Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Random forest assessment of correlation between environmental factors and genetic differentiation of populations: Case of marine mussels Mytilus
Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
Show others and affiliations
Number of Authors: 72019 (English)In: Oceanologia, ISSN 0078-3234, Vol. 61, no 1, p. 131-142Article in journal (Refereed) Published
Abstract [en]

The novel machine learning technique Random Forest (RF) was used to test if the genetic differentiation of populations of marine species may be related to any of the key environmental variables known to shape species distributions. The study was performed in North and Baltic Sea characterized by strong gradients of environmental factors and almost continuous distributions of Mytilus mussel populations. Assessment of the species identity was performed using four nuclear DNA markers, and previously published single nucleotide polymorphism (SNP) data. A general pattern of cline variation was observed with increasing Mytilus trossulus share towards the eastern Baltic Sea. Average allele share rose to 61% in Hoga Kusten, Gulf of Bothnia. All Baltic Sea samples revealed a strong introgression of Mytilus edulis and a limited introgression of M. trossulus through the Danish Straits. The studied environmental variables described 67 and 68% of the variability in the allele frequencies of M. edulis and M. trossulus. Salinity defined over 50% of the variability in the gene frequencies of the studied Mytilus spp. populations. Changes along this environmental gradient were not gradual but instead a significant shift from gene dominance was found at a salinity of 12 PSU. Water temperature and the trophic status of the sea area had only moderate association with the gene frequencies. The obtained results showed that the novel machine learning technique can be successfully used for finding correlations between genetic differentiation of populations and environmental variables and for defining the functional form of these linkages.

Place, publisher, year, edition, pages
2019. Vol. 61, no 1, p. 131-142
Keywords [en]
Marine environment, Spatial distribution, Seascape genetics, Nuclear DNA markers EFbis, Glu-5 ', ITS, M7 and Single Nucleotide Polymorphism, Baltic Sea
National Category
Biological Sciences Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:su:diva-165784DOI: 10.1016/j.oceano.2018.08.002ISI: 000455022500012OAI: oai:DiVA.org:su-165784DiVA, id: diva2:1290305
Available from: 2019-02-20 Created: 2019-02-20 Last updated: 2019-02-20Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Kautsky, HansWenne, Roman
By organisation
Department of Ecology, Environment and Plant Sciences
In the same journal
Oceanologia
Biological SciencesEarth and Related Environmental Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 5 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf