Abstract
Understanding the genetic architecture of functional traits can provide key insights into the ecological dynamics and adaptive potential of species. We investigated whether genetic data can predict growth rate variation in a natural population of the widespread kelp, Ecklonia radiata. We tagged kelps and tracked their growth in situ over spring when growth is maximal. Individual kelps were then genotyped using reduced representation sequencing (ddRAD) and we employed multiple approaches to assess whether genetic variation corresponded with growth rate variation. Despite a limited sample size, we found evidence that growth rate can be strongly predicted from genetic variation, with approximately half of the variation in growth rate predicted by only 18 loci (R2 = 0.499). Leveraging published transcriptomic data, we confirm that most of these loci are expressed or are linked to expressed putative genes. However, many of these genes are of unknown function and do not match well-known gene families. These findings have important implications for understanding natural kelp forest dynamics and for applied approaches such as selective breeding and aquaculture. While our study offers an important first assessment of the possible genomic architecture underlying growth rate in E. radiata, future work is needed to confirm this apparent link between genetic and functional variation.
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Data availability
Raw sequence reads are available on the NCBI Sequence Read Archive (SRA) under BioProject ID: PRJNA1368432. The processed datasets generated and/or analysed during the current study are available on Figshare, [http://doi.org/10.6084/m9.figshare.30528407](http:/doi.org/10.6084/m9.figshare.30528407).
References
Barshis, D. et al. (ed, J.) Genomic basis for coral resilience to climate change. Proc. Natl. Acad. Sci. 110 1387–1392 (2013).
Deng, C. et al. AquaGWAS: A genome-wide association study pipeline for aquatic animals and its application to reference-required and reference-free genome-wide association study for abalone. Front. Mar. Sci. 9, 841561 (2022).
Hauser, L. & Carvalho, G. Paradigm shifts in marine fisheries genetics: ugly hypotheses slain by beautiful facts. Fish Fish. 9, 333–362 (2008).
Limborg, M. T. et al. Environmental selection on transcriptome-derived SNPs in a high gene flow marine fish, the Atlantic herring (Clupea harengus). Mol. Ecol. 21, 3686–3703 (2012).
Starko, S. et al. Marine heatwaves threaten cryptic coral diversity and erode associations among coevolving partners. Sci. Adv. 9, eadf0954 (2023).
Altmann, J. & Alberts, S. Growth rates in a wild primate population: ecological influences and maternal effects. Behav. Ecol. Sociobiol. 57, 490–501 (2005).
Monro, K. & Marshall, D. Faster is not always better: selection on growth rate fluctuates across life history and environments. Am. Nat. 183, 798–809 (2014).
Pontier, O., Rhoades, O., Twist, B., Okamoto, D. & Hessing-Lewis, M. Bull Kelp (Nereocystis luetkeana) growth rates as climate stress indicators for canada’s Pacific Coast. FACETS 9, 1–19 (2024).
Layton, C. et al. Resilience and stability of Kelp forests: the importance of patch dynamics and environment-engineer feedbacks. PLoS ONE 14 (1), e0210220 (2019).
Reed, D., Rassweiler, A. & Arkema, K. Biomass rather than growth rate determines variation in net primary production by giant Kelp. Ecology 89 9, 2493–2505 (2008).
Smale, D. A. et al. Chapter 3. Biodiversity and ecosystem services. In Into the Blue: Securing a Sustainable Future for Kelp Forests. United Nations Environment Programme 45–70. (Nairobi, Kenya, 2023).
Bennett, S. et al. The ‘Great Southern reef’: social, ecological and economic value of australia’s neglected Kelp forests. Mar. Freshw. Res. 67, 47–56 (2016).
Pessarrodona, A. et al. Global seaweed productivity. Sci. Adv. 8, eabn2465 (2022).
Duggins, D. O., Simenstad, C. A. & Estes, J. A. Magnification of secondary production by Kelp detritus in coastal marine ecosystems. Science 245, 170–173 (1989).
Teagle, H., Hawkins, S. J., Moore, P. J. & Smale, D. A. The role of Kelp species as biogenic habitat formers in coastal marine ecosystems. J. Exp. Mar. Biol. Ecol. 492, 81–98 (2017).
Bearham, D., Vanderklift, M. & Gunson, J. Temperature and light explain Spatial variation in growth and productivity of the Kelp Ecklonia radiata. Mar. Ecol. Prog. Ser. 476, 59–70 (2013).
Mabin, C. J. T., Johnson, C. & Wright, J. T. Physiological response to temperature, light, and nitrates in the giant Kelp macrocystis pyrifera, from Tasmania, Australia. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/MEPS12900 (2019).
Starko, S. et al. Microclimate predicts Kelp forest extinction in the face of direct and indirect marine heatwave effects. Ecol. Appl. e2673 https://doi.org/10.1002/eap.2673 (2022).
Alsuwaiyan, N. A. et al. Genotypic variation in response to extreme events May facilitate Kelp adaptation under future climates. Mar. Ecol. Prog. Ser. 672, 111–121 (2021).
Camus, C., Faugeron, S. & Buschmann, A. H. Assessment of genetic and phenotypic diversity of the giant kelp, Macrocystis pyrifera, to support breeding programs. Algal Res. 30, 101–112 (2018).
Allen, H. L. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).
Kroymann, J. & Mitchell-Olds, T. Epistasis and balanced polymorphism influencing complex trait variation. Nature 435, 95–98 (2005).
Remington, D. L., Purugganan, M. D. & Candidate Genes Quantitative trait Loci, and functional trait evolution in plants. Int. J. Plant Sci. 164, S7–S20 (2003).
Fornoni, J., Valverde, * Pedro, L. & Núñez-Farfán, J. Quantitative genetics of plant tolerance and resistance against natural enemies of two natural populations of Datura stramonium. Evol Ecol Res 5, 1049–1065 (2003).
Hill, W. G. Quantitative genetics in the genomics era. Curr. Genom. 13, 196–206 (2012).
Viana, J., Piepho, H. & Silva, F. F. Quantitative genetics theory for genomic selection and efficiency of breeding value prediction in open-pollinated populations. Scientia Agricola. 74, 41–50 (2016).
Wang, X., Yang, Z. & Xu, C. A comparison of genomic selection methods for breeding value prediction. Sci. Bull. 60, 925–935 (2015).
Chimura, K., Akita, S., Iwasaki, T., Nagano, A. J. & Shimada, S. Phylogeography of a canopy-forming kelp, Eisenia bicyclis (Laminariales, Phaeophyceae), based on a genome-wide sequencing analysis. J. Phycol. 58, 318–329 (2022).
Minne, A. et al. Strong environmental and genome-wide population differentiation underpins adaptation and high genomic vulnerability in the dominant Australian kelp (Ecklonia radiata). Ecol. Evol. 15, e71158 (2025).
Reynes, L. et al. Temporal genomics help in Deciphering neutral and adaptive patterns in the contemporary evolution of Kelp populations. J. Evol. Biol. 37, 677–692 (2024).
Vranken, S. et al. Genotype–Environment mismatch of Kelp forests under climate change. Mol. Ecol. 30, 3730–3746 (2021).
Bringloe, T. T. et al. Phylogeny and evolution of the brown algae. CRC. Crit. Rev. Plant Sci. 39, 281–321 (2020).
Coleman, M. A. et al. Restore or redefine: future trajectories for restoration. Front. Mar. Sci. 7, 237 (2020).
Forbes, H., Shelamoff, V., Visch, W. & Layton, C. Farms and forests: evaluating the biodiversity benefits of Kelp aquaculture. J. Appl. Phycol. 34, 3059–3067 (2022).
Wood, G. V. et al. Upscaling marine forest restoration: challenges, solutions and recommendations from the green gravel action group. Front. Mar. Sci. 11, 1364263 (2024).
Champion, C., Coleman, M. A. & Wernberg, T. Enhancing productivity in restoration and assited adaptation under ocean warming. Front. Mar. Sci. 15, 38796 (2025).
Mao, X. et al. Population genetics of sugar Kelp throughout the Northeastern united States using genome-wide markers. Front. Mar. Sci. 7, 694 (2020).
Baral, K. & Rotwein, P. The insulin-like growth factor 2 gene in mammals: organizational complexity within a conserved locus. PLOS ONE. 14, e0219155 (2019).
De-Santis, C. & Jerry, D. R. Candidate growth genes in finfish — Where should we be looking? Aquaculture 272, 22–38 (2007).
Rasheed, H. et al. Plant growth regulators: an overview of WOX gene family. Plants 13, 3108 (2024).
Santure, A. W. & Garant, D. Wild GWAS-association mapping in natural populations. Mol. Ecol. Resour. 18, 729–738 (2018).
Burkholz, C. Harnessing Intraspecific Variation to Enable Future-Proofing Practices in Kelp Forests. (2024). https://doi.org/10.26182/na5n-3m56
Kirkman, H. Standing stock and production of Ecklonia radiata (C.Ag). J. Exp. Mar. Biol. Ecol. 76, 119–130 (1984).
de Bettignies, T., Wernberg, T., Lavery, P. S., Vanderklift, M. A. & Mohring, M. B. Contrasting mechanisms of dislodgement and erosion contribute to production of Kelp detritus. Limnol. Oceanogr. 58, 1680–1688 (2013).
Wernberg, T. et al. Biology and ecology of the globally significant Kelp Ecklonia radiata. Oceanogr. Mar. Biol. 57, 265–374 (2018).
Champion, C., Wernberg, T. & Coleman, M. C. Future projections of kelp productivity under climate change and assisted adaptation scenarios. Current Biology (in review.).
Vitezica, Z. G., Varona, L. & Legarra, A. On the additive and dominant variance and covariance of individuals within the genomic selection scope. Genetics 195, 1223–1230 (2013).
Lynch, M., Walsh, B., Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits (Oxford University Press, 1998).
Pessarrodona, A., Howard, J., Pidgeon, E., Wernberg, T. & Filbee-Dexter, K. Carbon removal and climate change mitigation by seaweed farming: A state of knowledge review. Sci. Total Environ. 918, 170525 (2024).
Ribaut, J. M. & Hoisington, D. Marker-assisted selection: new tools and strategies. Trends Plant Sci. 3, 236–239 (1998).
Coleman, M. A. & Goold, H. D. Harnessing synthetic biology for Kelp forest conservation1. J. Phycol. 55, 745–751 (2019).
Dwivedi, S. L., Heslop-Harrison, P., Amas, J., Ortiz, R. & Edwards, D. Epistasis and pleiotropy-induced variation for plant breeding. Plant Biotechnol. J. 22, 2788–2807 (2024).
Ackerly, D. D. et al. The evolution of plant ecophysiological traits: recent advances and future directions: new research addresses natural selection, genetic constraints, and the adaptive evolution of plant ecophysiological traits. BioScience 50, 979–995 (2000).
Bradshaw, H. D. Jr & Stettler, R. F. Molecular genetics of growth and development in populus. IV. Mapping QTLs with large effects on growth, form, and phenology traits in a forest tree. Genetics 139, 963–973 (1995).
Verhaegen, D. et al. Quantitative trait dissection analysis in Eucalyptus using RAPD markers: 1. Detection of QTL in interspecific hybrid progeny, stability of QTL expression across different ages. Theor. Appl. Genet. 95, 597–608 (1997).
Kaya, Z., Sewell, M. M. & Neale, D. B. Identification of quantitative trait loci influencing annual height- and diameter-increment growth in loblolly pine (Pinus Taeda L). Theor. Appl. Genet. 98, 586–592 (1999).
Chen, Z. Q. et al. Genetic architecture behind developmental and seasonal control of tree growth and wood properties in Norway Spruce. Front. Plant Sci. 13, 927673 (2022).
Bishop, A. P., Hart, T., Wang, I. J. & D. E. & Optimising sampling design for landscape genomics. Mol. Ecol. Resour. 25, e14052 (2025).
Spencer, C. C. A., Su, Z., Donnelly, P. & Marchini, J. Designing Genome-Wide association studies: sample Size, Power, Imputation, and the choice of genotyping chip. PLoS Genet. 5, e1000477 (2009).
Delph, L. F. & Kelly, J. K. On the importance of balancing selection in plants. New Phytol. 201, 45–56 (2014).
Hatcher, B. G., Kirkman, H. & Wood, W. F. Growth of the Kelp Ecklonia radiata near the Northern limit of its range in Western Australia. Mar. Biol. 95, 63–73 (1987).
Rehfeldt, G. E. Early selection in Pinus ponderosa: compromises between growth potential and growth rhythm in developing breeding strategies. For. Sci. 38, 661–677 (1992).
Starko, S. & Martone, P. T. Evidence of an evolutionary-developmental trade-off between drag avoidance and tolerance strategies in wave-swept intertidal kelps (Laminariales, Phaeophyceae). J. Phycol. 52, 54–63 (2016).
Wernberg, T. Holdfast aggregation in relation to morphology, age, attachment and drag for the Kelp Ecklonia radiata. Aquat. Bot. 82, 168–180 (2005).
Merkulov, V. M., Leberfarb, E. Y. & Merkulova, T. I. Regulatory SNPs and their widespread effects on the transcriptome. J. Biosci. 43, 1069–1075 (2018).
Gupta, V., Jain, M. & Reddy, C. R. K. Macroalgal functional genomics: A missing area. In Systems Biology of Marine Ecosystems (eds Kumar, M. & Ralph, P.) 3–12 (Springer International Publishing, 2017). https://doi.org/10.1007/978-3-319-62094-7_1.
Teng, L. et al. Exon shuffling and alternative splicing of ROCO genes in brown algae enables a diverse repertoire of candidate immune receptors. Front. Plant Sci. 15, 1445022 (2024).
Marín, I., van Egmond, W. N. & van Haastert, P. J. M. The Roco protein family: a functional perspective. FASEB J. 22, 3103–3110 (2008).
Zhong, R., Morrison, W. H., Himmelsbach, I. I. I., Poole, D. S., Ye, Z. H. & F. L., II & Essential role of caffeoyl coenzyme A O-Methyltransferase in lignin biosynthesis in Woody Poplar plants. Plant Physiol. 124, 563–578 (2000).
Deniaud-Bouët, E. et al. Chemical and enzymatic fractionation of cell walls from fucales: insights into the structure of the extracellular matrix of brown algae. Ann. Bot. 114, 1203–1216 (2014).
Kloareg, B. & Quatrano, R. S. Structure of the cell walls of marine algae and ecophysiological functions of the matrix polysaccharides. Oceanogr. Mar. Biology Annual Rev. 26, 259–315 (1988).
Varona, L., Legarra, A., Toro, M. A. & Vitezica, Z. G. Non-additive effects in genomic selection. Front. Genet. 9, 78 (2018).
Mann, E. H. & Kirkman, H. Biomass method for measuring productivity of Echlonia radiata, with the potential for adaptation to other large brown algae. Mar. Freshw. Res. 32, 297–304 (1981).
Kregting, L., Blight, A., Elsäßer, B. & Savidge, G. The influence of water motion on the growth rate of the Kelp Laminaria hyperborea. J. Exp. Mar. Biol. Ecol. 448, 337–345 (2013).
Campbell, J. & Starko, S. Allometric models effectively predict Saccharina latissima (Laminariales, Phaeophyceae) fresh weight at local scales. J. Appl. Phycol. 33, 491–500 (2021).
Kim, S., Kang, Y. H., Kim, T. H., Lee, H. J. & Park, S. R. Use of morphological characteristics for calculating individual biomass in the Kelp Ecklonia Cava. J. Appl. Phycol. 29, 2587–2593 (2017).
Wernberg, T. & Vanderklift, M. A. Contribution of Temporal and Spatial components to morphological variation in the Kelp Ecklonia (laminariales). J. Phycol. 46, 153–161 (2010).
Severn-Ellis, A. A. et al. Genotyping for species identification and diversity assessment using Double-Digest restriction Site-Associated DNA sequencing (ddRAD-Seq). Methods Mol. Biol. 2107, 159–187 (2020).
Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: an analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).
Andrews, S. FastQC: A quality control tool for high throughput sequence data. (2010).
Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
Danecek, P. et al. The variant call format and vcftools. Bioinformatics 27, 2156–2158 (2011).
Knaus, B. J. & Grünwald, N. J. Vcfr: a package to manipulate and visualize variant call format data in R. Mol. Ecol. Resour. 17, 44–53 (2017).
Wickham, H. et al. Welcome to the tidyverse. J. Open. Source Softw. 4, 1686 (2019).
Gruber, B., Unmack, P. J., Berry, O. F. & Georges, A. Dartr: an r package to facilitate analysis of SNP data generated from reduced representation genome sequencing. Mol. Ecol. Resour. 18, 691–699 (2018).
Oksanen, J. et al. Vegan: community ecology package. R package version 1.17-4. (2010). (2010) http://cran.r-project.org. Acesso em 23.
Zheng, X. & Zheng, M. X. Package ‘SNPRelate’. A package for parallel computing toolset for relatedness and principal component analysis of SNP data (2013). https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=95e93191e93984495923be473d9d6f49f4d33b3b
Privé, F., Aschard, H., Ziyatdinov, A. & Blum, M. G. Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and Bigsnpr. Bioinformatics 34, 2781–2787 (2018).
Paradis, E. & Schliep, K. Ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).
Endelman, J. B. Ridge regression and other kernels for genomic selection with R package RrBLUP. Plant Genome 4, 250–255 (2011).
Dabney, A. & Storey, J. D. Package ‘qvalue’. Medicine 344, 539–548 (2011).
Fadista, J., Manning, A. K., Florez, J. C. & Groop, L. The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants. Eur. J. Hum. Genet. 24, 1202 (2016).
Uffelmann, E. et al. Genome-wide association studies. Nat. Rev. Methods Primers. 1, 1–21 (2021).
Caye, K., Jumentier, B., Lepeule, J. & François, O. LFMM 2: fast and accurate inference of gene-environment associations in genome-wide studies. Mol. Biol. Evol. 36, 852–860 (2019).
Xuereb, A. et al. Effect of missing data and sample size on the performance of genotype-environment association methods. In US-IALE 2017 Annual Meeting, People, Places, Patterns: Linking Landscape Heterogeneity and Socio-Environmental Systems (2017).
Carvalho, C. S. et al. Combining genotype, phenotype, and environmental data to delineate site-adjusted provenance strategies for ecological restoration. Mol. Ecol. Resour. 21, 44–58 (2021).
Jackson, C., Salomaki, E. D., Lane, C. E. & Saunders, G. W. Kelp transcriptomes provide robust support for interfamilial relationships and revision of the little known arthrothamnaceae (Laminariales). J. Phycol. 53, 1–6 (2017).
Kearse, M. et al. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
Cock, J. M. et al. The ectocarpus genome and the independent evolution of multicellularity in brown algae. Nature 465, 617–621 (2010).
Acknowledgements
This work was supported by funding from the Australian Research Council (DP240100230 to TW, SS, MAC; LP190100346 to TW, KFD, MAC; DP200100201 to TW, MAC and FL240100015 to TW), the Norwegian Research Council (GecoKelp project no. 335371 to TW, KFD, MAC), the Ecological Society of Australia, the Holsworth Wildlife Research Endowment – Equity Trustees Charitable Foundation and the Forrest Foundation through a Forrest Research Scholarship and a Forrest Research Fellowship to CB and SS, respectively. We thank H. Denham, M. Sullivan, D. Sahin, A. Pessarrodona, T. Simpkins, J. Valckenaere, G. Wood, A. Harwood, M. Sanchez and J. Edgeloe for assistance during field work.
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SS, CB, JMB, KFD, JB, MAC & TW collected the data. SS, CB & DW conducted data analyses. SS wrote the original manuscript with input from all authors. All authors reviewed the manuscript.
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Starko, S., Burkholz, C., Edgeloe, J.M. et al. Evidence of a genomic basis for growth rate variation in a natural kelp population.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-36286-8
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DOI: https://doi.org/10.1038/s41598-026-36286-8
Keywords
- ddRAD
- Genomic prediction
- GWAS
- Heritability
- LFMM
- Population genomics
- Polygenic traits
- Reduced representation sequencing
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