Global genetic diversity status and trends: towards a suite of Essential Biodiversity Variables (EBVs) for genetic composition

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 analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic biodiversity monitoring regionally and globally.

Keywords: biodiversity monitoring; environmental policy; indicators; interoperability; metadata; molecular ecology.

Fig. 1

The four Genetic Essential Biodiversity Variables (EBVs; bullet points) are indicated below each level of biological organization (Species, Populations, Individuals) for which they can be calculated. The species level corresponds to the combined genetic diversity for the species. The population level pie charts reflect the relative population sizes and the proportion of genotypes in each population (i.e. population genetic structure resulting from gene flow and migration). The smallest circles represent unique individuals with the colors depicting their genotypes.

Fig. 2

A sample of four diploid individuals from a population, with various representations of genetic composition data structures. The workflow process includes genetic sequencing, aligning the sequences from each individual, and polymorphic loci identification. The data from the polymorphic sites (single nucleotide polymorphisms; SNPs) in the sequence can be summarized as a matrix of genotypes for each locus (L1–L4). When these loci are bi‐allelic SNPs, the data can be summarized as the Minor Allele Count – the number of occurrences of the least frequent allele at that locus, a convenient summary format for certain statistics and models. A Genetic Diversity EBV for evenness, such as observed heterozygosity (H _o), can also be summarized for each individual (rows), or by locus (columns), as illustrated for the SNP matrix. Note that some measures of diversity include invariant sites which are calculated from sequence alignments, not a matrix of SNP genotypes.

Fig. 3

The four Genetic Composition Essential Biodiversity Variables (EBVs). Green background shading indicates the preferred genetic state (high or low levels) in many conservation/management situations. The preferred state for genetic differentiation is context dependent, represented by a lighter shade of green (see text). Distance of genetic units illustrates high genetic distance (black versus white), and low genetic distance (dark gray versus light gray). The contemporary effective population size (N _e) is represented with black (breeding) and gray (non‐breeding) individuals and the graphs denote projections after the present time (p) of the future losses of genetic diversity.

Fig. 4

Steps in generating Genetic Composition Essential Biodiversity Variables (EBVs) include field (or archive) collection of DNA, laboratory work, computational processing of raw data, analysis/calculation, publishing, archiving, modeling and/or synthesis and communication to inform management decisions. GEOME, Genomic Observatories MetaDatabase; Pop, population; QA/QC, quality assurance/quality control.

Fig. 5

Sources of DNA for genetic analyses. Genetic material may be obtained directly from tissue samples from extant populations (biopsy or non‐invasive), biological collections (e.g. museums), or sub‐fossils (sometimes called ancient DNA) or from the environment (i.e. environmental DNA, eDNA). Older samples may have low‐quality and low‐quantity DNA, restricting the use of certain Genetic Essential Biodiversity Variables (EBVs); eDNA is challenging to use for Genetic EBVs since the DNA is typically of lower quality and quantity. These examples indicate information typically assessed with these types of data, and do not represent all possibilities. N _e, effective population size.