11-17-2016
The South Atlantic LCC’s Third Thursday Web Forum
Understanding marine ecosystem changes in the South Atlantic region using an integrated modeling approach
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Agenda
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Tom Okey, University of Victoria
Understanding marine ecosystem changes in the South Atlantic region using an integrated modeling approach
11-17-16
AN INTEGRATIVE ECOPATH WITH ECOSIMMODEL FOR THE SOUTH ATLANTIC REGION FOR UNDERSTANDING COASTAL MARINE
ECOSYSTEM CHANGESTom Okey, Ocean Integrity Research
Roger Pugliese, South Atlantic Fishery Management Council
South Atlantic LCC Fall Seminar seriesOctober 17th, 2016
FOOD WEB MODEL
ECOPATH WITH ECOSIM
No fish is an island
7000+ users in 150+ countries (google analytics)
800+ peer-reviewed publication (ISI Web of Knowledge)
ECOPATH WITH ECOSIM
ECOPATH / ECOSIM / ECOSPACE
ECOPATH with ECOSIM (EwE) is a suite of combined models
developed for ecosystem and fishery analysis. These commonly
used programs can explore management or policy options and
address growing ranges of fisheries and ecological questions.
THE ECOPATH MODEL OF THE SOUTH
ATLANTIC BIGHT
SOUTH ATLANTIC BIGHT FOOD WEB
Figure by Kelly Kearney
BROAD PROJECT OBJECTIVES
A. Advance and integrate existing models
and data
B. Advance and refine the LCC
conservation blueprint
C. Support the SA Fishery Management
Council’s move to ecosystem-based
management and its Fishery Ecosystem
Plan
To build a flexible and integrative ecosystem modelling
suite for the South Atlantic Region
HOW DO WE GET THERE?
Establish an inter-disciplinary regional science team to ensure
the approaches taken include leading-edge thinking and
produce the best possible information for resource decision-
making
Work with managers and decision-makers to ensure models
meet their needs for conservation, management, and science
planning
Highlight the connectivities and dynamics between
coastal/ocean and estuarine-riverine ecosystems
Develop ecosystem models to understand ongoing changes in
the system and develop effective management strategies
OUR PROJECT TEAMS– Principal investigators –
Dr. Marcel J. Reichert – Marine Resources Research Institute, South Carolina Department of Natural Resources
Dr. Luiz Barbieri – Florida Fish and Wildlife Conservation Commission, FWRI
Dr. Thomas Okey – Ocean Integrity Research, Victoria, BC, Canada
Dr. Jerald S. Ault – Professor and Director, Fisheries Ecosystem Modeling and Assessment Research, Rosenstiel School of Marine and Atmospheric Science, University of Miami
Dr. Ruoying He – Department of Marine, Earth and Atmospheric Sciences, North Carolina State University
Dr. Peter Sheng – Professor and Director, Coastal and Oceanographic Engineering Program, University of Florida
Vembu Subramanian – RCOOS Program Manager and Data Management Coordinator, SECOORA
Dr. Patrick N. Halpin – Director, Geospatial Analysis Program Duke University
Roger Pugliese – Senior Fisher Biologist, South Atlantic Fishery Management Council
THE MODELS WILL:
Link to hydrodynamic oceanographic models and satellite data
Provide more realistic predictions about spatial policy options
Predict impacts of episodic events that are limited in space (oil spills, red tides, upwelling)
Meet the immediate needs of the SSC and the South Atlantic Council
PHASE 1- UPDATE
SOUTH ATLANTIC
ECOPATH MODEL
Element 1Develop South Atlantic Ecopath Model - Tom Okey
Element 2Data Conditioning- Marcel Reichert
Element 3Review of Estuarine Data and Models - Peter Sheng
Element 4A Coupled Marine Environmental Assessment and Prediction System for the Southeastern U.S. Coastal Ocean in Support of Effective Marine Ecosystem-Based Management - Ruoying He
Element 5South Atlantic Fisheries Ecosystem Modeling & Prediction- Jerald S. Ault
PHASE 2 –CONNECT TO OTHER MODELS
Respiration
Predation
Yield
Net migration
UnassimilatedOther mortality
Food
consumptionProduction
Polovina, J.J. 1984. Coral Reefs, 3:1-11; Pauly et al. 2000. ICES J. Mar. Sci., 57: 697-706; Christensen and Walters. 2004. Ecol. Model., 172(2-4): 109-139
Bi Biomass
Pi/Bi Production
Qi/Bi Specific consumption
DCij Fraction of prey i in diet of predator j
BiAi Biomass accumulation
EEi Production used in the system
1-EEi Unexplained mortality
ii
i
iii
n
1Pred_jijj
j
i
i
EE1BB
PBAYEDCB
B
QB
B
P
1.
2.
iii
i
i
i
UNRBB
PB
B
Q
ECOPATH
Diet compositione.g., for a tuna
Use volume or weight!
Partly digested
fish 31.6%
Others 19.3%Portunids 15.8%
Euphausiids 3.5%
Squids 12.3%
Anchovies 8.8%
Sardines 7%Auxids 1.7%
HISTORY OF THE SAB MODEL
2001 - Strawman 48-box model constructed
2004 - Preliminary 98-box model developed
2013 - Model refined to address forage fish
questions
Construction
& refinement
Sponsored by
SAFMC
42-box model
98-box model
Application to
forage question
Sponsored by
Pew Charitable
Trusts
Forage groups
articulated
99-box model
COLLABORATIVE CONSTRUCTION
Adult Herring
Arrowtooth Flounder
CetaceansPinnipeds
SablefishLingcod
Pacific Cod
Seabirds
Omnivorous Zooplankton Herbivorous
Zooplankton
Shallow Small
Epifauna
Fishery
Primary contributors Behzad Mahmoudi (FMRI) Bob Feller (USC) David Whitaker (SCDNR) Doug Vaughan (NMFS) Marty Levissen (SCDNR) Jack McGovern (NMFS) Larry DeLancey (SCDNR) Bill Sharp (FMRI) Whit Gibbons (UGA) Joan Browder (NMFS) John Carlson (NMFS) Larry Cahoon (UNC) Galen Johnson (UNC)
Megan Gamble (ASMFC) Brad Spear (ASMFC) Toni Kearns (ASMFC) Peter Verity (SKIO) Wilson Laney (USFWS)
Secondary contributors
Elizabeth Wenner (SCDNR)
Robert George (GIBS)
Carolyn Currin (NOAA)
Chuck Hunter (USFWS)
Craig Watson (USFWS)
Damon Gannon (Mote Lab)
Desmond Kahn (DEDNR)
Enric Cortez (NMFS)
George Sedberry (SCDNR)
Greg McFall (GRNMS)
Hans Paerl (UNC)
Jennifer Wheaton (FMRI)
Jenny Purcell (WWU)
Jim Nance (NMFS)
John Merriner (NOAA)
Doug Forsell (USFWS)
Jon Hare (NOAA)
Jose Castro (Mote Lab)
Ken Lindeman (ED)
Mark Epstein (USFWS)
Martin Posey (UNC)
Paul Carlson (FMRI)
Steve Ross (UNC)
Buddy Powell (WT)
Alan Bolten (UFL)
Karen Bjorndal (UFL)
Bob Noffsinger (USFWS)
Sean McKenna (NCDENR)
Pat Tester (NOAA)
Lance Garrison (NMFS)
Species / Groups in SAB 99-box model
Dogfish sharks Demersal coastal omnivores Birds -- shelf piscivores Benthic meiofauna
Adult mackerel Benthic oceanic piscivores Rock shrimps Deep-burrowing infauna
Juvenile mackerel Benthic oceanic invertivores Penaeid shrimps Carnivorous zooplankton
Bluefish Benthic coastal piscivores Megafaunal predators Aquatic and other insects
Weakfish Benthic coastal invertivoresEchinoderms and
gastropodsOther zooplankton
Red drum Benthic coastal planktivoresEstuarine infaunal
crustaceansIchthyoplankton
Atlantic menhaden Reef associated piscivores Birds -- herbivores Microbial heterotrophs
Mullets Reef associated omnivores Birds -- wading piscivores Phytoplankton
Other Drums & Croakers Triggerfish Birds -- shelf invertivores Microphytobenthos
Striped bass Shallow water grouper/tilefish Birds -- raptors Benthic macroalgae
Highly migratory pelagics Goliath grouper Encrusting fauna Pelagic macroalgae
Dolphinfish Nassau grouper Squids Seagrasses
Pelagic oceanic
piscivoresDeep-water grouper/tilefish Stomatopods Marsh vegetation
Pelagic coastal piscivores Shallow-water snapper Octopods Estuarine benthic detritus
Nearshore piscivores Mid-shelf snapper Blue crabs Offshore benthic detritus
Pelagic oceanic
planktivoresJacks Horseshoe crabs Water-column detritus
Sardines Red porgy Golden crabs Dead carcasses
Scads Grunts and porgys Calico scallops
VALUED FISH SPECIES
FORAGE GROUPS IN THE 99 BOX MODELGroup Species included B
(tˑkm-2)P/B
(year-1)Q/B
(year-1)Anchovies Bay (Anchoa mitchilli), striped (A. hepsetus), silver
(Engraulis eurystole)
3.75 1.45 17.50
Atlantic menhaden Brevoortia tyrannus (not B. patronus) 7.05 1.70 7.84
Atlantic silverside Menidia menidia 1.18 2.00 14.90
Halfbeaks Ballyhoo (Hemiramphus brasiliensis), balao (H.
balao), common or Atlantic silverstripe
(Hyporhamphus unifasciatus)
1.22 2.60 11.70
Mullets Striped (Mugil cephalus), other (Mugil spp.) 0.11 0.70 11.03
Sardines Spanish (Sardinella aurita), scaled (Harengula
jaguana)
1.93 1.11 11.82
Scads Round (Decapterus punctatus), rough (Trachurus
lathami), bigeye (Selar crumenophthalmus)
2.28 0.92 10.00
Shad Alosa spp. 3.97 0.50 3.80
Thread herring Atlantic thread herring (Ophistonema oglinum) 0.28 1.60 13.26
Pelagic oceanic
planktivores
Chub mackerel (Scomber japonicus), lanternfish
(Diaphus spp.), antenna codlet (Bregmaceros
atlanticus), striated argentine (Argentina striata),
flyingfish (Exocoetidae)
3.95 0.87 11.71
Squids Shortfin (Illex illecebrosus), longfin (Loligo pealei) 0.45 2.67 36.50
Shrimps Rock shrimps and penaeid shrimps 2.53 5.38 19.20
NEW 99 BOX SAB MODEL (FORAGE)
ELEMENT 2: SC DEPT. OF NATURAL
RESOURCES LONG-TERM SURVEY DATA
CONTRIBUTIONS
Coastal Trawl Survey
SEAMAP-SA
Annual, seasonal
Nearshore (depths <10m)
75 ft. Falcon trawls
Common taxa:
Croakers, mackerels, anchovies
Penaeid shrimp
Coastal sharks
Rays
SE Reef Fish Survey
Begun by MARMAP, now with SEAMAP-SA and SEFIS
Annual (May-Sept.)
Offshore (15-300 m)
Traps, longlines
Common taxa:
Groupers and sea basses
Porgies
Snappers
Triggerfishes
Element 3: Reviews of Estuarine Data and Models
Element 3: Reviews of Estuarine Data and Models
Peter Sheng is looking at a few estuaries in Florida
Relate fishery data with salinity and other parameters obtained from numerical model simulations using dynamic models.
St. John's River Apalachicola Bay Rookery Bay
Element 4A Coupled Marine Environmental Assessment and Prediction System for the Southeastern U.S. Coastal Ocean in Support of Effective Marine Ecosystem-Based Management - Ruoying He
ECOSIM
Temporal-dynamic module of EwE, initialized from Ecopath
Includes biomass and size structure dynamics
Requires only a few extra parameters
Used, among others, to assessQuantify combined effect of species dynamics, fishing
impacts, and environmental impacts on a food web over time
Replicate past scenarios (time series fitting)
Explore future scenarios
Explore fishing policy alternatives
Test model robustness
ECOSIM
Walters et al 1997 RFBF, Ahrens et al 2012 Fish and Fisheries
Foraging arena:
CALIBRATING THE MODEL
TIME PREDICTIONS FROM AN ECOSYSTEM
MODEL OF THE GEORGIA STRAIT, 1950-2000
Okey, T. A. 2004. Can oil spills shift marine ecosystems to alternate stable states?: Preliminary simulations with an Ecopath model of Prince William Sound,
Alaska. Pages 84-103 In: Shifted community states in four marine ecosystems: some potential mechanisms. PhD. University of British Columbia, Vancouver.
Prince William Sound, Alaska
1
2
3
0Bio
mas
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rigi
nal b
iom
ass
0 5 10
System-wide changes in biomass
Time (years)(V = 3)
REMOVAL OF A SEA CUCUMBERS
EXPLAINS GALÁPAGOS ANEMONE
BARRENS
Anemones
Damselfishes
Hexaplex gastropod
Sessile filter and suspension
Pepino (S. fuscus)
Fishing zone
No fishingzone
Island
Sea cucumber recovery on a
Galápagos rocky reef
SPATIAL SIMULATION
ECOSPACE: SEASONAL OR FULL-TIME CLOSURES
Advancing Ecospace
Jeroen Steenbeek, Ecopath International Initiative
ECOSPACE
Spatial temporal component of EwE, executes
Ecosim for every ‘water’ cell in a grid
Requires extra inputs, related to movement,
habitat, fishing, environment
Groups and fleets try to move to
nearby optimal conditions
ECOSPACE
ECOSPACE
ECOSPACE
Used, among others, to assess
Distribution of marine species and fishing effort
Spatial impact of fishing
Management options, e.g. impact of MPAs
Impact of environmental change (EwE version
6.3+)
Running model has been linked to Marxan & Atlantis
Includes an IBM approach
ECOSPACE
2011 Ecospace had three major limitations
1. Unable to represent sub-cell features
2. Unable to explicitly incorporate environmental
effects on species: “why are the species where
they are?”
3. Limited facilities to exchange data with the
outside world, thus unable to include
environmental variability
RECENT DEVELOPMENTS IN ECOSPACE
1. REPRESENTING SUB-CELL FEATURES
PROBLEM
Cells may contain small but important features that
cannot be represented well, such as coral reefs
Old Ecospace accepted only one habitat type per
cell, and yes/no habitat usage
Using ever smaller cells is no option: computationally
demanding and wasteful, with risk of over-
representing features and dynamics
Example: small but ecologically important coral reef
SOLUTION
Ecospace (EwE version 6.3+) allows habitats to
overlap, and habitats can occupy a fraction
[0,1] of a cell
Species can utilize a fraction [0, 1] of each
habitat type
Old Ecospace models are directly translated
to this structure, and work as expected
2. WHY ARE SPECIES WHERE THEY ARE?
HABITAT CAPACITY MODEL
Christensen et al. 2014. Ecosystems, E17, 1397-1412.
Original Ecospace could not explain species distributions. Habitat usage was an aggregated assumption implying environmental preferences
Ecospace (EwE version 6.3+) defines spatial foraging arena size from species’ response to environmental conditions
Ecospace has become an integrated food-web / species envelope model
HABITAT CAPACITY MODEL
Dynamic habitat model predicts how productive individual cells
are for each species, based on multiplicative effect of
environmental responses
Christensen et al. 2014. Ecosystems, E17, 1397-1412.
SETTING UP THE
HABITAT FORAGING CAPACITY MODEL
1. SELECT GROUP CAPACITY MODEL
2. DEFINE ENVIRONMENTAL DRIVERS
3. POPULATE ENV. DRIVER MAPS
4. DEFINE ENV. RESPONSE CURVES
HAB. CAP. CASE STUDY
Full Mediterranean EwE model
90+ functional groups, assigned to 4 MSFD zones
Time frame 1950 – 2010
Entire basin at 0.167 dd grid
Piroddi et al (in progress)
Foraging habitat capacity model case study
Primary production
Salinity (surface and bottom)
Temperature (surface and bottom)
Depth
MSFD area restrictions
HAB. CAP. CASE STUDY
1. Define environmental drivers
HAB. CAP. CASE STUDY
Piroddi et al (in progress)
2. Define environmental responses
Here we are using a plug-in to import environmental
responses from AquaMaps species envelopes
HAB. CAP. CASE STUDY
4. Ecospace computes capacity (cetaceans - depth)
HAB. CAP. CASE STUDY
4. More capacity (Western sardine - depth, MSFD W)
HAB. CAP. CASE STUDY
4. Run
ECOSPACE SPIN-OFF
Functional responses are also available to
Ecosim
Previous Ecosim only supported two
normal distributed response functions
New Ecosim shares functional response
curves with Ecospace
Environmental drivers in Ecosim are
functions; not maps
Affects foraging arena size over time
System is backwards compatible with
older models
Not published yet, application in
progress in PhD thesis
3. EXCHANGING DATA
WITH THE OUTSIDE WORLD
Ecospace internal data model was hard to access• Almost impossible to vary input maps over time
• Almost impossible to exchange data with other
models
• Changing environmental conditions could not be
included in spatial temporal analysis
ECOSPACE SHORTCOMINGS
SPATIAL TEMPORAL DATA FRAMEWORK
Steenbeek et al. 2013. Ecological Modelling 263, 139-151.
SPATIAL TEMPORAL DATA FRAMEWORK
SPATIAL TEMPORAL DATA FRAMEWORK
CASE STUDY
Steenbeek et al (2013) Ecological Modeling
GIS DATA FOR MANY ECOSPACE LAYERSConnected to existing Ecospace driver
layers
Primary production
Environmental drivers
Habitats
Fishing cost
MPA layouts
Contaminants
Migration
Computed foraging capacity
Coming soon
Advection
1. CALCULATE ADVECTION
CURRENT STATUS
Connected to most Ecospace layers
Reads and writes 20+ GIS data formats, both raster and vector
Designed upon EwE plug-in system. Easy to extend with new capabilities
Not (yet) publicly released: more R&D needed.
Can only be applied by directly involving EwEteam
Needs better support for projections
Needs integration of new data types
Needs user testing to streamline workflow
2. CHOOSE ADVECTED GROUPS
ADVECTION
Computed advection, per month, is stored
with the EwE model
Advection patterns can be overwritten with
external hydrological model output via the
spatial temporal data framework
Will be made available in a next EwE version,
but development version can be applied by
involving the EwE development team
Publication is in progress
The evolution of the South Atlantic Conservation Blueprint
Next Third Thursday Web Forum
1-19-2017
10:00 am
Rua Mordecai
Science Coordinator, South Atlantic LCC
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How to get involved with your cooperative
• Join the South Atlantic LCC web community
• Connect with a staff or other cooperative member
• Explore the Conservation Blueprint
southatlanticlcc.org/blueprint
southatlanticlcc.org
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