1 changing storminess and global capture fisheries
TRANSCRIPT
ORE Open Research Exeter
TITLE
Changing storminess and global capture fisheries
AUTHORS
Sainsbury, NC; Genner, MJ; Saville, GR; et al.
JOURNAL
Nature Climate Change
DEPOSITED IN ORE
18 July 2018
This version available at
http://hdl.handle.net/10871/33479
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Open Research Exeter makes this work available in accordance with publisher policies.
A NOTE ON VERSIONS
The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date ofpublication
1
Changing storminess and global capture fisheries 1
Climate change-driven alterations in storminess pose a significant threat to global capture 2
fisheries. Understanding how storms interact with fishery social-ecological systems can 3
inform adaptive action and help to reduce the vulnerability of those dependent on fisheries 4
for life and livelihood. 5
Nigel C. Sainsbury*, Environment and Sustainability Institute, College of Life and 6
Environmental Sciences, University of Exeter, Treliever Road, Penryn, TR10 9FE, UK. 7
Martin J. Genner, School of Biological Sciences, University of Bristol, Life Sciences 9
Building, 24 Tyndall Avenue, Bristol BS8 1TQ, UK. [email protected]. 10
Geoffrey R. Saville, Willis Research Network, Willis Towers Watson, The Willis Building, 51 11
Lime St, London EC3M 7DQ, UK. [email protected]. 12
John K. Pinnegar, Centre for Environment, Fisheries and Aquaculture Science, Pakefield 13
Rd, Lowestoft NR33 0HT, UK. [email protected]. 14
Clare K. O’Neill, Met Office, Fitzroy Rd, Exeter EX1 3PB, UK. 15
Stephen D. Simpson, Biosciences, College of Life and Environmental Sciences, Geoffrey 17
Pope Building, University of Exeter, Stocker Road, Exeter, EX4 4QD, UK. 18
Rachel A. Turner, Environment and Sustainability Institute, College of Life and 20
Environmental Sciences, University of Exeter, Treliever Road, Penryn, TR10 9FE, UK, 21
23
24
25
26
2
Changing storminess and global capture fisheries 27
Climate change-driven alterations in storminess pose a significant threat to global 28
capture fisheries. Understanding how storms interact with fishery social-ecological 29
systems can inform adaptive action and help to reduce the vulnerability of those 30
dependent on fisheries for life and livelihood. 31
Fisheries are an important source of food, nutrition, livelihoods and cultural identity on a 32
global scale. Fish provide 3.1 billion people with close to 20% of their animal protein1, and 33
are relied upon for vital micronutrients, which are particularly critical to the health of children 34
and pregnant women2. Capture fisheries and aquaculture are estimated to support the 35
livelihoods of 12% of the global population and 38 million fishers regularly risk their lives in 36
one of the most dangerous jobs on Earth1. Despite its dangers, fishing is an important 37
source of cultural identity and well-being for fishing communities around the world3. 38
In addition to ocean warming and acidification, changing storminess is a climate stressor that 39
affects marine life and habitats (Fig. 1a), with potential negative consequences for fish catch 40
and the well-being of coastal communities. Changing storminess also poses a direct risk to 41
fisheries: storms disrupt fishing effort and pose a physical threat to fishers, their vessels and 42
gear, as well as to fishing communities and their infrastructure. Although ocean warming 43
may alter the potential fish catch over the next 50 to 100 years4, changing storminess has 44
the potential to cause more immediate and catastrophic impacts. The twenty-first century 45
has already witnessed many tropical, extra-tropical and thunder storms that have claimed 46
thousands of fishers’ lives, destroyed fishery-dependent livelihoods and assets, and 47
disrupted the production of commercial inland and marine capture fisheries (Fig. 1b). 48
49
The number of storminess reanalysis and projection studies is growing, as is their 50
geographic scope (Fig. 2). However, uncertainty in past and future storminess from global 51
and regional climate models remains high as a result of widespread variation in analytical 52
3
methods, poor historic observational data5 and the challenge of distinguishing externally 53
forced climate changes from natural internal climate variability6. The attribution of particular 54
extreme weather events to anthropogenic climate forcing is challenging — particularly for 55
storms7. Thus, extreme weather event attribution is an expanding area of research and 56
examples for storm events are beginning to emerge8. 57
Despite the difficulties in modelling the location, frequency and intensity of storms, there is 58
sufficient certainty for the IPCC to conclude for the North Atlantic basin (where fisheries 59
productivity is high and historic storm data is particularly rich) that the frequency of the most 60
intense tropical storms has increased since the 1970s5. A recent review of future winter 61
storminess studies in Europe, ranging over periods spanning 2020–2190, predicts increases 62
in storm frequency and intensity in Western and Central Europe, and decreasing storminess 63
over the North Atlantic north of 60° N and in Southern Europe9. Evidence of changing 64
storminess from studies outside the North Atlantic includes a northward shift in Western 65
North Pacific tropical cyclone exposure towards the East China Sea10 and increased post-66
Monsoon storminess in the Arabian Sea8. However, substantial uncertainties in storminess 67
projections remain, and represent a real barrier to effective assessment of global fishery 68
vulnerability. 69
The uncertainties surrounding the changing nature of storm hazards is paralleled by a lack of 70
knowledge about how storm events directly interact with social and economic variables to 71
influence the behaviour of fishers. In addition, the impacts of storms on marine ecosystems, 72
and the linkages by which these cause indirect social and economic perturbations to 73
fisheries, are little understood. An interdisciplinary research effort is now required to clarify 74
the climatic, social and ecological dimensions of changing storminess to support the 75
assessment of fishery vulnerability and inform adaptive action. 76
77
78
4
Plotting the course ahead 79
We advocate a roadmap that draws on climate science, environmental social science, 80
psychology, economics, and ecology, and is based on four interlinked research areas (Fig. 81
3): (1) developing climate modelling to better understand changing storm hazards; (2) 82
understanding fishers’ behavioural response to storms; (3) examining the effects of storms 83
on coastal marine ecosystems and socio-economic linkages; and, (4) assessing fisheries 84
vulnerability and adaptation strategies for changing storminess. 85
Modelling changing storm frequency and severity 86
Identifying the risk to fisheries of changes in storminess requires climate models that provide 87
a reliable spatial and temporal view of past and future frequency and intensity of tropical, 88
extra-tropical and thunder storms. To achieve this, improvements are required in the explicit 89
representation of the sub-grid scale physical processes by which the most intense storms 90
form and develop, such as convection. Advances in ocean-atmosphere coupled models are 91
also necessary to capture the boundary layer processes that drive storms. Progress is being 92
made in these areas, for instance in developing climate models that better represent the 93
coupled ocean-atmosphere processes in tropical cyclones11. 94
Improving the characterization of storms in climate models demands finer spatial resolution 95
and a shortening of time steps, which will intensify the trade-off between resolution and 96
timescale of simulations that results from limited computing resources. Supported by greater 97
computing power, enhanced representation of storms in climate models will improve both 98
reanalysis and predictions of storminess and strengthen our understanding of the influence 99
of climate variability at seasonal to decadal timeframes on storm events. 100
Fishers’ behavioural response to storms 101
The effect of storms on fisheries is in part a function of fishers’ behavioural response to 102
meteorological conditions. The heterogeneity of fisher decisions regarding whether to 103
participate, and where to fish, in adverse weather conditions for different fishery types, 104
5
vessel characteristics and social and cultural contexts around the world should be explored. 105
Fishers’ decisions on where and when to fish are known to be affected by a complex array of 106
socio-economic factors12. However, the way in which fishers make weather-related decisions 107
is poorly understood. We do not know how projected weather information is used or if it 108
accessible to fishers. It will be important to understand fisher decisions to go to sea, or stay 109
at sea, during storms, how weather conditions affect the distribution of fishing activity, the 110
performance of different gears in adverse weather and the interaction of perceptions of 111
physical and economic risk in decision-making. 112
113
Explaining the behavioural response of fishers to storms will require the involvement of 114
psychologists, sociologists, anthropologists and economists employing research methods 115
across the epistemological spectrum. Qualitative approaches can unravel the complexity of 116
factors, motivations and processes underpinning decision-making, whereas experimental 117
methods, such as economic choice experiments, offer the potential to reveal how decisions 118
are made where observational data are not readily available, as is the case in many tropical 119
fisheries. The increasing availability of on-board satellite vessel tracking technology and 120
wind and wave hindcast modelled data is creating the potential to model the behavioural 121
response of fishers to weather conditions at unprecedented temporal and spatial resolutions. 122
In addition, the emerging application of agent-based modelling approaches to fisheries could 123
reveal the weather-related behaviour of fleets based on the decisions and interactions of 124
individual fishers. 125
Coastal marine ecosystems and socio-economic linkages 126
Storms have the capacity to cause extensive disturbance to marine ecosystems and habitats 127
that support productive fisheries. Several areas require investigation to improve our 128
knowledge: little is known about the manner in which fish lifecycle events (including 129
spawning migrations, larval growth and dispersal during the planktonic larval phase) and the 130
use of shallow nursery ground habitats, are influenced by storm disturbance. There is some 131
6
evidence that fish may evacuate storm areas or be redistributed by storm waves and 132
currents (Fig. 1a), but this requires further exploration. Storm-induced fish mortality events, 133
such as the death of 400,000 fish in the Nyanza Gulf of Lake Victoria following post-storm 134
deoxygenation and turbidity in 198413, are poorly understood. Finally, the way that changing 135
storminess interacts with other marine impacts of climate change (such as ocean warming, 136
acidification and deoxygenation) to affect marine ecosystems remains unexplored. 137
Interdisciplinary efforts are required to uncover how direct marine ecosystem impacts are 138
linked with indirect social and economic impacts on fisheries. Although there are examples 139
of storm damage to key habitats, we know little of how this consequently influences the 140
abundance or catchability of targeted fish species. We lack knowledge of how storm-induced 141
changes in fish distribution affect fishery catches, but fishers’ logbooks may offer a rich 142
source of data to address this gap. 143
Vulnerability and adaptation strategies 144
Assessing the vulnerability of fisheries to changing storminess is essential for prioritizing 145
limited adaptation resources and informing adaptation strategies. The exposure of fisheries 146
will vary spatially with projected changes in storm risk, target fish species, the resilience of 147
infrastructure and the extent of natural and man-made storm defences. It is probable that the 148
impact of changing storminess on fisheries will be socially differentiated, with severe impacts 149
more likely to affect small-scale fisheries. The vulnerability of fisheries to changes in 150
storminess is unclear at present. Fishery vulnerability assessments developed over the past 151
decade have acknowledged, but not reflected, changing storminess14, largely because of the 152
gaps in knowledge outlined here. These assessments can be enhanced by incorporating 153
appropriate measures of exposure, sensitivity and adaptive capacity to storms. 154
Fishery adaptation measures will require evaluation in local contexts. Possibilities include 155
technological advances, improvements in the accuracy and communication of weather 156
forecasts, and innovative financial solutions. In Kerala, India, a weather forecast service 157
7
called Radio Monsoon (https://twitter.com/radiomonsoon) provides daily information over 158
loudspeaker in harbours and through social media. Insurance schemes triggered by 159
environmental indexes are growing in popularity in terrestrial agriculture15 and could increase 160
the resilience of fisheries to increased storminess. Modifications of this concept would have 161
to reflect the nature of daily harvesting activity and the dynamic nature of marine resources. 162
Some fishers may also have opportunities to adapt to take advantage of reduced 163
storminess, which may exacerbate existing challenges to sustainable natural resource use. 164
Conclusion 165
Greater attention to the research priorities outlined here could help inform adaptation and 166
protect the well-being of billions of people worldwide. Although scientists are actively working 167
in some of these areas, research gaps remain, and existing knowledge is yet to be applied to 168
this social-ecological climate issue. The potentially catastrophic impacts of changing 169
storminess for global fisheries across relatively short timescales mean that enhanced 170
integration across disciplines is urgently needed to address this challenge. 171
Acknowledgements 172
N.C.S. acknowledges the financial support of the UK Natural Environment Research Council 173
(NERC; GW4+ studentship NE/L002434/1), Centre for Environment, Fisheries and 174
Aquaculture Science and Willis Research Network. We thank Emma M. Wood, who provided 175
design services for the figures. 176
Competing Interests statement 177
J.K.P. is a co-chair of the “ICES-PICES Strategic Initiative on Climate Change Impacts on 178
Marine Ecosystems” and will be a Lead Author for the “Small Islands” chapter within the 179
IPCC 6th Assessment Report (AR6 – WGII). 180
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201
202
203
204
205
206
207
208
209
9
210
Figure 1. Ecological, social and economic impacts of storms on fisheries. (a) 211
Examples of storm-induced marine ecosystem disturbances. For further detail see 212
10
Supplementary Information Section 1a. (b) Examples of social and economic impact 213
case studies from the twenty-first century. Case studies were selected based on scale 214
of the impacts, global geographic spread and availability of data. For further detail see 215
Supplementary Information Section 1b. 216
217
Figure 2. The spatially heterogeneous nature of changing global storminess. The 218
selection of studies is not systematic, but is designed to reflect a range of studies 219
carried out for the Atlantic, Pacific and Indian Oceans, which account for the majority 220
of global fish catch. For further detail see Supplementary Information Section 2. 221
11
222
Figure 3. Schematic of a research roadmap to understand the impact of changing 223
storminess on fisheries. Straight arrows between boxes demonstrate the 224
dependencies within and between research streams. Curved arrows represent the 225
feedback loop in which changes in fisher behaviour affect the ecosystem and 226
changes to the ecosystem affect fisher behaviour. Collaboration will be required 227
between research streams. The order of research streams does not represent 228
importance or priority. 229
230
231
232
233
234
235
12
Changing storminess and global capture fisheries 236
Nigel C. Sainsbury, Martin J. Genner, Geoffrey R. Saville, John K. Pinnegar, Clare K. 237
O’Neill, Stephen D. Simpson, Rachel A. Turner 238
Supplementary Information Section 1a 239
This section provides references and additional detail for Figure 1a. 240
241
Supplementary Figure 1a. Figure 1a with additional case study reference numbers 242 linking to Supplementary Table 1a. 243
244
245
246
247
248
249
250
251
13
252
Sup
ple
me
nta
ry T
able
1a.
Ad
dit
ion
al d
eta
il an
d r
efer
ence
s fo
r Fi
gure
1a.
Su
pp
lem
en
tary
Fig
ure
1a M
ap
Re
fere
nce
Lo
cati
on
Sp
ecie
sS
torm
typ
eIm
pacts
(an
d s
ou
rce
re
fere
nce
)T
ime
pe
rio
d o
f im
pact
No
tes
1K
ona, H
aw
aii
Cora
ls a
nd v
arious r
eef fis
h in
clu
din
g
Para
cirrh
ites a
rcatu
s, C
irrh
itops fascia
tus a
nd
Chro
mis
vanderb
ilti
Unnam
ed s
torm
(1980)
Specie
s r
edis
trib
utio
n; C
ora
l dam
age
1Long term
Aft
er
16 m
on
ths
wh
ilst
so
me
fis
h h
ad r
etu
rne
d t
o t
he
ir p
re-s
torm
are
as, o
the
r re
mai
ne
d in
sh
ifte
d
loca
tio
ns
2M
issis
sip
pi /
Louis
iana, U
SA
Shellf
ish a
nd o
ffshore
habita
tH
urr
icanes R
ita, W
ilma a
nd K
atr
ina
(2005)
Pollu
tion (
debris);
Shellf
ish m
ort
alit
ies;
Reoxy
genate
d c
oasta
l wate
rs2
Pollu
tion (
long term
); s
hellf
ish (
long term
);
reoxy
genatio
n o
f w
ate
r (m
ediu
m term
)
Po
llu
tio
n in
clu
de
s ch
em
ical
fro
m o
nsh
ore
an
d o
ffsh
ore
ind
ust
ry, o
rgan
ic p
oll
uta
nts
an
d d
eb
ris
fro
m
dam
age
d in
fras
tru
ctu
re
3F
lorida, U
SA
Mangro
ves
Hurr
icane W
ilma (
2005)
Mangro
ve d
am
age; seagra
ss b
ed
dam
age; cora
l dam
age
2
Mangro
ve d
am
age (
long term
); s
eagra
ss
bed d
am
age (
mediu
m term
); c
ora
l
dam
age (
mediu
m term
)
Tim
ing
of
dam
age
ass
ess
me
nt
pla
ces
seag
rass
be
d a
nd
co
ral d
amag
e a
s m
ed
ium
te
rm im
pac
ts.
Man
gro
ve d
amag
e s
tate
d a
s lo
nge
r th
an o
ne
ye
ar
4C
hesapeake
Bay,
Washin
gto
n/V
irgin
ia, U
SA
Pela
gic
and b
enth
o-p
ela
gic
fis
h s
pecie
s
inclu
din
g A
nchoa m
itchill
i, A
meiu
rus
nebulo
sus, Lepom
is s
p., E
theosto
ma o
lmste
di
and P
erc
a fla
vescens
Hurr
icane Is
abel (
2003)
Specie
s r
edis
trib
utio
n3
Mediu
m term
Fish
su
rve
ys t
oo
k p
lace
s in
th
e m
on
ths
foll
ow
ing
Hu
rrri
can
e Is
abe
l
5N
ort
h C
aro
lina, U
SA
Blu
e c
rab C
alli
necte
s s
apid
us
Hurr
icanes D
ennis
and F
loyd
(1999)
Specie
s re
dis
trib
utio
n4
Mediu
m term
Sto
rm c
ause
d r
ive
r fl
oo
din
g th
at f
lush
ed
blu
e c
rab
s d
ow
nst
ream
into
off
sho
re w
ate
rs w
he
re t
he
y
we
re h
eav
ily
har
vest
ed
by
com
me
rcia
l fis
he
rie
s
6O
nslo
w B
ay,
Nort
h C
aro
lina, U
SA
Atla
ntic
menhaden B
revoort
ia tyra
nnus
Unnam
ed s
torm
(1986)
Reductio
n in
larv
al g
row
th r
ate
5Long term
Dat
a co
lle
cte
d w
ith
in t
wo
mo
nth
s o
f st
orm
. Im
pac
t fo
r fi
sh p
op
ou
lati
on
wil
l be
gre
ate
r th
an o
ne
year
7D
om
inic
an R
epublic
Mangro
ves
Hurr
icane G
eorg
es (
1998)
Mangro
ve d
am
age
6Long term
Dam
age
su
rve
yed
at
7 an
d 1
8 m
on
ths
afte
r th
e s
torm
8Ja
maic
aC
ora
lsH
urr
icane A
llen (
1980)
Cora
l dam
age
7Long term
Po
st-s
torm
re
cru
itm
en
t b
y th
e c
ora
l, Acropora
, was
no
min
al. O
the
rs w
ere
sh
ow
ing
sign
s o
f
reco
very
ove
r th
e t
hre
e y
ear
s af
ter
the
sto
rm
9
Charlotte H
arb
or
estu
ary
and
Peace R
iver
wate
rshed, F
lorida,
US
A
Various e
stu
arine fis
h in
clu
din
g M
icro
pte
rus
salm
oid
es, Lepom
is m
acro
chirus, P
ara
lichth
ys
alb
igutta, Lutja
nus g
riseus, A
rius felis
,
Epin
ephelu
s it
aja
ra, and C
entr
opom
us
undecim
alis
, H
oplo
ste
rnum
littora
le a
nd
Pte
rygoplic
hth
ys s
pp.
Hurr
icane C
harley
(2004)
Specie
s r
edis
trib
utio
n8
Mediu
m term
Ch
ange
s in
fis
h a
sse
mb
lage
s o
bse
rve
d in
th
e t
wo
mo
nth
s fo
llo
win
g th
e s
torm
. Alt
era
tio
ns
asso
ciat
ed
wit
h s
torm
-re
late
d h
ypo
xia
10
Terr
a C
eia
Bay,
Flo
rida, U
SA
Bla
ckt
ip s
hark
s C
arc
harh
inus li
mbatu
sH
urr
icane G
abrielle
(2001)
Specie
s r
edis
trib
utio
n9
Short
term
Bla
ckti
p s
har
ks e
vacu
ate
d t
he
aff
ect
ed
are
a in
th
e p
eri
od
lead
ing
up
to
th
e s
torm
an
d r
etu
rne
d
imm
ed
iate
ly a
fte
rwar
ds
11
Icela
nd
Ocean q
uahog A
rctic
a is
landic
aU
nnam
ed s
trorm
(2006)
Shellf
ish r
edis
trib
utio
n; S
hellf
ish
mort
alit
y1
0Long term
Oce
an q
uah
og
mo
ved
by
sto
rm t
o a
har
d o
cean
bo
tto
m w
he
re, a
ye
ar la
ter,
th
ey
we
re f
ou
nd
to
hav
e
be
en
su
bje
ct t
o e
asy
pre
dat
ion
12
Nya
nza G
ulf
of Lake
Vic
toria,
Kenya
Fis
h s
pecie
s (
Late
s n
ilotic
us
and O
reochro
mis
nilo
ticus)
Unnam
ed s
torm
(1984)
Alg
al b
loom
; R
un-o
ff p
ollu
tion; D
e-
oxy
genatio
n; F
ish m
ort
alit
ies
11
Alg
al b
loom
(m
ediu
m term
); R
un-o
ff
pollu
tion (
mediu
m term
); D
e-o
xygenatio
n
(mediu
m term
); F
ish m
ort
alit
ies (
long
term
)
Low
er
than
ave
rage
lake
leve
ls c
om
bin
ed
wit
h r
un
-off
se
dim
en
t, c
hu
rne
d-u
p la
ke b
ott
om
mu
d,
wat
er
hyp
oxi
a an
d a
lgal
blo
om
to
cau
se m
ass
fish
mo
rtal
ity
eve
nt.
Wh
ilst
th
e e
nvi
ron
me
nta
l
con
dit
ion
s ca
use
d b
y th
e s
torm
we
re m
ed
ium
te
rm, t
he
fis
h m
ort
alit
y e
ven
t h
as b
ee
n c
lass
ifie
d a
s
lon
g te
rm
13
Andava
doaka
, M
adagascar
Seagra
ss
Tro
pic
al C
yclo
ne H
aru
na (
2013)
Seagra
ss b
ed d
am
age
12
Mediu
m term
D
amag
e a
sse
sse
d w
ith
in a
mo
nth
of
the
sto
rm. F
urt
he
r st
ud
ies
wo
uld
hav
e b
ee
n r
eq
uir
ed
to
est
abli
sh w
he
the
r d
amag
e la
ste
d m
ore
th
an a
ye
ar
14
Mya
nm
ar
Mangro
ves a
nd fis
h s
pecie
sC
yclo
ne N
arg
is (
2008)
Mangro
ve d
am
age; R
educed fis
h
pro
ductiv
ity1
3Long term
Cyc
lon
e N
argi
s d
est
roye
d 3
8,00
0 h
ect
are
s o
f m
angr
ove
s. It
has
be
en
ass
um
ed
th
at t
he
re
cove
ry w
ill
take
mo
rwe
th
an o
ne
ye
ar. T
he
loss
of
man
grio
ves
de
stro
yed
fis
h b
ree
din
g gr
ou
nd
s, r
ed
uci
ng
fish
pro
du
ctiv
ity
(as
wit
h m
angr
ove
imp
acts
, th
is h
as b
ee
n a
ssu
me
d t
o b
e lo
ng
term
)
15
Warn
bro
Sound,W
este
rn
Austr
alia
, A
ustr
alia
Various r
eef fis
h in
clu
din
g A
ustr
ola
bru
s
macula
tus a
nd P
arm
a m
ccullo
chi
Four
unnam
ed s
torm
s (
2013)
Specie
s r
edis
trib
utio
n1
4S
hort
term
Stu
dy
no
ted
var
iati
on
in t
he
se
nsi
tivi
ty o
f sp
eci
es
to s
torm
-re
late
d e
nvi
ron
me
nta
l fac
tors
du
rin
g
sto
rms.
16
Phill
ipin
es
Mangro
ves
Typ
hoon H
aiy
an (
2013)
Mangro
ve d
am
age
15
Long term
Dam
age
to
man
gro
ves
rem
ain
ed
wh
en
stu
dy
are
as w
ere
re
visi
ted
18
mo
nth
s af
ter
the
sto
rm
17
Liz
ard
Isla
nd (
nort
hern
Gre
at
Barr
ier
Reef)
, A
ustr
alia
R
eef fis
h (
ext
ensiv
e li
st of specie
s)
Cyc
lone E
ddie
(1981)
Specie
s r
edis
trib
utio
n; F
ish m
ort
alit
y1
6S
pecie
s r
edis
trib
utio
n (
mediu
m term
);
Fis
h m
ort
alit
y (long term
)
Hig
h m
ort
alit
y ra
tes
of
juve
nil
e f
ish
(cl
assi
fie
d a
s lo
ing
term
). S
ub
-ad
ult
fis
h r
e-d
istr
ibu
ted
bu
t ad
ult
fish
did
no
t ap
pe
ar t
o b
e a
ffe
cte
d b
y th
e s
torm
. Stu
die
s to
ok
pla
ce r
egu
larl
y in
th
e le
ad u
p t
o, a
nd
two
day
s af
ter,
th
e s
torm
18
New
Calid
onia
Reef fis
h a
nd c
ora
lC
yclo
ne E
rica (
2003)
Cora
l dam
age; S
pecie
s r
edis
trib
utio
n1
7Long term
Dat
a co
lle
cte
d w
ith
in a
mo
nth
of
the
sto
rm a
nd
20
mo
nth
s af
ter
the
sto
rm. I
mp
act
on
fis
h
asse
mp
lage
s fo
un
d t
o b
e g
reat
er
afte
r 20
mo
nth
s th
an b
efo
re o
r ju
st a
fte
r th
e s
torm
19
Fiji
Cora
lsC
yclo
ne W
insto
n (
2016)
Cora
l dam
age
18
Mediu
m term
Dam
age
to
co
ral a
sse
sse
d w
ith
in a
mo
nth
of
the
sto
rm. N
o f
oll
ow
up
stu
die
s w
ere
re
po
rte
d, s
o
imp
act
has
be
en
cla
ssif
ied
as
me
diu
m t
erm
14
Supplementary Information Section 1b 253
This section provides references and additional detail for Figure 1b. 254
255
Supplementary Figure 1b. Figure 1b with additional case study reference numbers 256 linking to Supplementary Table 1b. 257
258
259
260
261
262
263
264
265
266
15
267
Sup
ple
me
nta
ry T
able
1b
. Ad
dit
ion
al d
etai
l an
d r
efer
ence
s fo
r Fi
gure
1b
.
Su
pp
lem
en
tary
Fig
ure
1b
Map
Re
fere
nce
Lo
cati
on
Ev
en
tIm
pact
typ
eE
xte
nt
of
imp
act
(an
d s
ou
rce
re
fere
nce
)N
ote
s
Vessels
/gear
dam
aged/lo
st/destr
oye
d87%
(n =
511)1
9%
of re
sid
ent lic
ensed M
issis
sip
pi c
om
merc
ial f
ishin
g u
nits
dam
aged e
stim
ate
d b
ased o
n s
am
ple
(of 1,0
30 li
censed v
essels
, 511 r
etu
rned
surv
eys
)
Vessels
/gear
dam
aged/lo
st/destr
oye
d$35.0
mill
ion
19
Estim
ate
calc
ula
ted u
sin
g a
vera
ge tota
l dam
ages r
eport
ed b
y re
sid
ent lic
ensed M
issis
sip
pi c
om
merc
ial f
ishin
g s
am
ple
units
(n =
511)
multi
plie
d b
y to
tal n
um
ber
of fis
hin
g u
nits
(n =
1030)
Fis
hin
g d
isru
ptio
n72%
gro
ss s
ale
s r
eductio
n in
2006
com
pare
d to 2
004
19
Based o
n e
stim
ate
s o
f pro
jecte
d g
ross s
ale
s r
eductio
n d
ue to lo
st m
ark
et channels
fro
m re
sid
ent lic
ensed M
issis
sip
pi c
om
merc
ial f
ishin
g
surv
ey
respondents
(n =
511)
Fis
hin
g d
isru
ptio
n79%
em
plo
yed c
rew
reductio
n1
9B
ased o
n r
eductio
n in
em
plo
yed c
rew
in 2
006 c
om
pare
d to 2
004 r
eport
ed b
y r
esid
ent lic
ensed M
issis
sip
pi c
om
merc
ial f
ishin
g s
urv
ey
respondents
(n =
511)
Vessels
dam
aged/lo
st/destr
oye
d69%
(n =
54)
of B
arb
udan v
essels
20
37 o
ut of 54 a
ctiv
e fis
hin
g v
essels
in B
arb
uda d
am
aged o
r destr
oye
d
Vessels
dam
aged/lo
st/destr
oye
d $
94,0
00 (
Barb
udan v
essels
only
)20
All
vessels
affecte
d w
ere
Barb
udan. X
CD
to U
S$ c
onve
rsio
n 1
:0.3
7 take
n fro
m w
ww
.xe.c
om
his
toric e
xchange r
ate
data
base for
01/0
9/1
7
(sourc
e r
eport
publis
hed S
epte
mber
2017)
Gear
dam
aged/lo
st/destr
oye
d 4
4%
(n =
4,8
99)2
0S
om
e lo
sses m
ay
be a
ttributa
ble
to H
urr
icanes J
ose a
nd M
aria. 2,1
77 o
f 4,8
99 fis
hin
g p
ots
lost
Gear
dam
aged/lo
st/destr
oye
d $
156,0
00
20
Som
e lo
sses m
ay
be a
ttributa
ble
to H
urr
icanes J
ose a
nd M
aria. Losses e
xperiences a
cro
ss A
ntig
ua a
nd B
arb
uda. X
CD
to U
S$ c
onve
rsio
n
1:0
.37 take
n fro
m w
ww
.xe.c
om
his
toric e
xchange r
ate
data
base for
01/0
9/1
7 (
sourc
e r
eport
publis
hed S
epte
mber
2017)
Vessels
/gear
dam
aged/lo
st/destr
oye
d$52.0
mill
ion
21
Based o
n s
urv
eys
conducte
d w
ith s
am
ple
of N
ew
york
and N
ew
Jers
ey
com
merc
ially
licensed fis
hers
(n =
292).
Estim
ate
based o
n
ave
rage v
alu
e o
f dam
ages a
nd lo
sses p
er
vessel m
ulti
plie
d b
y to
tal n
um
ber
of lic
ensed v
essels
Vessels
dam
aged/lo
st/destr
oye
d57%
(n =
292)2
1B
ased o
n s
urv
eys
conducte
d w
ith s
am
ple
of N
ew
york
and N
ew
Jers
ey
com
merc
ially
licensed fis
hers
(n =
292)
Fis
hin
g d
isru
ptio
n94 fis
hin
g d
ays
per
fisher
on a
vera
ge
21
Based o
n s
urv
eys
conducte
d w
ith s
am
ple
of N
ew
york
and N
ew
Jers
ey
com
merc
ially
licensed fis
hers
(n =
292)
Vessels
dam
aged/lo
st/destr
oye
d29%
(n =
437)2
2,
23
128 o
ut of 437 fis
hin
g v
essels
dam
aged o
r destr
oye
d
Vessels
and e
ngin
e
dam
aged/lo
st/destr
oye
d$1.7
mill
ion
22
Estim
ate
Gear
dam
aged/lo
st/destr
oye
d10%
(n =
7,2
41)2
3746 o
ut of 7,2
41 g
ears
affecte
d
Gear
dam
aged/lo
st/destr
oye
d$156,0
00
23
Initi
al e
stim
ate
. X
CD
to U
S$ c
onve
rsio
n 1
:0.3
7 take
n fro
m w
ww
.xe.c
om
his
toric e
xchange r
ate
data
base for
01/1
0/1
7 (
sourc
e r
eport
publis
hed O
cto
ber
2017)
5K
enya
/ T
anzania
/
Uganda
Daily
thunders
torm
sF
isher
/ fis
hery
work
er
fata
litie
s3000–5
000 a
nnually
24
Estim
ate
Gear
dam
aged/lo
st/destr
oye
d$682,0
00
25
Based o
n the v
alu
e o
f cla
ims m
ade b
y fis
hers
under
the U
K G
ove
rnm
ent's
Gear
Repla
cem
ent S
chem
e. G
B£ to U
S$ c
onve
rsio
n 1
:1.7
10
take
n fro
m w
ww
.xe.c
om
his
toric e
xchange r
ate
data
base for
30/0
/6/1
4 (
date
applic
atio
ns c
losed for
the g
rear
repla
cem
ent schem
e)
Fis
hin
g d
isru
ptio
n$11.0
mill
ion in
com
e lo
st2
6E
stim
ate
made b
ased o
n r
educed c
atc
h a
t port
of N
ew
lyn, C
orn
wall
during J
anuary
and F
ebru
ary
2014. G
B£ to U
S$ c
onve
rsio
n 1
:1.5
67
take
n fro
m w
ww
.xe.c
om
his
toric e
xchange r
ate
data
base for
19/1
1/1
4 (
date
sourc
e r
eport
publis
hed)
Fis
hery
infr
astr
uctu
re d
am
age
$1.8
mill
ion
27
Leve
l of fu
ndin
g s
upport
pro
vided b
y U
K G
ove
rnm
ent to
repair d
am
age to fis
hin
g p
ort
s. G
B£ to U
S$ c
onve
rsio
n 1
:1.6
22 take
n fro
m
ww
w.x
e.c
om
his
toric e
xchange r
ate
data
base for
01/1
0/1
4 (
date
sourc
e r
eport
publis
hed)
Fis
her
/ fis
hery
work
er
fata
litie
s28,0
00 d
ead o
r m
issin
g2
8E
stim
ate
Vessels
dam
aged/lo
st/destr
oye
d101,5
00 d
estr
oye
d2
8M
ostly
sm
all
inla
nd v
essels
Gear
dam
aged/lo
st/destr
oye
d70%
28
Estim
ate
Vessels
/gear/
facili
ties/ tr
ansport
and
infr
astr
uctu
re d
am
aged/lo
st/destr
oye
d$23.3
mill
ion
29
Estim
ate
. K
YA
T to U
S$ c
onve
rsio
n 1
:0.0
009 a
s u
sed e
lsew
here
with
in the s
ourc
e d
ocum
ent
Fis
hin
g d
isru
ptio
n$89.9
mill
ion in
com
e lo
st2
9E
stim
ate
of fo
regone in
com
e. K
YA
T to U
S$ c
onve
rsio
n 1
:0.0
009 a
s u
sed e
lsew
here
with
in the s
ourc
e d
ocum
ent
Mya
nm
ar
Cyc
lone N
arg
is 2
009
6U
KW
inte
r sto
rms
2013–2
014
1U
SA
(M
issis
sip
pi o
nly
)H
urr
icanes K
atr
ina a
nd
Rita
2005
4D
om
inic
aH
urr
icane M
aria 2
017
Hurr
icane Ir
ma 2
017
Antig
ua a
nd B
arb
uda
2 3U
SA
(N
ew
Jers
ey
and
New
York
)H
urr
icane S
andy
2012
7
16
268
Sup
ple
me
nta
ry T
able
1b
(co
nti
nu
ed).
Ad
dit
ion
al d
etai
l an
d r
efe
ren
ces
for
Figu
re 1
b.
Su
pp
lem
en
tary
Fig
ure
1b
Map
Re
fere
nce
Lo
cati
on
Ev
en
tIm
pact
typ
eE
xte
nt
of
imp
act
(an
d s
ou
rce
re
fere
nce
)N
ote
s
Vessels
/gear
dam
age/lo
st/destr
oye
d$2.6
mill
ion
30
Dam
age to b
oats
and g
ear.
Estim
ate
s r
ange fro
m U
S$1.9
mill
ion to U
S$3.3
mill
ion. A
n a
vera
ge o
f th
e tw
o h
as b
een u
sed. B
ased o
n fie
ld
trip
s a
nd c
ross c
hecke
d w
ith in
dependent estim
ate
s
9P
hill
ippin
es
Typ
hoon H
aiy
an 2
013
Vessels
dam
aged/lo
st/destr
oye
d30,0
00
31
Estim
ate
.
Gear
dam
aged/lo
st/destr
oye
d$627,0
00
32
Tota
l of estim
ate
s m
ade b
y fis
hers
during s
urv
eys
conducte
d w
ith a
cro
ss a
sam
ple
of affecte
d v
illages (
74%
, n =
207)
with
in s
ix p
rovi
nces.
Bam
boo r
afts (
bili
bili
) w
ere
not in
clu
ded. F
JD to U
S$ c
onve
rsio
n 1
:0.4
85 take
n fro
m w
ww
.xe.c
om
his
toric e
xchange r
ate
data
base for
01/0
5/1
6 (
mid
-poin
t of surv
ey
period)
Tota
l of estim
ate
s m
ade b
y fis
hers
during s
urv
eys
conducte
d w
ith a
cro
ss a
sam
ple
of affecte
d v
illages (
74%
, n =
207)
with
in s
ix p
rovi
nces.
Bam
boo r
afts (
bili
bili
) w
ere
not in
clu
ded. F
JD to U
S$ c
onve
rsio
n 1
:0.4
85 take
n fro
m w
ww
.xe.c
om
his
toric e
xchange r
ate
data
base for
01/0
5/1
6 (
mid
-poin
t of surv
ey
period)
$586,0
00
32
Vessels
and e
ngin
e
dam
aged/lo
st/destr
oye
d
Based o
n fie
ld trips to e
ight dis
tric
ts a
nd c
ross c
hecke
d w
ith d
am
age e
stim
ate
s c
arr
ied o
ut by
Bangla
desh D
epart
ment of F
isheries
3,9
80
30
Vessels
dam
aged/lo
st/destr
oye
d
8B
angla
desh
Cyc
lone S
idr
2007
10
Fiji
Cyc
lone W
insto
n 2
016
17
Supplementary Information Section 2 269
This section provides references and additional detail for Figure 2. 270
271
Supplementary Figure 2. Figure 2 with additional case study reference numbers 272 linking to Supplementary Table 2. 273
274
275
276
277
278
279
280
281
282
283
18
284
Sup
ple
me
nta
ry T
able
2. A
dd
itio
nal
de
tail
and
ref
eren
ces
for
Figu
re 2
.
Su
pp
lem
en
tary
Fig
ure
2 M
ap
Re
fere
nce
Stu
dy
typ
eA
rea
Typ
e o
f sto
rmR
ean
aly
sis
or
Pro
jecti
on
Tim
e p
eri
od
Ch
an
ge
de
scri
be
d (a
nd
so
urc
e r
efe
ren
ce
)T
ime
of
year
1R
evi
ew
Weste
rn E
uro
pe
Ext
ra-t
ropic
al
Pro
jectio
nM
ix s
pannin
g 2
020–2190 a
cro
ss 3
3 s
tudie
s
Incre
ase in
fre
quency
and in
tensity
of sto
rms
33
Mix
spannin
g S
epte
mber–
April a
cro
ss 3
3 s
tudie
s
2R
evi
ew
Easte
rn N
ort
h A
tlantic
south
of 60°N
Ext
ra-t
ropic
al
Pro
jectio
nM
ix s
pannin
g 2
020–2190 a
cro
ss 1
6 s
tudie
sIn
cre
ase in
fre
quency
and in
tensity
of sto
rms
33
Mix
spannin
g S
epte
mber–
April a
cro
ss 1
4 s
tudie
s, 1 s
tudy
May–
Decem
ber,
1 s
tudy
not specifi
ed
3R
evi
ew
Nort
h A
tlantic
nort
h o
f
60°N
Ext
ra-t
ropic
al
Pro
jectio
nM
ix s
pannin
g 2
020–2190 a
cro
ss 1
1 s
tudie
sD
ecre
ase in
fre
quency
of ext
rem
e c
yclo
nes a
nd d
ecre
ase in
cyc
lone in
tensity
33
Mix
spannin
g S
epte
mber–
April a
cro
ss 1
1 s
tudie
s
4R
evi
ew
South
ern
Euro
pe
Ext
ra-t
ropic
al
Pro
jectio
nM
ix s
pannin
g 2
020–2190 a
cro
ss 1
1 s
tudie
sD
ecre
ase b
ehavi
our
of sto
rmin
ess o
ver
long term
33
Mix
spannin
g S
epte
mber–
April a
cro
ss 9
stu
die
s, 2 s
tudie
s
not specifi
ed
5R
evi
ew
Nort
h A
tlantic
tro
pic
sT
ropic
al
Reanaly
sis
1970–2013
Most in
tense tro
pic
al c
yclo
nes a
re b
ecom
ing m
ore
fre
quent sin
ce 1
970s
34
Not specifi
ed
6N
ew
data
Nort
h A
tlantic
tro
pic
sT
ropic
al
Reanaly
sis
1900–2000
Hurr
icanes m
aki
ng la
ndfa
ll in
US
A h
ave
not becom
e m
ore
fre
quent ove
r la
st centu
ry3
5A
ll ye
ar
7N
ew
data
Mid
-latit
ude N
ort
h P
acifi
cE
xtra
-tro
pic
al
Reanaly
sis
1958–1977 a
nd 1
982–2001
Incre
asin
g tre
nd in
str
ong c
yclo
nic
activ
ity3
6Ja
nuary
/Febru
ary
/Marc
h
8N
ew
data
Mid
-latit
ude N
ort
h A
tlantic
Ext
ra-t
ropic
al
Reanaly
sis
1958–1977 a
nd 1
982–2001
Decre
asin
g tre
nd in
str
ong c
yclo
nic
activ
ity3
6Ja
nuary
/Febru
ary
/Marc
h
9N
ew
data
Weste
rn p
art
of W
este
rn
Nort
h P
acifi
cT
ropic
al
Pro
jectio
n2075–2099
Decre
ase in
fre
quency
of tr
opic
al c
yclo
nes
37
Peak
tropic
al c
yclo
ne s
eason in
nort
hern
hem
isphere
10
New
data
Centr
al P
acifi
cT
ropic
al
Pro
jectio
n2075–2099
Incre
ase in
fre
quency
of tr
opic
al c
yclo
nes
37
Peak
tropic
al c
yclo
ne s
eason in
each h
em
isphere
11
New
data
Weste
rn N
ort
h P
acifi
cT
ropic
al
Pro
jectio
n2075–2099
Decre
ase in
fre
quency
of tr
opic
al c
yclo
nes a
ppro
achin
g c
oasta
l regio
ns
37
Peak
tropic
al c
yclo
ne s
eason in
nort
hern
hem
isphere
12
New
data
Nort
h-W
este
rn N
ort
hern
Pacifi
cT
ropic
al
Pro
jectio
n2075–2099
Incre
ase in
fre
quency
of m
ost in
tense tro
pic
al c
yclo
nes
37
Peak
tropic
al c
yclo
ne s
eason in
nort
hern
hem
isphere
13
New
data
Nort
h P
acifi
c n
ear
the
Ale
utia
n Is
lands
Ext
ra-t
ropic
al
Pro
jectio
n2081–2100
Enhanced s
torm
iness
38
Not specifi
ed
14
New
data
Weste
rn N
ort
hern
Pacifi
cT
ropic
al
Reanaly
sis
and
Pro
jectio
n
Reanaly
sis
: 1980–2013; P
roje
ctio
n:
2070–2099
Decre
ased tro
pic
al c
yclo
ne e
xposure
in the P
hili
ppin
e a
nd S
outh
Chin
a S
ea r
egio
ns
and in
cre
ased e
xposure
in the E
ast C
hin
a S
ea r
egio
n3
9Ju
ly–N
ove
mber
15
New
data
South
Pacifi
cT
ropic
al
Pro
jectio
n2075–2099
Decre
ase in
fre
quency
of tr
opic
al c
yclo
nes
37
Peak
tropic
al c
yclo
ne s
eason in
south
ern
hem
isphere
16
New
data
South
Pacifi
cT
ropic
al
Pro
jectio
n2075–2099
Decre
ase in
fre
quency
of tro
pic
al c
yclo
nes a
ppro
achin
g c
oasta
l regio
ns
37
Peak
tropic
al c
yclo
ne s
eason in
south
ern
hem
isphere
17
Revi
ew
South
Pacifi
cT
ropic
al
Pro
jectio
nM
ix fro
m 2
061–2200
Tro
pic
al c
yclo
ne fre
quency
will
decre
ase. T
he in
tensity
of th
e m
ost in
tense s
torm
s w
ill
likely
incre
ase
40
Not specifi
ed
18
New
data
Austr
alia
Tro
pic
al
Pro
jectio
n2046–2065 a
nd 2
081–2100
Decre
ase in
num
bers
of tr
opic
al c
yclo
nes o
vera
ll, s
mall
incre
ase in
the m
ost in
tense
tropic
al c
yclo
nes
41
All
year
19
New
data
Nort
h In
dia
n O
cean
Tro
pic
al
Reanaly
sis
1901–1951 a
nd 1
951–2001
No in
cre
ase in
sto
rms d
espite
incre
ase in
sea s
urf
ace tem
pera
ture
in B
ay
of B
engal
and A
rabia
n S
ea
42
Win
ter/
Pre
-Monsoon / M
onsoon / P
ost M
onsoon
20
New
data
Ara
bia
n S
ea
Tro
pic
al
Reanaly
sis
Contr
ol e
xperim
ents
for
1860 (
600 y
ears
),
1940 (
200 y
ears
), 1
990 (
300 y
ears
), 2
015
(200 y
ears
)
Glo
bal w
arm
ing h
as in
cre
ased the p
robabili
ty o
f post-
monsoon e
xtre
mely
seve
re
cyc
lonic
sto
rms o
ver
the A
rabia
n S
ea
43
Octo
ber–
Decem
ber
21
New
data
South
India
n O
cean
Tro
pic
al
Pro
jectio
n2075–2099
Decre
ase in
num
ber
of tr
opic
al c
yclo
nes
37
Peak
tropic
al c
yclo
ne s
eason in
south
ern
hem
isphere
(Nove
mber–
April)
19
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