Species Distribution Modelling of Corals and Sponges in the Eastern Arctic for Use in the Identification of Significant Benthic Areas
Species distribution modelling using a random forest (RF) machine learning approach was used to predict the probability of occurrence and biomass of sponges, sea pens, and large and small gorgonian corals in the Hudson Strait portion of Fisheries and Oceans, Canada's (DFO) Hudson Bay Complex Bi...
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2019
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Online Access: | https://doi.org/10.17632/mcb726kcbx.1 https://doi.org/10.17632/MCB726KCBX |
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fttriple:oai:gotriple.eu:50|dedup_wf_001::8763ebd3f6631afa0b9110bf03d6c697 2023-05-15T14:56:51+02:00 Species Distribution Modelling of Corals and Sponges in the Eastern Arctic for Use in the Identification of Significant Benthic Areas Lirette, Camille Beazley, Lindsay Murillo, Javier Kenchington, Ellen Guijarro-Sabaniel, Javier Siferd, Tim Treble, Margaret Baker, Emily Bouchard Marmen, Marieve Tompkins-MacDonald, Gabrielle Lindsay Beazley 2019-01-21 https://doi.org/10.17632/mcb726kcbx.1 https://doi.org/10.17632/MCB726KCBX undefined unknown Mendeley http://dx.doi.org/10.17632/mcb726kcbx.1 https://dx.doi.org/10.17632/mcb726kcbx.1 http://dx.doi.org/10.17632/MCB726KCBX https://dx.doi.org/10.17632/mcb726kcbx lic_creative-commons 10.17632/mcb726kcbx.1 oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:115936 oai:oai.datacite.org:18054945 oai:easy.dans.knaw.nl:easy-dataset:115936 doi:10.17632/mcb726kcbx doi:10.17632/mcb726kcbx.1 10.17632/mcb726kcbx oai:oai.datacite.org:18054944 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|re3data_____::db814dc656a911b556dba42a331cebe9 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 Benthic Ecology Modelling Interdisciplinary sciences envir geo Dataset https://vocabularies.coar-repositories.org/resource_types/c_ddb1/ 2019 fttriple https://doi.org/10.17632/mcb726kcbx.1 https://doi.org/10.17632/MCB726KCBX https://doi.org/10.17632/mcb726kcbx 2023-01-22T16:52:25Z Species distribution modelling using a random forest (RF) machine learning approach was used to predict the probability of occurrence and biomass of sponges, sea pens, and large and small gorgonian corals in the Hudson Strait portion of Fisheries and Oceans, Canada's (DFO) Hudson Bay Complex Biogeographic Zone (sponges only), and in the eastern extent (Davis Strait and Southern Baffin Bay) of the Eastern Arctic Biogeographic Zone. A suite of 54 environmental predictor variables from different data sources were used. Models utilized catch records from the DFO multispecies trawl surveys and DFO/industry northern shrimp surveys collected between 2006 and 2014. For each taxonomic group in each region, both presence-absence random forest models using data collected across gear types (Alfredo, Campelen, and Cosmos trawls), and biomass random forest models using data collected within gear types were run. Most presence-absence models had good predictive capacity with cross-validated Area Under the Receiver Operating Characteristic Curve (AUC) values ranging from 0.643 to 0.894. The lower AUC was produced from the Hudson Strait sponge model, which also had poor sensitivity and specificity relative to the models performed in the Eastern Arctic Biogeographic Zone. The random forest biomass models performed inconsistently within taxa by gear type, with the models for sponges using data from Alfredo and Campelen trawl surveys perfoming best (R2 = 0.327 and 0.480 respectively). Generalized additive models (GAMs) were developed to predict the biomass distribution of each taxonomic group and serve as a comparison to the RF models. Aside from providing continuous prediction maps of significant benthic taxa for these regions, our results will be useful in ecosystem management decision-making processes. In particular, good SDM models could be used to refine the outer boundaries of significant concentrations of these organisms identified by kernel density analyses and identify new suitable habitat not sampled by the trawl surveys ... Dataset Arctic Baffin Bay Baffin Bay Baffin Davis Strait Hudson Bay Hudson Strait northern shrimp Unknown Arctic Baffin Bay Hudson Hudson Bay Hudson Strait ENVELOPE(-70.000,-70.000,62.000,62.000) |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
unknown |
topic |
Benthic Ecology Modelling Interdisciplinary sciences envir geo |
spellingShingle |
Benthic Ecology Modelling Interdisciplinary sciences envir geo Lirette, Camille Species Distribution Modelling of Corals and Sponges in the Eastern Arctic for Use in the Identification of Significant Benthic Areas |
topic_facet |
Benthic Ecology Modelling Interdisciplinary sciences envir geo |
description |
Species distribution modelling using a random forest (RF) machine learning approach was used to predict the probability of occurrence and biomass of sponges, sea pens, and large and small gorgonian corals in the Hudson Strait portion of Fisheries and Oceans, Canada's (DFO) Hudson Bay Complex Biogeographic Zone (sponges only), and in the eastern extent (Davis Strait and Southern Baffin Bay) of the Eastern Arctic Biogeographic Zone. A suite of 54 environmental predictor variables from different data sources were used. Models utilized catch records from the DFO multispecies trawl surveys and DFO/industry northern shrimp surveys collected between 2006 and 2014. For each taxonomic group in each region, both presence-absence random forest models using data collected across gear types (Alfredo, Campelen, and Cosmos trawls), and biomass random forest models using data collected within gear types were run. Most presence-absence models had good predictive capacity with cross-validated Area Under the Receiver Operating Characteristic Curve (AUC) values ranging from 0.643 to 0.894. The lower AUC was produced from the Hudson Strait sponge model, which also had poor sensitivity and specificity relative to the models performed in the Eastern Arctic Biogeographic Zone. The random forest biomass models performed inconsistently within taxa by gear type, with the models for sponges using data from Alfredo and Campelen trawl surveys perfoming best (R2 = 0.327 and 0.480 respectively). Generalized additive models (GAMs) were developed to predict the biomass distribution of each taxonomic group and serve as a comparison to the RF models. Aside from providing continuous prediction maps of significant benthic taxa for these regions, our results will be useful in ecosystem management decision-making processes. In particular, good SDM models could be used to refine the outer boundaries of significant concentrations of these organisms identified by kernel density analyses and identify new suitable habitat not sampled by the trawl surveys ... |
author2 |
Beazley, Lindsay Murillo, Javier Kenchington, Ellen Guijarro-Sabaniel, Javier Siferd, Tim Treble, Margaret Baker, Emily Bouchard Marmen, Marieve Tompkins-MacDonald, Gabrielle Lindsay Beazley |
format |
Dataset |
author |
Lirette, Camille |
author_facet |
Lirette, Camille |
author_sort |
Lirette, Camille |
title |
Species Distribution Modelling of Corals and Sponges in the Eastern Arctic for Use in the Identification of Significant Benthic Areas |
title_short |
Species Distribution Modelling of Corals and Sponges in the Eastern Arctic for Use in the Identification of Significant Benthic Areas |
title_full |
Species Distribution Modelling of Corals and Sponges in the Eastern Arctic for Use in the Identification of Significant Benthic Areas |
title_fullStr |
Species Distribution Modelling of Corals and Sponges in the Eastern Arctic for Use in the Identification of Significant Benthic Areas |
title_full_unstemmed |
Species Distribution Modelling of Corals and Sponges in the Eastern Arctic for Use in the Identification of Significant Benthic Areas |
title_sort |
species distribution modelling of corals and sponges in the eastern arctic for use in the identification of significant benthic areas |
publisher |
Mendeley |
publishDate |
2019 |
url |
https://doi.org/10.17632/mcb726kcbx.1 https://doi.org/10.17632/MCB726KCBX |
long_lat |
ENVELOPE(-70.000,-70.000,62.000,62.000) |
geographic |
Arctic Baffin Bay Hudson Hudson Bay Hudson Strait |
geographic_facet |
Arctic Baffin Bay Hudson Hudson Bay Hudson Strait |
genre |
Arctic Baffin Bay Baffin Bay Baffin Davis Strait Hudson Bay Hudson Strait northern shrimp |
genre_facet |
Arctic Baffin Bay Baffin Bay Baffin Davis Strait Hudson Bay Hudson Strait northern shrimp |
op_source |
10.17632/mcb726kcbx.1 oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:115936 oai:oai.datacite.org:18054945 oai:easy.dans.knaw.nl:easy-dataset:115936 doi:10.17632/mcb726kcbx doi:10.17632/mcb726kcbx.1 10.17632/mcb726kcbx oai:oai.datacite.org:18054944 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|re3data_____::db814dc656a911b556dba42a331cebe9 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 |
op_relation |
http://dx.doi.org/10.17632/mcb726kcbx.1 https://dx.doi.org/10.17632/mcb726kcbx.1 http://dx.doi.org/10.17632/MCB726KCBX https://dx.doi.org/10.17632/mcb726kcbx |
op_rights |
lic_creative-commons |
op_doi |
https://doi.org/10.17632/mcb726kcbx.1 https://doi.org/10.17632/MCB726KCBX https://doi.org/10.17632/mcb726kcbx |
_version_ |
1766328909336412160 |