Massimo Ventrucci

Climate change in the Alps is affecting glaciers, altering stream flow and biological communities. Aquatic macroinvertebrates are useful indicators for describing biological temporal trends in glacial ecosystems, but comprehensive long‐term data are rarely available. Here, we studied 18 Alpine glacially influenced streams in northwestern Italy over a 15‐year period (2010–2024), focussing on multiple taxonomic and functional metrics. The stream sites were classified into four groups according to the percentage of glacial coverage within the basin. In parallel, to contextualize climate warming in the area, we analysed daily air temperature data recorded by 54 weather monitoring stations in the same period. Macroinvertebrate‐based metrics were then assessed using linear mixed‐effects models within a Bayesian framework to investigate temporal trends. The temperature increase was 0.084°C per year, and winter was the season most affected. Functional and taxonomic richness showed a heterogeneous temporal pattern, with an increasing trend in spring. Community‐level weighted means based on temperature‐preference traits revealed a reduction in summer for hot stenotherms and an increase in spring for eurytherms. Interestingly, crustaceans have been found to be distributed in a way that is inversely related to glacial influence, and they are currently absent from rivers with higher glacial influence. Our results revealed a complex picture of changes in freshwater macroinvertebrates, which overall showed some level of resilience across multiple descriptors, but also reflected uneven signs of climate change. However, abundance‐based metrics showed a clear declining trend for both insects and the total macroinvertebrate community. Findings also suggested that in the analysed period glacial retreat has not yet caused a profound change in multiple macroinvertebrate‐based metrics commonly used when biomonitoring. These results highlight the need for closer attention to existing metrics and for the development of additional ones tailored to fluvial networks in the context of global change. Alpine streams are likely facing a crucial transitional phase and this long‐term monitoring revealed mixed signals of climate change. To disentangle these dynamic glacial environments, continued monitoring is essential to better track trends and tendencies in their communities. Expanding datasets on an annual basis while accounting for seasonal and sub‐seasonal variability will help clarify invertebrate patterns and provide a solid foundation for future ecological research.

This paper examines the state of child well-being survey data in Italy, highlighting the lack of longitudinal, child-centred studies, discussing the importance of such data for evidence-based policymaking and comparing Italy’s situation with that of other European countries where cohort studies are more established. We emphasize the importance of high-quality, longitudinal data to inform social policy and capture the dynamic factors impacting well-being, such as economic, health, and educational conditions. Although international initiatives by organisations like UNICEF and the OECD, have advanced child well-being research, gaps remain in Italy, where data are fragmented and cross-sectional, limiting the understanding of long-term outcomes. We introduce GUIDE (Growing Up In Digital Europe), the proposed first pan-European birth cohort study, aimed at addressing this gap by providing harmonized, longitudinal data on child and youth well-being, collected by directly surveying children and their families. This would provide a novel perspective for social research in Italy, where data on children’s viewpoints remain underdeveloped. As part of this initiative, Italy will conduct a pilot study for GUIDE in 2025, with a focus on adapting international survey tools to Italy’s cultural context. We describe Italy’s pilot study for GUIDE, including pre-test findings and sampling strategies, emphasizing the project’s potential to inform social policy in Italy and across Europe. By enhancing Italy’s child well-being data, GUIDE is positioned to inform policy and intervention strategies, contributing to a more comprehensive understanding of the factors shaping child and youth well-being in Italy and Europe within a life-course framework.

Latent Gaussian models (LGMs) are a subset of Bayesian Hierarchical models where Gaussian priors, conditional on variance parameters, are assigned to all effects in the model. LGMs are employed in many fields for their flexibility and computational efficiency. However, practitioners find prior elicitation on the variance parameters challenging because of a lack of intuitive interpretation for them. Recently, several papers have tackled this issue by representing the model in terms of variance partitioning (VP) and assigning priors to parameters reflecting the relative contribution of each effect to the total variance. So far, the class of priors based on VP has been mainly applied to random effects and fixed effects separately. This work presents a novel standardization procedure that expands the applicability of VP priors to a broader class of LGMs, including both fixed and random effects. The practical advantages of standardization are demonstrated with simulated data and a real dataset on survival analysis.

This chapter discusses the methodological innovations and challenges related to surveying child well-being in Italy, and the role of the GUIDE (Growing Up in Digital Europe) project. The GUIDE research infrastructure aims to develop a comparative longitudinal survey on child well-being across European countries. The proposed survey would follow two cohorts of children, one from ages 8 to 24, and one between ages 1 and 24. The data collected through this longitudinal survey will provide a holistic evaluation of child well-being and constitute an evidence base for public policies at different territorial levels in participating countries, as well as creating a unique dataset for comparative policy analysis. Building on pilots conducted in five countries in 2023, the Italian GUIDE team at the University of Bologna pre-tested the implementation of the survey in Italy. In collaboration with the Italian National Research Council (CNR-IRPPS), the team also produced a design for the pilot in Italy. This design will facilitate the evaluation of different survey modes, with particular interest in the option for a “push to web” via self-completion Computer Assisted Web Interviews (CAWI). The pilot will also permit assessment of the robustness of the survey instrument to regional heterogeneity, as data will be collected in northern (Lombardy and Emilia-Romagna) and southern (Campania and Apulia) regions; specific technical elements of the sampling strategy are discussed. After outlining the state-of-the-art regarding data on child well-being in Italy, the chapter analyses the work conducted so far to establish GUIDE in Italy, and presents pathways to the full-scale launch of the GUIDE survey in 2027 in Italy and Europe.

Entropy is widely used in ecological and environmental studies, where data often present complex interactions. Difficulties arise in linking entropy to available covariates or data dependence structures, thus, all existing entropy estimators assume independence. To overcome this limit, we take a Bayesian model-based approach which focuses on estimating the probabilities that compose the index, accounting for any data dependence and correlation. An estimate of entropy can be constructed from the model fitted values, returning an observation-specific measure of entropy rather than an overall index. This way, the latent heterogeneity of the system can be represented by a curve in time or a surface in space, according to the characteristics of the survey study at hand. An empirical study illustrates the flexibility and interpretability of our results over temporally and spatially correlated data. An application is presented about the biodiversity of spatially structured rainforest tree data.

Bayesian disease mapping, yet if undeniably useful to describe variation in risk over time and space, comes with the hurdle of prior elicitation on hard-to-interpret random effect precision parameters. We introduce a reparametrized version of the popular spatio-temporal interaction models, based on Kronecker product intrinsic Gaussian Markov random fields, that we name the variance partitioning model. The variance partitioning model includes a mixing parameter that balances the contribution of the main and interaction effects to the total (generalized) variance and enhances interpretability. The use of a penalized complexity prior on the mixing parameter aids in coding prior information in an intuitive way. We illustrate the advantages of the variance partitioning model using two case studies.