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Genetic monitoring of populations currently attracts interest in the context of the Convention on Biological Diversity but needs long-term planning and investments. However, genetic diversity has been largely neglected in biodiversity monitoring, and when addressed, it is treated separately, detached from other conservation issues, such as habitat alteration due to climate change. We report an accounting of efforts to monitor population genetic diversity in Europe (genetic monitoring effort, GME), the evaluation of which can help guide future capacity building and collaboration towards areas most in need of expanded monitoring. Overlaying GME with areas where the ranges of selected species of conservation interest approach current and future climate niche limits helps identify whether GME coincides with anticipated climate change effects on biodiversity. Our analysis suggests that country area, financial resources and conservation policy influence GME, high values of which only partially match species’ joint patterns of limits to suitable climatic conditions. Populations at trailing climatic niche margins probably hold genetic diversity that is important for adaptation to changing climate. Our results illuminate the need in Europe for expanded investment in genetic monitoring across climate gradients occupied by focal species, a need arguably greatest in southeastern European countries. This need could be met in part by expanding the European Union’s Birds and Habitats Directives to fully address the conservation and monitoring of genetic diversity.

A data set of submitted candidate projects for evaluation of validity as Category II population genetic monitoring, with repeated sampling over time and at least one indicator of population genetic diversity., A data set of submitted candidate projects for evaluation of validity as Category II population genetic monitoring, with repeated sampling over time and at least one indicator of population genetic diversity.

Released 4 years ago, the Wallace EcoMod application (R package wallace) provided an open-source and interactive platform for modeling species niches and distributions that served as a reproducible toolbox and educational resource. wallace harnesses R package tools documented in the literature and makes them available via a graphical user interface that runs analyses and returns code to document and reproduce them. Since its release, feedback from users and partners helped identify key areas for advancement, leading to the development of wallace 2. Following the vision of growth by community expansion, the core development team engaged with collaborators and undertook a major restructuring of the application to enable: simplified addition of custom modules to expand methodological options, analyses for multiple species in the same session, improved metadata features, new database connections, and saving/loading sessions. wallace 2 features nine new modules and added functionalities that facilitate data acquisition from climate-simulation, botanical and paleontological databases; custom data inputs; model metadata tracking; and citations for R packages used (to promote documentation and give credit to developers). Three of these modules compose a new component for environmental space analyses (e.g., niche overlap). This expansion was paired with outreach to the biogeography and biodiversity communities, including international presentations and workshops that take advantage of the software's extensive guidance text. Additionally, the advances extend accessibility with a cloud-computing implementation and include a suite of comprehensive unit tests. The features in wallace 2 greatly improve its expandability, breadth of analyses, and reproducibility options, including the use of emerging metadata standards. The new architecture serves as an example for other modular software, especially those developed using the rapidly proliferating R package shiny, by showcasing str, Released 4 years ago, the Wallace EcoMod application (R package wallace) provided an open-source and interactive platform for modeling species niches and distributions that served as a reproducible toolbox and educational resource. wallace harnesses R package tools documented in the literature and makes them available via a graphical user interface that runs analyses and returns code to document and reproduce them. Since its release, feedback from users and partners helped identify key areas for advancement, leading to the development of wallace 2. Following the vision of growth by community expansion, the core development team engaged with collaborators and undertook a major restructuring of the application to enable: simplified addition of custom modules to expand methodological options, analyses for multiple species in the same session, improved metadata features, new database connections, and saving/loading sessions. wallace 2 features nine new modules and added functionalities that facilitate data acquisition from climate-simulation, botanical and paleontological databases; custom data inputs; model metadata tracking; and citations for R packages used (to promote documentation and give credit to developers). Three of these modules compose a new component for environmental space analyses (e.g., niche overlap). This expansion was paired with outreach to the biogeography and biodiversity communities, including international presentations and workshops that take advantage of the software's extensive guidance text. Additionally, the advances extend accessibility with a cloud-computing implementation and include a suite of comprehensive unit tests. The features in wallace 2 greatly improve its expandability, breadth of analyses, and reproducibility options, including the use of emerging metadata standards. The new architecture serves as an example for other modular software, especially those developed using the rapidly proliferating R package shiny, by showcasing str

Context: Human-induced changes in landscape structure are among the main causes of biodiversity loss. Despite their important contribution to biodiversity and ecosystem functioning, microbes—and particularly protists—remain spatially understudied. Soil microbiota are most often driven by local soil properties, but the influence of the surrounding landscape is rarely assessed. Objectives: We assessed the effect of landscape structure on soil protist alpha and beta diversity in meadows in the western Swiss Alps. Methods: We sampled 178 plots along an elevation gradient representing a broad range of environmental conditions and land-use. We measured landscape structure around each plot at 5 successive spatial scales (i.e. neighbourhood windows of increasing radius, ranging from 100 to 2000 m around a plot). We investigated the changes of protist alpha and beta diversity as a function of landscape structure, local environmental conditions and geographic distance. Results: Landscape structures, especially percentage of meadows, forests, or open habitats, played a key role for protist alpha and beta diversity. The importance of landscape structure was comparable to that of environmental conditions and spatial variables, and increased with the size of the neighbourhood window considered. Conclusions: Our results suggest that dispersal from neighbouring habitats is a key driver of protist alpha and beta diversity which highlight the importance of landscape-scale assembly mechanisms for microbial diversity. Landscape structure emerges as a key driver of microbial communities which has profound implications for our understanding of the consequences of land-use change on soil microbial communities and their associated functions.

Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high‐resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high‐performance computing (HPC) pipeline, we developedN‐SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments.N‐SDMwas built around a spatially‐nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales.N‐SDMallows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state‐of‐the‐art SDM features embodied inN‐SDMincludes a newly devised covariate selection procedure, five modelling algorithms, an algorithm‐specific hyperparameter grid search, and the ensemble of small‐models approach.N‐SDMis designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and runningN‐SDMis openly available on the GitHub repositoryhttps://github.com/N‐SDM/N‐SDM.