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Utilizing remote sensing, modeling and data assimilation to sustain and protect fisheries: ecological forecasting at work Francisco Chavez, M. Messie Monterey.

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Presentation on theme: "Utilizing remote sensing, modeling and data assimilation to sustain and protect fisheries: ecological forecasting at work Francisco Chavez, M. Messie Monterey."— Presentation transcript:

1 Utilizing remote sensing, modeling and data assimilation to sustain and protect fisheries: ecological forecasting at work Francisco 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)

2 Approach Develop remote sensing products for fisheries decision support systems Develop remote sensing products for fisheries decision support systems Develop strong theoretical basis for forecasting using in situ and satellite data Develop strong theoretical basis for forecasting using in situ and satellite data Develop 50 year model hindcasts and test theory Develop 50 year model hindcasts and test theory Develop 2-9 month model forecasts and incorporate into fisheries decision support systems Develop 2-9 month model forecasts and incorporate into fisheries decision support systems

3 Mean Trend Anomaly Mean trend MODIS chlorophyll - first biological parameter explicitly included in the CPC report Dave Foley, NOAA

4 Science at the leading and/or bleeding edge Long term (9 month) forecasts of chlorophyll Why Peru?

5 Progress in Oceanography 2008

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

7 Two Primary States Change? Varia- bility SST 1880 - 2006 SSH 1983 – 2006 black line

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9 Regional Ocean Model Systems (ROMS)-CoSiNE CoSiNE: Carbon, Silicate, and Nitrogen Ecosystem (Chai and Chao) Eddy-Resolving Ocean Model at 12-km

10 10 Pacific Basin ROMS-CoSINE (12-km) Simulation Annual Mean Sea Surface Temperature (SST) Modeled SST ( o C) Satellite SST ( o C) 10

11 11 Zooplankton(ROMS-CoSINE) Averaged from 1991-2007 by ROMS-CoSINE (blended wind forcing)

12 50 year 50 km hindcast simulation Data Model SST

13 Model Data Sea levelSST

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15 Large regime shift documented in Monterey Bay, CA

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19 EGGS DURATION: 24 HR MORTALITY RATE>99% YOLK-SAC LARVE LEN: 2-4MM DURATION: 24-28 HR MORTALITY RATE 80%-98% FIRST-FEEDER FEED BY PHYTOPL. LEN: 4.25CM, WT: ~2 gm DURATION: 80 DAYS AGE-1(JUVENILE) BECOME SEXUAL MATRUE LEN: 8-10CM WT: ~10 gm AGE-2 LEN: ~20CM WT: ~55 gm OPT TEMP: 18.6°C SPAWN ~20 TIMES/YR AGE-2+ LIFE SPAN ~3 YR PREDATOR: SEA BIRDS, MARINE MAMMALS Life Cycle of Peruvian Anchovy Individual Based Model with ROMS-CoSINE ROMS-CoSINE (12 km) Temperature, Currents, Plankton ROMS-CoSINE (12 km) Temperature, Currents, Plankton ROMS-CoSINE (12 km) Temperature, Currents, Plankton ROMS-CoSINE (12 km) Temperature, Currents, Plankton Yi Xu, U of Maine

20 20 Anchovy Distribution Statistics Start with same amount of eggs Start with same amount of eggs Release eggs each year/month Release eggs each year/month Calculate the total survivors after 6 months with spatial distribution Calculate the total survivors after 6 months with spatial distribution Temperature and food (phyto+zoo) control survivorship Temperature and food (phyto+zoo) control survivorship

21 21 Anchovy Distribution Averaged from 1991-2007 by IBM

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23 23 Latitudinal direction

24 Next steps Continue to improve forecasts and insert into DSS Continue to improve forecasts and insert into DSS Retrospective analysis to get at mechanisms behind changes Retrospective analysis to get at mechanisms behind changes Clearly identified changes in the ecosystem – 1972 anchoveta decline, sardine increase, 1989 anchoveta recovery and sardine decline, 1992 humboldt squid appearance-jack mackerel/hake disappearance, 1998 appearance of cool water species Clearly identified changes in the ecosystem – 1972 anchoveta decline, sardine increase, 1989 anchoveta recovery and sardine decline, 1992 humboldt squid appearance-jack mackerel/hake disappearance, 1998 appearance of cool water species


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