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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Bibliographic Details
Main Author: Lirette, Camille
Other Authors: Kenchington, Ellen, Murillo-Perez, Javier, Beazley, Lindsay, Guijarro-Sabaniel, Javier, Wareham, Vonda, Gilkinson, Kent, Koen-Alonso, Mariano, Benoit, Hugues, Bourdages, Hugo, Sainte-Marie, Bernard, Treble, Margaret, Siferd, Tim, Ellen Kenchington
Format: Dataset
Language:unknown
Published: Mendeley 2018
Subjects:
Benthic Ecology
Kernel Density Estimation
Interdisciplinary sciences
envir
geo
Canada
Northwest Atlantic
Online Access:https://doi.org/10.17632/DTK86RJM86.2
https://doi.org/10.17632/dtk86rjm86.1
https://doi.org/10.17632/dtk86rjm86
Description
Summary: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.

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