pdf, 3 mb - pices
TRANSCRIPT
NEMURO &NEMURO & NEMURO.FISHNEMURO.FISHShin-ichi Ito (Fisheries Research Agency)
Michio Kishi (Hokkaido Univ.)NEMURO Mafia
Today's contentsToday's contents
1. Brief history of ecosystem model(go through Prof. Fei Chai's lecture in the 1st PSS)
2. NEMURO (a lower trophic level EM)3. NEMURO.FISH (a higher trophic level EM)4. hands on
EcosystemEcosystemEcosystem is one component of the earth system.It is not static but dynamic.
Difficulties of Earth System ScienceDifficulties of Earth System ScienceHarte (2002)
1. The global scale of human activities and the historically unprecedented magnitude of human disturbance of the planet mean that past experience is often not a reliable guide to predicting the consequences of our actions.
2. The Earth system is rife with feedback, nonlinear synergies, thresholds, and irreversibilities, that confound our intuition.
3. Ecosystem is dynamically changing.4. Conducting large scale experiments on this system is
impossible.
Ecosystem modelingEcosystem modelingTherefore, we need models to test the ecosystem functions and responses and to understand its structure and functioning.
• Always we meet with tradeoff between "resolution or coverage in biological components" & "spatial resolution or coverage".
• Computational power is still limited.
ProblemsProblems
Strategy for Ecosystem ModelingStrategy for Ecosystem Modeling1. Nesting in spatial resolution2. Rhomboidal approach (deYoung et al., 2004)
increase resolution of target species.
From JCOPE home pagedeYoung et al. (2004)
Functional complexity
Tro
phic
leve
l
FermiFermi approach (simple models)approach (simple models)1. Complex models, which look as inscrutable as nature
itself, have numerous adjustable parameters and are generally unfalsifiable. However, it is impossible to validate the models.
2. Simple models that capture the essence of the problem, but not all the details, might get us farther (Fermi approach).
3. This "Fermi approach" to ESS will only be effective, of course, if decision makers can be weaned from their awe of computer-simulated complexity. Harte (2002)
Another or similar solutionAnother or similar solution
Everything should be as simple as possible, but no simpler.By Albert EinsteinBy Albert Einstein
history of marine ecosystem modelhistory of marine ecosystem modelRiley G. A. (1946) "Factors controlling phytoplankton population on Georges Bank.", J. Mar. Res., 6, 54-73.This is the first paper introduced differential equation to marine ecosystem modeling.
Dots:observationLine:model
Memoirs (G. A. Riley, 1984)Memoirs (G. A. Riley, 1984)
H. Bigelow said "Anybody who thinks he can predict more than 10% of plankton variability is a damn fool, but good luck".
To be sure, I have never pretended that [models] are truly realistic. My only defense has been that they help us to think….
They frequently yield results that are not intuitively obvious, and they teach us caution about drawing conclusions that seem to violate mathematical logic.
That is the way physics and astronomy have grown.
Biological oceanography, messy though it may be, needs the same kind of disciplined thinking.
milestones of NPZD modelsmilestones of NPZD modelsGorden Riley (1946)
Factors controlling phytoplankton population on Georges Bank.J. Mar. Res. 6, 54-73
John Steele (1974)The structure of marine ecosystems
Evans and Parslow (1985)A model of annual plankton cycles, Biol. Oceanogr., 24, 483-494
Fasham, Ducklow, McKelvie (1990)A Nitrogen-based model of plankton dynamics in the ocean mixed layerJ. Mar. Res., 48 (3): 591-639
Fasham (1995)Variations in the seasonal cycle of biological production in sub-arctic oceans - a model sensitivity analysis.Deep-Sea Res. Part I: 42 (7): 1111-1149
courtesy of Fei Chai
NEMURONEMUROAs a science project of PICES, CCCC (Climate Change and Carrying Capacity) was started in 1993.Under CCCC, "Conceptual theoretical modeling studies Task Team" was formed. =>MODEL Task Team
To develop a lower trophic ecosystem model which can be applied commonly to the North Pacific, PICES LTL model workshop was held in Nemuro in 2000.The developed model was names as NEMURO (North Pacific Ecosystem Model for Understanding Regional Oceanography).
NEMURONEMUROAs a science project of PICES, CCCC (Climate Change and Carrying Capacity) was started in 1993.Under CCCC, "Conceptual theoretical modeling studies Task Team" was formed. =>MODEL Task Team
To develop a lower trophic ecosystem model which can be applied commonly to the North Pacific, PICES LTL model workshop was held in Nemuro in 2000.The developed model was names as NEMURO (North Pacific Ecosystem Model for Understanding Regional Oceanography).
NEMURONEMURO(North Pacific Ecosystem Model for (North Pacific Ecosystem Model for
Understanding Regional Oceanography)Understanding Regional Oceanography)
1.1. NutrientNutrient--PhytoplanktonPhytoplankton--Zooplankton Zooplankton modelmodel
2.2. Developed under PICES Model Task Developed under PICES Model Task Team since 2000Team since 2000
3.3. Coupled with fish bioenergetics modelsCoupled with fish bioenergetics models4.4. Published special issue on Ecological Published special issue on Ecological
ModellingModelling in 2007in 20075.5. Published 37 papers on peer reviewed Published 37 papers on peer reviewed
journalsjournals6.6. Used in more than 8 countries (Canada, Used in more than 8 countries (Canada,
Russia, South Korea, China, USA, Japan, Russia, South Korea, China, USA, Japan, Mexico, Greek, etc.)Mexico, Greek, etc.)
NEMURONEMURONorth Pacific Ecosystem Model for Understanding
Regional Oceanography
Kishi et al. (2001), Kishi et al. (2007)
nutrient
phyto
zoo
ZLnGraPSZSnGraPSExcPSnMorPSnPSnResGppPSndt
dPSn 22 −−−−−=
GraPL2ZPnGraPL2ZLnExcPLnMorPLnsPLnRenGppPLdt
dPLn−−−−−=
EgeZSnExcZSnMorZSnZPnGraZSZLnGraZSZSnGraPSdt
dZSn−−−−−= 222
EgeZLnExcZLnMorZLnGraZL2ZPnGraZS2ZLnnZLaPLrGnZLaPSrGdt
dZLn−−−−++= 22
UPWnNitRnewLResPLn)nGppPLRnewSPSnResGppPSndt
dNO++−−−−= ()(3
ExcZPnExcZLnExcZSnDecD2NDecP2NNit
RnewL)ResPLn)(1(GppPLnRnewS)ResPSn)(1(GppPSndt
dNH
+++++−
−−−−−−=4
SEDnDecP2DDecP2NEgeZPn
EgeZLnEgeZSnMorZPnMorZLnMorZSnMorPLnMorPSndt
dPON
−−−+
++++++=
NDecDDDecPnExcPLExcPSndt
dDON 22 −++=
EgeZPnExcZPnMorZPnGraZL2ZPnnZPaZSrGnZPaPLrGdt
dZPn−−−++= 22
Nitrogen equationsNitrogen equations
GraPL2ZPsiGraPL2ZLsiExcPLiMorPLsisiPLRessiGppPLdt
dPLsi−−−−−=
EgeZLsisiZLLParGdt
dZLsi−= 2
EgeZPsisiZPLParGdt
dZPsi−= 2
SiDecPSEDsiEgeZPsiEgeZLsiMorPLsidt
dOpal 2−−++=
SiDecPUPWsiExcPLsiisResPLsiGppPLdtOHdSi 24)(
++++−=
Silicon equationsSilicon equations
ZLnGraPSZSnGraPSExcPSnMorPSnPSnResGppPSndt
dPSn 22 −−−−−=
e.g. small phytoplankton equatione.g. small phytoplankton equation
photosynthesisrespiration
mortalityExtracellular
excretionGrazing from
small zooplankton
Grazing from large
zooplankton
)()exp(
*1exp*)*exp(*
44)*exp(
33*
21
0
0
44
3max
PLnPSnZII
PSndzI
II
ITMPk
KNHNHNH
KNONOVGppPSn
optSH optSGppS
SNHS
SNOS
++=
−=
⎟⎠⎞
⎜⎝⎛ −
⎟⎠⎞
⎜⎝⎛
++Ψ−
+=
∫−
αακκ
SNHS
SNO
SSNO
KNHNHNH
KNONO
NHKNO
NO
RnewS
43
3
44)4*exp(
33
)4*exp(3
3
++Ψ−
+
Ψ−+=
ResPSn = ResPS0 * exp( kResPS * TMP ) * PSnMorPSn = MorPS0 * exp( kMorPS * TMP ) * PSn2
ExcPSn = γPS* GppPSn
{ }[ ]ZSnPSn)ZS*(PSTMPkSpsGRZSnGraPS SGraS * )2exp(-1 *)*exp(* 0,Max2 *max −= λ
{ }[ ]ZLnPSn)ZL*(PSTMPkLGRZLnGraPS LGraLps * )2exp(-1 *)*exp(* 0,Max2 *max −= λ
small phytoplankton equationssmall phytoplankton equations
1
1/2
K C
C C+K Michaelis-Menten
1
Iopt I
Light dependency
1
C* C
Ivlev function
1. Limiting factor for PhotosynthesisMultiplier of nutrient concentration, temp. and light.Another possibility is f(T)*min(f(N), f(L))
2. Natural mortalityProportional to square of plankton densityDue to stabilize the system (no theoretical reason) Insensitive to high frequency variability
(Wainwright et al. 2007)Since predation pressure from higher trophic already acts on planktons, it is possible to modify such as D**1.5.
3. Predation pressureCritical value is defined to avoid extinction
4. Many of parameters were borrowed from other models.PL photosynthesis parameters must be revised.
problemsproblems
NEMURONEMURONorth Pacific Ecosystem Model for Understanding
Regional Oceanography
Kishi et al. (2001), Kishi et al. (2007)
Ont
ogen
etic
ve
rtic
al m
igra
tion
Ecosystem resolution or coverage
Spac
e/tim
e re
solu
tion
or c
over
age
3D-PLANKTON10
0D-NPZDBox-NEMURO.anchovyBox-NEMURO.sardineBox-NEMURO.FISH
NEMPORT
SEAPODYMOSMOSE
NEMURO family (NEMURO family (GR: publishedGR: published, , RD:developingRD:developing))
3D-eNEMURO3D-FeNEMURO
tradeoff between (finer individual species) & (basin scale model)
box-NEMURO
1D-NEMURO
3D-NPZD
NEMUROMSCHOPE-NEMURO3D-NEMURO(1/4)
migrate-NEMURO.salmonmigrate-NEMURO.squid
box-eNEMURObox-FeNEMURO
Sponge-NEMURO
eNEMUROMSCHOPE-eNEMURO
NEMURO.SAN
eNEMURO.SAN
NEMUROMS-SAN
3D-PLANKTON53D-NEMURO
LTL modelsLTL models
Kuroshio path
Log10(Chl) [mg/m-3]SeaWiFS
model
2005/4/22
CHOPE + CHOPE + eNEMUROeNEMUROKomatsu et al. (in prep.)
CHOPE + CHOPE + eNEMUROeNEMURO
High resolution ocean circulation model was assimilated to satellite altimetry data.Therefore, physical part is close to the true.LTL model can capture the variability of phytoplankton well in this case.
(Yoshie et al., in prep.)
NEMURO.FISHNEMURO for Including Saury and Herring
Megrey et al. (2007a), Ito et al. (2004) etc.
Bioenergetics Model for herring and saury
change of weight
[ ]f
z
CALCALPEFSRC
dtWdW
⋅++++−=⋅
)(
E: excretion
C: consumption
R: respiration (loses through metabolism)
S: specific dynamic action(digesting food)
F: egestion
P: egg production
30
35
40
45
130 135 140 145 150 155 160 165
Kuroshio Extension
Oyashio Front
spawning groundin winter
spawning groundin spring & autumn
Mixed water region
Migration
Migration
Feeding groundin summer
Fishing groundin autumn
Life History of Pacific Saury with Oceanographic Features
Ito et al. (2004a)
Table 2. Life stages of Pacific saury in the saruy bioenergetics modelStage regionlarvae Kuroshiojuvenile & young mixed regionsmall Oyashioadult mixed regionadult matured Kuroshioadult mixed regionadult Oyashioadult mixed regionadult matured Kuroshio
3-box version
OyashioMixed Waterregion
Kuroshio
9 life stages
Ito et al. (2004b)Ito et al . (2007)
Mukai et al. (2007)
Simulated wet weight & observed growth (Kurita et al.)
maximum consumption rate multiplied by temperature function (solid line) & water temperature (dotted line).
Terms of the bioenergetics equationblack solid: consumptionred solid: respirationblue solid: egestionblack dotted: excretionred dotted: specific dynamic actiongreen: egg productionopen circle: observed consumption
by Kurita (2002)
Ito et al. (2004b)
Dynamic linkage between LTL & HTL
[ ]f
z
CALCALPEFSRC
dtWdW
⋅++++−=⋅
)(
NH4
Decrease ZS, ZL, ZP
PON
dN/dt = - (F+M) N
Population dynamics model
(b) Coupled
Year0 2 4 6 8 10
Ave
rage
wei
ght
(g w
et w
eigh
t)
0
50
100
150
200
250
(a) Uncoupled
0 2 4 6 8 10
Ave
rage
wei
ght
(g w
et w
eigh
t)
0
50
100
150
200
250
Age-0Age-1Age-2Age-3
Age-4Age-5Age-6Age-7
Age-8Age-9Age-10
Effect of Dynamic Linkage and Predation Pressure
Reduction in large zooplankton
Slower herring growth
(Megrey et al. 2007a)
Ecosystem resolution or coverage
Spac
e/tim
e re
solu
tion
or c
over
age
3D-PLANKTON10
0D-NPZDBox-NEMURO.anchovyBox-NEMURO.sardineBox-NEMURO.FISH
NEMPORT
SEAPODYMOSMOSE
NEMURO family (NEMURO family (GR: publishedGR: published, , RD:developingRD:developing))
3D-eNEMURO3D-FeNEMURO
tradeoff between (finer individual species) & (basin scale model)
box-NEMURO
1D-NEMURO
3D-NPZD
NEMUROMSCHOPE-NEMURO3D-NEMURO(1/4)
migrate-NEMURO.salmonmigrate-NEMURO.squid
box-eNEMURObox-FeNEMURO
Sponge-NEMURO
eNEMUROMSCHOPE-eNEMURO
NEMURO.SAN
eNEMURO.SAN
NEMUROMS-SAN
3D-PLANKTON53D-NEMURO
NEMURO.SANNEMURO.SAN
Held a workshop in Tokyo in 2005 to compare sardine & anchovy in California, Benguera, Funbolt, Oyashio/KuroshioCurrent systems.0
150
300
450
0
15
30
45Japan
0
150
300
450
0
350
700
1050Chile-Peru
0
50
100
150
1920 1930 1940 1950 1960 1970 1980 1990020406080100
Benguela
Sard
ine
catc
h x
104
(tons
)
Anchovy catch x 104(tons)
0
20
40
60
80
0
10
20
30
40
California
0
150
300
450
0
15
30
45Japan
0
150
300
450
0
350
700
1050Chile-Peru
0
50
100
150
1920 1930 1940 1950 1960 1970 1980 1990020406080100
Benguela
Sard
ine
catc
h x
104
(tons
)
Anchovy catch x 104(tons)
0
20
40
60
80
0
10
20
30
40
California
Common features:asynchronicity between sardine &
anchovygeographical expansion and reduction of
distribution
We decided model extensions:Two species (sardine & anchovy)Dynamic predator on sardine & anchovy
Provided by: Salvador E. Lluch-CotaSource: Schwartzlose et al., 1999
California Current SystemCalifornia Current System
Ligh
t (ly
/min
)
0.0
0.1
0.2
0.3
Mix
ed L
ayer
Dep
th (m
)
30
45
60
75
Day of Year0 60 120 180 240 300 360
Tem
pera
ture
(o C)
8
10
12
14
Day of Year0 60 120 180 240 300 360
Nut
rient
Exc
hang
e
0
5
10
15
Environmental VariablesAt West Vancouver Island
Light
Temp.
MLD
Nut. Exc.
Rose et al.(in prep.)
Experiment 1Experiment 1Year 1-10: Spin-upYear 11-20: warm (+2 degC)Year 21-30: cold (-2 degC)Year 31-40: warm (+2 degC)Year 41-50: cold (-2 degC)
Spa
wni
ng S
tock
Bio
mas
s (1
05 M
T)
0
3
6
9 A n c h o v yS a r d in e
Y e a r0 1 0 2 0 3 0 4 0 5 0
Mea
n W
eigh
t
at A
ge-4
(g)
3 0
4 5
6 0
7 5
spawning stock biomass (105MT)
mean weight at age-4 (g)
Rose et al.(in prep.)
Experiment 1Experiment 1
Year 1
5
10
15
20
25
30
35
40
0 1e+9 2e+9 3e+9 4e+9 5e+9 6e+9 7e+9 8e+9 9e+9 1e+10
Year 1
5
10
15
20
25
30
35
40
0.0 1.0e+9 2.0e+9 3.0e+9 4.0e+9 5.0e+9 6.0e+9 7.0e+9 8.0e+9 9.0e+9 1.0e+10 1.1e+10 1.2e+10 1.3e+10
4 8 12 16 20
5
10
15
20
25
30
35
40
0 5000 10000 15000 20000 25000 30000 35000 40000
1 10 20 30 40 50
Anchovy
Sardine
Predator
Rose et al.(in prep.)
NEMURONEMURO & NEMURO.FISH& NEMURO.FISH•LTL-HTL coupled model.•Now interspecies interaction is included.•Therefore, it is possible to apply NEMURO.FISH to ecosystem based management
•However, we must keep the fact in memories that the model parameters are not enough validated and other factors such as adaptability may change the parameters.•Models include errors (uncertainty) because of
lack of dynamics,insufficient determination of parameters,inapposite forcings, etc.
•Make model simple as possible as we can.
NEMURO.FISHNEMURO.FISHVery simple exercisesVery simple exercises
1. One box model and one fish.2. Physical forcing only includes SST.3. Deeper layer temperature is fixed.4. If the SST becomes cooler than DL temp, vertical mixing
ratio is increased by unstable stratification and nutrient is transported to the surface layer.
5. Idealized seasonal light intensity is assumed.6. Fish has two year life history and the parameters are
similar to Pacific saury.7. Fish natural mortality depends on body length.8. Annual fishery mortality is assumed to 30%.9. Reproduction depends on the size and food availability of
spawning stock.
Ex. 1. Climatological forcingEx. 1. Climatological forcing
0.0E+00
5.0E-02
1.0E-01
1.5E-01
2001/1/1 2003/1/1 2004/12/31
light
inte
nsity
(ly/
min
)
051015202530
wat
er te
mp.
(deg
C)
Lint0 TMP
0.0E+00
5.0E-03
1.0E-02
1.5E-02
2.0E-02
2.5E-02
2001/1/1 2003/1/1 2004/12/31
conv
ectio
n tim
e (1
/day
)
0
50
100
150
200
mix
ed la
yer d
epth
(m)
ExcTime MLD
Climatological forcingClimatological forcing
05
101520253035
2001/1/1
2003/1/1
2004/12/31
2006/12/31
2008/12/30
2010/12/30
(um
ol/l)
NO3 NH4 SiOH4
0
0.5
1
1.5
2
2001/1/1
2003/1/1
2004/12/31
2006/12/31
2008/12/30
2010/12/30
(um
ol/l)
PS PL
00.10.20.30.40.50.60.7
2001/1/1
2003/1/1
2004/12/31
2006/12/31
2008/12/30
2010/12/30
(um
ol/l)
ZS ZL ZP
00.10.20.30.40.50.60.7
2001/1/1
2003/1/1
2004/12/31
2006/12/31
2008/12/30
2010/12/30
(um
ol/l)
PON DON Opal
Climatological forcingClimatological forcing
0.0E+00
5.0E-01
1.0E+00
1.5E+00
2.0E+00
2.5E+00
3.0E+00
2001/1/1
2003/1/1
2004/12/31
2006/12/31
2008/12/30
2010/12/30
density(1) density(2) density(3)
0
20
40
60
80
100
120
140
160
2001/1/1
2003/1/1
2004/12/31
2006/12/31
2008/12/30
2010/12/30
2012/12/29
w(1) w(2) w(3)
0.0E+00
1.0E-03
2.0E-03
3.0E-03
4.0E-03
5.0E-03
6.0E-03
7.0E-03
8.0E-03
2001/1/1
2003/1/1
2004/12/31
2006/12/31
2008/12/30
2010/12/30
2012/12/29
biomass(1) biomass(2) biomass(3)
0.0E+00
5.0E-03
1.0E-02
2001/1/1
2003/1/1
2004/12/31
2006/12/31
2008/12/30
2010/12/30
2012/12/29
biomass total
Ex. 2. decadal forcingEx. 2. decadal forcing
0.0E+002.0E-024.0E-026.0E-028.0E-021.0E-011.2E-011.4E-011.6E-01
2001/1/1
2003/1/1
2004/12/31
2006/12/31
2008/12/30
2010/12/30
light
inte
nsity
(ly/
min
)
051015202530
wat
er te
mp.
(deg
C)
Lint0 TMP
1 degC decadal fluctuation is added to idealized seasonal forcing.
decadal forcingdecadal forcing
0
20
40
60
80
100
120
140
160
2001/1/1
2003/1/1
2004/12/31
2006/12/31
2008/12/30
2010/12/30
2012/12/29
w(1) w(2) w(3)
Cooler condition increases wet weight of adult fish because of rich food.
decadal forcingdecadal forcing
0.0E+00
5.0E-03
1.0E-02
1.5E-02
2001/1/1 2003/1/1 2004/12/31 2006/12/31 2008/12/30 2010/12/30
biomass total
0.0E+00
5.0E-01
1.0E+00
1.5E+00
2.0E+00
2.5E+00
3.0E+00
2001/1/1 2003/1/1 2004/12/31 2006/12/31 2008/12/30 2010/12/30
density(1) density(2) density(3)
However, cooler condition increases mortality of larvae and reduces biomass.
Ex. 3. Fisheries impactEx. 3. Fisheries impact
Fisheries pressure is doubled in 2005 only.
In normal year, the annual fisheries catch is 30% of adult biomass.
In 2005, the annual fisheries catch is increased to 60%.
Fisheries impact in 2005 (doubled)Fisheries impact in 2005 (doubled)
0.0E+00
5.0E-03
1.0E-02
2001/1/1 2003/1/1 2004/12/31 2006/12/31 2008/12/30 2010/12/30
biomass total
0.0E+00
5.0E-01
1.0E+00
1.5E+00
2.0E+00
2.5E+00
3.0E+00
2001/1/1 2003/1/1 2004/12/31 2006/12/31 2008/12/30 2010/12/30
density(1) density(2) density(3)
Memoirs (G. A. Riley, 1984)Memoirs (G. A. Riley, 1984)
H. Bigelow said "Anybody who thinks he can predict more than 10% of plankton variability is a damn fool, but good luck".
To be sure, I have never pretended that [models] are truly realistic. My only defense has been that they help us to think….
They frequently yield results that are not intuitively obvious, and they teach us caution about drawing conclusions that seem to violate mathematical logic.That is the way physics and astronomy have grown.Biological oceanography, messy though it may be, needs the same kind of disciplined thinking.