570 Biowissenschaften; Biologie
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This study examined how early childhood (EC) teachers' instructional quality predicted children's development in mathematics across two measurement occasions. Therefore, EC teachers' (n = 25) instructional quality was assessed using one standardized observation instrument covering both domain-specific and general aspects of instructional quality. Additionally, data on children's (n = 208) outcome in early number skills was collected applying a standardized test. Multilevel structural equation modeling was used accounting for nested data. Children's age and the average size of preschool groups were controlled for. Results revealed that EC teachers' instructional quality predicted children's development but was not associated with their initial achievement. The findings suggest that instruments covering domain-specific and general aspects might be helpful in order to measure EC teachers' instructional quality in mathematics and predict children's learning growth. Understanding the mechanisms between instructional quality and children's development may help EC teachers to enhance their math teaching in practice.
Management of agricultural soil quality requires fast and cost-efficient methods to identify multiple stressors that can affect soil organisms and associated ecological processes. Here, we propose to use soil protists which have a great yet poorly explored potential for bioindication. They are ubiquitous, highly diverse, and respond to various stresses to agricultural soils caused by frequent management or environmental changes. We test an approach that combines metabarcoding data and machine learning algorithms to identify potential stressors of soil protist community composition and diversity. We measured 17 key variables that reflect various potential stresses on soil protists across 132 plots in 28 Swiss vineyards over 2 years. We identified the taxa showing strong responses to the selected soil variables (potential bioindicator taxa) and tested for their predictive power. Changes in protist taxa occurrence and, to a lesser extent, diversity metrics exhibited great predictive power for the considered soil variables. Soil copper concentration, moisture, pH, and basal respiration were the best predicted soil variables, suggesting that protists are particularly responsive to stresses caused by these variables. The most responsive taxa were found within the clades Rhizaria and Alveolata. Our results also reveal that a majority of the potential bioindicators identified in this study can be used across years, in different regions and across different grape varieties. Altogether, soil protist metabarcoding data combined with machine learning can help identifying specific abiotic stresses on microbial communities caused by agricultural management. Such an approach provides complementary information to existing soil monitoring tools that can help manage the impact of agricultural practices on soil biodiversity and quality.