ecosim* overview for nemow *and spawn of ecosim: related dynamic models including ecospace sarah...
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Ecosim* overview for NEMoW
*and spawn of Ecosim: related dynamic models including Ecospace
Sarah Gaichas and Kerim Aydin, AFSCChris Harvey, NWFSC
John Field, SWFSCFrank Parrish, PIFSC Clay Porch, SEFSC
Howard Townsend, NCBO
What is/has/will the model be used for?
• Describing ecosystems and improving understanding of how simultaneous physical, ecological, and fisheries interactions affect commercial and bycatch species
• Examining apex predator (and or protected species) carrying capacity and predicting responses to changing fishing and primary production
• Examining ecosystem effects of – changing water quality – changing fishing gear– different MPA scenarios
• Evaluating tradeoffs between management strategies
• Providing foundation for developing proposals to integrate ecosystem-based management approaches into current management regimes
Has the model been published in the peer reviewed literature?
Yes. Early version:Walters, C., Christensen, V., and Pauly, D. 1997. Structuring
dynamic models of exploited ecosystems from trophic mass-balance assessments. Rev. Fish. Biol. Fish. 7: 139-172.
Most recent version with “multistanza” age structure:Christensen, V., and C. Walters, 2004. Ecopath with Ecosim:
methods, capabilities, and limitations. Ecological Modelling 172: 109-139.
Ecospace (also covered in Christensen & Walters 04):Walters, C., Pauly, D., and Christensen, V. 1999. Ecospace:
prediction of mesoscale spatial patterns in trophic relationships of exploited ecosystems, with emphasis on the impacts of marine protected areas. Ecosystems, 2: 539–554.
Static food web to dynamic simulation requires functional response
+ age structured population dynamics
?0
20
40
60
1 51 101 151 201
Year
Bio
ma
ss
Nessasaurus PiscivorousBirdsArcticChar Brow nTroutPiscivorousFish EelsSticklebacks ForageFishAquaticInsects TerrestrialInsectsCladocerans CopepodsDetritivores MacroalgaePhytoplankton
Blanco
Mendocino
Habitats (depth, substrate)
Hypothetical MPAcoverage
Where bottomtrawlingoccurs
Ecospace: sim in space
ColumbiaRiver
• Traceable spatial features in grid space– habitats, fleets, ports, management areas, advection
fields, seasonal migrations, etc.
Biomass dynamics equations
• For Biomass of group i,
dBi /dt = GEi ∑prey Q(BiBprey) consumption gain
- FiBi fishing loss
- M0iBi other mortality loss
- ∑pred Q(BpredBi) predation loss
+I immigration rate
0
20
40
60
1 51 101 151 201
Year
Bio
ma
ss
Nessasaurus PiscivorousBirdsArcticChar Brow nTroutPiscivorousFish EelsSticklebacks ForageFishAquaticInsects TerrestrialInsectsCladocerans CopepodsDetritivores MacroalgaePhytoplankton
Picturing the “foraging arena” (Walters et al 1997)
Unavailable prey
Bi - Vij
Vulnerable prey VijPredator Bj
vijVijvij (Bi-Vij)
aijVijBj
dVij /dt = vij(Bi-Vij) - vijVij - aijVijBj
Assume fast equilibrium
for Vij
V
B-V
“It’s cold down there!”
Sophisticated functional response behavior ranges from stable donor-controlled to chaotic Lotka-Volterra
Single “vulnerability” parameter X ~ 2v/aBj ratio
Gulf of Alaska (GOA) simulation
0
1
2
3
4
5
6
7
8
0 10 20 30 40 50
W. Pollock
P. Cod
Arrowtooth
P. Halibut
0
1
2
3
4
5
6
7
8
0 10 20 30 40 50
W. Pollock
P. Cod
Arrowtooth
P. Halibut
Simulation year
Bio
mass
(t/
km2)
Low vulnerability versusHigh vulnerability
The full consumption equation: complex functional
response
Qt = alinkvlinkBpredBpreyTpredTprey / Dpred
vlink + vlinkTprey + alinkBpredTpred / Dpred
Where Dpred = hpredTpred
1 + ∑pred’sprey alinkBpreyTprey
0
20
40
60
1 51 101 151 201
Year
Bio
ma
ss
Nessasaurus PiscivorousBirdsArcticChar Brow nTroutPiscivorousFish EelsSticklebacks ForageFishAquaticInsects TerrestrialInsectsCladocerans CopepodsDetritivores MacroalgaePhytoplankton
Functional response parameters
• Vulnerability: how much prey biomass is available to predators?
• Foraging Time: if I’m hungry, should I spend more time vulnerable?
• Handling Time: at some point, my consumption is limited even if there are more prey
V
B-V
“It’s cold down there!”
“Our food is up there, but so are those big guys!”
“Don’t worry, I’m still chewing.”
Ecospace equations and assumptions
• Same biomass dynamics equation as Ecosim, except with coordinates x, y to designate location on map grid, and movement terms take on greater importance:
• Growth efficiency, predation, mortality are now spatially explicit (habitat quality, abundance of other spp., fishing, etc.)
• Instantaneous movement mi,x,y reflects organism’s ability to discern fitness trade-offs between x,y and surrounding cells
dBi,x,y/dt = GEi prey Q(Bi,x,yBprey,x,y) consumption gain - Fi,x,yBi,x,y fishing loss- M0iBi,x,y other mortality
loss- pred Q(Bpred,x,yBi,x,y) predation loss+ Ii,x,y immigration gain- mi,x,yBi,x,y emigration loss
Ecospace equations and assumptions
• Fishing mortality by fleet k over all N cells of system is equal to N · Fk
• For each model time step, that mortality is distributed spatially by assigning a weight G to each cell c:
Gkc = Okc · Ukc · i pkiqkiBic / Ckc
Okc = status of fleet k in cell c (0=closed, 1=open) Ukc = ability of fleet k to fish in cell c habitat type (0, 1)pki = price fleet k receives for species iqki = catchability of species i in fleet kBic = biomass of species i in cell cCkc = cost for fleet k to operate in cell c
Data requirements I, Ecosim and Ecospace
• All food web parameters from Ecopath, plus
• Growth information for age structured groups
• General habitat preferences
• Dispersal and/or migratory characteristics
• Time series to “drive” trajectories for some groups – Single species F, and/or Gear specific effort with bycatch
– Primary Production or other group production/recruitment, B
• Port locations, habitats where fishing occurs• For ecosystem map
– Habitat distribution, including land– Advection patterns– 1° production patterns (can use Sea Around Us data)– Location of management zones (statistical areas, MPAs, etc.)
Data requirements II, calibration/fitting
• Time series to “fit” (by estimating functional response Vulnerability)– B most common
– Species total catch, recruitment
• Values for functional response parameters Foraging time, Handling time– Alternatively, estimate these parameters* (see next
slides)
– Also, include time series of diet data to estimate functional response*
• Known species interactions modeled as “mediation functions”
* Not available in current version of Ecosim
What key data gaps have been identified?
• Many regions missing time series of primary production• Time series that are NOT model output already• Mid TL forage fish and low TL zooplankton group
dynamics are key low data interactions in many systems
• Often, high TL unexploited predator dynamics (killer whales, seals) are unknown and influential
• Nobody really knows functional response parameters
Are these data gaps informing monitoring efforts?• Strategic data collections implemented from model
gaps at PIFSC• NCBO can inform, but still need money approved• Much other data collection still opportunistic
1800 1850 1900 1950 2000
01
23
45
6
Year
PO
P_B
iom
ass
1800 1850 1900 1950 2000
01
23
45
6
Year
PO
P_B
iom
ass
1800 1850 1900 1950 2000
01
23
45
6
Year
PO
P_B
iom
ass
1800 1850 1900 1950 2000
01
23
45
6
Year
PO
P_B
iom
ass
Experience: equilibrium + uninformative data + vulnerability estimation in the
GOA*
• Today’s rules (path equilibruim) can’t recreate yesterday’s GOA. Species and or ecosystem production was different historically.
• Supports both climate and fishing-related hypotheses for change, but with different predator prey relationships implied by estimated vulnerability parameters
*Analyses in Sim alternative
Different drivers for different species*
Model AIC SS
L
Po
llock
Co
d
Her
rin
g
Arr
ow
too
th
Hal
ibu
t
Sab
lefi
sh
PO
P
Th
orn
yhea
d
Sal
mo
n
F, default vul 421,898
F, fit vuls 19,442
F, fit vuls, recruitment 18,336
F, fit vuls, PDO 30,584
**
*
*
Key: Lower AIC is better overall fit; Each species fit varies by model
Extinct OK fit Best fit
-4 -2 0 2 4
Transient KillersSalmon shark
Resident KillersSteller Sea Lion_Juv
Steller Sea LionSleeper shark
Sperm and Beaked WhalesLongnosed skate
PorpoisesN. Fur Seal_Juv
N. Fur SealResident seals
FulmarsAlbatross Jaeger
ShearwaterMurres
CormorantsGulls
P. HalibutKittiwakes
PuffinsStorm Petrels
DogfishArrowtooth
Big skateMisc. fish deep
GreenlingsMinke whales
P. CodSablefish
GrenadiersP. Halibut_Juv
Dusky RockLg. SculpinsHumpbacks
Arrowtooth_JuvShortraker RockRougheye Rock
Shortspine ThornsOther sculpins
Sea OttersFH. Sole
Other SebastesOctopi
Salmon returningFin WhalesSei whalesW. Pollock
P. Cod_JuvOther skates
Shortspine Thorns_JuvSquidsAuklets
W. Pollock_JuvYF. Sole
FH. Sole_JuvSablefish_Juv
EelpoutsPOP_Juv
POPGray WhalesRight whalesHerring_Juv
HerringN. Rock soleS. Rock sole
AK PlaiceRex Sole
Misc. FlatfishSharpchin Rock
Northern RockAtka mackerel_Juv
Atka mackerelMisc. fish shallowSalmon outgoing
BathylagidaeMyctophidae
CapelinSandlanceEulachon
Oth. managed forageOth. pelagic smelt
Sea starsDover Sole
BairdiKing Crab
Scyphozoid JelliesMisc. crabs
Hermit crabsPandalidaeNP shrimp
SnailsChaetognaths
Misc. CrustaceanBenthic Amphipods
AnemonesCorals
HydroidsUrochordata
Sea PensSpongesBivalves
PolychaetesMisc. worms
Fish LarvaeEuphausiids
MysidsPelagic Amphipods
Gelatinous filter feedersPteropodsCopepods
Brittle starsUrchins dollars cucumbers
Pelagic microbesBenthic microbes
MacroalgaeLg Phytoplankton
Sm Phytoplankton
FitF_PHP_PredV
FitF_PHP_PreyV
-4 -2 0 2 4
Transient KillersSalmon shark
Resident KillersSteller Sea Lion_Juv
Steller Sea LionSleeper shark
Sperm and Beaked WhalesLongnosed skate
PorpoisesN. Fur Seal_Juv
N. Fur SealResident seals
FulmarsAlbatross Jaeger
ShearwaterMurres
CormorantsGulls
P. HalibutKittiwakes
PuffinsStorm Petrels
DogfishArrowtooth
Big skateMisc. fish deep
GreenlingsMinke whales
P. CodSablefish
GrenadiersP. Halibut_Juv
Dusky RockLg. SculpinsHumpbacks
Arrowtooth_JuvShortraker RockRougheye Rock
Shortspine ThornsOther sculpins
Sea OttersFH. Sole
Other SebastesOctopi
Salmon returningFin WhalesSei whalesW. Pollock
P. Cod_JuvOther skates
Shortspine Thorns_JuvSquidsAuklets
W. Pollock_JuvYF. Sole
FH. Sole_JuvSablefish_Juv
EelpoutsPOP_Juv
POPGray WhalesRight whalesHerring_Juv
HerringN. Rock soleS. Rock sole
AK PlaiceRex Sole
Misc. FlatfishSharpchin Rock
Northern RockAtka mackerel_Juv
Atka mackerelMisc. fish shallowSalmon outgoing
BathylagidaeMyctophidae
CapelinSandlanceEulachon
Oth. managed forageOth. pelagic smelt
Sea starsDover Sole
BairdiKing Crab
Scyphozoid JelliesMisc. crabs
Hermit crabsPandalidaeNP shrimp
SnailsChaetognaths
Misc. CrustaceanBenthic Amphipods
AnemonesCorals
HydroidsUrochordata
Sea PensSpongesBivalves
PolychaetesMisc. worms
Fish LarvaeEuphausiids
MysidsPelagic Amphipods
Gelatinous filter feedersPteropodsCopepods
Brittle starsUrchins dollars cucumbers
Pelagic microbesBenthic microbes
MacroalgaeLg Phytoplankton
Sm Phytoplankton
FitF_HP_PredV
FitF_HP_PreyV
-4 -2 0 2 4
Transient KillersSalmon shark
Resident KillersSteller Sea Lion_Juv
Steller Sea LionSleeper shark
Sperm and Beaked WhalesLongnosed skate
PorpoisesN. Fur Seal_Juv
N. Fur SealResident seals
FulmarsAlbatross Jaeger
ShearwaterMurres
CormorantsGulls
P. HalibutKittiwakes
PuffinsStorm Petrels
DogfishArrowtooth
Big skateMisc. fish deep
GreenlingsMinke whales
P. CodSablefish
GrenadiersP. Halibut_Juv
Dusky RockLg. SculpinsHumpbacks
Arrowtooth_JuvShortraker RockRougheye Rock
Shortspine ThornsOther sculpins
Sea OttersFH. Sole
Other SebastesOctopi
Salmon returningFin WhalesSei whalesW. Pollock
P. Cod_JuvOther skates
Shortspine Thorns_JuvSquidsAuklets
W. Pollock_JuvYF. Sole
FH. Sole_JuvSablefish_Juv
EelpoutsPOP_Juv
POPGray WhalesRight whalesHerring_Juv
HerringN. Rock soleS. Rock sole
AK PlaiceRex Sole
Misc. FlatfishSharpchin Rock
Northern RockAtka mackerel_Juv
Atka mackerelMisc. fish shallowSalmon outgoing
BathylagidaeMyctophidae
CapelinSandlanceEulachon
Oth. managed forageOth. pelagic smelt
Sea starsDover Sole
BairdiKing Crab
Scyphozoid JelliesMisc. crabs
Hermit crabsPandalidaeNP shrimp
SnailsChaetognaths
Misc. CrustaceanBenthic Amphipods
AnemonesCorals
HydroidsUrochordata
Sea PensSpongesBivalves
PolychaetesMisc. worms
Fish LarvaeEuphausiids
MysidsPelagic Amphipods
Gelatinous filter feedersPteropodsCopepods
Brittle starsUrchins dollars cucumbers
Pelagic microbesBenthic microbes
MacroalgaeLg Phytoplankton
Sm Phytoplankton
FitF_PDO_PredV
FitF_PDO_PreyV
-4 -2 0 2 4
Transient KillersSalmon shark
Resident KillersSteller Sea Lion_Juv
Steller Sea LionSleeper shark
Sperm and Beaked WhalesLongnosed skate
PorpoisesN. Fur Seal_Juv
N. Fur SealResident seals
FulmarsAlbatross Jaeger
ShearwaterMurres
CormorantsGulls
P. HalibutKittiwakes
PuffinsStorm Petrels
DogfishArrowtooth
Big skateMisc. fish deep
GreenlingsMinke whales
P. CodSablefish
GrenadiersP. Halibut_Juv
Dusky RockLg. SculpinsHumpbacks
Arrowtooth_JuvShortraker RockRougheye Rock
Shortspine ThornsOther sculpins
Sea OttersFH. Sole
Other SebastesOctopi
Salmon returningFin WhalesSei whalesW. Pollock
P. Cod_JuvOther skates
Shortspine Thorns_JuvSquidsAuklets
W. Pollock_JuvYF. Sole
FH. Sole_JuvSablefish_Juv
EelpoutsPOP_Juv
POPGray WhalesRight whalesHerring_Juv
HerringN. Rock soleS. Rock sole
AK PlaiceRex Sole
Misc. FlatfishSharpchin Rock
Northern RockAtka mackerel_Juv
Atka mackerelMisc. fish shallowSalmon outgoing
BathylagidaeMyctophidae
CapelinSandlanceEulachon
Oth. managed forageOth. pelagic smelt
Sea starsDover Sole
BairdiKing Crab
Scyphozoid JelliesMisc. crabs
Hermit crabsPandalidaeNP shrimp
SnailsChaetognaths
Misc. CrustaceanBenthic Amphipods
AnemonesCorals
HydroidsUrochordata
Sea PensSpongesBivalves
PolychaetesMisc. worms
Fish LarvaeEuphausiids
MysidsPelagic Amphipods
Gelatinous filter feedersPteropodsCopepods
Brittle starsUrchins dollars cucumbers
Pelagic microbesBenthic microbes
MacroalgaeLg Phytoplankton
Sm Phytoplankton
FitF_PredV
FitF_PreyV
Fitted Vuls in each
model*
Group DataType BaseF FitF FitF_HP FitF_PHP BaseF_PDO FitF_PDOJuvenile Steller Sea Lion Biomass 44.33 53.26 51.74 36.32 162.96 56.08Adult Steller Sea Lion Biomass 62.60 67.78 61.33 66.12 194.51 119.22Pollock Biomass 230.65 313.02 295.19 130.55 233.78 179.52Cod Biomass 66.23 8.42 16.45 -12.40 283.14 8.66Herring Biomass 20,061.59 188.14 53.68 43.61 19,432.91 27.95Arrowtooth Biomass 335.39 780.98 691.69 642.27 460.54 11.43Halibut Biomass 20,387.76 -1.23 99.53 67.03 37,547.54 451.63Sablefish Biomass 1,495.09 118.63 103.56 110.00 1,627.72 17.81Pacific Ocean Perch (POP) Biomass 119,821.10 981.78 861.15 806.68 150,761.90 575.78Shortspine Thornyhead Biomass 41,443.02 13.98 -8.50 -17.70 47,856.55 508.36Salmon Biomass 65.66 57.47 56.13 57.10 5,885.64 5,892.15Pandalid Shrimp Biomass 441.97 401.30 434.63 326.52 720.83 494.99Sea Otter Catch 101.06 101.06 101.06 101.06 101.06 101.06Northern Fur Seal Catch 157.38 157.38 157.38 157.38 157.38 157.38Right Whale Catch 33.80 33.80 33.80 33.80 33.80 33.80Fin Whale Catch 1,029.36 1,029.36 1,029.36 1,029.36 1,029.36 1,029.36Humpback Whale Catch 40.47 40.47 40.47 40.47 40.47 40.47Sei Whale Catch 60.32 60.32 60.32 60.32 60.32 60.32Sperm Whale Catch 36.46 36.46 36.46 36.46 36.46 36.46Pollock Catch 28.26 28.12 28.13 28.13 28.19 28.12Cod Catch 79.43 78.42 78.42 78.42 79.22 78.41Herring Catch 109.16 108.40 105.86 106.00 108.21 109.07Arrowtooth Catch 32.40 30.22 30.22 30.21 30.32 30.20Halibut Catch 0.42 -1.71 -1.71 -1.71 0.43 -1.72Sablefish Catch 29.87 29.64 29.64 29.64 31.95 29.65Pacific Ocean Perch (POP) Catch 52.21 52.04 52.03 52.03 54.98 52.03Shortspine Thornyhead Catch 16.64 16.64 16.64 16.64 15.31 16.64Salmon Catch 83.67 83.67 83.67 83.67 93.80 94.16Tanner Crab (C. bairdi) Catch 26.34 26.34 26.37 26.34 26.38 26.34King Crabs Catch 529.08 529.08 529.08 529.08 529.03 529.08Pandalid Shrimp Catch 47.51 47.51 47.51 47.51 47.51 47.51
BaseF FitF FitF_HP FitF_PHP BaseF_PDO FitF_PDOSum Biomass -log Likelihood 204,455.39 2,983.53 2,716.58 2,256.10 265,168.02 8,343.58
Sum Catch -log Likelihood 2,493.84 2,487.22 2,484.71 2,484.81 2,504.18 2,498.34
Total -log Likelihood 206,949.23 5,470.75 5,201.29 4,740.91 267,672.20 10,841.92
Fishing forcing parameters 4,000 4,000 4,000 4,000 4,000 4,000Predator-Prey vulnerabilities 0 250 250 250 0 250
Production forcing parameters 0 0 135 177 200 200
Total Parameters 4,000 4,250 4,385 4,427 4,200 4,450
AIC 421,898 19,442 19,173 18,336 543,744 30,584
AIC minus minimum AIC 403,563 1,106 837 0 525,409 12,248
Likelihood and AIC
for all models*
250 estimated vul parameters
Even more functional response
parameters*
NMDS Ordination of all biomass
Axis 1
Axi
s 2
Fishing
NoF1xF2xF3xF70sF
NMDS Ordination of all biomass
Axis 1
Axi
s 2
Fishing
NoF1xF2xF3xF70sF
NMDS Ordination of all biomass
Axis 1
Axi
s 2
Fishing
NoF1xF2xF3xF70sF
NMDS Ordination of all biomass
Axis 1
Axi
s 2
Fishing
NoF1xF2xF3xF70sF
NMDS Ordination of all biomass
Axis 1
Axi
s 2
Fishing
NoF1xF2xF3xF70sF
Data-free simulation testing using randomly sampled Vul, Ftime, and Htime
(324 parameters)
gave a wide range of alternative GOA ecosystems
The art part: Pick your poison
Too many parameters, not enough data. Options:
– The Walters bias: Fix many parameters, fit only vulnerabilities (in blocks), assume systematic residuals are “primary production anomaly.”
– The Aydin bias: Group by predator and prey, fit all functional response parameters, assume systematic residuals are difference between start state being “in equilibrium” and the true equilibrium (initial spin-up to “true fitted” equilibrium).
– Many other “biases” are possible, and possibly reasonable.
Best practice would require more formal evaluation of these hypotheses within a statistical framework. Current EwE software allows only the first hypothesis, “manual adjustment” may be used to achieve the second.
Model improvement: Ecosim equations (re-written)
link
link
preylink
preylink
predlink
predlinklinkpreypred
YD
YD
YX
YXQBBc
11),( *
*BBY t
where B* and Q* are biomass and consumption in a reference year (1991)
pred
ipredprey
preyii BBcFBBMBBcGE
dt
dB),(),( 0
)3( KZZAGE
EqF
1991
1991,
19911991
19910
f
pred fpredpredpred
ff B
DCRationBZM
Parameters fit using likelihood criteria to available time series, parallel search algorithms
coded in C.
Whole guilds may move from equilibrium
Mzero (increase means more mortality)
-6-4-20246
Tra
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What are the strengths of this model?
• Ecosim is freely available, large user community• Improved understanding of data systems (multiple
agency, multiple scale data assimilation)• Functional response parameterization is very flexible,
much more advanced than many published forms• Simulates a wide variety of fishing scenarios,
including spatial management in Ecospace• Simulates changes in production regimes• Ability to represent age structure for many groups• Biomass dynamics of whole ecosystem considered,
see both direct effects and side effects of scenarios
What are the weaknesses of this model?
• Functional response:– In some cases, results sensitive to (difficult to estimate)
functional response parameters– Full functional response flexibility means more parameters to
estimate than data available • Model weakness or data weakness???• True of many stock assessments…
– EwE statistical estimation of vulnerability only; manual adjustment of other parameters during calibration difficult to repeat if not well documented
• Inability to estimate uncertainty in projections (Sim)• In big models, sensitivity analysis for all parameters is
an overwhelming (but necessary) task• Ecospace relatively untested, few published examples
What remains for model development/improvement/enhancement?
• More users for Ecospace, comparisons with Atlantis, etc.
• Improved data (but when isn’t that the case?)• More rigorous
– documentation of parameter estimation process in many applications (e.g. “manual adjustment” vs. statistical fitting)
– statistical parameter estimation ability, including fitting to time varying diet composition data
– estimation of uncertainty
• Direct comparison of outputs with alternative models
• Improved compatibility with complementary models– High resolution ocean circulation models– Fishery interaction and management system models– Age structured stock assessments