Minding the Data-Gap Trap: Exploring Dynamics of Abundant Dolphin Populations Under Uncertainty - Ashe, Erin; Williams, Rob; Clark, Christopher; Erbe, Christine; Gerber, Leah R.; Hall, Ailsa J.; Hammond, Philip S.; Lacy, Robert C.; Reeves, Randall; Vollmer, Nicole L. (Frontiers in Marine Science, 2021)
Preventing declines in common species is key to sustaining the structure and function of marine ecosystems. Yet for many common marine mammals, including oceanic dolphins, statistical power to detect declines remains low due to patchy distribution and large variability in group sizes. In this study, population viability analyses (PVA) were used to model the dynamics of four oceanic dolphin populations off the United States West Coast: eastern North Pacific long-beaked common dolphins (Delphinus delphis capensis), short-beaked common dolphins (D. delphis delphis), Pacific white-sided dolphins (Lagenorhynchus obliquidens), and “offshore” common bottlenose dolphins (Tursiops truncatus). We calibrated the PVA with life-history tables, studies on proxy species, and stock assessment reports. We explored the sensitivity of populations to demographic variation and projected how they may respond to changes in three sublethal threats (prey limitation, ocean noise, and chemical pollution) and one lethal threat (fisheries bycatch). We found the most serious projected declines in long-beaked common dolphins, which showed the lowest birth rate. Most threat scenarios resulted in declines that would not be detected by existing monitoring programs in the United States, which are among the most data-rich surveys of their kind. The cumulative effects of the three sublethal stressors exceeded the effect of the one lethal stressor (fisheries bycatch). To implement pro-active management and monitoring programs, anticipating which cetaceans are more at risk and which anthropogenic threats could cause declines is paramount. Our study highlights the value of model testing with PVA when monitoring data are poor, thereby identifying priorities for future research, monitoring, and management.