The Influence of Life History Variability on Population Connectivity: Development and Application of a Trait-Based Biophysical Model of Individuals

Connectivity estimates, which measure the exchange of individuals among populations, are necessary to create effective reserves for marine life. Connectivity can be influenced by a combination of biology (e.g. spawning time) and physics (e.g. currents). In the past a dispersal model was created in an effort to explain connectivity for the highly sought after reef fish Lau‘ipala (Yellow Tang, Zebrasoma flavescens) around Hawai‘i Island using physics alone, but this was shown to be insufficient. Here we created an individual based model (IBM) to describe Lau‘ipala life history and behavior forced with ocean currents and temperature (via coupling to a physical model) to examine biophysical interactions. The IBM allows for tracking of individual fish from spawning to settlement, and individual variability in modeled processes. We first examined the influence of different reproductive (e.g. batch vs. constant spawners), developmental (e.g. pelagic larval duration), and behavioral (e.g. active vs. passive buoyancy control) traits on modeled connectivity estimates for larval reef fish around Hawai‘i Island and compared results to genetic observations of parent-offspring pair distribution. Our model is trait-based which allows individuals to vary in life history strategies enabling mechanistic links between predictions and underlying traits and straightforward applications to other species and sites.