The hidden biodiversity knowledge split in biological collections

Note

This manuscript is submitted as a preprint here

Code and data can be downloaded from zenodo

General overview

This repository contains the data and code used in the analysis of the manuscript entitled “The hidden biodiversity knowledge split in biological collections”.

In this study we characterized different aspects of spatial and temporal patterns of fish Name Bearing Types (NBT) among countries and world regions. The characteristics comprises the number of total NBT, the NBT flowing among different world regions, the characteristics of regions and countries regarding the source of NBT in their biological collection, the level of underepresentation of native species and the level of overepresentation of non-native species for each country.

We discuss how the fundamental knowledge in fish species is distributed and its implications for science development and knowledge sharing.

Repository structure

data

This folder stores raw and processed data used to perform all the analysis presented in this study

raw

• flow_period_region_country.csv a data frame in the long format containing the flowing of NBT per regions per per time (50-year time frame). Variables:

  • period numeric variable representing 50-year time intervals

  • region_type character representing the name of the World Bank region of the country where the NBT was sourced

  • country_type character. A three letter code (alpha-3 ISO3166) representing the country of the museum where the NBT was sourced

  • region_museum character. Name of the World Bank region of the country where the NBT is housed

  • country_museum character. A three letter code (alpha-3 ISO3166) representing the country of the museum where the NBT is housed

  • n numeric. The number of NBT flowing from one country to another

• spp_native_distribution.csv data frame in the long format containing the native composition at the country level. Variables:

  • species character. The name of a species in the format genus_epithet according to the Catalog of Fishes (including synonym names)

  • country_distribution character. Three letter code (alpha-3 ISO3166) indicating the name of the country where a species is native to

  • region_distribution character. The name of the region acording with World Bank where a species is native to

• spp_type_distribution.csv data frame in the long format containing the composition of NBT by country. Variables:

  • species character. The name of a species in the format genus_epithet according to the Catalog of Fishes (including synonym names)

  • country_distribution character. Three letter code (alpha-3 ISO3166) indicating the name of the country where a species is housed

  • region_distribution character. The name of the region acording with World Bank where a species is housed

• bio-dem_data.csv data frame with data downloaded from Bio-Dem containing information on biological and social information at the country level. Variables:

  • country character. A three letter code (alpha-3 ISO3166) representing a country

  • records numeric. Total number of species occurrence records from Global Biodiverity Facility (GBIF)

  • records_per_area numeric. Records per area from gbif

  • yearsSinceIndependence numeric. Years since independence for each country

  • e_migdppc numeric. GDP per capta

• museum_data.csv data frame with museums’ acronyms and the world region of each. Variables:

  • code_museum character. Three letter code of the museum

  • country_museum character. A three letter code (alpha-3 ISO3166) representing a country

  • region_museum character. The name of the region acording with World Bank

processed

• flow_region.csv a data frame containing flowing of NBT among world regions and the total number of NBT derived from the source region

  • region_type character. The region, according to World bank classification, where the type was collected

  • region_museum character. The region, according to World bank classification, where the type is housed

  • n numeric. The number of types that flowed from region_type to region_museum

  • total_region_type numeric. The total number of name bearers sampled in each region

• flow_period_region.csv a data frame with the number of NBT between the world regions per 50-year time frame and the total number of NBT in each time frame for each world region

  • period numeric. The year in which the name bearer was discovered

  • region_type character. The region, according to World bank classification, where the type was collected

  • region_museum character. The region, according to World bank classification, where the type is housed

  • n numeric. The number of name bearers that flowed from region_type to region_museum

  • total_region_type numeric. The total number of name bearers sampled in each region in each time period

• flow_period_region_prop.csv a data frame with the number of NBT, the Domestic Contribution and Domestic Retention between the world regions in a 50-year time frame

  • period numeric. The year in which the name bearer was discovered

  • region_type character. The region, according to World bank classification, where the type was collected

  • region_museum character. The region, according to World bank classification, where the type is housed

  • n numeric. The number of name bearers that flowed from region_type to region_museum

  • total_period_region_type numeric. The number of name bearers samples in a region in a time period

  • total_period_region_museum numeric. The number of name bearers housed in biological collections in a region in a time period

  • total_period numeric. The total number of name bearers described in a period

  • prop_DC numeric. The proportion of name bearers in biological collection of a region sampled within the region in a time period. This variable is not used in the final version of the study.

  • prop_DR numeric. The proportion of all name bearers sampled that were retained in a region in a time period. This variable is not used in the final version of the study.

• flow_region_prop.csv data with the total number of species flowing between world regions, Domestic Contribution and Domestic Retention

  • region_type character. The region, according to World bank classification, where the type was collected

  • region_museum character. The region, according to World bank classification, where the type is housed

  • total_region_type numeric. The total number of name bearers sampled in a region

  • total_region_museum numeric. The total number of name bearers housed in biological collections in a region

  • prop_DC numeric. The proportion of name bearers in biological collection of a region sampled within the region. This variable is not used in the final version of the study

  • prop_DR numeric. The proportion of all name bearers sampled that were retained in a region. This variable is not used in the final version of the study

• flow_country.csv data frame with flowing information of NBT among countries

  • country_type character. A three letter code (alpha-3 ISO3166) representing a country where the name bearer was sampled

  • country_museum character. A three letter code (alpha-3 ISO3166) representing a country where the name bearer was housed

  • n numeric. The number of name bearers that flowed from country_type to country_museum

  • total_country_type numeric. The number of name bearers sampled in a country

• df_country_native.csv data frame with the number of native species at the country level

  • country_distribution character. A three letter code (alpha-3 ISO3166) representing a country

  • region_distribution character. The name of a region according to World Bank

  • native.richness numeric. The number of native species in a country according to Catalog of Fishes

• df_country_type.csv data frame with the number of NBT at the country level

  • country_museum character. A three letter code (alpha-3 ISO3166) representing a country

  • region_museum character. The name of a region according to World Bank

  • type_region numeric. The number of name bearers in biological collections in countries

• df_endemic_beta.csv data frame with values of endemic deficit and non-endemic representation at the country level using only species with restricted occurrences (only one occurrence per country) at the country level

  • native.beta numeric. The endemic deficit calculated as the number of endemic species outside of the country of origin

  • type.beta numeric. The number of non-endemic name bearers

• df_all_beta.csv data frame with values of endemic deficit and non-endemic representation at the country level. This is used in the analysis of Supplementary material, specifically to generate Figure S2.

  • countries character. A three letter code (alpha-3 ISO3166) representing a country

  • native.beta numeric. The endemic deficit calculated as the number of endemic species outside of the country of origin

  • type.beta numeric. The number of non-endemic name bearers

R

The letters D, A and V represents scripts for, respectively, data processing (D), data analysis (A) and results visualization (V). The script sequence to reproduce the workflow is indicated by the numbers at the beginning of the name of the script file.

• 01_D_data_preparation.qmd initial data preparation

• 02_A_beta-endemics-countries.qmd This script is used to calculate non endemic name bearers and endemic deficit that will be used in the script 07_V_beta_endemics_Fig2.qmd

• 03_D_data_preparation_models.qmd script used to build data frames that will be used in statistical models (04_A_model_NBTs.qmd)

• 04_A_model_NBTs.qmd statistical models for the total number of NBT, endemic deficit and non endemic name bearers

• 05_V_chord_diagram_Fig1.qmd code used to produce circular flow diagram. This is the Figure 1 of the study

• 06_V_world_map_Fig1.qmd code used to produce the world map in the Figure 1 of the main text

• 07_V_beta_endemics_Fig2.qmd code used to build Figure 2 of the main text

• 08_V_model_Fig3.qmd code used to build the Figure 3 of the main text. This is the representation of the results of the models present in the script 04_A_model_NBTs.qmd

• 09_Supplementary_analysis.qmd code to produce all the tables and figures presented in the Supplementary material of this study

functions

• function_beta_types_success_fail.R function used to calculate endemic deficit and non endemic name bearers.

• function_scale_back.R function used to transform back normalized variables

Summary statistics

• 011_Summary_stats.qmd code needed to reproduce summary statistics reported in the Results section of the main text of the study “The hidden biodiversity knowledge split in biological collections”

output

Figures

In this folder you will find all figures used in the main text and supplementary material of this study

• Fig1_flow_circle_plot.png Figure with circular plots showing the flux of NBT among regions of the world in a 50-year time window

• Fig2_turnover_metrics_endemics.png Cartogram with 3 maps showing the level of endemic deficit, non endemic name bearers and the combination of both metrics biscale map. This is the Figure 2 in the main text

Fig3_models.png Figure showing the predictions of the number of name bearers, endemic deficit and non endemic name bearers for different predictors. Corresponds to Figure 3 This is derived from the statistical models scripts

Supp-material

This folder contains the figures in the Supplementary material

• FigS1_native_richness.png World map with countries colored according to the number of native species richness according to the Catalog of Fishes. This corresponds to Figure S1 in Supplementary material

• FigS3_turnover_metrics.png Cartogram with 3 maps showing the level of native turnover, NBT turnover and the combination of both metrics in a combined map. This corresponds to Figure S2 in Supplementary material

Packages

Package	Version	Documentation
bbmle	1.0.25.1	bbmle
betapart	1.6	betapart
biscale	1.0.0	biscale
circlize	0.4.15	circlize
countrycode	1.6.0	countrycode
cowplot	1.1.1	cowplot
DHARMa	0.4.6	DHARMa
dplyr	1.1.4	dplyr
ggarrow	0.0.0.9000	ggarrow
ggplot2	3.5.0	ggplot2
glmmTMB	1.1.8	glmmTMB
glue	1.6.2	glue
here	1.0.1	here
patchwork	1.2.0.9000	patchwork
performance	0.12.1	performance
phyloregion	1.0.8	phyloregion
readr	2.1.4	readr
rmapshaper	0.5.0	rmapshaper
rnaturalearth	0.3.4	rnaturalearth
scales	1.4	scales
sf	1.0-14	sf
tidyr	1.3.1	tidyr

Contact

Gabriel Nakamura and Bruno Mioto

If you have any suggestion or commentary, please open an issue

Source code

Session Info

sessionInfo()
# R version 4.3.1 (2023-06-16 ucrt)
# Platform: x86_64-w64-mingw32/x64 (64-bit)
# Running under: Windows 11 x64 (build 26100)
# 
# Matrix products: default
# 
# 
# locale:
# [1] LC_COLLATE=Portuguese_Brazil.utf8  LC_CTYPE=Portuguese_Brazil.utf8   
# [3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C                      
# [5] LC_TIME=Portuguese_Brazil.utf8    
# 
# time zone: America/Sao_Paulo
# tzcode source: internal
# 
# attached base packages:
# [1] stats4    stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
#  [1] biscale_1.0.0       cowplot_1.1.3       rmapshaper_0.5.0    sf_1.0-14          
#  [5] rnaturalearth_1.0.1 DHARMa_0.4.7        bbmle_1.0.25.1      performance_0.15.0 
#  [9] glmmTMB_1.1.11      countrycode_1.6.1   tidyr_1.3.0         patchwork_1.2.0    
# [13] ggplot2_3.5.0.9000  scales_1.4.0        dplyr_1.1.3         tibble_3.2.1       
# [17] ggeffects_2.3.0     here_1.0.1          readr_2.1.4        
# 
# loaded via a namespace (and not attached):
#   [1] RColorBrewer_1.1-3  rstudioapi_0.15.0   jsonlite_1.9.0      datawizard_1.1.0   
#   [5] magrittr_2.0.3      farver_2.1.1        nloptr_2.0.3        ragg_1.2.6         
#   [9] vctrs_0.6.4         minqa_1.2.6         terra_1.7-55        forcats_1.0.0      
#  [13] htmltools_0.5.7     itertools_0.1-3     clustMixType_0.4-2  haven_2.5.3        
#  [17] curl_5.1.0          betapart_1.6        KernSmooth_2.23-21  plyr_1.8.9         
#  [21] TMB_1.9.17          igraph_2.0.2        mime_0.12           lifecycle_1.0.4    
#  [25] minpack.lm_1.2-4    iterators_1.0.14    pkgconfig_2.0.3     sjlabelled_1.2.0   
#  [29] phyloregion_1.0.8   gap_1.6             Matrix_1.6-1.1      R6_2.5.1           
#  [33] fastmap_1.1.1       snakecase_0.11.1    rbibutils_2.3       shiny_1.8.0        
#  [37] magic_1.6-1         digest_0.6.33       numDeriv_2016.8-1.1 colorspace_2.1-0   
#  [41] rprojroot_2.0.4     textshaping_0.3.7   qgam_2.0.0          vegan_2.7-1        
#  [45] labeling_0.4.3      fansi_1.0.5         httr_1.4.7          abind_1.4-5        
#  [49] mgcv_1.9-3          compiler_4.3.1      proxy_0.4-27        bit64_4.0.5        
#  [53] withr_2.5.2         doParallel_1.0.17   DBI_1.1.3           MASS_7.3-60        
#  [57] classInt_0.4-10     permute_0.9-8       units_0.8-4         tools_4.3.1        
#  [61] ape_5.8-1           httpuv_1.6.13       glue_1.6.2          quadprog_1.5-8     
#  [65] rcdd_1.6            nlme_3.1-162        promises_1.2.1      grid_4.3.1         
#  [69] cluster_2.1.4       generics_0.1.3      snow_0.4-4          predicts_0.1-19    
#  [73] gtable_0.3.4        tzdb_0.4.0          class_7.3-22        hms_1.1.3          
#  [77] sp_2.1-3            utf8_1.2.4          foreach_1.5.2       pillar_1.9.0       
#  [81] vroom_1.6.4         later_1.3.1         splines_4.3.1       lattice_0.21-8     
#  [85] bit_4.0.5           tidyselect_1.2.0    knitr_1.45          reformulas_0.4.1   
#  [89] V8_6.0.4            xfun_0.41           smoothr_1.1.0       geojsonsf_2.0.3    
#  [93] boot_1.3-28.1       codetools_0.2-19    maptpx_1.9-7        cli_3.6.1          
#  [97] xtable_1.8-4        geometry_0.5.2      systemfonts_1.2.1   Rdpack_2.6.4       
# [101] Rcpp_1.0.11         doSNOW_1.0.20       bdsmatrix_1.3-7     parallel_4.3.1     
# [105] picante_1.8.2       ellipsis_0.3.2      gap.datasets_0.0.6  lme4_1.1-35.1      
# [109] phangorn_2.12.1     mvtnorm_1.2-3       slam_0.1-50         e1071_1.7-13       
# [113] insight_1.3.1       purrr_1.0.2         crayon_1.5.2        combinat_0.0-8     
# [117] rlang_1.1.2         fastmatch_1.1-6