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Layer: Maritimes Large Gorgonians Presence Probability Model 3 (ID: 15)

Parent Layer: Maritimes Large Gorgonians

Name: Maritimes Large Gorgonians Presence Probability Model 3

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Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Species distribution modelling using a random forest (RF) machine learning approach was used to predict the probability of occurrence and biomass of sponges, sea pens, large and small gorgonians and Vazella pourtalesi in the Maritimes Region. A suite of 66 environmental predictor variables from different data sources were used. Species occurrence was predicted using all presence and absence data (unbalanced model), and a balanced species prevalence model (i.e. an equal number of presences and absences). Also, for such taxonomic groups whose distribution was felt was not fully sampled by the multispecies stock assessment surveys, or when the number of trawl records for a group was insufficient for producing accurate predictions of distribution, additional random forest models were run using trawl survey data augmented with data from other sources: 1) in situ benthic imagery observations from scientific surveys, 2) DFO scallop stock assessment surveys, and 3) commercial records from the Fisheries Observer Program (FOP). The model unbalanced presence and absence catch data from DFO multispecies trawl surveys and in addition with other sources was chosen for the sea pens, small and large gorgonian corals and Vazella pourtalesi as the better prediction surface and the model produced from the balanced data was chosen for sponges.</SPAN></P><P STYLE="margin:0 0 7 0;"><SPAN>Three measures of accuracy were used to assess model performance: sensitivity, specificity, and AUC, or Area Under the Receiver Operating Curve. The accuracy measures for the random forest model using all large gorgonian presence and absence data, in situ benthic imagery observations and a threshold equal to species prevalence (0.09) were AUC= 0.928, sensitivity= 0.833 and specificity=0.892; indicating excellent model performance.</SPAN></P></DIV></DIV></DIV>

Service Item Id: 76cdc1f191f14c268583cac76ff8e300

Copyright Text: Fisheries and Oceans Canada, Bedford Institute of Oceanography, P.O. Box 1006, Dartmouth, NS, Canada B2Y 4A2

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