Kernel Density Analyses of Coral and Sponge Catches from Research Vessel Survey Data for Use in Identification of Significant Benthic Areas

Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbour-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hots...

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Main Author: Lirette, Camille
Format: Dataset
Language:unknown
Published: Mendeley 2018
Subjects:
Online Access:https://dx.doi.org/10.17632/dtk86rjm86
https://data.mendeley.com/datasets/dtk86rjm86
id ftdatacite:10.17632/dtk86rjm86
record_format openpolar
spelling ftdatacite:10.17632/dtk86rjm86 2023-05-15T17:45:40+02:00 Kernel Density Analyses of Coral and Sponge Catches from Research Vessel Survey Data for Use in Identification of Significant Benthic Areas Lirette, Camille 2018 https://dx.doi.org/10.17632/dtk86rjm86 https://data.mendeley.com/datasets/dtk86rjm86 unknown Mendeley https://dx.doi.org/10.17632/dtk86rjm86.2 https://dx.doi.org/10.17632/dtk86rjm86.1 Creative Commons Attribution 4.0 International info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Benthic Ecology Kernel Density Estimation dataset Dataset 2018 ftdatacite https://doi.org/10.17632/dtk86rjm86 https://doi.org/10.17632/dtk86rjm86.2 https://doi.org/10.17632/dtk86rjm86.1 2021-11-05T12:55:41Z Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbour-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hotspots, that is, areas of relatively high biomass/abundance, and in 2010 was used by Fisheries and Oceans Canada to delineate significant concentrations of corals and sponges. The same approach has been used successfully in the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area. Here, we update the previous analyses with the catch records from up to 5 additional years of trawl survey data from Eastern Canada, including the Gulf of St. Lawrence. We applied kernel density estimation to create a modelled biomass surface for each of sponges, small and large gorgonian corals, and sea pens, and applied an aerial expansion method to identify significant concentrations of theses taxa. We compared our results to those obtained previously and provided maps of significant concentrations as well as point data co-ordinates for catches above the threshold values used to construct the significant area polygons. The borders of the polygons can be refined using knowledge of null catches and species distribution models of species presence/absence and/or biomass. Dataset Northwest Atlantic DataCite Metadata Store (German National Library of Science and Technology) Canada
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Benthic Ecology
Kernel Density Estimation
spellingShingle Benthic Ecology
Kernel Density Estimation
Lirette, Camille
Kernel Density Analyses of Coral and Sponge Catches from Research Vessel Survey Data for Use in Identification of Significant Benthic Areas
topic_facet Benthic Ecology
Kernel Density Estimation
description Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbour-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hotspots, that is, areas of relatively high biomass/abundance, and in 2010 was used by Fisheries and Oceans Canada to delineate significant concentrations of corals and sponges. The same approach has been used successfully in the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area. Here, we update the previous analyses with the catch records from up to 5 additional years of trawl survey data from Eastern Canada, including the Gulf of St. Lawrence. We applied kernel density estimation to create a modelled biomass surface for each of sponges, small and large gorgonian corals, and sea pens, and applied an aerial expansion method to identify significant concentrations of theses taxa. We compared our results to those obtained previously and provided maps of significant concentrations as well as point data co-ordinates for catches above the threshold values used to construct the significant area polygons. The borders of the polygons can be refined using knowledge of null catches and species distribution models of species presence/absence and/or biomass.
format Dataset
author Lirette, Camille
author_facet Lirette, Camille
author_sort Lirette, Camille
title Kernel Density Analyses of Coral and Sponge Catches from Research Vessel Survey Data for Use in Identification of Significant Benthic Areas
title_short Kernel Density Analyses of Coral and Sponge Catches from Research Vessel Survey Data for Use in Identification of Significant Benthic Areas
title_full Kernel Density Analyses of Coral and Sponge Catches from Research Vessel Survey Data for Use in Identification of Significant Benthic Areas
title_fullStr Kernel Density Analyses of Coral and Sponge Catches from Research Vessel Survey Data for Use in Identification of Significant Benthic Areas
title_full_unstemmed Kernel Density Analyses of Coral and Sponge Catches from Research Vessel Survey Data for Use in Identification of Significant Benthic Areas
title_sort kernel density analyses of coral and sponge catches from research vessel survey data for use in identification of significant benthic areas
publisher Mendeley
publishDate 2018
url https://dx.doi.org/10.17632/dtk86rjm86
https://data.mendeley.com/datasets/dtk86rjm86
geographic Canada
geographic_facet Canada
genre Northwest Atlantic
genre_facet Northwest Atlantic
op_relation https://dx.doi.org/10.17632/dtk86rjm86.2
https://dx.doi.org/10.17632/dtk86rjm86.1
op_rights Creative Commons Attribution 4.0 International
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
cc-by-4.0
op_rightsnorm CC-BY
op_doi https://doi.org/10.17632/dtk86rjm86
https://doi.org/10.17632/dtk86rjm86.2
https://doi.org/10.17632/dtk86rjm86.1
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