Range-Wide Comparison of Gray Whale Body Condition Reveals Contrasting Sub-Population Health Characteristics and Vulnerability to Environmental Change

Abstract

Climate change is a global phenomenon, yet impacts on resource availability to predators may be spatially and temporally diverse and asynchronous. As capital breeders, whales are dependent on dense, predictable prey resources during foraging seasons. An Unusual Mortality Event (UME) of Eastern North Pacific (ENP) gray whales (Eschrichtius robustus) was declared in 2019 due to a dramatic rise in stranded animals, many emaciated. Climate change impacts may have affected prey availability on the primary foraging grounds of ENP gray whales (~20,000 individuals) in the Arctic and sub-Arctic region and in coastal habitats between northern California, USA and British Columbia, Canada where a small sub-group of ENP whales called the Pacific Coast Feeding Group (PCFG; ~230 individuals) forages. To investigate variability of gray whale body condition relative to changing ocean conditions, we compare two datasets of gray whale aerial photogrammetry images collected via Unoccupied Aircraft Systems (UAS) on the ENP wintering grounds in San Ignacio Lagoon, Mexico (SIL; n=111) and on the PCFG feeding grounds in Oregon, USA (n=72) over the same three-year period (2017–2019). We document concurrent body condition improvement of PCFG whales in Oregon while body condition of whales in SIL declined. This result indicates that the UME may have affected ENP whales due to reduced energetic gain on some Arctic/sub-Arctic foraging grounds, while PCFG whales are recovering from poor prey conditions during the NE Pacific marine heatwave event of 2014–2016. Surprisingly, we found that PCFG whales in Oregon had significantly worse body condition than whales in SIL, even when accounting for year and phenology. We derive support for this unexpected finding via photogrammetry analysis of opportunistic aerial images of gray whales on Arctic foraging grounds (n=18) compared to PCFG whales in Oregon (n=30): the body condition of PCFG whales was significantly lower (t=2.96, p=0.005), which may cause PCFG whales to have reduced reproductive capacity or resilience to environmental perturbations compared to ENP whales. Overall, our study elucidates divergent gray whale body condition across sub-groups and time, and we demonstrate the value of UAS to effectively monitor and identify the physiological response of whales to climate change.

Trade-offs in prey quantity and quality in gray whale foraging

“…Furthermore, gray whales in poor body condition have higher fecal cortisol stress levels (Lemos et al 2022), showcasing the physiological consequences of foraging success. Decreased prey quality has resulted in reduced body condition and fitness (Trites & Donnelly 2003, Cohen et al 2014, reproductive failure (Harris et al 2007, Grémillet et al 2008, and population declines (Crawford et al 1995, Trites & Donnelly 2003, Ludynia et al 2010) in a number of other marine predators and is one of the hypothesized causes of the unusual mortality event for ENP gray whales (2019−present; Christiansen et al 2021, Torres et al 2022). Variation Indeed, we noted a sharp de cline in kelp abundance across years in the study area, particularly in the TC site (L. Torres pers.…”

Seasonal gain in body condition of foraging humpback whales along the Western Antarctic Peninsula

“…We measured body condition as Body Area Index (BAI), which is a unitless and scale-invariant metric standardized by total length (Burnett et al, 2018). BAI is useful for comparing body condition across individuals, as it has been shown to have low measurement uncertainty with high precision (Bierlich et al, 2021a;Torres et al, 2022) (Figure 2). We calculated BAI for each individual using a Head-Tail Range of 25-85% for juvenile and mature humpback whales and 20-85% for calves (Figure 2) (Bierlich et al, 2021a).…”

“…We summarized uncertainty by calculating the variance, 95% highest posterior density (HPD) intervals, and the coefficient of variation (CV%) of the posterior predictive distributions for total length and BAI for each individual. We used the CV% to compare measurement uncertainty associated with each UAS, which compares the relative width of each individual's posterior distribution, with a lower CV% translating to a more precise estimated measurement (Bierlich et al, 2021a;Torres et al, 2022).…”

“…We first investigated whether BAI increased linearly or nonlinearly with respect to time over the course of the season for each reproductive class by comparing the fit between a linear regression model and a quadratic polynomial regression model. Similar to Torres et al (2022), we used Monte Carlo methods to propagate uncertainty associated with BAI into the regression analysis by averaging the results of 1,000,000 replications each of a linear or quadratic model, where reproductive class and date within season (i.e., 10, 11, 12 days since start of season, etc.) are the predictor variables and BAI is the response variable.…”

“…Photogrammetry from unoccupied aircraft systems (UAS, UAVs, or drones) is an effective non-invasive tool for obtaining measurements of cetaceans in remote environments (Durban et al, 2015). UAS have been used to measure body condition in blue (Balaenoptera musculus), gray (Eschrichtius robustus), and northern (Eubalaena glacialis) and southern right whales (Eubalaena australis) (Christiansen et al, 2016;Durban et al, 2016;Christiansen et al, 2018;Lemos et al, 2020;Christiansen et al, 2021;Stewart et al, 2022;Torres et al, 2022). For humpback whales, studies have described trends in body condition along the migration routes (Christiansen et al, 2020b;Russell et al, 2022), the North Atlantic feeding grounds (Aoki et al, 2021), and the Western Australian breeding grounds (Christiansen et al, 2016).…”