1. FISHING FOR PROFIT, NOT FISH: AN ECONOMIC ASSESSMENT OF MARINE RESERVE EFFECTS ON FISHERIES Crow White, Bruce Kendall, Dave Siegel, and Chris Costello University of California – Santa Barbara

2. Compared to traditional (open access) management… …reserves maintain yields: ▪ Hastings and Botsford 1999 …reserves enhance yield: ▪ Gerber et al. 2003 (a review) ▪ Neubert 2003 ▪ Gaylord et al. 2005

3. θ = 5 θ = 0 Cost of catching one fish = Density of fish at that location θ

4. θ = 5 θ = 0 Bottom line for fishermen: Profit = Revenue - cost Cost of catching one fish = Density of fish at that location θ

5. θ = 20 θ = 0 Bottom line for fishermen: Profit = Revenue - cost Cost of catching one fish = Density of fish at that location θ

6. PROFIT = Revenue - Cost Initial fish density Final fish density This year’s harvest at location x

7. integrate PROFIT = Revenue - Cost Final fish density Initial fish density This year’s harvest at location x

8. Incorporating Density Dependence Post-dispersal: Larva settlement and/or recruitment success increases with decreasing adult population density at that location.

9. For coastal fish species: Myers & Cadigan 1993 Botsford & Hobbs 1995 Carr et al. 1995 Caley et al. 1996 Fokvord 1997 Hixon & Webster 2002 Webster 2003 Skajaa et al. In Prep.

10. Including stock effect does not influence optimal reserve design Reserve design that maximizes population density in fishery area:  Maximizes harvest  Minimizes cost of fishing, thereby maximizing profit

13. Including stock effect does influence optimal fisheries management With increasing stock effect severity…  Reserves less effective at increasing profit beyond that attainable under traditional management  Higher escapement and less emphasis on reserves more appropriate for maximizing profit

14. Max yield without reserves

15. Max profit without reserves

19. M = 0.05 (dash) M = 0.1 (solid) P = 1, 2, 3

20. Cost of fishing: LowCost of fishing: Moderate Cost of fishing: Higher Cost of fishing: Highest

21. Max profit without reserves

25. Reserves = 20% of the coastline (M = 0.1, P = 1)

26. θ = 0 - 20 θ = 5 - 20 20% of the coast dedicated to reserves 35% escapement across all fisheries

27. θ = 0 - 20 θ = 5 - 20 30% of the coast dedicated to reserves 30% escapement across all fisheries

28. θ = 0 - 20 θ = 5 - 20 40% of the coast dedicated to reserves 30% escapement across all fisheries

29. θ = 0 - 20 θ = 5 - 20 50% of the coast dedicated to reserves 30% escapement across all fisheries

30. Reserves = 60% of the coastline

32. 60% of the coast dedicated to reserves θ % escapement across all fisheries θ = 0 - 20 θ = 5 - 20

33. Options for fishery management Traditional No reserves, & high escapement levels (34 - 47% K) individually set for each fishery Moderately difficult to enforce Mix 20 – 50% coast in reserves, & ~30% escapement across all fisheries Equivalent/enhanced profit; moderately difficult to enforce Reserve 60% coast in reserves, & no regulation of escapement Equivalent/much enhanced profit; simple to enforce

34. University of California – Santa Barbara National Science Foundation Coastal Environmental Quality Initiative The Canon National Parks Science Scholars Program THANK YOU!!

35. FISHING FOR PROFIT, NOT FISH: AN ECONOMIC ASSESSMENT OF MARINE RESERVE EFFECTS ON FISHERIES Crow White, Bruce Kendall, Dave Siegel, and Chris Costello University of California – Santa Barbara

36. Compared to traditional (open access) management… …reserves maintain yields: ▪ Hastings and Botsford 1999 …reserves enhance yield: ▪ Gerber et al. 2003 (a review) ▪ Neubert 2003 ▪ Gaylord et al. 2005

37. θ = 5 θ = 0 Cost of catching one fish = Density of fish at that location θ

38. θ = 5 θ = 0 Bottom line for fishermen: Profit = Revenue - cost Cost of catching one fish = Density of fish at that location θ

39. θ = 20 θ = 0 Bottom line for fishermen: Profit = Revenue - cost Cost of catching one fish = Density of fish at that location θ

40. No Fishing

42. For coastal fish species: Myers & Cadigan 1993 Botsford & Hobbs 1995 Carr et al. 1995 Caley et al. 1996 Fokvord 1997 Hixon & Webster 2002 Webster 2003 Skajaa et al. In Prep.

43. An integro-difference model describing coastal fish population dynamics: Adult abundance at location x during time-step t+1 Number of adults harvested Natural mortality of adults that escaped being harvested Fecundity Larval survival Larval dispersal (Gaussian) (Siegel et al. 2003) Larval recruitment at x Number of larvae that successfully recruit to location x

44. Incorporating Density Dependence Post-dispersal: Larva settlement and/or recruitment success increases with decreasing adult population density at that location.

45. Incorporating Density Dependence Post-dispersal: Larva settlement and/or recruitment success increases with decreasing adult population density at that location.

46.  Mean lifespan = 1 / Mortality rate  Meta-analysis of 124 nearshore Pacific fishery species (Cailliet 2000):  Most nearshore fishery species live 10++ years  M < 0.1 for most species Fish Mortality and Lifespan Atlantic cod: 20+ yrs CA sheephead 20+ yrs Cabezon 17 yrs

47. P = Density independent fish replacement rate per generation: P = F*L Meta-analysis of 700 fish (Myers et al. 1999)

48. Mean larval dispersal distance

49. To maximize profits, should reserves be… …few and large, What is the optimal reserve design? …or many and small? SLOSS debate

50. PROFIT = Revenue - Cost Initial fish density Final fish density This year’s harvest at location x

51. integrate PROFIT = Revenue - Cost Final fish density Initial fish density This year’s harvest at location x

52. FEW LARGE RESERVES SEVERAL SMALL RESERVES

53. Scale bar = 100 km

64. Max yield without reserves

67. Max profit without reserves

71. M = 0.1 (solid) P = 1

72. M = 0.05 (dash) M = 0.1 (solid) P = 1

73. M = 0.05 (dash) M = 0.1 (solid) P = 1, 2, 3

74. Cost of fishing: LowCost of fishing: Moderate Cost of fishing: Higher Cost of fishing: Highest

75. 12 reserves constituting ~20% of the C.I. coastline

76. MPA process along the central CA coast. Deliberating dedicating ~20% of the coast to a network of ~30 reserves (Pending approval by CDFG and Gov. Szchchweschcwchchcnggchcerrrr)

77. Georges Bank and two nearby reserves constitute ~20-30% of regional groundfish habitat (Murawski 2000)

78. Max profit without reserves

83. Reserves = 20% of the coastline (M = 0.1, P = 1)

84. θ = 0 - 20 θ = 5 - 20 20% of the coast dedicated to reserves 35% escapement across all fisheries

85. θ = 0 - 20 θ = 5 - 20 20% of the coast dedicated to reserves 35% escapement across all fisheries

86. θ = 0 - 20 θ = 5 - 20 30% of the coast dedicated to reserves 30% escapement across all fisheries

87. θ = 0 - 20 θ = 5 - 20 40% of the coast dedicated to reserves 30% escapement across all fisheries

88. θ = 0 - 20 θ = 5 - 20 50% of the coast dedicated to reserves 30% escapement across all fisheries

89. Max profit without reserves

90. Cost = θ /density (Stop fishing when cost = $1)

91. Max profit without reserves Cost = θ /density (Stop fishing when cost = $1) Escapement = % of virgin K (K = 100)

92. Max profit without reserves Cost = θ /density (Stop fishing when cost = $1) Escapement = % of virgin K (K = 100) Zero-profit escapement level = θ /K = 20%

93. Max profit without reserves Cost = θ /density (Stop fishing when cost = $1) Escapement = % of virgin K (K = 100) Zero-profit escapement level = θ /K = 20%

94. Max profit without reserves θ /K = 15/100 = 15%

95. Max profit without reserves θ /K = 15/100 = 15%

96. Max profit without reserves θ /K = 10/100 = 10% 10%

97. Max profit without reserves θ /K = 5/100 = 5% 10%

98. Reserves = 60% of the coastline

100. 60% of the coast dedicated to reserves θ % escapement across all fisheries θ = 0 - 20 θ = 5 - 20

101. 60% of the coast dedicated to reserves θ % escapement across all fisheries θ = 0 - 20 θ = 5 - 20

102. Summary 1.Profit is bottom line for fishermen and fisheries. 2.Fishery yield and profit maximized via…  A small proportion of the coastline in reserves …A variety of reserve spacing options.  A large proportion of the coastline in reserves …Several small or few medium-sized reserves.

103. Summary 4.Reserves effects on fishery profit: ▪ Cost of fishing low/moderate: Increases profit ▪ Cost of fishing exorbitant: Maintains profit

104. Summary 4.Reserves effects on fishery profit: ▪ Cost of fishing low/moderate: Increases profit ▪ Cost of fishing exorbitant: Maintains profit 5.Near-maximum profits are maintained across a spectrum of reserve and harvest scenarios: ReservesNone Many EscapementHighLow

105. Summary 4.Reserves effects on fishery profit: ▪ Cost of fishing low/moderate: Increases profit ▪ Cost of fishing exorbitant: Maintains profit 5.Near-maximum profits are maintained across a spectrum of reserve and harvest scenarios: ReservesNone Many EscapementHighLow 6. 20-50% coast in reserves and ~30% escapement

106. Summary 4.Reserves effects on fishery profit: ▪ Cost of fishing low/moderate: Increases profit ▪ Cost of fishing exorbitant: Maintains profit 5.Near-maximum profits are maintained across a spectrum of reserve and harvest scenarios: ReservesNone Many EscapementHighLow 6. 20-50% coast in reserves and ~30% escapement 7. 60% coast in reserves and θ % escapement

107. An integro-difference model describing coastal fish population dynamics: Adult abundance at location x during time-step t+1 Number of adults harvested Natural mortality of adults that escaped being harvested Fecundity Larval survival Larval dispersal (Gaussian) (Siegel et al. 2003) Larval recruitment at x Number of larvae that successfully recruit to location x

108. Smooth, Gaussian larval dispersal kernel Based on MCMC particle simulations in a 2-D field characterized by flow velocities obtained from buoys and drifters along the central CA coast. Siegel et al. 2003

109. Stochastic larval dispersal kernel Heterogeneous dispersal pattern due to stochastic ocean flow field dynamics Siegel et al. 2003 Delivery of larval “packet”

110. SeaWiFS - NASA California Current San Francisco Santa Barbara Los Angeles

111. SeaWiFS - NASA California Current San Francisco Santa Barbara Los Angeles

112. With fish abundance Nishimoto & Washburn (2002) Elwood Beach

113. SETTLEMENT TIME SERIES Data Courtesy - PISCO [UCSB]

114. A STIRRED OCEAN Larval dispersal is stochastic driven by turbulent “eddies” –Not smooth diffusion processes Makes recruitment and stock distribution predictions challenging

115. Estimate stochastic dispersal patterns: BIO-PHYSICAL SIMULATIONS Simulate coastal circulation processes in California current system –Using ROMS (Rutgers) –Driven by real data Buoys and transects Release & track “larvae” Obtain connectivity Mean wind Mean offshore current at surface due to Coriolis Force - Creates upwelling

116. Estimate stochastic dispersal patterns: BIO-PHYSICAL SIMULATIONS TARGET AREA: Central California –Relatively straight coast –Wind is dominant Equatorward Mean wind Mean offshore current at surface due to Coriolis Force - Creates upwelling

117. IDEALIZED SIMULATION Top view Alongshore pressure gradient obtained from observation data Poleward geostrophic force applied as an external force Stochastic wind stress estimated from observation data Predominantly southward Side view Periodic Coast = Wall Open  Poleward

118. °C

119. SIMULATION VALIDATION: MEAN TEMPERATURE Simulation Shows good agreement with CalCOFI seasonal mean (Line 70) CalCOFI seasonal mean °C

120. SIMULATION VALIDATION: LAGRANGIAN STATISTICS Time scale Length scale Diffusivity zonal/meridional zonal/meridional zonal/meridional 2.7/2.9 days 29/31 km 4.0/4.3 x10 7 cm 2 /s 2.9/3.5 days 32/38 km 4.3/4.5 x10 7 cm 2 /s Surface drifter data (Swenson & Niiler) Simulation data Data set Shows good agreement with surface drifter data

121. ADD LARVAE Release many (10 5 ) Lagrangian particles as model larvae Pattern after rocky reef fish –Larvae are released daily for 90 days, uniformly distributed in habitat –Settlement competency window = day 20-40 –Nearshore habitat = waters within 10 km from the coast

122. Eddies sweep newly released larvae together into “packets” which stay coherent through much of their pelagic stage

123. ALL larvae Red dots = settling larvae

124. ALL larvae Red dots = settling larvae

125. DEPARTURE, ARRIVAL & CONNECTIVITY Connectivity is inherently heterogeneous No bathymetric or coastline variability necessary

126. THREE MORE REALIZATIONS Different realizations -> different connectivity

127. THREE MORE REALIZATIONS … Connectivity is not diffusive

128. DISPERSAL KERNEL Sample dispersal kernel (from a 10-km subpopulation) Ensemble averaged (& normalized) Gaussian fit

129. Stochastic settlement patterns will be most pronounced for species with… Short and periodic spawning seasons Late-stage settlement competency windows Little/no active swimming behaviors

130. Population Dynamics Implications of Stochastic Dispersal Kernel Habitat connectivity on annual scales is uncertain (“flow-induced uncertainty”) –Hotspots shift from year to year –Habitat connectivity is heterogeneous & intermittent & NOT diffusive Even if coastline and bathymetry is smooth –Spatial variance in hotspots likely reduced by inclusion of variable bathymetry and coastal contours Modeling implications –Must account for massive, local recruitment events Add larvae-on-larvae density dependence to represent competition for limited settlement niches at a site

131. Even with perfect knowledge of current stock distribution & productivity –Recruitment and future stock predictions uncertain Difficult to assess appropriate escapement level –High potential for over-exploitation at recruitment-failure location Reliance on reserve management more practical –Network of reserves provide many potential sources Consequences of over-exploitation reduced due to reserves –Fisheries can track locate and exploit “hot spots” Fishermen can act in real time (managers have to forecast) Fisheries Management Implications of Stochastic Dispersal Kernel

132. Flow, Fish, and Fishing (F 3 ) Biocomplexity Project Dave Siegel Physical Oceanographer Kraig Winters Physical Oceanographer Bob Warner Population biologist Steve Polasky Environmental economist Bruce Kendall Theoretical Ecologist Ray Hilborn Fishery Scientist Chris Costello Environmental economist Steve Gaines Population ecologist Satoshi Mitarai Oceanography Post-Doc

133. University of California – Santa Barbara National Science Foundation Coastal Environmental Quality Initiative The Canon National Parks Science Scholars Program THANK YOU!!

135. SIDE VIEW ALL LARVAE Red dots = settling larvae

136. SIDE VIEW ALL LARVAE Red dots = settling larvae

137. Effects of age/stage structure on fish population and fishery dynamics Per capita production increases (exponentially) with age. But K (#fish/km) will decrease…

138. 25% of the coast dedicated to reserves 30% escapement across all fisheries θ = 0 - 20 θ = 5 - 20