This dataset contains two geotiff layers. The first layer (1) represents the coastwide distribution of Dungeness crab as predicted from a geostatistical model. The model predicts the mean coastwide probability of Dungeness crab detection using trap sampling gear. The second layer (2) represent the uncertainty in those predictions. Detailed descriptions of these data products can be found in Nephin et al. (2023) and the code used to produce them can be found at https://gitlab.com/dfo-msea/dungeness-sdm/.

The objectives of this work was to model the habitat of Dungeness crab (_Metacarcinus magister_), a data-limited coastal marine species, to evaluate the efficacy of data integration when making predictions to geographic areas larger than the area covered by any one data source. In British Columbia, Dungeness crab are sampled regionally and sporadically with a variety of sampling gears and survey protocols, making them an ideal case study to investigate whether the integration of disparate surveys can improve habitat predictions. To that aim, we assemble data from dive, trawl, and baited-trap surveys to generate six candidate generalized linear mixed-effect models with spatial random fields. This dataset contains the mean (1) and difference (2) between the Survey-effect and Gear-effect model predictions.

This dataset contains two geotiff layers. The first layer (1) represents the coastwide distribution of Dungeness crab as predicted from a geostatistical model. The model predicts the mean coastwide probability of Dungeness crab detection using trap sampling gear. The second layer (2) represent the uncertainty in those predictions. Detailed descriptions of these data products can be found in Nephin et al. (2023) and the code used to produce them can be found at https://gitlab.com/dfo-msea/dungeness-sdm/.

The objectives of this work was to model the habitat of Dungeness crab (_Metacarcinus magister_), a data-limited coastal marine species, to evaluate the efficacy of data integration when making predictions to geographic areas larger than the area covered by any one data source. In British Columbia, Dungeness crab are sampled regionally and sporadically with a variety of sampling gears and survey protocols, making them an ideal case study to investigate whether the integration of disparate surveys can improve habitat predictions. To that aim, we assemble data from dive, trawl, and baited-trap surveys to generate six candidate generalized linear mixed-effect models with spatial random fields. This dataset contains the mean (1) and difference (2) between the Survey-effect and Gear-effect model predictions.