Ecosim* overview for NEMoW *and spawn of Ecosim: related dynamic models including Ecospace

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

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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

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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

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P. Cod

Arrowtooth

P. Halibut

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(t/

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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

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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

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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

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Co

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Arr

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Sab

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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

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ShearwaterMurres

CormorantsGulls



DogfishArrowtooth



P. CodSablefish





Sea OttersFH. Sole







EelpoutsPOP_Juv



AK PlaiceRex Sole







Sea starsDover Sole

BairdiKing Crab



SnailsChaetognaths


AnemonesCorals

HydroidsUrochordata









Sm Phytoplankton

FitF_HP_PredV

FitF_HP_PreyV

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ShearwaterMurres

CormorantsGulls



DogfishArrowtooth



P. CodSablefish





Sea OttersFH. Sole







EelpoutsPOP_Juv



AK PlaiceRex Sole







Sea starsDover Sole

BairdiKing Crab



SnailsChaetognaths


AnemonesCorals

HydroidsUrochordata









Sm Phytoplankton

FitF_PDO_PredV

FitF_PDO_PreyV

-4 -2 0 2 4








ShearwaterMurres

CormorantsGulls



DogfishArrowtooth



P. CodSablefish





Sea OttersFH. Sole







EelpoutsPOP_Juv



AK PlaiceRex Sole







Sea starsDover Sole

BairdiKing Crab



SnailsChaetognaths


AnemonesCorals

HydroidsUrochordata









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

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s 2

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NoF1xF2xF3xF70sF


Axis 1

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NoF1xF2xF3xF70sF


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Axis 1

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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

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preylink

preylink

predlink

predlinklinkpreypred

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where B* and Q* are biomass and consumption in a reference year (1991)

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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)

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Growth efficiency (increase means more production)

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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

Ecosim* overview for NEMoW *and spawn of Ecosim: related dynamic models including Ecospace

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Transcript of Ecosim* overview for NEMoW *and spawn of Ecosim: related dynamic models including Ecospace