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Population changes in a whale breeding ground revealed by citizen science noninvasive genetics unique microsatellite profiles of southern right whales

Citation

Neveceralova, Petra et al. (2022), Population changes in a whale breeding ground revealed by citizen science noninvasive genetics unique microsatellite profiles of southern right whales, Dryad, Dataset, https://doi.org/10.5061/dryad.pvmcvdnnb

Abstract

Historical exploitation, and a combination of current anthropogenic impacts, such as climate change and habitat degradation, impact the population dynamics of marine mammalian megafauna. Right whales (Eubalaena spp.) are large cetaceans recovering from hunting, whose reproductive and population growth rate appear to be impacted by climate change. We apply noninvasive genetic methods to monitor southern right whale (E. australis, SRW) and test the application of noninvasive genetics to minimise the observer effects on the population. Our aim is to describe population structure, and interdecadal and interannual changes to assess species status in the Great Acceleration period of Anthropocene. As a basis for population genetic analyses, we collected samples from sloughed skin during post-migration epidermal moult. Considering the exploration-exploitation dilemma, we collaborated with whale-watching companies, as part of a citizen science approach and to reduce ad hoc logistic operations and biopsy equipment. We used mitochondrial and microsatellite data and population genetic tools. We report for the first time the genetic composition and differentiation of the Namibian portion of the range. Population genetic parameters suggest that South Africa hosts the largest population. This corresponds with higher estimates of current gene flow from Africa compared to older samples. We have observed considerable interannual variation in population density at the breeding ground and an interdecadal shift in genetic variability, evidenced by an increase in the point estimate inbreeding. Clustering analyses confirmed differentiation between the Atlantic and Indo-Pacific, presumably originating during the ice ages. We show that population monitoring of large whales, essential for their conservation management, is feasible using noninvasive sampling within non-scientific platforms. Observed patterns are concurrent to changes of movement ecology and decline in reproductive success of the South African population, probably reflecting a large-scale restructuring of pelagic marine food webs.

Methods

The majority of samples used in this study were obtained noninvasively by collecting sloughed skin from whale watching boats conducting commercial trips during the austral winters of 2016 – 2018 in the area of Gansbaai and Walker Bay, South Africa. Pieces of skin were spotted in the water, picked up by a dip net and transferred with sterile tweezers to a tube containing 96% ethanol. Additional samples were collected from a research boat by remote biopsy using a crossbow and Cetadart darts (Lambertsen, 1987). All samples were stored at − 18 °C. Another 32 biopsy samples were available in archive held by University of Pretoria Mammal Research Institute Whale Unit. These samples were collected in two different regions, South Africa and Namibia, between 2003 and 2013.

Tissue was pulverised in liquid nitrogen and DNA was extracted using either the QIAGEN DNeasy Blood & Tissue Kit or the GENEAID Genomic DNA Mini Kit. Seventeen microsatellite loci were grouped into multiplexes and amplified in 10 μl PCR reactions (Carroll et al., 2015). Multi-locus microsatellite genotyping was done according to sample type. For noninvasive samples, a multi-tube approach (Taberlet et al., 1996) was attempted, with each DNA extraction being amplified at all loci up to three times. For biopsy samples, all loci were amplified once.

Sex was determined by amplification of the male specific SRY gene, multiplexed with an amplification of the ZFY/ZRX region as a positive control (Aasen and Medrano, 1990, Gilson et al., 1998). An approximately 550 base pair fragment of the left hypervariable domain of mtDNA control region adjacent to the Pro-tRNA gene was amplified according to Baker et al. (1999). The resulting PCR product was purified by either QIAGEN QIAquick PCR Purification Kit or GENEAID GenepHlow PCR Cleanup Kit and sequenced using BigDye chemistry on a 3130 Genetic Analyzer (Applied Biosystems). Chromatograms were visualized and edited in Geneious Prime v2020.2.4 (© Biomatters Ltd.).

For the microsatellite data, quality filtering, allele calling and binning was performed in the program Genemapper 5 (Applied Biosystems). For samples where loci were run more than once, the final genotype was constructed by choosing the highest quality allele calls for each locus, as determined by the quality score in Genemapper. Any samples where the allele calls disagreed between runs were removed from the dataset. Genotypes that failed to amplify for seven or more loci were considered poor quality and were removed from the dataset. Genotype error rates were calculated per allele (Pompanon et al., 2005) using the internal control samples amplified in every PCR and replicate samples. We report the completeness of the final dataset in terms of number of loci per sample.

First, genotypes within a year were reconciled to identify the number of unique whales sampled per austral winter season. Then, unique genotypes across years were compared to understand between year recaptures and the total number of whales sampled over the survey period. Cervus 3.0.7 was used to identify these within and between years genotype matches (Kalinowski et al., 2007) with the minimum number of matching loci set to at least eight. Pairs of genotypes that matched at eight loci but mismatched at up to three loci were reviewed and repeated if necessary to verify the individual’s identity or difference (Constantine et al., 2012).

Usage Notes

This dataset contains 190 unique genotypes of southern right whales collected in South Africa. Additional samples were added by Carroll et al., 2020, which can be downloaded at https://datadryad.org/stash/dataset/doi:10.5061/dryad.vv5347p

Funding

Grantová Agentura, Univerzita Karlova, Award: grant number 1140217