Carlson M. Büth

Area-based conservation remains the principal strategy for preserving biodiversity, yet current planning approaches inadequately consider two critical uncertainties: future trajectories of socio-environmental drivers of environmental degradation and preferences for alternate conservation objectives. This study demonstrates how spatial conservation prioritization can be integrated with scenario-based simulation modelling to systematically evaluate conservation strategies under these uncertainties. We simulated conservation area expansion strategies (e.g. timing and extent of interventions) in Switzerland (2020-2060) using a comprehensive full-factorial design testing approaches across five scenarios of climate change, land-use change, and conservation priorities, with strategy robustness assessed through impacts on ten ecosystem services (ES) characterized across both space and time. Results revealed substantial variation in ES provision under alternative futures, with pronounced spatial shifts in service delivery. Critically, only limited spatial overlap existed between regions displaying the highest ES robustness and areas prioritized for protection based on current ecological value, demonstrating the inadequacy of planning approaches that ignore future uncertainties. The most consistently robust strategy for securing ES provision involved rapidly expanding conservation areas to 30% coverage (aligned with the Kunming-Montreal Global Biodiversity Framework target), prioritizing small patches in human-dominated landscapes, and employing mixed management combining both preservation and restoration. However, strategy effectiveness varied across different ES and scenarios, indicating that optimal approaches must account for societal preferences regarding trade-offs between services. This research addresses a recognized gap in conservation planning science and provides both methodological guidance for uncertainty-informed planning and specific recommendations for Switzerland’s pursuit of the 30x30 target under accelerating environmental change.

Within the endeavour of modelling and understanding the propagation of delays in transportation networks, an approach that has attracted increasing interest in the last decade is the creation of functional network representations. These graphs map elements of interest (e.g. airports or stations) as nodes, and derive pairwise propagation patterns from their dynamics through correlation and causality tests. In spite of multiple notable results, this approach still lacks a coherent framework, with decisions related to many fundamental steps being left to the judgement of the researcher. We here provide an introduction to the theory behind functional networks for transportation systems, detailing the main steps and the associated pitfalls. We further introduce a Python package, delaynet, designed to support the researcher in the reconstruction and analysis of such networks. We finally present an analysis of the propagation of delays in the Swiss train system; and discuss future research steps.

Within the endeavour of describing and analysing delays and their propagations in air transport, a major limitation is represented by the validation of the obtained results. While this can be overcome through synthetic models, those available in the literature mostly aim at simulating the system in a detailed and realistic way, resulting in high complexity and substantial computational costs. We here present SynthATDelays, a minimalist and modular Python package designed to simulate a virtual customisable air transport system and to provide synthetic delay data under tuneable conditions; it is thus designed to support the validation of data-based studies and pipelines. We describe its internal structure and provide examples about how scenarios can be designed and executed. We further show how it can be used to tackle two relevant questions, i.e., the role of operational buffer times in the absorption of delays and the comparison and optimisation of causality tests to detect the propagation thereof.

Information theory, i.e. the mathematical analysis of information and of its processing, has become a tenet of modern science; yet, its use in real-world studies is usually hindered by its computational complexity, the lack of coherent software frameworks, and, as a consequence, low reproducibility. We here introduce infomeasure, an open-source Python package designed to provide robust tools for calculating a wide variety of information-theoretic measures, including entropies, mutual information, transfer entropy and divergences. It is designed for both discrete and continuous variables; implements state-of-the-art estimation techniques; and allows the calculation of local measure values, \p\-values and \t\-scores. By unifying these approaches under one consistent framework, infomeasure aims to mitigate common pitfalls, ensure reproducibility, and simplify the practical implementation of information-theoretic analyses. In this contribution, we explore the motivation and features of infomeasure; its validation, using known analytical solutions; and exemplify its utility in a case study involving the analysis of human brain time series.

To mitigate the risk of injuries, many countries recommend bicycle helmets. The current paper seeks to examine the effectiveness of bicycle helmets by performing a systematic review focusing on meta-analyses. First, the current paper explores the findings of studies that employ meta-analyses using bicycle crash data. Second, the results are discussed considering the findings from research analyzing bicycle helmet effectiveness in a laboratory using simulation, and then are complemented with key methodological papers that address cycling and the overall factors contributing to the injury severity. The examined literature confirms that wearing a helmet while cycling is beneficial, regardless of age, crash severity, or crash type. The relative benefit is found to be higher in high-risk situations and when cycling on shared roads and particularly preventing severe head injuries. The results from the studies performed in laboratories also suggest that the shape and size of the head itself play a role in the protective effects of helmets. However, concerns regarding the equitability of the test conditions were found as all reviewed studies used a fifty-percentile male head and body forms. Lastly, the paper discusses the literature findings in a broader societal context.