Comparative biological studies often investigate the morphological, physiological or ecological divergence (or overlap) between entities such as species or populations. Here we discuss the weaknesses of using existing methods to analyse patterns of phenotypic overlap and present a novel method to analyse co‐occurrence in multidimensional space. We propose a ‘hyperoverlap’ framework to detect qualitative overlap (or divergence) between point datasets and present the hyperoverlap r package which implements this framework, including functions for visualization. hyperoverlap uses support vector machines (SVMs) to train a classifier based on point data (such as morphological or ecological data) for two entities. This classifier finds the optimal boundary between the two sets of data and compares the predictions to the original labels. Misclassification is an evidence of overlap between the two entities. We demonstrate the theoretical and practical advantages of this method compared to existing approaches (e.g. single‐entity hypervolume models) using the bioclimatic data extracted from global occurrence records of conifers. We find that there are instances where single‐entity hypervolume models predict overlap, but there are no observations of either entity in the shared hypervolume. In these instances, hyperoverlap reports nonoverlap. We show that our method is stable and less likely to be affected by sampling biases than current approaches. We also find that hyperoverlap is particularly effective for situations involving entities with a small number of data points (e.g. narrowly endemic species) for which single‐entity models cannot be reliably constructed. We argue that overlap can be reliably detected using hyperoverlap, particularly for descriptive studies. The method proposed here is a valuable tool for studying patterns of overlap in a multidimensional space.

SummaryThe World Checklist of Vascular Plants (WCVP) is an extremely valuable resource that is being used to address many fundamental and applied questions in plant science, conservation, ecology and evolution. However, databases of this size require data manipulation skills that pose a barrier to many potential users. Here, we present rWCVP, an open‐source R package that aims to facilitate the use of the WCVP by providing clear, intuitive functions to execute many common tasks. These functions include taxonomic name reconciliation, geospatial integration, mapping and generation of multiple different summaries of the WCVP in both data and report format. We have included extensive documentation and tutorials, providing step‐by‐step guides that are accessible even to users with minimal programming experience. rWCVP is available on cran and GitHub.

The leaf epidermis is the interface between a plant and its environment. The epidermis is highly variable in morphology, with links to both phylogeny and environment, and this diversity is relevant to several fields, including physiology, functional traits, palaeobotany, taxonomy and developmental biology.Describing and measuring leaf epidermal traits remains challenging. Current approaches are either extremely labour-intensive and not feasible for large studies or limited to measurements of individual cells.Here, we present a method to characterise individual cell size, shape (including the effect of neighbouring cells) and arrangement from light microscope images. We provide the first automated characterisation of cell arrangement (from traced images) as well as multiple new shape characteristics. We have implemented this method in an R package, EPIDERMALMORPH, and provide an example workflow using this package, which includes functions to evaluate trait reliability and optimal sampling effort for any given group of plants. We demonstrate that our new metrics of cell shape are independent of gross cell shape, unlike existing metrics.EPIDERMALMORPH provides a broadly applicable method for quantifying epidermal traits that we hope can be used to disentangle the fundamental relationships between form and function in the leaf epidermis.

(1) Evolution of genome size is shaped by various intrinsic and extrinsic factors. We explored three long-standing hypotheses concerning factors shaping the global distribution of genome size: the mutational hazard hypothesis, the polyploidy-mediated hypothesis and the climate-mediated hypothesis. (2) We compiled the largest genome size dataset to date, encompassing >5% of known angiosperm species and analyzed genome size distribution using a comprehensive geographic distribution dataset for all angiosperms across all continents. (3) Angiosperm species with large range sizes only have small genomes, consistent with the mutational hazard hypothesis. We also uncovered a unique latitudinal pattern in the distribution of genome size diversity. Climate had a strong effect on the latitudinal pattern in genome size, while the effect of polyploidy was small. Contrary to the unimodal latitudinal patterns found for plant size traits and polyploidy, the increase in angiosperm genome sizes from the equator to 40-50 degrees N/S is probably mediated by different (mostly climatic) mechanisms from those underpinning the decrease in genome sizes observed from 40-50 degrees northwards. (4) Overall, our global analyses highlight that species range size and climate factors are the main drivers shaping the global distribution of angiosperm genome sizes, with their relative importance varying across the latitudinal gradient.