Data from: Plant dispersal in the sub-Antarctic inferred from anisotropic genetic structure
Climatic conditions and landscape features often strongly affect species’ local distribution patterns, dispersal, reproduction and survival, and may therefore have considerable impacts on species' fine-scale spatial genetic structure (SGS). In this paper we demonstrate the efficacy of combining...
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ftdans:oai:easy.dans.knaw.nl:easy-dataset:82233 2023-07-02T03:29:52+02:00 Data from: Plant dispersal in the sub-Antarctic inferred from anisotropic genetic structure Born, Céline le Roux, Peter C. Spohr, Colin McGeoch, Melodie A. Van Vuuren, Bettine Jansen 2011-10-19T18:31:53.000+02:00 http://nbn-resolving.org/urn:nbn:nl:ui:13-2v-o6zn https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:82233 unknown doi:10.5061/dryad.4f1r5vg8/1 doi:10.1111/j.1365-294X.2011.05372.x PMID:22129220 http://nbn-resolving.org/urn:nbn:nl:ui:13-2v-o6zn doi:10.5061/dryad.4f1r5vg8 https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:82233 OPEN_ACCESS: The data are archived in Easy, they are accessible elsewhere through the DOI https://dans.knaw.nl/en/about/organisation-and-policy/legal-information/DANSLicence.pdf Life sciences medicine and health care 2011 ftdans https://doi.org/10.5061/dryad.4f1r5vg8/110.1111/j.1365-294X.2011.05372.x10.5061/dryad.4f1r5vg8 2023-06-13T13:04:37Z Climatic conditions and landscape features often strongly affect species’ local distribution patterns, dispersal, reproduction and survival, and may therefore have considerable impacts on species' fine-scale spatial genetic structure (SGS). In this paper we demonstrate the efficacy of combining fine-scale SGS analyses with isotropic and anisotropic spatial autocorrelation techniques to infer the impact of wind patterns on plant dispersal processes. We genotyped 1304 Azorella selago (Apiaceae) specimens, a wind-pollinated and wind-dispersed plant, from four populations distributed across sub-Antarctic Marion Island. SGS was variable with Sp values ranging from 0.001 to 0.014, suggesting notable variability in dispersal distance and wind velocities between sites. Nonetheless, the data supported previous hypotheses of a strong NW – SE gradient in wind strength across the island. Anisotropic autocorrelation analyses further suggested that dispersal is strongly directional, but varying between sites depending on the local prevailing winds. Despite the high frequency of gale-force winds on Marion Island, gene dispersal distance estimates (σ) were surprisingly low (< 10 m), most likely because of a low pollen dispersal efficiency. An SGS approach in association with isotropic and anisotropic analyses provides a powerful means to assess the relative influence of abiotic factors on dispersal, and allow inferences that would not be possible without this combined approach. Other/Unknown Material Antarc* Antarctic Marion Island Data Archiving and Networked Services (DANS): EASY (KNAW - Koninklijke Nederlandse Akademie van Wetenschappen) Antarctic |
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Open Polar |
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Data Archiving and Networked Services (DANS): EASY (KNAW - Koninklijke Nederlandse Akademie van Wetenschappen) |
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topic |
Life sciences medicine and health care |
spellingShingle |
Life sciences medicine and health care Born, Céline le Roux, Peter C. Spohr, Colin McGeoch, Melodie A. Van Vuuren, Bettine Jansen Data from: Plant dispersal in the sub-Antarctic inferred from anisotropic genetic structure |
topic_facet |
Life sciences medicine and health care |
description |
Climatic conditions and landscape features often strongly affect species’ local distribution patterns, dispersal, reproduction and survival, and may therefore have considerable impacts on species' fine-scale spatial genetic structure (SGS). In this paper we demonstrate the efficacy of combining fine-scale SGS analyses with isotropic and anisotropic spatial autocorrelation techniques to infer the impact of wind patterns on plant dispersal processes. We genotyped 1304 Azorella selago (Apiaceae) specimens, a wind-pollinated and wind-dispersed plant, from four populations distributed across sub-Antarctic Marion Island. SGS was variable with Sp values ranging from 0.001 to 0.014, suggesting notable variability in dispersal distance and wind velocities between sites. Nonetheless, the data supported previous hypotheses of a strong NW – SE gradient in wind strength across the island. Anisotropic autocorrelation analyses further suggested that dispersal is strongly directional, but varying between sites depending on the local prevailing winds. Despite the high frequency of gale-force winds on Marion Island, gene dispersal distance estimates (σ) were surprisingly low (< 10 m), most likely because of a low pollen dispersal efficiency. An SGS approach in association with isotropic and anisotropic analyses provides a powerful means to assess the relative influence of abiotic factors on dispersal, and allow inferences that would not be possible without this combined approach. |
author |
Born, Céline le Roux, Peter C. Spohr, Colin McGeoch, Melodie A. Van Vuuren, Bettine Jansen |
author_facet |
Born, Céline le Roux, Peter C. Spohr, Colin McGeoch, Melodie A. Van Vuuren, Bettine Jansen |
author_sort |
Born, Céline |
title |
Data from: Plant dispersal in the sub-Antarctic inferred from anisotropic genetic structure |
title_short |
Data from: Plant dispersal in the sub-Antarctic inferred from anisotropic genetic structure |
title_full |
Data from: Plant dispersal in the sub-Antarctic inferred from anisotropic genetic structure |
title_fullStr |
Data from: Plant dispersal in the sub-Antarctic inferred from anisotropic genetic structure |
title_full_unstemmed |
Data from: Plant dispersal in the sub-Antarctic inferred from anisotropic genetic structure |
title_sort |
data from: plant dispersal in the sub-antarctic inferred from anisotropic genetic structure |
publishDate |
2011 |
url |
http://nbn-resolving.org/urn:nbn:nl:ui:13-2v-o6zn https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:82233 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic Marion Island |
genre_facet |
Antarc* Antarctic Marion Island |
op_relation |
doi:10.5061/dryad.4f1r5vg8/1 doi:10.1111/j.1365-294X.2011.05372.x PMID:22129220 http://nbn-resolving.org/urn:nbn:nl:ui:13-2v-o6zn doi:10.5061/dryad.4f1r5vg8 https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:82233 |
op_rights |
OPEN_ACCESS: The data are archived in Easy, they are accessible elsewhere through the DOI https://dans.knaw.nl/en/about/organisation-and-policy/legal-information/DANSLicence.pdf |
op_doi |
https://doi.org/10.5061/dryad.4f1r5vg8/110.1111/j.1365-294X.2011.05372.x10.5061/dryad.4f1r5vg8 |
_version_ |
1770273095792197632 |