1.   Development of a climate-to-fish-to-fishers model: proof-of-principle using long-term population dynamics of anchovies and sardines in the California Current Jerome Fiechter, UC Santa Cruz (on Behalf of the NEMUROMS.SAN Group) ePOPf meeting, Portland, 21 Sep 2010

2. Chris Edwards University of California – Santa Cruz Dave Checkley Scripps Institution of Oceanography Alec MacCall National Marine Fisheries Service Tony Koslow SIO - CALCOFI Sam McClatchie Ken Denman Institute for Ocean Sciences (Canada) Francisco Werner Rutgers University
Kenneth A. Rose Louisiana State University Enrique N. Curchitser Rutgers University Kate Hedstrom Arctic Region Supercomputing Center Jerome Fiechter University of California – Santa Cruz Miguel Bernal Instituto Español de Oceanografía (Spain) Shin-ichi Ito Fisheries Research Agency Salvador Lluch-Cota CIBNOR (Mexico) Bernard A. Megrey National Marine Fisheries Service

3. Motivation Much emphasis on climate to fish linkages
Global change issues
Bottom-up, middle-out, top-down controls
Increasing pressure for ecosystem-based considerations in management
Full array of interactions within an ecosystem, including humans (instead of single species)
Development of end-to-end ecosystem models
Multi-species, individual-based, physics to fish
Proof of principle, continuation of NEMURO effort

4. Motivation
The human component…

5. Proof of Principle Sardine – Anchovy cycles
Well-studied species with population cycles observed in many systems
Teleconnections across basins
Good case study
Forage fish tightly coupled to NPZ
Important ecologically and widely distributed
Low frequency variability
Provided by: Salvador E. Lluch-Cota
Source: Schwartzlose et al., 1999

6. Californian Anchovy Larval Abundance
April 1952
Low Anchovy Abundance
April 1965
High Anchovy Abundance
Source: MacCall, 1990

7. End-to-End (E2E) Ecosystem Model (NEMUROMS.SAN)
Sub-Model 1: 3-D ROMS for ocean circulation
Sub-Model 2: NEMURO for NPZ
Sub-Model 3: Multiple-species IBM for fish
Sub-Model 4: Fishing fleet dynamics
Today: progress to date
Solved many numerical and bookkeeping issues
Next is to add more realistic biology

8. Regional Ocean Circulation Model
Why ROMS Framework
Regional Ocean Circulation Model
NPZ Component (multiple)
Floats Component
Climate Coupling
Data Assimilation
Fish IBM
Sardines
Anchovies
Predators
Fishing Fleet

9. ROMS-CCSM Coupling – Climate Forcing
Focus on Upwelling Regions
California Current System
Humboldt Current System
Benguela Current System

10. 3-D ROMS for Ocean Circulation
California Current Grid
- 30 km horiz. resolution
- 30 vertical levels
Run duration
- 40 years ( )
- hourly time step
BC/IC: SODA-POP
- Monthly SSH, U, V, T, S
(Carton et al., 2000)
Surface forcing: CORE2
- 6-hrly wind stress, heat fluxes
- Daily radiation, monthly E-P
(Large and Yeager, 2008)

11. NEMURO for Lower Trophic Levels (Nutrients, Phytoplankton, and Zooplankton)

12. Fish IBM: Species Types
Why individual-based model (IBM)
Natural unit in nature
Allow for complicated life history
Plasticity and size-based interactions
Conceptually easier movement
Sardines and anchovy (full life-cycle)
Reproduction, growth, mortality, movement
Competitors for food (P,Z)
Predator species (e.g., albacore)
Consumption and movement only
Relative biomass to scale mortality

13. Fish IBM: Growth and Mortality
Bioenergetics for weight
Consumption from multi-species functional response
At maturity, allocate energy to growth or reproduction
Mortality
Natural (temperature and stage dependent)
Predation (predator species dynamics, vary spatially)
Fishing (fishing fleet dynamics, vary spatially)
Maximum consumption: f(W, T)
Holling Type II feeding curve

14. Fish IBM: Movement (Happiness-based behavior; Humston et al, 2004)
Eggs, yolk-sac, and larvae moved by physics
- Vertical position assumed at 50 m depth
Juveniles and adults moved by behavior
- Day-to-day vs. seasonal migrations
Kinesis for sardines and anchovies
(Happiness-based behavior; Humston et al, 2004)
- Fitness for predator species
(Maximized growth behavior; Railsback et al., 1999)
Each fish has continous x, y, and z position
- mapped to 3-D grid at each time step to determine cell location and local conditions

15. Fishing Fleet Targeting sardines in the California Current
Movement based on engineering, economics, behavior (evaluated once daily in model)
Maximize revenue based on expected CPUE
- Num. of ports: 6 (CA(3), OR(1), WA(2))
- Num of boats per ports: 10-30
- Average catch per boat: tons
Boat motoring speed: 20 km/h
Time to fish/process catch: 2 hours
Average price for catch: $/kg

16. CA Commercial Fish Landings (2009)
Source: CA Department of Fish and Game
Pacific Sardine (~38K tons)
P. Mackerel (~5K tons)
Fisheries Peak: ~700K tons (1936)
N. Anchovy (~3K tons)
Dover Sole (~3K tons)
Sablefish (~2K tons)

17. Numerical Details Major numerical and bookkeeping challenges
Solving everything simultaneously, eventually physics with an offline option
However, it is the fish IBM that takes computing time
Super-individual approach for IBM
Sheffer et al., 1995
Allocation of super-individuals (array management)
Super-individuals eating super-individuals
Bisection to start new super-individuals as eggs each day based on spawner locations

18. NEMUROMS.SAN ( )
Fully coupled ROMS + NEMURO + Fish + Predators + Fleet
(20,000 individuals; 1,000 predators; 100 fishing boats)

19. Sample NEMUROMS.SAN output (1960-2000)
Weight at Age
Total Egg Production
Diet Composition
Mean Depth (m)
Mean Temperature (C)

20. Next Steps It can be done – proof of principle
2 days to run 40 years with 20,000 individuals
Target: 500,000 super-individuals
Can we calibrate and validate this model?
Very challenging: Physics, NPZ, Fish
Is it useful?
Need to increase biological realism
Investigate the causes of low-frequency cycles in sardine and anchovy
Parallel effort in Japan to provide a geographic and ecosystem contrast

21. Next Leaps Relate to other E2E ecosystem models
Atlantis, ECOPATH w/ECOSIM , FEAST, …
Assess use for ecosystem-based management
Other species, fisheries in the California Current
California Sea Lion foraging in 2004 and 2005 (Weise et al., 2006)
Coho Salmon Survival (Peterson and Schwing, 2003)

22. ROMS
NEMURO
IBM

24. Biological Resolution
3D-NEMURO
(1/12º)
NEMUroms.SAN
3D-eNEMURO
3D-FeNEMURO
3D-NPZ
3D-PlankTOM5
3D-NEMURO(1º)
SEAPODYM
2D-NEMURO.FISH
Spatial Resolution
2D-NEMURO
multi-box
NEMURO.FISH
3-box
NEMURO.FISH
MFW- NEMURO
1D-NEMURO
0D-NPZ
1-box NEMURO.FISH
Biological Resolution
Prepared by Shin-ichi Ito

25. ROMS time-stepping (hourly) Age and clean old individuals
Loop over cells in grid
Loop over days in year
Next year
Growth
NEMURO – NPZ
Initial conditions
If midnight then reproduction
Hydrodynamics
Determine fish in each cell
ROMS time-stepping (hourly)
Age and clean old individuals
Next
Loop over years in run
Consumption from previous hour
Small, large, and
predatory zooplankton
Loop over fish in cell
If midnight then add new super-individuals
Mortality
Movement
NEMUROMS.SAN
FLOWCHART

26. Fish IBM: Kinesis Movement
Inertial:
f(Vt-1) = Vt-1·multiplier
Random:
g(ε) = ε·multiplier
Velocity:
f(Vt-1) + g(ε)

27. Fish IBM: Development Eggs, yolk-sac larvae, larvae
Temperature-dependent stage durations
Birthday on January 1 of each year
Juvenile to subadult on first January 1
Subadult to adult with maturity at second birthday

28. Fish IBM: Reproduction
Two strategies
Capital and income
Hybrid that allows switching
Beginning and ending temperatures (or days)
Check energy to initiate a batch
Capital using condition
income using yesterday’s weight change
Batches develop based on temperature
Accumulate eggs each day and locations of spawners

29. Fish IBM: Mortality Natural Starvation: too skinny Eggs: 0.1/day
Yolk-sac: 0.05/day
Larvae: 0.05/day
Juveniles: 0.3/year
Adults: 0.3/year
Starvation: too skinny

30. Bisection

31. Fish IBM: Fitness Option for Movement (Railsback et al. 1999)
Evaluate entire water
column defined by 9 cells
Project growth rate using
interpolated fields at center
of each cell
Go towards center of cell with fastest growth
Position updated hourly

32. Fish IBM: Kinesis Option for Movement (Humston et al. 2004)
X and Y velocities of each individual is computed hourly based on kinesis behavior (response to temperature)
Kinesis is the sum of random and inertial velocities (happiness)
Horizontal done 24 times a day using first hour’s conditions; vertical is hourly

33. Density-Dependence Growth via feedback effects on prey
Starvation
Fecundity
Other possibilities:
Maturation (mediated through growth)
Mortality via predation (movement only species) and fishing
Movement
Spreading out under high abundances
Costs of inferior habitats?

34. Sample IBM output: early life history

35. ROMS and NEMURO Fields (1960-2000 means)

36. Sample IBM output: individuals and biomass

37. Sample IBM output: individuals and biomass

38. Sample IBM output: individuals and biomass

39. Sample IBM output: fish trajectories
Locations every 5 days for one year
Grey shading shows bottom topography

40. Sample IBM output: vertical position (every 5 days)