Acculturation Orientations towards ‘Valued’ and ‘Devalued’ Immigrants in South Korea

This study, based on the Interactive Acculturation Model, investigates the acculturation orientations of undergraduates (n=279) in South Korea. Results show that Korean respondents considered South-East Asian immigrants to be less valued than Western immigrants. They were more welcoming towards ‘valued’ Western immigrants than they were towards ‘devalued’ South-East Asian immigrants. As in the case of undergraduates in North America & Europe, Korean undergraduates mainly endorsed integration and individualism towards both Western and South-East Asian immigrants, but they also strongly endorsed the segregationist orientations towards both ‘valued’ and ‘devalued’ immigrants reflecting the still contentious view of Korea as an immigration country

Aménagement linguistique et vitalité des communautés francophone et anglophone du Québec

Le cas du Québec français montre qu'un aménagement linguistique soutenu en faveur d'une langue minoritaire à l'échelle continentale peut mettre un terme au glissement linguistique vers la langue la plus puissante de ce millénaire : l'anglais. L'objectif de cet article est de fournir un survol de la situation sociolinguistique au Québec suite à l'adoption de lois linguistiques destinées à asseoir le statut du français par rapport à l'anglais dans le seul territoire majoritairement francophone ..

Pour en finir avec le Bronze final ? Les haches à douille de type armoricain en France

A discussion about socket armorican bronze axes datation. They are from Ha D period (VII th & VIt h century B.C.)Révision de la datation des haches à douille de type armoricain, au seul Hallstatt D (VIIe-VIe s; av. J.-C.

Translating surveillance data into incidence estimates

Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which we may estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence does not change between monitoring rounds, resulting in an underestimation of the disease incidence. In this paper we develop an incidence estimation model accounting for epidemic growth with monitoring rounds that sample varying incidence. We also show how to accommodate the asymptomatic period that is characteristic of most diseases. For practical use, we produce an approximation of the model, which is subsequently shown to be accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations. The estimation model is made available as an online application. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’

Epidemiologically-based strategies for the detection of emerging plant pathogens

Emerging pests and pathogens of plants are a major threat to natural and managed ecosystems worldwide. Whilst it is well accepted that surveillance activities are key to both the early detection of new incursions and the ability to identify pest-free areas, the performance of these activities must be evaluated to ensure they are fit for purpose. This requires consideration of the number of potential hosts inspected or tested as well as the epidemiology of the pathogen and the detection method used. In the case of plant pathogens, one particular concern is whether the visual inspection of plant hosts for signs of disease is able to detect the presence of these pathogens at low prevalences, given that it takes time for these symptoms to develop. One such pathogen is the ST53 strain of the vector-borne bacterial pathogen Xylella fastidiosa in olive hosts, which was first identified in southern Italy in 2013. Additionally, X. fastidiosa ST53 in olive has a rapid rate of spread, which could also have important implications for surveillance. In the current study, we evaluate how well visual surveillance would be expected to perform for this pathogen and investigate whether molecular testing of either tree hosts or insect vectors offer feasible alternatives. Our results identify the main constraints to each of these strategies and can be used to inform and improve both current and future surveillance activities

Explainable neural networks for trait-based multispecies distribution modelling—A case study with butterflies and moths

Species response traits mediate environmental effects on species distribution. Traits are used in joint and multispecies distribution models (JSDMs and MSDMs) to enable community-wide shared parameters that characterise niche filtering along environmental gradients. Multispecies machine learning SDMs, however, do not use traits as their inclusion requires an additional taxonomic dimension that is incompatible with their usual tabular inputs. This has confined trait mediation in SDMs to hierarchical Bayesian models. Here we provide a novel artificial neural network (ANN) architecture that solves this dimensionality problem. Our ANN includes species traits (via a time distributed layer) and is therefore able to identify not only species-specific responses to the environment, but also shared responses across the community that are mediated by species traits. Model performance evaluated at the species level not only quantifies the reliability of species predictions, but also their departure from an average response dictated by traits only. We apply our model to two unique long-term spatio-temporal of butterfly and moth datasets collected across the United Kingdom between 1990 and 2019. In addition to species traits, predictors include numerous metrics derived from weather, land-cover and topology data. For butterflies and moths we show convincing model performance for classifying species occupancy. We use SHAP (Shapley Additive exPlanations) to explain the ANN and show how trait-mediated and species-specific responses can be approximated, hence yielding ecological insights on the key drivers of species distribution. We highlight a range of drivers of change that determine occupancy, including wind, temperature as well as habitat type. We demonstrate that a trait-based approach can be encoded as an ANN by using a time distributed layer. This brings ANNs unmatched predictive capabilities to the field of MSDMs, at the same time of lifting their reputed drawback of poor explainability