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Purpose: Habitat selection in animals is a hierarchal process that operates across multiple temporal and spatial scales, adapting to changes in environmental conditions, human disturbances, and predation risks. Despite its significance, previous research often oversimplifies temporal dynamics by categorizing them into broad seasonal and diel patterns, overlooking the continuous nature of temporal variability and habitat specificity. Methods: We investigated the temporal patterns in habitat selection of moose (Alces alces) in highly heterogenous landscapes at the southwestern edge of their European range using step-selection functions. Utilizing over 700,000 GPS locations from 34 adult moose, we aimed to assess seasonal and diel patterns in their selectivity for both natural and human-related habitats. Results: Our findings revealed significant overall temporal variation in moose habitat selection at both seasonal and diel scales. Moose selectivity toward different habitats showed low repeatability over time, with 35% of cases displaying negative correlation between selectivity in different time windows. Diel changes were more pronounced, showing 5.6-fold difference in cumulative selectivity, compared to 1.4-fold difference in seasonal dynamics. Notably, moose exhibited lower selectivity during nighttime hours throughout the year compared to daytime hours. The study also highlighted distinct habitat selection patterns across different habitat types: natural habitats (deciduous forests, coniferous forests, wetlands) exhibited pronounced seasonal variation, while anthropogenic habitats (grasslands, arable land, roads and settlements) showed more diel variability. Moose generally avoided human-related habitats during daytime hours, but their preferences during nighttime varied depending on the habitat type and time of year. Conclusion: This research advances our understanding of the complex temporal patterns in habitat selection by large herbivores and underscores the importance of considering temporal dynamics in habitat selection modelling. [ABSTRACT FROM AUTHOR]

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Biological invasions threaten global biodiversity and can have severe economic and social impacts. The complexity of this problem challenges effective management of invasive alien species as the contribution of many factors involved in the invasion processes across different spatial scales is not well understood.Here, we identify the most important determinants associated with the occurrence of two invasive alien plants, the North American goldenrods (Solidago canadensis and S. gigantea), commonly found in agricultural landscapes of Europe. We used Google Street View images to perform a remote, large‐scale inventory of goldenrods along 1347 roadside transects across Poland. Using open access geospatial data and machine learning techniques, we investigated the relative role of nearly 50 variables potentially affecting the distribution of studied species at five spatial scales (from within 0.25 to 5 km of the studied locations).We found that the occurrence of goldenrods along roadsides was simultaneously associated with multiple drivers among which those related to human impacts, climate, soil properties and landscape structure were the most important, while local characteristics, such as road parameters or the presence of other alien plants were less influential. However, the relative contribution of different variables in predicting goldenrod distribution changed across spatial scales.Synthesis: Mechanisms underlying plant invasions are highly complex and a number of factors can jointly influence the outcomes of this process. However, since different invasion drivers operate at different spatial scales, some important associations may be overlooked when focusing on a single spatial context. Although associations were consistent in direction (positive or negative) across scales, their relative influence on goldenrod occurrence often changed. Socio‐economic factors were largely important at local scales, while the effect of landscape factors broadly increased with increasing spatial scale. We highlight that using multi‐scale approaches involving a wide range of variables may enable setting priorities for the management of invasive alien plants. [ABSTRACT FROM AUTHOR]