DOI: 10.5061/dryad.0zpc8678d
title: Data and R code for: Biogeography and conservation of bycatch decapods

description: Aim To investigate the spatial patterns of species richness (SR),
 phylogenetic diversity (PD), and phylogenetic endemism (PE) in bycatch
 decapod crustaceans along the Brazilian Exclusive Economic Zone and to
 assess the influence of environmental variables and trawling effort on
 these diversity indices. Location Brazilian Exclusive Economic Zone. Taxa
 Decapoda Results Decapod diversity showed strong spatial heterogeneity
 across the EEZ. SR peaked in southeastern Brazil in upwelling-influenced
 ecotones, while PD and PE were highest in offshore and equatorial regions.
 PD was strongly associated with bottom temperature, primary productivity
 (PP) and current velocity, SR was mainly driven by salinity, light
 availability, and PP, and PE was best explained by temperature and PP.
 Most hotspots of phylogenetic diversity and endemism fell outside Marine
 Protected Areas (MPAs), revealing significant conservation gaps in
 offshore and southern regions. Main conclusions This study provides the
 first macroecological framework of decapod diversity in the Brazilian EEZ,
 revealing that temperature, salinity, productivity, and current dynamics
 differentially shape biodiversity metrics. Southeastern and offshore zones
 host unique evolutionary lineages but remain underrepresented in current
 MPA networks. By integrating phylogenetic and environmental data, our
 findings offer a baseline to improve spatial biodiversity assessments and
 support more representative conservation planning under shifting ocean
 conditions.
description: We compiled <em>bycatch</em> occurrence data from
 peer-reviewed studies and global biodiversity repositories. Diversity
 indices (SR, PD, PE, ED and WE) were estimated using a phylogenetic
 approach incorporating mitogenomic markers. Environmental and trawling
 effort data were extracted from global databases, and statistical
 analyses, including redundancy analysis (RDA), spatial regression and
 random forest models, were applied to determine the drivers of
 diversity.
description: # Dataset: Data, phylogenies, and R workflows for analyzing species
 richness, phylogenetic diversity, and conservation gaps of bycatch decapod
 crustaceans in the Brazilian Exclusive Economic Zone (EEZ) 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](https://doi.org/10.1111/jbi.70076) All
 files are described below to ensure independent interpretation and reuse
 without consulting the manuscript. --- ## 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. --- ### 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. ---
 ### 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. --- ### 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. --- ### 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. --- ###
 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 &lt;- st_read("eez.shp") ``` The EEZ layer is
 used exclusively to constrain analyses to Brazilian marine waters. --- ##
 R workflow description ### Editing_mitogenomic_tree.R Edits and prunes the
 mitogenomic phylogeny to match species occurrence data. --- ###
 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. --- ###
 Indices_correlations.R Generates correlation matrices among biodiversity
 indices. --- ### Analisys_Models_LMER__GAM_and_SPAMM.R Fits environmental
 models using: * Linear Mixed-Effects Models (LMER) * Generalized Additive
 Models (GAM) * Spatial mixed models (spaMM) --- ### RDA_models.R
 Redundancy Analysis (RDA) evaluating multivariate relationships between
 environmental predictors and biodiversity metrics. --- ###
 Spatial_random_forest.R Spatial Random Forest models (spatialRF)
 identifying key environmental drivers. --- ### Maps.R Produces spatial
 maps of biodiversity indices across the EEZ. --- ###
 MPAs_hotspot_and_coldspot.R Identifies biodiversity hotspots and coldspots
 and overlaps them with Marine Protected Areas to quantify conservation
 gaps. --- ## 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 --- ## External data
 sources * OBIS * Global Fishing Watch * GEOMAR * Bio-ORACLE * GenBank *
 Protected Planet Mitochondrial sequences used for phylogenetic
 reconstruction were retrieved from GenBank. --- ## 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](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