Species Distribution Modelling of Corals and Sponges from Research Vessel Survey Data in the Newfoundland and Labrador Region for Use in the Identification of Significant Benthic Areas

• Main Author: Guijarro-Sabaniel, J
• Other Authors: Javier Guijarro-Sabaniel, Guijarro-Sabaniel, Javier, Beazley, Lindsay, Kenchington, Ellen, Wareham-Hayes, Vonda, Gilkinson, Kent, Koen-Alonso, Mariano, Murillo-Perez, Javier
• Format: Dataset
• Language: unknown
• Published: Data Archiving and Networked Services (DANS) 2019
• Subjects: Interdisciplinary sciences; Benthic Ecology; Modelling; envir; geo; Newfoundland; Gam; Labrador region; northern shrimp
• Online Access: https://doi.org/10.17632/zwvv3xx3rc.1; https://doi.org/10.17632/ZWVV3XX3RC

We used a species distribution modelling approach called random forest (RF) to predict the probability of occurrence and biomass of sponges, sea pens, and large and small gorgonian corals across the entire spatial extent of Fisheries and Oceans, Canada's (DFO) Newfoundland and Labrador Region. A suite of 66 environmental variables from different data sources were used. Models utilized catch records from the DFO multispecies trawl survey, DFO/industry northern shrimp surveys, and Spanish trawl surveys. Most models had excellent predictive capacity with cross-validated Area Under the Receiver Operating Characteristic Curve (AUC) values ranging from 0.786 to 0.926. Areas of suitable habitat were identified for each taxon and were contrasted against their known distribution. Generalized additive models (GAMs) were developed to predict the biomass distribution of each taxonomic group and serve as a comparison to the RF models. The RF and GAM models provided similar results, although GAMs provided superior predictions of biomass along the slopes of Newfoundland and Labrador for some taxonomic groups. Aside from providing continuous prediction maps of significant benthic taxa for the entire Newfoundland and Labrador Region that will be useful in ecosystem management decision-making processes, these results 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.