well on academic tests. Above all, they revealed subtle patterns of mediating processes. Specifically, individual differences in WM processing, and to a lesser extent in ASC accounted for most part of the negative impact of low SES on academic achievement. These findings indicate that being a member of disadvantaged groups impair both WM processing and ASC, and provide a clearer picture of the complex involvements of socioeconomic, cognitive and self-perception factors in academic achievement.

The Covid-19 pandemic has led millions of students worldwide to intensify their use of digital education. This massive change is not reflected by the scant scientific research on the effectiveness of methods relying on digital learning compared to other innovative and more popular methods involving face-to-face interactions. Here, we tested the effectiveness of computer-assisted instruction (CAI) in Science and Technology compared to inquiry-based learning (IBL), another modern method which, however, requires students to interact with each other in the classroom. Our research also considered socio-cognitive factors–working memory (WM), socioeconomic status (SES), and academic self-concept (ASC)–known to predict academic performance but usually ignored in research on IBL and CAI. Five hundred and nine middle-school students, a fairly high sample size compared with relevant studies, received either IBL or CAI for a period varying from four to ten weeks prior to the Covid-19 events. After controlling for students’ prior knowledge and socio-cognitive factors, multilevel modelling showed that CAI was more effective than IBL. Although CAI-related benefits were stable across students’ SES and ASC, they were particularly pronounced for those with higher WM capacity. While indicating the need to adapt CAI for students with poorer WM, these findings further justify the use of CAI both in normal times (without excluding other methods) and during pandemic episodes.