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.
Resources in the ocean are ephemeral, and effective management must therefore account for the dynamic spatial and temporal patterns of ecosystems and species of concern. We focus on the South ...Taranaki Bight (STB) of New Zealand, where upwelling generates productivity and prey to support an important foraging ground for blue whales that overlaps with anthropogenic pressure from industrial activities.
We incorporate regional ecological knowledge of upwelling dynamics, physical–biological coupling and associated lags in models to forecast sea surface temperature (SST) and net primary productivity (NPP) with up to 3 weeks lead time. Forecasted environmental layers are then implemented in species distribution models to predict suitable blue whale habitat in the STB. Models were calibrated using data from the austral summers of 2009–2019, and ecological forecast skill was evaluated by predicting to withheld data.
Boosted regression tree models skilfully forecasted SST (CV deviance explained = 0.969–0.970) and NPP (CV deviance explained = 0.738–0.824). The subsequent blue whale distribution forecast models had high predictive performance (AUC = 0.889), effectively forecasting suitable habitat on a daily scale with 1–3 weeks lead time.
The spatial location and extent of forecasted blue whale habitat were variable, with the proportion of petroleum and mineral permit areas that overlapped with daily suitable habitat ranging from 0% to 70%. Hence, the STB and these forecast models are well‐suited for dynamic management that could reduce anthropogenic threats to whales while decreasing regulatory burdens to industry users relative to a traditional static protected area.
Synthesis and applications. We develop and test ecological forecast models that predict sea surface temperature, net primary productivity and blue whale suitable habitat up to 3 weeks in the future within New Zealand's South Taranaki Bight region. These forecasts of whale distribution can be effectively applied for dynamic spatial management due to model foundation on quantified links and lags between physical forcing and biological responses. A framework to operationalize these forecasts through a user‐driven application is in development to proactively inform conservation management decisions. This framework is implemented through stakeholder engagement, allows flexibility based on management objectives, and is amenable to improvement as new knowledge and feedback are received.
We develop and test ecological forecast models that predict sea surface temperature, net primary productivity and blue whale suitable habitat up to 3 weeks in the future within New Zealand's South Taranaki Bight region. These forecasts of whale distribution can be effectively applied for dynamic spatial management due to model foundation on quantified links and lags between physical forcing and biological responses. A framework to operationalize these forecasts through a user‐driven application is in development to proactively inform conservation management decisions. This framework is implemented through stakeholder engagement, allows flexibility based on management objectives, and is amenable to improvement as new knowledge and feedback are received.
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.
Links in the trophic chain Barlow, Dawn R.; Bernard, Kim S.; Escobar-Flores, Pablo ...
Marine ecology. Progress series,
05/2020, Letnik:
642
Journal Article
Recenzirano
Odprti dostop
The response of marine predators to global climate change and shifting ocean conditions is tightly linked with their environment and prey. Environmental data are frequently used as proxies for prey ...availability in marine predator distribution models, as the ephemeral nature of prey makes sampling difficult. For this reason, the functional, ecological links between environment, prey, and predator are rarely described or explicitly tested. We used 3 years of vessel-based whale survey data paired with oceanographic sampling and hydroacoustic backscatter to model trophic relationships between water column structure, krill availability, and blue whale Balaenoptera musculus brevicauda distribution in New Zealand’s South Taranaki Bight region under typical (2014 and 2017) and warm (2016) austral summer oceanographic regimes. The warm regime was characterized by a shallower mixed layer, and a stronger, thicker, and warmer thermocline. Boosted regression tree models showed that krill metrics predicted blue whale distribution (typical regime = 36% versus warm regime = 64% cross-validated deviance explained) better than oceanography (typical regime = 19% versus warm regime = 31% cross-validated deviance explained). However, oceanographic features that predicted more krill aggregations (typical regime) and higher krill density (warm regime) aligned closely with the features that predicted higher probability of blue whale presence in each regime. Therefore, this study confirms that environmental drivers of prey availability can serve as suitable proxies for blue whale distribution. Considering changing ocean conditions that may influence the distribution of marine predators, these findings emphasize the need for models based on functional relationships, and calibrated across a broad range of conditions, to inform effective conservation management.
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 (
) 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.
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).
Our work links animal behavior and oceanography to provide insights into species life history and investigate the impacts of environmental change. We examine environmental correlates of blue whale vocalization patterns in Aotearoa New Zealand. Our results demonstrate the vulnerability of marine predator populations to marine heatwaves due to the links between environmental factors that control prey availability, foraging effort, and reproductive effort.
Describing spatial and temporal occurrence patterns of wild animal populations is important for understanding their evolutionary trajectories, population connectivity, and ecological niche ...specialization, with relevance for effective management. Throughout the world, blue whales produce stereotyped songs that enable identification of separate acoustic populations. We harnessed continuous acoustic recordings from five hydrophones deployed in the South Taranaki Bight (STB) region of Aotearoa New Zealand from January 2016 to February 2018. We examined hourly presence of songs from three different blue whale populations to investigate their contrasting ecological use of New Zealand waters. The New Zealand song was detected year-round with a seasonal cycle in intensity (peak February–July), demonstrating the importance of the region to the New Zealand population as both a foraging ground and potential breeding area. The Antarctic song was present in two distinct peaks each year (June–July; September–October) and predominantly at the offshore recording locations, suggesting northbound and southbound migration between feeding and wintering grounds. The Australian song was only detected during a 10-day period in January 2017, implying a rare vagrant occurrence. We therefore infer that the STB region is the primary niche of the New Zealand population, a migratory corridor for the Antarctic population, and outside the typical range of the Australian population.
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
-tests,
> 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:
= 0.277,
< 0.0001; song:
= 0.080,
= 0.028); however, these same relationships were true for 'null' periods without earthquakes (D calls:
= 0.262,
< 0.0001; song:
= 0.149,
= 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.
Skin condition assessment of wildlife can provide insight into individual and population health. Yet, logistics can limit skin condition assessment of large whales. We developed a standardized, ...quantitative protocol using photographs to assess skin condition of blue whales in New Zealand, and demonstrate the value gained by testing hypotheses, documenting new morphologies, and establishing baselines that can be monitored for change. We reviewed a photo-identification catalog to compile common markings, categorized markings according to existing definitions, and described markings not previously documented. Photographs of blue whale skin (n=1,466) were assessed to quantify marking prevalence, severity, and co-occurrence patterns. Of the whales assessed (n=148), 96.6% had cookie cutter shark bites, 80.4% had blister lesions, 56.0% had pigmentation blazes on the dorsal fin, and 33.7% had holes in the dorsal fin. Additionally, 35.8% had “starburst” lesions, a previously undocumented marking. Blister and cookie cutter shark bite severity did not accumulate linearly, indicating that the two marking types are unrelated. There was a positive relationship between blister severity and number of starbursts, indicating that the two could be related; based on morphological similarities, starburst lesions may derive from ruptured blisters. Whales with holes in their dorsal fin had significantly higher blister severity than those without, indicating that these markings could be related; this is supported by observed blisters on dorsal fins of blue whales. There was a significantly higher probability of fresher cookie cutter shark bites on whales observed at more northerly latitudes, but no relationship between blister severity or number of starbursts and latitude. These latitudinal patterns indicate that blue whales in New Zealand accumulate cookie cutter shark bites at more northerly latitudes; this finding is supported by the known range of cookie cutter sharks in New Zealand waters. Of the eight individual whales re-sighted across multiple years, there was no uniform pattern in lesion change over time, however individual cases revealed lesion healing over a multi-year timeframe. Our protocol for quantifying skin condition can be applied to any cetacean photo-identification catalog, and can be used to compare across individuals and populations, and explore causal links between skin condition and cetacean health.
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