Parental background and daughters' and sons' educational outcomes - application of the Trivers-Willard hypothesis.

This study uses Trivers-Willard hypothesis to explain the differences in daughters' and sons' educational outcomes by parental background. According to the Trivers-Willard hypothesis (TWH), parental support and investments for sons and daughters display an asymmetrical relationship according to parental status because of the different reproductive advantage of the sexes. It predicts that high-status parents support sons more than daughters, and low-status parents support daughters more than sons. In modern societies, where education is the most important mediator of status, the TW hypothesis predicts that sons from high-status families will achieve higher educational outcomes than daughters. Using cohorts born between 1987 and 1997 from the reliable full population Finnish register data that contain the data of over 600.000 individuals, children's educational outcomes were measured using data on school dropout rate, academic grade point average (GPA), and general secondary enrollment in their adolescence. OLS and sibling fixed-effect regression that permitted an examination of opposite-sex siblings' educational outcomes within the same family were applied. Sons with high family income and parental education, compared to daughters of the same family, have lower probability of dropping out of school and are more likely to enroll into academic secondary school track. In families with low parental education or income daughters have lower probability for school dropout and enroll more likely to academic school track related to sons of the same family. The effect of family background by sex can be interpreted to support TWH in dropout and academic school track enrollment but not in GPA.

Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

Interaction modification among decomposers impairs ecosystem processes in lead-polluted soil.

Environmental pollution by toxicants is generally believed to lead both to direct toxic effects and indirect effects via altered species interactions in a stressed community. We examined how contamination of a coniferous forest soil floor by lead (from an abandoned shooting range area) may alter trophic interactions and decomposition processes. We constructed laboratory microcosms containing microbial-based food webs with various trophic structures: microbes alone; microbes with microbivorous-detritivorous enchytraeid worms (Cognettia sphagnetorum, a potential keystone species of boreal forest soil); and microbes, worms, and predatory mites (Lysigamasus spp.) together. The direct toxic effect of lead and the effectiveness of the predators in preying upon the worms were studied in separate experiments. Polluted humic soil (acid-extractable lead concentration was 2,720 +/- 349 mg/kg of dry soil [mean +/- standard error]) was toxic to enchytraeids, and predators effectively reduced worm populations. Positive density-dependent effects of worms on microbes and a trophic cascade from predatory mites to microbes, observed in the unpolluted humus, were not observed in the polluted humus, indicating pollution-induced interaction modification among organisms. Concomitantly, lead pollution altered the process of nutrient mineralization in the humic soil. However, causality and relative importance of direct toxicity and indirect effects of lead on the dynamics of the decomposer food web could not be pointed out. Examination of our results indicates that traditional food web models are incapable of predicting mechanisms that alter dynamics of pollution-stressed belowground food webs.