Functional relationships & decision tools for EAF:

Functional relationships & decision tools for EAF:

Evaluating environmental, ecosystem, and anthropogenic

impact

Evaluating environmental, ecosystem, and anthropogenic

impact

NOAA WS, Key Largo, Feb 2005NOAA WS, Key Largo, Feb 2005 Villy Christensen, UBCVilly Christensen, UBC

Modeling process

Fishing

Ecosystem model

(predation, competition,

age structured)

(B0, BCC) Log Likelihood

PredictedC, B, Z, W,

diets(Z0)

(Diet0)

Climate Nutrientloading

ObservedC,B,Z,W,

dietsHabitat area

Strait of Georgia time series from assessments or surveys (dots) compared to Ecosim (line)

Strait of Georgia time series from assessments or surveys (dots) compared to Ecosim (line)

Model

Data

1950 2000

Ecosystems where models have been tested using historical trend data

Ecosystems where models have been tested using historical trend data

• E Bering Sea• Aleutian Islands• W&C GoAlaska• E GoAlaska• W Vancouver Island• Hecate Strait• Strait of Georgia • NE Pacific• Central N Pacific • FF Shoals, Hawaii• Central Chile

• E Bering Sea• Aleutian Islands• W&C GoAlaska• E GoAlaska• W Vancouver Island• Hecate Strait• Strait of Georgia • NE Pacific• Central N Pacific • FF Shoals, Hawaii• Central Chile

• Bay of Quinte• Oneida Lake• Scotian Shelf• Cheasapeake Bay• Tampa Bay• S Brazil Bight• North Sea • Baltic • S Benguela• Gulf of Thailand

• Bay of Quinte• Oneida Lake• Scotian Shelf• Cheasapeake Bay• Tampa Bay• S Brazil Bight• North Sea • Baltic • S Benguela• Gulf of Thailand

Estimated Primary Production Anomaly from NE Pacific ecosystem model compared to PDO

0.5

0.7

0.9

1.1

1.3

1.5

1950 1960 1970 1980 1990 2000

year

EwE

PPA

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

PDO

inde

x

NEPac PPA

PDO

Dave Preikshot, UBC FC

Estimated Primary Production Anomaly from BC shelf ecosystem model & BC upwelling index

0.5

0.7

0.9

1.1

1.3

1.5

1950 1960 1970 1980 1990 2000

year

EwE

PPA

-25-20-15-10-5051015

Upw

ellin

g in

dex

BC PPA

54N UI

Dave Preikshot, UBC FC

Nutrient loading• Florida Bay, Tampa Bay

and Chesapeake Bay;• Monthly wind, rain, river

runoff, nutrient loading;• The derived nutrient

loading time series is used to force simulations;

• Sea grass density mediation effects:– Chl. a and sea grass;– Epiphytes; – Habitat area for some

fishes, a.o.

Chlorophyll a and Nutrients

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

1 29 57 85 113 141 169 197 225 253 281 309 337 365 393 421 449 477 505 533 561

Sea grass density

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

1 29 57 85 113 141 169 197 225 253 281 309 337 365 393 421 449 477 505 533 561

Chesapeake Bay• Ecosystem

model is driven with nutrient loading and fishing pressure;

• Model used as ‘CB Fisheries Ecosystem Model’– Companion to

the CB FEP.

Why have Steller sea lions declined?Why have Steller sea lions declined?

Sylvie GuénetteSheila HeymansVilly ChristensenAndrew Trites

Sylvie GuénetteSheila HeymansVilly ChristensenAndrew Trites

Abu

ndan

ce

1960 1980 20000

4,000

8,000

12,000Adult sea lions

Two N Pacific ecosystems

Aleutian IslandsAleutian Islands

SE AlaskaSE Alaska

Abu

ndan

ce

1960 1980 20000

20,000

40,000

Adult sea lions

Hypotheses for the SSL decline

1. Nutritional stress?2. Disturbance?3. Fishing practices?4. Predators?5. Climate?6. Disease?7. Parasites?

David RosenDavid Rosen

Fitting to time series dataEcosim predictionForced catches

1960 1980 20000

4

8

12Halibut

1960 1980 20002

6

10

14

Atka mackerel

Bio

mas

s (t/

km2)

1960 1980 20000.30.50.70.91.11.3

Arrowtooth Cat

ches

(t/k

m2)

1960 1980 20000

0.020.040.060.08

Halibut

1960 1980 20000

1

2 Atka mackerel

1960 1980 20000

0.1

0.2 Arrowtooth

Model results, SSL dataEcosim prediction

Aleutians

1960 1980 20000

20,000

40,000

Abu

ndan

ce

Adult sea lions

Why does the model reproduce the sea lion dynamics so well?

We evaluated the relative impact of:Changes in ocean productivity (PDO)Fishing (catches)Predation (orcas)Competition (flatfish)

Impact of ocean productivity (PDO)Aleutians

Adult sea lions

dataEcosim prediction

Ignore PDO

1960 1980 20000

10,000

20,000

30,000

40,000

Abu

ndan

ce

Impact of fishing

dataEcosim prediction

Ignore PDONo fishing

1960 1980 20000

10,000

20,000

30,000

40,000

Abu

ndan

ce

Adult sea lions

Aleutians

Impact of orca predation

dataEcosim prediction

No predation (orca)

Ignore PDONo fishing

Adult sea lions

1960 1980 20000

10,000

20,000

30,000

40,000

Abu

ndan

ce

Aleutians

Impact of competition from flatfishAleutians

No predation (orca)

Ignore PDONo fishing

No competition

Ecosim prediction

1960 1980 20000

10,000

20,000

30,000

40,000

Abu

ndan

ce

Adult sea lions

data

Model indicates:

changes in competition, predation and ocean productivity are required to explain the sea lion decline;fishing had the least impact;

pollock fisheries is not a major enterprise in the Aleutian Islands.

• Prey behavior limits predation (foraging arena assumptions)

• Prey behavior limits predation (foraging arena assumptions)

A key aspect of the modeling:A key aspect of the modeling:

Andrew Trites

Organisms are not chemicals!Organisms are not chemicals!Ecological interactions are highly organizedEcological interactions are highly organized

Big effects from small changes in space/time scale

Reaction vat model Foraging arena model

Preyeaten

Prey density

Preyeaten

Prey density

Prey behaviorlimits ratePredator handling

limits rate

Predator abundancePredator abundance

Predicted predation mortality

‘Traditio

nal’

‘Traditio

nal’

Base estimate from EcopathBase estimate from Ecopath

EcosimEcosim

Predation mortality & carrying capacityPredation mortality & carrying capacity

Carrying capacityCarrying capacity

?

Are we finally able to develop useful predictive models for

ecosystem management?

Are we finally able to develop useful predictive models for

ecosystem management?

• It’s beginning to look like it;• We can with some credibility describe agents of

mortality and trophic interdependencies;• Evaluation of relative impact of fisheries and

environmental factors is progressing (at the ‘looking for correlation’-stage.)

• It’s beginning to look like it;• We can with some credibility describe agents of

mortality and trophic interdependencies;• Evaluation of relative impact of fisheries and

environmental factors is progressing (at the ‘looking for correlation’-stage.)

• Prim. productivity anomalies may or may not be amplified through the food web;

• Possible to replicate development over time– often by incorporating environmental as well as

fisheries impact;• Requires more data

– but mainly data we should have at hand in any case: ‘the ecosystem history’;

• Supplements single species assessment, does not replace it;

• Prim. productivity anomalies may or may not be amplified through the food web;

• Possible to replicate development over time– often by incorporating environmental as well as

fisheries impact;• Requires more data

– but mainly data we should have at hand in any case: ‘the ecosystem history’;

• Supplements single species assessment, does not replace it;

Experience so far:Experience so far:

• When we have a modelthat can replicate development over time we can (with some confidence) use it for ecosystem-based policy exploration.

• When we have a modelthat can replicate development over time we can (with some confidence) use it for ecosystem-based policy exploration.

Acknowledgements