Utilizing remote sensing, Utilizing remote sensing, modeling and data modeling and data

assimilation to sustain and assimilation to sustain and protect fisheries: ecological protect fisheries: ecological

forecasting at workforecasting at workFrancisco Chavez, M. Messie

Monterey Bay Aquarium Research Institute

F. Chai (U of Maine), Y. Chao (NASA/JPL), David Foley (NOAA/NMFS), and R.T. Barber (Duke)

ApproachApproach• Develop remote sensing products for Develop remote sensing products for

fisheries decision support systemsfisheries decision support systems

• Develop strong theoretical basis for Develop strong theoretical basis for forecasting using in situ and satellite forecasting using in situ and satellite datadata

• Develop 50 year model hindcasts and Develop 50 year model hindcasts and test theorytest theory

• Develop 2-9 month model forecasts and Develop 2-9 month model forecasts and incorporate into fisheries decision incorporate into fisheries decision support systemssupport systems

Mean

TrendAnomalyAnomaly

Mean trend

MODIS chlorophyll - first biological parameter explicitly included in the CPC report

Dave Foley, NOAA

Science at the leading and/or bleeding edge

Long term (9 month) forecasts of chlorophyll

Why Peru?

Progress in Oceanography 2008

More fish (total and per unit primary production) than any other place in the world!

TwoPrimaryStates

Change?

Varia-bility

SST1880 - 2006

SSH1983 – 2006

black line

Regional Ocean Model Systems (ROMS)-CoSiNECoSiNE: Carbon, Silicate, and Nitrogen Ecosystem (Chai and Chao)

Eddy-Resolving Ocean Model at 12-kmEddy-Resolving Ocean Model at 12-km

1010

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

ModeledSST (oC)

SatelliteSST (oC)

10

1111

Zooplankton(ROMS-Zooplankton(ROMS-CoSINE)CoSINE)

Averaged from 1991-2007 by ROMS-CoSINE (blended wind forcing)

50 year 50 km hindcast simulation

Data

Model

SST

Model

Data

Sea levelSST

Large regime shift documented in Monterey Bay, CA

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-CoSINE (12 km)

Temperature, Curre

nts,

Plankton

ROMS-CoSIN

E (12 km

)

Temperature

, Curre

nts,

Plankton

ROMS-CoSINE (12 km)

Temperature, Currents,

Plankton

ROMS-CoSINE (12 km)

Temperature, Currents,

Plankton

Yi Xu, U of Maine

2020

Anchovy Distribution Anchovy Distribution StatisticsStatistics

• Start with same amount of eggsStart with same amount of eggs

• Release eggs each year/monthRelease eggs each year/month

• Calculate the total survivors after 6 Calculate the total survivors after 6 months with spatial distributionmonths with spatial distribution

• Temperature and food (phyto+zoo) Temperature and food (phyto+zoo) control survivorshipcontrol survivorship

2121

Anchovy DistributionAnchovy Distribution

Averaged from 1991-2007 by IBM

2222

2323

Latitudinal directionLatitudinal direction

Next stepsNext steps

• Continue to improve forecasts and insert Continue to improve forecasts and insert into DSSinto DSS

• Retrospective analysis to get at Retrospective analysis to get at mechanisms behind changesmechanisms behind changes

• Clearly identified changes in the ecosystem Clearly identified changes in the ecosystem – 1972 anchoveta decline, sardine increase, – 1972 anchoveta decline, sardine increase, 1989 anchoveta recovery and sardine 1989 anchoveta recovery and sardine decline, 1992 humboldt squid appearance-decline, 1992 humboldt squid appearance-jack mackerel/hake disappearance, 1998 jack mackerel/hake disappearance, 1998 appearance of cool water speciesappearance of cool water species