Inherent Uncertainties in Nearshore Fisheries: The Biocomplexity of Flow, Fish and Fishing Dave Siegel 1, Satoshi Mitarai 1, Crow White 1, Heather Berkley 1, Chris Costello 1, Steve Gaines 1, Ray Hilborn 2, Bruce Kendall 1, Steve Polasky 3, Bob Warner 1 & Kraig Winters 4 1 = [UCSB], 2 = [UW], 3 = [UMn] & 4 = [SIO/UCSD] Flow, Fish & Fishing (F3) Biocomplexity

Flow, Fish & Fishing Human-natural system biocomplexity project Oceanography, population dynamics, marine ecology, fishery management, fisherman behavior & economics all wrapped up together Focus on California nearshore fisheries & role of uncertainty in management [but in a general way] Today – environmental uncertainties & their role on the stocks & harvest of a long-lived fish

Flow Fish Settlement Habitat Recruitment Harvest Regulation Fishermen Market INFO Climate

Flow Fish Settlement Habitat Recruitment Harvest Regulation Fishermen Market INFO Climate

Stock / Harvest Modeling Next generation stocks = survivors - harvest + new recruits SURVIVORS are surviving adults from previous time HARVEST are those extracted from the fishery NEW RECRUITS are a function of fecundity of the survivors, larval dispersal & mortality, settlement & recruitment to adult stages

Mathematically...

Model System Long lived, sessile, harvested fish –M = 0.05 y -1, density dependence parameterized using Beverton-Holt on larval settling densities Larval dispersal scales (Gaussian kernel) –PLD = 60 d, D d = 150 km & T spawn = 60 d Virgin carrying capacity set to 100 units –Fixes fecundity 1-D coastline domain –2000 km long,  x = 5 km & absorbing BC

Harvesting Total allowable catch (TAC) = f(recruitment) TAC = 20% of the measured recruitment Enables TAC to be set dynamically Spatial harvest allocation = f(adult density) Fishermen fish where there are the highest fish densities & harvest up to the set TAC So-called “ideal free distribution”

Base Case Diffusive larval kernel, no sources of uncertainty Adults (~60) Recruitment (~4) Settlement (~6) Harvest (~0.9)

Flow Fish Settlement Habitat Recruitment Harvest Regulation Fishermen Market INFO Climate Variability in Fecundity CV = 50%

Climate Case Adults Recruitment Settlement Harvest Diffusive larval kernel - fecundity variability (CV = 50%)

Regional means are same as the base case Adults (~60) Recruitment (~4) Settlement (~6) Harvest (~0.9) Recruitment variability sets TAC

Flow Fish Settlement Habitat Recruitment Harvest Regulation Fishermen Market INFO Climate Short time scales of the process makes larval transport stochastic

Larval Connectivity is a Stochastic Driven by flow scales, short spawning durations & the low probability of survival Model stochastically which matches Gaussian kernel when # of settlement events is large Siegel et al. [in review] Mitarai et al. [in prep.] Destination Location (km) Source Location (km) Self settlement

Patchy Settlement Case Adults Recruitment Settlement Harvest Patchy larval kernel - PLD = 60 d, D d = 150 km & T spawn = 60 d

Adult densities are lower, why?

Climate Case Patchy Case Settling densities are 2x the base case due to the spatial focusing of successful settlement events Larval density dependence on post-settlement recruitment rates reduces overall adult populations Role of Density Dependence

Flow Fish Settlement Habitat Recruitment Harvest Regulation Fishermen Market INFO Climate Sample recruitment at only 5% of the sites to set the TAC

Uncertainty Case Adults Recruitment Settlement Harvest Patchy larval kernel, varying fecundity & assessment area = 5%

Regional scale harvest & recruitment are weakly correlated Times when fishery is closed when TAC = 0 Increases risk to sustainability of the stock & fishing profits Uncertainty Case

Stock-recruitments do not exists for these systems No relationship between total harvest and recruitment Shows danger of setting TAC based on little data (5% sites) Stock-Recruitment & Harvest- Recruitment Relationships for Uncertainty Case ?

Conclusions to Date Created a caricature of a CA nearshore fishery Climate forcing creates temporal variations though its effects are linear (time average = base case) Flow-induced stochastic settlement creates spatial- temporal variability to stocks, recruitment & harvest Larvae/larvae density dependence mitigates extreme settlement event densities Information is critical Poor information leads to overfishing & profit losses

Completed Next Steps Add spatial variability in habitat Habitat quality affected post-settlement recruitment Used Markov chain with a 25 km length scale Habitat quality varied by factor of 0.5 about 1 Calculate optimal harvesting level strategy Ran to steady state changing the control parameter for the TAC rule => Optimal_TAC = 0.3 * Aggregate_Recruitment

Optimal!!

Next Steps to Do Can now do Value of Information calculations Repeat the recruitment sampling experiments & assess value of next recruitment sample location OR?? Package this up as a publication… Need help completing this!

Thank You!! Photo credit: Steve Churchill

Determine # of settlement packets N = (T/  ) (L/ ) f NUMBER OF SETTLEMENT PACKETS T: Larval release duration  : Lagrangian correlation time L: domain size : Rossby radius f: survivability of packet Siegel et al. [in rev.], Mitarai et al. [in prep.]

DIFFUSION LIMIT Packet model 1 season 6 seasons 12 seasons 120 seasons 1 season 6 seasons 12 seasons Diffusion Flow simulation Diffusion model

MODEL PREDICTIONS SummerWinter Accounts for spatial structures

CONNECTIVITY MATRIX Summer Winter

Larval Transport & Fish Life Cycles