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Alaska Marine Sciences SymposiumGulf of Alaska – Wednesday, January 24th, 2007
Session 2: Lower Trophic LevelsPoster Presentations
Intertidal and subtidal
2) Temporal and Spatial Variability of Nearshore Crab LarvaeBen Daly* School of Fisheries and Ocean Sciences, University of Alaska Fairbanks, 245 O'Neill Bldg. P.O. Box 757220 Fairbanks, Alaska 99775-7220, (907)474-7074 [email protected] Konar. Global Undersea Research Unit, University of Alaska Fairbanks, 217 O'Neill Bldg. P.O. Box 757220 Fairbanks, AK 99775-7220 (907)474-5028 [email protected]
Compare temporal variability between species in the zoeal and megalopa stages and compare habitat use between species over time regardless of larval stage in Kachemak Bay.
3) Remote Sensing of Seagrass Resources in Kachemak Bay, AlaskaDon, Field, NOAA Center for Coastal Fisheries and Habitat Research, [email protected]; *Kris, Holderied, NOAA Kasitsna Bay Lab, [email protected]; Mark, Fonseca, NOAA Center for Coastal Fisheries and Habitat Research, [email protected]
Seagrass beds were mapped in Kachemak Bay in 2005 using aerial photographs.
4) Development and Testing of a Probability-based Intertidal Monitoring Scheme for Sitka National Historic Park, AlaskaGail V. Irvine*, USGS-Alaska Science Center, [email protected]
Presents an intertidal monitoring program at the Sitka National Historic Park
5) www.seaweedsofalaska.com - a photo-rich portal to the taxonomy of Alaskan seaweeds and their habitatsMandy Lindeberg, Auke Bay Laboratories Alaska Fisheries Science Center NOAA/NMFS, [email protected]*Sandra Lindstrom, University of British Columbia, [email protected] Saupe, Cook Inlet RCAC, [email protected]
Information on a website to aid in taxonomic classification and mapping of seaweeds
6) Community structuring impacts of Enteroctopus dofleini in Prince William Sound, AlaskaCourtney Lyons*, Alaska Pacific University, [email protected] Scheel, Alaska Pacific University, [email protected] Cornick, Alaska Pacific University, [email protected] Dial, Alaska Pacific University, [email protected]
Role of an octopus in structuring intertidal communities in Prince William Sound.
Plankton
1) Zooplankton populations on the Alaskan Shelf and northern Gulf of AlaskaSonia Batten, Sir Alister Hardy Foundation for Ocean Science, [email protected]
Examines continuous plankton recorder data from the northern Gulf of Alaska collected from 2000 to present.
Autonomous Zooplankton Sampling for Ocean Observing Systems
J.M. Napp1, D.V. Holliday2, C.F. Greenlaw2, P.J. Stabeno3, and A.J. Jenkins3
1NOAA – Alaska Fisheries Science Center2BAE Systems
3NOAA – Pacific Marine Environmental Laboratory
1) Calibration of a nutrient - phytoplankton - zooplankton model for use with a three dimensional physical model to simulate ecological mechanisms on the northern Gulf of Alaska shelfK. O. Coyle, Institute of Marine Science, University of Alaska, [email protected]. Hinckley, Alaska Fisheries Science Center/NMFS, [email protected]. J. Hermann, Joint Institute for the Study of the Atmosphere and Ocean,University of Washington, [email protected]
2) Rocky Intertidal Benthos in Iniskin/Iliamna Bay: A 28-Year Baseline and Hints of Climate Change?Jon Houghton, Pentec Environmental/Hart Crowser, Inc., [email protected] Dennis Lees, Littoral Environmental and Ecological Services, [email protected] Lindstrom, University of British Columbia [email protected], andJason Stutes, Pentec Environmental/Hart Crowser, Inc. [email protected]
3) Role of Grazers in the Recolonization of Hard Bottom Communities in Kachemak Bay, AlaskaNick Harman, University of Alaska Fairbanks, [email protected]
4) Clams and Armor: Were They Casualties of the War on the Beaches?Dennis Lees, Littoral Ecological & Environmental Services, [email protected] Driskell, [email protected]
Calibration of a nutrient - phytoplankton - zooplankton model for use with a three dimensional physical model to simulate ecological mechanisms on the northern
Gulf of Alaska shelfK. Coyle, Institute of Marine Science, University of Alaska
S. Hinckley, Alaska Fisheries Science Center, 7600 Sand Point Way NE, Seattle, WA 98115.
A.J. Hermann, Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115.
Funding Agency: North Pacific Research Board
Understand the mechanistic links between physical forcing and the ecosystem response.
Major Goal of Biological Oceanographic Programs
1) To accomplish this goal we aim to develop and verify the quantitative relationships between the physical and biological data collected during field observations.
2) The quantitative relationships between physical and biological observations are expressed by equations in mathematical models.
3) The simulations generated by the mathematical models must be compared with actual measurements to insure that the model output is an accurate reflection of actual conditions in the environment.
1) Site description
2) Brief description of the model.
3) Comparison of model output with GLOBEC results
4) The model as a research tool.
Description of how the model is used to generate data sets permitting direct comparison of simulated results with field measurements.
1. Site Description
May 26, 20000 5 0 1 0 0 1 5 0 2 0 0
D ista n ce (k m )
-1 0 0
-8 0
-6 0
-4 0
-2 0
0
Dep
th (
m)
T em p eratu re ( o
C )
3.8
4.3
4.8
5.3
5.8
6.3
6.8
7.3
0 5 0 1 0 0 1 5 0 2 0 0
D ista n ce (k m )
-1 0 0
-8 0
-6 0
-4 0
-2 0
0
Dep
th (
m)
S a lin ity (P S U )
30.4
30.8
31.1
31.5
31.8
32.2
32.6
32.9
33.3
33.6
0 5 0 1 0 0 1 5 0 2 0 0
D ista n ce (k m )
-1 0 0
-8 0
-6 0
-4 0
-2 0
0
F lu orescen ce
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Any attempt to understand the potential influence of climate on the Gulf of Alaska shelf ecology must consider the highly complex physical regime shown above.
The physical model which drives the biological simulation must be capable of reproducing the physical environment, at least in the statistical sense: density distribution, flow patterns and eddies.
1. Model Description
Physical Model:1)Regional Ocean Modeling System (ROMS)2)Northeast Pacific (NEP) Component has 11 km
resolution3)Coastal Gulf of Alaska (CGOA) component has
3 km resolution4)Ocean boundaries of the CGOA open allowing
entry and exit of Alaska Stream waters5)Bathymetry was derived from ETOPO5 and
finer-scale bathymetric data. 6)The model has 30 layers, layers are
concentrated near the surface (surface layer thickness varies from 0.3 to 15 m depth).
7)Physical model is driven by MM5 climatology.
11 Component ModelSlide by G. Gibson
Iron
Is this level of model complexity necessary?
For optimal utility, the complexity of mathematical models should not exceed that required to address the problem under consideration.
Highly complex models can:1) Be difficult to parameterize because of the large number of potentially unconstrained variables.2) Require unreasonably long times to run because of the additional algorithms which must be executed to generate model output.
0
10
20
30
40
50
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Fraction of total chl >20 µm
0.0
0.2
0.4
0.6
0.8
1.0
0.1 1.0 10.0 100.0
Total chlorophyll (µg/liter)
ISMSOS
large (>20 µm)small (<20 µm)
Neocalanus
other mesozoo
microzoo
2.62.6
20.1
1.6
1.6
sinking?advection?
67
41.7
A. Spring large-cell dominated food web
microzoo
other mesozoo
13
?
larvaceans
4
13
B. Summer small-cell dominated food web (dashed line = episodic event)
Units: µgC liter-1 d-1
pink salmon?
?
Data from Dagg and Strom
Month
March April May June July August Sept October
Me
an B
iom
ass
(g
m-3
)
0.0
0.1
0.2
0.3
0.4
0.5
OthersSalpidaeChaetognathaLarvaceansEuphausiidsCnidariaPteropodsCalanoida
Mean Zooplankton Biomass: 1998 to 2003GLOBEC Seward Line Data
Month
March April May June July August Sept October
Mea
n B
iom
ass
(g m
-3)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
Calanus marshallaeEucalanus bungiiMetridia spp.Neocalanus cristatusN. plumchrus & N. flemingeriPseudocalanus spp.Oithona similis
Mean copepod biomass: 1998 to 2003GLOBEC Seward Line Data
Complexity of the 11 box model is probably the minimum required to accurately simulate observed conditions in the northern Gulf of Alaska
Model Validation Using One Dimension Model Output
Model validation and parameterization requires direct comparison of simulated values with actual measurements generated from field collections.
The fully three dimensional model with embedded biological component requires about three and a half weeks for a single simulation for the northern Gulf of Alaska.
Model output for direct comparison of simulated biological variables with field data are generated using a one – dimensional model forced with physical results for any selected location from the three dimensional simulations for the entire Gulf of Alaska grid.
PrinceWilliamSound
ResurrectionBay
Knight Island Passage
Middleton Island
Station locations for biological data from the northern Gulf of Alaska.
GAK6 2001 Model Simulation
Model Simulation: May 2001
Distance (km)
0 50 100 150 200
Bio
mas
s (m
g C
m-3
)
10
15
20
25
30
35
Large PhytoplanktonSmall Phytoplankton
Average in upper 50 m
0
20
40
60
80Measured Biomass
Measured and simulated biomass of phytoplankton along the Seward Line in May Measured values from a poster by Lessard and Foy
Station Numbers
Model Simulation: Jun 2001
Distance (km)
0 50 100 150 200
Bio
mas
s (m
g C
m-3
)
0
20
40
60
80
100
Large PhytoplanktonSmall Phytoplankton
Average in upper 50 m
0
20
40
60
80
Measured and simulated biomass of phytoplankton along the Seward Line in June - July
Measured values from a poster by Lessard and Foy
Measured Biomass
Station Numbers
GAK6 2001 Model Simulation
Model Simulation: 2001; Gak6
Time (days)
Mar Apr May Jun Jul Aug Sep Oct
Bio
mas
s (m
g C
m-3
)
0
5
10
15
20
25
Neocalanus spp.Small Copepods
Average in upper 100 m
Month
March April May June July August Sept October
Mea
n B
iom
ass
(mg
C m
-3)
0
5
10
15
20
25
N. plumchrus & N. flemingeriPseudocalanus spp.
Mean (1998 – 2003) Copepod Carbon Biomass Measured Values
Model as a research tool
How does a regime shift impact the shelf ecosystem?Strong
Southerly
MarineWet
Weak
Northerly
ContinentalDry
Elevated freshwater input from runoff and glacial melt
Low freshwater input; less precipitation and runoff; glacial growth rather than retreat
Modified from Gargett (1997)
What might the model tell us about climate influences on the shelf
1) Will declines or increases in freshwater input to the shelf result in increases or declines in lower trophic – level production? What is the potential magnitude of the response?
2) Are specific regions more sensitive to shifts in freshwater input than others? If so, where and by what magnitude?
3) What sample density and frequency would be required to detect a climate – related change in lower trophic level production? What would be the optimal station distribution?
4) Will a shift in freshwater input lead to higher or lower cross – shelf transport? What regions will be most impacted?
5) What is the potential effect of elevated temperatures on lower trophic level production?
Questions (Conclusions) that the fully implemented 3 – dimensional model with
embedded biological model might address
Most of the data for model calibration were collected by Global Ocean Ecosystem Dynamics (GLOBEC) program for the northern Gulf of Alaska. Participants included: Tom Weingartner, Tom Royer, Evelyn Lessard, Suzanne Strom, Terry Whitledge, Dean Stockwell, Jeff Napp, Phyllis Stabeno, Lew Haldarson, Jennifer Bolt, Russell Hopcroft, Alexei Pinchuk, Mike Foy, Hue Liu, Michael Dagg, Seth Danielson.
The ROMS model was implemented for the Gulf of Alaska by Elizabeth Dobbs, Al Hermann and Kate Hedstrom. The biological model was originally developed by Sarah Hinckley.
The GLOBEC program was jointly funded by the National Science Foundation and NOAA. Additional data for model calibration was provided by the North Pacific Research Board monitoring project along the Seward Line.
Acknowledgments
