This repository contains occurrence records, environmental predictors, phylogenetic trees, biodiversity indices, spatial layers, and R scripts used in the study:

“Decapod Biodiversity Hotspots and Environmental Drivers: A Macroecological Approach About Bycatch Species in Brazil”
Journal of Biogeography
https://doi.org/10.1111/jbi.70076

All files are described below to ensure independent interpretation and reuse without consulting the manuscript.

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File inventory and description

CCDB_OBIS_GeoMar_data.csv

This file contains raw occurrence records of decapod crustaceans compiled from multiple sources, including:

• Crustacean Collection Database (CCDB)
• Ocean Biodiversity Information System (OBIS)
• GEOMAR Helmholtz Centre for Ocean Research
• Peer-reviewed literature

Each row represents a single occurrence record.

Variables:

• sp – Species name (as originally reported in the source database or publication).
• x – Longitude (decimal degrees, WGS84).
• y – Latitude (decimal degrees, WGS84).

Coordinates were standardized to decimal degrees (WGS84). No additional environmental or sampling metadata are included in this file.

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clados_info.csv

This file links decapod crustacean species to higher taxonomic groupings used for phylogenetic tree visualization and downstream analyses.

Each row corresponds to a single species.

Variables:

• ID – Species identifier used in phylogenetic trees and analyses (formatted as Genus_species).
• family – Taxonomic family of each species.
• name_ok – Validated scientific name (Genus species).
• grupo – Higher-level taxonomic grouping (e.g., Achelata, Anomura) used for clade assignment and visualization.

Taxonomic names were standardized prior to analysis.

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Tree_genoma_OK.treefile

Maximum likelihood mitogenomic phylogeny generated with IQ-TREE 2 using mitochondrial sequences retrieved from GenBank.

This file contains the full phylogenetic tree prior to pruning.

Format: Newick.

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tree_pruned.treefile

Pruned phylogenetic tree including only species with spatial occurrence data.

This tree was used to calculate the following phylogenetic diversity metrics:

• PD – Phylogenetic Diversity (total branch length connecting species within a spatial unit).
• PE – Phylogenetic Endemism (phylogenetic diversity weighted by geographic range restriction).
• ED – Evolutionary Distinctiveness (amount of unique evolutionary history represented by each species).
• WE – Weighted Endemism (species endemism weighted by inverse range size).

Format: Newick.

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VariablesDataOK_pix1x1.csv

Environmental predictors and biodiversity indices aggregated to a spatial grid of 1° × 1° covering the Brazilian Exclusive Economic Zone (EEZ).

Each row represents one grid cell.

Geographic coordinates

• Longitude – Longitude of grid cell centroid (decimal degrees)
• Latitude – Latitude of grid cell centroid (decimal degrees)

Environmental variables

All environmental variables were spatially summarized per grid cell from global marine datasets.

• ph – Seawater pH
• chlomean – Mean chlorophyll-a concentration
• chloRange – Chlorophyll-a range
• chloSS – Chlorophyll-a seasonal variability
• curvel – Mean ocean current velocity
• O2 – Dissolved oxygen concentration
• O2range – Dissolved oxygen range
• O2Lmax – Maximum dissolved oxygen
• nit – Nitrate concentration
• phosp – Phosphate concentration
• sal – Mean salinity (PSU)
• salrange – Salinity range
• salLmax – Maximum salinity
• tempmean – Mean bottom temperature (°C)
• temprange – Temperature range
• tempSS – Temperature seasonal variability
• tempLmax – Maximum temperature
• bathym – Bathymetry (depth, meters)
• iron – Dissolved iron concentration
• sil – Silicate concentration
• pp – Primary productivity
• ppSS – Primary productivity seasonal variability
• pprange – Primary productivity range
• light – Surface light availability
• carbophyto – Carbon biomass of phytoplankton
• carbophytoLmax – Maximum carbon phytoplankton biomass
• carbophytorange – Range of carbon phytoplankton biomass
• carbophytoSS – Seasonal variability of carbon phytoplankton biomass
• calcite – Calcite concentration
• SalSS – Salinity seasonal variability

Environmental layers were primarily obtained from Bio-ORACLE and related global marine products.

Fishing pressure

• fishing_effort – Fishing trawling effort derived from Global Fishing Watch

Biodiversity metrics

• SR – Species Richness (number of species per cell)
• PD – Faith’s Phylogenetic Diversity
• WE – Weighted Endemism
• PE – Phylogenetic Endemism
• ED – Evolutionary Distinctiveness

Standardized effect sizes:

• PD.SES – Standardized phylogenetic diversity
• WE.SES – Standardized weighted endemism
• PE.SES – Standardized phylogenetic endemism
• ED.SES – Standardized evolutionary distinctiveness

Missing values:

Cells filled with “n/a” indicate unavailable environmental or biodiversity values due to lack of coverage in the original raster layers or absence of biological records after spatial aggregation.

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eez.shp and eez.shx

Shapefile representing the Brazilian Exclusive Economic Zone (EEZ), used as spatial mask and reference area for all analyses.

The EEZ spatial layer is provided as a shapefile and requires companion files to open correctly:

• eez.shp – geometry
• eez.shx – shape index

These files must be kept in the same folder for proper visualization and use.

Coordinate reference system: WGS84 (EPSG:4326)

Geometry type: polygon

Usage notes:
This shapefile can be opened using QGIS, ArcGIS, or R packages such as sf or terra.

Example in R:

library(sf)
eez <- st_read("eez.shp")

The EEZ layer is used exclusively to constrain analyses to Brazilian marine waters.

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R workflow description

Editing_mitogenomic_tree.R

Edits and prunes the mitogenomic phylogeny to match species occurrence data.

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Get_merge_predictors_and_calculate_PD__PE__SR.R

Calculates SR, PD, PE, ED, and WE using:

• picante
• phylobase
• phyloraster

Environmental predictors are merged with biodiversity metrics at 1° resolution.

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Indices_correlations.R

Generates correlation matrices among biodiversity indices.

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Analisys_Models_LMER__GAM_and_SPAMM.R

Fits environmental models using:

• Linear Mixed-Effects Models (LMER)
• Generalized Additive Models (GAM)
• Spatial mixed models (spaMM)

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RDA_models.R

Redundancy Analysis (RDA) evaluating multivariate relationships between environmental predictors and biodiversity metrics.

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Spatial_random_forest.R

Spatial Random Forest models (spatialRF) identifying key environmental drivers.

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Maps.R

Produces spatial maps of biodiversity indices across the EEZ.

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MPAs_hotspot_and_coldspot.R

Identifies biodiversity hotspots and coldspots and overlaps them with Marine Protected Areas to quantify conservation gaps.

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Software requirements

Core software

• R ≥ 4.4
• IQ-TREE 2
• Geneious Prime

Main R packages

tidyverse, dplyr, readr, CoordinateCleaner
sf, raster, terra
picante, ape, phylobase, phyloraster, LetsR, SESraster
sdmpredictors, robis
vegan, adespatial, spaMM
randomForest, spatialRF
ggplot2, ggpubr, gridExtra

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External data sources

• OBIS
• Global Fishing Watch
• GEOMAR
• Bio-ORACLE
• GenBank
• Protected Planet

Mitochondrial sequences used for phylogenetic reconstruction were retrieved from GenBank.

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Citation

If using this dataset, please cite:

Teles, J. N. & Mantelatto, F. L. (2025). Decapod Biodiversity Hotspots and Environmental Drivers: A Macroecological Approach About Bycatch Species in Brazil. Journal of Biogeography.
https://doi.org/10.1111/jbi.70076

For the dataset:

Teles, J. N. (2025). Macroecology of bycatch decapods in the Brazilian EEZ: data and R workflows. Dryad Digital Repository. DOI: 10.5061/dryad.0zpc8678d