Skip to main content
Dryad logo

Data from: Genotype-environment mismatch of kelp forests under climate change

Citation

Vranken, Sofie et al. (2021), Data from: Genotype-environment mismatch of kelp forests under climate change, Dryad, Dataset, https://doi.org/10.5061/dryad.fn2z34ttg

Abstract

Climate change is increasingly impacting ecosystems globally. Understanding adaptive genetic diversity and whether it will keep pace with projected climatic change is necessary to assess species’ vulnerability and design efficient mitigation strategies such as assisted adaptation. Kelp forests are the foundations of temperate reefs globally but are declining in many regions due to climate stress. A lack of knowledge of kelps’ adaptive genetic diversity hinders assessment of vulnerability under extant and future climates. Using 4245 single nucleotide polymorphisms (SNPs), we characterise patterns of neutral and putative adaptive genetic diversity for the dominant kelp in the southern hemisphere (Ecklonia radiata) from ~1000 km coastline off Western Australia. Strong population structure and isolation-by-distance was underpinned by significant signatures of selection related to temperature and light. Gradient forest analysis of temperature-linked SNPs under selection revealed a strong association with mean annual temperature range suggesting adaptation to local thermal environments. Critically, modelling revealed that predicted climate-mediated temperature changes will likely result in high genomic vulnerability via a mismatch between current and future predicted genotype-environment relationships such that kelp forests off Western Australia will need to significantly adapt to keep pace with projected climate change. Proactive management techniques such as assisted adaptation to boost resilience may be required to secure the future of these kelp forests and the immense ecological and economic values they support.

Usage Notes

Population & Environmental data: latgrad_pop_env.csv

Sample ID, site ID and Environmental data for variables used in lfmm and RDA analysis

id = sample ID

site = site ID

LT_meantemp = long term annual mean temperature, calculated from https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplMURSST41.html

LT_rangetemp = Long term annual mean temperature range, calculated from https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplMURSST41.html

BO_damean = mean diffuse attenuation, downloaded from Bio-ORACLE v2.0 (Assis et al., 2018)

BO2_curvelmean_bdmax = mean seawater velocity at depth, downloaded from Bio-ORACLE v2.0 (Assis et al., 2018)

SNP datafile: latgrad_bayescan_nodup

Bayescan input file

SNP datafile: latgrad_geno

Snmf input file in geno format, missing values are coded as 9

SNP datafile: latgrad_imputed

SNP input file, imputed with snmf and used for lfmm, RDA and GF analysis