We fit geostatistical models: generalized linear mixed-effect models (GLMMs) with spatial Gaussian random fields using sdmTMB in R. We modelled probability of detection using a Bernoulli model with a complementary log-log link. We sourced Dungeness crab observations from four different research surveys conducted by Fisheries and Oceans Canada and their partners: SCUBA dive multi-species surveys (dive), small-mesh trawl surveys targeting shrimp (trawl), baited-trap surveys targeting Dungeness crab (trap DC), and baited-trap surveys targeting _Carcinus maenas_, Green crab (trap GC). To model Dungeness crab distribution, we relied on environmental variables that were known or hypothesised to have an influence on the distribution of Dungeness crab: bottom depth (meters), slope (degrees), fine- and broad-scale bathymetric position index (BPI), mean bottom temperature (celcius), mean bottom salinity (Practical Salinity Unit, PSU), bottom salinity range (PSU), and substrate indices representing the amount of sandy and muddy substrate within an area.

Gear-effect and Survey-effect integrated models accounted for differences between surveys with a fixed effect for sampling gear (dive, trawl, trap) or survey (dive, trawl, trap GC, trap DC). We included the Survey-effect model to assess whether sampling protocol differences between trap GC and trap DC surveys were large enough to merit a unique fixed effect for each trap survey.

Detailed methods can be found in Nephin et al. (2023) and the code can be found at https://gitlab.com/dfo-msea/dungeness-sdm/.