DOI: 10.5061/dryad.d2547d89s
title: 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

description: 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.
description: A total of 342 individual <em>P. australis</em> 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 <em>bitwise.dist</em> in the R
 package <em>poppr</em> 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 <em>R</em> package <em>dartR</em>
 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
description: # 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
 [https://doi.org/10.5061/dryad.d2547d89s](https://doi.org/10.5061/dryad.d2547d89s) ## Description of the data and file structure We used single nucleotide polymorphisms (SNP data provded here) and genotype by environment analyses (environmental data provided here) to identify candidate loci under putative selection to environmental stressors and assess genomic variation and allelic turnover along stressor gradients for the seagrass Posidonia australis in estuaries along the New South Wales (Australia) coast. ### Files and variables #### File: PosidoniafiltereddataGEASNPs.csv **Description:** DArT Sequence generated single nucleotide polymorphisms for individual loci from multiple individuals ##### Variables * Individual: Samples from estauries in New South Wales - see estuarine covariables dataset for explainers of individual origin * SNP sequence: short read sequence for individual SNPs. Some cells contain "NA" values representing no data available #### File: Environmental\_Covariables\_NSW\_estuaries.csv **Description:** Corresponding environmental data for individuals and populations in the PosidoniafiltereddataGEASNPs.csv dataset ##### Variables * Ind: Name of individual samples * Pop: Name of population to which each individual belongs * AvTemp: Average water temperature (degrees Celsius) for the relevant population/individual averaged over the last 20 years * MinTemp: Minimum temperature (degrees Celsius) for the relevant population/individual over the last 20 years * MaxTemp: Maximum temperature (degrees Celsius) for the relevant population/individual over the last 20 years * TempRange: Difference between maximum and minimum temperatures (degrees Celsius) for the relevant population/individual over the last 20 years * AvpH: Average water pH (pH units) for the relevant population/individual  over the last 20 years * MinpH: Minimum water pH (pH units)for the relevant population/individual  over the last 20 years * MaxpH: Maximum water pH (pH units)for the relevant population/individual  over the last 20 years * pHRange: Difference (pH units) between maximum and minimum pH value for the relevant population/individual over the last 20 years * AvSal: Average water salinity (practical salinity units) meaure for the relevant population/individual  over the last 20 years * MaxSal: Maximum water salinity (practical salinity units meaure for the relevant population/individual  over the last 20 years * AvTurb: Average water turbidity (nephelometric turbidity units) measure for the relevant population/individual  over the last 20 years * MinTurb: Maximum water turbidity (nephelometric turbidity units) measure for the relevant population/individual  over the last 20 years * MaxTurb: Minimum water turbidity (nephelometric turbidity units) measure for the relevant population/individual over the last 20 years * TurbRange:  Difference between maximum and minimum water turbidity (nephelometric turbidity units) value for the relevant population/individual over the last 20 years ## Code/software 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*. A data filtering strategy was employed using several functions in the *R* package *dartR* v.2.7.2. Genomic scans for adaptive divergence were carried out to identify candidate SNPs potentially under selective pressure using three different models: Redundancy analysis (RDA) , Principal Component Analysis for Outlier Detection (PCAdapt) and Latent Factor Mixed Models (LFMM2) To infer population structure, individual ancestral coefficients were estimated based on a sparse non-negative matrix factorisation (SNMF) method. This was implemented using the *snmf* function in the *R *package LEA* v3.10.2. The optimal factor, *K*=8, *was used to inform the LFMM to identify whether allele frequencies were correlated with any of the environmental variables. Statistical power of associations was increased by imputing missing genotype data via the *gl.impute *function in the *dartR* package using the *nearest neighbour* option. Subsequently, the function *lfmm_ridge *was used* *to compute a regularised least-squares estimate using a ridge penalty. Individual associations between each SNP frequency and each environmental variable were assessed using statistics test calibrated using genomic inflation factor (function *lfmm_test*). Corrections for multiple comparisons were applied with the Benjamini-Hochberg algorithm with a false discovery rate (FDR) threshold of 5% . Significance associations was determined using a threshold of 0.001 as the probability of finding a false positive result increases with lower thresholds. Candidate SNP loci were retained for downstream analysis when they were identified by at least two out of the three methods. The GF analysis was run in the *R* package *gradientForest* using a regression tree-based approach to fit a model of responses between genomic data and environmental variables . Turnover in adaptive genetic variation were modelled on the predictor variables using the candidate SNPs, identified via GEAs, as the response variables. The machine learning algorithm partitioned allele frequencies at numerous split values along each environmental gradient and calculated the change in allele frequencies for each split. The split importance (i.e., the amount of genomic variation explained by each split value) was cumulatively summed along the environmental gradient and aggregated across alleles to build a non-linear turnover function to identify loci that were significantly influenced by the predictor variable. The analysis was run over 500 regression trees for each of the nine environmental predictor variables with all other parameters at default settings. ## Access information Other publicly accessible locations of the data: * Nil Data was derived from the following sources: * Nil