and francisco chavez 柴扉 fei chai - home - pices...fc 6 deyoung, heath, werner, chai, megrey,...

Fei CHAI (柴扉) and Francisco Chavez

University of Maine Monterey Bay Aquarium Research Institute

Outlines:

Needs & Challenges of Modeling Ecosystems

Physical and Ecosystem Models

Peruvian Anchoveta, ENSO Forecasts

1

An ecosystem sandwich

Marine Ecosystems

CLIMATEFISHING

FISHING/HARVEST: a combination of effort and abundance

CLIMATE: exerts important influences on habitat

2

FAO (2004)

(Chavez et al. Science 2003)

PDO

Anchoveta Sardine

Trends of climatic and oceanographic variables in the Pacific Basin

Benthivorous Fish

Pelagic Invertebrate

Predators

Phytoplankton

Seabirds

Deposit-feedingBenthos

Suspension- feeding Benthos

DOAmmonia

Fishing

Detritus

Micro-Zooplankton

Meso-Zooplankton

Nitrate+Nitrite

Nano-Phytoplankton

PlanktivorousFish

Piscivorous Fish

Pre-recruits Pre-recruits Pre-recruitsMarine

Mammals

spawning

recruitment

Bacteria

How to Link?

5

FC 6

deYoung, Heath, Werner, Chai, Megrey, Monfray

Science, 2004

The difficulty arises because organisms at higher trophic

levels are longer lived, with important variability in

abundance and distribution at basin and decadal scales.

7

The rhomboids indicate the conceptual characteristics for models with differentspecies and differing areas of primary focus.

Rhomboid is broadest wheremodel has its greatest functional complexity i.e., at the level of the target organism.

deYoung, Heath, Werner, Chai, Megrey, MonfrayScience, 2004

Fei CHAI (柴扉) and Francisco Chavez

University of Maine Monterey Bay Aquarium Research Institute

Outlines:

Needs & Challenges of Modeling Ecosystems

Physical and Ecosystem Models Peruvian Anchoveta, ENSO Forecasts

8

FC9

Sea Surface Temperature (SST)

Sea Surface Height (SSH)

3 day averaged, 1993 to 2006

Regional Ocean Model System (ROMS): 12-kmRes.

PhysicalModel

Nitrate[NO3]

Advaction

& Mixing

SmallPhytoplankton

[P1]NO3

Uptake

Micro-Zooplankton

[Z1]

Grazing

Ammonium[NH4]

Excretion

NH4

Uptake

Detritus-N[DN]

Fecal

Pellet

Sinking

Silicate[Si(OH)4]

Diatoms[P2]

Si

Uptake

N-Uptake

Meso-zooplankton

[Z2]

Sinking

Detritus-Si[DSi]

GrazingFecal

Pellet

Sinking

Predation

Lost

Total CO2

[TCO2]

Biological

Uptake

Air-Sea

Exchange

Carbon, Silicate, Nitrogen Ecosystem ModelCoSiNE, Chai et al. 2002; Dugdale et al. 2002

Iron

Iron

Chai et al., 1996

Eddy-Resolving Ocean Modeling at 12-km

Regional Ocean Model Systems (ROMS)-CoSiNECoSiNE: Carbon, Silicate, and Nitrogen Ecosystem (Chai et al., 2002)

ROMS

SST

Jan. 1993

Fei CHAI (柴扉) and Francisco Chavez

University of Maine Monterey Bay Aquarium Research Institute

Outlines:

Needs & Challenges of Modeling Ecosystems

Physical and Ecosystem Models

Peruvian Anchoveta, ENSO Forecasts

12

Lima

Why Peru?

A fishing village in Peru

13

The State of World Fisheries and Aquaculture, FAO (2004)14

15

Daily sampling covers all the landing points, and 10% of the active fishing fleet.

Birds and

mammals

17

El Nino and Peruvian Anchovy Fishery

Sea Surface Temperature Anomaly

Annual Anchovy Catch

10 x 106 MT

Pacific Basin ROMS-CoSINE (12-km) SimulationAnnual Mean Sea Surface Temperature (SST)

ModeledSST (oC)

SatelliteSST (oC)

18

Surfare Chlorophyll Comparisonin situ, the modeled, and SeaWiFS

Historical DataSeaWiFS

1997-2006

19

FC

Si(OH)4

Meso

zooplankton

ROMS-CoSINE (12-km), Annual Mean Si(OH)4, Diatoms, Meso-zooplankton along 10oS

2021

FC21

ROMS-CoSINE (12-km) Si(OH)4 and Diatoms along 10oS

Jan. 1999

Jan. 1999

Jan.1998

Si(OH)4

Diatoms

Offshore Distance400 km

40

20

0

4

2

0

mmol/m3

mmol/m3

22

FC

in situ

Modeled

SeaWiFS

Seasonal Cycle of Surface Chlorophyllalong the Coast of Peru, 0-100km, 4oS-18oS

23

Science at the leading and/or bleeding edge

First ever long term forecast of chlorophyll?

Surface chlorophyll, Nov. 2008

Forecasting Skill for Peru Coast - about 6 months

Forecasted

Simulated

Data

EGGSDURATION: 24 HR

MORTALITY RATE>99%

YOLK-SAC LARVELEN: 2-4MM

DURATION: 24-28 HRMORTALITY RATE 80%-98%

FIRST-FEEDERFEED BY PHYTOPL.

LEN: 4.25CM, WT: ~2 gmDURATION: 80 DAYS

AGE-1(JUVENILE)BECOME SEXUAL MATRUE

LEN: 8-10CMWT: ~10 gm

AGE-2LEN: ~20CM WT: ~55 gm

OPT TEMP: 18.6°CSPAWN ~20 TIMES/YR

AGE-2+LIFE SPAN ~3 YR

PREDATOR: SEA BIRDS, MARINE MAMMALS

Life Cycle of Peruvian AnchovyIndividual Based Model with ROMS-CoSINE

ROMS-CoSIN

E (12 km)

Temperature, Curre

nts,

Planktons

ROM

S-CoSIN

E (12 k

m)

Tem

peratu

re, C

urrents

,

Plankt

ons

ROM

S-CoSINE (12 km)

Temperature, Currents,

Planktons

RO

MS-C

oSINE (12 km

)

Temperature, C

urrents,

Planktons

25

ROMS-CoSINE-IBM

Current, Food, Temperature, and Behavior

Wind

Coast

Zooplankton

Phytoplankton

Currents

Anchovy

Surface

100 m

27

Fish Growth Curves

28

Days to recruit to 5cm

Total Zooplankton

Total Phytoplankton

Recruitment: Seasonal Cycle

There is a clear seasonal and interannual variability characterized by anchovy recruitment to 5cm.

Strong El NinoModerate El Nino

Temperature

diatommesozooplankton

Recruitment

Anchovy Recruitment in Response to ENSO

Current, Food, Temperature, and Behavior

Wind

Coast

Zooplankton

Phytoplankton

Currents

Anchovy

Surface

100 m

31

3-D model results Passive driftDay 31Day 1 Day 61

0m

release

30m

release

February 1991

32

1

3

2

Fish is able to search a given round area, the radius is

determined by swimming speed.

The new location is determined by the highest potential

growth rate within the search area.

If there are more than one preferred place, then randomly

choose one.

3-D model results Swimming Assumptions

33

Dep0m

Dep30m

3-D model results Swimming Results

6 month1 month

0m

release

30m

release

January1991

Fei CHAI (柴扉) and Francisco Chavez

University of Maine Monterey Bay Aquarium Research Institute

Outlines:

Needs & Challenges of Modeling Ecosystems

Physical and Ecosystem Models

Peruvian Anchoveta, ENSO Forecasts

ROMS-CoSiNE-IBM34

Some final thoughts:

New understanding: food web structure, Fe, remote

forcing, ENSO and decadal variability and

predication.

Technical advancement: computing power, observing

power and information infrastructure.

Cost to resource managers of operational ecological

forecasting for fisheries management will be small

because society as a whole has already paid for.

Need to train new generation of marine scientists and

resource managers to integrated all information and

understanding.

35

Human Consumption of Anchoveta

A Japanese restaurant in Lima, Peru

36

Fei CHAI (柴扉) and Francisco Chavez

University of Maine Monterey Bay Aquarium Research Institute

Outlines:

Needs & Challenges of Modeling Ecosystems

Physical and Ecosystem Models

Peruvian Anchoveta, ENSO Forecasts

ROMS-CoSiNE-IBM37