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Abstract Current models of island biogeography treat endemic and non‐endemic species as if they were functionally equivalent, focussing primarily on species richness. Thus, the functional composition of island biotas in relation to island biogeographical variables remains largely unknown. Using plant trait data (plant height, leaf area and flower length) for 895 native species in the Canary Islands, we related functional trait distinctiveness and climate rarity for endemic and non‐endemic species and island ages. Endemics showed a link to climatically rare conditions that is consistent with island geological change through time. However, functional trait distinctiveness did not differ between endemics and non‐endemics and remained constant with island age. Thus, there is no obvious link between trait distinctiveness and occupancy of rare climates, at least for the traits measured here, suggesting that treating endemic and non‐endemic species as functionally equivalent in island biogeography is not fundamentally wrong. 

Modeling species distributions over space and time is one of the major research topics in both ecology and conservation biology. Joint Species Distribution models (JSDMs) have recently been introduced as a tool to better model community data, by inferring a residual covariance matrix between species, after accounting for species' response to the environment. However, these models are computationally demanding, even when latent factors, a common tool for dimension reduction, are used. To address this issue, Taylor-Rodriguez et al. ( 2017 ) proposed to use a Dirichlet process, a Bayesian nonparametric prior, to further reduce model dimension by clustering species in the residual covariance matrix. Here, we built on this approach to include a prior knowledge on the potential number of clusters, and instead used a Pitman–Yor process to address some critical limitations of the Dirichlet process. We therefore propose a framework that includes prior knowledge in the residual covariance matrix, providing a tool to analyze clusters of species that share the same residual associations with respect to other species. We applied our methodology to a case study of plant communities in a protected area of the French Alps (the Bauges Regional Park), and demonstrated that our extensions improve dimension reduction and reveal additional information from the residual covariance matrix, notably showing how the estimated clusters are compatible with plant traits, endorsing their importance in shaping communities. 

ABSTRACT BackgroundHuman pressures are driving the emergence of unprecedented, ‘novel’, ecological and environmental systems. The concept of novel (eco)systems is well accepted by the scientific community, but the use and measurement of novelty has outgrown initial definitions and critiques. There are still unresolved methodological and conceptual differences in quantifying novelty that prevent a unified research approach. FrameworkHere we present a conceptual framework and guidelines to unify past and future measurement of ecological novelty. Under this framework, novelty is a property of an ecological or environmental entity of interest. Novelty is quantified as the comparison between the target entity and a reference set, measured as the summary of degrees of difference across one or more dimensions. Choices in these components, particularly the reference set, can change resulting novelty measurements and inferences. ShowcaseWe provide a case‐study to showcase our framework, measuring pre‐ and post‐European novelty in 99 pollen assemblages in Midwest USA forests. We paired this quantitative exploration with a five‐step process designed to improve the utility and outcomes of novelty analyses. ConclusionsQuantitative novelty has immense value in studies of abrupt ecological change, linking climatic and ecological change, biotic interactions and invasions, species range shifts and fundamental theories. Our framework offers a unified overview and is also primed for integration into management and restoration workflows, providing consistent and robust measurements of novelty to support decision making, priority setting and resource allocation. 

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades—including a maturation of relevant theory and key concepts—but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an “Overview” talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.