DOI: 10.5281/zenodo.15074854
title: Field and Genetic Data to" Small-scale thermal habitat variability may not determine seagrass resilience to  climate change"

description: Field and Genetic Data to” Small-scale thermal habitat variability may not determine seagrass resilience to  climate change”

The study investigated the effect of microclimate differences across ten seagrass meadows in the Koster Sea archipelago, located on the Swedish west coast. Environmental data were combined with genetic and experimental analyses to evaluate how local microclimatic conditions influenced seagrass resilience and response to a changing climate. This integrated approach provided insights into the interaction between environmental variability, genetic diversity, and the adaptive capacity of seagrass meadows.

Experimental data related to this can be found : 10.5281/zenodo.15074712

 
description: Environmental data: 

HOBO_Data.txt

·       Seawater temperature was recorded every 15 minutes at 1 m above the seafloor in ten meadows from July 5–29 2021, using HOBO loggers. Data logger at one meadow (Ram-Ramnekroken) was excluded due to burial in sediment. 

Other_Environmental_Data.txt

·       Effective Fetch The exposure of seagrass meadows to wave energy was modeled using their coordinates in a high-resolution fetch model that covers the entire Swedish coastline. The model provides spatially detailed estimates of wave exposure.

·       Water content was calculated as the difference between wet and dry weight (drying at 105°C for 16 hours) divided by the wet weight. 

·       Organic content was calculated as the difference between dry and burned weight (burning at 550°C for 4 hours) divided by the dry weight. 

·       Coordinates of the 10 meadows 

 

Genetic data:



zostera_miss25_sorted_filt_noshared.recode.vcf


This final filtered (as described below) vcf file contains the genotyping data for the seagrass Zostera marina in the Koster Sea, Sweden. The file contains 111 multi locus lineages (MLLs), i.e. duplicates clones within sites have been removed. The vcf file contains MLLs from 10 sampling sites genotyped using 1,639 SNPs (of which 1,588 are polymorphic) derived from 2b-RAD sequencing following the protocol by Matz & Aglyamova, (2019; available in https://github.com/z0on/2bRAD_denovo). 

For sequencing, the libraries were pooled in three separate pools that were sequenced in different flow cells (Pool 1- GAS, KOD, Pool2 – KOC, NYC, TJB, TAN, VAT; Pool 3 - FLA, RAM, STY). Single-read sequencing (51 bp read-length) was done on the NovaSeq 6000 system and v1.5 Illumina sequencing chemistry. Raw reads were filtered to remove low-quality reads and redundant sequences (i.e. restriction sites and pcr-duplicates). The quality-filtered reads were aligned to the Zostera marina reference genome v3.1 (Ma et al., 2021) using Bowtie2 (v.2.5.1; Langmead & Salzberg, 2012). UnifiedGenotyper (GATK v3.8; McKenna et al., 2010) was used to call SNPs. VCFtools (v.4.2; Danecek et al., 2011) was used to implement a minDP=3 filter, which allowed us to keep only genotype calls with more than three reads. Variant Quality Score Recalibration (VQSR) was then performed using SNPs identically genotyped in each pair of technical replicated to generate a training model that would allow us to estimate the probability of each variant being a “true” SNP. VQSR was applied using 95% truth sensitivity with a Ti/Tv ratio of 2.573 to call SNPs from the overall dataset. A final filter was applied with VCFtools (v.4.2; Danecek et al., 2011) to keep only those individuals with less than 25% missing data and to select polymorphic biallelic loci genotyped in at least 90% of the individuals with a maximum heterozygosity rate of 0.5. 

·       zostera_miss25_sorted.vcf

This final filtered vcf file contains the genotyping data for the seagrass Zostera marina in the Koster Sea, Sweden. This vcf file contains genotyping data on all 189 samples genotyped with 1,639 SNPs (i.e. before clone removal), and is provided for reference purposes. 

 

References: 

Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E., DePristo, M. A., Handsaker, R. E., Lunter, G., Marth, G. T., Sherry, S. T., McVean, G., Durbin, R., & 1000 Genomes Project Analysis Group. (2011). The variant call format and VCFtools. Bioinformatics, 27(15), 2156–2158. https://doi.org/10.1093/bioinformatics/btr330

 

Dray, S., & Dufour, A.-B. (2007). The ade4 Package: Implementing the Duality Diagram for Ecologists. Journal of Statistical Software, 22, 1–20. https://doi.org/10.18637/jss.v022.i04

 

Frichot, E., & François, O. (2015). LEA: An R package for landscape and ecological association studies. Methods in Ecology and Evolution, 6(8), 925–929. https://doi.org/10.1111/2041-210X.12382

 

Kamvar, Z. N., Tabima, J. F., & Grünwald, N. J. (2014). Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ, 2, e281. https://doi.org/10.7717/peerj.281

 

Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4), Article 4. https://doi.org/10.1038/nmeth.1923

 

Ma, X., Olsen, J. L., Reusch, T. B. H., Procaccini, G., Kudrna, D., Williams, M., Grimwood, J., Rajasekar, S., Jenkins, J., Schmutz, J., & Van de Peer, Y. (2021). Improved chromosome-level genome assembly and annotation of the seagrass, Zostera marina (eelgrass). F1000Research, 10, 289. https://doi.org/10.12688/f1000research.38156.1

 

Matz, M. V. (2021). 2bRAD de novo AND reference-based walkthrough [Computer software]. https://github.com/z0on/2bRAD_denovo

 

Matz, M. V., & Aglyamova, G. (2019). 2bRAD sample preparation. https://github.com/z0on/2bRAD_denovo

 

McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303. https://doi.org/10.1101/gr.107524.110

 

R Core Team. (2023). R: A language and environment for statistical computing. [Computer software]. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/