Three decades of nearshore surveys reveal long-term patterns in gray whale habitat use, distribution, and abundance in the Northern California Current.

The nearshore waters of the Northern California Current support an important seasonal foraging ground for Pacific Coast Feeding Group (PCFG) gray whales. We examine gray whale distribution, habitat use, and abundance over 31 years (1992-2022) using standardized nearshore (< 5 km from shore) surveys spanning a large swath of the PCFG foraging range. Specifically, we generated density surface models, which incorporate detection probability into generalized additive models to assess environmental correlates of gray whale distribution and predict abundance over time. We illustrate the importance of coastal upwelling dynamics, whereby increased upwelling only yields higher gray whale density if interspersed with relaxation events, likely because this combination optimizes influx and retention of nutrients to support recruitment and aggregation of gray whale prey. Several habitat features influence gray whale distribution, including substrate, shelf width, prominent capes, and river estuaries. However, the influence of these features differs between regions, revealing heterogeneity in habitat preferences throughout the PCFG foraging range. Predicted gray whale abundance fluctuated throughout our study period, but without clear directional trends, unlike previous abundance estimates based on mark-recapture models. This study highlights the value of long-term monitoring, shedding light on the impacts of variable environmental conditions on an iconic nearshore marine predator.

Environmental conditions and marine heatwaves influence blue whale foraging and reproductive effort.

Animal behavior is motivated by the fundamental need to feed and reproduce, and these behaviors can be inferred from spatiotemporal variations in biological signals such as vocalizations. Yet, linking foraging and reproductive effort to environmental drivers can be challenging for wide-ranging predator species. Blue whales are acoustically active marine predators that produce two distinct vocalizations: song and D calls. We examined environmental correlates of these vocalizations using continuous recordings from five hydrophones in the South Taranaki Bight region of Aotearoa New Zealand to investigate call behavior relative to ocean conditions and infer life history patterns. D calls were strongly correlated with oceanographic drivers of upwelling in spring and summer, indicating associations with foraging effort. In contrast, song displayed a highly seasonal pattern with peak intensity in fall, which aligned with the timing of conception inferred from whaling records. Finally, during a marine heatwave, reduced foraging (inferred from D calls) was followed by lower reproductive effort (inferred from song intensity).

Shaken, not stirred: blue whales show no acoustic response to earthquake events.

Quantifying how animals respond to disturbance events bears relevance for understanding consequences to population health. We investigate whether blue whales respond acoustically to naturally occurring episodic noise by examining calling before and after earthquakes (27 040 calls, 32 earthquakes; 27 January-29 June 2016). Two vocalization types were evaluated: New Zealand blue whale song and downswept vocalizations ('D calls'). Blue whales did not alter the number of D calls, D call received level or song intensity following earthquakes (paired t-tests, p > 0.7 for all). Linear models accounting for earthquake strength and proximity revealed significant relationships between change in calling activity surrounding earthquakes and prior calling activity (D calls: R 2 = 0.277, p < 0.0001; song: R 2 = 0.080, p = 0.028); however, these same relationships were true for 'null' periods without earthquakes (D calls: R 2 = 0.262, p < 0.0001; song: R 2 = 0.149, p = 0.0002), indicating that the pattern is driven by blue whale calling context regardless of earthquake presence. Our findings that blue whales do not respond to episodic natural noise provide context for interpreting documented acoustic responses to anthropogenic noise sources, including shipping traffic and petroleum development, indicating that they potentially evolved tolerance for natural noise sources but not novel noise from anthropogenic origins.

Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence.

Understanding relationships between physical drivers and biological response is central to advancing ecological knowledge. Wind is the physical forcing mechanism in coastal upwelling systems, however lags between wind input and biological responses are seldom quantified for marine predators. Lags were examined between wind at an upwelling source, decreased temperatures along the upwelling plume's trajectory, and blue whale occurrence in New Zealand's South Taranaki Bight region (STB). Wind speed and sea surface temperature (SST) were extracted for austral spring-summer months between 2009 and 2019. A hydrophone recorded blue whale vocalizations October 2016-March 2017. Timeseries cross-correlation analyses were conducted between wind speed, SST at different locations along the upwelling plume, and blue whale downswept vocalizations (D calls). Results document increasing lag times (0-2 weeks) between wind speed and SST consistent with the spatial progression of upwelling, culminating with increased D call density at the distal end of the plume three weeks after increased wind speeds at the upwelling source. Lag between wind events and blue whale aggregations (n = 34 aggregations 2013-2019) was 2.09 ± 0.43 weeks. Variation in lag was significantly related to the amount of wind over the preceding 30 days, which likely influences stratification. This study enhances knowledge of physical-biological coupling in upwelling ecosystems and enables improved forecasting of species distribution patterns for dynamic management.

Insight into the kinematics of blue whale surface foraging through drone observations and prey data.

To understand how predators optimize foraging strategies, extensive knowledge of predator behavior and prey distribution is needed. Blue whales employ an energetically demanding lunge feeding method that requires the whales to selectively feed where energetic gain exceeds energetic loss, while also balancing oxygen consumption, breath holding capacity, and surface recuperation time. Hence, blue whale foraging behavior is primarily driven by krill patch density and depth, but many studies have not fully considered surface feeding as a significant foraging strategy in energetic models. We collected predator and prey data on a blue whale (Balaenoptera musculus brevicauda) foraging ground in New Zealand in February 2017 to assess the distributional and behavioral response of blue whales to the distribution and density of krill prey aggregations. Krill density across the study region was greater toward the surface (upper 20 m), and blue whales were encountered where prey was relatively shallow and more dense. This relationship was particularly evident where foraging and surface lunge feeding were observed. Furthermore, New Zealand blue whales also had relatively short dive times (2.83 ± 0.27 SE min) as compared to other blue whale populations, which became even shorter at foraging sightings and where surface lunge feeding was observed. Using an unmanned aerial system (UAS; drone) we also captured unique video of a New Zealand blue whale's surface feeding behavior on well-illuminated krill patches. Video analysis illustrates the whale's potential use of vision to target prey, make foraging decisions, and orient body mechanics relative to prey patch characteristics. Kinematic analysis of a surface lunge feeding event revealed biomechanical coordination through speed, acceleration, head inclination, roll, and distance from krill patch to maximize prey engulfment. We compared these lunge kinematics to data previously reported from tagged blue whale lunges at depth to demonstrate strong similarities, and provide rare measurements of gape size, and krill response distance and time. These findings elucidate the predator-prey relationship between blue whales and krill, and provide support for the hypothesis that surface feeding by New Zealand blue whales is an important component to their foraging ecology used to optimize their energetic efficiency. Understanding how blue whales make foraging decisions presents logistical challenges, which may cause incomplete sampling and biased ecological knowledge if portions of their foraging behavior are undocumented. We conclude that surface foraging could be an important strategy for blue whales, and integration of UAS with tag-based studies may expand our understanding of their foraging ecology by examining surface feeding events in conjunction with behaviors at depth.