Trophic interactions in northern Chile upwelling ecosystem ...
Decision support systems for ocean ecosystem-climate interactions
description
Transcript of Decision support systems for ocean ecosystem-climate interactions
Decision support systems for Decision support systems for ocean ecosystem-climate ocean ecosystem-climate
interactionsinteractionsFrancisco Chavez
Monterey Bay Aquarium Research Institute
and mostly others
ConclusionsConclusions• Sophisticated observing systems, increased computing power Sophisticated observing systems, increased computing power
for modeling and improved ecosystem theory provide the for modeling and improved ecosystem theory provide the framework framework todaytoday to develop ecological forecasts useful for to develop ecological forecasts useful for societysociety
• Nature will continue to surprise (at a faster rate due to global Nature will continue to surprise (at a faster rate due to global change) and our theory will need to constantly evolvechange) and our theory will need to constantly evolve
• NASA will need to develop new direct or indirect estimates NASA will need to develop new direct or indirect estimates (and models) of ecologically relevant properties beyond (and models) of ecologically relevant properties beyond chlorophyllchlorophyll
• Better working relationships between resource managers and Better working relationships between resource managers and NASA should be fosteredNASA should be fostered
RoadmapRoadmap
• How did we get hereHow did we get here
• ApproachApproach
• Case StudiesCase Studies
• The way forwardThe way forward
During the 1970s Bob Smith, Dick Dugdale and Dick Barber believed that, in coastal upwelling research, physics and biology together would be more than the sum of the parts and together could deliver an applied science product regarding management of living marine resources.
CUEA was successful as an interdisciplinary basic research project; but CUEA failed to deliver an applied science product it had promised regarding management of living marine resources.
What didn’t CUEA deliver? Why not? Is progress being made?
The CUEA proposal funded by NSF in the 70s said:
“The goal of the Coastal Upwelling Ecosystems Analysis Program is to understand the coastal upwelling ecosystem well enough to predict its response far enough in advance to be useful to mankind.” [in the management of living marine resources].
According to Barber
Conclusions regarding “useful to mankind” forecasting:
1. Goal was inherently unattainable in 1972 to 1980.
2. Limitations (deficiencies) in both in theory and technology.
3. The deficiencies in theory (food web structure, Fe, remote forcing, decadal variability) were serious, but a lot (~75%?) of the 1972/1980 physical and biological theory was correct.
4. Technological limitations in computation, observing systems and information handling infrastructure were fatal due to the constraints they imposed (undersampling, inadequate space and time resolution and inadequate model complexity.)
5. These technical limitations were unconceivable at the time and had to change many orders of magnitude before “useful” forecasting could be done.
But the goal is now within reach
Science at the leading and/or bleeding edge
First ever long term forecast of chlorophyll?
We know forecast “drifts” after 5 months
Comparison of forecast anomalies with SeaWIFS anomalies
Barber, Chai, Chao, Chavez
Basin-scale model run for 10 years forced by NOAA blended winds then forced by NCEP 9 month forecast
ApproachApproach
• Retrospective analysis (of Retrospective analysis (of in situin situ and and remote sensing data)remote sensing data)
• Identify changes in ecosystem and Identify changes in ecosystem and environmentenvironment
• Develop conceptual and numerical modelsDevelop conceptual and numerical models
• Look at model solutions (physics, Look at model solutions (physics, biogeochemistry, fish) for driversbiogeochemistry, fish) for drivers
• Forecast and hindcastForecast and hindcast
El Viejo La Vieja
El Viejo
La Vieja
It is a familiar storyChildEl Niño La Niña
Parent
1900 to 2000
Once ever 3-8 years
Once ever 25-40 years
TwoPrimaryStates
Change
Varia-bility
Low oxygen
Realistic model of the Pacific at 12.5 km resolution - SST
Yi Chao, JPL
Computing power allows for model simulations at the right scale
Yamanaka et al. (2005)
PhysicalModel
Nitrate[NO3]
Advection& Mixing
SmallPhytoplankton
[P1]NO3
Uptake
Micro-Zooplankton
[Z1]
Grazing
Ammonium[NH4]
Excretion
NH4Uptake
Detritus-N[DN]
FecalPellet
Sinking Silicate[Si(OH)4]
Diatoms[P2]
Si-Uptake
N-UptakeMeso-zooplankton
[Z2]
Sinking
Detritus-Si[DSi]
GrazingFecalPellet
Sinking
Predation
Lost
Total CO2[TCO2]
BiologicalUptake
Air-Sea Exchange
Dissolution
Carbon, Silicate, Nitrogen Ecosystem ModelCoSiNE, Chai et al. 2002; Dugdale et al. 2002
Chai et al., DSR 1996
Iron
Iron
Iron
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
Need to gain better access to US ocean ecosystem scientists and their data and if data not available then determine how to collect
Case StudiesCase Studies
• The “Right” WhaleThe “Right” Whale
• The Sablefish in Gulf of AlaskaThe Sablefish in Gulf of Alaska
• Leatherback turtlesLeatherback turtles
• Productivity in the Arabian SeaProductivity in the Arabian Sea
181818
Right Whale, Wrong Right Whale, Wrong Time?Time?
• Only 350-400 right Only 350-400 right whales in N. whales in N. AtlanticAtlantic
• Recovery is limited Recovery is limited by hight mortalityby hight mortality
– Ship strikesShip strikes– Fishing gearFishing gear
• All management All management options depend on options depend on knowing where knowing where whales whales areare
Andy Pershing et al.
191919
Saving the Saving the WhalesWhales
• During spring, summer, and fall, During spring, summer, and fall, whales follow foodwhales follow food
– How can we find whale food on How can we find whale food on operational time scales?operational time scales?
– Use remote sensing of SST and Chl Use remote sensing of SST and Chl coupled with model circulation to coupled with model circulation to predict predict CalanusCalanus
Courtesy PCCS
Andy Pershing et al.
202020
Predicting Whales from Predicting Whales from CalanusCalanus
Whale Arrival Date
Whales arrive early when food is abundant
Whale
Arr
ival D
ate
Predicted Calanus Abundance
Andy Pershing et al.
Alaskan Sablefish ProjectAlaskan Sablefish ProjectAuthors: S. Kalei Shotwell & Dana H. HanselmanAuthors: S. Kalei Shotwell & Dana H. Hanselman
• Sablefish (Sablefish (Anoplopoma fimbriaAnoplopoma fimbria))– Fast growing, wide distribution, highly valuable commercial speciesFast growing, wide distribution, highly valuable commercial species– Adults generally at 200+ meters in continental slope, gullies, fjordsAdults generally at 200+ meters in continental slope, gullies, fjords
• Early life history (ELH) largely unknownEarly life history (ELH) largely unknown– Spawning at depth (400+m), larvae swim to surface, collect at Spawning at depth (400+m), larvae swim to surface, collect at
shelf breakshelf break– Juveniles move nearshore to overwinter, then offshore in summerJuveniles move nearshore to overwinter, then offshore in summer– Reach adult habitat and recruit to fishery or survey in 4 to 5 yearsReach adult habitat and recruit to fishery or survey in 4 to 5 years
• Recruitment calculated in age-structured modelRecruitment calculated in age-structured model– Recruitments are estimated as two year-oldsRecruitments are estimated as two year-olds– Estimates for most recent years are highly variable with large Estimates for most recent years are highly variable with large
uncertainty and excluded from model projectionsuncertainty and excluded from model projections
• ObjectiveObjective– Evaluate ELH data and explore integrating satellite derived Evaluate ELH data and explore integrating satellite derived
environmental time series into the sablefish stock assessment to environmental time series into the sablefish stock assessment to reduce recruitment uncertaintyreduce recruitment uncertainty
Distribution, Movements, and Behaviors of Critically Distribution, Movements, and Behaviors of Critically Endangered Eastern Pacific Leatherback Turtles: Endangered Eastern Pacific Leatherback Turtles:
Conservation Implications for an Imperiled PopulationConservation Implications for an Imperiled Population
Shillinger, Palacios, Bailey, BogradShillinger, Palacios, Bailey, BogradBlock Lab, Stanford UniversityBlock Lab, Stanford University
NOAA-SWFSC-ERDNOAA-SWFSC-ERD
Mean Kinetic Energy (cm2 s-2)
1. Turtles must negotiate gauntlet of zonal currents
Chlorophyll (mg m-3)10 year Sea-WiFS
2. Turtles move into zones of low phytoplankton density
CRD (~10cm s-CRD (~10cm s-1)1)NECC(~30cm s-NECC(~30cm s-
1) 1)
SEC (n) (~30cm s-SEC (n) (~30cm s-1)1)
EUC (~5cm s-1)EUC (~5cm s-1)
SEC (s) (~15cm s-SEC (s) (~15cm s-1)1)
Surface velocities cm Surface velocities cm s-1s-1 (CHL vs. Speed linear regression: (CHL vs. Speed linear regression: = 0.964 = 0.964 ± 0.057, ± 0.057, FF1,95771,9577 = 281, = 281, P P < 0.001, < 0.001, rr22 = 0.029) = 0.029)
INTERANNUAL TRENDS IN PHYTOPLANKTON DYNAMICS IN THE ARABIAN SEA LINKED TO EURASIAN
WARMINGJoaquim I. Goes and Helga Gomes Bigelow Laboratory for Ocean Sciences, Maine, USA
Prasad ThoppilNaval Research Laboratory, Stennis Space Centre, Mississippi, USA
Adnan Al Azri Sultan Qaboos University Oman
Prabhu MatondkarNational Institute of Oceanography, Goa, India
R. M. Dwivedi Space Applications Centre, Indian Space Research Organization, India
Warming of SW Eurasia mirrors the global-land signal, but recent warming anomalies are >50% larger than global temperature trends.
SW Eurasian-Land Warming
Interannual changes in chlorophyll along coast of Somalia since 1997 (Goes et al., Science, 2005 )
0
0.4
0.8
1.2
1.6
2
1997 1998 1999 2000 2001 2002 2003 2004
Year
Chlor
ophy
ll (mg
m-3
)
High chlorophyll concentrations during the NEM are being caused by blooms of dinoflagellate Noctiluca miliaris (not
all chlorophyll is the same!!!!)
A similar phenomena in Monterey Bay where (as in the Arabian Sea) the nutricline is shallow and dinos vertically migrate to depth at night for nutrients and surface during day to take up carbon dioxide
Monterey Bay Time Series- El Niños during 92-93 and 97-98- Transition from El Viejo to La Vieja- The age of dinoflagellates?
Dinoflagellate regime associated with failures in fish and seabirds
Export production
Oxygen at 150 m
Gutierrez et al. - Paleopeces
Longer Centennial changes (in oxygen)
Summary, during LIA ocean off Peru high oxygen/few fish, low oxygen/high fish after
The low oxygen expanded The low oxygen expanded southward in to Chile, what southward in to Chile, what about the recent record (~50 about the recent record (~50 years)years)• CaliforniaCalifornia
• PeruPeru
Stramma, L., G.C. Johnson, J. Sprintall, and V. Mohrholz (2008), Expanding oxygenminimum zones in the tropical oceans, Science, in press. May 2
Long-Term Trends in Dissolved Oxygen off California-2.1 mol/kg/y
Zmean = -41 m Zmax = -92 m
Expansion of Low-Oxygen Habitat
(Bograd et al. in press)
10:00 (Abstract ID:2647) MondayMessié, M; Calienes, R; Ledesma, J; Barber, R T; Pennington, J T; Chavez, F P; INTERANNUAL VARIABILITY AND LONG TERM TRENDS IN EASTERN PACIFIC UPWELLING ECOSYSTEMS
In situ oceanographic data from Peru
It appears as if the eastern It appears as if the eastern Pacific low oxygen regions Pacific low oxygen regions reformed after the Little reformed after the Little Ice Age and continue to Ice Age and continue to
expand todayexpand today
Are there biological indicators Are there biological indicators of this expansion?of this expansion?
The Hake off Peru has retreated and gotten more concentrated
1966-1977 1978-1987
1988-1995 1996-2001
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20000
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Hake inEcuador Merluza durante La Niña (1996)
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1967
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1991
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1997
1999
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2003
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2007
Catchability (F
/f)
Index of hake concentración
Hake habitat restricted by oxygen
July 2005Tracy Arm (Sitka), AK
Post 1997/98 expansion of Dosidicus gigas range
British ColumbiaSept. 2005
1984
Long Beach, WAOct 2004
2004Outer Coast, BC
La Jolla Cove, CA.July, 2002
2001
2004
Including variability and Including variability and change in managementchange in management
• Has not been the norm for Has not been the norm for management of exploited management of exploited populations; typically managed using populations; typically managed using population models that do not population models that do not parameterize the environment.parameterize the environment.
• Needs to be built in early on in our Needs to be built in early on in our “new” ecosystem based “new” ecosystem based management approachmanagement approach
ConclusionsConclusions• Sophisticated observing systems, increased computing power Sophisticated observing systems, increased computing power
for modeling and improved ecosystem theory provide the for modeling and improved ecosystem theory provide the framework framework todaytoday to develop ecological forecasts useful for to develop ecological forecasts useful for societysociety
• Nature will continue to surprise (at a faster rate due to global Nature will continue to surprise (at a faster rate due to global change) and our theory will need to constantly evolvechange) and our theory will need to constantly evolve
• NASA will need to develop new direct or indirect estimates NASA will need to develop new direct or indirect estimates (and models) of ecologically relevant properties beyond (and models) of ecologically relevant properties beyond chlorophyllchlorophyll
• Better working relationships between resource managers and Better working relationships between resource managers and NASA should be fosteredNASA should be fostered
Where climate meets the global economy
Global Climate
GlobaleconomySmall Pelagic Fish
AquacultureHogs
Poultry
6Mt Fishmeal + 1.2 Mt Fish oil(from 30 Mt fish or 25 % Global
catch)