DOI: 10.5061/dryad.np5hqbzzg
title: Integration of genomic and ecologic methods inform management of an undescribed, yet highly exploited, sardine species

description: Assessing genetic diversity within species is key for conservation
 strategies in the context of human-induced biotic changes. This is
 important in marine systems where many species remain undescribed while
 being overfished, and conflicts between resource-users and conservation
 agencies are common. Combining niche modelling with population genomics
 can contribute to resolving those conflicts by identifying management
 units and understanding how past climatic cycles resulted in current
 patterns of genetic diversity. We addressed these issues on an undescribed
 but already overexploited species of sardine of the genus Harengula. We
 find that the species distribution is determined by salinity and depth,
 with a continuous distribution along the Brazilian mainland and two
 disconnected oceanic archipelagos. Genomic data indicates that such
 biogeographic barriers are associated with two divergent intraspecific
 lineages. Changes in habitat availability during the last glacial cycle
 led to different demographic histories among stocks. One coastal
 population experienced a 3.6-fold expansion, whereas an island-associated
 population contracted 3-fold, relative to the size of the ancestral
 population. Our results indicate that the island population should be
 managed separately from the coastal population, and that a Marine
 Protected Area covering part of the island population distribution can
 support the viability of this lineage.
description: This README file was generated by Jessica FR Coelho on 07-Feb-2024.
 \-------------------- ## GENERAL INFORMATION \-------------------- 1\.
 Title of Dataset: Integration of genomic and ecologic methods inform
 management of an undescribed, yet highly exploited, sardine species DOI:
 10.5061/dryad.np5hqbzzg 2\. Author information: Coelho, Jéssica Fernanda
 Ramos, Universidade Federal do Rio Grande do Norte, jessicovsky@gmail.com
 Mendes, Liana, Universidade Federal do Rio Grande do Norte Di Dario,
 Fabio, Universidade Federal do Rio de Janeiro Carvalho, Pedro Hollanda,
 Universidade Federal do Rio de Janeiro Dias, Ricardo, Universidade Federal
 do Rio de Janeiro Lima, Sergio, Universidade Federal do Rio Grande do
 Norte Verba, Julia, Ludwig Maximilian University of Munich Pereira,
 Ricardo, Staatliches Museum für Naturkunde Stuttgart 3\. Short summary of
 the study: In this study, we used niche modeling and population genomics
 to investigate how historical climate changes led to the current genetic
 diversity patterns of the tropical scaled-sardine Harengula sp. We
 observed that the distribution of this species is shaped by depth and
 salinity along the Atlantic Southwest and that these barriers are linked
 to two distinct populations: one that is spread across the Brazilian
 mainland coast and one in the oceanic island of Fernando de Noronha. 4\.
 Date of data collection: 2021 (for ecological data deposited on online
 databases from 1946-2021) and 2020-2021 (fieldwork collection for genomic
 data) 5\. Geographic location of data collection: Atlantic Southwest,
 coast of Brazil 6\. Funding sources that supported the collection of the
 data: J.F.R.C. was financed in part by the Coordenação de Aperfeiçoamento
 de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
 Financial support to F.D.D. was provided by CNPq (Conselho Nacional de
 Desenvolvimento Científico e Tecnológico – PROTAX 443302/2020). SMQL
 receives a Conselho Nacional de Desenvolvimento Científico e Tecnologico
 (CNPq) productivity research grant (№ Proc 312066/2021-0). This study was
 developed in the context of the "Projeto MULTIPESCA- Ciência para a
 sustentabilidade da pesca, pescado e pescadores do Rio de Janeiro",
 which received support from the "Marine and Fisheries Research
 Project". The Marine and Fisheries Research Project is an offset
 measure established under a consent decree agreed between the company PRIO
 and the Federal Public Prosecutors’ Office in Rio de Janeiro. It is
 implemented by FUNBIO. \---------------------------------------------
 Overview of folders/files and their contents
 \--------------------------------------------- Files List: (1) Genomic
 data \* Reduced representation (DArTseq data) of the genome of 91
 scaled-sardines of the genus Harengula. (2) Ecological data \*
 Georeferenced occurrence records. (3) R Scripts \* Scripts used to filter
 and analyze genomic and ecological data. \----------------- (1) GENOMIC
 DATA \----------------- The folder Report-DHare21-6652.zip contains the
 genomic data generated by Diversity Arrays Technology (DArTseq) for the
 Brazilian scaled sardines Harengula sp. SNPs can be coded as: 0=homozygous
 for the reference allele; 1=heterozygous; and 2=homozygous for the
 alternate allele. NA, blank cells or - [hyphen] indicate missing values.
 SNPs can also be coded as: A/A, A/C, C/T, G/A and so on, with ‐/‐
 indicating missing values. Details on file columns also available in
 Gruber et al. (2022). The files are: I.
 Report_DHare21-6652_SilicoDArT_1.csv - 'silicodart'
 (presence/absence) dataset. Columns are CloneID: Identifier of the
 sequence tag that is unique for each tag AlleleSequence: Refers to the DNA
 sequence of the tag found in samples with a genotype score of
 '1' TrimmedSequence: Same as the full sequence, but with
 sequencing adapters trimmed out CallRate: Ratio of samples where the
 genotype call is either '1' or '0' as opposed to
 '‐' [hyphen] OneRatio: Ratio of samples where the genotype score
 is '1' PIC: Polymorphism Information Content AvgReadDepth:
 Average read depth, calculated by adding the read counts for all samples
 then dividing by the number of samples that have non-zero tag read counts
 StDevReadDepth: Standard deviation read depth is the standard deviation of
 the count of tag reads for all samples that have non-zero tag read counts
 Qpmr: Mean of normalized non-zero tag read counts divided by the standard
 deviation of normalized non-zero tag read counts Reproducibility: Ratio of
 technical replicate assay pairs with consistent marker scores -&gt;
 Asterisks and header in the dataset characterize the format of the file
 and serve for software reading purposes. -&gt; Cells from K6 to CW6 refer
 to individuals' collection identification; cells from K7 to CW7
 identify individuals with location IDs, which are: FNO Fernando de Noronha
 [island] CE Ceara RN Rio Grande do Norte PB Paraiba PE Pernambuco AL
 Alagoas BA Bahia ABR Abrolhos [island] RJ Rio de Janeiro SP Sao Paulo SC
 Santa Catarina II. Report_DHare21-6652_SNP_2.csv - 'SNP' (single
 nucleotide polymorphism) dataset. Individuals in this dataset were ordered
 by sampling latitude. In this file these columns were defined above:
 CloneID, AlleleSequence, TrimmedSequence and CallRate. Cells from S7 to
 DE7 identify individuals with location IDs abbreviated as defined above.
 Columns are AlleleID: Same as CloneID with details on SNP position SNP:
 Single Nucleotide polymorphism; refers to the mutation identified in the
 sequence tag SnpPosition: Position of the SNP indentified in the sequence
 tag (zero is position 1) OneRatioRef: Ratio of samples where genotype
 score is zero (0) OneRatioSnp: Ratio of samples where genotype score is
 two (2) FreqHomRef: Frequency of homozygotes for the reference allele
 FreqHomSnp: Frequency of homozygotes for the mutation (SNP) FreqHets:
 Frequency of heterozygotes (genotype score is one (1)) PICRef:
 Polymorphism Information Content for the reference allele PICSnp:
 Polymorphism Information Content for the mutation (SNP) AvgPIC: Mean of
 Polymorphism Information Content of both reference and SNP alleles
 AvgCountRef: Mean of PIC for both reference and SNP alleles AvgCountSnp:
 Sum of the tag read counts for all samples, divided by the number of
 samples for which tag read counts is not zero for the SNP allele RepAvg:
 Ratio of technical replicate assay pairs with consistent marker scores
 III. SampleFile-DHare21-6652-my_metadata.csv - Metadata associated to the
 SNP dataset; required when uploading the above dataset as a genlight
 object in R. Columns are id Unique individual identification pop
 Population abbreviation (as defined above, sampling site collection) lat
 Latitude of sample collection in decimal degrees lon Longitude of sample
 collection in decimal degrees \-------------------- (2) ECOLOGICAL DATA
 \-------------------- Harengulasp_OccurrenceRecords.csv comprises the
 dataset of occurrence records used in the ecological niche model. Columns
 are species names, latitude, and longitude, in decimal degrees as detailed
 above. Climate data was downloaded from MARSPEC and can be accessed in R,
 using the script "enm.txt" avaliable in this repository and
 listed below. Folder abiotic_data.zip contains the climate data in two
 folders: I. current_bioregion: climate data for the present climate croped
 into marine bioregions as in Spalding et al. (2007); bathy Depth of sea
 floor (meters, scaling factor: 1x) slope Bathymetric slope (degrees,
 scaling factor: 1x) sss_mean Mean Annual Sea Surface Salinity (practical
 salinity unit, scaling factor: 100x) sst_mean Mean Annual Sea Surface
 Temperature (celsius, scaling factor: 100x) II. lgm_bioregion: climate
 data for the Last Glacial Maximum (LGM) climate croped into marine
 bioregions as in Spalding et al. (2007). bathy Depth of sea floor (meters,
 scaling factor: 1x) slope Bathymetric slope (degrees, scaling factor: 1x)
 sss_mean Mean Annual Sea Surface Salinity (practical salinity unit,
 scaling factor: 100x) sst_mean Mean Annual Sea Surface Temperature
 (celsius, scaling factor: 100x) \--------------- (3) R Scripts
 \--------------- Folder r_scripts.zip: For genomic analyses, R scripts
 (.txt format) follow the order: 1\. filtering.txt 2\. outlier_removal.txt
 3\. pcoa_colors.txt 4\. pop_analyses.txt Ecological niche models: 1\.
 enm.txt Extract all files from .zip folder, then copy/paste the text in
 each .txt file to R to run the analyses. \------------ REFERENCES
 \------------ Gruber B, Georges A, Mijangos JL, Pacioni C, Unmack PJ,
 Berry O, Clarck LV, Devloo-Delva F, Archer E. Importing and analysing SNP
 and Silicodart data generated by genome-wide restriction fragment
 analysis. Package: dartR 2022 v. 2.7.2.
 https://green-striped-gecko.github.io/dartR/ Spalding MD, Fox HE, Allen
 GR, Davidson N, Ferdaña ZA, Finlayson MA, Halpern BS, Jorge MA, Lombana
 AL, Lourie SA, Martin KD. Marine ecoregions of the world: a
 bioregionalization of coastal and shelf areas. BioScience. 2007 Jul
 1;57(7):573-83.