Luise Quoss’s research while affiliated with Martin Luther University Halle-Wittenberg and other places

The nested species-area relationship, obtained by counting species in increasingly larger areas in a nested fashion, exhibits robust and recurring qualitative and quantitative patterns. When plotted in double logarithmic scales it shows three phases: rapid species increase at small areas, slower growth at intermediate scales, and faster rise at large scales. Despite its significance, the theoretical foundations of this pattern remain incompletely understood. Here, we develop a theory for the species-area relationship using extreme value theory, and show that the species-area relationship is a mixture of the distributions of minimum distances to a starting sampling focal point for each individual species. A key insight of our study is that each phase is determined by the geographical distributions of the species, i.e., their ranges, relative to the focal point, enabling us to develop a formula for estimating the number of species at phase transitions. We test our approach by comparing empirical species-area relationships for different continents and taxa with our predictions using Global Biodiversity Information Facility data. Although a SAR reflects the underlying biological attributes of the constituent species, our interpretations and use of the extreme value theory are general and can be widely applicable to systems with similar spatial features.

Based on an extensive model intercomparison, we assessed trends in biodiversity and ecosystem services from historical reconstructions and future scenarios of land-use and climate change. During the 20th century, biodiversity declined globally by 2 to 11%, as estimated by a range of indicators. Provisioning ecosystem services increased several fold, and regulating services decreased moderately. Going forward, policies toward sustainability have the potential to slow biodiversity loss resulting from land-use change and the demand for provisioning services while reducing or reversing declines in regulating services. However, negative impacts on biodiversity due to climate change appear poised to increase, particularly in the higher-emissions scenarios. Our assessment identifies remaining modeling uncertainties but also robustly shows that renewed policy efforts are needed to meet the goals of the Convention on Biological Diversity.

The rapid growth of clean energy technologies is driving a rising demand for critical minerals. In 2022 at the 15th Conference of the Parties to the Convention on Biological Diversity (COP15), seven major economies formed an alliance to enhance the sustainability of mining these essential decarbonization minerals. However, there is a scarcity of studies assessing the threat of mining to global biodiversity. By integrating a global mining dataset with great ape density distribution, we estimated the number of African great apes that spatially coincided with industrial mining projects. We show that up to one-third of Africa’s great ape population faces mining-related risks. In West Africa in particular, numerous mining areas overlap with fragmented ape habitats, often in high-density ape regions. For 97% of mining areas, no ape survey data are available, underscoring the importance of increased accessibility to environmental data within the mining sector to facilitate research into the complex interactions between mining, climate, biodiversity, and sustainability.

The Americas contain highly biodiverse yet vulnerable ecosystems, with many threatened species inadequately protected. Finer-scale, localized habitat assessments are crucial for effective conservation planning, but continental-scale high-resolution vegetation maps remain limited. This study addresses this gap by identifying critical vegetation types across the Americas using the standardized framework of the International Vegetation Classification (IVC) system at the macrogroup level, representing the finest vegetation classification available across the region, as well as the highest-resolution Area of Habitat (AOH) maps currently available for birds and mammals. By combining these high-resolution IVC macrogroup maps with detailed AOH maps, we highlight at-risk vegetation types based on 1) threatened and macrogroup-associated species (species that have at least 50% of their AOH in one macrogroup), 2) current protection levels, and 3) projected threats from land use changes, and 4) develop a conservation value index (CVI) that accounts for all these factors. The results highlighted the remarkable diversity of high conservation value macrogroups across the Americas, emphasizing their significance in regions such as the Andes, montane Mesoamerica, the Caribbean, Brazil's Cerrado, and the Atlantic Forest. Among the highest-scoring macrogroups, the Northern Andean Montane & Upper Montane Humid Forest emerged as critically important, harboring a high number of threatened and macrogroup-associated species. Other macrogroups of immediate conservation concern include the Brazilian Atlantic Montane Humid Forest, Pacific Mesoamerican Seasonal Dry Forest, Caribbean Lowland Humid Forest, and Central Midwest Oak Forest, Woodland and Savanna. However, the study revealed that nearly three-quarters of the over 300 macrogroups in the Americas fall below the global target of 30% protection. Notably, a fifth of all species were macrogroup-associated species, including over 40% of threatened species. Our findings emphasize the need for targeted conservation strategies that consider finer-scale habitat classifications and paired with high-quality species distribution data to guide conservation strategies for biodiversity across the Americas.

The Americas contain highly biodiverse yet vulnerable ecosystems, with many threatened species inadequately protected. Finer-scale, localized habitat assessments are crucial for effective conservation planning, but continental-scale high-resolution vegetation maps remain limited. This study addresses this gap by identifying critical vegetation types across the Americas using the standardized framework of the International Vegetation Classification (IVC) system at the macrogroup level, representing the finest vegetation classification available across the region, as well as the highest-resolution Area of Habitat (AOH) maps currently available. By combining these high-resolution IVC macrogroup maps with detailed AOH maps, we highlight at-risk vegetation types based on 1) threatened and macrogroup-associated species (species that have at least 50% of their AOH in one macrogroup), 2) current protection levels, and 3) projected threats from land use changes, and 4) develop a conservation value index (CVI) that accounts for all these factors. The results highlighted the remarkable diversity of high conservation value macrogroups across the Americas, emphasizing their significance in regions such as the Andes, montane Mesoamerica, the Caribbean, Brazil's Cerrado, and the Atlantic Forest. Among the highest-scoring macrogroups, the Northern Andean Montane & Upper Montane Humid Forest emerged as critically important, harboring a high number of threatened and macrogroup-associated species. Other macrogroups of immediate conservation concern include the Brazilian Atlantic Montane Humid Forest, Pacific Mesoamerican Seasonal Dry Forest, Caribbean Lowland Humid Forest, and Central Midwest Oak Forest, Woodland and Savanna. However, the study revealed that nearly three-quarters of the over 300 macrogroups in the Americas fall below the global target of 30% protection. Notably, a fifth of all species were macrogroup-associated species, including over 40% of threatened species. Our findings emphasize the need for targeted conservation strategies that consider finer-scale habitat classifications and paired with high-quality species distribution data to guide conservation strategies for biodiversity across the Americas.

The rapid growth of clean energy technologies is driving a rising demand for critical minerals. In 2022 at the UN Biodiversity Conference (COP 15), seven major economies formed an alliance to enhance the sustainability of mining these essential decarbonization minerals. However, there is a scarcity of studies assessing the threat of mining to global biodiversity. By integrating a global mining dataset with ape density distribution estimates, we explored the potential negative impact of industrial mining on African great apes. Our findings reveal that up to one-third of Africa’s great ape population faces mining-related risks. This is especially pronounced in West Africa, where numerous mining areas overlap with fragmented ape habitats, often occurring in high-density ape regions. For 97% of mining areas, no ape survey data are available, underscoring the importance of increased accessibility to environmental data within the mining sector to facilitate research into the complex interactions between mining, climate, biodiversity and sustainability. Teaser Mining for clean energy minerals could put one-third of Africa’s ape population at risk.

The EBV Data Portal API is specifically designed for the machine-readable integration, sharing, and utilization of EBV datasets and currently supports only GET requests in its initial version. The ebv_download function of the R package utilizes this REST API to download the EBV datasets. The API is accessible via the URL https://portal.geobon.org/api/swagger-ui/

Essential Biodiversity Variables (EBVs) are used to monitor the status and trends in biodiversity at multiple spatiotemporal scales. These provide an abstraction level between raw biodiversity observations and indicators, enabling better access to policy-relevant biodiversity information. Furthermore, the EBV vision aims to support detection of critical change, among other things, with easy to use tools and dashboards accessible to a variety of users and stakeholders. We present the EBV Data Portal, a platform for distributing and visualizing EBV datasets. It contains a geographic cataloging system that supports a large number of spatiotemporal and EBV specific attributes and enables their discoverability. To facilitate user interaction, it offers a web-based interface where users can upload, discover and share essential biodiversity spatiotemporal data through intuitive interaction with cataloging and visualization tools. Using the EBV Catalog, the user can explore the characteristics of the data based on the definition of the EBV Cube standard*1. The Catalog also allows browsing of the description of the metadata in the specifications of the Attribute Convention for Data Discovery (ACDD) and in the Ecological Metadata Language (EML) vocabulary. This enables easy interoperability with other metadata catalogs. An example application is the calculation of summary statistics for selected countries. Using the EBV Data Portal, users can select EBV datasets and calculate basic biodiversity change metrics from spatiotemporal subsets and conveniently visualize complex, multidimensional biodiversity datasets. These visualization and analysis tools of the EBV Data Portal are a first step towards an EBV-based dashboard for biodiversity analyses.

The concept of Essential Biodiversity Variables (EBVs) was conceived to study, report, and manage biodiversity change. The EBV netCDF structure was developed in order to support publication and interoperability of biodiversity data. This standard is based on the Network Common Data Format (netCDF). Additionally, it follows the Climate and Forecast Conventions (CF, version 1.8) and the Attribute Convention for Data Discovery (ACDD, version 1.3). The standard allows several datacubes per netCDF file (see Fig. 1). These cubes have four dimensions: longitude, latitude, time and entity, whereby the last dimension can, for example, encompass different species or groups of species, ecosystem types or other aspects. The usage of hierarchical groups enables the coexistence of multiple EBV cubes (see Fig. 2). The first level (netCDF group) are scenarios, e.g., the modelling for different Shared Socioeconomic Pathways (SSP) scenarios. The second level (netCDF group) are metrics, e.g., the percentage of protected area per pixel and its proportional loss over a certain time span per pixel. All metrics are repeated per scenario, if any are present. The result is a rather complex raster dataset (see example dataset in Fig. 3). This is where the ebvcube R package comes into play. This R package enables scientists to create their own netCDFs in the EBV cube standard. Its functionality covers the creation, opening/reading and visualizing the EBV netCDFs. The ebvcube package is part of the overall EBV infrastructure and works together with the EBV Data Portal. Users can work with the downloaded EBV netCDFs or upload their own EBV netCDFs to the portal. Generally, the package aims to condense the output for the users and assist in the understanding of the file structure to overcome the complexity. The output is reduced to the necessary information, e.g., not displaying coordinate variables or any technical attributes. Moreover, functionality for a quick data exploration is implemented.