Dr Megan Cimino1, Jarrod Santora1, Elliott Hazen1, Michael Jacox1, Steven Bograd1
1UC Santa Cruz/NOAA SWFSC, Pacific Grove, United States
Euphausiids, or krill, are an important prey item for many marine organisms due to their global distribution, high biomass and energy content. Krill often form large and dense aggregations that are constantly on the move (both passively and actively), making them extremely patchy in space and time. An understanding of krill habitat associations is essential for predicting and planning for range shifts or variability in the future. In this study, we used 15 years (2002-2017) of shipboard net haul data to investigate the factors influencing krill distributions in the central California Current System. We built boosted regression tree species distribution models for each of the two dominate krill species (Thysanoessa spinifera and Euphausia pacifica) given that T. spinifera is often more abundant over the continental shelf and E. pacifica is more abundant offshore over the slope. We found the distribution and abundance of the two species in late spring/early summer was related to bathymetric/geomorphic features, winter preconditioning upwelling dynamics, and late spring transport dynamics and water column properties. The understanding of krill distributions provides a guide for ecosystem-based management and conservation strategies for their predators, including fish, seabirds and whales. Our modeling framework sets up the infrastructure to predict krill distributions under future climate scenarios, to guide fishing locations to reduce bycatch or whale entanglements, and to inform fishers on the most probable areas to catch fish that prey on krill.
Megan received her BS in 2009 from Cal Poly San Luis Obispo and her PhD in oceanography in 2016 from the University of Delaware. She was a postdoctoral scholar at Scripps Institution of Oceanography from 2016 to 2018 and is currently a project scientist at UC Santa Cruz. Broadly,
Megan’s research utilizes many remote sensing technologies (for example, robots, satellites, and bio-logging) coupled with
biological observations to understand the relationships between animals and their habitat.