Research ArticleMarine Ecology

Blue whales (Balaenoptera musculus) optimize foraging efficiency by balancing oxygen use and energy gain as a function of prey density

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Science Advances  02 Oct 2015:
Vol. 1, no. 9, e1500469
DOI: 10.1126/sciadv.1500469
  • Fig. 1 Theoretical response of blue whale diving-foraging performance as a function of prey density and depth on high-density (left) and low-density (right) density prey patches.

    (A) Portions of an individual’s dive are referenced by equation parameters including dive time (t), bottom time (τ), surface time (s), and number of lunges (L) marked with black circles. Recovery time at the surface increases greater than a 1:1 relationship with oxygen use; thus, conservation strategies significantly decrease time spent at the surface. (B and C) Foraging on two prey patches that vary in density and depth demonstrates the trade-off between the two approaches. Solid black points show hypothetical lunges, hollow white points show abandoned lunges (for example, the dive is aborted before these lunges are performed), and the dashed gray line shows the time at which the whale aborted the foraging dive. The two foraging scenarios illustrate the difference between the strategies (B) maximizing energy gain by increasing lunges per dive at the expense of oxygen when prey density is high and (C) minimizing oxygen use by decreasing lunges per dive. The red line shows hypothetical oxygen stores termed the theoretical aerobic dive limit (TADL). The blue line shows relative energy gain, whereas the red line shows the oxygen use, demonstrating the trade-off between oxygen and energy when feeding at depth. Energy gain is greater per lunge when foraging on dense prey, but lunge costs remain the same. To optimize foraging efficiency (energy gain relative to energy used), when dense prey patches are available, we hypothesize that a whale will perform more lunges resulting in (i) a longer dive, (ii) more energy gained, and (iii) more oxygen consumed (B). This differs from a low-density prey scenario, where minimizing oxygen use while foraging is more important than maximizing energy gain (C).

  • Fig. 2 Vertical partitioning of feeding lunges and prey.

    Whale foraging parameters (left) and krill patch metrics (right) are plotted with common depths on the y axis from 2011 to 2013 individuals. The total number of lunges across all whales (gray bars) and maximum number of lunges per dive (blue) are shown on the left, whereas the number of krill patches by centroid depth bin (white) and krill density per patch (red) are shown on the right. Both the number of patches and krill density are important metrics to understand dive behavior of foraging blue whales.

  • Fig. 3 Blue whale optimal foraging behaviors relative to krill density.

    (A) Predicted number of lunges as a function of dive depth based on models of energy gain (blue dashed line) and oxygen conservation (black line). The estimated TADL for a 22-m whale, the point at which an individual goes hypoxic, is shown in red (8). The maximum number of lunges per dive with concurrent measured krill densities (size and color of circles) are plotted against dive depth (n = 14 whales). Historical maximum lunges per dive (n = 41 whales) that were collected in the absence of prey measurements are plotted with gray symbols. (B) The difference in residuals (residualsEq. 2 − residualsEq. 1) between maximum lunges per individual whale and the two foraging models shows the point at which individuals switch from maximizing energy (positive) to conserving oxygen (negative; R2 = 0.66). The reference of 100 krill m−3 below which it has been hypothesized to be energetically inefficient to forage (8) is shown in dashed gray line. CIs (95%) are shaded in gray. (C) Efficiency rates, the ratio of the energy gained divided by all energy expenditures, estimated on actual krill density measurements using both foraging models (oxygen use and energy gain) are shown in black circles and blue squares, respectively. The two efficiencies have significantly different slopes [analysis of covariance (ANCOVA), P < 0.001]. CIs (95%) around both models are represented by dotted lines.

Supplementary Materials

  • Supplementary Materials

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    • Table S1. Reparameterization of equations used in optimal foraging models from Houston and Carbone (1), Doniol-Valcroze et al. (18), and Mori et al. (26).

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