Marine Environment Around Iceland: Hydrography, Sediments and First Predictive Models of Icelandic Deep-sea Sediment Characteristics
Abstract Sediment samples and hydrographic conditions were studied at 28 stations around Iceland. At these sites, Conductivity-Temperature-Depth (CTD) casts were coducted to collect hydrographic data and multicorer casts were conducted to collect data on sediment characteristics including grain size...
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Walter de Gruyter GmbH
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Online Access: | http://dx.doi.org/10.2478/popore-2014-0021 http://content.sciendo.com/view/journals/popore/35/2/article-p151.xml https://www.degruyter.com/view/j/popore.2014.35.issue-2/popore-2014-0021/popore-2014-0021.pdf |
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crdegruyter:10.2478/popore-2014-0021 2023-05-15T16:44:51+02:00 Marine Environment Around Iceland: Hydrography, Sediments and First Predictive Models of Icelandic Deep-sea Sediment Characteristics Ostmann, Alexandra Schnurr, Sarah Martínez Arbizu, Pedro 2014 http://dx.doi.org/10.2478/popore-2014-0021 http://content.sciendo.com/view/journals/popore/35/2/article-p151.xml https://www.degruyter.com/view/j/popore.2014.35.issue-2/popore-2014-0021/popore-2014-0021.pdf unknown Walter de Gruyter GmbH http://creativecommons.org/licenses/by-nc-nd/3.0/ CC-BY-NC-ND Polish Polar Research volume 35, issue 2, page 151-176 ISSN 2081-8262 Ecology Ecology, Evolution, Behavior and Systematics journal-article 2014 crdegruyter https://doi.org/10.2478/popore-2014-0021 2022-06-16T13:42:00Z Abstract Sediment samples and hydrographic conditions were studied at 28 stations around Iceland. At these sites, Conductivity-Temperature-Depth (CTD) casts were coducted to collect hydrographic data and multicorer casts were conducted to collect data on sediment characteristics including grain size distribution, carbon and nitrogen concentration, and chloroplastic pigment concentration. A total of 14 environmental predictors were used to model sediment characteristics around Iceland on regional scale. Two approaches were used: Multivariate Adaptation Regression Splines (MARS) and randomForest regression models. RandomForest outperformed MARS in predicting grain size distribution. MARS models had a greater tendency to over-and underpredict sediment values in areas outside the environmental envelope defined by the training dataset. We provide first GIS layers on sediment characteristics around Iceland, that can be used as predictors in future models. Although models performed well, more samples, especially from the shelf areas, will be needed to improve the models in future. Article in Journal/Newspaper Iceland Polar Research De Gruyter (via Crossref) Polish Polar Research 35 2 151 176 |
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Open Polar |
collection |
De Gruyter (via Crossref) |
op_collection_id |
crdegruyter |
language |
unknown |
topic |
Ecology Ecology, Evolution, Behavior and Systematics |
spellingShingle |
Ecology Ecology, Evolution, Behavior and Systematics Ostmann, Alexandra Schnurr, Sarah Martínez Arbizu, Pedro Marine Environment Around Iceland: Hydrography, Sediments and First Predictive Models of Icelandic Deep-sea Sediment Characteristics |
topic_facet |
Ecology Ecology, Evolution, Behavior and Systematics |
description |
Abstract Sediment samples and hydrographic conditions were studied at 28 stations around Iceland. At these sites, Conductivity-Temperature-Depth (CTD) casts were coducted to collect hydrographic data and multicorer casts were conducted to collect data on sediment characteristics including grain size distribution, carbon and nitrogen concentration, and chloroplastic pigment concentration. A total of 14 environmental predictors were used to model sediment characteristics around Iceland on regional scale. Two approaches were used: Multivariate Adaptation Regression Splines (MARS) and randomForest regression models. RandomForest outperformed MARS in predicting grain size distribution. MARS models had a greater tendency to over-and underpredict sediment values in areas outside the environmental envelope defined by the training dataset. We provide first GIS layers on sediment characteristics around Iceland, that can be used as predictors in future models. Although models performed well, more samples, especially from the shelf areas, will be needed to improve the models in future. |
format |
Article in Journal/Newspaper |
author |
Ostmann, Alexandra Schnurr, Sarah Martínez Arbizu, Pedro |
author_facet |
Ostmann, Alexandra Schnurr, Sarah Martínez Arbizu, Pedro |
author_sort |
Ostmann, Alexandra |
title |
Marine Environment Around Iceland: Hydrography, Sediments and First Predictive Models of Icelandic Deep-sea Sediment Characteristics |
title_short |
Marine Environment Around Iceland: Hydrography, Sediments and First Predictive Models of Icelandic Deep-sea Sediment Characteristics |
title_full |
Marine Environment Around Iceland: Hydrography, Sediments and First Predictive Models of Icelandic Deep-sea Sediment Characteristics |
title_fullStr |
Marine Environment Around Iceland: Hydrography, Sediments and First Predictive Models of Icelandic Deep-sea Sediment Characteristics |
title_full_unstemmed |
Marine Environment Around Iceland: Hydrography, Sediments and First Predictive Models of Icelandic Deep-sea Sediment Characteristics |
title_sort |
marine environment around iceland: hydrography, sediments and first predictive models of icelandic deep-sea sediment characteristics |
publisher |
Walter de Gruyter GmbH |
publishDate |
2014 |
url |
http://dx.doi.org/10.2478/popore-2014-0021 http://content.sciendo.com/view/journals/popore/35/2/article-p151.xml https://www.degruyter.com/view/j/popore.2014.35.issue-2/popore-2014-0021/popore-2014-0021.pdf |
genre |
Iceland Polar Research |
genre_facet |
Iceland Polar Research |
op_source |
Polish Polar Research volume 35, issue 2, page 151-176 ISSN 2081-8262 |
op_rights |
http://creativecommons.org/licenses/by-nc-nd/3.0/ |
op_rightsnorm |
CC-BY-NC-ND |
op_doi |
https://doi.org/10.2478/popore-2014-0021 |
container_title |
Polish Polar Research |
container_volume |
35 |
container_issue |
2 |
container_start_page |
151 |
op_container_end_page |
176 |
_version_ |
1766035101672538112 |