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Glaciers host a variety of cold-adapted taxa, many of which have not yet been described. Interactions among glacier organisms are even less clear. Understanding ecological interactions is crucial to unravelling the functioning of glacier ecosystems, particularly in light of current glacier retreat. Through a review of the existing literature, we aim to provide a first overview of the biodiversity, primary production, trophic networks, and matter flow of a glacier ecosystem. We use the Forni Glacier (Central Italian Alps) - one of the best studied alpine glaciers in the world - as a model system for our literature review and integrate additional original data. We reveal the importance of allochthonous organic matter inputs, of Cyanobacteria and eukaryotic green algae in primary production, and the key role of springtails (Vertagopus glacialis) on the glacier surface in sustaining populations of two apex terrestrial predators: Nebria castanea (Coleoptera: Carabidae) and Pardosa saturatior (Araneae: Lycosidae). The cryophilic tardigrade Cryobiotus klebelsbergi is the apex consumer in cryoconite holes. This short food web highlights the fragility of nodes represented by invertebrates, contrasting with structured microbial communities in all glacier habitats. Although further research is necessary to quantify the ecological interactions of glacier organisms, this review summarises and integrates existing knowledge about the ecological processes on alpine glaciers and supports the importance of glacier-adapted organisms in providing ecosystem services., (© 2024 The Author(s). Biological Reviews published by John Wiley & Sons Ltd on behalf of Cambridge Philosophical Society.)

Various modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as species abundance or presence-absence) and a set of predominantly abiotic covariates. The choice of the modeling approach, i.e., the algorithm, is itself a significant source of variability, as different algorithms applied to the same dataset can yield disparate outcomes. This inter-model variability has led to the adoption of ensemble modelling techniques, among which stacked generalisation, which has recently demonstrated its capacity to produce robust results. Stacked ensemble modelling incorporates predictions from multiple base learners or models as inputs for a meta-learner. The meta-learner, in turn, assimilates these predictions and generates a final prediction by combining the information from all the base learners. In our study, we utilized a recently published dataset documenting egg abundance observations of Aedes albopictus collected using ovitraps. and a set of environmental predictors to forecast the weekly median number of mosquito eggs using a stacked machine learning model. This approach enabled us to (i) unearth the seasonal egg-laying dynamics of Ae. albopictus for 12 years; (ii) generate spatio-temporal explicit forecasts of mosquito egg abundance in regions not covered by conventional monitoring initiatives. Our work establishes a robust methodological foundation for forecasting the spatio-temporal abundance of Ae. albopictus, offering a flexible framework that can be tailored to meet specific public health needs related to this species., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)