Divergent foraging strategies between populations of sympatric matrilineal killer whales
Creators
- 1. University of Washington
- 2. Northwest Fisheries Science Center
- 3. Fisheries and Oceans Canada
- 4. Cascadia Research
- 5. University of Cumbria
Description
In gregarious species, collective behavior maximizes individual fitness benefits while minimizing costs. Despite the relevance of behavior to conservation, the link between the robustness of behavioral patterns across populations and population health is poorly understood. We studied the collective foraging behavior of two closed, sympatric populations of piscivorous killer whales, leveraging two contemporaneously-collected data sets from suction cup-attached bio-logging tags, to quantify patterns of fine-scale foraging movements and their relationships with demography. We reveal striking plasticity in collective foraging behavior between populations. Prey capture rate and foraging efficiency were greater for males in the endangered Southern Resident (SRKW) population, yet greater for females in the Northern Resident (NRKW) population. The presence of a calf (≤ 3 y) reduced the number of prey captured by adult females in both populations, with the greatest effect in SRKW, in which no mothers with calves captured prey while bearing tags. SRKW adult males with a living mother tended to capture more prey than those whose mother had died, whereas the opposite was true for NRKW adult males. Moreover, males generally tended to forage in areas with deeper bathymetry than females, and SRKW captured prey deeper than NRKW. These population-level differences in sex-specific foraging behavior challenge the existing paradigm that mothers are disproportionate foragers in gregarious killer whales, underscoring that an endangered population is employing a potentially unstable collective foraging strategy mismatched to recovery. Thus, our study provides a mechanistic link between fine-scale, collective foraging behavior and population health in an apex marine predator.
Notes
Files
Model_a.csv
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