Hutchinson's n‐dimensional hypervolume concept underlies many applications in contemporary ecology and evolutionary biology. Estimating hypervolumes from sampled data has been an ongoing challenge due to conceptual and computational issues. We present new algorithms for delineating the boundaries and probability density within n‐dimensional hypervolumes. The methods produce smooth boundaries that can fit data either more loosely (Gaussian kernel density estimation) or more tightly (one‐classification via support vector machine). Further, the algorithms can accept abundance‐weighted data, and the resulting hypervolumes can be given a probabilistic interpretation and projected into geographic space. We demonstrate the properties of these methods on a large dataset that characterises the functional traits and geographic distribution of thousands of plants. The methods are available in version ≥2.0.7 of the hypervolume r package. These new algorithms provide: (i) a more robust approach for delineating the shape and density of n‐dimensional hypervolumes; (ii) more efficient performance on large and high‐dimensional datasets; and (iii) improved measures of functional diversity and environmental niche breadth.

Life-history traits represent organisms' strategies to navigate the fitness trade-offs between survival and reproduction. Eric Charnov developed three dimensionless metrics to quantify fundamental life-history trade-offs. Lifetime reproductive effort (LRE), relative reproductive lifespan (RRL) and relative offspring size (ROS), together with body mass can be used to classify life-history strategies across the four major classes of tetrapods: amphibians, reptiles, mammals and birds. First, we investigate how the metrics have evolved in concert with body mass within tetrapod lineages. In most cases, we find evidence for correlated evolution among body mass and the three dimensionless metrics. Second, we compare life-history strategies across the four classes of tetrapods and find that LRE, RRL and ROS delineate a space in which the major tetrapod classes occupy mostly unique subspaces. These distinct combinations of life-history strategies provide us with a framework to understand the impact of major evolutionary transitions in energetics, physiology and ecology.

Estimates of species' ranges can inform many aspects of biodiversity research and conservationmanagement decisions. Many practical applications need high-precision range estimates that are sufficiently reliable to use as input data in downstream applications. One solution has involved expert-generated maps that reflect on-the-ground field information and implicitly capture various processes that may limit a species' geographic distribution. However, expert maps are often subjective and rarely reproducible. In contrast, species distribution models (SDMs) typically have finer resolution and are reproducible because of explicit links to data. Yet, SDMs can have higher uncertainty when data are sparse, which is an issue for most species. Also, SDMs often capture only a subset of the factors that determine species distributions (e.g., climate) and hence can require significant post-processing to better estimate species' current realized distributions. Here, we demonstrate how expert knowledge, diverse data types, and SDMs can be used together in a transparent and reproducible modeling workflow. Specifically, we show how expert knowledge regarding species' habitat use, elevation, biotic interactions, and environmental tolerances can be used to make and refine range estimates using SDMs and various data sources, including high-resolution remotely sensed products. This range-refinement approach is primed to use various data sources, including many with continuously improving spatial or temporal resolution. To facilitate such analyses, we compile a comprehensive suite of tools in a new R package, maskRangeR, and provide worked examples. These tools can facilitate a wide variety of basic and applied research that requires high-resolution maps of species' current ranges, including quantifications of biodiversity and its change over time.

Life history traits represent organism's strategies to navigate the fitness trade-offs between survival and reproduction. Eric Charnov developed three dimensionless metrics to quantify fundamental life history trade-offs. Lifetime reproductive effort (LRE), relative reproductive lifespan (RRL), and relative offspring size (ROS), together with body mass, can be used classify life history strategies across the four major classes of tetrapods: amphibians, reptiles, mammals, and birds. First, we investigate how the metrics have evolved in concert with body mass. In most cases, we find evidence for correlated evolution between body mass and the three metrics.Finally, we compare life history strategies across the four classes of tetrapods and find that LRE, RRL, and ROS delineate a space in which the major tetrapod clades occupy mostly unique subspaces. These distinct combinations of life history strategies provide us with a framework to understand the impact of major evolutionary transitions in energetics, physiology, and ecology.