Single nucleotide polymorphisms, environmental data and R scripts used in the work: A donor registry: Genomic analyses of Posidonia australis seagrass meadows identifies adaptive genotypes for future-proofing

Globally, anthropogenic climate change has caused declines of seagrass ecosystems necessitating proactive restoration approaches which would ideally anticipate future conditions. In eastern Australia, environmental conditions in estuaries with meadows of the endangered seagrass Posidonia australis have warmed and acidified over the past decade and seagrass communities have declined in some estuaries. Securing these valuable habitats will require proactive conservation and restoration efforts that could be augmented with restoration focussed on boosting resilience to future change.  Understanding patterns of selection and where seagrass meadows are adapted to particular environmental conditions is key for identifying optimal donor material for restoration. We use single nucleotide polymorphisms and genotype by environment analyses to identify candidate loci under putative selection to environmental stressors and assess genomic variation and allelic turnover along stressor gradients. The most important estuarine variables driving selection were associated with temperature, water turbidity and pH. We developed a preliminary ‘donor registry’ of pre-adapted Posidonia australis genotypes by mapping the distribution of alleles to visualise allelic composition of each sampled seagrass meadow. The registry could be used as a first step to select source material for future-proofing restoration projects however, manipulative experiments will be required to test that pre-adapted genotypes confer increased resistance to multiple environmental stressors.

Methods
A total of 342 individual P. australis were initially genotyped with the DArTseq™ platform yielding a total of 11,382 SNP loci with a mean read depth of 7.07 and 20.73% missing data. Sequencing error was estimated by calculating the maximum proportion of allelic differences (bitwise distance) found between six pairs of technical replicates using bitwise.dist in the R package poppr which was used as a threshold. No sequencing errors were detected and technical replicates were then removed from the dataset. To enhance the quality of SNPs and to optimise the number of loci available for identification of candidate SNPs under potential selection, a data filtering strategy was employed using several functions in the R package dartR v.2.7.2. Data were filtered applying a locus call rate of 0.67, and individual call rate of 0.25 and a reproducibility threshold of 0.99. Read depth filter parameters were set at 2 to 50 and SNPs were thinned by setting the MAF to default (0.01). After filtering, a total of 3,277 SNP loci for 311 genotypes across the 13 populations were retained.

Environmental data were sourced from various repositories, please see the published manuscript for source details