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Glyphosate residues in soil alter herbivore‐induced plant volatiles and affect predatory insect behaviour
No full textPlants under herbivore attack emit distinct blends of herbivore-induced plant volatiles (HIPVs) which serve as signalling cues for predatory insects. This concept of indirect plant defence has tremendous potential in sustainable insect pest control. It represents a cornerstone of alternatives to synthetic pesticides in integrated pest management (IPM) strategies. The composition of HIPVs determines the effectiveness of predatory insect attraction and is vulnerable to disturbance by multiple biotic and abiotic factors above- and belowground. Residues of the most widely used herbicide (glyphosate) are persistent pollutants in agricultural soils, where they increasingly affect plant physiology, with cascading effects on species interactions.
Here, we tested whether herbicide legacy in soil affects plant performance, aphid herbivory, and aphid-induced volatile organic compound (VOC) emissions in oat plants, and tested whether the preference of predatory ladybirds towards aphid-infested plants is affected by herbicide legacy in the soil.
Soil herbicide legacy reduced chlorophyll activity and plant height, but did not affect plant biomass nor aphid populations. Five compounds in the emitted VOC profile were significantly affected by soil history of herbicide use, which, in turn, affected ladybird orientation behaviour. In a choice assay, ladybirds preferred the odour of plants growing in herbicide-free soil.
These results reveal a subtle layer of effects of herbicide legacy in soil on emission of HIPVs, with cascading effects on predatory insect behaviour. Our results demonstrate that essential ecosystem services in the aboveground plant space, such as natural pest control, may be reduced by soil pollution with anthropogenic pesticides such as glyphosate, causing mismatches in plant–insect communication
Parisuhdeväkivaltaan liittyvät erityispiirteet rangaistuksen ankaruuteen vaikuttavien ratkaisujen perusteluissa
No full textKorkeakoulujen opiskelijavalintauudistuksen merkitys toisella asteella mahdollisuuksien tasa-arvon näkökulmasta - Toisen asteen opinto-ohjaajien kokemuksia opiskelijavalintauudistuksesta
No full textExpectations Directed at Psychologists: A Membership Categorisation Analysis of Interviews with Retired Psychologists
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Cross-spectral purity—a fundamental property of light: tutorial
No full textCross-spectral purity was conceived in the context of coherence research and has remained largely unknown to the wider optics community. Over the past few decades, coherence researchers have made progress in uncovering the intricate properties of this fundamental concept. Cross-spectral purity has been shown to be intimately connected to the concept of spatiotemporal separability, generalizing the concept of separability from fields to correlation functions. It has an observable effect on a multitude of experiments, and purity is in fact assumed in many well-known experiments. In this tutorial, we discuss the basic definitions related to cross-spectral purity and investigate some special cases. Additionally, we show how to generate and detect this property, and consider its experimental significance in areas such as ultrashort pulses, quantum optics, scattering, and ghost imaging
Job characteristics and commitment among health personnel in primary care: associations that help understand the phenomenon of turnover
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Machine learning-based downscaling of aerosol size distributions from a global climate model
No full textAir pollution, particularly exposure to ultrafine particles (UFPs) with diameters below 100 nm, poses significant health risks, yet their spatial and temporal variability complicates impact assessments. This study explores the potential of machine learning (ML) techniques in enhancing the accuracy of a global aerosol-climate model's outputs through statistical downscaling to better represent observed data at specific sites. Specifically, the study focuses on the particle number size distributions from the global aerosol-climate model ECHAM-HAMMOZ. The coarse horizontal resolution of ECHAM-HAMMOZ (approx. 200 km) makes modeling sub-gridscale phenomena, such as UFP concentrations, highly challenging. Data from three European measurement stations (Helsinki, Leipzig, and Melpitz) were used as target of downscaling, covering nucleation, Aitken, and accumulation particle size ranges during years 2016–2018. Six different ML methods (Random Forest, XGBoost, Neural Networks, Support Vector Machine, Gaussian Process Regression and Generalized Linear Model) were employed, with hyperparameter optimization and feature selection integrated for model improvement. A separate ML model was trained for each of the sites and size ranges. Results showed a notable improvement in prediction accuracy for all particle sizes compared to the original global model outputs, particularly for the accumulation subrange. Challenges remained particularly in downscaling the nucleation subrange, likely due to its high variability and the discrepancy in spatial scale between the climate model representation and the underlying processes. Additionally, the study revealed that the choice of downscaling method requires careful consideration of spatial and temporal dimensions as well as the characteristics of the target variable, as different particle size ranges or variables in other studies may necessitate tailored approaches. The study demonstrates the feasibility of ML-based downscaling for enhancing air quality assessments. This approach could support future epidemiological studies and inform policies on pollutant exposure. Future integration of ML models dynamically into global climate model frameworks could further refine climate predictions and health impact studies
