Beta diversity patterns are essential for understanding how biological communities are structured. Geographical and environmental factors, as well as species dispersal ability, are important drivers of beta diversity, but their relative importance may vary across spatial scales. In this study, we evaluate whether beta diversity changes across geographical scales and analyse how different drivers affect turnover patterns of native seed plants in an oceanic archipelago, the Azores (Portugal). Using a 500 x 500 m resolution grid, we selected cells that are covered by one of the following habitats: native forest, naturalized vegetation and seminatural pastures. We calculated species turnover at three spatial scales: i) between islands, ii) between cells within each island, and finally iii) between cells of each of the habitats of interest in each island. We then calculated the contribution of dispersal syndromes (endozoochory, epizoochory, hydrochory and anemochory) to turnover at each of the scales. Lastly, we assessed the relationship between geographical and climatic distances and habitat composition with turnover. Turnover was higher at the smallest scale, particularly in seminatural pastures, and decreased with increasing spatial scales, a pattern potentially associated with the historical fragmentation and current patchy distribution of native forest and seminatural habitats in the Azores. Dispersal syndromes and habitat composition had a negligible effect on turnover at all scales. Geographical distance had a positive effect on turnover at all scales, increasing with scale. The relationship between turnover and climatic distance was only significant at the intermediate and small scales in specific islands and habitats. Scale plays an important role at determining the effect of the drivers of turnover, in particular geographical and climatic distance. These results highlight the need to carefully select the scale of analysis when studying turnover patterns, as well as identifying the potential drivers associated with each scale.

The distribution ranges and spatio-temporal patterns in the occurrence and activity of boreal bats are yet largely unknown due to their cryptic lifestyle and lack of suitable and efficient study methods. We approached the issue by establishing a permanent passive-acoustic sampling setup spanning the area of Finland to gain an understanding on how latitude affects bat species composition and activity patterns in northern Europe. The recorded bat calls were semi-automatically identified for three target taxa; Myotis spp., Eptesicus nilssonii or Pipistrellus nathusii and the seasonal activity patterns were modeled for each taxa across the seven sampling years (2015–2021). We found an increase in activity since 2015 for E. nilssonii and Myotis spp. For E. nilssonii and Myotis spp. we found significant latitude -dependent seasonal activity patterns, where seasonal variation in patterns appeared stronger in the north. Over the years, activity of P. nathusii increased during activity peak in June and late season but decreased in mid season. We found the passive-acoustic monitoring network to be an effective and cost-efficient method for gathering bat activity data to analyze spatio-temporal patterns. Long-term data on the composition and dynamics of bat communities facilitates better estimates of abundances and population trend directions for conservation purposes and predicting the effects of climate change.

Early warning signals (EWSs) represent a potentially universal tool for identifying whether a system is approaching a tipping point, and have been applied in fields including ecology, epidemiology, economics, and physics. This potential universality has led to the development of a suite of computational approaches aimed at improving the reliability of these methods. Classic methods based on univariate data have a long history of use, but recent theoretical advances have expanded EWSs to multivariate datasets, particularly relevant given advancements in remote sensing. More recently, novel machine learning approaches have been developed but have not been made accessible in the R environment. Here, we present EWSmethods – an R package that provides a unified syntax and interpretation of the most popular and cutting edge EWSs methods applicable to both univariate and multivariate time series. EWSmethods provides two primary functions for univariate and multivariate systems respectively, with two forms of calculation available for each: classical rolling window time series analysis, and the more robust expanding window. It also provides an interface to the Python machine learning model EWSNet which predicts the probability of a sudden tipping point or a smooth transition, the first of its form available to R users. This note details the rationale for this open-source package and delivers an introduction to its functionality for assessing resilience. We have also provided vignettes and an external website to act as further tutorials and FAQs.

A document by Douglas Sponsler. Click on the document to view its contents.