More than passive drifters: a stochastic dynamic model for the movement of Antarctic krill

Marine Ecology Progress Series

Jarrod A. Santora¹  Kate Richerson¹  George M. Watters¹  Marc Mangel¹  Isaac D. Schroeder¹ 

关键词: Euphausia superba;    Behavior;    Stochastic dynamic programming;    Management;    Advection;    Southern Ocean;   

DOI  :  10.3354/meps11324

学科分类：海洋学与技术

来源: Inter-Research

【 摘 要 】

ABSTRACT: Antarctic krill Euphausia superba are a key part of the marine food web and are the target of the largest fishery in the Southern Ocean. Although ecosystem and management models typically assume that krill are passive drifters, their relatively large size and strong swimming ability suggest that the active movement of krill may play an important role in their spatial distribution. Thus, active swimming behavior by krill may influence spatial structure of food web interactions (e.g. feeding behavior of seabirds and marine mammals) and regional commercial fishery activity. The objective of this work was to model the potential for active movement to affect krill distribution, and consequently growth, reproductive success, and survival. We used state-dependent life history theory, implemented by stochastic dynamic programming, in combination with spatial information on food availability, current velocity, temperature, and predation risk, to predict krill swimming behavior near the northern Antarctic Peninsula. We found that including active krill behavior resulted in distribution patterns that are associated with increased survival, growth, and reproductive success compared to a model that treats krill as passive drifters. The expected reproductive success of actively behaving krill was about 70% greater than that of passively diffusing krill, suggesting that there are strong selective pressures for active behavior along oceanic drift trajectories. This modeling framework will benefit assessments of new catch limits as krill fishing grounds are partitioned into smaller spatial management units.

【 授权许可】

Unknown