The macroecology of knowledge: Spatio-temporal patterns of name-bearing types in biodiversity science

Authors

Gabriel Nakamura

Lívia Estéfane F. Frateles

Bruno H. Mioto Stabile

Matheus Lima de Araujo

Emanuel Neuhaus

Manoela Maria Ferreira Marinho

Melina de Souza Leite

Aline Richter

Liuyong Ding

Tiago Magalhães da Silva Freitas

Bruno Eleres Soares

Weferson Júnio da Graça

José Alexandre Felizola Diniz-Filho

Published

July 24, 2024

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 macroecology of knowledge: Spatio-temporal patterns of name-bearing types in biodiversity science”.

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. The acronym (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
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
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
flow_region_prop.csv data with the total number of species flowing between world regions, Domestic Contribution and Domestic Retention
flow_country.csv data frame with flowing information of NBT among countries
df_country_native.csv data frame with the number of native species at the country level
df_country_type.csv data frame with the number of NBT at the country level
df_endemic_beta.csv data frame with values of native and NBT turnover at the country level using only species with restricted occurrences at the country level
df_all_beta.csv data frame with values of native and NBT turnover at the country level. This is used in the analysis of Supplementary material

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 analysis of beta diversity metrics. This script is used to calculate turnover NBT and native NBT
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, native and NBT turnover, Domestic Contribution and Domestic Retention
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_scatterplot_Fig2.qmd code used to reproduce the Figure 2 of the main text
08_V_beta_endemics_Fig3.qmd code used to build Figure 3 of the main text
09_V_model_Fig4.qmd code used to build the Figure 4 of the main text. This is the representation of the results of the models present in the script 04_A_model_NBTs.qmd
0010_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 turnover metrics.
function_scale_back.R function used to transform back normalized variables

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_DC_DR.png Scaterplot with World regions characterized by their Domestic Contribution and Domestic Retention values in a 50-year time frame

Fig3_turnover_metrics_endemics.png Cartogram with 3 maps showing the level of native endemic turnover, NBT turnover and the combination of both metrics in a combined map

Fig4_models.png Figure showing the predictions of the number of NBT, DC, DR, native turnover and NBT turnover for different predictors. This is derived from the statistical models

Supp-material

This folder contains the figures in the Supplementary material

FigS1_native_richness.png World map with countries coloured according to the number of native species richness according to the Catalog of Fishes
FigS2_scatterplot.png All-time Domestic contribution (DC) and Domestic retention (DR) for world regions
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

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	1.3@420*	scales
sf	1.0-14	sf
tidyr	1.3.1	tidyr

* Pull request 420 version

Contact

Gabriel Nakamura and Bruno Mioto

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

Source code

Session Info

sessionInfo()
#> R version 4.3.2 (2023-10-31 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19045)
#> 
#> 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: Etc/GMT+3
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] biscale_1.0.0        cowplot_1.1.1        patchwork_1.2.0.9000
#>  [4] ggarrow_0.0.0.9000   rmapshaper_0.5.0     sf_1.0-14           
#>  [7] rnaturalearth_0.3.4  circlize_0.4.15      DHARMa_0.4.6        
#> [10] bbmle_1.0.25.1       performance_0.12.1   glmmTMB_1.1.8       
#> [13] ggplot2_3.5.0        scales_1.3.0.9000    countrycode_1.6.0   
#> [16] phyloregion_1.0.8    readr_2.1.4          here_1.0.1          
#> [19] glue_1.6.2           tidyr_1.3.1          dplyr_1.1.4         
#> 
#> loaded via a namespace (and not attached):
#>  [1] DBI_1.1.3           phangorn_2.11.1     sandwich_3.0-2     
#>  [4] rlang_1.1.2         magrittr_2.0.3      multcomp_1.4-25    
#>  [7] predicts_0.1-11     e1071_1.7-13        compiler_4.3.2     
#> [10] mgcv_1.9-0          vctrs_0.6.4         quadprog_1.5-8     
#> [13] pkgconfig_2.0.3     shape_1.4.6         fastmap_1.1.1      
#> [16] clustMixType_0.3-9  utf8_1.2.4          rmarkdown_2.26     
#> [19] tzdb_0.4.0          nloptr_2.0.3        purrr_1.0.2        
#> [22] xfun_0.43           reprex_2.0.2        jsonlite_1.8.8     
#> [25] styler_1.10.2       terra_1.7-55        parallel_4.3.2     
#> [28] R6_2.5.1            RColorBrewer_1.1-3  boot_1.3-28.1      
#> [31] numDeriv_2016.8-1.1 estimability_1.4.1  Rcpp_1.0.11        
#> [34] knitr_1.46          zoo_1.8-12          R.utils_2.12.2     
#> [37] Matrix_1.6-2        splines_4.3.2       R.cache_0.16.0     
#> [40] igraph_2.0.3        tidyselect_1.2.1    rstudioapi_0.16.0  
#> [43] yaml_2.3.8          TMB_1.9.10          codetools_0.2-19   
#> [46] curl_5.1.0          lattice_0.22-5      tibble_3.2.1       
#> [49] withr_3.0.0         coda_0.19-4.1       evaluate_0.23      
#> [52] survival_3.5-7      units_0.8-4         proxy_0.4-27       
#> [55] pillar_1.9.0        KernSmooth_2.23-22  smoothr_1.0.1      
#> [58] insight_0.20.2      generics_0.1.3      rprojroot_2.0.4    
#> [61] sp_2.1-3            maptpx_1.9-7        hms_1.1.3          
#> [64] munsell_0.5.0       minqa_1.2.6         xtable_1.8-4       
#> [67] class_7.3-22        slam_0.1-50         emmeans_1.8.9      
#> [70] tools_4.3.2         lme4_1.1-35.1       fs_1.6.3           
#> [73] mvtnorm_1.2-3       fastmatch_1.1-4     grid_4.3.2         
#> [76] ape_5.8             bdsmatrix_1.3-6     colorspace_2.1-0   
#> [79] nlme_3.1-163        cli_3.6.1           fansi_1.0.6        
#> [82] V8_4.4.0            gtable_0.3.4        R.methodsS3_1.8.2  
#> [85] digest_0.6.33       classInt_0.4-10     TH.data_1.1-2      
#> [88] htmltools_0.5.8.1   R.oo_1.25.0         lifecycle_1.0.4    
#> [91] httr_1.4.7          GlobalOptions_0.1.2 MASS_7.3-60