Marine mammal hotspots across the circumpolar Arctic

Aim: Identify hotspots and areas of high species richness for Arctic marine mammals. Location: Circumpolar Arctic. Methods: A total of 2115 biologging devices were deployed on marine mammals from 13 species in the Arctic from 2005 to 2019. Getis-Ord Gi* hotspots were calculated based on the number of individuals in grid cells for each species and for phyloge-netic groups (nine pinnipeds, three cetaceans, all species) and areas with high spe-cies richness were identified for summer (Jun-Nov), winter (Dec-May) and the entire year. Seasonal habitat differences among species’ hotspots were investigated using Principal Component Analysis. Results: Hotspots and areas with high species richness occurred within the Arctic continental-shelf seas and within the marginal ice zone, particularly in the “Arctic gateways” of the north Atlantic and Pacific oceans. Summer hotspots were generally found further north than winter hotspots, but there were exceptions to this pattern, including bowhead whales in the Greenland-Barents Seas and species with coastal distributions in Svalbard, Norway and East Greenland. Areas with high species rich-ness generally overlapped high-density hotspots. Large regional and seasonal dif-ferences in habitat features of hotspots were found among species but also within species from different regions. Gap analysis (discrepancy between hotspots and IUCN ranges) identified species and regions where more research is required. Main conclusions: This study identified important areas (and habitat types) for Arctic marine mammals using available biotelemetry data. The results herein serve as a benchmark to measure future distributional shifts. Expanded monitoring and teleme-try studies are needed on Arctic species to understand the impacts of climate change and concomitant ecosystem changes (synergistic effects of multiple stressors). While efforts should be made to fill knowledge gaps, including regional gaps and more com-plete sex and age coverage, hotspots identified herein can inform management ef-forts to mitigate the impacts of human activities and ecological changes, including creation of protected areas

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Do beluga whales truly migrate? Testing a key trait of the classical migration syndrome

Abstract Background Migration enables organisms to access resources in separate regions that have predictable but asynchronous spatiotemporal variability in habitat quality. The classical migration syndrome is defined by key traits including directionally persistent long-distance movements during which maintenance activities are suppressed. But recently, seasonal round-trip movements have frequently been considered to constitute migration irrespective of the traits required to meet this movement type, conflating common outcomes with common traits required for a mechanistic understanding of long-distance movements. We aimed to test whether a cetacean ceases foraging during so-called migratory movements, conforming to a trait that defines classical migration. Methods We used location and dive data collected by satellite tags deployed on beluga whales (Delphinapterus leucas) from the Eastern Beaufort Sea population, which undertake long-distance directed movements between summer and winter areas. To identify phases of directionally persistent travel, behavioural states (area-restricted search, ARS; or Transit) were decoded using a hidden-Markov model, based on step length and turning angle. Established dive profiles were then used as a proxy for foraging, to test the hypothesis that belugas cease foraging during these long-distance transiting movements, i.e., they suppress maintenance activities. Results Belugas principally made directed horizontal movements when moving between summer and winter residency areas, remaining in a Transit state for an average of 75.4% (range = 58.5–87.2%) of the time. All individuals, however, exhibited persistent foraging during Transit movements (75.8% of hours decoded as the Transit state had ≥ 1 foraging dive). These data indicate that belugas actively search for and/or respond to resources during these long-distance movements that are typically called a migration. Conclusions The long-distance movements of belugas do not conform to the traits defining the classical migration syndrome, but instead have characteristics of both migratory and nomadic behaviour, which may prove adaptive in the face of unpredictable environmental change. Such patterns are likely present in other cetaceans that have been labeled as migratory. Examination of not only horizontal movement state, but also the vertical behaviour of aquatic animals during directed movements is essential for identifying whether a species exhibits traits of the classical migration syndrome or another long-distance movement strategy, enabling improved ecological inference

Migratory culture, population structure and stock identity in North Pacific beluga whales <i>(Delphinapterus leucas)</i>

<div><p>The annual return of beluga whales, <i>Delphinapterus leucas</i>, to traditional seasonal locations across the Arctic may involve migratory culture, while the convergence of discrete summering aggregations on common wintering grounds may facilitate outbreeding. Natal philopatry and cultural inheritance, however, has been difficult to assess as earlier studies were of too short a duration, while genetic analyses of breeding patterns, especially across the beluga’s Pacific range, have been hampered by inadequate sampling and sparse information on wintering areas. Using a much expanded sample and genetic marker set comprising 1,647 whales, spanning more than two decades and encompassing all major coastal summering aggregations in the Pacific Ocean, we found evolutionary-level divergence among three geographic regions: the Gulf of Alaska, the Bering-Chukchi-Beaufort Seas, and the Sea of Okhotsk (<i>Φ</i>_st = 0.11–0.32, <i>R</i>_st = 0.09–0.13), and likely demographic independence of (<i>F</i>_st-mtDNA = 0.02–0.66), and in many cases limited gene flow (<i>F</i>_st-nDNA = 0.0–0.02; <i>K</i> = 5–6) among, summering groups within regions. Assignment tests identified few immigrants within summering aggregations, linked migrating groups to specific summering areas, and found that some migratory corridors comprise whales from multiple subpopulations (P_BAYES = 0.31:0.69). Further, dispersal is male-biased and substantial numbers of closely related whales congregate together at coastal summering areas. Stable patterns of heterogeneity between areas and consistently high proportions (~20%) of close kin (including parent-offspring) sampled up to 20 years apart within areas (<i>G</i> = 0.2–2.9, <i>p</i>>0.5) is the first direct evidence of natal philopatry to migration destinations in belugas. Using recent satellite telemetry findings on belugas we found that the spatial proximity of winter ranges has a greater influence on the degree of both individual and genetic exchange than summer ranges (r_winter-<i>F</i>_st-mtDNA = 0.9, r_summer-<i>F</i>_st-nDNA = 0.1). These findings indicate widespread natal philopatry to summering aggregation and entire migratory circuits, and provide compelling evidence that migratory culture and kinship helps maintain demographically discrete beluga stocks that can overlap in time and space.</p></div

The proportion of pairwise genealogical relationships estimated for beluga whales sampled within and between years across two decades near Kasegaluk Lagoon, Alaska.

<p>Maximum likelihood estimates of four relationship categories were estimated from genotypic data using the program ml-relate. The stacked bars represent the proportions of distantly/unrelated individuals to closely related individuals (i.e., parent-offspring, full-sib and half-sib or equivalent) for a subset of the 20-year data set comprising the first three years (1988, 1993, 1994) and the last three years (2005, 2006, 2007).</p

Summary plots generated in Clumpak of model-based cluster analysis of population structure in Pacific beluga whales using STRUCTURE 2.3.4.

<p>The major modes for <i>K</i> = 4 to 6 (based on five separate runs for each value of <i>K</i>) are presented for the analysis using prior sample group information and no admixture which revealed <i>K</i> = 5 clusters as the most likely (see panel 2). However, in a number of analyses <i>K</i> = 6 was the most or second-most likely resulting in the separation of Anadyr into a discrete cluster (see panel 3). Each genotyped individual is represented by a vertical line with estimated membership, Q, in each cluster denoted by different colors. The analysis was based on using all individuals (n = 1032) scored at 6 or more loci (n_loci≥6).</p

Genetic differentiation (<i>F</i>_st) between post-dispersal age cohorts of beluga whales from the eastern Chukchi (Kasegaluk Lagoon) and the Beaufort Seas (Mackenzie-Amundsen).

<p>Pairwise estimates for mtDNA are below the diagonal and for microsatellites above the diagonal. Analyses were conducted on all adults (A) and on all large, and presumably older, adults (B). Sample sizes (n) for the mtDNA comparisons are in column 2 and for microsatellites in row 3. Estimates of age were based on the number of growth layer groups (GLGs) in sectioned teeth.</p

The probability distribution of population proportions of groups of beluga whales sampled on northbound migration in spring.

<p> Stock-mixture analysis was conducted in Bayes with the eastern Beaufort Sea (blue) and the eastern Chukchi Sea (red) as baseline populations and the migrating groups as the potential ‘mixtures’. The ordinate axis indicates the number of runs.</p

Distribution (light blue) of beluga whales, <i>Delphinapterus leucas</i>, in the North Pacific Ocean.

<p>The ten major nearshore concentration areas during the summer months are highlighted (dark blue). These areas along with a small resident group of beluga whales in the Gulf of Alaska are numbered according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0194201#pone.0194201.t001" target="_blank">Table 1</a>.</p

The likely population of origin of beluga whales on spring migration sampled at four locations in the Bering, Chukchi and Beaufort Seas.

<p>Maximum likelihood assignments to two candidate populations, the eastern Chukchi Sea (Kasegaluk Lagoon) and the eastern Beaufort Sea (Mackenzie-Amundsen), were conducted in Whichrun and are reported both as likelihood ratios (P(n)/P(max)) and the Log of these ratios (LOD) for each individual. Bayesian assignments, using prior sample group information (i.e., LOCPRIOR models), were made using STRUCTURE and are reported as the estimated ancestry, Q, in Clusters 1 (Chukchi) and 2 (Beaufort). Assignments of individual migrants were also estimated using the stock-mixture method in BAYES, summarized here as the proportion of times, <i>P</i>, an individual was assigned to each baseline population.</p