Next generation conservation genetics at sea: detecting and conserving adaptive potential

Overview

Background: There is a long tradition in conservation genetics of using neutral genetic markers to assess demographic patterns and processes relevant to conservation. For example, the identification of the units of conservation requires information on population genetic structure and patterns of connectivity. These analyses assume differentiation by neutral processes. Neutral markers also provide information on effective population size and historical population dynamics, and reflect inbreeding (and can therefore be correlated with fitness; Reed & Frankham 2003). At the same time, a primary objective (and almost a mantra) of conservation genetics has been the conservation of adaptive potential. This is necessarily associated with phenotype and local adaptation, each of which will be determined by functional genetic loci, and gene-environment interactions. The objective is to conserve the ability of wildlife species to respond to changing environments, essential in the context of anthropogenic global climate change. However, this has been difficult to assess and support. Although there is weak correlation between quantitative trait diversity (QST, reflecting phenotypic diversity) and Wright’s inbreeding coefficient measure of population structure (FST, based on neutral markers; McKay & Latta 2002), a clear relationship has remained elusive. Variation at adaptive markers could be expected to show a more direct relationship, but the relationship between phenotype and genotype is poorly understood. A meta-analyses of plant studies showed a stronger relationship between adaptive diversity and ecological and community structure than for neutral markers in plant species using indirect inference for adaptation (e.g. ecotypes and cultivars; Whitlock 2014), but to better understand the broader relationship we can now use genomic data (see Hoelzel et al. 2019).

In the marine environment there are few obvious barriers to gene flow, and in fact marine species of management concern (such as pelagic fish species) often show little evidence of structure at neutral genetic markers. This was the case for several deep sea fish species studied in our lab, where 15-20 polymorphic microsatellite DNA markers showed no evidence of structure across a broad geographic range (e.g. White et al. 2009). However, for each of these species, genome sampling (by restriction-associated DNA analysis: RADseq), showed evidence of weak structure at neutral loci, consistent with isolation by distance in most cases. However, RADseq also permits an analysis of structure at putative functional loci, detected as outliers from predicted divergence metrics under neutral theory. At those loci, a different pattern of structure emerged, highlighting especially stand out differentiation for populations south of the sub-polar front (in warmer waters, influenced by a different current system; e.g. see Figure 1; Goncalves da Silva et al. 2019). Some similar patterns associated instead with ecotype were found for marine mammal species (e.g. Moura et al. 2015). The differential pattern of structure suggests local adaptation, but the more specific mechanisms associated with specific loci or genetic pathways could not easily be determined from the RADseq data (where 1000’s of single nucleotide polymorphic (SNP) sites are revealed, but millions may be required). For example, particular loci involved in local adaption to habitat depth were revealed for the roundnose grenadier (Coryphaenoides rupestris) by re-sequencing methods revealing 6M SNP loci (Gaither et al. 2018).

Aims & novelty: To test hypotheses about the more specific mechanisms that generate distinct patterns of diversity at functional loci across environmental gradients using high resolution data and working with reference genomes. The key objectives will be to identify loci or gene systems that reflect diversity requiring conservation management that are not detectable using conventional methods based on neutral loci. At the same time, these data will enhance our understanding of the process of adaptive evolution, providing novel inference about the evolution of diversity in the marine ecosystem.

Methodology

Extensive existing sample sets across relevant environmental boundaries will be investigated using high-resolution genome sampling and re-sequencing methodologies on an Illumina platform (available in Durham), in comparison with new datasets as required and appropriate. Analyses will be based on comparison against available annotated reference sequences using GWAS and similar bioinformatic methods. A particular focus will be on oceanic gradients associated with thermal transitions, such as the sub-polar front in the North Atlantic, and the Almeria-Oran Front in the Mediterranean Sea. Habitat preference will be used as a trait variable to search for loci under selection, e.g. using Manhattan plot analyses. The relationship between high-resolution genetic structure and environmental variables will be explored, e.g. by redundancy analysis or latent factor mixed models.

Project Timeline

Year 1

Identification of focal sample sets and study systems, DNA extraction as required and the building of DNA libraries for sequencing. Sequencing will be completed in this year.

Year 2

Bioinformatic work will be the focus of year 2, together with initial analytical work. The acquisition of environmental data from available databases will also be undertaken this year.

Year 3

Data analysis and writing will be the focus of year 3, together with a focus on the integration of genomic and environmental data.

Year 3.5

The primary focus of year 3.5 will be on thesis and paper writing, on presentations and on the consideration and facilitation of impact.

Training
& Skills

Training will be provided in the generation of next generation genome sequencing data and its bioinformatic analysis, and interpretation in the context of evolutionary process and conservation. Skills associated with the generation of sequencing libraries, sequence quality assessment and control, bioinformatic processing and analysis of sequence data, and with a broad range of population genetic analytical tools will be developed. Analytical skills associated with the integration of data and drawing robust inference about patterns of adaptation and environmental drivers will also be developed. Data interpretation, presentation, writing papers and generating impact will provide skills in scientific communication, and training through the Iapetus program will facilitate these objectives.

References & further reading

Gaither, M.R., Gkafas, G.A., de Jong. M., Sarigol, F., Neat, F., Regnier, T., Moore, D., Grocke, D.R., Hall, N., Liu, X., Kenny, J., Lucaci, A., Hughes, M., Haldenby, S., Hoelzel, A.R. (2018) Genomics of habitat choice and adaptive evolution in the deep sea. Nature Ecology & Evolution 2, 680-687

Goncalves da Silva, A., Barendse, B., Kijas, J., England, P.R., Hoelzel, A.R. (2019) Genomic data suggest environmental drivers of fish population structure in the deep sea; a case study for the orange roughy (Hoplostethus atlanticus) J. Applied Ecol. 57, 296-306.

Hoelzel, A.R., Bruford, M.W. & Fleischer, R.C. (2019) Conservation of adaptive potential and functional diversity. Cons. Gen. 20,1-5

McKay, J.K., Latta, R.G. (2002) Adaptive population divergence: markers, QTL and traits. TREE 17, 285-291.

Moura, A.E., Kenny, J.G., Chaudhuri, R., Hughes, M.A., Welch, A., Reisinger, R.R., de Bruyn, P.J.N., Dahlheim, M.E., Hall, N., Hoelzel, A.R. 2014. Population genomics of the killer whale indicates ecotype evolution in sympatry involving both selection and drift. Mol. Ecol. 23, 5179-5192

Reed, D.H., Frankham, R. (2003) Correlation between fitness and genetic diversity. Cons. Biol. 17, 230-237.

White, T.A., Stefanni, S., Stamford, J. & Hoelzel, A.R. 2009. Ocean basin panmixia in a long-lived, deep-sea fish with well defined habitat dependence and relatively low fecundity. Mol. Ecol. 18: 2563-2573.

Whitlock, R. (2014) Relationships between adaptive and neutral genetic diversity and ecological structure and functioning: a meta-analysis. J. Ecol. 102, 857-872.

Further Information

Lead supervisor contact details: Rus Hoelzel, Department of Biosciences, Durham University, email: a.r.hoelzel@dur.ac.uk; phone: 0191-334-1325.

Apply Now