Hydrodynamic Connectivity in Marine Population Dynamics Satoshi Mitarai 1, David A. Siegel 1, Bruce E. Kendall 1, Robert R. Warner 1, Steven D. Gaines 1, Christopher E. Costello 1 and Kraig B Winters 2 1 Institute for Computational Earth System Science University of California, Santa Barbara, CA Integrative Oceanographic Division, Scripps Institution of Oceanography, La Jolla, CA “Flow, Fish & Fishing” Project Destination location (km) Connectivity in physical space N Season #1#2#3 Source location (km) Spawning season #1 Spawning season #2 Spawning season #3 Three independent spawning seasons y x Mean wind Mean offshore current at surface Santa Barbara San Francisco x Target Area Stay near the top surface Question: Will understanding marine life cycle in turbulence improve predictablity of marine ecosystems? Habitat Harvest Regulation Fishermen Market INFO Flow Fish Settlement Recruitment Climate Flow Fish Settlement Recruitment Harvest Fishermen Biophysical Model Role of Stochastic Connectivity in Population Dynamics # of adults at x in year n+1 # of recruits to x from everywhere # of survivors at x in year n =+ # of larvae produced at y Fraction of larvae transported to x Recruitment success (%) # of adults harvested Natural mortality Connectivity matrix Connectivity estimated from coastal circulation simulations Particles are released daily for 90 days uniformly in nearshore waters. Particles are passively transported in horizontal directions while they can actively change their vertical positions. Settlement is defined when pparticles are found in nearshore during competency window (20 to 40 d) Consider dynamics of species among sites on a long straight coastline Marine species have a two life stage (larva-adult) and a sessile adult stage Connectivity is Stochastic Connectivity is stochastic on annual time scales Larval behavior can change connectivity Stay surfaceMigrate 50 m + Larvae are transported by coastal eddies as coherent packets Coastal eddy motion is chaotic Only a few arriving larval packets for single spawning season Siegel et al, Proceedings of National Academy of Science (accepted for publication) Mitarai et al., Journal of Marine Systems (in press) Spatially-explicit population dynamics model We propose to examine five areas in which issues of predictability or scale render decision-making process difficult. These are the missing links in: 1. The physical drivers of larval settlement 2. The spatial scales of nearshore fish populations 3. The role of uncertainty and use of information in fishery management 4. The mismatch in scale between harvesting and regulatory decisions 5. The difficulties associated with multi-species management Smoothed out if averaged 10+ seasons Consider single unharvested species 2. Stochastic connectivity Turbulent structures exist Diffusion model Turbulence is ignored Diffusion model Stochastic connectivity Predictions... Mean Adult Population (%) Thoughtful experiment 1 - single species case Thoughtful experiment 2 - two species case Consider two similar species A & B Species A has a slightly better ability to utilize resources They compete for limited resources at settlement sites 1. Same Behavior2. Different behavior A = 100 B = 0 Without difference in behaviorWith a difference in behavior A = 60 B = 40 Different behavior leads to species coexistence Consider single unharvested species This work is a contribution of the Flow, Fish and Fishing biocomplexity project work and is supported by the National Science Foundation (NSF grant # ). Understanding marine life cycle in turbulence changes population dynamics predictions A small difference in biological processes can change community structure Sea surface temperature = Focus of this poster Density of settlers (%) Recruitment Rate Particle trajectories & sea level