Sponge Biomass Hotspots in the Gulf of St. Lawrence

Identification of significant sponge concentration areas in the Gulf of St. Lawrence biogeographic unit using Kernel Density Estimation and random forest species distribution models. The analysis is based on geo-referenced biomass data from research vessel trawl surveys, refined with knowledge of null catches. Results are available in SHP, ESRI REST, and CSV formats.

Use Cases

• Apply Kernel Density Estimation (KDE) on geo-referenced biomass data to model sponge distribution surfaces.
• Use random forest machine-learning technique on trawl survey data to produce predictive species distribution models.
• Refine the borders of significant concentration areas by integrating knowledge of null catches from survey data.
• Analyze sponge biomass hot spots to support the delineation of sensitive benthic marine ecosystems for conservation planning.

Strengths

• Analysis incorporates multiple data sources, including research vessel trawl surveys and other available data.
• Methodology combines Kernel Density Estimation (KDE) for biomass surfaces with random forest machine-learning for predictive modeling.
• Results are peer-reviewed and documented in a detailed 178-page DFO scientific advisory report from 2016.

Limitations

• Dataset is limited to the Gulf of St. Lawrence biogeographic unit; other east coast areas are not included.
• Analysis focuses only on sponges (Porifera) and sea pens (Pennatulacea) for this region, excluding coral taxa.
• Underlying sample size, row count, and specific column structure of the source data are not provided.

Provenance

Source

Fisheries and Oceans Canada.

Collection Method

Quantitative analysis of research vessel trawl survey data, supplemented with other sources, using KDE and random forest models.

Time Range

null

Freshness

Metadata was last updated on 2026-03-24, though the core analysis and report date from 2016.

Geography

Gulf of St. Lawrence biogeographic unit on the east coast of Canada.