Modelling dispersal of sea scallop ( Placopecten magellanicus) larvae on Georges Bank: The influence of depth-distribution, planktonic duration and spawning seasonality

Abstract

The giant sea scallop ( Placopecten magellanicus) metapopulation on Georges Bank (GB) in the northwest Atlantic constitutes an economically important fishery, which is sustained by retention of planktonic larvae that are advected among three subpopulations. Dispersal of GB scallop larvae spawned in the fall is known to be sensitive to climate-related variability in the currents. Associated changes in temperature and stratification may elicit variations in biological responses that can also affect transport and these need to be examined. Additionally, no investigation has been made into transport of larvae spawned in spring, despite this being a consistently recurring phenomenon. Here, we quantified the influence of larval depth-distribution, planktonic larval duration (PLD) and spawning phenology on the retention and exchange of P. magellanicus larvae on GB. We developed a 3-D particle-tracking model to simulate larval transport in both spawning seasons using realistic circulation fields and assumptions about both larval behavior and PLD. Results were analyzed in terms of settlement distributions and dispersal matrices. Passive vertical behavior led to unrealistic depth-distribution in fall, while pycnocline-seeking behavior improved predicted settlement distributions and altered dispersal by factors of 2–5 for certain subpopulations. Simulations examining temperature-dependent larval development demonstrated significant effects of thermal history on PLD, and that associated changes in mean PLD of a few days can greatly alter connectivity. Larval transport in the spring exhibited increased downstream losses as well as sensitivity of retention to larval depth and spawning location. Our results demonstrate that biological factors significantly influence larval dispersal, and we need to reduce the uncertainty associated with estimates of these factors in order to predict effects of climate change on population connectivity.