Aim To understand how functional traits and evolutionary history shape the geographic distribution of plant life on Earth, we need to integrate high‐quality and global‐scale distribution data with functional and phylogenetic information. Large‐scale distribution data for plants are, however, often restricted to either certain taxonomic groups or geographic regions. Range maps only exist for a small subset of all plant species and digitally available point‐occurrence information is biased both geographically and taxonomically. Floras and checklists represent an alternative, yet rarely used potential source of information. They contain highly curated information about the species composition of a clearly defined area, and together virtually cover the entire global land surface. Here, we report on our recent efforts to mobilize this information for macroecological and biogeographical analyses in the GIFT database, the Global Inventory of Floras and Traits. Location Global. Taxon Land plants (Embryophyta). Methods GIFT integrates plant distributions from regional Floras and checklists with functional traits, phylogenetic information, and region‐level geographic, environmental and socio‐economic data. It contains information about the floristic status (native, endemic, alien and naturalized) and takes advantage of the wealth of trait information in the regional Floras, complemented by data from global trait databases. Results GIFT 1.0 holds species lists for 2,893 regions across the whole globe including ~315,000 taxonomically standardized species names (i.e. c. 80% of all known land plant species) and ~3 million species‐by‐region occurrences. Based on a hierarchical and taxonomical derivation scheme, GIFT contains information for 83 functional traits and more than 2.3 million trait‐by‐species combinations and achieves unprecedented coverage in categorical traits such as woodiness (~233,000 spp.) or growth form (~213,000 spp.). Main conclusions Here, we present the structure, content and automated workflows of GIFT and a corresponding web‐interface (http://gift.uni-goettingen.de) as proof of concept for the feasibility and potential of mobilizing aggregated biodiversity data for global macroecological and biogeographical research.

Predictions of species' current and future ranges are needed to effectively manage species under environmental change. Species ranges are typically estimated using correlative species distribution models (SDMs), which have been criticized for their static nature. In contrast, dynamic occupancy models (DOMs) explicitily describe temporal changes in species’ occupancy via colonization and local extinction probabilities, estimated from time series of occurrence data. Yet, tests of whether these models improve predictive accuracy under current or future conditions are rare. Using a long‐term data set on 69 Swiss birds, we tested whether DOMs improve the predictions of distribution changes over time compared to SDMs. We evaluated the accuracy of spatial predictions and their ability to detect population trends. We also explored how predictions differed when we accounted for imperfect detection and parameterized models using calibration data sets of different time series lengths. All model types had high spatial predictive performance when assessed across all sites (mean AUC > 0.8), with flexible machine learning SDM algorithms outperforming parametric static and DOMs. However, none of the models performed well at identifying sites where range changes are likely to occur. In terms of estimating population trends, DOMs performed best, particularly for species with strong population changes and when fit with sufficient data, while static SDMs performed very poorly. Overall, our study highlights the importance of considering what aspects of performance matter most when selecting a modelling method for a particular application and the need for further research to improve model utility. While DOMs show promise for capturing range dynamics and inferring population trends when fitted with sufficient data, computational constraints on variable selection and model fitting can lead to reduced spatial accuracy of predictions, an area warranting more attention.

Poly(p-phenylene terephthalamide) fiber sections of ∼15μm length were prepared by a laser microdissection system. On-axis mesh scans by combined small- and wide-angle x-ray scattering using a micron-sized synchrotron radiation beam confirms a radial texture of crystalline domains with orientational order differing for skin, central core and intermediate layers. A skin-core variation in small-angle scattering demonstrates the evolution of fiber structure due to the heat-treatment process.

This article focuses on the pattern of relationship that develops over time between a state-owned firm and its owner. The main hypothesis is that there is not a single, permanent pattern of relationship. Instead, it is proposed that there are three possible modes of interaction, which can be combined into a cycle. The state-SOE relationships are thus shown to evolve from mutual dependance and co-operation to autonomy via an adversarial stage.The article first provides detailed examples of the various types of relations that have been revealed by research, then combines them into the idea of cycle, before investigating the forces that lie under the cycle. It finally offers several implications for both practice and research.