SlideShare a Scribd company logo
1 of 33
TROPOMOD modelling of six
SABBAC aquaculture zones
PHILMINAQ project
Chris Cromey
Patrick White
Cesar Villanoy, Evangeline Mandong
All of the PHILMINAQ team made
contributions to this modelling effort

© www.akvaplan.niva.no

1
Particles settling under a fish cage

result in a ‘footprint’ of deposition of solids on the sea bed
© www.akvaplan.niva.no

2
Currents move in different speeds and direction at
different depths. Faeces settle more slowly and so
are transported further away from cages
0

C u r re n t V e lo c ity
S o u rc e

Milkfish waste
faeces settle
very slowly

F in e

C o a rs e

M e d iu m

Waste feed particles settle quickly
© www.akvaplan.niva.no

3
Contour map of waste flux
Benthic
community

(grams waste feed and faeces m d )
-2

-2

-1

grams solids m bed d

-1

75

Severe impact
(no animals)
75

15

High impact
(some effect)
15

1

Moderate impact
1

© www.akvaplan.niva.no

4
PHILMINAQ project modelling approach with TROPOMOD
3 important aspects:
1. How severe is the impact – what is the maximum impact underneath
cages?
2. How far to the boundary of the impact? (Scotland = Allowable Zone of
Effect)
3. How can husbandry practices be optimised to use the zone most
productively?

Objectives
Predict if impact is SEVERE underneath cages
as shown by this deposition footprint
Zone colour
Predict distance to boundary of MODERATE impact

Edge of zone

Zone colour

© www.akvaplan.niva.no

5
PHILMINAQ project modelling approach with TROPOMOD
Maintain enough spacing between cage rows so that remediation of
sediments can take place – impact should be LOW between rows in each
zone
Zone
colour

Maintain enough space between cage rows to prevent reduction of currents
by high aggregation of cages

Not predicted by TROPOMOD, but this effect is known to exist and has
been shown by MSI models
© www.akvaplan.niva.no

6
PHILMINAQ project modelling approach with TROPOMOD
Encourage careful feeding, so that there is less waste feed and
less wastage of money
Encourage better quality feed:
Feed digestibility is increased
Less feed is needed
Better quality feed also breaks up less, so more goes to
growth
Test these scenarios
Prevent overlap of zones by predicting the extent of the zones
– e.g. 600 m between zones for this site

S c a le ( m )

0

200

400

600

© www.akvaplan.niva.no

800

7
Method - common model input data between scenarios
Model input data

Value

Zone size

600 m by 200 m (12 ha)

Current (modelled by MSI)

Modelled

Feed settling rate for different
scenarios
FCR 2.8 – pellet break up (estimated)
FCR 2.0 – intact pellets (measured
by PHILMINAQ)
Faeces settling rate – measured by
PHILMINAQ for Milkfish

8.9 cm/s (5%), 4.5 cm/s (65%), 1.6 cm/s (30 %)
8.9 cm/s (100%)
0.84 (cm/s)

© www.akvaplan.niva.no

8
Scenarios tested
A range of feed inputs are used between 0 and maximum to
simulate fish at different stages of growing cycle in each zone
Therefore, these are not worse case scenarios
Method – what is varied between scenarios?
Model input data

Scenario 1
Poor feeding
Low digest.

Scenario 3
Careful feeding
Better digest.

Feed wasted

27%

10%

Feed digestibility

49%

56%

FCR

2.8:1

2.0:1

Feed input

Between 0 and 323
kg/cage/d

Between 0 and 231
kg/cage/d

Data source: EMMA and PHILMINAQ projects
© www.akvaplan.niva.no

9
Square cages – 12m * 12m * 8m
Zones 1 – 3, 5 and 6: feed ration used in the simulations – each cage
randomly allocated a feed ration to simulate fish at different stages of cycle
FCR = 2.8:1
Days

Size

Number

Biomass
(kg)

Feed rate
(%/day)

Feed/day
(kg)

April

0

0

0

0

0

0

May

25

20

27247

545

8.5

46

June

30

41

26873

1091

8.9

97

July

31

91

26498

2406

7.1

171

August

31

162

26124

4224

5.1

214

September

30

247

25749

6358

4.4

278

October

31

386

25375

9799

3.3

323

November

19

433

25000

10825

1.8

193

Data source: EMMA project
© www.akvaplan.niva.no

10
Large circular cages – 20m diameter * 8m means 2.18 times more
biomass can be contained in cages if stocking density is maintained
Zones 4: feed ration used in the simulations – each cage randomly allocated
a feed ration to simulate fish at different stages of cycle
FCR = 2.8:1

Days

Size

Number

Biomass
(kg)

Feed rate
(%/day)

Feed/day
(kg)

April

0

0

0

0

0

0

May

25

20

59444

1189

8.5

101

June

30

41

58628

2404

8.9

214

July

31

91

57810

5261

7.1

374

August

31

162

56994

9233

5.1

471

September

30

247

56176

13875

4.4

611

October

31

386

55360

21369

3.3

705

November

19

433

54542

23616

1.8

425

Data source: EMMA project data on previous slide scaled by 2.18
© www.akvaplan.niva.no

11
Aquazones identified by the hydrodynamic model

Current used in
TROPOMOD were
taken from the MSI
hydrodynamic model
of the area (left) – 30
days of current were
used

Zones 1 to 6 are
shown in the map

© www.akvaplan.niva.no

12
Current velocity distribution

North

e.g. At zone 1, current is flowing east and west in the
Bolinao Narrows
Zone 1

Zone 3

Zone 2

35
25

25

25

15

15

15

5
-35 -25 -15

35

35

5

5

-5
-5

5

15

25

35

-35

-25 -15

-5
-5

5

15

25

-35

35

-25

-15

-5
-5

-15

-15
-25
-35

15

25

35

Zone 6

Zone 5

25

25

15

15

5

35

35

5

-5
-5

5

15

25

35

-35 -25

-15

-5
-5

-15

-15

-25

-25

-35

-35

25
15

5

15

25

35

v (cm/s)

35

-25 -15

35

-35

Zone 4

-35

25

-25

-35

15

-15

-25

5

5
-35

-25

-15

-5
-5

5

-15
-25
-35
u (cm/s)

For each site, lines of cages were orientated parallel with the main axis
of current e.g. cages in © www.akvaplan.niva.no
zone 4 were aligned NE-SW
13
Modelled current at the zones from MSI Bolinao model
showing a rank of the most dispersive and deepest

Zone

Depth (m)

Depth rank

Mean
speed
(cm/s)

Max
speed
(cm/s)

Current
rank

1

20

1

10.8

28.6

3

2

14

2

11.5

30.6

2

3

13

3

8.9

23.7

4

4

10

4

7.9

24.8

5*

5

10

5

6.0

18.8

6

6

9

6

13.3

39.9

1

This table shows tidal currents – site 4 is exposed to waves from the east,
which will increase dispersion

© www.akvaplan.niva.no

14
Zone colouring
Definitions
Impact areas:
Low/None
Moderate
High
Severe

Zone colour

Predicted flux
(g m-2 d-1)
<1
1 – 15
15 – 75
75 +

% of zone area HIGH
and SEVERE impact

>15

Is more than 1 % of
zone SEVERE
impact? Yes or No?

> 75

Distance to boundary
of zone of effect - 1 g
m-2 d-1 contour

1

Measured sediment flux at Bolinao during workshops by
PHILMINAQ project - 114.0 g m-2 d-1 (0 m) and 148.7 (25 m) - Both
stations devoid of fauna
© www.akvaplan.niva.no

15
Aquaculture Zone 1 – Bolinao Narrows
M o d e l w a s t e f lu x - g r a m s m

-2

-1

d

Impact
FCR 2.8:1
Severe

75
High
Less feed
wasted,
higher
digestibility
FCR 2.0:1

N

15
Moderate

S c a le ( m )

1
0

200

© www.akvaplan.niva.no

400

600

800

16
Aquaculture Zone 2 – ??
M o d e l w a s te flu x - g r a m s m

-2

d

-1

N

FCR 2.8:1

Impact
Severe
75

High
15

Less feed
wasted,
higher
digestibility
FCR 2.0:1

S c a le ( m )

0

200

© www.akvaplan.niva.no

400

Moderate
1
600

800

17
Aquaculture Zone 3 – ??

M o d e l w a s te flu x - g r a m s m

-2

d

-1

Impact
N

FCR 2.8:1

Severe
75

High

Less feed
wasted,
higher
digestibility
FCR 2.0:1
S c a le ( m )

0

200

15

400

600

Moderate

800

1

© www.akvaplan.niva.no

18
Aquaculture Zone 4 – 2 rows of 12 large cages
M o d e l w a s te flu x - g r a m s m
N

Less feed
wasted,
higher
digestibility
FCR 2.0:1

FCR 2.8:1

-2

d

-1

Impact
Severe

75
High

15
Moderate

S c a le ( m )

0

200

400

600

800

1
Zone 4 is exposed to waves from the east, so the model will overpredict
impact
© www.akvaplan.niva.no

19
Aquaculture Zone 5 – ??

M o d e l w a s te flu x - g r a m s m

-2

d

-1

Impact

Less feed
wasted,
higher
digestibility
FCR 2.0:1

FCR 2.8:1

Severe

75
High

N

15

S c a le ( m )

0

200

© www.akvaplan.niva.no

400

600

800

Moderate

1

20
Aquaculture Zone 6 – Pens

M o d e l w a s te flu x - g r a m s m

-2

d

-1

Impact

Less feed
wasted,
higher
digestibility
FCR 2.0:1

FCR 2.8:1

Severe
75

High
N

S c a le ( m )

0

200

© www.akvaplan.niva.no

400

600

15

800

1

Moderate

21
Prohibited Aquaculture zones
1 6 .4
1 6 .3 9
1 6 .3 8
1 6 .3 7

Hr 61

G u ig iw a n e n

29
27

B O L IN A O

25
23
21

S ia p a r

19

1 6 .3 5
D e p th (m )

L a titu d e (N )

1 6 .3 6

1 6 .3 4
1 6 .3 3

17
15
13
11
9

ANDA

1 6 .3 2

7
5

1 6 .3 1

3
1 6 .3
1 6 .2 9
1 6 .2 8
1 1 9 .8 8

1
C a q u ip u ta n
S tr a it

1 1 9 .9

R e fe r e n c e V e c to r s (m /s )
0
0
0
0

1 1 9 .9 2

1 1 9 .9 4
L o n g itu d e (E )

© www.akvaplan.niva.no

1 1 9 .9 6

1 1 9 .9 8

.1
.3
.6
.9 3

120
22
Avoiding impact overlap between aqua zones

© www.akvaplan.niva.no

23
Recommended cage culture zones
1 6 .4
1 6 .3 9
1

Hr 61

29
27

B O L IN A O

3

1 6 .3 7

G u ig iw a n e n

2

1 6 .3 8

25
23

1 6 .3 6

21

S ia p a r

D e p th (m )

1 6 .3 4
7

L a titu d e (N )

4

19

6

1 6 .3 5

1 6 .3 3

17
15
13
11
9

ANDA

1 6 .3 2

7
5

1 6 .3 1

3
1 6 .3
1 6 .2 9
1 6 .2 8
1 1 9 .8 8

1
C a q u ip u ta n
S tr a it

1 1 9 .9

R e fe re n c e V e c to r s (m /s )
0
0
0
0

1 1 9 .9 2

1 1 9 .9 4

1 1 9 .9 6

1 1 9 .9 8

.1
.3
.6
.9 3

120

L o n g itu d e (E )
© www.akvaplan.niva.no

24
Recommended cage culture zones
1 6 .4
1 6 .3 9
1

1 6 .3 7

G u ig iw a n e n

Hr 61

2

1 6 .3 8

29
27

3

B O L IN A O

25
23

1 6 .3 6

21

S ia p a r

D e p th (m )

1 6 .3 4

6

7

L a titu d e (N )

4

19

1 6 .3 5

1 6 .3 3

17
15
13
11
9

ANDA

1 6 .3 2

7
5

1 6 .3 1

3
1 6 .3
1 6 .2 9
1 6 .2 8
1 1 9 .8 8

1
C a q u ip u ta n
S tr a it

1 1 9 .9

R e fe r e n c e V e c to r s (m /s )
0
0
0
0

1 1 9 .9 2

1 1 9 .9 4

© www.akvaplan.niva.no

1 1 9 .9 6

1 1 9 .9 8

.1
.3
.6
.9 3

120

25
Zones – what area is impacted
Zone

Scenario

Area impacted - % of
zone HIGH/SEVERE

SEVERE
(% of zone)

Rank

1

FCR 2.8

54

6.1

1

2

FCR 2.8

53

6.5

2

3

FCR 2.8

50

9.3

4

4

FCR 2.8

53

20.3

6(Exposed)

5

FCR 2.8

45

13.5

5

6

FCR 2.8

52

7.2

3

1

FCR 2.0

36

0.0

2

FCR 2.0

35

0.0

3

FCR 2.0

36

0.0

4

FCR 2.0

44

5.9

5

FCR 2.0

35

0.0

6

FCR 2.0

35

0.0

Exposed

Zone 4 is exposed and therefore model probably over estimates impact
because the model does not take account of waves
© www.akvaplan.niva.no

26
Zones – how severe is the impact
Zone

Scenario

Average flux in
HIGH/SEVERE zone
(g/m2/d)

1.0

FCR 2.8

43

1

2.0

FCR 2.8

44

2

3.0

FCR 2.8

50

4

4.0

FCR 2.8

73

6 (exposed)

5.0

FCR 2.8

58

5

6.0

FCR 2.8

46

3

1.0

FCR 2.0

24

2.0

FCR 2.0

26

3.0

FCR 2.0

29

4.0

FCR 2.0

45

5.0

FCR 2.0

33

6.0

FCR 2.0

30

Rank

Zone 4 is exposed and therefore model probably over estimates impact
because the model does not takewww.akvaplan.niva.no
account of waves
©

27
Zone

Cages

Spacing between cages

Zone biomass
modelled
Average situation
with all different
fish sizes in zone
(EMMA data)

Zone biomass if all
fish 386 grams in all cages
Maximum biomass in zone

1, 2,
3, 5,
6

2 rows of 18

20 m between cages
120 m between rows

137 tonnes

353 tonnes

A

4

2 rows of
12

30 m between cages
120 m between rows

277 tonnes

514 tonnes

B

Zone 4 cages are large circular cages (20 m diameter* 8m deep)
Zones 1,2,3,5,6, are square cages (12m* 12m*8m deep)
386 gram fish require highest feed, 9.8 tonnes per cage *36 = 353 tonnes in
zone (square cages)
A

386 gram fish require highest feed, 21.4 tonnes per cage *24 = 514 tonnes in
zone (large circular cages)
B

© www.akvaplan.niva.no

28
Large, circular cages versus small, square cages

Scenario – for zone 5 which has the lowest currents, can the zone support a
higher biomass if large circular cages are used instead of small square
cages?

Scenario

Configuration

Area impacted - %
of zone
HIGH/SEVERE

FCR 2.8

2 * 18 square cages

45

13.5

353 tonnes

FCR 2.0

2 * 18 square cages
(353 tonnes)

35

0.0

353 tonnes

SEVERE
(% of zone)

Zone peak
biomass

FCR 2.8
FCR 2.0

YES - ? tonnes can be at peak biomass as opposed to 353 tonnes
with small cages ( a x % increase)

© www.akvaplan.niva.no

29
Zones ranked in terms of dispersiveness
Overall rank

Zone

Rank magnitude of
SEVERE
impact

Rank extent of
SEVERE
impact

Rank - depth

Rank current

1

1

1

1

1

3

2

2

2

2

2

2

3

6

3

3

6

1

4

3

4

4

3

4

5

4

6

6

4

5

6

5

5

5

5

6

However, zone 4 is exposed to waves which are not modelled so zone 4 is
likely to be more dispersive in reality
© www.akvaplan.niva.no

30
Summary

TROPOMOD model predicted impact with 2 scenarios, FCR of 2.8:1 and 2.0:1.

Results are for different sized fish throughout the zone, which is realistic, not worse
case.

Improvement of FCR of 2.8 to 2.0:1 resulted in:
•

reduced feed needed by 29 %

•

there was no/little SEVERE impact under cages

•

HIGH impact areas were less than 50 % of the total zone area

© www.akvaplan.niva.no

31
Recommendations
Zone

Cage
configuration

Peak biomass
(tonnes)

Spacing

1, 2, 3, 5, 6

2 * 18 square
cages

353

20 m between
cages, 120 m
between rows

4

2 * 12 large
circular cages

514

30 m between
cages, 120 m
between rows

As the deposition footprints extend between 200 and 400 m from the edge
of the zone, it is recommended the distance between zones should be a
minimum of 600 m.

© www.akvaplan.niva.no

32
TROPOMOD has been developed using a similar concept to the following
models:
MERAMOD – Sea bass/bream in the Mediterranean – scientifically tested at
several sites with sediment traps and benthic fauna sampling
DEPOMOD – Salmon/cod for North Atlantic – used in the Scottish regulatory
system to consent farms in Scotland
DEPOMOD and MERAMOD have 100+ users worldwide
Tests with TROPOMOD at Bolinao were satisfactory – the model predicted on
average 104 g m-2 d-1, which corresponded to an average value measured in traps
of 131 g m-2 d-1 in May 2007.
ECASA toolbox - a tool box of environmental impact and socioeconomic
indicators, including descriptions of models and where to find them
www.ecasatoolbox.org.uk

© www.akvaplan.niva.no

33

More Related Content

Similar to Modelling of aquaculture impact and carrying capacity in the Philippines using Tropomod

System design, sustainable production and water quality research for Recircul...
System design, sustainable production and water quality research for Recircul...System design, sustainable production and water quality research for Recircul...
System design, sustainable production and water quality research for Recircul...IRJET Journal
 
Effects of limiting water on growth, development and yield of alfalfa grown i...
Effects of limiting water on growth, development and yield of alfalfa grown i...Effects of limiting water on growth, development and yield of alfalfa grown i...
Effects of limiting water on growth, development and yield of alfalfa grown i...Caleb M Carter
 
Science Forum Day 2 - Diaa Al Kenawy - Integrated agric-aquaculture, science...
Science Forum  Day 2 - Diaa Al Kenawy - Integrated agric-aquaculture, science...Science Forum  Day 2 - Diaa Al Kenawy - Integrated agric-aquaculture, science...
Science Forum Day 2 - Diaa Al Kenawy - Integrated agric-aquaculture, science...WorldFish
 
Water management in sri
Water management in sriWater management in sri
Water management in sriAshutosh Pal
 
Poster AIL - Ultrasound as control strategy for bryozoans
Poster AIL - Ultrasound as control strategy for bryozoansPoster AIL - Ultrasound as control strategy for bryozoans
Poster AIL - Ultrasound as control strategy for bryozoansToscano Línea Electrónica
 
Sarah stephenson csiro
Sarah stephenson csiroSarah stephenson csiro
Sarah stephenson csiroSas_Benthos1
 
GRM 2013: Drought phenotyping and modeling across crops -- V Vadez
GRM 2013: Drought phenotyping  and modeling  across crops -- V VadezGRM 2013: Drought phenotyping  and modeling  across crops -- V Vadez
GRM 2013: Drought phenotyping and modeling across crops -- V VadezCGIAR Generation Challenge Programme
 
INTEGRATED SEAWEED/ABALONE MULTITROPHIC RECIRCULATING AQUACULTURE (IMTA) IN S...
INTEGRATED SEAWEED/ABALONE MULTITROPHIC RECIRCULATING AQUACULTURE (IMTA) IN S...INTEGRATED SEAWEED/ABALONE MULTITROPHIC RECIRCULATING AQUACULTURE (IMTA) IN S...
INTEGRATED SEAWEED/ABALONE MULTITROPHIC RECIRCULATING AQUACULTURE (IMTA) IN S...Deborah Robertson-Andersson
 
Shawn Rummel, Trout Unlimited, "Recovery of Coldwater Ecosystems Following Tr...
Shawn Rummel, Trout Unlimited, "Recovery of Coldwater Ecosystems Following Tr...Shawn Rummel, Trout Unlimited, "Recovery of Coldwater Ecosystems Following Tr...
Shawn Rummel, Trout Unlimited, "Recovery of Coldwater Ecosystems Following Tr...Michael Hewitt, GISP
 
Effectiveness Analysis of Best Management Practices by SWAT for Appropriate C...
Effectiveness Analysis of Best Management Practices by SWAT for Appropriate C...Effectiveness Analysis of Best Management Practices by SWAT for Appropriate C...
Effectiveness Analysis of Best Management Practices by SWAT for Appropriate C...ExternalEvents
 
Sydney Harbour: Innovative Environmental Data Science in Australia's most ico...
Sydney Harbour: Innovative Environmental Data Science in Australia's most ico...Sydney Harbour: Innovative Environmental Data Science in Australia's most ico...
Sydney Harbour: Innovative Environmental Data Science in Australia's most ico...Luke Hedge
 
ICES VME & fisheries foot print
ICES VME & fisheries foot printICES VME & fisheries foot print
ICES VME & fisheries foot printMark Dickey-Collas
 
Dr Peter Miller, Plymouth Marine Laboratory - ShellEye: Satellite-based monit...
Dr Peter Miller, Plymouth Marine Laboratory - ShellEye: Satellite-based monit...Dr Peter Miller, Plymouth Marine Laboratory - ShellEye: Satellite-based monit...
Dr Peter Miller, Plymouth Marine Laboratory - ShellEye: Satellite-based monit...IES / IAQM
 

Similar to Modelling of aquaculture impact and carrying capacity in the Philippines using Tropomod (20)

Bringing Back Seasonality into Coastal Aquatic Agricultural Systems
Bringing Back Seasonality into Coastal Aquatic Agricultural SystemsBringing Back Seasonality into Coastal Aquatic Agricultural Systems
Bringing Back Seasonality into Coastal Aquatic Agricultural Systems
 
Water Productivity Mapping (WPM) at various Resolutions (scales) using Remote...
Water Productivity Mapping (WPM) at various Resolutions (scales) using Remote...Water Productivity Mapping (WPM) at various Resolutions (scales) using Remote...
Water Productivity Mapping (WPM) at various Resolutions (scales) using Remote...
 
System design, sustainable production and water quality research for Recircul...
System design, sustainable production and water quality research for Recircul...System design, sustainable production and water quality research for Recircul...
System design, sustainable production and water quality research for Recircul...
 
Effects of limiting water on growth, development and yield of alfalfa grown i...
Effects of limiting water on growth, development and yield of alfalfa grown i...Effects of limiting water on growth, development and yield of alfalfa grown i...
Effects of limiting water on growth, development and yield of alfalfa grown i...
 
Science Forum Day 2 - Diaa Al Kenawy - Integrated agric-aquaculture, science...
Science Forum  Day 2 - Diaa Al Kenawy - Integrated agric-aquaculture, science...Science Forum  Day 2 - Diaa Al Kenawy - Integrated agric-aquaculture, science...
Science Forum Day 2 - Diaa Al Kenawy - Integrated agric-aquaculture, science...
 
Water management in sri
Water management in sriWater management in sri
Water management in sri
 
Developing a Place-Based Tool
Developing a Place-Based ToolDeveloping a Place-Based Tool
Developing a Place-Based Tool
 
Poster AIL - Ultrasound as control strategy for bryozoans
Poster AIL - Ultrasound as control strategy for bryozoansPoster AIL - Ultrasound as control strategy for bryozoans
Poster AIL - Ultrasound as control strategy for bryozoans
 
Sarah stephenson csiro
Sarah stephenson csiroSarah stephenson csiro
Sarah stephenson csiro
 
GRM 2013: Drought phenotyping and modeling across crops -- V Vadez
GRM 2013: Drought phenotyping  and modeling  across crops -- V VadezGRM 2013: Drought phenotyping  and modeling  across crops -- V Vadez
GRM 2013: Drought phenotyping and modeling across crops -- V Vadez
 
GROUNDWATER FLOW SIMULATION IN GUIMARAS ISLAND, PHILIPPINE
GROUNDWATER FLOW SIMULATION IN GUIMARAS ISLAND, PHILIPPINEGROUNDWATER FLOW SIMULATION IN GUIMARAS ISLAND, PHILIPPINE
GROUNDWATER FLOW SIMULATION IN GUIMARAS ISLAND, PHILIPPINE
 
INTEGRATED SEAWEED/ABALONE MULTITROPHIC RECIRCULATING AQUACULTURE (IMTA) IN S...
INTEGRATED SEAWEED/ABALONE MULTITROPHIC RECIRCULATING AQUACULTURE (IMTA) IN S...INTEGRATED SEAWEED/ABALONE MULTITROPHIC RECIRCULATING AQUACULTURE (IMTA) IN S...
INTEGRATED SEAWEED/ABALONE MULTITROPHIC RECIRCULATING AQUACULTURE (IMTA) IN S...
 
Routine Quantification of Lipophilic Marine Biotoxins in Shellfish by LC/MS/M...
Routine Quantification of Lipophilic Marine Biotoxins in Shellfish by LC/MS/M...Routine Quantification of Lipophilic Marine Biotoxins in Shellfish by LC/MS/M...
Routine Quantification of Lipophilic Marine Biotoxins in Shellfish by LC/MS/M...
 
Hermesquispecuadros
HermesquispecuadrosHermesquispecuadros
Hermesquispecuadros
 
Shawn Rummel, Trout Unlimited, "Recovery of Coldwater Ecosystems Following Tr...
Shawn Rummel, Trout Unlimited, "Recovery of Coldwater Ecosystems Following Tr...Shawn Rummel, Trout Unlimited, "Recovery of Coldwater Ecosystems Following Tr...
Shawn Rummel, Trout Unlimited, "Recovery of Coldwater Ecosystems Following Tr...
 
Hossler coastal impoundments decision making
Hossler coastal impoundments decision makingHossler coastal impoundments decision making
Hossler coastal impoundments decision making
 
Effectiveness Analysis of Best Management Practices by SWAT for Appropriate C...
Effectiveness Analysis of Best Management Practices by SWAT for Appropriate C...Effectiveness Analysis of Best Management Practices by SWAT for Appropriate C...
Effectiveness Analysis of Best Management Practices by SWAT for Appropriate C...
 
Sydney Harbour: Innovative Environmental Data Science in Australia's most ico...
Sydney Harbour: Innovative Environmental Data Science in Australia's most ico...Sydney Harbour: Innovative Environmental Data Science in Australia's most ico...
Sydney Harbour: Innovative Environmental Data Science in Australia's most ico...
 
ICES VME & fisheries foot print
ICES VME & fisheries foot printICES VME & fisheries foot print
ICES VME & fisheries foot print
 
Dr Peter Miller, Plymouth Marine Laboratory - ShellEye: Satellite-based monit...
Dr Peter Miller, Plymouth Marine Laboratory - ShellEye: Satellite-based monit...Dr Peter Miller, Plymouth Marine Laboratory - ShellEye: Satellite-based monit...
Dr Peter Miller, Plymouth Marine Laboratory - ShellEye: Satellite-based monit...
 

More from Patrick White

Mankind's relationship with alcohol
Mankind's relationship with alcoholMankind's relationship with alcohol
Mankind's relationship with alcoholPatrick White
 
Evolution of the house and home
Evolution of the house and homeEvolution of the house and home
Evolution of the house and homePatrick White
 
Evolution of clothes
Evolution of clothesEvolution of clothes
Evolution of clothesPatrick White
 
Mankind's relationship with alcohol through the ages
Mankind's relationship with alcohol through the agesMankind's relationship with alcohol through the ages
Mankind's relationship with alcohol through the agesPatrick White
 
Mankinds journey from the dawn of civilisation
Mankinds journey from the dawn of civilisationMankinds journey from the dawn of civilisation
Mankinds journey from the dawn of civilisationPatrick White
 
Mankind’s relationship with money
Mankind’s relationship with moneyMankind’s relationship with money
Mankind’s relationship with moneyPatrick White
 
Climate change and AsianAquaculture
Climate change and AsianAquacultureClimate change and AsianAquaculture
Climate change and AsianAquaculturePatrick White
 
Development of language
Development of languageDevelopment of language
Development of languagePatrick White
 
Mankind’s relationship with food
Mankind’s relationship with foodMankind’s relationship with food
Mankind’s relationship with foodPatrick White
 
Meramed article 2002
Meramed article 2002Meramed article 2002
Meramed article 2002Patrick White
 
Fernandes_et_al._2001
Fernandes_et_al._2001Fernandes_et_al._2001
Fernandes_et_al._2001Patrick White
 
FAO EAAF Pages from FAO M+10 i2734e
FAO EAAF Pages from FAO M+10 i2734eFAO EAAF Pages from FAO M+10 i2734e
FAO EAAF Pages from FAO M+10 i2734ePatrick White
 
Akvaplan-niva International project examples
Akvaplan-niva International project examplesAkvaplan-niva International project examples
Akvaplan-niva International project examplesPatrick White
 
AQGR and Climate Change (Aquaculture and fisheries) reduced
AQGR and Climate Change (Aquaculture and fisheries) reducedAQGR and Climate Change (Aquaculture and fisheries) reduced
AQGR and Climate Change (Aquaculture and fisheries) reducedPatrick White
 
AQUATIC GENETIC RESOURCES AND CLIMATE CHANGE_Clean
AQUATIC GENETIC RESOURCES AND CLIMATE CHANGE_CleanAQUATIC GENETIC RESOURCES AND CLIMATE CHANGE_Clean
AQUATIC GENETIC RESOURCES AND CLIMATE CHANGE_CleanPatrick White
 
03. Carrying Capacity estimation White 1
03. Carrying Capacity estimation White 103. Carrying Capacity estimation White 1
03. Carrying Capacity estimation White 1Patrick White
 
Better Management Practices for good aquaculture plannimng and management by ...
Better Management Practices for good aquaculture plannimng and management by ...Better Management Practices for good aquaculture plannimng and management by ...
Better Management Practices for good aquaculture plannimng and management by ...Patrick White
 
Better practice guidelines for fish farmers
Better practice guidelines for fish farmersBetter practice guidelines for fish farmers
Better practice guidelines for fish farmersPatrick White
 
Environmental impacts from Aquaculture in Late Taal, Philippines
Environmental impacts from Aquaculture in Late Taal, PhilippinesEnvironmental impacts from Aquaculture in Late Taal, Philippines
Environmental impacts from Aquaculture in Late Taal, PhilippinesPatrick White
 

More from Patrick White (20)

Mankind's relationship with alcohol
Mankind's relationship with alcoholMankind's relationship with alcohol
Mankind's relationship with alcohol
 
Evolution of the house and home
Evolution of the house and homeEvolution of the house and home
Evolution of the house and home
 
Evolution of clothes
Evolution of clothesEvolution of clothes
Evolution of clothes
 
Mankind's relationship with alcohol through the ages
Mankind's relationship with alcohol through the agesMankind's relationship with alcohol through the ages
Mankind's relationship with alcohol through the ages
 
Mankinds journey from the dawn of civilisation
Mankinds journey from the dawn of civilisationMankinds journey from the dawn of civilisation
Mankinds journey from the dawn of civilisation
 
Mankind’s relationship with money
Mankind’s relationship with moneyMankind’s relationship with money
Mankind’s relationship with money
 
Climate change and AsianAquaculture
Climate change and AsianAquacultureClimate change and AsianAquaculture
Climate change and AsianAquaculture
 
Development of language
Development of languageDevelopment of language
Development of language
 
Mankind’s relationship with food
Mankind’s relationship with foodMankind’s relationship with food
Mankind’s relationship with food
 
Meramed article 2002
Meramed article 2002Meramed article 2002
Meramed article 2002
 
Fernandes_et_al._2001
Fernandes_et_al._2001Fernandes_et_al._2001
Fernandes_et_al._2001
 
Emma description
Emma descriptionEmma description
Emma description
 
FAO EAAF Pages from FAO M+10 i2734e
FAO EAAF Pages from FAO M+10 i2734eFAO EAAF Pages from FAO M+10 i2734e
FAO EAAF Pages from FAO M+10 i2734e
 
Akvaplan-niva International project examples
Akvaplan-niva International project examplesAkvaplan-niva International project examples
Akvaplan-niva International project examples
 
AQGR and Climate Change (Aquaculture and fisheries) reduced
AQGR and Climate Change (Aquaculture and fisheries) reducedAQGR and Climate Change (Aquaculture and fisheries) reduced
AQGR and Climate Change (Aquaculture and fisheries) reduced
 
AQUATIC GENETIC RESOURCES AND CLIMATE CHANGE_Clean
AQUATIC GENETIC RESOURCES AND CLIMATE CHANGE_CleanAQUATIC GENETIC RESOURCES AND CLIMATE CHANGE_Clean
AQUATIC GENETIC RESOURCES AND CLIMATE CHANGE_Clean
 
03. Carrying Capacity estimation White 1
03. Carrying Capacity estimation White 103. Carrying Capacity estimation White 1
03. Carrying Capacity estimation White 1
 
Better Management Practices for good aquaculture plannimng and management by ...
Better Management Practices for good aquaculture plannimng and management by ...Better Management Practices for good aquaculture plannimng and management by ...
Better Management Practices for good aquaculture plannimng and management by ...
 
Better practice guidelines for fish farmers
Better practice guidelines for fish farmersBetter practice guidelines for fish farmers
Better practice guidelines for fish farmers
 
Environmental impacts from Aquaculture in Late Taal, Philippines
Environmental impacts from Aquaculture in Late Taal, PhilippinesEnvironmental impacts from Aquaculture in Late Taal, Philippines
Environmental impacts from Aquaculture in Late Taal, Philippines
 

Recently uploaded

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAndrey Devyatkin
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Orbitshub
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfOrbitshub
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businesspanagenda
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontologyjohnbeverley2021
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamUiPathCommunity
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...Zilliz
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...apidays
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...apidays
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Victor Rentea
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusZilliz
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Zilliz
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobeapidays
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingEdi Saputra
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoffsammart93
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityWSO2
 

Recently uploaded (20)

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
 

Modelling of aquaculture impact and carrying capacity in the Philippines using Tropomod

  • 1. TROPOMOD modelling of six SABBAC aquaculture zones PHILMINAQ project Chris Cromey Patrick White Cesar Villanoy, Evangeline Mandong All of the PHILMINAQ team made contributions to this modelling effort © www.akvaplan.niva.no 1
  • 2. Particles settling under a fish cage result in a ‘footprint’ of deposition of solids on the sea bed © www.akvaplan.niva.no 2
  • 3. Currents move in different speeds and direction at different depths. Faeces settle more slowly and so are transported further away from cages 0 C u r re n t V e lo c ity S o u rc e Milkfish waste faeces settle very slowly F in e C o a rs e M e d iu m Waste feed particles settle quickly © www.akvaplan.niva.no 3
  • 4. Contour map of waste flux Benthic community (grams waste feed and faeces m d ) -2 -2 -1 grams solids m bed d -1 75 Severe impact (no animals) 75 15 High impact (some effect) 15 1 Moderate impact 1 © www.akvaplan.niva.no 4
  • 5. PHILMINAQ project modelling approach with TROPOMOD 3 important aspects: 1. How severe is the impact – what is the maximum impact underneath cages? 2. How far to the boundary of the impact? (Scotland = Allowable Zone of Effect) 3. How can husbandry practices be optimised to use the zone most productively? Objectives Predict if impact is SEVERE underneath cages as shown by this deposition footprint Zone colour Predict distance to boundary of MODERATE impact Edge of zone Zone colour © www.akvaplan.niva.no 5
  • 6. PHILMINAQ project modelling approach with TROPOMOD Maintain enough spacing between cage rows so that remediation of sediments can take place – impact should be LOW between rows in each zone Zone colour Maintain enough space between cage rows to prevent reduction of currents by high aggregation of cages Not predicted by TROPOMOD, but this effect is known to exist and has been shown by MSI models © www.akvaplan.niva.no 6
  • 7. PHILMINAQ project modelling approach with TROPOMOD Encourage careful feeding, so that there is less waste feed and less wastage of money Encourage better quality feed: Feed digestibility is increased Less feed is needed Better quality feed also breaks up less, so more goes to growth Test these scenarios Prevent overlap of zones by predicting the extent of the zones – e.g. 600 m between zones for this site S c a le ( m ) 0 200 400 600 © www.akvaplan.niva.no 800 7
  • 8. Method - common model input data between scenarios Model input data Value Zone size 600 m by 200 m (12 ha) Current (modelled by MSI) Modelled Feed settling rate for different scenarios FCR 2.8 – pellet break up (estimated) FCR 2.0 – intact pellets (measured by PHILMINAQ) Faeces settling rate – measured by PHILMINAQ for Milkfish 8.9 cm/s (5%), 4.5 cm/s (65%), 1.6 cm/s (30 %) 8.9 cm/s (100%) 0.84 (cm/s) © www.akvaplan.niva.no 8
  • 9. Scenarios tested A range of feed inputs are used between 0 and maximum to simulate fish at different stages of growing cycle in each zone Therefore, these are not worse case scenarios Method – what is varied between scenarios? Model input data Scenario 1 Poor feeding Low digest. Scenario 3 Careful feeding Better digest. Feed wasted 27% 10% Feed digestibility 49% 56% FCR 2.8:1 2.0:1 Feed input Between 0 and 323 kg/cage/d Between 0 and 231 kg/cage/d Data source: EMMA and PHILMINAQ projects © www.akvaplan.niva.no 9
  • 10. Square cages – 12m * 12m * 8m Zones 1 – 3, 5 and 6: feed ration used in the simulations – each cage randomly allocated a feed ration to simulate fish at different stages of cycle FCR = 2.8:1 Days Size Number Biomass (kg) Feed rate (%/day) Feed/day (kg) April 0 0 0 0 0 0 May 25 20 27247 545 8.5 46 June 30 41 26873 1091 8.9 97 July 31 91 26498 2406 7.1 171 August 31 162 26124 4224 5.1 214 September 30 247 25749 6358 4.4 278 October 31 386 25375 9799 3.3 323 November 19 433 25000 10825 1.8 193 Data source: EMMA project © www.akvaplan.niva.no 10
  • 11. Large circular cages – 20m diameter * 8m means 2.18 times more biomass can be contained in cages if stocking density is maintained Zones 4: feed ration used in the simulations – each cage randomly allocated a feed ration to simulate fish at different stages of cycle FCR = 2.8:1 Days Size Number Biomass (kg) Feed rate (%/day) Feed/day (kg) April 0 0 0 0 0 0 May 25 20 59444 1189 8.5 101 June 30 41 58628 2404 8.9 214 July 31 91 57810 5261 7.1 374 August 31 162 56994 9233 5.1 471 September 30 247 56176 13875 4.4 611 October 31 386 55360 21369 3.3 705 November 19 433 54542 23616 1.8 425 Data source: EMMA project data on previous slide scaled by 2.18 © www.akvaplan.niva.no 11
  • 12. Aquazones identified by the hydrodynamic model Current used in TROPOMOD were taken from the MSI hydrodynamic model of the area (left) – 30 days of current were used Zones 1 to 6 are shown in the map © www.akvaplan.niva.no 12
  • 13. Current velocity distribution North e.g. At zone 1, current is flowing east and west in the Bolinao Narrows Zone 1 Zone 3 Zone 2 35 25 25 25 15 15 15 5 -35 -25 -15 35 35 5 5 -5 -5 5 15 25 35 -35 -25 -15 -5 -5 5 15 25 -35 35 -25 -15 -5 -5 -15 -15 -25 -35 15 25 35 Zone 6 Zone 5 25 25 15 15 5 35 35 5 -5 -5 5 15 25 35 -35 -25 -15 -5 -5 -15 -15 -25 -25 -35 -35 25 15 5 15 25 35 v (cm/s) 35 -25 -15 35 -35 Zone 4 -35 25 -25 -35 15 -15 -25 5 5 -35 -25 -15 -5 -5 5 -15 -25 -35 u (cm/s) For each site, lines of cages were orientated parallel with the main axis of current e.g. cages in © www.akvaplan.niva.no zone 4 were aligned NE-SW 13
  • 14. Modelled current at the zones from MSI Bolinao model showing a rank of the most dispersive and deepest Zone Depth (m) Depth rank Mean speed (cm/s) Max speed (cm/s) Current rank 1 20 1 10.8 28.6 3 2 14 2 11.5 30.6 2 3 13 3 8.9 23.7 4 4 10 4 7.9 24.8 5* 5 10 5 6.0 18.8 6 6 9 6 13.3 39.9 1 This table shows tidal currents – site 4 is exposed to waves from the east, which will increase dispersion © www.akvaplan.niva.no 14
  • 15. Zone colouring Definitions Impact areas: Low/None Moderate High Severe Zone colour Predicted flux (g m-2 d-1) <1 1 – 15 15 – 75 75 + % of zone area HIGH and SEVERE impact >15 Is more than 1 % of zone SEVERE impact? Yes or No? > 75 Distance to boundary of zone of effect - 1 g m-2 d-1 contour 1 Measured sediment flux at Bolinao during workshops by PHILMINAQ project - 114.0 g m-2 d-1 (0 m) and 148.7 (25 m) - Both stations devoid of fauna © www.akvaplan.niva.no 15
  • 16. Aquaculture Zone 1 – Bolinao Narrows M o d e l w a s t e f lu x - g r a m s m -2 -1 d Impact FCR 2.8:1 Severe 75 High Less feed wasted, higher digestibility FCR 2.0:1 N 15 Moderate S c a le ( m ) 1 0 200 © www.akvaplan.niva.no 400 600 800 16
  • 17. Aquaculture Zone 2 – ?? M o d e l w a s te flu x - g r a m s m -2 d -1 N FCR 2.8:1 Impact Severe 75 High 15 Less feed wasted, higher digestibility FCR 2.0:1 S c a le ( m ) 0 200 © www.akvaplan.niva.no 400 Moderate 1 600 800 17
  • 18. Aquaculture Zone 3 – ?? M o d e l w a s te flu x - g r a m s m -2 d -1 Impact N FCR 2.8:1 Severe 75 High Less feed wasted, higher digestibility FCR 2.0:1 S c a le ( m ) 0 200 15 400 600 Moderate 800 1 © www.akvaplan.niva.no 18
  • 19. Aquaculture Zone 4 – 2 rows of 12 large cages M o d e l w a s te flu x - g r a m s m N Less feed wasted, higher digestibility FCR 2.0:1 FCR 2.8:1 -2 d -1 Impact Severe 75 High 15 Moderate S c a le ( m ) 0 200 400 600 800 1 Zone 4 is exposed to waves from the east, so the model will overpredict impact © www.akvaplan.niva.no 19
  • 20. Aquaculture Zone 5 – ?? M o d e l w a s te flu x - g r a m s m -2 d -1 Impact Less feed wasted, higher digestibility FCR 2.0:1 FCR 2.8:1 Severe 75 High N 15 S c a le ( m ) 0 200 © www.akvaplan.niva.no 400 600 800 Moderate 1 20
  • 21. Aquaculture Zone 6 – Pens M o d e l w a s te flu x - g r a m s m -2 d -1 Impact Less feed wasted, higher digestibility FCR 2.0:1 FCR 2.8:1 Severe 75 High N S c a le ( m ) 0 200 © www.akvaplan.niva.no 400 600 15 800 1 Moderate 21
  • 22. Prohibited Aquaculture zones 1 6 .4 1 6 .3 9 1 6 .3 8 1 6 .3 7 Hr 61 G u ig iw a n e n 29 27 B O L IN A O 25 23 21 S ia p a r 19 1 6 .3 5 D e p th (m ) L a titu d e (N ) 1 6 .3 6 1 6 .3 4 1 6 .3 3 17 15 13 11 9 ANDA 1 6 .3 2 7 5 1 6 .3 1 3 1 6 .3 1 6 .2 9 1 6 .2 8 1 1 9 .8 8 1 C a q u ip u ta n S tr a it 1 1 9 .9 R e fe r e n c e V e c to r s (m /s ) 0 0 0 0 1 1 9 .9 2 1 1 9 .9 4 L o n g itu d e (E ) © www.akvaplan.niva.no 1 1 9 .9 6 1 1 9 .9 8 .1 .3 .6 .9 3 120 22
  • 23. Avoiding impact overlap between aqua zones © www.akvaplan.niva.no 23
  • 24. Recommended cage culture zones 1 6 .4 1 6 .3 9 1 Hr 61 29 27 B O L IN A O 3 1 6 .3 7 G u ig iw a n e n 2 1 6 .3 8 25 23 1 6 .3 6 21 S ia p a r D e p th (m ) 1 6 .3 4 7 L a titu d e (N ) 4 19 6 1 6 .3 5 1 6 .3 3 17 15 13 11 9 ANDA 1 6 .3 2 7 5 1 6 .3 1 3 1 6 .3 1 6 .2 9 1 6 .2 8 1 1 9 .8 8 1 C a q u ip u ta n S tr a it 1 1 9 .9 R e fe re n c e V e c to r s (m /s ) 0 0 0 0 1 1 9 .9 2 1 1 9 .9 4 1 1 9 .9 6 1 1 9 .9 8 .1 .3 .6 .9 3 120 L o n g itu d e (E ) © www.akvaplan.niva.no 24
  • 25. Recommended cage culture zones 1 6 .4 1 6 .3 9 1 1 6 .3 7 G u ig iw a n e n Hr 61 2 1 6 .3 8 29 27 3 B O L IN A O 25 23 1 6 .3 6 21 S ia p a r D e p th (m ) 1 6 .3 4 6 7 L a titu d e (N ) 4 19 1 6 .3 5 1 6 .3 3 17 15 13 11 9 ANDA 1 6 .3 2 7 5 1 6 .3 1 3 1 6 .3 1 6 .2 9 1 6 .2 8 1 1 9 .8 8 1 C a q u ip u ta n S tr a it 1 1 9 .9 R e fe r e n c e V e c to r s (m /s ) 0 0 0 0 1 1 9 .9 2 1 1 9 .9 4 © www.akvaplan.niva.no 1 1 9 .9 6 1 1 9 .9 8 .1 .3 .6 .9 3 120 25
  • 26. Zones – what area is impacted Zone Scenario Area impacted - % of zone HIGH/SEVERE SEVERE (% of zone) Rank 1 FCR 2.8 54 6.1 1 2 FCR 2.8 53 6.5 2 3 FCR 2.8 50 9.3 4 4 FCR 2.8 53 20.3 6(Exposed) 5 FCR 2.8 45 13.5 5 6 FCR 2.8 52 7.2 3 1 FCR 2.0 36 0.0 2 FCR 2.0 35 0.0 3 FCR 2.0 36 0.0 4 FCR 2.0 44 5.9 5 FCR 2.0 35 0.0 6 FCR 2.0 35 0.0 Exposed Zone 4 is exposed and therefore model probably over estimates impact because the model does not take account of waves © www.akvaplan.niva.no 26
  • 27. Zones – how severe is the impact Zone Scenario Average flux in HIGH/SEVERE zone (g/m2/d) 1.0 FCR 2.8 43 1 2.0 FCR 2.8 44 2 3.0 FCR 2.8 50 4 4.0 FCR 2.8 73 6 (exposed) 5.0 FCR 2.8 58 5 6.0 FCR 2.8 46 3 1.0 FCR 2.0 24 2.0 FCR 2.0 26 3.0 FCR 2.0 29 4.0 FCR 2.0 45 5.0 FCR 2.0 33 6.0 FCR 2.0 30 Rank Zone 4 is exposed and therefore model probably over estimates impact because the model does not takewww.akvaplan.niva.no account of waves © 27
  • 28. Zone Cages Spacing between cages Zone biomass modelled Average situation with all different fish sizes in zone (EMMA data) Zone biomass if all fish 386 grams in all cages Maximum biomass in zone 1, 2, 3, 5, 6 2 rows of 18 20 m between cages 120 m between rows 137 tonnes 353 tonnes A 4 2 rows of 12 30 m between cages 120 m between rows 277 tonnes 514 tonnes B Zone 4 cages are large circular cages (20 m diameter* 8m deep) Zones 1,2,3,5,6, are square cages (12m* 12m*8m deep) 386 gram fish require highest feed, 9.8 tonnes per cage *36 = 353 tonnes in zone (square cages) A 386 gram fish require highest feed, 21.4 tonnes per cage *24 = 514 tonnes in zone (large circular cages) B © www.akvaplan.niva.no 28
  • 29. Large, circular cages versus small, square cages Scenario – for zone 5 which has the lowest currents, can the zone support a higher biomass if large circular cages are used instead of small square cages? Scenario Configuration Area impacted - % of zone HIGH/SEVERE FCR 2.8 2 * 18 square cages 45 13.5 353 tonnes FCR 2.0 2 * 18 square cages (353 tonnes) 35 0.0 353 tonnes SEVERE (% of zone) Zone peak biomass FCR 2.8 FCR 2.0 YES - ? tonnes can be at peak biomass as opposed to 353 tonnes with small cages ( a x % increase) © www.akvaplan.niva.no 29
  • 30. Zones ranked in terms of dispersiveness Overall rank Zone Rank magnitude of SEVERE impact Rank extent of SEVERE impact Rank - depth Rank current 1 1 1 1 1 3 2 2 2 2 2 2 3 6 3 3 6 1 4 3 4 4 3 4 5 4 6 6 4 5 6 5 5 5 5 6 However, zone 4 is exposed to waves which are not modelled so zone 4 is likely to be more dispersive in reality © www.akvaplan.niva.no 30
  • 31. Summary TROPOMOD model predicted impact with 2 scenarios, FCR of 2.8:1 and 2.0:1. Results are for different sized fish throughout the zone, which is realistic, not worse case. Improvement of FCR of 2.8 to 2.0:1 resulted in: • reduced feed needed by 29 % • there was no/little SEVERE impact under cages • HIGH impact areas were less than 50 % of the total zone area © www.akvaplan.niva.no 31
  • 32. Recommendations Zone Cage configuration Peak biomass (tonnes) Spacing 1, 2, 3, 5, 6 2 * 18 square cages 353 20 m between cages, 120 m between rows 4 2 * 12 large circular cages 514 30 m between cages, 120 m between rows As the deposition footprints extend between 200 and 400 m from the edge of the zone, it is recommended the distance between zones should be a minimum of 600 m. © www.akvaplan.niva.no 32
  • 33. TROPOMOD has been developed using a similar concept to the following models: MERAMOD – Sea bass/bream in the Mediterranean – scientifically tested at several sites with sediment traps and benthic fauna sampling DEPOMOD – Salmon/cod for North Atlantic – used in the Scottish regulatory system to consent farms in Scotland DEPOMOD and MERAMOD have 100+ users worldwide Tests with TROPOMOD at Bolinao were satisfactory – the model predicted on average 104 g m-2 d-1, which corresponded to an average value measured in traps of 131 g m-2 d-1 in May 2007. ECASA toolbox - a tool box of environmental impact and socioeconomic indicators, including descriptions of models and where to find them www.ecasatoolbox.org.uk © www.akvaplan.niva.no 33