No abstract

Biodiversity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well-being. Understanding how biodiversity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying biodiversity data. A major challenge is that biodiversity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed the Essential Biodiversity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret biodiversity observation data from diverse sources. Mapping and analyzing EBVs can help to evaluate how aspects of biodiversity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of biodiversity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within-species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic biodiversity monitoring with respect to theory, sampling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: (i) Genetic Diversity; (ii) Genetic Differentiation; (iii) Inbreeding; and (iv) Effective Population Size (N e ). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large-scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for biodiversity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all biodiversity and species' long-term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytica...

Ecological restoration is essential in rehabilitating degraded areas and safeguarding biodiversity, ecosystem services and human welfare. Using functional traits to plan restoration strategies has been suggested as they are the main ecological attributes that underlie ecosystem processes and services. However, few studies have translated ecological theory into actual restoration practices that can be easily used by different stakeholders. In this article, we applied a multiple-trait approach to select plant species for the restoration of degraded lands inside the Brazilian Amazon Forests. We selected 10 traits encompassing ease of management, geographical distribution and interactions with animals and other ecosystem services and scored these traits using 118 native species. Then, we ranked all species according to the total number of traits that they exhibited to obtain a list of 53 highly ranked species. In addition, we employed non-metric multidimensional scaling (NMDS) to assess the variation in these traits across the entire group of species. Based on the results, we selected a subset of species that maximizes functional diversity (high variability). We performed a sparse linear discriminant analysis (SLDA) to highlight a minimum set of traits to effectively discriminate botanical families. The final list of species and their traits highlight the importance of preserving not only the historical reference of a focused ecosystem but also its functional diversity to restore the interaction with local fauna, enrich the food chain and guarantee ecosystem services for local communities.