Western gray whales on their summer feeding ground off Sakhalin Island in 2015: who is foraging where?

In 2015, two oil and gas companies conducted seismic surveys along the northeast coast of Sakhalin Island, Russia, near western gray whale (Eschrichtius robustus) feeding areas. This population of whales was listed as Critically Endangered at the time of the operations described here but has been reclassified as Endangered since 2018. The number and duration of the 2015 seismic surveys surpassed the level of previous seismic survey activity in this area, elevating concerns regarding disturbance of feeding gray whales and the potential for auditory injury. Exxon Neftegas Limited (ENL) developed a mitigation approach to address these concerns and, more importantly, implemented a comprehensive data collection strategy to assess the effectiveness of this approach. The mitigation approach prioritized completion of the seismic surveys closest to the nearshore feeding area as early in the season as possible, when fewer gray whales would be present. This was accomplished by increasing operational efficiency through the use of multiple seismic vessels and by establishing zones with specific seasonal criteria determining when air gun shutdowns would be implemented. These zones and seasonal criteria were based on pre-season modeled acoustic footprints of the air gun array and on gray whale distribution data collected over the previous 10 years. Real-time acoustic and whale sighting data were instrumental in the implementation of air gun shutdowns. The mitigation effectiveness of these shutdowns was assessed through analyzing short-term behavioral responses and shifts in gray whale distribution due to sound exposure. The overall mitigation strategy of an early survey completion was assessed through bioenergetics models that predict how reduced foraging activity might affect gray whale reproduction and maternal survival. This assessment relied on a total of 17 shore-based and 5 vessel-based teams collecting behavior, distribution, photo-identification, prey, and acoustic data. This paper describes the mitigation approach, the implementation of mitigation measures using real-time acoustic and gray whale location data, and the strategy to assess impacts and mitigation effectiveness.

Section: Part Iii: Strategy To Assess Impacts and Mitigation Effectiv...mentioning

confidence: 99%

Section: Part Iii: Strategy To Assess Impacts and Mitigation Effectiv...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Seismic surveys near gray whale feeding areas off Sakhalin Island, Russia: assessing impact and mitigation effectiveness

Aerts¹,

Jenkerson

Nechayuk

et al. 2022

“…Overall, prey energy density is much higher and a higher proportion of energy is provided by amphipods in the offshore feeding area (Maresh et al, 2022). Young of the year only utilize the nearshore area, and as they age, gray whales tend to increase their use of the offshore area (Schwarz et al, 2022).…”

Section: Study Areamentioning

confidence: 99%

Gray whale habitat use and reproductive success during seismic surveys near their feeding grounds: comparing state-dependent life history models and field data

Schwarz

McHuron

Mangel

et al. 2022

We used a stochastic dynamic programming (SDP) model to quantify the consequences of disturbance on pregnant western gray whales during one foraging season. The SDP model has a firm basis in bioenergetics, but detailed knowledge of minimum reproductive length of females (Lmin) and the relationship between length and reproductive success (Rfit) was lacking. We varied model assumptions to determine their effects on predictions of habitat use, proportion of animals disturbed, reproductive success, and the effects of disturbance. Smaller Lmin values led to higher predicted nearshore habitat use. Changes in Lmin and Rfit had little effect on predictions of the effect of disturbance. Reproductive success increased with increased Lmin and with higher probability of reproductive success by length. Multiple seismic surveys were conducted in 2015 off the northeast coast of Sakhalin Island, with concomitant benthic prey surveys, photo-identification studies, and whale distribution sampling, thus providing a unique opportunity to compare output from SDP models with empirical observations. SDP model predictions of reproductive success and habitat use were similar with and without acoustic disturbance, and SDP predictions of reproductive success and large-scale habitat use were generally similar to values and trends in the data. However, empirical estimates of the proportion of pregnant females nearshore were much higher than SDP model predictions (a large effect, measured by Cohen’s d) during the first week, and the SDP model overestimated whale density in the south and underestimated density around the mouth of Piltun Bay. Such differences in nearshore habitat use would not affect SDP predictions of reproductive success or survival under the current seismic air gun disturbance scenario.

“…In 2015, multiple seismic surveys were planned adjacent to the nearshore portion of the western gray whale feeding ground, prompting development of a comprehensive monitoring program with the purpose of assessing impacts on gray whale behavior, distribution, and energetics from sound exposure and vessel presence (Aerts et al, 2022 ; Gailey et al, 2022 ; Schwarz et al, 2022 ). Blanchard et al ( 2022 ) determined that biomass concentrations of some prey groups in 2015 declined from early and mid-summer to late summer and fall.…”

Section: Introductionmentioning

confidence: 99%

Benthic studies adjacent to Sakhalin Island, Russia 2015 III: benthic energy density spatial models in the nearshore gray whale feeding area

Blanchard¹,

Ainsworth²,

Gailey³

et al. 2022

Energy densities of six dominant benthic groups (Actinopterygii, Amphipoda, Bivalvia, Cumacea, Isopoda, and Polychaeta) and total prey energy were modeled for the nearshore western gray whale feeding area, Sakhalin Island, Russia, as part of a multi-disciplinary research program in the summer of 2015. Energy was modeled using generalized additive mixed models (GAMM) with accommodations for zero-inflation (logistic regression and hurdle models) and regression predictions combined with kriging to interpolate energy densities across the nearshore feeding area. Amphipoda energy density was the highest nearshore and in the south whereas Bivalvia energy density was the highest offshore and in the northern portion of the study area. Total energy was the highest in mid-range distances from shore and in the north. Amphipoda energy density was higher than minimum energy estimates defining gray whale feeding habitats (312–442 kJ/m2) in 13% of the nearshore feeding area whereas total prey energy density was higher than the minimum energy requirement in 49% of the habitat. Inverse distance-weighted interpolations of Amphipoda energy provided a broader scale representation of the data whereas kriging estimates were spatially limited but more representative of higher density in the southern portion of the study area. Both methods represented the general trend of higher Amphipoda energy density nearshore but with significant differences that highlight the value of using multiple methods to model patterns in highly complex environments.