Trait model for Mediterranean fish invasions
Year: 2025
Type: article
Abstract: This repository contains the data and code used for the study on predicting potential marine fish invaders in the Mediterranean Sea using ecological, morphological, and anthropogenic traits. ## 📁 Contents ### 01_Traits_extractions.R# Description:# 1) Load species list with invasion status# 2) Retrieve trait data from FishBase, FAO, and IUCN# 3) Clean and merge all trait sources# 4) Filter out variables with ≥10% missing data# 5) Save the final trait dataset# 6) Report missingness for invasive species (informative)# 7) Generate and save Figure 1: trait distributions ### 02_Imputation_traits.R`Description:# 1) Load processed trait dataset# 2) Drop non-informative metadata columns# 3) Filter out species with >20% missing data# 4) Prepare predictor matrix:# - numeric vars (DepthMax/Min log-transformed)# - dummy-encoded factors# 5) Impute missing values via Random Forest (missForest)# 6) Compute imputation diagnostics (OOB‐error, % missing) and plot Figure 2# 7) Derive composite fishing‐exposure flags# 8) Subset to modeling traits only (drop unused columns)# 9) Save final imputed dataset for downstream analysis ### 03_Correlation.R`Description:# 1) Load final imputed & trimmed dataset# 2) Split into numeric vs factor# 3) Chi-square tests for categorical predictors vs Invasive# 4) Pearson correlation heatmap among numeric traits# 5) Identify highly correlated pairs (|r|>0.7)# 6) Univariate t-tests of numeric traits vs Invasive# 7) Plot and save:# • Figure 3: correlation heatmap# • Figure 4: –log10(p-values) barplot for key predictors# 8) Save high-correlation table ### 04. Modelling.R# Description:# 1) Load required libraries# 2) Load final dataset & recode Invasive# 3) (Fishing_AnyGear already present) Factorize fishing flags# 4) Scale continuous predictors# 5) Define final candidate set (5 traits)# 6) Random‐sampling model selection (60 formulas)# 7) Fit final INLA model# 8) Bootstrap coefficient stability (n = 50)# 9) Plot & save bootstrap results (Figure 5)# 10) (Optional) Predictive threshold evaluation & plotting --- ## Dependencies The R scripts require the following packages:- `tidyverse`- `missForest`- `INLA`- `caret`- `ggplot2`- `cowplot`- `patchwork` R version ≥ 4.1 is recommended. --- The main dataset (Complete_Med_list.csv) includes trait and invasion status data for 833 Mediterranean species. ## 📌 Notes - All code has been anonymized and stripped of author metadata for review purposes.- This dataset excludes any personal identifiers and is designed for reproducibility. --- ## 📬 Contact This repository was prepared for anonymous peer review. Questions will be addressed upon manuscript acceptance.
Author: Anonymous for reviewers
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: green
OpenAlex ID: W6893372894
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