MAR 580: Models for Marine Ecosystem Based Management

Whole of System Modeling

Ecosystem MSE, Atlantis

Acknowledgements: Beth Fulton, Erik Olsen, �Bec Gorton, Isaac Kaplan, Sarah Gaichas

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Today’s Outline

• Project presentation scheduling
• MSP revisited
• Whole of System Modeling
• Ecosystem MSE
• Atlantis

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Stock/Single Species

Ecosystem

Multi-species

Aggregate Biomass

Single species models, forget ecosystem issues

Gadids

Flatfish

Pelagics

Multiple single species assessments in “harmony”

Extended single species models with predation mortality, habitat or climate effects

Multi-species assessments

Aggregate Biomass Models

Whole system models

food web models

Gradient of possible modeling tools

Whole of system models: focus is at system level, domains span multiple spatial, ecological, societal scales

Plagányi 2007

“Sunlight to Dinner plate”�End-to-end model types (Fulton 2010)

• Qualitative (and conceptual) models
• e.g. Loop analysis
• Aggregate system (or network based) models
• e.g. Ecopath with Ecosim (EwE)
• Biogeochemically based models
• ERSEM, NEMURO, Atlantis
• Coupled and hybrid models
• SEAPODYM, APECOSM
• Agent-based models (OSMOSE, InVitro)

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Coupled social-ecological systems

(Some) uses of ecosystem models

• Understanding ecosystem structure and function.
• Impact of target fish species on other species in the ecosystem
• Effect on top predators of removing predators and prey
• What ecosystem considerations need to be taken into account to rebuild depleted stocks?
• Are single-species assessments biased or wrong because of a failure to include multispecies interactions?
• Are there better (economic, ecological) ways to distribute fishing effort in an ecosystem?
• Is fishing driving ecosystem to less productive/desirable state?
• Identifying effective monitoring schemes
• Expanding thinking about whole of systems (rather than simply sub-sections)
• Allow for identification of new forms of adaptive management that are appropriate for changing system states.

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Plagányi (2007), Fulton (2010)

Whole of system models

Intended Purpose:

• Evaluate management strategies in context of all potential uses and drivers of an ecosystem.
• Try to look more holistically at system dynamics, yet retain necessary detail.
• Models can quickly become unwieldy, output difficult to summarize and communicate.
• Particularly in the model development stage, flexibility is important.
• Coupled/hybrid modeling approaches have benefits of being able to stitch together existing tools, perhaps swapping model types.

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Best practices in model development�(FAO 2007, Fulton 2010)

• Only include components needed for capturing critical dynamics.
• Important to explicitly include feedbacks.
• Address uncertainty: parameterization, model structure, associated requirements to try multiple management options.
• Across all the dimensions of model construction do not default to the finest scales, often inappropriate and unnecessary.
• Spatial and temporal resolution
• Taxonomic resolution
• Process resolution and external forcing
• Anthropogenic components
• Multiple model forms should be considered. �There is no one “right” model.

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Management Strategy Evaluation

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• Simulation method for testing robustness of management methods to system drivers & pressures.

DESIGN &

ANALYSIS

DEFINE

OBJECTIVES

PERFORMANCE

MEASURES

JUDGING OUTCOMES

BIOPHYSICAL

HUMAN USE

(INDUSTRY)

MONITORING

& REPORTING

ASSESSMENT

MANAGEMENT

& DECISION MAKING

IMPLEMENTATION

(ECONOMIC, SOCIAL & PSYCHOLOGICAL INFLUENCES)

SIMULATION CYCLE

Management Strategy Evaluation

Assessment

Management

Contents - Whole Of System Model

Play out alternative futures

Why Model?

$

Define objectives

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MODEL

SPACE

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Behavioural Uncertainty

< 0.06% of literature

End to end models allow for evaluation of tradeoffs across broader suite of objectives at system level.

Fulton EA, Smith ADM, Smith DC, Johnson P (2014) An Integrated Approach Is Needed for Ecosystem Based Fisheries Management: Insights from Ecosystem-Level Management Strategy Evaluation. PLoS ONE 9(1): e84242. doi:10.1371/journal.pone.0084242

http://127.0.0.1:8081/plosone/article?id=info:doi/10.1371/journal.pone.0084242

Atlantis – a whole of system model

Purpose

• What-if sandbox
• model complexity
• ecological understanding
• whole of system understanding
• fisheries
• management systems
• social and economic behavior
• cumulative (climate) impacts
• ‘Flight simulator for managers’

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Atlantis - Putting It Together

ENVIRONMENT

HABITAT

PLANKTON

FOOD WEB

PREDATORS

INDUSTRIES

SOCIOECONOMIC

MANAGEMENT

Atlantis - Putting It Together

ENVIRONMENT

HABITAT

PLANKTON

FOOD WEB

PREDATORS

INDUSTRIES

SOCIOECONOMIC

MANAGEMENT

Atlantis - Putting It Together

ENVIRONMENT

HABITAT

PLANKTON

FOOD WEB

PREDATORS

INDUSTRIES

SOCIOECONOMIC

MANAGEMENT

Atlantis - Putting It Together

ENVIRONMENT

HABITAT

PLANKTON

FOOD WEB

PREDATORS

INDUSTRIES

SOCIOECONOMIC

MANAGEMENT

Usefulness of Atlantis

• Strategic insights
• synthesize information, identify key processes
• simulate possible ecosystem responses (e.g. climate change)
• set management reference points

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• NOT for tactical management

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• How does the system behave given:
• Hypotheses for system structure & function
• Impacts of large scale change (pressures)
• What management levers work?
• How do levels of human activities tradeoff against management objectives?
• What monitoring/assessment methods are reliable?
• Are there robust management rules?

Ecological models

• Multiple options for major processes (e.g. feeding, movement, reproduction)�Model currency is Nitrogen – converted to biomass

Model development – stages

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• Usually, incremental calibration at each stage.
• Each provides own kind of information even if don’t progress further.

Ecology Alone

Forcing History

Match History

Predictions

Calibration and fits to history

• Training and test set concept, are general trends ok?
• Individual humans = HARD to predict
• aggregate behaviour predictable
• Phase I: unfished scenarios
• Phase 2: sensitivity to fixed fishing mortalities, estimates of MSY and FMSY
• Phase 3: comparison to historical trends
• Goal is for calibrated model productivity to be biologically plausible, as evidenced by tests of no fishing, simple forced F, and historical fishing.
• 1000s of model parameters. Calibration of recruitment and consumption parameters primarily.
• also interaction coefficients between predator and prey, and additional mortality terms.

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Handling Uncertainty

• Atlantis is a (largely) deterministic model
• 1000s of parameters
• Make do with:
• loop analysis
• multiple specifications (& multiple models)
• functionality filters (look at sensitive subsets)
• perturbation analysis
• bounded parameterisations
• but see recent work on the Norwegian/Barents Sea, Gulf of Mexico, and NEUS models…

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Handling Uncertainty

Scenario

Strategy

Specification

Atlantis Northeast US

Link JS, Gamble RJ, Fulton EA. 2011. NEUS – Atlantis: Construction, Calibration, and Application of an Ecosystem Model with Ecological Interactions, Physiographic Conditions, and Fleet Behavior. NOAA Tech Memo NMFS NE-218 247 p. Available at http://www.nefsc.noaa.gov/nefsc/publications/.

18 Fishing fleets

• here

Video game style output using Virtual Ecosystem Viewer (VES-V)�videos �combining�animation and�narratives

Web link here if now live?

Suggested reading

• Fulton, E.A., 2010. Approaches to end-to-end ecosystem models. Journal of Marine Systems, 81(1), pp.171-183.
• Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C., 2011. Lessons in modelling and management of marine ecosystems: the Atlantis experience. Fish and Fisheries, 12(2), pp.171-188.
• Fulton, E.A., Smith, A.D., Smith, D.C. and Johnson, P., 2014. An integrated approach is needed for ecosystem based fisheries management: insights from ecosystem-level management strategy evaluation. PloS one, 9(1), p.e84242.
• Griffith, G.P., Fulton, E.A., Gorton, R. and Richardson, A.J., 2012. Predicting Interactions among Fishing, Ocean Warming, and Ocean Acidification in a Marine System with Whole‐Ecosystem Models. Conservation Biology, 26(6), pp.1145-1152.
• Harfoot, M.B., Newbold, T., Tittensor, D.P., Emmott, S., Hutton, J., Lyutsarev, V., Smith, M.J., Scharlemann, J.P. and Purves, D.W., 2014. Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model. PLoS Biol, 12(4), p.e1001841.
• Kaplan, I.C., Horne, P.J. and Levin, P.S., 2012. Screening California Current fishery management scenarios using the Atlantis end-to-end ecosystem model. Progress in Oceanography, 102, pp.5-18.
• Kaplan, I.C., Gray, I.A. and Levin, P.S., 2013. Cumulative impacts of fisheries in the California Current. Fish and Fisheries, 14(4), pp.515-527.
• Link, J.S., Fulton, E.A. and Gamble, R.J., 2010. The northeast US application of ATLANTIS: a full system model exploring marine ecosystem dynamics in a living marine resource management context. Progress in Oceanography, 87(1), pp.214-234.
• Punt, A.E., Butterworth, D.S., Moor, C.L., De Oliveira, J.A. and Haddon, M., 2014. Management strategy evaluation: best practices. Fish and Fisheries
• Smith, M.D., Fulton, E.A. and Day, R.W., 2014. An investigation into fisheries interaction effects using Atlantis. ICES Journal of Marine Science: Journal du Conseil, p.fsu114.
• Steele, J.H., Aydin, K., Gifford, D.J. and Hofmann, E.E., 2013. Construction kits or virtual worlds; Management applications of E2E models. Journal of Marine Systems, 109, pp.103-108.
• Travers, M., Shin, Y.J., Jennings, S. and Cury, P., 2007. Towards end-to-end models for investigating the effects of climate and fishing in marine ecosystems. Progress in oceanography, 75(4), pp.751-770.