The concept of green infrastructure (GI) seeks to identify and prioritize areas of high ecological value for wildlife and people, to improve the integration of natural values in landscape planning decisions. In 2018, the canton of Geneva, Switzerland, established a roadmap for biodiversity conservation, which includes the operationalization of GI covering 30% of the territory by 2030. In this paper, we demonstrate a GI mapping framework in the canton of Geneva. Our approach is based on the combined assessment of three ‘pillars’, namely species’ distribution, landscape structure and connectivity, and ecosystem services, to optimize the allocation of conservation actions using the spatial prioritization software, Zonation. The identified priority conservation areas closely overlap existing natural reserves. Including the three pillars in the landscape prioritization should also improve adhesion to the GI idea, without undermining the protection of threatened species. With regards to land use planning, public and private land parcels with high values for GI may require specific incentives to maintain their desirable characteristics, as they are more likely to be degraded than areas with more building restrictions. Visualizing priority conservation areas in a spatially explicit manner will support decision-makers in Geneva to optimally allocate limited resources for ecosystem preservation.

Facing the decline of biodiversity worldwide, the conservation of the remaining natural and semi-natural areas is fundamental. To do so, the concept of green infrastructure has gained attention recently. This case study presents the method developed to identify the green infrastructure in a cross-border, urbanized territory between Switzerland and France in the area of influence of the city of Geneva. The first part of the methodology consists of calculating and mapping the inputs aggregated in four pillars: (i) the distribution of habitats as well as the predicted distribution of hundreds of plant and animal species, (ii) the supply of five ecosystem services, (iii) the functional connectivity for three animal species and the light pollution and (iv) five indices of landscape’s structure. These inputs are then used to run a prioritization model to identify the areas with the highest ecological interest according to these weighted inputs. The cross-border situation of this case study had impacts on the way the input data were gathered and weighted and on the way the output was created to consider the expectations of the three main local authorities involved, without creating any legal obligations on the implementation of the green infrastructure. As a positive sign of the usefulness of these results, the resulting maps were immediately transferred to the land use planners in charge of developing ambitious visions of the “Grand Genève” territory for 2050 in alignment with 10 objectives of ecological transition as recently agreed and signed by local authorities. The method presented in this article is flexible and includes a broad description of biodiversity, supporting a reliable network of areas with high ecological values for conservation purposes and human well-being.

Biodiversity is declining globally principally because of land degradation and more because of climate change. Its effective conservation is vital for species and habitats, but also to maintain the related ecosystem services they provide for human well-being. In this context, evaluating the ability of Protected Areas (PAs) to cover species distribution under current and future environmental conditions is highly valuable. Considering the distributions of 1692 species of plants in the cross-border region of Grand Genève, located between France and Switzerland, the effectiveness of existing PAs in preserving plant diversity through local hotspots and priority areas for rare and vulnerable species was evaluated. The results show that PAs are moderately effective in conserving plant diversity, but are not expected to lose effectiveness in future conditions because important areas for plant diversity conservation will remain at similar locations. To address this gap, a spatial conservation network combining hotspots and priority areas was identified to cover 30% of the study area. It captures a significantly higher proportion of species distributions under both current and future conditions, and covers a greater representation of rare and ecologically important habitats, such as subalpine meadows and wetlands. The proposed solution aims to inform local stakeholders about areas of high ecological value that could be used to identify the Blue-Green Infrastructure, supporting the expansion of PAs and the improvement of conservation strategies in the face of environmental change.

Accurate knowledge of phylogenetic relationships and divergence time among plant species composing a regional flora is crucial for understanding the origin of the biodiversity patterns we observe today. Despite advances in phylogenomics, resources available for reconstructing regional phylogenies are still largely incomplete and poorly resolved at the species level. We developed a workflow to assemble a phylogenetic dataset for a regional flora using Angiosperms353 target‐enrichment and high‐throughput genome sequencing. We sampled tissues from freshly collected plants and herbarium specimens representing the angiosperm diversity of the Geneva canton in Switzerland (1098 species). We used ORTHOSKIM to recover targeted nuclear genes and plastid loci from off‐target reads, estimated relationships and time divergence using IQTREE, weighted‐ASTRAL and treePL, and compared the results obtained from nuclear and plastid genes. We recovered a mean of 289 nuclear and 54 plastid genes for 1316 taxa, representing 92% of the species native to the Geneva canton. We demonstrate that nuclear genes provide increased resolution and phylogenetic support, but similar divergence time estimates compared to plastomes. Our workflow based on Angiosperms353 data provides a phylogenetic framework for an entire regional flora that is consistent with current systematic knowledge and more resolved and complete at the infrageneric level than trees obtained from phylogenetic resources available for angiosperms. This type of genomic data opens perspectives for fine‐scale biodiversity and conservation research that integrates evolutionary history.