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Here is a series of scripts to extract enviromental variables, select depth layers from 4D netCDFs, and fit BRT models

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davidruizgarci/Shark_predictAVM

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Shark_predictAVM

Here is a series of scripts to extract enviromental variables, select depth layers from 4D netCDFs, and fit BRT models. Cite using the following doi: DOI

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1_Preparing_dataframe: Data organising.

2_Obtaining_Enviro_data: Enables downloading data from CMEMS (future research - requiered update to Python) and ERA5 (Copernicus) and enables extracting the enviromental data to the surveyed points.

3_Subsetting_Checking_data: Conduct pre-fitting checks on data.

4_Fitting_brt: Enables fitting a brt model applied to predict at-vessel mortality (AVM) for demersal sharks using biological, enviromental and fishing operation drivers.

5_Plots: Create study map and raincloud plots as data summary.

Appendix 1: theoretical recommendations and training on BRT fitting

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Here is a series of scripts to extract enviromental variables, select depth layers from 4D netCDFs, and fit BRT models

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