Annex 1 Overview of the spatial analyses and data sources · Overview of the spatial analyses and data sources ... such as roads or rivers) and point feature classes (containing such

85

Annex 1

Overview of the spatial analyses and data sources

This annex briefly describes the processing steps used to create the results of the spatial analyses presented in this technical paper.

1. Hardware and softwareThe GIS software used in this study was Manifold (CDA International Ltd.) and ArcGIS 9.3 (ESRI). Manifold, versions up to 8.0.27, was used because it is a very affordable (currently about one-fifth of the cost of the most widely used GIS software) but fully functional GIS. ArcGIS 9.3 was used to prepare the raw data and to perform more complex analysis.

The text below describes the conceptual steps necessary to replicate the analysis described in this technical paper. Readers should be aware that most of the ArcGIS analysis could not be done with the standard ArcGIS tools, and, therefore, required custom VBA (Visual Basic for Applications) functions (i.e. codes) were required to conduct the analysis. The VBA computer codes written for this technical paper are available upon request from the authors of this technical paper, but they will only be useful to readers using ArcGIS with VBA installed and licensed.

2. Spatial dataSpatial data used for this technical paper were: (i) exclusive economic zones (EEZ); (ii) bathymetry; (iii) current speeds; (iv) world ports; (v) sea surface temperature (SST); (vi) chlorophyll-a; (vii) marine protected areas; (viii) Global Administrative Area from the GADM database; and (ix) geographic zones (see Table A1.1 for details).

All data sets used in this study are presented in Section 4 of this Annex and are available for download in FAO’s GeoNetwork portal (www.fao.org/geonetwork). 3. Spatial analysisThis study identifies areas that satisfy criteria for offshore mariculture development. The criteria include the suitability of depth and current speed for sea cages and longlines, and the temperatures favouring grow-out of representative species: cobia (Rachycentron canadum), Atlantic salmon (Salmo salar) and blue mussel (Mytilus edulis), as well as chlorophyll-a concentration for the last species.

There were two important limitations to this study. First, only already-digitized or computer-ready data could be used for the analysis to save costs, and second, because offshore mariculture potential is being predicted for areas where it largely does not yet exist, verification was limited to using the location of a few offshore fish farms and relied mainly on comparisons of offshore potential with existing inshore mariculture. Another limitation was that the data had to be comparable for all maritime countries. In overview, this study consisted of three major analytical stages (Figure A1.1):

(i) data preparation;(ii) integration of data sets; and(iii) verification.

86 A global assessment of offshore mariculture potential from a spatial perspective

3.1 Data preparationVarious aspects of this analysis required analysing and processing data in both raster and vector formats. The general strategy with raster data was to do all analysis at the finest resolution of the data, and then to convert the final to vector format for further analysis. For example, if a particular analysis was required to identify regions that met thresholds using multiple rasters, then the new raster that was generated would have the same resolution as the finest resolution of the multiple input rasters. The new raster would then be converted to a polygon feature class7 for further analysis.

7 A polygon feature class is a geographic data set of polygonal vector objects (i.e. entities that cover an area, such as administrative units or analysis areas), plus associated attribute information for each polygon. Other examples of vector data sets include polyline feature classes (containing linear features such as roads or rivers) and point feature classes (containing such things as port locations).

FIGURE A1.1Major analytical stages

Note: Fail acknowledges that verification could be incomplete, or in some cases fail.Note: Areas with potential within EEZs, but presently outside of cost-effective areas for development were estimated by setting aside the cost-effective area for development (see Table 1, Criterion 4).

87

EEZ boundaries to define the spatial limits for near-future offshore developmentExclusive economic zone boundaries were taken from the Flanders Marine Institute (Vlaams Instituut voor de Zee, or VLIZ) data, Version 5.

Depth and current speed to define the spatial limits on offshore cages and longlinesRegions suitable for offshore cages and longlines were defined according to current speed and depth (Figures A1.2–A1.3) based on data from manufacturers and mariculture practice (Table A1.2 (depth) and A1.3a (current speed).

FIGURE A1.2Steps to define spatial limits on offshore cages and longlines

based on depth and current speed

Annex 1


Depth: bathymetric data were extracted from the 2008 version of General Bathymetric Chart of the Ocean (GEBCO), which is a raster data set with cell edge lengths of approximately 0.9 km. In all analyses using this bathymetric data, regions with depths in the desired ranges were converted to polygon feature classes for further analysis.

Horizontal cell size: the GEBCO bathymetric data had 43 200 columns covering 360 degrees of longitude (40 075 km equatorial circumference). This equals to 0.92766 km per cell width along the equator. This east-west distance decreases when moving towards the poles. The extreme north and south rows that actually had data were < 1 metre in width.

Vertical cell size: the GEBCO bathymetric data had 21 600 rows covering 180 degrees of latitude (20 004 km from the North Pole to South Pole), equal to 0.92611 km per cell. This north-south distance is constant for all cells.

Current speed: the current speed data (from HYCOM, representing current speed at 30 m depth) included separate monthly data sets over a 5-year period from 2004 to 2008. Therefore, data were pooled by month before calculating the confidence intervals. Note: the original HYCOM current speed units are in metres per second, so

FIGURE A1.3HYCOM current speed confidence intervals subprocess

89Annex 1

these values were converted to centimetres per second for the final threshold analysis. Summarized monthly mean, standard deviation and upper/lower 95 percent confidence limits for current speed were calculated as follows:

Horizontal cell size: HYCOM current speed data had 4 500 columns covering 360 degrees longitude (40 075 km equatorial circumference). This equals 8.90556 km per cell width along the equator. This east-west distance decreases when moving towards the poles. The extreme north and south rows that actually had data were < 2 km in width.

Vertical cell size: HYCOM current speed had 2 100 rows covering approximately 168 degrees latitude (~18,665 km from North Pole to –78°), equal to 8.88810 km per cell. This north-south distance is constant for all cells.

Distance from a port and reliable access to offshore spatially define the cost-effective area for offshore mariculture development

Cost-effective areas around ports: several steps were conducted to identify regions that were within 25 nm (46.3 km) of a port, intersected with depth range, current speed and VLIZ exclusive economic zone (Figure A1.4).


•Beginning with the 2009 World Port Index, 25 nm (46.3 km) buffer circles were first created around each port location using a custom VBA function. This function creates circles with 180 vertices distributed every 2° around the circle. Each vertex is created a specified distance and bearing from the port, and the new vertex locations are calculated accurately over the curved surface of the planet spheroid using spherical trigonometric functions so that the circles are undistorted by any projection issues.

•This study was only interested in the marine portion of the port buffers, so all land portions were clipped off based on Global Administrative Areas (GADM) polygons.

•This study was only interested in the portion of the port buffers that were within 25 nm travel distance from the ports, so the port buffers were further clipped to this region using a custom VBA function to create a cost-distance raster over each GADM-clipped port buffer polygon. This function calculates the cumulative distance from the port location, where travel is restricted to only the water. Note: this function is reasonably accurate but not perfect. Because of how cost-

FIGURE A1.4Steps to define cost-effective areas around ports

91Annex 1

distance functions work with raster surfaces, the final data set correctly identifies all locations within approximately 23.5 nm (43.5 km) of the port. It correctly identifies approximately half the locations between 23.5 and 25 nm of the port, and it incorrectly identifies approximately half the locations between 25 and 25.5 nm of the port. Therefore, there is some uncertainty about the area between 23.5 and 25.5 nm of the port. This problem is inherent in raster-based cost-distance algorithms and is unavoidable.

•The port buffer feature class was then intersected with GEBCO-derived Depth Range polygons (–1 m to –25 m), (–25 m to –100 m), (< –100 m).

•Finally, the port buffer feature class was intersected with VLIZ exclusive economic zone polygons.

•This final feature class reflects only maritime areas within 25 nm travel distance of ports, combined by country, and split by depth range and EEZ.

Eventually, the final feature class was intersected with areas favourable for the grow-out of the three species and integrated multitrophic aquaculture (IMTA).

Offshore mariculture potential of three representative species and IMTA of two of them spatially defined by environments favourable for grow-outChlorophyll-a, sea surface temperature and current speed: the raw data for chlorophyll-a (CHL2), sea surface temperature (SST) and current speed (CS) included mean values, number of observations and standard deviations per cell in raster format. Using a confidence level of = 0.05, these original rasters were used to generate 95 percent confidence intervals around the mean values. A location would be considered to fall within a threshold if the full confidence interval around the observed value at that location was completely within the upper and lower threshold values. For example, the temperature threshold for cobia was 22–32 °C. A location would only be considered to fall within this temperature range if both the lower confidence limit at that location was ≥ 22° and the upper confidence limit was ≤ 32°. Steps for identifying suitable regions for cobia, Atlantic salmon, blue mussel and IMTA are illustrated in Figures A1.5–A1.10.


FIGURE A1.5Steps to define regions favorable for cobia grow-out

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FIGURE A1.6Steps to define regions favorable for grow-out of Atlantic salmon


FIGURE A1.7Steps to define regions favorable for grow-out of blue mussel

95Annex 1

FIGURE A1.8Steps to define regions favorable for grow-outof Integrated Multi-Trophic Aquaculture (IMTA)


FIGURE A1.9Chlorophyll-a confidence intervals subprocess

FIGURE A1.10Sea surface temperature confidence intervals subprocess

97Annex 1

CHL2 data was often unavailable at extreme latitudes in the colder months of the year, which complicated the task of identifying areas that met CHL2 thresholds. Therefore, analyses of CHL2 were done both seasonally and by the full year. In the Northern Hemisphere, seasonal data sets were calculated that met threshold requirements for the combined months of March, April, May, June, July, August and September. In the Southern Hemisphere, data sets were calculated that met threshold requirements for the combined months of September, October, November, December, January, February, March and April. These monthly data sets are the monthly averages for the years 2003–2009 (i.e. the “March” data represents the average CHL2 of all the months of March from 2003 through 2009). The final analysis only used the seasonal CHL2 data sets.

The CHL2, CS, SST and bathymetry data were in raster format at different resolutions. The CS had cell edge lengths of approximately 8.9 km, SST cell sizes were ~4.9 km, and CHL2 cell sizes were ~4.6 km. When identifying regions that met various combinations of CHL2, CS and SST thresholds, the finest resolution data set was used to define the resolution of the final raster. For example, an analysis that incorporated both CS and SST rasters would produce a raster with a cell size equal to the SST data because SST had the finest resolution. Bathymetry was at the highest resolution (~0.9 km) and was always converted to a vector polygon feature class of polygons meeting various depth thresholds before additional analysis.

After deriving a final raster delineating all areas that met some combination of thresholds, this final raster was then converted to a polygon feature class for further analysis.

Sea surface temperature: the sea surface temperature data were available as monthly values and, therefore, did not require pooling any data across years. However, to convert them to degrees Celsius, they needed to be rescaled according to the following formula:

True Sea Surface (SST) = [Original SST from HDF files) * 0.075] - 3

Furthermore, original SST values of 1 indicated that they were on land, and values of 0 indicated missing data, so these regions were excluded from the analysis.

95 percent confidence intervals around the mean SST value were calculated according to the following definition:


Horizontal cell size: sea surface temperature data had 8 192 columns covering 360 degrees longitude (40 075 km equatorial circumference). This equals to 4.89197 km per cell width along the equator. This east-west distance decreases when moving towards the poles. The extreme north and south rows that actually had data were < 1 km in width.

Vertical cell size: sea surface temperature had 4 096 rows covering 180 degrees latitude (20 004 km from the North Pole to South Pole), equal to 4.88379 km per cell. This north-south distance is constant for all cells.

Chlorophyll-a: the Chlorophyll-a data were available as monthly values and, therefore, did not require pooling of any data. The 95 percent confidence intervals were calculated according to the following definition:

Horizontal cell size: chlorophyll-a data had 8 640 columns covering 360 degrees longitude (40 075 km equatorial circumference). This equals to 4.63831 km per cell width along the equator. This east-west distance decreases when moving towards the poles. The extreme north and south rows that actually had data were < 3 km in width.

Vertical cell size: chlorophyll-a had 4 320 rows covering 180 degrees latitude (20 004 km from the North Pole to the South Pole), equal to 4.63056 km per cell. This north-south distance is constant for all cells.

Marine protected areas: a data set of marine protected areas was derived from the World Dataset of Protected Areas. This data set was clipped so that it only represents marine areas, and was intersected with geographic zones.

Shorelines: the coastline length data were obtained from the Global Administrative Areas database of administrative boundaries (GADM, Version 1.0), available at www.gadm.org. The polyline data set of marine shorelines was derived by: (i) creating an “ocean” polygon data set by clipping out the GADM polygons from a general background polygon covering the entire earth; (ii) deleting all small polygons from the “ocean” data set that represented lakes or internal holes in the GADM data set; and then (iii) creating a coastline polyline data set by intersecting the GADM polygons with the ocean polygons. This last polyline data set is the linear intersection of all coastal countries with the oceans and, therefore, represents the coastline of all countries that face the ocean. This shorelines layer was essential to determine exactly how much shoreline each country has, and was intersected with other layers (such as the geographic zones data set) to determine how much shoreline lies within specific regions. The steps to generate shorelines are illustrated in Figure A1.11.

99Annex 1

3.2 Integration of data setsThe compiled spatial data were used in two ways: (i) to identify all of the areas meeting the thresholds associated with each criterion; and (ii) to estimate temperatures and chlorophyll-a concentrations at specific mariculture locations. This approach also allowed these suitability thresholds to be compared with temperatures and chlorophyll-a concentrations actually experienced in inshore mariculture practice and to measure temperature and chlorophyll-a offshore of inshore mariculture locations.

FIGURE A1.11Steps to define coastlines


Raster data sets were manipulated in ArcGIS 9.3 as described above, vectorized and imported to Manifold as shapefiles. In Manifold, the shapefiles became drawing (map) components in map projects. Each map project represented a separate analytical step (e.g. identifying the areas with depths suitable for cages and longlines). The output from each project consisted of a drawing and an associated table. Component outputs from individual projects were then sequentially integrated in subsequent projects (e.g. spatial integration of depths and current speeds) to obtain the results set out in Chapter 4. Topology overlay was the basic GIS tool used to spatially integrate the spatial data sets. Selection by query using spatial Structured Query Language was employed to organize the results into meaningful classes. Tables were exported to Microsoft Excel 2010, where the data were manipulated in pivot tables to provide the estimates of potential by EEZ and nation as surface area, which were then reported as tables and charts. Manifold also was used to arrange individual drawings into layers in maps and to add legends and labels to them, which were then exported as images that, in turn, became the map figures in this document.

The analysis conducted for this technical paper was primarily interested in cumulative areas that met various criteria (in which case slivers contributed very little to the cumulative total) and, therefore, the results were not significantly influenced by the potential effects of sliver8 polygons. The data were also not the types that typically cause large numbers of slivers.

3.3 Comparisons of offshore potential with inshore mariculture locations and verification

Comparisons of predicted offshore potential with inshore mariculture practice at national and subnational levels and verifications at offshore mariculture sites

Three kinds of comparisons were made: (i) national-level comparisons of mariculture production based on FAO statistics (FAO Statistics and Information Branch of the Fisheries and Aquaculture Department, 2012) of the three species with national-level offshore mariculture potential of the species; (ii) known inshore mariculture locations of cobia, Atlantic salmon and blue mussel, obtained through a literature review, contacts with government entities in British Columbia, Canada, Ireland, the Kingdom of Norway and the People’s Democratic Republic of China, and with commercial farmers in eastern Canada and several other countries, were compared with areas identified by the analyses as possessing offshore mariculture potential; and (iii) offshore mariculture potential was examined at several offshore cobia farm locations using locational information communicated by commercial farmers. For the first two kinds of comparisons, good correspondence between established inshore mariculture practice and offshore potential indicates that offshore mariculture could be more easily developed using the existing inshore experience, goods and services, and access to markets. Good correspondence also suggests that favourable conditions for grow-out (water temperature and also food availability for the blue mussel) are likely to be found offshore from existing inshore mariculture installations. The third kind of comparison actually tests predicted potential against the locations of functioning offshore farms. Results from this analysis are described in detail in Chapter 5 of the technical paper.

8 A sliver polygon is a small remnant polygon resulting from an intersection operation between two or more polygons.

101Annex 1

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t.

Dat

a ar

e p

rovi

ded

in s

hap

efile

fo

rmat

, an

d G

IS

cap

able

so

ftw

are

is r

equ

ired

to

vie

w it

. Dat

a ar

e fr

eely

do

wn

load

able

aft

er r

egis

trat

ion

.

ww

w.w

dp

a.o

rg/

Geo

gra

ph

ic z

on

esN

.A.

2010

Arc

tic,

No

rth

ern

Tem

per

ate,

Inte

rtro

pic

al,

Sou

ther

n T

emp

erat

e an

d A

nta

rcti

c Zo

nes

.Th

ese

po

lyg

on

s w

ere

gen

erat

ed w

ith

in t

his

stu

dy

bas

ed o

n t

he

Tro

pic

s o

f C

ance

r an

d C

apri

corn

, an

d

on

th

e A

rcti

c an

d A

nta

rcti

c C

ircl

es

GA

DM

dat

abas

e o

f G

lob

al A

dm

inis

trat

ive

Are

as,

Ver

sio

n 1

N.A

.20

10G

AD

M is

a f

reel

y d

ow

nlo

adab

le s

pat

ial d

atab

ase

of

the

loca

tio

n o

f th

e w

orl

d’s

ad

min

istr

ativ

e b

ou

nd

arie

s. T

he

dat

a ar

e av

aila

ble

in s

hap

efile

, ES

RI g

eod

atab

ase,

RD

ata

and

Go

og

le E

arth

km

z fo

rmat

.

htt

p://

gad

m.o

rg/h

om

e

Co

astl

ine

len

gth

(km

)N

.A.

2010

Dat

a b

y co

un

try

and

ter

rito

ry, d

eriv

ed f

rom

GA

DM

(G

lob

al A

dm

inis

trat

ive

Are

as)

v. 1

.C

alcu

lati

on

s m

ade

wit

hin

th

is s

tud

y. S

ee F

igu

re

A1.

11 a

bo

ve t

hat

des

crib

es t

he

app

roac

h in

det

ail

term

ino

log

y: N

.A. =

No

t ap

plic

able

as

they

are

in v

ecto

r fo

rmat

.


TAB

LE A

1.2

Dep

th c

har

acte

rist

ics

of

exp

erim

enta

l an

d c

om

mer

cial

sea

cag

e an

d lo

ng

line

inst

alla

tio

ns

and

sp

ecif

icat

ion

s fr

om

man

ufa

ctu

rers

Enti

tyLo

cati

on

Syst

em t

ype

Dep

th a

t si

te (

m),

o

r m

anu

fact

ure

r’s

spec

ific

atio

n

Sou

rces

Sea

cag

es f

or

fish

Snap

per

farm

, In

c.Pu

erto

Ric

o,

Un

ited

Sta

tes

of

Am

eric

a

SeaS

tati

on

™

off

sho

re

sub

mer

sib

le

30O

’Han

lon

et

al. (

2001

)

Cat

es In

tern

atio

nal

Haw

aii,

Un

ited

Sta

tes

of

Am

eric

a

SeaS

tati

on

™

off

sho

re

sub

mer

sib

le 3

000

31B

ybee

an

d B

aile

y-B

rock

, (20

03),

p. 1

21

Ko

na

Blu

e W

ater

Far

ms

Haw

aii,

Un

ited

Sta

tes

of

Am

eric

a

SeaS

tati

on

™

off

sho

re

sub

mer

sib

le 3

000

61–6

7K

on

a B

lue

Wat

er F

arm

s (2

003)

Un

iver

sity

of

New

Ham

psh

ire

New

Ham

psh

ire,

U

nit

ed S

tate

s o

f A

mer

ica

SeaS

tati

on

™

fish

cag

e (S

S600

)

52La

ng

an a

nd

Ho

rto

n (

2005

)

Gu

lf o

f M

exic

o O

ffsh

ore

Aq

uac

ult

ure

C

on

sto

rtiu

mM

issi

ssip

pi,

Un

ited

Sta

tes

of

Am

eric

a

SeaS

tati

on

™

fish

cag

e 60

0 m

3

25w

ww

.mas

gc.

org

/oac

/Ph

ase%

201%

20R

P1.p

df

Oce

an S

par

LLC

(m

anu

fact

ure

r)W

ash

ing

ton

, U

nit

ed S

tate

s o

f A

mer

ica

SeaS

tati

on

fu

lly s

ub

mer

ged

o

r fl

oat

ing

> 2

5w

ww

.oce

ansp

ar.c

om

/file

s/O

cean

Spar

_Se

aSta

tio

n.p

df

Farm

oce

an In

tern

atio

nal

(m

anu

fact

ure

r)Sw

eden

Farm

oce

an 4

500

25w

ww

.far

mo

cean

.se/

Gen

eral

.pd

f

SUB

flex

Isra

elSU

Bfl

ex

sin

gle

po

int

mo

ori

ng

40–5

5 (1

2 m

dia

met

er c

age)

0–

60

(16

m d

iam

eter

cag

e)

50–8

0 (1

8 m

dia

met

er c

age)

SUB

flex

Op

en O

cean

Net

SPM

cag

es;

ww

w.s

ub

flex

.org

Oce

an F

arm

Tec

hn

olo

gie

s M

anu

fact

ure

r’s

spec

ific

atio

nA

qu

apo

d A

212,

A36

00,

A70

0020

–50

- 32

–50

- 35

–50

Oce

an F

arm

Tec

hn

olo

gie

s “A

qu

apo

d S

ite

Sele

ctio

n”

(ww

w.o

cean

farm

tech

.co

m)

and

C. S

tock

, per

son

al c

om

mu

nic

atio

n (

2009

)

Wo

rksh

op

on

op

en o

cean

aq

uac

ult

ure

Un

ited

Sta

tes

of

Am

eric

aSe

a ca

ges

w

ith

mu

ltip

le

anch

ors

Co

mm

ent:

Mo

ori

ng

> 1

00 m

a c

hal

len

ge

ow

ing

to

larg

e fo

otp

rin

t, c

apit

al

and

mai

nte

nan

ce c

ost

of

anch

ori

ng

sys

tem

Bro

wd

y an

d H

arg

reav

es (

2009

)

Asi

a-Pa

cifi

c O

cean

Res

earc

h C

ente

r, N

atio

nal

Su

n Y

at-s

en U

niv

ersi

tyTa

iwan

Pro

vin

ce o

f C

hin

aG

ravi

ty c

ages

30–5

0 id

eal r

ange

of

wat

er d

epth

fo

r n

et-c

age

imp

lem

enta

tio

n in

th

e o

pen

sea

H

uan

g, C

.C.,

Tan

g, H

.J. a

nd

Liu

, J.Y

. (20

08)

105Annex 1

TAB

LE A

1.3A

Cu

rren

t sp

eed

in m

aric

ult

ure

pra

ctic

e fo

r se

a ca

ges

an

d lo

ng

lines

Cu

rren

t sp

eed

(cm

/s)

Co

mm

ent

Cag

e ty

pe

Sou

rce

60N

ot

to e

xcee

dG

ener

al c

om

men

t to

avo

id d

efo

rmat

ion

B

ever

idg

e (1

996)

103–

129

No

t to

exc

eed

Farm

Oce

an 4

500

ww

w.f

arm

oce

an.s

e/G

ener

al.p

df

100

90%

of

cag

e vo

lum

e is

ret

ain

edO

cean

Sp

ar S

eaSt

atio

nSc

ott

an

d M

uir

(20

06);

h

ttp

://re

sso

urc

es.c

ihea

m.o

rg/o

m/p

df/

b30

/006

0065

1.p

df

150–

170

“An

ti-c

urr

ent”

off

sho

reD

FC t

ype

Ch

en e

t al

. (20

07)

in C

age

Aq

uac

ult

ure

(Ta

ble

4, p

. 59)

; w

ww

.fao

.org

/do

crep

/010

/a12

90e/

a129

0e00

.htm

100–

120

“An

ti-c

urr

ent”

sem

i-o

pen

loca

tio

nPD

W t

ype

50–1

00“A

nti

-cu

rren

t” s

emi-

op

en lo

cati

on

HD

PE t

ype

150

“An

ti-c

urr

ent”

HD

PE t

ype

sub

mer

ged

ww

w.a

libab

a.co

m/c

atal

og

/116

4467

0/Su

bm

erg

ed_S

tyle

_H

DPE

_Dee

p_W

ater

_Sea

_Cag

e.h

tml

72M

axim

um

fo

r a

144–

625

m3

cag

eLM

S ty

pe

Hu

nte

r,Tel

fer

and

Ro

ss (

2006

), T

able

3.1

p. 1

4;

ww

w.a

qua.

stir.

ac.u

k/pu

blic

/GIS

AP/

pdfs

/SA

RF00

3_Fu

ll.pd

f

82M

axim

um

fo

r a

700–

800

m3

cag

eC

-250

typ

e

93M

axim

um

fo

r a

3 00

0–17

000

m3

typ

eC

-315

typ

e

10–7

5R

ang

e g

iven

fo

r th

ree

cag

e m

od

els

Oce

an F

arm

Tec

hn

olo

gie

s A

qu

apo

d A

212,

A36

00 a

nd

A

7000

Oce

an F

arm

Tec

hn

olo

gie

s “A

qu

apo

d S

ite

Sele

ctio

n”

dat

ed 6

/5/0

9 (w

ww

.oce

anfa

rmte

ch.c

om

) an

d

e-m

ail d

ated

24

Jun

e 20

09 f

rom

Ch

ris

Sto

ck in

Ou

tlo

ok

Fold

er M

arn

e G

IS

100

Cu

rren

ts t

hat

exc

eed

100

cm

/s n

ot

gen

eral

ly

reco

mm

end

edSe

a ca

ges

in g

ener

alJa

mes

an

d S

lask

i (20

07)

(in

PN

A)


TAB

LE A

1.3B

C

urr

ent

spee

d in

mar

icu

ltu

re p

ract

ice

for

fin

fish

an

d m

uss

els

Cu

rren

t sp

eed

cm

/sC

om

men

tSp

ecie

sSo

urc

e

> 1

0To

ass

ure

suff

icie

nt w

ater

qua

lity

wit

hin

a ca

ge, c

urre

nts

shou

ld r

emai

n ab

ove

10 c

m/s

for

a m

ajor

par

t of

the

ti

dal c

ycle

.

Salm

on

Tlu

sty,

Pep

per

an

d A

nd

erso

n (

2005

) ci

tin

g P

uls

an

d

Sun

der

man

(19

90).

1–9,

1–7

, 1–2

2, 1

–10,

1–4

55–

10, 2

–6O

bse

rvat

ion

s at

fiv

e in

sho

re s

ea s

ites

an

d t

wo

lake

fa

rmin

g s

ites

tak

en d

uri

ng

tw

o v

isit

s, f

rom

5 t

o 8

h

ou

rs (

five

sit

es)

up

to

th

ree

day

s (t

wo

loca

tio

ns)

at

dep

ths

of

fro

m 2

2 to

75

m w

ith

rec

ord

er 1

–2 m

fro

m

the

bo

tto

m.

Atl

anti

c sa

lmo

nSo

to e

t al

. (20

03)

3–18

(av

erag

e)Tw

enty

sal

mo

n f

arm

s, B

ay o

f Fu

nd

y, C

anad

a, w

ith

24

-ho

ur

dep

loym

ents

co

rres

po

nd

ing

to

tw

o t

idal

cyc

les.

Atl

anti

c sa

lmo

nPe

ters

on

et

al. (

2001

);

htt

p://

pu

blic

atio

ns.

gc.

ca/c

olle

ctio

ns/

colle

ctio

n_2

012/

mp

o-d

fo/F

s97-

6-23

37-e

ng

.pd

f

42 (

max

imu

m)

Twen

ty s

alm

on

far

ms,

Bay

of

Fun

dy,

Can

ada,

wit

h

24-h

ou

r d

eplo

ymen

ts c

orr

esp

on

din

g t

o t

wo

tid

al c

ycle

s.A

tlan

tic

salm

on

Pete

rso

n e

t al

. (20

01);

h

ttp

://p

ub

licat

ion

s.g

c.ca

/co

llect

ion

s/co

llect

ion

_201

2/m

po

-dfo

/Fs9

7-6-

2337

-en

g.p

df

> 1

00“S

ites

wh

ere

curr

ent

spee

ds

exce

ed 1

00 c

m/s

fo

r ex

ten

ded

per

iod

s m

ay n

ot

be

suit

able

fo

r g

row

ing

sa

lmo

n”.

Atl

anti

c sa

lmo

nC

han

g, P

age

and

Hill

(20

05);

w

ww

.gn

b.c

a/00

27/A

qu

/pd

fs/B

arry

DFO

2585

.pd

f

10–1

10“E

stim

ated

ran

ge

of

no

less

th

an 1

0 cm

/s f

or

seve

ral

ho

urs

an

d n

o m

ore

th

an 1

10 c

m/s

fo

r m

ore

th

an 2

4 h

ou

rs”.

Rel

ates

to

Atl

anti

c sa

lmo

n a

nd

blu

e m

uss

el

amo

ng

oth

er s

pec

ies

con

sid

ered

Mac

leod

(20

07)

citi

ng M

assa

chus

etts

Bay

Env

iron

men

tal

Fore

cast

Sys

tem

Mo

del

, UM

ass

Bo

sto

n.

(M. J

ian

g, p

erso

nal

co

mm

un

icat

ion

, 200

7);

htt

p://

envs

tud

ies.

bro

wn

.ed

u/t

hes

es/a

rch

ive2

0062

007/

mer

riel

lem

acle

od

_th

esis

.pd

f

10–6

0“…

site

s co

nsi

der

ed o

pti

mal

fo

r fi

sh f

arm

ing

in

pen

s h

ave

curr

ent

ran

ges

fro

m 1

0 to

60

cm/s

bu

t va

ries

wit

hin

th

is r

ang

e d

epen

din

g o

n s

ize

and

sp

ecie

s o

f fi

sh, s

tock

ing

den

sity

an

d c

age

des

ign

an

d

con

fig

ura

tio

n”.

Rel

ates

to

“m

arin

e o

r sa

lmo

nid

fis

h”

Ren

sell

et a

l. (2

007)

, p. 1

15;

ww

w.f

ra.a

ffrc

.go

.jp/b

ulle

tin

/bu

ll/b

ull1

9/13

.pd

f

< 1

54C

urr

ent

spee

ds

are

to 3

kn

ots

(i.e

. 154

cm

/s)

(off

sho

re

site

). C

ob

ia

Aq

ual

ider

: w

ww

.aq

ual

ider

.co

m.b

r (o

ut

of

bu

sin

ess

late

201

0)

13–7

7 (r

ang

e)

Co

bia

Sn

app

erfa

rm, I

nc.

, Pu

erto

Ric

o,

Un

ited

Sta

tes

of

Am

eric

a; B

enet

ti e

t al

. (20

10)

< 2

6C

urr

ent

is v

aria

ble

bu

t m

ain

ly n

ort

h t

o s

ou

th w

ith

p

eaks

of

26 c

m/s

an

d d

ays

of

slac

k cu

rren

t.C

ob

ia; D

ou

ble

rin

g o

larc

irke

l, 40

, 60

and

10

0 m

cir

cum

fere

nce

Mar

ine

Farm

s B

eliz

e;

ww

w.m

arin

efar

msb

eliz

e.co

m

13–1

29C

urr

ent

0.25

kn

ot

(13

cm/s

) av

erag

e, m

ax 2

.5 k

no

ts

(129

cm

/s).

Co

bia

; Sea

Stat

ion

640

0, A

qu

apo

d 7

000

and

10

0 m

Aq

ual

ine

Op

en B

lue

Sea

Farm

s, P

anam

a;

ww

w.o

pen

blu

esea

farm

s.co

m

10–5

0Si

tin

g s

tud

y fo

r Sn

app

erfa

rm; m

ean

is 1

0 an

d m

ax is

50

ob

serv

ed.

Co

bia

Ren

sell,

Kie

fer

and

O’B

rien

(20

06);

ww

w.li

b.n

oaa

.go

v/re

tire

dsi

tes/

do

caq

ua/

rep

ort

s_n

oaa

rese

arch

/co

bia

_aq

uam

od

el_f

inal

_rep

ort

.pd

f

107Annex 1

Cu

rren

t sp

eed

cm

/sC

om

men

tSp

ecie

sSo

urc

e

< 5

0 M

ain

ly t

rad

itio

nal

co

bia

cag

es, b

ut

som

e o

ffsh

ore

ca

ges

at

11 lo

cati

on

s.C

ob

iaG

uan

gd

on

g a

nd

Hai

nan

pro

vin

ces,

Ch

ina.

(C

. Zh

u, p

erso

nal

co

mm

un

icat

ion

, 201

1)

55O

ffsh

ore

wat

ers

of

Pen

gh

u Is

lan

ds,

Tai

wan

Pro

vin

ce

of

Ch

ina.

Co

bia

Mia

o e

t al

. (20

09)

9O

ffsh

ore

wat

ers

of

Pin

gtu

ng

Co

un

ty, T

aiw

an P

rovi

nce

o

f C

hin

a.

70–1

04H

igh

est

spee

ds

enco

un

tere

d a

s o

cean

-gyr

e cu

rren

ts.

Seri

oal

a ri

volia

na;

Oce

an S

par

Sea

Stat

ion

ca

ges

inst

alle

d a

t K

eah

ole

Po

int,

Haw

aii,

(Un

ited

Sta

tes

of

Am

eric

a) 1

.6 k

m o

ffsh

ore

Love

rich

(20

10)

31–1

03Se

aSta

tio

n 3

000

and

540

0, 4

.5 k

m o

ffsh

ore

, Je

ju p

rovi

nce

, Rep

ub

lic o

f K

ore

a.Pa

rro

tfis

h (

op

leg

nat

hu

s fa

scia

tus)

Lim

an

d L

ee (

no

dat

e); w

ww

.ysl

me.

org

/do

c/rm

c/Pr

esen

tati

on

/Han

Kyu

%20

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df

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Des

ign

sp

ecif

icat

ion

.G

ilth

ead

bre

am (

Spar

us

aura

ta);

SU

Bfl

exsi

ng

le-p

oin

t m

oo

rin

g12

km

off

sho

re in

65

m

(R. T

ish

ler,

per

son

al c

om

mu

nic

atio

n, 2

011)

.

51–7

7C

on

stan

t sp

eed

exp

erie

nce

d.

51–7

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on

stan

t sp

eed

exp

erie

nce

d.

103

Max

imu

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xper

ien

ced

.10

3M

axim

um

exp

erie

nce

d.

5 –50

•“C

lass

I Pr

od

uct

ion

up

to

20

000

po

un

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yea

r (9

07

2 kg

per

yea

r).

•M

inim

um

dep

th o

f 35

fee

t (1

0.6

m)

for

a cu

rren

t ve

loci

ty o

f 5

cm/s

ec (

0.1

kno

ts)

to a

min

imu

m d

epth

o

f 20

fee

t (6

.1 m

) fo

r cu

rren

t ve

loci

ties

of

40 c

m/s

ec

(0.8

kn

ots

) o

r g

reat

er C

lass

II P

rod

uct

ion

bet

wee

n 2

0 00

0 to

100

000

po

un

ds

per

yea

r (9

072

to

45

360

kg

per

yea

r).

•M

inim

um

dep

th o

f 45

fee

t (1

3.7

m)

for

a cu

rren

t ve

loci

ty o

f 5

cm/s

ec (

.01

kno

ts)

to a

min

imu

m d

epth

o

f 25

fee

t (7

.6 m

) fo

r cu

rren

t ve

loci

ties

of

50 c

m/s

ec

(1.0

kn

ots

) o

r g

reat

er C

lass

III P

rod

uct

ion

ove

r 10

0 00

0 p

ou

nd

s p

er y

ear

(4

5 36

0 kg

per

yea

r).

• M

inim

um

dep

th o

f 60

fee

t (1

8.2

m)

for

a cu

rren

t ve

loci

ty o

f 5

cm/s

ec (

0.1

kno

ts)

to a

min

imu

m d

epth

o

f 40

fee

t (1

2.1)

fo

r cu

rren

t ve

loci

ties

of

50 c

m/s

ec

(1.0

kn

ots

) o

r g

reat

er.

Fin

fish

Lan

gen

bu

rg a

nd

Stu

rges

(19

99),

Tab

le 2

. Th

ere

is

an a

pp

end

ix w

ith

a c

har

t re

lati

ng

cu

rren

t sp

eed

, p

rod

uct

ion

an

d d

epth

fro

m w

hic

h t

he

dat

a o

n t

he

left

h

ave

bee

n d

eriv

ed. T

hes

e es

tim

ates

are

fro

m t

he

po

int

of

view

of

envi

ron

men

tal p

rote

ctio

n r

ath

er t

han

fro

m

max

imiz

ing

pro

du

ctio

n, i

t se

ems.

> 5

0Se

aSta

tio

n S

S600

an

d S

S300

0 ca

ges

10

km

off

sho

re o

f N

ew H

amp

shir

e, U

nit

ed S

tate

s o

f A

mer

ica,

wit

h c

urr

ent

spee

d “

oft

en e

xcee

din

g 5

0 cm

/s”.

Atl

anti

c h

alib

ut,

Atl

anti

c h

add

ock

, A

tlan

tic

cod

Fre

dri

ckss

on

et

al. (

2003

)

10–5

0O

pti

mal

wit

h le

sser

sp

eed

s af

fect

ing

fill

et q

ual

ity

and

g

reat

er s

pee

ds

resu

ltin

g in

a r

apid

incr

ease

of

the

feed

co

nve

rsio

n r

atio

wit

h c

ult

ivat

ion

bec

om

ing

fin

anci

ally

u

nat

trac

tive

.

Gilt

hea

d b

ream

Fe

rrei

ra, S

aure

l an

d F

erre

ira

(201

2)


Cu

rren

t sp

eed

cm

/sC

om

men

tSp

ecie

sSo

urc

e

N/A

“At

a cu

rren

t sp

eed

of

15 c

m/s

ec, t

he

wat

er a

t a

site

is

exch

ang

ed a

bo

ut

100

tim

es p

er d

ay. A

n e

xch

ang

e ra

te

of

2–3

tim

es is

typ

ical

ly n

eed

ed t

o k

eep

th

e le

vels

of

nu

trie

nts

in t

he

wat

er c

olu

mn

low

er t

han

th

e cr

itic

al lo

ad.”

N/A

Grø

ttu

m a

nd

Bev

erid

ge

(200

7), c

itin

g O

lsen

, Sla

gst

ad

and

Vad

stei

n (

2005

) p

. 148

.

5–20

go

od

; < 1

an

d >

50

po

or

No

t n

amed

Hal

ide

(200

8) A

pp

end

ix 1

(ap

pea

rs t

o b

e m

od

ellin

g f

or

un

nam

ed f

ish

sp

ecie

s in

tro

pic

al w

ater

s); h

ttp

://d

ata.

aim

s.g

ov.

au/c

ads/

CA

DS_

TOO

L_Te

chn

ical

_Gu

ide.

pd

f

< 5

Ver

y w

eak

curr

ent,

po

or

mas

s fl

ux

and

inco

nsi

sten

t cu

rren

t d

irec

tio

n. D

eple

tio

n li

kely

at

the

cen

tre

of

fa

rms.

On

ly s

uit

able

fo

r lo

w-d

ensi

ty f

arm

ing

or

spat

h

old

ing

.

pern

a ca

nal

icu

lus

and

Myt

ilus

gal

lop

rovi

nci

alis

Ing

lis, H

ayd

en a

nd

Ro

ss (

2000

), B

ox

tab

le, p

. 21

5–10

Wea

k cu

rren

t ve

loci

ties

of

gen

eral

ly w

idel

y va

ryin

g

dir

ecti

on

lead

ing

to

so

me

dep

leti

on

at

cen

tre

of

farm

.pe

rna

can

alic

ulu

s an

d M

ytilu

s g

allo

pro

vin

cial

isIn

glis

, Hay

den

an

d R

oss

(20

00),

Bo

x ta

ble

, p. 2

1

10–2

0M

od

erat

e-lo

w d

eple

tio

n t

hat

may

be

mo

re m

arke

d a

t d

ow

nst

ream

en

d o

f fa

rm. D

eple

tio

n is

mo

re li

kely

to

b

e o

bse

rved

in c

entr

e o

f fa

rmed

are

a.

pern

a ca

nal

icu

lus

and

Myt

ilus

gal

lop

rovi

nci

alis

Ing

lis, H

ayd

en a

nd

Ro

ss (

2000

), B

ox

tab

le, p

. 21

> 2

0St

ron

g c

urr

ent

flo

w. L

ittl

e d

eple

tio

n b

ut

cum

ula

tive

ef

fect

of

man

y ro

pes

lon

glin

es in

th

e d

irec

tio

n o

f fl

ow

co

uld

res

ult

in (

rem

ain

der

of

text

is m

issi

ng

fro

m t

he

ori

gin

al a

rtic

le).

pern

a ca

nal

icu

lus

and

Myt

ilus

gal

lop

rovi

nci

alis

Ing

lis, H

ayd

en a

nd

Ro

ss (

2000

), B

ox

tab

le, p

. 21

15–2

0B

ott

om

cu

ltu

re (

mo

der

ate

gro

w-o

ut

den

siti

es).

Myt

ilus

edu

lisN

ewel

l (20

01)

5Lo

ng

lines

, if

giv

en a

deq

uat

e sp

acin

g.

Myt

ilus

edu

lisN

ewel

l (20

01)

10–1

5R

afts

in o

rder

to

pre

ven

t d

eple

tio

n.

Myt

ilus

edu

lisN

ewel

l (20

01)

10–3

0O

ffsh

ore

lon

glin

e.M

ytilu

s ed

ulis

R. L

ang

an, p

erso

nal

co

mm

un

icat

ion

, 200

9

< 1

0; in

freq

uen

tly

up

to

50

Haw

ke B

ay, N

ort

h Is

lan

d,

New

Zea

lan

d.

Biv

alve

sh

ellf

ish

, in

clu

din

g g

reen

shel

l mu

ssel

s,

pern

a ca

nal

icu

lus

Ch

eney

et

al. (

2010

)

< 1

5–30

Op

oti

ki, N

ort

h Is

lan

d,

New

Zea

lan

d.

Biv

alve

sh

ellf

ish

, in

clu

din

g g

reen

shel

l mu

ssel

s,

pern

a ca

nal

icu

lus

Ch

eney

et

al. (

2010

)

109Annex 1

TAB

LE A

1.4A

Tem

per

atu

res

for

gro

w-o

ut

of

cob

ia

Tem

per

atu

re o

r ra

ng

eC

om

men

tLo

cati

on

Sou

rce

< 2

2 N

o g

row

thV

iet

Nam

Nh

u e

t al

. (20

09)

< 1

8 St

op

eat

ing

Vie

t N

amN

hu

et

al. (

2009

)

15 M

ass

mo

rtal

ity

Five

wee

ks in

200

8.V

iet

Nam

Nh

u e

t al

. (20

09)

22–3

0 M

ean

mo

nth

ly r

ang

eV

iet

Nam

Nh

u e

t al

. (20

09)

<16

Sev

ere

stre

ss, m

ort

alit

ies

Un

ited

Sta

tes

of

Am

eric

aM

. Ost

erlin

g, p

erso

nal

co

mm

un

icat

ion

, 201

1

16–3

2; >

20

pre

ferr

edC

on

dit

ion

s in

th

e w

ild.

No

t st

ated

Kai

ser

and

Ho

lt (

2005

)

> 2

6 O

pti

mal

gro

wth

Cu

ltu

re c

on

dit

ion

s.N

ot

stat

edK

aise

r an

d H

olt

(20

05)

21–2

2Fe

edin

g a

ctiv

ity

red

uce

d.

Taiw

an P

rovi

nce

of

Ch

ina

Mia

o e

t al

. (20

09)

19Fe

edin

g c

ease

s.Ta

iwan

Pro

vin

ce o

f C

hin

aM

iao

et

al. (

2009

)

< 1

6 an

d >

36

May

res

ult

in m

ass

mo

rtal

ity.

Taiw

an P

rovi

nce

of

Ch

ina

Mia

o e

t al

. (20

09)

22–3

2O

pti

mal

tem

per

atu

re r

ang

e.N

ot

spec

ifie

dM

iao

et

al.,

2009

, ci

tin

g C

han

g e

t al

., 19

99

25–2

7 m

ean

sp

rin

g t

o a

utu

mn

;21

–22

mea

n w

inte

r <

16

low

tem

per

atu

rePe

ng

hu

Arc

hip

elag

o, T

aiw

an P

rovi

nce

of

Ch

ina

Shih

, Ch

ou

an

d C

hia

u (

2009

)

27–2

9 A

mb

ien

t co

nd

itio

ns

Snap

per

farm

, Isl

a C

ule

bra

, U

nit

ed S

tate

s o

f A

mer

ica

Ren

sel,

Kie

fer

and

O’B

rien

, 20

06

< 1

6 H

igh

mo

rtal

ity

Pen

gh

u A

rch

ipel

ago

, Ta

iwan

Pro

vin

ce o

f C

hin

aLi

ao e

t al

. (20

04)

23.5

–28

year

ro

un

dSe

a ca

ge

area

s in

so

uth

ern

Tai

wan

Pro

vin

ce

of

Ch

ina

Liao

et

al. (

2004

)

28–3

2 H

igh

est

gro

wth

rat

es;

< 2

0 g

row

th r

ates

dec

reas

edFi

sh f

ed t

o s

atia

tio

n.

Pen

gh

u A

rch

ipel

ago

, Pr

ovi

nce

of

Ch

ina

Uen

g e

t al

. ( 2

001)

26 a

nd

ab

ove

On

gro

win

g.

No

t st

ated

Kai

ser

and

Ho

lt (

2007

); w

ww

.fao

.org

/fis

her

y/cu

ltu

red

spec

ies/

Rac

hyc

entr

on

_can

adu

m/e

n

27–2

8 ye

ar r

ou

nd

On

gro

win

g.

Op

en B

lue

Sea

Farm

s,

Pan

ama

B. O

’Han

lon

, per

son

al C

om

mu

nic

atio

n, 2

011

25–3

2O

n g

row

ing

.Sn

app

erfa

rm, I

nc.

, Pu

erto

Ric

o,

Un

ited

Sta

tes

of

Am

eric

aB

enet

ti e

t al

. (20

10)


TAB

LE A

1.4B

Tem

per

atu

res

for

gro

w-o

ut

of

Atl

anti

c sa

lmo

n

Tem

per

atu

re o

r ra

ng

eC

om

men

tLo

cati

on

Sou

rce

6–16

Gro

w b

est

in s

ites

wit

h

thes

e te

mp

erat

ure

ext

rem

es.

Gen

eral

ized

FAO

(20

11);

w

ww

.fao

.org

/fis

her

y/cu

ltu

red

spec

ies/

Salm

o_s

alar

/en

0.6–

15.6

Win

ter

and

su

mm

er t

emp

erat

ure

s n

ot

to b

e ex

ceed

ed f

or

any

len

gth

o

f ti

me.

Mai

ne,

Un

ited

Sta

tes

of

Am

eric

aU

niv

ersi

ty o

f M

ain

e;

ww

w.u

mai

ne.

edu

/mai

nes

ci/A

rch

ives

/Mar

ineS

cien

ces/

salm

on

-fa

rmin

g.h

tm

8–18

Op

tim

al r

ang

e fo

r cu

ltu

re.

Tasm

ania

, Au

stra

liaA

no

n, (

2002

);

ww

w.p

ir.sa

.go

v.au

/__d

ata/

asse

ts/p

df_

file

/001

0/33

895/

salm

on

_fs

.pd

f

12–1

6B

est

gro

wth

.Ta

sman

ia, A

ust

ralia

An

on

, (20

02);

w

ww

.pir.

sa.g

ov.

au/_

_dat

a/as

sets

/pd

f_fi

le/0

010/

3389

5/sa

lmo

n_

fs.p

df

0–21

Tole

rate

th

is r

ang

e.Ta

sman

ia, A

ust

ralia

An

on

, (20

02);

w

ww

.pir.

sa.g

ov.

au/_

_dat

a/as

sets

/pd

f_fi

le/0

010/

3389

5/sa

lmo

n_

fs.p

df

6–16

Gro

w-o

ut

ran

ge.

N/A

Ver

spo

or

et a

l. (2

007)

12–1

6Pr

efer

red

ran

ge.

Gip

psl

and

, Vic

tori

a, A

ust

ralia

Gip

psl

and

Aq

uac

ult

ure

Ind

ust

ry N

etw

ork

Inc.

(20

11);

h

ttp

://g

row

fish

.co

m.a

u/G

row

/Pag

es/S

pec

ies/

Tro

ut.

htm

> 2

2N

ot

reco

mm

end

ed f

or

farm

ing

if e

xcee

ded

on

a

reg

ula

r b

asis

.G

ipp

slan

d, V

icto

ria,

Au

stra

liaG

ipp

slan

d A

qu

acu

ltu

re In

du

stry

Net

wo

rk In

c. (

2011

);

htt

p://

gro

wfi

sh.c

om

.au

/Gro

w/P

ages

/Sp

ecie

s/Tr

ou

t.h

tm

0.7

Low

est

tem

per

atu

re

for

surv

ival

.B

ay d

’Esp

oir,

N

ewfo

un

dla

nd

, Can

ada

An

on

. (20

09);

w

ww

.dfo

-mp

o.g

c.ca

/sci

ence

/en

viro

/aq

uac

ult

ure

/acr

dp

-pcr

da/

fsh

eet-

ftec

hn

iqu

e/p

df/

02-e

ng

.pd

f

0–15

This

ran

ge,

alo

ng

wit

h s

tro

ng

cu

rren

ts a

nd

h

igh

tid

es, p

rovi

de

idea

l co

nd

itio

ns.

Mai

ne,

U

nit

ed S

tate

s o

f A

mer

ica

Mai

ne

Aq

uac

ult

ure

Inn

ova

tio

n C

ente

r (2

011)

; w

ww

.mai

nea

qu

acu

ltu

re.o

rg/in

du

stry

/sp

ecie

s.h

tml#

salm

on

0.5–

15.2

Mea

sure

men

ts f

rom

20

far

ms

ove

r tw

o y

ears

.B

ay o

f Fu

nd

y, C

anad

aPe

ters

on

et

al. (

2001

);

htt

p://

pu

blic

atio

ns.

gc.

ca/c

olle

ctio

ns/

colle

ctio

n_2

012/

mp

o-d

fo/F

s97-

6-23

37-e

ng

.pd

f

111Annex 1

TAB

LE A

1.4C

Tem

per

atu

res

for

gro

w-o

ut

of

blu

e m

uss

el

Tem

per

atu

re o

r ra

ng

e oC

No

teLo

cati

on

Sou

rce

-1.0

8–15

.6R

ang

e at

tw

o c

om

mer

cial

far

min

g s

ites

, wit

h

two

sta

tio

ns

per

sit

e.N

otr

e D

ame

Bay

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. Jo

hn

’s, N

ewfo

un

dla

nd

, Can

ada

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an,

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ish

an

d S

hah

idi (

2006

)

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arm

. W

est

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nit

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ssel

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rld

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y (2

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um

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his

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th A

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ralia

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on

. (20

00);

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ir.sa

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ssel

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ge

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ure

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ain

e, U

nit

ed S

tate

s o

f A

mer

ica

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nd

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itu

te (

1999

).

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Mai

ne

gu

ide

to r

aft

cult

ure

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eal f

or

cult

ure

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ain

e, U

nit

ed S

tate

s o

f A

mer

ica

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nd

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itu

te (

1999

).

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ne

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in t

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se b

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l str

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ited

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tes

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eric

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lan

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e M

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uid

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re

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tim

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r g

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th

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ince

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. (20

09);

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ter

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aily

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ada

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fess

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ers

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oci

atio

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ear

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ort

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atly

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uid

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glis

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den

an

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oss

(20

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wn

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nal

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mb

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ents

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eric

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idel

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lis, H

ayd

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uss

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hel

l gro

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bec

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ith

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per

atu

res

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ch K

ish

orn

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otl

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cel &

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cel (

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)

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– 5

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re e

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tal s

ite

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oo

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esu

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an

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row

-ou

t.N

ear

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s o

f Sh

oal

s,

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psh

ire,

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nit

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tate

s o

f A

mer

ica

Lan

gan

an

d H

ort

on

(20

05)

113Annex 1

GADM database of global administrative areas

http://www.gadm.org/

Boundaries of sovereign nations

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= d8e81680-e070-11dc-9d70-0017f293bd28

Exclusive economic zones of the world - version 5

http://www.gebco.net/

General bathymetric chart of the Oceans

Depth and current speed as the fundamental criteria characterizing the technicallimits of present offshore submerged cage and longline culture systems

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= 44752cb4-d5f9-4a61-a8ce-eab9bb6ab8f9

Regions with depths ranging from lower than 25 m,between 25–100 m and greater than 100 m

Regions with HYCOM current speedsat 30 m depth between 1 and 10 cm/s

for all months in the year

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= 485bb2c1-f84c-40e8-83c6-0d1eb97a922a

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= 73fee8e5-fcb6-47ad-bef0-365f8bd0368b

Regions with HYCOM current speeds at 30m depth between 1–10, 10–100 and > 100 cm/s

for all months in the year

4. Global data sets for estimates of offshore mariculture


Regions with HYCOM current speeds at 30m depth between 10 and 100 cm/s for all months in the year

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= acc9f071-d42f-4a5e-938b-d23f97f7d28f

Regions with no HYCOM current speed dataavailable 30m depth

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= 88d32204-77e5-4ed6-9749-a7aada6895d7

GeoNetwork URL:www.fao.org/geonetwork/srv/en/main.home?uuid=4208693e-c4b8-448c-824f-ae3f4632305c

Regions with HYCOM current speeds at 30m depth> 100 cm/s for all months in the year

Areas within 25 nautical miles (46.3 km) of a port

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= 626f7bfd-e11d-444b-9335-ba85f7474c62

Distance offshore from onshore infrastructure related to economic cost limitson transportation and on reliable access from a port to the sea

Note: Areas with no current speed data are those with depths less than 30m (i.e. all of the areas close to the shorelines) so they are difficult to see on a world map).

115Annex 1

Aqua MODIS climatology sea surface temperature (Spring 2002–2009)

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= 85c03e2d-edce-4881-be4c-8ed240e1d4fb

Regions with sea surface temperatures, as defined by 95% confidence intervals, between 1.5 and 16°C

over entire year for Atlantic salmon

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= ae29adcb-5128-4fb9-adef-5a6440916031

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= 10531883-6f07-47b0-88cf-67c3cd484a77

Regions with sea surface temperatures,as defined by 95% confidence intervals, between

22 and 32° C over entire year for Cobia

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= 88cb7b48-7d13-4116-83c0-682ef3bb281c

Regions with sea surface temperatures, as defined by 95% confidence intervals, between 2.5 and 19°C

over entire year for blue mussel

Favourable offshore grow-out environment based on temperature requirementsof representative fish and mussels and on food availability measured as chlorophyllconcentration for the latter

Regions with chlorophyll 2 concentrations, asdefined by 95% confidence intervals, greater than

0.5 mg/m3 that were combined for the monthsavailable in each hemisphere for the blue mussel

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= 34e73ea7-d62e-403c-9217-826cc2c314b5


2009 World database on protected areas

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= 5749f8b6-d36c-4cbc-9423-2fcc2fc22fcc

Competing, conflicting and complementary uses of ocean space

GeoNetwork URL:http://www.fao.org/geonetwork/srv/en/main.home?uuid= fcc3eb74-f3c9-484d-93e3-7c94e9226e2b

National and international marine protected areas within exclusive economic zones

Documents

Annex 1 Overview of the spatial analyses and data sources · Overview of the spatial analyses and data sources ... such as roads or rivers) and point feature classes (containing such