Memory and resource tracking drive blue whale migrations

Abstract

In terrestrial systems, the green wave hypothesis posits that migrating animals can enhance foraging opportunities by tracking phenological variation in high-quality forage across space (i.e., "resource waves"). To track resource waves, animals may rely on proximate cues and/or memory of long-term average phenologies. Although there is growing evidence of resource tracking in terrestrial migrants, such drivers remain unevaluated in migratory marine megafauna. Here we present a test of the green wave hypothesis in a marine system. We compare 10 years of blue whale movement data with the timing of the spring phytoplankton bloom resulting in increased prey availability in the California Current Ecosystem, allowing us to investigate resource tracking both contemporaneously (response to proximate cues) and based on climatological conditions (memory) during migrations. Blue whales closely tracked the long-term average phenology of the spring bloom, but did not track contemporaneous green-up. In addition, blue whale foraging locations were characterized by low long-term habitat variability and high long-term productivity compared with contemporaneous measurements. Results indicate that memory of long-term average conditions may have a previously underappreciated role in driving migratory movements of long-lived species in marine systems, and suggest that these animals may struggle to respond to rapid deviations from historical mean environmental conditions. Results further highlight that an ecological theory of migration is conserved across marine and terrestrial systems. Understanding the drivers of animal migration is critical for assessing how environmental changes will affect highly mobile fauna at a global scale.

Keywords: marine megafauna; migration; movement ecology; resource wave; spatial memory.

Fig. 1.

(A and B) Average timing of (A) peak chlorophyll-a concentration and (B) 15 to 17 °C sea surface temperature along the western coast of North America between 1998 and 2010. (C) Observed blue whale tracks (blue points) and simulated random migrants (red points) used in the analysis.

Fig. 2.

(Top) Annual blue whale latitudinal movements averaged over the population (black line) overlaid on Hovmoller plots of (A) chlorophyll-a and (B) SST. (Bottom) Annual linear regressions of whale movement phenology with contemporaneous (A) chlorophyll-a and (B) SST (observed tracks in blue; simulated tracks in red). Gray lines in the 1999 regressions indicate a 1:1 relationship.

Fig. 3.

(A and B) Climatology of blue whale migrations (black lines ± 1 SE) along the US west coast and Baja California between 1998 and 2010 in relation to climatological (A) chlorophyll-a concentration and (B) SST. (C and D) Relationship between phenology of tracks and long-term climatologies of (C) chlorophyll-a; observed tracks (blue; linear regression mean 0.14 ± 0.03; P < 0.001) and random tracks (red; mean −0.04 ± 0.02; P = 0.015), and (D) SST; observed tracks (mean 0.26 ± 0.04; P < 0.001) and random tracks (mean = 0.24 ± 0.03; P < 0.001). Shading indicates 95% confidence interval around fitted values. Gray lines indicate a 1:1 relationship. Results of all tracks pooled and averaged are shown.

Fig. 4.

(A) Distributions of 10-y average and contemporaneous chlorophyll-a concentrations for n = 2,373 area-restricted search locations, and 10-y average chlorophyll-a concentrations from 10,000 randomly generated locations in the study area. (B) Distributions of 10-y temporal SDs in chlorophyll-a concentrations for area-restricted search locations and randomized locations.