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Support Vector Machine (SVM), Random Forest and Bayesian Additive Regression 
Trees (BART). Each model was trained to capture the environmental requirements 
of individual species, using the 10 year-average in the period 2005-2014 of 
the environmental variables: near-bed and sea surface temperature, near-bed 
and sea surface salinity, and their differences, sea surface chlorophyll 
concentrations, water depth and substrate properties. 

Once the models were trained, they were used to study future habitat 
suitability for the 19 vulnerable marine species selected for this piece of 
work, in 10-year averages to the end of century. To produce maps of the 
suitable habitat, the model projections were converted to binary 
presence/absence data using the thresholds that optimized the True Skill 
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to 1 ("1" denotes all ENMs agree an area is suitable, "0" denotes all ENMs 
agree an area is not suitable). The number of ENMs considered for each 
combination of species and emission scenario depends on model performance. A 
ENM model was considered if its Area Under the Curve (AUC) score of the 
Receiver Operator Characteristic (ROC) curve was at least 0.7. Model 
performance is listed in the file "Model performance.csv".

Model input data: Climate change projection data from Plymouth Marine 
Laboratory (PML) (as referenced in Marine Biology paper). Species presence 
records from online databases (Ocean Biodiversity Information System (OBIS), 
Global Biodiversity Information System (GBIF) (references provided in 
associated Marine Biology paper and model_input_data_read_me), and from 
additional data sources as described in the Marine Biology paper and 
model_input_data_read_me. Models were trained using the R software. 

Study region: northeast Atlantic shelf. Latitudes between -17° and 9.25°; 
longitudes between 44° and 65°.

These datasets were produced for the study in the article "Climate change 
resilience of vulnerable marine species in northwest Europe" in Marine 
Biology.</gco:CharacterString>
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