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Ecosystem modelling as a tool for carrying
capacity estimations in aquaculture sites
Tracadie Bay, PEI (Canada)
Ramón Filgueira
Ecological modelling. What is a model?
Mathematical representations of ecosystems
Understand the interactions within complex manipulated ecosystems
Problem & Hypothesis
New hypothesis
Solution
Conceptual model
Mathematical model
Parameterization
Boundary
conditions
Scenario analysis
Prediction
Optimization
Sensitivity analysis
Groundtruthing
Yes
No
Good?
Yes
More results
Still good?
No
Outline
Background. Ecological modelling and aquaculture. Why? How?
Dynamic simulation of trophic interactions. Carrying Capacity
1. Tracadie Bay, Canada
2. Lysefjord, Norway
3. St. Ann’s Bay, Canada
Fluxes of matter and energy in aquaculture sites
4. Rope-scale model, Norway
5. CIMTAN, Canada
6. MUMIHUS, Denmark
7. Spatial connectivity, Spain, Canada & France
Environmental impacts and ecological indicators
8. Meat yield project, Canada
Observational science
9. Change in estuarine productivity forced by climatic events, Canada
10. Change in phenology triggered by climate change, Canada
Ecological modelling in aquaculture sites. Why?
Scenario building, prediction and
optimization
How can science help farmers?
Management strategies
Growth predictions
Disease transmissions
Farm location
Where and how big?
Cultured density
Hainan Island, China
How can science help regulators?
Ecosystem-based management
Impacts in the far- and near-field
Mitigation alternatives
Decision support systems
Marine spatial planning
Lorbé, Spain
Ecological modelling in aquaculture sites. How?
Ecosystem models – multiple approaches depending on our needs
0-D models – static in time
no predictive power
no hydrodynamics
no spatial resolution
2-D models
– dynamic
3-D models
predictive
exchange coefficients or
hydrodynamic models
spatial resolution
maps to link with GIS
Dame & Prins 1998
X
Depth (m)
1-D models – dynamic
predictive
basic “hydrodynamics”
no spatial resolution
Tidal turnover vs filtration time
Ferreira et
al. 2008
Dowd
2003
Ecological modelling in aquaculture sites. How?
At which spatial resolution?
Ecological modelling in aquaculture sites. How?
At which spatial resolution?
Bay of Fundy, Canada
Ecological modelling in aquaculture sites. How?
At which spatial scale?
Dynamic
Energy
Budget
Individual
Community
Ecosystem
Outline
Background. Ecological modelling and aquaculture. Why? How?
Dynamic simulation of trophic interactions. Carrying Capacity
1. Tracadie Bay, Canada
2. Lysefjord, Norway
3. St. Ann’s Bay, Canada
Fluxes of matter and energy in aquaculture sites
4. Rope-scale model, Norway
5. CIMTAN, Canada
6. MUMIHUS, Denmark
7. Spatial connectivity, Spain, Canada & France
Environmental impacts and ecological indicators
8. Meat yield project, Canada
Observational science
9. Change in estuarine productivity forced by climatic events, Canada
10. Change in phenology triggered by climate change, Canada
1. Carrying capacity. Tracadie Bay
Mussel culture in a
complex manipulated
ecosystem
Explore the effects of interannual variability on ecosystem performance
1. Carrying capacity. Tracadie Bay
Boundary Conditions
Time series (chla, seston…) outside the
model domain that force the model
Huge influence on model performance
Different estimations of ecosystem variables
Important for management
Goal
Manage mussel culture in a changing environment based on an
ecosystem perspective
1. Carrying capacity. Tracadie Bay
Ecosystem-based management
Goal
Maintain ecosystem functioning to
provide services humans want and need
McLeod et al. 2005
Resilience
The capacity of a system to absorb disturbance and reorganize while undergoing
change, that is, to maintain essentially the same function, structure, identity, and
feedbacks
Walker et al. 2004
Ecosystem
State of the system
Perturbation
Ecological resilience
Peterson et al. 1998
1. Carrying capacity. Tracadie Bay
EBM + Resilience = Carrying Capacity
Carrying Capacity
EBM + Resilience
alteration of ecosystem functioning
within the bounds of natural
variation
Grant and Filgueira 2011
Ecological resilience
Natural Variation
Ecosystem
State of the system
Tipping points
Safety margins
Sustainability
Aquaculture activity
1. Carrying capacity. Tracadie Bay
Carrying Capacity in shellfish aquaculture
The most relevant interaction in the system: shellfish feeding on phytoplankton
Ferreira et al. 2009
Phytoplankton or chlorophyll depletion
Zooplankton
Mussels
Phytoplankton
Detrital
matter
Nutrients
Spatial Connections
Boundary Conditions
1. Carrying capacity. Tracadie Bay
Grant et al. 2007 Depletion Index (D.I.)
D.I. (%) =
chl-a Box i
chl-a Boundary
1
2
3
4
5
Two consecutive years
Sustainability criterion
x100
Not sustainable (D.I.<73%)
Acceptable (73% <D.I.<100%)
Sustainable (D.I.>100%)
Year 1
Boundary
1998
Depletion index (%)
Depletion index (%)
1. Carrying capacity. Tracadie Bay
Inner part
Year 2
Boundary
Inner part
-­‐ Higher deple4on in second year-­‐> less suitable for growing mussels -­‐ To obtain the same values both years 40% biomass reduc4on in 1999 -­‐ The datasets highlight the importance of inter-­‐annual variability and allows us to improve bay management Filgueira & Grant 2009
Filgueira & Grant 2009, Filgueira et al. 2012 2. Carrying capacity. Lysefjord
Mussel culture in a
pristine area
Optimize the system to provide the best management scenario
2. Carrying capacity. Lysefjord
Aure et al. 1996
Kristianssan Fjord
20.0
25.0
20.0
25.0
30.0
30.0
32.0
Time (d) 20.0
25.0
30.0
32.0
32.5
Salinity vertical profile through time
15.0
32.0
32.5
32.5
Aure et al. 2007
Salinity vertical profile through fjord
10
Stratification
Reduces the exchange between the upper
Long,
deep glacialdeep
valleywaters
layers
andnarrow,
the nutrient-rich
40 km long
0.5 – 2 kminwide
Depletion of nutrients
the euphotic layer
14 – 460 m depth
9
8
7
6
5
4
3
2
1
Box 4
Box 2
Box 1
2 km
Box 3
0
2. Carrying capacity. Lysefjord
Artificial pump
Hypothesis: Can we use deep nutrients to enhance primary
production in the euphotic layer?
Artificial pump
Chlorophyll vertical distribution (mg m-3)
Aure et al. 2007
2. Carrying capacity. Lysefjord
Questions?
1.- Where is the
optimal location
for the pump?
2.- What is the
optimal mussel
biomass to grow
according to CC?
And where?
CC criterion: Introducing mussels in order to compensate
artificial upwelling and maintain chlorophyll level similar to
background conditions
2. Carrying capacity. Lysefjord
Physical-biogeochemical coupling
* *Also
for FVCOM and RMA
Filgueira et al. 2012
2. Carrying capacity. Lysefjord
Where is the optimal location for the pump? (Scenario analysis)
Scenario 1
Scenario 2
Scenario 3
Scenario 4
10 0 5 10 Standardized chlorophyll
enrichment
0 Scenario 2 is the best one:
-  Highest averaged chla
-  Most homogeneous chla
distribution in the
enriched area
Filgueira et al. In review 2. Carrying capacity. Lysefjord
What is the optimal mussel biomass to grow according to CC? And where?
Optimization tools
Estimate the value of a parameter in order to adjust the results of
the model to a dataset chosen by the user
Zooplankton
Zooplankton
Phyto.
Detrital
matter
Nutrients
Mussels
Phyto.
Detrital
matter
Nutrients
Forced nutrients
CC: maintain chlorophyll level similar to background condi4ons How many mussels and where? 2. Carrying capacity. Lysefjord
0
95
2000 m
190
Optimal mussel density
(mg m-3 WW )
What is the optimal mussel biomass to grow according to CC? And where?
Optimal biomass according to CC criterion
Box model 482 tons wet weight (Filgueira et al. 2011)
Fully-spatial model 660 tons wet weight (Filgueira et al. in review)
Filgueira et al. 2010 & In review 2. Carrying capacity. Lysefjord
Box model
Fully-spatial model
-  Lower spatial resolution
-  Coarser maps
-  Simplified hydrodynamics
-  Higher spatial resolution
-  Detailed maps and links with GIS
-  Coupled hydrodynamic model
-  Computational simplicity
-  Simple optimization
-  Cheaper
-  Computational complexity
-  Complex optimization
-  More expensive
-  Ecosystem-level hypothesis
-  Ecosystem-level hypothesis
Local-level hypothesis
3. Carrying capacity. St. Ann’s Harbour
Ecosystem models in
data poor environments
Explore alternative tools to parameterize and force ecosystem models
3. Carrying capacity. St. Ann’s Harbour
Land use maps
Diffusive
nitrogen inputs
Digital elevation maps
River discharge
3. Carrying capacity. St. Ann’s Harbour
NASA
3. Carrying capacity. St. Ann’s Harbour
Depletion index (%)
3. Carrying capacity. St. Ann’s Harbour
In the field of applied sciences, researchers must be able to make objective
decisions without full knowledge, but by using fully what is known at the time
Polasky et al. 2011. Trends in Ecology and Evolution 26(8): 398-404
ADAPTIVE MANAGEMENT
Filgueira et al. In review Outline
Background. Ecological modelling and aquaculture. Why? How?
Dynamic simulation of trophic interactions. Carrying Capacity
1. Tracadie Bay, Canada
2. Lysefjord, Norway
3. St. Ann’s Bay, Canada
Fluxes of matter and energy in aquaculture sites
4. Rope-scale model, Norway
5. CIMTAN, Canada
6. MUMIHUS, Denmark
7. Spatial connectivity, Spain, Canada & France
Environmental impacts and ecological indicators
8. Meat yield project, Canada
Observational science
9. Change in estuarine productivity forced by climatic events, Canada
10. Change in phenology triggered by climate change, Canada
4. Fluxes of matter and energy. Rope-scale modelling
Quantify the temporal variation in nutrient
fluxes of mussel ropes in relation to
mussel biomass, invasive tunicates and
organic material
Explore the effects of scaling up individual rates
4. Fluxes of matter and energy. Rope-scale modelling
0-D
Individual based model
Observed Nitrogen Flux
(µmol l-1 h-1)
2.5
Individual Chamber
r2 = 0.91 b≈1 a≈0
2.0
Pelagic Chamber
r2 =0.57 b≈1 a≠0
1.5
1.0
0.5
0.0
0
Community approach
0.5
1
1.5
2
2.5
Modelled Nitrogen Flux (µmol l-1 h-1)
- Individual: we understand the physiology
- Pelagic: some indications for additional fluxes
- Remineralization of organic material (≠intercept)
- Associated fauna (red hollow dots)
Jansen, Filgueira et al. In review 5. Fluxes
Fluxes of
ofmatter
matterand
andenergy.
energy.
IMTA
IMTA
Goal: Optimize the recycling of organic matter improve production
mitigate ecosystem impact
5. Fluxes of matter and energy. IMTA
5. Fluxes
Fluxes of
ofmatter
matterand
andenergy.
energy.
IMTA
Spatial connectivity
Filter Feeder
Phytoplankton
Detrital
matter
Nutrients
Finfish
Spatial
Connections
Boundary
Conditions
Zooplankton
Macroalgae
Food
Highly influenced by hydrodynamics
Engineering concept
Finfish
Filter-feeder
Algae
Bay management
- Optimal farm location
- Implications for
Marine Spatial Planning
6. Fluxes of matter and energy. MUMIHUS
Production of Mussels – Mitigation and Feed for Husbandry (MUMIHUS)
Extract blue mussels to
remove the excess of
nutrients in coastal
waters and recycle them
into valuable products
Goal: Evaluate mussel production as a source of food for humans
a tool to mitigate pollution: N credits
6. Fluxes of matter and energy. MUMIHUS
Resuspension model – evaluate the contribution of resuspended material to
mussel’s diet
Time (d)
Quality of resuspended material
6. Fluxes of matter and energy. MUMIHUS
2
Chlorophyll - SCUFA 1m lag
-Vy - Wind - rotated
3
2
1
1
0
0
-1
-1
-2
-2
-3
Time (d)
-Vy - Wind rotated
Chlorophyll (standardized
Chla lag
averaged mean)
3
-Vy – rotated (standardized)
Effect of wind on resuspension
7. Spatial connectivity
Farmers asked:
Where should we
deploy collector
ropes?
7. Spatial connectivity
Miranda
Settlement (indiv col-1)
Arnela
-QL-lag0 (m2 s-1)
Link between mussel settlement, upwelling, longitudinal
transport and spatial connectivity
Peteiro, Filgueira et al. 2007, 2011 7. Spatial connectivity
Release point 1
0
5
Conservative Tracer Concentration
Release point 2
10
7. Spatial connectivity
Transfer time from one element (white) to the other elements
7. Spatial connectivity
Body water divided in 17 specific areas Spatial connectivity but also risk assessment for disease (Infectious
salmon anemia virus) or sea lice transmission. Bay management
Outline
Background. Ecological modelling and aquaculture. Why? How?
Dynamic simulation of trophic interactions. Carrying Capacity
1. Tracadie Bay, Canada
2. Lysefjord, Norway
3. St. Ann’s Bay, Canada
Fluxes of matter and energy in aquaculture sites
4. Rope-scale model, Norway
5. CIMTAN, Canada
6. MUMIHUS, Denmark
7. Spatial connectivity, Spain, Canada & France
Environmental impacts and ecological indicators
8. Meat yield project, Canada
Observational science
9. Change in estuarine productivity forced by climatic events, Canada
10. Change in phenology triggered by climate change, Canada
8. Environmental impacts and ecological indicators
Ferreira et al. 2009
Phytoplankton or chlorophyll depletion: research tool
- 
- 
- 
- 
Difficult to measure
Difficult for farmers
Not a good operational indicator
Need for other indicators
8. Environmental impacts and ecological indicators
Overstocking bivalves leads to increased competition for food
resources, phytoplankton, which ultimately could have a significant
effect on bivalve growth performance and condition index
Condition Index (Arcsin transformed)
8. Environmental impacts and ecological indicators
Lease Coverage (Biomass per acre)
Overstocking
?
Phytoplankton
?
Condition index
Filgueira et al. 2013 8. Environmental impacts and ecological indicators
Can we relate stocking biomass, phytoplankton deple8on and condi8on index and establish thresholds of sustainability based on bivalve measurements? Filgueira et al. In prep. 8. Environmental impacts and ecological indicators
Can we correlate phytoplankton deple8on with condi8on index and establish thresholds of sustainability based on bivalve measurements? Depletion index
25
→
75
75
Deple8on Index (%) ↓
175
0
2000
4000
175
6000
Growth rate (DW)
25
75
→
125
175
225
↑
0
2000
4000
6000
8000
0.013
0.012
0.011
0.010
0.009
0.008
0.007
0.006
0.005
0.004
8000
225
26
→
125
125
225
Final CI
25
2000
4000
22
8000
6000
Growth rate (L)
25
75
0.0025
0.0020
→
125
175
225
24
23
↑
0
25
0.0010
↑
0
2000
4000
0.0015
6000
0.0005
8000
Standing stock biomass (tons) Filgueira et al. In prep. Outline
Background. Ecological modelling and aquaculture. Why? How?
Dynamic simulation of trophic interactions. Carrying Capacity
1. Tracadie Bay, Canada
2. Lysefjord, Norway
3. St. Ann’s Bay, Canada
Fluxes of matter and energy in aquaculture sites
4. Rope-scale model, Norway
5. CIMTAN, Canada
6. MUMIHUS, Denmark
7. Spatial connectivity, Spain, Canada & France
Environmental impacts and ecological indicators
8. Meat yield project, Canada
Observational science
9. Change in estuarine productivity forced by climatic events, Canada
10. Change in phenology triggered by climate change, Canada
9. Observations. Estuarine productivity & Climate Change
Hurricanes can induce severe changes in geomorphology
Pine Island, Florida (before 2004)
Breach formed after Ivan, 2004.
US Geological Survey
9. Observations. Estuarine productivity & Climate Change
Tracadie Bay, PEI (Canada)
Prior to breach opening (2000)
2 km
Present with new breach (2010)
9. Observations. Estuarine productivity & Climate Change
Breaching induces changes in hydrodynamics
Prior to breach opening (2000)
2 km
Present with new breach (2010)
9. Observations. Estuarine productivity & Climate Change
Tracadie Bay: Highly manipulated ecosystem
Hypothesis:
Did the change in coastal
geomorphology and the subsequent
hydrodynamic alterations affect
estuarine productivity, and
consequently ecosystem
sustainability?
Depletion Index 9. Observations. Estuarine productivity & Climate Change
B
Breach
brings
phytoplankton inside the bay
1 x 106 kg
Wet Weight
Year 1
Box 2
Box 3
Box 5
Year 2
Box 2
Box 3
Box 5
20
40
60
80
100
120
Depletion Index (D.I. %)
140
Aquaculture implies:
-  High pressure on autochthonous phyto
-  High demand of allochthonous phyto
Breach implies:
-  More dynamic system
-  Bringing phytoplankton inside the bay
-  Sustainability increase
2
3
5
9. Observations. Estuarine productivity & Climate Change
Observed Aquaculture Production in Tracadie Bay
10. Observations. Change in phenology
Farmer: We are seeding earlier and earlier in the year since I can remember! I: Hmmm… how old are you? Farmer: I am 65, all my life in the sea! Why? I: Because you are the best 8me series that I’ve ever had! Weekly larvae survey Size and abundance since 2001 10. Observations. Change in phenology
10. Observations. Change in phenology
Filgueira et al. In prep. Summarizing…
Ecosystem modeller:
- Expertise in aquaculture sites
- Farm and bay management using an ecosystem perspective
- Carrying capacity
- Ecological indicators
- Multiple trophic levels
- Spatial connectivity
- Feed MSP
Science-based
management Acknowledgements. Why am I a lucky guy?
Jon Grant
JG’s lab
Rune Rosland
Cedric Bacher
Luc Comeau
Thomas Landry
Thomas Guyondet
Peter Cranford
Henrice Jansen
Aad Smaal
Jens Petersen
Bernardino G. Castro
Robin Stuart
Øivind Strand
Jan Aure
Lars Asplin
Other government agencies
Aquaculture companies
Funding agencies
Thank you! Gracias! [email protected]