Single nucleotide polymorphism genotypes for the Australian blackspot shark and the milk shark in Northern Australian waters

Banks, Sam 1 ; Kirke, Amy2 ; Alves, Fernanda3 ; Johnson, Grant4 ; Crook, David5

Research facility: Charles Darwin University

Published Nov 07, 2024 on Dryad. https://doi.org/10.5061/dryad.7h44j103q

Data files

Nov 07, 2024 version files 169.66 MB

CARCOA_sample_data.csv

32.89 KB
CARCOA_SNP_data_unfiltered.csv

109.46 MB
metadata_Dcarcha21-5958.xlsx
27.33 KB
metadata_DRHIzn21-5993.xlsx
23.33 KB
README.md
5.66 KB
Report_DRhizn21-5993_SNP_mapping_2.csv

60.10 MB
RHIACU_sample_data.csv

10.16 KB

Abstract

Charles Darwin University and the Northern Territory (NT) Department of Industry, Tourism and Trade (DITT) Fisheries Division used genetic data to investigate the population structure of two small tropical shark species (Milk Shark [Rhizoprionodon acutus] and Australian Blackspot Shark [Carcharhinus coatesi]), which are caught as bycatch from commercial fisheries in the NT.

The aim of this study was to gain information on the genetic stock structure to inform the future management of these two species in the NT. This project was conducted in parallel with a PhD project investigating the biology and ecology of both species for applications to fisheries management. There is motivation by the NT Government to develop these two shark species into a commercial product. This project used genetic analysis to understand the patterns of connectivity of populations of these two shark species in NT waters and adjacent regions, including Northern Western Australia and Papua New Guinea.

Background

These two shark species that are captured as bycatch in the NT Demersal Trawl fishery have the potential to be developed into a byproduct to add value to that fishery. A sustainable commercial harvest of these two species could greatly reduce the waste from fisheries, where they are currently abundant and caught in relatively large numbers. We address current knowledge gaps in biological information about populations of R. acutus and C. coatesi to inform the potential development of a byproduct fishery for these two species in the NT.

Aims

Our research aimed to:

· identify the genetic population structure for R. acutus and C. coatesi in NT waters

· develop capacity for genetic research and monitoring of shark species in the NT

· provide baseline information on genetic structure to inform potential genetic monitoring of these species, including initial estimates of effective population size.

Methods

We used single-nucleotide polymorphism genetic analyses to measure genetic structure among R. acutus and C. coatesi samples obtained from commercial trawl fishing in NT waters between May 2018 and November 2019. Our aim was to determine whether the two species each occur as a single population in NT waters or as a set of discrete populations that may warrant separate monitoring and management. We also analysed samples of these species from Western Australia and Papua New Guinea to provide broader context for the degree of genetic differentiation among the samples from different regions in the NT.

Our secondary aim was to provide a baseline for deciding whether genetic estimates of effective population size could be used to monitor trends in abundance of these species, and whether samples from across the NT could be combined for the genetic estimation of effective population size for this purpose.

Results

Genetic data from R. acutus and C. coatesi strongly suggest that each species exists as a single, highly connected population in the NT. Genetic differentiation among the sampling locations for each species was low, and genetic clustering analyses provided strong support for a single population of each species in the region. Sharks of both species captured within a single location (within 50 km of one another) were more genetically related than those further apart; however, this does not constitute evidence for multiple, spatially discrete populations of either species in NT waters. Preliminary applications of effective population size estimators were used, but further work is needed to determine if these can be used to indicate trends in abundance.

Implications for relevant stakeholders

The immediate implications of our research are for fisheries scientists and managers from NT DITT. Our results indicate that these two shark species can be monitored and managed in the NT under the assumption that each species occurs as a single population in this region. Further information relevant to shorter-term movements of individuals may refine management strategies for the two species.

Our research has potential implications for commercial fishers, particularly from the Demersal Trawl Fishery and Australia Bay Seafoods company. Currently, those implications are indirect, as the information from our research will flow through to the industry by contributing to the information required to develop a byproduct fishery for the two species, mitigating bycatch and increasing economic return.

Recommendations

Future research could develop genetic methods, such as effective population size or close-kin mark-recapture, for population monitoring. Comparing the genetic data against other data that indicate individual movement patterns on shorter timescales would help develop a holistic understanding of shark movement and population connectivity to inform sustainable harvest strategies.

https://doi.org/10.5061/dryad.7h44j103q

Description of the data and file structure

Sample collection

We took tissue samples from 196 individual Rhizoprionodon acutus (Figure 1) and 634 individual Carcharhinus coatesi *(Figure 2) that were collected in Northern Territory (Australia) waters between May 2018 and November 2019. These sharks were caught as bycatch from the commercial trawl fisheries from Australia’s Exclusive Economic Zone (EEZ) and retained for our research use. Australia’s EEZ around the NT encompasses the Timor Sea in the north-west, the Arafura Sea in the north and the Gulf of Carpentaria in the east (Figure 1, Figure 2). For initial analyses of patterns of population genetics, we grouped the NT samples into four regions. We included three additional regions (Pilbara, Kimberley and Papua New Guinea [PNG]) for *R. acutus (Figure 1) and two additional regions (Kimberley and PNG) for C. coatesi (Figure 2). We included these additional samples from Western Australia (WA) and PNG to provide broader context to help understand the degree of genetic differentiation and connectivity among populations in NT waters relative to a broader sample across the regional distribution of these species. The Western Australian samples were provided by Alister Harry (WA Department of Primary Industries and Regional Development), and the PNG samples were provided by Will White (CSIRO).

We extracted DNA for single-nucleotide polymorphism (SNP) genotyping using the DArTSeq protocol through Diversity Arrays P/L (Kilian et al., 2012). We sent tissue samples in 100% ethanol to Diversity Arrays for DNA extraction and genomic library preparation for sequencing. DArTSeq involves an initial step of ‘genome reduction’ to subset a small fraction of the genome of each individual for high-throughput sequencing, followed by bioinformatics analysis to identify DNA sequences containing single nucleotide positions that vary among individuals within and among populations. The DArTSeq protocols for *Carcharhinus *and *Rhizoprionodon *are available for future projects via Diversity Arrays.

The data were used for genetic analyses to infer population structure as described in the published report for the Fisheries Research and Development Corporation associated with this dataset.

Kilian, A., Wenzl, P., Huttner, E., Carling, J., Xia, L., Blois, H., Caig, V., Heller-Uszynska, K., Jaccoud, D., & Hopper, C. (2012). Diversity arrays technology: A generic genome profiling technology on open platforms. In Data production and analysis in population genomics (pp. 67–89). Springer.

Files and variables

Description:

Unfiltered SNP datasets for Rhizoprionodon acutus and Carcharinus coatesi as well as CSV files with individual sample information including sampling location (‘id’ = individual sample identifier; ‘pop’ = stratum variable for calculation of genetic diversity metrics based on broad sampling region; ‘lat’ = latitude of sampling location (rounded); ‘lon’ = longitude of sampling location (rounded); and ‘Full pop name’ = full name of population strata for analysis. The files are in the default format required for filtering and analysis in the dartR package for the R statistical environment. The two metadata files describe the unfiltered SNP data provided by Diversity arrays P/L.

Gruber, B., Unmack, P. J., Berry, O. F., & Georges, A. (2018). dartr: An r package to facilitate analysis of SNP data generated from reduced representation genome sequencing. Molecular Ecology Resources, 18(3), 691–699. https://doi.org/10.1111/1755-0998.12745

R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org

Code/software

Do, C., Waples, R. S., Peel, D., Macbeth, G. M., Tillett, B. J., & Ovenden, J. R. (2014). NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Molecular Ecology Resources, 14(1), 209–214. https://doi.org/10.1111/1755-0998.12157

Jombart, T., & Ahmed, I. (2011). adegenet 1.3-1: New tools for the analysis of genome-wide SNP data. Bioinformatics, 27(21), 3070–3071. https://doi.org/10.1093/bioinformatics/btr521

Peakall, R., & Smouse, P. E. (2012). GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics, 28(19), 2537–2539. https://doi.org/10.1093/bioinformatics/bts460

Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics, 155(2), 945–959. https://doi.org/10.1093/genetics/155.2.945