Marine Ecology and Telemetry Research

BackgroundAn animal’s movement reflects behavioral decisions made to address ecological needs; specifically, that movement will become less directional in regions with high prey availability, indicating foraging behavior. In the marine realm, animal behavior occurs below the sea surface and is difficult to observe. We used an extensive satellite tagging dataset to explore how physical and biological habitat characteristics influence blue (Balaenoptera musculus) and fin (B. physalus) whale movement and foraging behavior in the California Current Ecosystem across four known bioregions.MethodsWe fitted movement models to 14 years of blue whale satellite tracking data and 13 years of fin whale data to characterize their movement persistence, with higher move persistence values representing more directional movement and lower move persistence values representing less directional movement. Models were evaluated against a range of physical and biological environmental predictors to identify significant correlates of low move persistence (i.e., presumed intensified foraging behavior). We then used data from a subset of sensor-equipped tags that monitored vertical behavior (e.g., dive and feeding), in addition to movement, to test the relationship between vertical behavior and movement persistence.ResultsLow move persistence was strongly correlated with shallower water depth and sea surface height for both species, with additional effects of chlorophyll-a concentration, vorticity and marine nekton biomass for blue whales. Data from sensor-equipped tags additionally showed that low move persistence occurred when whales made more numerous feeding dives. Temporal patterns of bioregion occupancy coincided with seasonal peaks in productivity. Most blue whale low-move-persistence movements occurred in the northern, nearshore bioregion with a late-season peak in productivity and were evenly distributed across all bioregions for fin whales.ConclusionsWe demonstrated that low move persistence is indicative of increased feeding behavior for both blue and fin whales. The environmental drivers of low move persistence were similar to those previously identified for survey-based species distribution models, linking environmental metrics to subsurface behavior. Occupancy and movement behavior patterns across bioregions indicate both species moved to exploit seasonal and spatial variability in productivity, with blue whales especially focusing on the bioregion of highest productivity during late summer and fall.

Risso’s dolphins (Grampus griseus), uncommon prior to the 1970’s, are now regularly observed within the Southern California Bight. During long-term cetacean monitoring programs on United States Navy range areas in the Southern California Bight from 2009–2019, we deployed 16 Argos-linked satellite tags on Risso’s to acquire objective, detailed depictions of their movements and behaviors. Individuals were tracked for a median of 10.7 days (range = 0.8 – 19.7). Kernel density estimation suggested individuals utilized the entire Southern California Bight with the 50% core use area centered around San Clemente and Santa Catalina Islands where most of the tag deployments occurred. Grand median dive depth was 101 m (max = 528) and dive duration was 5.6 min (max = 11.1). We used generalized mixed models to assess seasonal and environmental effects on distribution and diving behavior including month, distance to shore, time of day, lunar phase, sea surface temperature, and chlorophyll-aresiduals. Animals were further from shore (including islands) during a full versus new moon and from the mainland during the last versus first quarter moon. Animals also tended to be closer to land in the fall and early winter months. Dives were deeper yet shorter during the night, during a full moon, and when animals were further offshore. Animals conducted nearly twice as many dives at night compared to day, though deep dives (> 500 m) occurred at all times of day. This study provides insights into Risso’s distribution and behavioral trends while identifying priorities for future research.

The population of humpback whales (Megaptera novaeangliae) wintering off eastern South America was exploited by commercial whaling almost to the point of extinction in the mid-twentieth century. Since cessation of whaling in the 1970s it is recovering, but the timing and level of recovery is uncertain. We implemented a Bayesian population dynamics model describing the population’s trajectory from 1901 and projecting it to 2040 to revise a previous population status assessment that used Sampling-Importance-Resampling in a Bayesian framework. Using our alternative method for model fitting (Markov chain Monte Carlo), which is more widely accessible to ecologists, we replicate a “base case scenario” to verify the effect on model results, and introduce additional data to update the status assessment. Our approach allowed us to widen the previous informative prior on carrying capacity to better reflect scientific uncertainty around historical population levels. The updated model provided more precise estimates for population sizes over the period considered (1901–2040) and suggests that carrying capacity (K: median 22,882, mean 22,948, 95% credible interval [CI] 22,711–23,545) and minimum population size (N1958: median 305, mean 319, 95% CI 271–444) might be lower than previously estimated (K: median 24,558, mean 25,110, 95% CI 22,791–31,118; N1958: median 503, mean 850, 95% CI 159–3,943). However, posterior 95% credible intervals of parameters in the updated model overlap those of the previous study. Our approach provides an accessible framework for investigating the status of depleted animal populations for which information is available on historical mortality (e.g., catches) and intermittent estimates of population size and/or trend.

To better understand pygmy blue whale movement and residency in New Zealand waters, we conducted a vessel-based survey with the aim of attaching satellite tags to several individuals. This survey also provided an opportunity to collect blue whale distribution data, photographic data for individual identification, and genetic data for subspecies confirmation. Both tracking and sighting data were linked to environmental data to quantify differences in oceanographic features between areas where animals were present and absent and in areas where animals spent significantly longer periods of time. Given the documented importance of the STB to blue whales, we expected satellite-tagged animals to spend some proportion of time in this region.

Management of gases during diving is not well understood across marine mammal species. Prior to diving, phocid (true) seals generally exhale, a behaviour thought to assist with the prevention of decompression sickness. Otariid seals (fur seals and sea lions) have a greater reliance on their lung oxygen stores, and inhale prior to diving. One otariid, the Antarctic fur seal (Arctocephalus gazella), then exhales during the final 50-85% of the return to the surface, which may prevent another gas management issue: shallow-water blackout. Here, we compare data collected from animal-attached tags (video cameras, hydrophones and conductivity sensors) deployed on a suite of otariid seal species to examine the ubiquity of ascent exhalations for this group. We find evidence for ascent exhalations across four fur seal species, but that such exhalations are absent for three sea lion species. Fur seals and sea lions are no longer genetically separated into distinct subfamilies, but are morphologically distinguished by the thick underfur layer of fur seals. Together with their smaller size and energetic dives, we suggest their air-filled fur might underlie the need to perform these exhalations, although whether to reduce buoyancy and ascent speed, for the avoidance of shallow-water blackout or to prevent other cardiovascular management issues in their diving remains unclear. This article is part of the theme issue 'Measuring physiology in free-living animals (Part I)'.

Fin whale 20 Hz calls were detected, localized, and tracked using a 10 km aperture network of three acoustic receivers deployed for 11 months in a Pacific Canadian fjord system. The area has been historically important for fin whales and is located along a route that tankers will begin using in 2024. A total of 6712 calls were localized, and trajectories were fitted for 55 acoustic tracks. Fin whale tracks occurred throughout the monitoring site. Call activity peaked in September and was low during winter months. Swimming characteristics varied significantly between day- and nighttime: at night, whales swam faster (7.1 vs 4.0 km/h median, +75.2%), which resulted in longer (+34.7%), less predictable (-70.6%) tracks as compared to daylight hours. Call frequencies varied between 16 and 32 Hz. Beside stereotypical song frequencies, fin whales also used irregular frequency components, which contributed the majority of calls in the summer but did not occur in the winter. The results suggest that the area is primarily used as a summer feeding ground, where fin whales follow a diel behavioral cycle. The observed activity patterns will aid in the assessment of strike risk and harassment mitigation and provide a baseline to document behavioral change.

We consider the classic problem of scheduling jobs with precedence constraints on identical machines to minimize makespan, in the presence of communication delays. In this setting, denoted by P | prec, c | Cmax, if two dependent jobs are scheduled on different machines, then at least c units of time must pass between their executions. Despite its relevance to many applications, this model remains one of the most poorly understood in scheduling theory. Even for a special case where an unlimited number of machines is available, the best known approximation ratio is 2/3·(c+1), whereas Graham's greedy list scheduling algorithm already gives a ( c+1) -approximation in that setting. An outstanding open problem in the top-10 list by Schuurman and Woeginger and its recent update by Bansal asks whether there exists a constant-factor approximation algorithm. In this work we give a polynomial-time O(logc·logm)-approximation algorithm for this problem, where m is the number of machines and c is the communication delay. Our approach is based on a Sherali-Adams lift of a linear programming relaxation and a randomized clustering of the semimetric space induced by this lift. The full version of this paper is available on arXiv.