Dataset for paper "Machine learning reveals key drivers of at-vessel mortality in demersal sharks"

The present database was used to fit the boosted regression trees models presented in this scientific article. 

 

 

### This database contains information on:

 

 

#### (1) The relative identification of each individual studied, including:

 

Database code   Full name Units

 

scientificName Scientific name of the study species Scyliorhinus canicula, Galeus melastomus

 

spCode Code given to name each species  Scyliorhinus canicula == Scanicula, Galeus melastomus == Gmelastomus

 

organismID identification number given to each specimen ranging from 1 to 3079 

 

towN identification number given to each tow analysed ranging from 1 to 66

 

vessel identification number given to each trawler collaborating in the study ranging from 1 to 8

 

date date when the tow occurred ranging from 02-12-2020 to 15-06-2022

 

Vessel name and fishing location was omitted as observation campaigns were conducted on board commercial trawlers and such information is confidential.

 

 

 

#### (2) Survival stage of the specimen at the time when sharks were released back to sea:

 

Database code   Full name Units

 

mortality Mortality stage of the specimen 0 == alive,  1 == dead

 

 

 

#### (3) Biological, environmental and fishing operation predictors considered into the modelling approach.

 

Database code   Full name Units

 

TL Body size centimeters

 

MAT Maturity 0 == immature , 1 == mature

 

SEX Sex 0 == male, 1 == female

 

DEPTH Tow depth meters

 

DUR Effective towing duration hours

 

SPEED Towing speed knots

 

TOWMASS Total catch biomass in the tow cod-end kilograms

 

DECKTIME Time exposed on deck minutes

 

CLOUD Cloud coverage %

 

SEASTATE Sea state Douglas scale (0 to 9)

 

WIND Wind force Beaufort scale (0 to 12)

 

ATEMP Atmospheric temperature ºC

 

DTEMP Change from atmospheric to sea bottom temperature ºC