The importance of genomic variation for biodiversity, ecosystems and people

Abstract

The 2019 United Nations Global assessment report on biodiversity and ecosystem services estimated that approximately 1 million species are at risk of extinction. This primarily human-driven loss of biodiversity has unprecedented negative consequences for ecosystems and people. Classic and emerging approaches in genetics and genomics have the potential to dramatically improve these outcomes. In particular, the study of interactions among genetic loci within and between species will play a critical role in understanding the adaptive potential of species and communities, and hence their direct and indirect effects on biodiversity, ecosystems and people. We explore these population and community genomic contexts in the hope of finding solutions for maintaining and improving ecosystem services and nature’s contributions to people.

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Fig. 1: Contributions of genetic and genomic effects to NCP.
Fig. 2: Ecosystem-level effects of genetic variation in individual trees mediated through keystone species.
Fig. 3: Classic genetic methods can inform NCP.
Fig. 4: Some current approaches to preserve or increase intraspecific genetic variation.
Fig. 5: Potential impact of genomic engineering on biodiversity and ecosystems.
Fig. 6: Incorporating population and community genomics into NCP studies.

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Acknowledgements

The authors thank S. Rudman, V. Glynn, S. van Moorsel, the anonymous reviewers, I. Porth and R. Waples for comments on an earlier version of the manuscript.

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Related links

Convention on Biological Diversity Aichi Biodiversity Targets: https://www.cbd.int/sp/targets/

Earth BioGenome Project: https://www.earthbiogenome.org

EpiDiverse European Training Network: https://epidiverse.eu/

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Genome 10K Project: https://genome10k.soe.ucsc.edu

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Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Global Assessment on Biodiversity and Ecosystem Services: https://www.ipbes.net/global-assessment-report-biodiversity-ecosystem-services

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Vertebrate Genomes Project: https://vertebrategenomesproject.org

Glossary

Intraspecific genetic variation

Variation in alleles of genes within and among populations of the same species.

Genetic diversity

Interspecific and intraspecific genetic variation.

Contemporary evolution

(Also known as rapid evolution). Natural selection that drives adaptive evolution in populations on timescales of less than a few hundred years.

Gene flow

Transfer of genetic variation from one population to another usually via migrating individuals.

Mutation

Change in the DNA sequence.

Genetic drift

Stochastic process altering the genetic variation in a population, usually reducing genetic diversity.

Population genetics

The study of genetic variation and evolutionary history within species using single-gene markers (population genetics) and multigene markers up to full genomes for consideration of structural and epigenetic variation (population genomics).

Community genetics

A community is the sum of populations formed by different species within a particular geographical area. Community genetics and genomics studies the effects of interactions among genomic variation between interacting species. Such interactions are mediated through phenotypes that are determined by heritable genetic variation and environmental influences.

Extended phenotypes

Phenotypes that include effects of genes on the environment, such as an organism’s behaviour or life history, or ecosystem.

Keystone species

A species with a disproportionate ecological effect in an ecosystem. Removal of that species would lead to a drastic change in the ecosystem.

Evolvability

The ability to evolve (that is, to produce genetic diversity on which selection can act).

Heterozygosity

Proportion of sites on the chromosome at which two randomly chosen copies differ in DNA sequence.

Additive genetic variance

The independent genetic effect of an allele on the phenotype of an individual organism resulting in deviation from the population mean phenotype. Additive genetic variance contributes to the evolvability of a population.

Dominance

A genetic interaction between the two alleles at a locus, such that the phenotype of heterozygotes deviates from the average of the two homozygotes.

Epistasis

Non-additive gene–gene interaction. A given allele might function well in one genetic background but poorly in another genetic background. We also refer to interspecific epistasis, in which alleles in different species interact (for example, gene–gene interactions between a native host and a parasite perform differently from an invasive host and the parasite genotype).

Biocontrol agent

In contrast to chemical control agents, biocontrol agents are natural predators or parasites of a pest.

Epialleles

Alternative chromatin states at a given locus, defined with respect to individuals in the population for a given time point and tissue type.

Population dynamics

A population is the sum of all individuals of the same species within a defined geographical area. Its dynamics are described as changes in the demographics of a given population (for example, age, composition or size) driven by biological and environmental factors.

Character displacement

Phenotypically (in a trait or ecological niche) similar but geographically or temporally co-occurring species diverge in the trait to minimize interspecific competition.

Allelopathic compound

As part of a plant’s defence mechanism, lethal biochemical compounds are released into the soil to suppress neighbouring organisms.

Mycorrhizal

A term describing the symbiotic interaction between a fungus and a plant’s rhizosphere.

Microbiome

The totality of microorganisms, their genetic information and the milieu in which they interact to perform a specific function.

Gene drives

Genetically engineered, synthetic genetic elements designed to increase in frequency over time in a population to propagate a certain gene variant.

Deep learning

A subdiscipline of machine learning, with the difference that no training data set is needed. The artificial neural network recognizes patterns from coarse to fine scale in multiple steps, so-called hidden layers, which compute increasingly more complex features by taking the results of preceding operations as input.

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Stange, M., Barrett, R.D.H. & Hendry, A.P. The importance of genomic variation for biodiversity, ecosystems and people. Nat Rev Genet (2020). https://doi.org/10.1038/s41576-020-00288-7

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