DOI: 10.5061/dryad.2v6wwpzxh
title: Data from: A latitudinal gradient of reference genomes

description: Global inequality rooted in legacies of colonialism and uneven development
 can lead to systematic biases in scientific knowledge. In ecology and
 evolutionary biology, findings, funding, and research effort are
 disproportionately concentrated at high latitudes, while biological
 diversity is concentrated at low latitudes. This discrepancy may have a
 particular influence in fields like phylogeography, molecular ecology, and
 conservation genetics, where the rise of genomics has increased the cost
 and technical expertise required to apply state-of-the-art methods. Here,
 we ask whether a fundamental biogeographic pattern – the latitudinal
 gradient of species richness in tetrapods – is reflected in
 available reference genomes, an important data resource for various
 applications of molecular tools for biodiversity research and
 conservation. We also ask whether sequencing approaches differ between the
 Global South and Global North, reviewing the last five years of
 conservation genetics research in four leading journals. We find that
 extant reference genomes are scarce relative to species richness at low
 latitudes, and that reduced-representation and whole-genome sequencing are
 disproportionately applied to taxa in the Global North. We conclude with
 recommendations to close this gap and improve international collaborations
 in biodiversity genomics.
description: We used the National Center for Biotechnology Information (NCBI)
 Datasets command-line tools v.16.19.0 (O’Leary et al. 2024) to download
 taxonomy metadata for the subset of species with an assembled reference
 genome in the following taxa: birds (Class: Aves), mammals (Class:
 Mammalia), squamates (Order: Squamata), amphibians (Class: Amphibia),
 turtles (Order: Testudines), crocodilians (Order: Crocodilia) and tuataras
 (Order: Rhynchocephalia). We selected these groups—together comprising
 extant tetrapods—to provide a snapshot of animal diversity in relatively
 well-studied clades with different ecologies and evolutionary histories,
 while restricting the total dataset to a computationally manageable size.
 From this initial list we retained species with an exact match to the
 Global Biodiversity Information Facility’s (GBIF) Backbone Taxonomy using
 rgbif v.3.8.0 (Chamberlain et al. 2024) and downloaded all observations of
 each backed by georeferenced voucher specimens in natural history museum
 collections (NHCs), excluding those without coordinates and those flagged
 for geospatial issues (n= 3,006,946). We repeated this process for all
 species in each higher-level taxon represented in our list of reference
 genomes (i.e., downloaded metadata for all georeferenced tetrapod
 specimens on GBIF; n= 9,303,258). DOIs for each download are available in
 the References section below (GBIF.org 2024a; GBIF.org 2024b; GBIF.org
 2024c; GBIF.org 2024d; GBIF.org 2024e; GBIF.org 2024f; GBIF.org 2024g;
 GBIF.org 2024h). Filtering these aggregated datasets to
 contain only species with 10 or more specimen records, we generated convex
 hull polygons for each as a coarse approximation of their geographic
 distribution using the R package sf v.1.0-16 (Pebesma 2018; Pebesma
 & Bivand 2023). Overlaying these on a shapefile of Earth’s
 landmasses from rnaturalearth v.1.0.1 (Massicotte & South 2024),
 we calculated species richness as the number of overlapping convex hulls
 in 2-degree x 2-degree grid cells, statistically standardizing this value
 by subtracting observed mean global species richness and dividing by its
 standard deviation. We subtracted the number of species with reference
 genomes from total species richness to determine the regions with the
 largest representation gap in genomic resources, again standardizing the
 difference. To assess the significance and slope of a correlation between
 species richness and the absolute value (or modulus) of latitude in
 decimal degrees, we performed simple linear regressions in R v.4.4.0 (R
 Core Team 2024), analyzing species with reference genomes and our full
 dataset separately. To evaluate how the geography of
 authorship might impact sequencing strategy of studies in conservation
 biology, we performed a restricted Web of Science literature search on 29
 June 2024 for English-language conservation genetics papers published in
 the last five years in the journals <em>Conservation
 Genetics</em>, <em>Molecular Ecology</em>,
 <em>Journal of Heredity</em>, and <em>Conservation
 Biology, </em>selected for frequently publishing empirical work on
 non-model organisms. We used the queries ‘SO=”Conservation Genetics”’ and
 ‘SO=("Molecular Ecology" OR "Journal of Heredity" OR
 "Conservation Biology") AND (TS="Conservation Genet*"
 OR KP="Conservation Genet*" OR TI="Conservation
 Genet*"’), excluding reviews, genome announcements, meta-analyses,
 preprints, and studies that were purely simulations. Our criteria aimed to
 achieve a tractable sample size for careful study (&lt;1000 papers)
 while covering the period in which WGS became commonly used for the
 conservation genetics of non-model organisms (Fuentes-Pardo &
 Ruzzante 2017; Hohenlohe et al. 2021).  We then
 manually reviewed each study, first assigning the home institution of its
 first and last author to the Global North, Global South or both (i.e.,
 joint affiliations) using the 2024 UN Trade and Development
 Classifications. Because the number of middle authors varied widely across
 our sample, we assessed their affiliations on a binary basis, indicating
 only whether a contributor from an institution from the Global South was
 present outside of the lead and senior positions. Synthesizing these data,
 we assigned papers to mutually exclusive groups based on whether they
 included one or more Global South authors or only Global North authors.
 Next, we categorized each study’s sequencing approach as reduced
 representation, WGS, Sanger sequencing, microsatellites, or other, and
 described its overall focus using tiered categories based on discussion in
 Bertola et al. 2024. These tiers were: 1) Taxonomy / systematics,
 identification, or sexing; 2) Phylogeography / population genetic
 structure, estimating genetic diversity, and inferring demographic
 history; and 3) Detecting outlier loci, quantifying runs of homozygosity,
 and evaluating adaptive potential. When studies employed more than one
 sequencing approach or addressed goals belonging to multiple tiers, we
 assigned them to a single category based on their most data-intensive
 method or question. To explore geographic patterns in sequencing effort,
 we assessed whether each study’s taxonomic sampling included 1) at least
 one species distributed in the Global South and 2) at least one species
 distributed in the Global North. Because some studies included multiple
 taxa and some species are broadly distributed or migrate between regions,
 these categories were not mutually exclusive. To evaluate whether
 geographic representation in conservation genetics changed over the period
 covered by our review, we performed logistic regression using the stats
 package R v.4.4.0, treating the presence or absence of an author from the
 Global South as a binary outcome variable and year as the sole independent
 variable.
description: # Data from: A latitudinal gradient of reference genomes Dataset DOI:
 [10.5061/dryad.2v6wwpzxh](10.5061/dryad.2v6wwpzxh) ## Description of the
 data and file structure Linck and Cadena 2024 *Mol. Ecol.* obtained two
 types of data: 1) metadata on published tetrapod reference genomes from
 NCBI's Genome Browser; and 2) georeferenced occurrence data from the
 Global Biodiversity Information Facility. Data were aggregated using
 the NCBI Datasets command-line tools v.16.19.0 and rgbif v.3.8.0. GBIF
 data are used in accordance with the organization's Data user
 agreement
 ([https://www.gbif.org/terms/data-user](https://www.gbif.org/terms/data-user)) ### GBIF Data The georeferenced occurrence data used in the study and necessary to knit `01_analysis.Rmd` are available directly from GBIF via dedicated DOIs and landing pages. These downloads are: [https://doi.org/10.15468/dl.59eyey](https://doi.org/10.15468/dl.59eyey) (producing `0013380-240626123714530.zip`); [https://doi.org/10.15468/dl.vybgce](https://doi.org/10.15468/dl.vybgce) (for `0013381-240626123714530.zip`); [https://doi.org/10.15468/dl.jd85b2](https://doi.org/10.15468/dl.jd85b2) (for `0013376-240626123714530.zip`); [https://doi.org/10.15468/dl.jbg6xy](https://doi.org/10.15468/dl.jbg6xy) (for `0085284-240506114902167.zip`);[https://doi.org/10.15468/dl.zw99uq](https://doi.org/10.15468/dl.zw99uq) (for `0083881-240506114902167.zip`); [https://doi.org/10.15468/dl.5pkrsd](https://doi.org/10.15468/dl.5pkrsd) (for `0085162-240506114902167.zip`); [https://doi.org/10.15468/dl.p4r6hf](https://doi.org/10.15468/dl.p4r6hf) (for `0071470-240506114902167.zip`); and [https://doi.org/10.15468/dl.njrdnn](https://doi.org/10.15468/dl.njrdnn) (for `0083708-240506114902167.zip`). ### Local Files and Variables `mammals.tsv`: Results of NCBI Datasets command-line tools query for metadata for all available Class Mammalia genome assemblies. ##### Variables * Assembly Accession: NCBI accession number—a unique identifier used to locate and access genome assemblies.  * Assembly Name: Author-input name for assembly version.  * Annotation Name: NCBI-generated identifier for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Annotation Release Date: Release date for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Organism Name: Latin binomial or trinomial for the species the assembly was generated from.  `squamates.tsv`: Results of NCBI Datasets command-line tools query for metadata for all available Class Squamata genome assemblies. ##### Variables * Assembly Accession: NCBI accession number—a unique identifier used to locate and access genome assemblies.  * Assembly Name: Author-input name for assembly version.  * Annotation Name: NCBI-generated identifier for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Annotation Release Date: Release date for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Organism Name: Latin binomial or trinomial for the species the assembly was generated from.  `birds.tsv`: Results of NCBI Datasets command-line tools query for metadata for all available Order Squamata genome assemblies. ##### Variables * Assembly Accession: NCBI accession number—a unique identifier used to locate and access genome assemblies.  * Assembly Name: Author-input name for assembly version.  * Annotation Name: NCBI-generated identifier for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Annotation Release Date: Release date for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Organism Name: Latin binomial or trinomial for the species the assembly was generated from.  `amphibia.tsv`: Results of NCBI Datasets command-line tools query for metadata for all available Class Amphibia genome assemblies. ##### Variables * Assembly Accession: NCBI accession number—a unique identifier used to locate and access genome assemblies.  * Assembly Name: Author-input name for assembly version.  * Annotation Name: NCBI-generated identifier for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Annotation Release Date: Release date for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Organism Name: Latin binomial or trinomial for the species the assembly was generated from.  `testudines.tsv`: Results of NCBI Datasets command-line tools query for metadata for all available Order Testudines genome assemblies. ##### Variables * Assembly Accession: NCBI accession number—a unique identifier used to locate and access genome assemblies.  * Assembly Name: Author-input name for assembly version.  * Annotation Name: NCBI-generated identifier for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Annotation Release Date: Release date for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Organism Name: Latin binomial or trinomial for the species the assembly was generated from.  `tuatara.tsv`: Results of NCBI Datasets command-line tools query for metadata for all available Order Rhynchocephalia genome assemblies.  ##### Variables * Assembly Accession: NCBI accession number—a unique identifier used to locate and access genome assemblies.  * Assembly Name: Author-input name for assembly version.  * Annotation Name: NCBI-generated identifier for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Annotation Release Date: Release date for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Organism Name: Latin binomial or trinomial for the species the assembly was generated from.  `crocodylia.tsv`: Results of NCBI Datasets command-line tools query for metadata for all available Order Crocodilia genome assemblies.  ##### Variables * Assembly Accession: NCBI accession number—a unique identifier used to locate and access genome assemblies.  * Assembly Name: Author-input name for assembly version.  * Annotation Name: NCBI-generated identifier for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Annotation Release Date: Release date for a genome assembly's annotation. Blank cells indicate N/A or not available (i.e., there is no associated annotation for this assembly).  * Organism Name: Latin binomial or trinomial for the species the assembly was generated from.  `congen_lit_review.csv`: Data from the manual literature review. ##### Variables * Authors: Authors of a given publication included in our review.  * Article Title: The title of the publication.  * Source Title: The journal the publication appeared in.  * DOI: The Digital Object Identifier for the publication.  * Publication Year: The year the article was published.  * First Author Origin: Geographic origin of the first author's affiliated institution, categorized as Global North, Global South, or Both.  * Senior Author Origin: Geographic origin of the first author's affiliated institution, categorized as Global North, Global South, or Both.  * Global South Middle Author?: Whether or not the study includes a middle author from the Global South, coded as Yes, No, or N/A as in not applicable (if no middle author).  * Sampled Focal Taxa Range: Geographic distribution of the study's focal taxa / taxon, coded as Global North, Global South, Both (Migratory), or Both (Shared Resident).  * Sequencing Method: DNA sequencing approach used by the study, assigned to the categories Sanger Sequencing, Microsatellites, Reduced Representation (e.g., UCEs or RADseq), WGS, or Other.  * Study Focus: Description of the focus of the study, categorized as Selection / GWAS / Runs of Homozygosity / Adaptive Potential / Gene Expression, Population Structure / Gene Flow / Diversity / Demographic History, or Identification / Sexing / Taxonomy / Systematics.  * Comments: Miscellaneous comments. Blank cells should be interpreted as N/A / Not Applicable. ## Code/software `ncbi.md`: Markdown file with NCBI CLI commands used to generate `.tsv` genome assembly metadata files.  `01_analysis.Rmd`: Rmarkdown file that performs all analyses and generates all figures for the manuscript.  ## Access information Other publicly accessible locations of the data: * Personal GitHub: [https://github.com/elinck/lat_grad_genome/](https://github.com/elinck/lat_grad_genome/) Data was derived from the following sources: * GBIF. All GBIF occurence records were associated with a CC BY-NC 4.0 license.  * NCBI Datasets. NCBI metadata used in the study are part of the public domain / available via an Open Database License.