Genomic Impact of Whaling in North Atlantic Fin Whales.

It is generally recognized that large-scale whaling in the 19th and 20th century led to a substantial reduction of the size of many cetacean populations, particularly those of the baleen whales (Mysticeti). The impact of these operations on genomic diversity of one of the most hunted whales, the fin whale (Balaenoptera physalus), has remained largely unaddressed because of the paucity of adequate samples and the limitation of applicable techniques. Here, we have examined the effect of whaling on the North Atlantic fin whale based on genomes of 51 individuals from Icelandic waters, representing three temporally separated intervals, 1989, 2009 and 2018 and provide a reference genome for the species. Demographic models suggest a noticeable drop of the effective population size of the North Atlantic fin whale around a century ago. The present results suggest that the genome-wide heterozygosity is not markedly reduced and has remained comparable with other baleen whale species. Similarly, there are no signs of apparent inbreeding, as measured by the proportion of long runs of homozygosity, or of a distinctively increased mutational load, as measured by the amount of putative deleterious mutations. Compared with other baleen whales, the North Atlantic fin whale appears to be less affected by anthropogenic influences than other whales such as the North Atlantic right whale, consistent with the presence of long runs of homozygosity and higher levels of mutational load in an otherwise more heterozygous genome. Thus, genome-wide assessments of other species and populations are essential for future, more specific, conservation efforts.

Mediterranean Fin Whales (Balaenoptera physalus) Threatened by Dolphin MorbilliVirus.

During 2011-2013, dolphin morbillivirus was molecularly identified in 4 stranded fin whales from the Mediterranean Sea. Nucleoprotein, phosphoprotein, and hemagglutinin gene sequences of the identified strain were highly homologous with those of a morbillivirus that caused a 2006-2007 epidemic in the Mediterranean. Dolphin morbillivirus represents a serious threat for fin whales.

Behavioural responses of fin whales to military mid-frequency active sonar.

The effect of active sonars on marine mammal behaviour is a topic of considerable interest and scientific investigation. Some whales, including the largest species (blue whales, Balaenoptera musculus), can be impacted by mid-frequency (1-10 kHz) military sonars. Here we apply complementary experimental methods to provide the first experimentally controlled measurements of behavioural responses to military sonar and similar stimuli for a related endangered species, fin whales (Balaenoptera physalus). Analytical methods include: (i) principal component analysis paired with generalized additive mixed models; (ii) hidden Markov models; and (iii) structured expert elicitation using response severity metrics. These approaches provide complementary perspectives on the nature of potential changes within and across individuals. Behavioural changes were detected in five of 15 whales during controlled exposure experiments using mid-frequency active sonar or pseudorandom noise of similar frequency, duration and source and received level. No changes were detected during six control (no noise) sequences. Overall responses were more limited in occurrence, severity and duration than in blue whales and were less dependent upon contextual aspects of exposure and more contingent upon exposure received level. Quantifying the factors influencing marine mammal responses to sonar is critical in assessing and mitigating future impacts.

Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (Balaenoptera physalus).

Density surface models (DSMs) are an important tool in the conservation and management of cetaceans. Most previous applications of DSMs have adopted a two-step approach to model fitting (hereafter referred to as the Two-Stage Method), whereby detection probabilities are first estimated using distance sampling detection functions and subsequently used as an offset when fitting a density-habitat model. Although variance propagation techniques have recently become available for the Two-Stage Method, most previous applications have not propagated detection probability uncertainty into final density estimates. In this paper, we describe an alternative approach for fitting DSMs based on Bayesian hierarchical inference (hereafter referred to as the Bayesian Method), which is a natural framework for simultaneously propagating multiple sources of uncertainty into final estimates. Our framework includes (1) a mark-recapture distance sampling observation model that can accommodate two team line transect data, (2) an informed prior for the probability a group of animals is at the surface and available for detection (i.e. surface availability) (3) a density-habitat model incorporating spatial smoothers and (4) a flexible compound Poisson-gamma model for count data that incorporates overdispersion and zero-inflation. We evaluate our method and compare its performance to the Two-Stage Method with simulations and an application to line transect data of fin whales (Balaenoptera physalus) off the east coast of the USA. Simulations showed that both methods had low bias (<1.5%) and confidence interval coverage close to the nominal 95% rate when variance was propagated from the first step. Results from the fin whale analysis showed that density estimates and predicted distribution patterns were largely similar among methods; however, the coefficient of variation of the final abundance estimate more than doubled (0.14 vs 0.31) when detection variance was correctly propagated into final estimates. An analysis of the variance components demonstrated that overall detectability as well as surface availability contributed substantial amounts of variance in the final abundance estimates whereas uncertainty in mean group size contributed a negligible amount. Our method provides a Bayesian alternative to DSMs that incorporates much of the flexibility available in the Two-Stage Method. In addition, these results demonstrate the degree to which uncertainty can be underestimated if certain components of a DSM are assumed fixed.