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Our interpretation of animal social structures is inherently dependent on our ability to define association criteria that are biologically meaningful. However, association thresholds are often based upon generalized preconceptions of a species’ social behaviour, and the impact of using these arbitrary definitions has been largely overlooked. In this study we suggest a probability-based method for defining association thresholds using lagged identification rates on photographic records of identifiable individuals. This technique uses a simple model of emigration/immigration from photographable clusters to identify the time-dependent lag value between identifications of two individuals that corresponds to approximately 75% probability of being in close spatial proximity and likely associating. This lag value is then used as the threshold to define associations for social analyses. We applied the technique to a dataset of northern resident killer whales (Orcinus orca) in the Northeast Pacific and tested its performance against two arbitrary thresholds. The probabilistic association maximized the variation in association strengths at different levels of the social structure, in line with known social patterns in this population. Furthermore, variability in inferred social structure metrics generated by different association criteria highlighted the consequential effect of choosing arbitrary thresholds. Data-driven association thresholds are a promising approach to study populations without the need to subjectively define associations in the field, especially in societies with prominent fission–fusion dynamics. This method is applicable to any dataset of sequential identifications where it can be assumed that associated individuals will tend to be identified in close proximity.

We present an ocean-basin-scale dataset that includes tail fluke photographic identification (photo-ID) and encounter data for the majority of living individual humpback whales (Megaptera novaeangliae) in the North Pacific Ocean. The dataset was built through a broad collaboration combining 39 separate curated photo-ID catalogs supplemented with community science data. All available images were compared using a recently developed machine learning artificial intelligence image recognition algorithm able to rapidly and accurately detect matches between individuals. For the study period of 2001 to 2021, a total of 27,956 unique individuals were documented in 157,379 encounters, with each individual encountered, on average, in 5.6 sampling periods (i.e., breeding and feeding seasons), and with an annual average of 87.1% of whales encountered in more than one season. The combined dataset and image recognition tool represents a living and accessible resource for collaborative, basin-wide studies of a keystone marine mammal in a time of rapid ecological change.

Culture, a pillar of the remarkable ecological success of humans, is increasingly recognized as a powerful force structuring nonhuman animal populations. A key gap between these two types of culture is quantitative evidence of symbolic markers—seemingly arbitrary traits that function as reliable indicators of cultural group membership to conspecifics. Using acoustic data collected from 23 Pacific Ocean locations, we provide quantitative evidence that certain sperm whale acoustic signals exhibit spatial patterns consistent with a symbolic marker function. Culture segments sperm whale populations into behaviorally distinct clans, which are defined based on dialects of stereotyped click patterns (codas). We classified 23,429 codas into types using contaminated mixture models and hierarchically clustered coda repertoires into seven clans based on similarities in coda usage; then we evaluated whether coda usage varied with geographic distance within clans or with spatial overlap between clans. Similarities in within-clan usage of both “identity codas” (coda types diagnostic of clan identity) and “nonidentity codas” (coda types used by multiple clans) decrease as space between repertoire recording locations increases. However, between-clan similarity in identity, but not nonidentity, coda usage decreases as clan spatial overlap increases. This matches expectations if sympatry is related to a measurable pressure to diversify to make cultural divisions sharper, thereby providing evidence that identity codas function as symbolic markers of clan identity. Our study provides quantitative evidence of arbitrary traits, resembling human ethnic markers, conveying cultural identity outside of humans, and highlights remarkable similarities in the distributions of human ethnolinguistic groups and sperm whale clans. Publisher PDF

Bowhead whales (Balaena mysticetusL., 1758) of the Eastern Canada-West Greenland population have been hunted by Inuit for millennia. Significant commercial harvests, conducted by European and American whalers for about 400 years, ended ca. 1915. A small co-managed subsistence harvest from this population has occurred inconsistently in Canada and Greenland, since 1996 and 2009, respectively. Since near extirpation from commercial whaling, population size has increased and the Inuit subsistence hunt now requires a harvest management framework that incorporates knowledge of abundance trends, population dynamics, and carrying capacity. Here, we use a model estimate of pre-commercial exploitation abundance to approximate carrying capacity and develop a management framework with reference points and corresponding stock status zones. When applied to recent abundance estimates, our framework indicates that the population is likely within the healthy (N50–N70) zone. Thus, an appropriate management objective is to support continued population increase, with concurrent marginal harvesting, while maintaining the population level above the target reference point (N70) of ca 12,000 whales. However, there remains large uncertainty about current population size and growth rate. The resulting data gaps require a plan for future research to monitor this population in the context of climate changes.

The extended female postreproductive life span found in humans and some toothed whales remains an evolutionary puzzle. Theory predicts demographic patterns resulting in increased female relatedness with age (kinship dynamics) can select for a prolonged postreproductive life span due to the combined costs of intergenerational reproductive conflict and benefits of late‐life helping. Here, we test this prediction using >40 years of longitudinal demographic data from the sympatric yet genetically distinct killer whale ecotypes: resident and Bigg's killer whales. The female relatedness with age is predicted to increase in both ecotypes, but with a less steep increase in Bigg's due to their different social structure. Here, we show that there is a significant postreproductive life span in both ecotypes with >30% of adult female years being lived as postreproductive, supporting the general prediction that an increase in local relatedness with age predisposes the evolution of a postreproductive life span. Differences in the magnitude of kinship dynamics however did not influence the timing or duration of the postreproductive life span with females in both ecotypes terminating reproduction before their mid‐40s followed by an expected postreproductive period of about 20 years. Our results highlight the important role of kinship dynamics in the evolution of a long postreproductive life span in long‐lived mammals, while further implying that the timing of menopause may be a robust trait that is persistent despite substantial variation in demographic patterns among populations., The rare life history trait of a long postreproductive period in females have been linked to demographic patterns leading to an increased relatedness to the local group with age (kinship dynamics). Here, we show that two killer whale ecotypes that are genetically distinct and exhibit different demographic patterns as well as different strengths of expected kinship dynamics have evolved a long postreproductive period in females, supporting the prediction that an increased female local relatedness with age predisposes for the evolution of this life history trait. Moreover, this results indicates that the onset of the postreproductive period may be robust to substantial variation in demographic patterns among populations.

The extended female post-reproductive lifespan found in humans and some toothed whales remains an evolutionary puzzle. Theory predicts demographic patterns resulting in increased female relatedness with age (kinship dynamics) can select for a prolonged post-reproductive lifespan due to the combined costs of inter-generational reproductive conflict and benefits of late-life helping. Here we test this prediction using >40 years of longitudinal demographic data from the sympatric yet genetically distinct killer whale ecotypes: resident and Bigg’s killer whales. The female relatedness with age is predicted to increase in both ecotypes, but with a less steep increase in Bigg’s due to their different social structure. Here, we show that there is a significant post-reproductive lifespan in both ecotypes with >30% of adult female years being lived as post-reproductive, supporting the general prediction that an increase in local relatedness with age predisposes the evolution of a post-reproductive lifespan. Differences in the magnitude of kinship dynamics however, did not influence the timing or duration of the post-reproductive lifespan with females in both ecotypes terminating reproduction before their mid-40s followed by an expected post-reproductive period of ~20 years. Our results highlight the important role of kinship dynamics in the evolution of a long post-reproductive lifespan in long-lived mammals, while further implying that the timing of menopause may be a robust trait that is persistent despite substantial variation in demographic patterns among population.

The hunting of bowhead whales (Balaena mysticetus) is an integral part of Inuit culture. An up-to-date abundance estimate of the entire Eastern Canada – West Greenland (EC-WG) bowhead population is necessary to support sustainable management of this harvest. The High Arctic Cetacean Survey (HACS) was conducted in August 2013, primarily to update abundance estimates for known stocks of Baffin Bay narwhal (Monodon monoceros). As the ranges of narwhal and bowhead largely overlap, the survey area was expanded to cover the summer range of bowhead whales. Bowhead whale abundance was estimated using 3 aircraft to cover the large survey area within a short time frame. Distance sampling methods were used to estimate detection probability away from the track line. Double platform with mark-recapture methods were used to correct for the proportion of whales missed by visual observers on the track line (perception bias). Abundance in Isabella Bay, an area known for high bowhead density, was estimated using density surface modelling to account for its complex shape and uneven coverage. Estimates were corrected for availability bias (whales that were not available for detection because they were submerged when the aircraft passed overhead) using a recent analysis of satellite-linked time depth recorders transmitting information on the diving behaviour of bowhead whales in the study area in August of the same survey year. The fully corrected abundance estimate for the EC-WG bowhead whale population was 6,446 (95% CI: 3,838–10,827). Possible sources of uncertainty include incomplete coverage and the diving behaviour of bowhead whales. These results confirm earlier indications that the EC-WG stock is continuing to recover from past overexploitation.

In summer, narwhals (Monodon monoceros) migrate from Baffin Bay to northeastern Canada and northwest Greenland, where they are hunted by Inuit for subsistence. To prevent localized depletion, management of narwhals is based on summer stocks. The High Arctic Cetacean Survey (HACS), conducted in August 2013, was the first survey to estimate abundance of all 4 Canadian Baffin Bay narwhal summer stocks, as well as putative stocks in Jones Sound and Smith Sound, in the same summer. Narwhal abundance was estimated using a double-platform aerial survey. Distance sampling methods were used to estimate detection probability away from the track line. Mark-recapture methods were used to correct for the proportion of narwhals missed by visual observers on the track line (i.e., perception bias). We used a data-driven approach to identify single and duplicate sightings, using 4 covariates to compare differences in sightings made by front and rear observers based on: time of sighting, declination angle, group size, and species identity. Abundance in fjords was estimated using density surface modelling to account for their complex shape and uneven coverage. Estimates were corrected for availability bias (narwhals that are not available for detection because they are submerged when the aircraft passes overhead) using a new analysis of August dive behaviour data from narwhals equipped with satellite-linked time depth recorders. Corrected abundance estimates were 12,694 (95% CI: 6,324–25,481) for the Jones Sound stock; 16,360 (95% CI: 3,833–69,836) for the Smith Sound stock; 49,768 (95% CI: 32,945–75,182) for the Somerset Island stock; 35,043 (95% CI: 14,188–86,553) for the Admiralty Inlet stock; 10,489 (95% CI: 6,342–17,347) for the Eclipse Sound stock; and 17,555 (95% CI: 8,473–36,373) for the East Baffin Island stock. Total abundance for these 6 stocks was estimated at 141,908 (95% CI: 102,464–196,536). Sources of uncertainty arise from the high level of clustering observed, in particular in Admiralty Inlet, Eclipse Sound, and East Baffin Island, as well as the difficulty in identifying duplicate sightings between observers when large aggregations were encountered.

International audience; Blue whale survival and fitness are highly contingent on successful food intake during an intense feeding season. Factors affecting time spent at the surface or at depth in a prey patch are likely to alter foraging effort, net energy gain, and fitness. We specifically examined the energetic consequences of a demonstrated reduction in dive duration caused by vessel proximity, and of krill density reductions potentially resulting from krill exploitation or climate change. We estimated net energy gain over a simulated 10-h foraging bout under baseline conditions, and three scenarios, reflecting krill density reductions, vessel interactions of different amplitudes, and their combined effects. Generally, the magnitude of the effects increased with that of krill density reductions and duration of vessel proximity. They were also smaller when peak densities were more accessible, i.e., nearer to the surface. Effect size from a reduction in krill density on net energy gain were deemed small to moderate at 5% krill reduction, moderate to large at 10% reduction, and large at 25 and 50% reductions. Vessels reduced cumulated net energy gain by as much as 25% when in proximity for 3 of a 10-h daylight foraging period, and by up to 47–85% when continuously present for 10 h. The impacts of vessel proximity on net energy gain increased with their duration. They were more important when whales were precluded from reaching the most beneficial peak densities, and when these densities were located at deeper depths. When krill densities were decreased by 5% or more, disturbing foraging blue whales for 3 h could reduce their net energy gain by ≥30%. For this endangered western North Atlantic blue whale population, a decrease in net energy gain through an altered krill preyscape or repeated vessel interactions is of particular concern, as this species relies on a relatively short feeding season to accumulate energy reserves and to fuel reproduction. This study highlights the importance of distance limits during whale-watching operations to ensure efficient feeding, as well as the vulnerability of this specialist to fluctuations in krill densities.

Fish stock assessments routinely integrate catch data. Misreported catches, however, can lead to biased estimates of stock size, production, reference points and poor advice on stock exploitation. Canadian Atlantic mackerel (Scomber scombrus) landings are thought to be significantly underestimated because this stock is subject to large bait and recreational fisheries that are not required to report catches. As this might lead to stock size underestimation, we developed a state-space age-structured model that accounts for catch data uncertainties using a censored catch method, which involves data on lower and upper catch limits. We explored how censoring influences parameter estimates and six common reference points, and their sensitivity to the choice of an upper catch limit. Modelling catch as a censored random variable led to more realistic estimation of state variables such as a higher estimate of SSB. The relationship between reference points and the range of possible catches was not straightforward, but F0.1, Fmax and F40% were more stable than SSBmsy, Fmsy and Fmed. Applying the censored catch approach to Canadian Atlantic mackerel highlighted the importance of informing the upper catch limit when faced with other sources of uncertainty and showed that it was crucial to provide realistic management advice.

Blue whales Balaenoptera musculus occur seasonally in the St. Lawrence Estuary, Canada, where they spend most of their time foraging. Their recurrent presence has stimulated the development of a large whale-watching industry. Here, we examine the effect of vessel distance on blue whale foraging behaviour by measuring changes in surface and diving patterns. Vessels were within 2000 m of blue whales during 70% of 33 follows, and 59% of total observation time. At vessel distances ≤400 m, surface and dive times were on average 49 and 36% shorter, respectively, and the number of breaths taken by the whales was reduced by 51% compared to control observations without vessel presence within 2000 m of whales. The consequent reduction in foraging time was likely greater than 36%, given that transit time is incompressible and foraging depth is dictated by where krill densities are located. We showed that the relative proportion of lost foraging time from vessel exposure increased exponentially with prey depth. Whales were unable to compensate for lost feeding opportunities by increasing diving rate or swim speed, except when feeding within 10 to 15 m of the surface. Our results indicate that preventing vessels from entering within a 400 m radius around blue whales can help reduce the negative effects of marine recreational activities on blue whale foraging.

Significance Why humans and some species of whales go through menopause remains an evolutionary puzzle. In humans, postreproductive females gain genetic benefits by helping family members—particularly increasing their number of surviving grandoffspring. The extent to which these grandmother benefits are important in the evolution of menopause in whales remains unclear. Here, we test the grandmother effect in resident killer whales, where females can live for decades after their last reproductive event. We show that grandmothers increase the survival of their grandoffspring, and these effects are greatest when grandmothers are no longer reproducing. These findings can help explain why killer whales have evolved the longest postreproductive life span of all nonhuman animals., Understanding why females of some mammalian species cease ovulation prior to the end of life is a long-standing interdisciplinary and evolutionary challenge. In humans and some species of toothed whales, females can live for decades after stopping reproduction. This unusual life history trait is thought to have evolved, in part, due to the inclusive fitness benefits that postreproductive females gain by helping kin. In humans, grandmothers gain inclusive fitness benefits by increasing their number of surviving grandoffspring, referred to as the grandmother effect. Among toothed whales, the grandmother effect has not been rigorously tested. Here, we test for the grandmother effect in killer whales, by quantifying grandoffspring survival with living or recently deceased reproductive and postreproductive grandmothers, and show that postreproductive grandmothers provide significant survival benefits to their grandoffspring above that provided by reproductive grandmothers. This provides evidence of the grandmother effect in a nonhuman menopausal species. By stopping reproduction, grandmothers avoid reproductive conflict with their daughters, and offer increased benefits to their grandoffspring. The benefits postreproductive grandmothers provide to their grandoffspring are shown to be most important in difficult times where the salmon abundance is low to moderate. The postreproductive grandmother effect we report, together with the known costs of late-life reproduction in killer whales, can help explain the long postreproductive life spans of resident killer whales.

International audience; Foraging efficiency (FE) is determined by the ratio of energy intake to energy expenditure and represents a metric for estimating the capacity to store energy. Blue whales Balaenoptera musculus rely mostly on stored energy reserves for reproduction. They feed almost exclusively on krill, which vary in density and abundance both spatially and temporally. We used 10 depth-velocity archival tags deployed on blue whales foraging in the St. Lawrence Estuary, Canada, to identify feeding events. We modeled krill densities required to equal or exceed energy expenditures and allow energy storage. During the daytime, blue whales generally dove deeper and performed fewer but longer feeding dives than at other times of the diel cycle (10 vs. 28 feeding dives h-1); however, they performed more lunges per dive during daytime (3 vs. 1 lunge dive-1), which resulted in a stable feeding rate around the clock. Only 11.7 and 5.5% of the Arctic and northern krill patches measured in situ contained densities allowing blue whales to achieve neutral energetic balance (FE = 1); less than 1.5% of patches allowed FE of ≥3. While FE leading to successful reproduction and adequate fitness is unknown, these results underscore the necessity for blue whales to seek the highest densities within patches to reach neutral balance or allow energy storage. These findings further our understanding of blue whale foraging ecology and habitat suitability, and may help predict the effects of climate and natural variability or of potential fisheries on krill densities and blue whale condition.

Like numerous species at risk, the resident killer whale populations of the Northeast Pacific are vulnerable to the cumulative effects of anthropogenic threats. A Pathways of Effects conceptual model summarised the current understanding of each threat (prey availability, acoustic and physical disturbance, and contaminants), threat interactions, and potential impacts to fecundity and mortality. A Population Viability Analysis utilised the most recent available data to quantify impacts of threats on population parameters. The impacts of individual and cumulative threat scenarios on modelled Southern and Northern Resident Killer Whale populations were compared to the observed population demographics to define a model that best captured the real world dynamics. Of the individual and combined threat models tested, the cumulative model incorporating all threats predicted demographic rates closest to those observed for both populations. Recent low Chinook salmon abundance and its interactions with vessel disturbance and contamination strongly influenced modelled killer whale population dynamics. The cumulative effects population viability analysis model projected a mean increase in the modelled Northern Resident Killer Whale population to the carrying capacity within 25 years. In contrast, the mean modelled Southern Resident Killer Whale population trajectory was projected to decline under current conditions, with a 26% probability of population extinction, and in those projections, extinction was estimated to occur after 86 (± 11) years. Our results highlight the importance of considering the collective impact of multiple threats to imperilled species and the necessity of testing management and mitigation measures aimed at recovery using a holistic, validated model.

Harp seals require pack ice as a platform for resting, to give birth and nurse their young. They are also subject to commercial and subsistence harvesting. In the late 1990's there were concerns that the Northwest Atlantic population would decline to very low levels unless a management system using Potential Biological Removals (PBR) was adopted. Canada followed a different approach and high harvests based on an alternative management framework continued throughout the next decade. We examined the status of the Northwest Atlantic harp seal population using a three parameter population model that incorporates information on reproductive rates, removals, and ice-related mortality acting on young of the year. By 1971, the population had declined to a minimum of 1.1 million animals and a quota was introduced, which allowed the population to increase. In 1996, the quota was raised and harvests increased substantially. Population growth continued, even as herd productivity declined. The population reached a maximum of 7.8 million animals in 2008 and has leveled off at around 7.4 million animals. Climate change is expected to result in a decline in the amount of seasonal pack ice in Atlantic Canada, which adds uncertainty to the future of this population. Although the results presented in this paper focused on how the status of this population has evolved over the last 60 years, our integrated modeling approach can also be used to examine scenarios that project into the future, to test the impacts of various management decisions in a changing environment.

Real-time passive acoustic monitoring (PAM) was conducted as part of the first large-scale systematic survey of marine mammal distribution and abundance in territorial outer-coastal waters off western Canada. The survey was conducted for approximately five weeks, from early July to mid-August, 2018 using a 69-m Canadian Coast Guard and Fisheries Vessel. A four-element, towed hydrophone array and sonobuoys (type AN/SSQ-53F and AN/SSQ-53G) were deployed and monitored in real-time 24/7 by experienced bio-acousticians. The towed array was deployed for approximately 640 h (78% of the time underway). Approximately 145 independent detections of marine mammals were made. Sperm whales (n = 53) and porpoises (n= 49) represented just over two-thirds of all detections. Ten near real-time localizations of sperm whales and 31 localization of porpoises were made, representing just over 60% of all (n = 67) all high-quality localizations. Sonobuoy effort consisted of semi-systematic deployments day and night and when sightings of large baleen whales occurred. A total of 112 sonobuoys were deployed successfully and monitored in directional (DIFAR) mode. Marine mammal calls were recorded on 32% (36) of sonobuoys. Sonobuoy detections included killer whales calls and clicks, sperm whale clicks, dolphin clicks, fin whale calls, and blue whale calls (both B and D type calls).

Effective conservation of animal species depends on accurate identification of their critical habitat. Marine mammals, however, often transit through heterogeneous habitats and perform various activities within short periods of time. The predictive power of habitat modelling techniques can thus suffer from variability in behaviour and the use of multiple habitat types. We used data loggers and ecological-niche factor analysis (ENFA) modelling techniques to determine blue whale Balaenoptera musculus associations with underwater topography on a feeding ground in the St. Lawrence River estuary, Canada. We compared a naive model that had no knowledge of behaviour with a model that used the locations of feeding events inferred from specific velocity signatures. Blue whales travelled over several habitat types with different characteristics, which confounded modelling efforts when pooled together. The model based on the feeding set had considerably higher predictive power but could not highlight all suitable habitats at the same time. Using cluster analysis, we identified 4 habitat types used for feeding, each corresponding to distinct underwater topographies. Feeding depth and behaviour varied across these habitats, which were used preferentially at different times of the tidal cycle and appeared linked to known prey aggregation mechanisms. Our results suggest that failure to identify feeding activity or to take into account the existence of multiple foraging habitats at a fine scale could result in spurious modelling results.

Accurate predictions of predator behavior remain elusive in natural settings. Optimal foraging theory predicts that breath-hold divers should adjust time allocation within their dives to the distance separating prey from the surface. Quantitative tests of these models have been hampered by the difficulty of documenting underwater feeding behavior and the lack of systems, experimental or natural, in which prey depth varies over a large range. We tested these predictions on blue whales (Balaenoptera musculus), which track the diel vertical migration of their prey. A model using simple allometric arguments successfully predicted diving behavior measured with data loggers. Foraging times within each dive increased to compensate longer transit times and optimize resource acquisition. Shallow dives were short and yielded the highest feeding rates, explaining why feeding activity was more intense at night. An optimal framework thus provides powerful tools to predict the behavior of free-ranging marine predators and inform conservation studies. Copyright 2011, Oxford University Press.

Understanding the factors influencing habitat selection is critical to improving manage- ment and conservation plans for large whales. Many studies have linked the distribution of cetaceans to basic environmental features such as underwater topography and sea surface temperature (SST), but the mechanisms underlying these relationships are poorly understood. Dynamic mesoscale pro- cesses like thermal fronts are prime candidates to link physiographic factors to whale distribution because they increase biological productivity and aggregate prey. However, previous studies of large whales have found little evidence of such associations, possibly because they were not at the appro- priate spatio-temporal scales. We quantified the relationship between SST fronts and the distribution of blue Balaenoptera musculus, finback B. physalus, humpback Megaptera novaeangliae and minke B. acutorostrata whales in the northern Gulf of St. Lawrence. We compared the distribution of 1094 whale sightings collected from boat surveys conducted in 1996 to 2000 to the locations of frontal areas determined from 61 satellite maps. The distributions of whales and thermal fronts were highly corre- lated (random resampling and Mantel tests of matrix similarity). Spatial distributions differed among species, probably reflecting differences in feeding strategies. Identification of surface fronts from satellite imagery thus effectively complemented field observations of whales. These findings signifi- cantly increase our understanding of habitat quality in rorqual whales, and encourage a greater use of dynamic environmental variables in future studies of whale habitat use.

Integrated population models combine data from several sources into a single model to allow the simultaneous estimation of demographic parameters and the prediction of population trajectories. They are especially useful when survey data alone are insufficient to estimate precise vital rates and abundance, and to understand mechanisms of population growth and decline. The St. Lawrence Estuary (SLE) beluga population was depleted by intensive hunting over the past century, and had declined to 1000 individuals or less when it was afforded protection in 1979. Despite protective measures, the SLE population has shown no signs of recovery. Low abundance estimates and high calf mortalities observed in recent years have raised concerns as to its current status. An age-structured Bayesian model was used to describe population dynamics by integrating information from two different monitoring programs. The model included information on population size and proportion of young (