Marine Policy 88 (2018) 295–302

Contents lists available at ScienceDirect

Marine Policy
journal homepage: www.elsevier.com/locate/marpol

How can climate predictions improve sustainability of coastal ﬁsheries in
Paciﬁc Small-Island Developing States?

T⁎

Piers K. Dunstana, , Bradley R. Mooreb, Johann D. Bellc,d, Neil J. Holbrooke, Eric C.J. Olivere,
James Risbeya, Scott D. Fosterf, Quentin Hanichc, Alistair J. Hobdaya, Nathan J. Bennettga

CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
Coastal Fisheries Programme, Paciﬁc Community (SPC), Noumea, New CaledoniacAustralian National Centre for Ocean Resources and Security, University of Wollongong, NSW 2522, AustraliadConservation International, Arlington, VA 22202, USAe
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australiaf
CSIRO Data61, Hobart, Tasmania, AustraliagInstitute for the Oceans and Fisheries and Institute for Resources, Environment and Sustainability, University of British ColombiaCenter for Ocean Solutions, Stanford
University, Canadab

A B S T R A C T
Climate and weather have profound eﬀects on economies, the food security and livelihoods of communities
throughout the Paciﬁc Island region. These eﬀects are particularly important for small-scale ﬁsheries and occur,for example, through changes in sea surface temperature, primary productivity, ocean currents, rainfall patterns,
and through cyclones. This variability has impacts over both short and long time scales. We diﬀerentiate climate
predictions (the actual state of climate at a particular point in time) from climate projections (the average stateof climate over long time scales). The ability to predict environmental conditions over the time scale of months
to decades will assist governments and coastal communities to reduce the impacts of climatic variability and takeadvantage of opportunities. We explore the potential to make reliable climate predictions over time scales of six
months to 10 years for use by policy makers, managers and communities. We also describe how climate predictions can be used to make decisions on short time scales that should be of direct beneﬁt to sustainable
management of small-scale ﬁsheries, and to disaster risk reduction, in Small-Island Developing States in the
Paciﬁc

1. Introduction
Variability in weather and climate across spatial and temporalscales has a range of human and ecological impacts. Variability overlong time scales (centennial and longer) has impacted viability ofhuman civilizations [93] and redeﬁned the distribution of major biomes[24]. At seasonal and interannual time scales, biomes, animal movements and human agricultural practices are inﬂuenced by climatic
events [40], while daily and monthly variations in weather, including
extreme events, also lead to dramatic impacts on people and environments [30,76,82]. These patterns of variability are superimposed onlong-term, global climate change trends. Oceans are warming and altering environmental conditions in many regions, with impacts apparent across all sectors of the blue economy, from ﬁshing to transport
to energy generation [43,69].
The well-being of Paciﬁc Island countries and territories (PICTs) istightly linked to oceans and climate. PICTs can largely be thought of as⁎

small-island, large-ocean, developing states: combined, the area of theirlandmasses is only 2% of that of their exclusive economic zones (EEZs).
Marine resources often provide the majority of income and protein forcoastal communities [13]. A recent study estimates the production of
Paciﬁc Island coastal ﬁsheries (i.e., those that harvest wild demersal
ﬁsh and invertebrates from inshore coastal habitats and pelagic ﬁshfrom nearshore waters for either commercial or subsistence purposes)
to have been around 163,936 mt in 2014, worth approximately US$453
million [31]. Consumption rates of fresh ﬁsh among Paciﬁc coastal
communities are among the highest in the world, with average consumption in the majority of PICTs being 2–4 times the global average
and with 50–90% of dietary animal protein in coastal, rural areas being
derived from ﬁsh [12]. Coastal ﬁsheries also provide many livelihoods
across the region, with an average of 50% of surveyed coastal households in 17 PICTs receiving their ﬁrst or second income from activities
related to ﬁshing [70].
Climate and weather have profound eﬀects on economies, and theCorresponding author.

http://dx.doi.org/10.1016/j.marpol.2017.09.033
Received 28 March 2017; Received in revised form 22 September 2017; Accepted 25 September 2017
Available online 08 October 20170308-597X/ Crown Copyright © 2017 Published by Elsevier Ltd. All rights reserved.

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climate system [61,64], such as the Paciﬁc Decadal Oscillation (PDO).
The basic principal is the same as weather forecasting – a longer-termclimate prediction will be initialised to the current phase of the PDO
and will attempt to track and predict the phase of the PDO. Climate
projections by contrast, do not, and cannot, keep track of the particularphase of processes like the PDO [75].

food security and livelihoods of communities, throughout the PaciﬁcIsland region [14]. Patterns of climate variability on the scale of days toyears aﬀect these communities in diﬀerent ways. The most well-known
oceanographic pattern aﬀecting Paciﬁc Island communities is the El
Niño-Southern Oscillation (ENSO) [76]. Multi-month El Niño and La
Niña events cause: (i) variation in the distribution, and hence regional
catch, of tuna [54,55]; (ii) extensive coral bleaching events that reduce
local ﬁsh abundance [46,72]; and (iii) inﬂuence the distribution oftropical cyclones, which can result in severe impacts to ﬁsh habitats andﬁshing infrastructure.
The ability to understand and anticipate changes in key climatic
variables, e.g., sea surface temperature (SST) and circulation patterns,
and predict weather events over the scale of months to decades oﬀers
immediate opportunities to governments and coastal communities tomitigate climate impacts and increase adaptive capacity. Developmentof models to forecast future climate states are developing quickly[61,64,75] and may allow predictive skill from six months out to a
decade [9,60,80,81,91]. This will require the development of new approaches to designing and implementing climate adaptation policies, as
management decisions respond to changing conditions over a range oftime scales. Further, by taking advantage of the ability to mitigate,
adapt and plan for change on inter-annual time scales, communities will
be more able to respond to changes that occur over longer time scales[41].
In this paper, we outline the potential for climate predictions to
support the adaptation and development of coastal ﬁsheries in Small
Island Developing States, focusing primarily on the Paciﬁc Islands. We
clarify the diﬀerences between climate prediction and projection, andwhich climate conditions might be skillfully predicted. We review
documented examples of weather impacts on ﬁsheries in the PaciﬁcIslands region and identify how predictions could inform management.
We then use this information to identify how predictions of climate
variability can help forge policies to reduce the impacts of climatic
variability on, and increase the adaptive capacity of, coastal ﬁsheries.

3. Current understanding of climate variability and extremes in
the Paciﬁc
The heat content of the ocean is dynamic and SST varies on multiple
temporal and spatial scales, with (for example) seasonal cycles being
generally larger at higher latitudes (see [29]) for the Australian region).
Although well studied, SST is not easily predicted and a notable sourceof uncertainty comes from the onset, severity and duration of ENSO
[83]. Extreme and prolonged variations in SST unrelated to seasonalvariations constitute events known as marine heatwaves [42]. The
dominant driver of marine heatwaves in the subtropical and tropicalPaciﬁc Ocean is ENSO [83]. The Oscillation forces marine heatwaves in
the central and eastern Paciﬁc directly as an expression of El Niño andelsewhere either directly or indirectly through the processes that ENSO
inﬂuences, including modiﬁed wind patterns and cloud coverage,
which in turn impact air-sea heat ﬂuxes.
Rainfall variability in tropical regions varies in conjunction withlocal convective processes and organised larger-scale convection inconvergence zones. It is also inﬂuenced by the proximity to the tracks oftropical cyclones, which can deliver extreme rainfall in some years.
Throughout the Paciﬁc basin, variability of rainfall is strongly linked to
the ENSO cycle, driven by shifts in the positions of large-scale convective zones. In the tropical Indian Ocean, precipitation is inﬂuenced
by local monsoon circulations and by ENSO and the Indian Ocean
Dipole (IOD).
Outside the tropics, variability in rainfall is set mainly by variationsin atmospheric jet streams and storm tracks. These respond in part totropical features such as ENSO and the IOD, but also to internal modulations and to changes in high latitudes that aﬀect meridional temperature gradients in the atmosphere and the formation of genesis zones
for storms. The Southern Annular Mode (SAM) represents north-south
variations in the storm tracks, while the major blocking regions of theSouthern Hemisphere [63] represent the east-west variation in storm
track activity [74]. Both SAM and blocking high pressure systems
modulate rainfall over the Southern Hemisphere extratropics.
Variability in rainfall is partly predictable on longer time scales
when it relates to slower varying modes in the oceans and cryosphere[61]. For example, rainfall variations related to ENSO are partly predictable on time scales of months, whereas variations due to the PDO
may have predictability spanning years [64]. These patterns are reﬂected in the large-scale spatial variations of upper ocean salinity,
which integrates rainfall, thereby oﬀering scope for enhanced predictability [65]. The controls of long-period variations in blocking andassociated extreme rainfall events are still not well understood, but may
also oﬀer some scope for predicting rainfall [74].
The ENSO state oﬀers some predictability of the frequency andpotential location of cyclones in the southwest Paciﬁc Ocean region[8,25,58,86]. During El Niño conditions, cyclones occur most frequently between Vanuatu and Fiji, and chances of occurrence are alsohigh further east towards Samoa, southern Cook Islands and French
Polynesia [52]. Under La Niña conditions, tropical cyclones are more
frequent in the Coral Sea, and absent from Cook Islands eastwards [25].

2. Diﬀerence between prediction and projection
The terms ‘climate prediction’ and ‘climate projection’ are often
confused as being the same. With regard to modelling the future, they
are diﬀerent activities with substantially diﬀerent objectives. A climate
prediction is aimed at describing the actual state of the climate system ata particular point in time, whereas a climate projection is aimed at describing the average or statistical properties of the climate system overa future time window.
For example, seasonal climate predictions attempt to forecast climate anomalies several months ahead, such as describing the actual
state of large-scale modes of the climate system (e.g. La Niña conditions) or environmental conditions (e.g. surface temperatures) at aparticular point in time. This is an initial-value problem that requires
the climate model to start oﬀ in the same state as the real climate
system and track the evolution of ENSO and other climate modes as
they progress through particular phases. Initialization of the correct
ENSO phase allows global climate prediction systems to forecast theevolution of tropical SSTs and the average weather over the next monthor season. A climate projection, on the other hand, characterises thechange in statistical properties of climate modes such as ENSO over a
future period. A projection does not attempt to specify the ENSO phaseat a given time, but rather whether one phase of ENSO will tend to befavoured more than another, or average temperatures, in a future
timeframe, typically on the scale of decades and longer. Models used tomake climate projections evolve in response to external forcing of theclimate system (e.g., increasing CO2). They do not need to be initialised
to a particular ENSO phase when they start because they are not intended to track speciﬁc changes in that phase [75].
Skillful climate predictions may be extended to longer time scales
than months by exploiting and focusing on slower processes in the

4. Climate impacts on small-scale ﬁsheries – case studies from the
Paciﬁc Islands region
4.1. Marine heatwaves
Marine heatwaves, deﬁned as ‘a prolonged discrete anomalously
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surface ﬁshery in the warm pool [54,55].

warm water event that can be described by its duration, intensity, rateof evolution, and spatial extent’ [42], can have devastating eﬀects oncoastal ﬁsheries in Small Island Developing States. Impacts can occur inmultiple ways, including: 1) through coral bleaching, resulting in reductions in quality and extent of critical habitat, 2) direct mortality ofmarine fauna through thermal stress, 3) indirect mortality of marine
fauna through reductions in dissolved O2 concentrations / increases inhypoxia in waters of elevated SST, and 4) avoidance of water bodies of
elevated SST by mobile ﬁsh species, including nearshore pelagics[20,68]. These eﬀects can result in long-term contraction or loss ofﬁshing grounds, shifts in species distributions, and declines in local
abundance, leading to depressed catch rates and declines in overall
catches.
Coral bleaching events are a common feature of marine heatwaves
in the tropical and subtropical Paciﬁc, and are typically associated withstrong El Nino phases [68,77]. The strong 1997/1998 El Niño caused
bleaching in every ocean, resulting in 16% of corals being destroyed
globally [44,45,88]. Severe bleaching was reported across much of thetropical and subtropical Paciﬁc during the 2015/16 El Niño, including
around the Cook Islands, Fiji, New Caledonia, Samoa, Tuvalu and Vanuatu. For example, in February 2016, a major coral bleaching event
occurred along a 20 km stretch of the Coral Coast on the island of Viti
Levu, Fiji. This event was caused by a severe, El Niño-induced marine
heatwave, resulting in SSTs that were 2–3 °C higher than average inJanuary, and a heat ‘spike’ over a ﬁve-day period in early February
where SSTs peaked between 34–36 °C and failed to drop below 30 °C(Victor Bonito, Reef Explorer Fiji Ltd, pers. comm.).
Coral bleaching events can signiﬁcantly alter the extent and qualityof coral reef habitats and are particularly devastating for coral reefdependent ﬁsheries if coral recovery does not occur and regime shifts
from coral-dominated systems to less desirable benthic assemblage
types (e.g., algal turf or macroalgae) occur [47,71,72]. While studies
have documented increases in the abundance of some functional groups
of ﬁsh (e.g., herbivores) immediately after coral bleaching events [71],over longer time-fames most reef-associated ﬁsh are expected to be lessabundant in habitats with low coral cover due to declines in the
structural complexity of reefs [35,72].
Marine heatwaves can also lead to direct or indirect ﬁsh mortality inboth coastal and nearshore waters. For example, the 2016 bleaching
event on the Coral Coast of Fiji described above was accompanied by amajor ﬁsh kill - thousands of ﬁsh and invertebrates died, presumably
from low dissolved O2 concentrations in the superheated water column(V. Bonito, pers. comm.). Similarly, the occurrence of a long period of
unseasonably warm, still weather was considered to be a signiﬁcantfactor driving a large phytoplankton bloom – which ultimately led to
declines in dissolved O2 and the death of thousands of marine fauna in alarge (> 20 km2) embayment in the Western Province of the Solomon
Islands (Simon Albert, University of Queensland, pers. Comm, [1]). This
event caused health complications amongst community members who
consumed dead ﬁsh and invertebrates, including dizziness, diarrhea,
aches and weakness, (S. Albert, pers. comm.), and resulted in a shift
from a coral-dominated to a cyanobacteria-dominated system [1]. Suchchanges in benthic cover have signiﬁcant negative long-term impacts
on local communities because cyanobacteria-dominated systems do notsupport as many ﬁsh as coral-dominated systems [1,62].
Marine heatwaves can also signiﬁcantly aﬀect the distribution of
pelagic species, reducing their availability for small-scale ﬁshers.
Climate cycles such as ENSO are key drivers of variation in the distribution of tuna and the location of tuna catches in the Paciﬁc Island
region. Under strong El Niño conditions, industrial ﬁshing for skipjack
tuna is generally displaced further eastward along the equator and at
higher latitudes - catches decline in western PICTs, such as Papua New
Guinea, Federated States of Micronesia and Solomon Islands, and increase in Kiribati [54,55]. The shallowing of the thermocline in thewestern Paciﬁc during El Niño events constrains the vertical habitat ofyellowﬁn tuna, resulting in higher catch rates of this species by the

4.2. Tropical cyclones and severe storms
Tropical cyclones and severe storms can have signiﬁcant eﬀects onﬁsh stocks, their supporting habitats, ﬁshing ﬂeets and ﬁsheries yields
and, consequently, protein intake and sources of livelihood for dependent communities. Tropical cyclones directly impact coral reefs, mangroves, seagrasses and intertidal areas through physical damage, resuspension of sediments, pulses of nutrient enrichment and freshwater
inundation, altering their extent, structural complexity and beneﬁt as
ﬁsh habitats [37,39,46,85,90], with subsequent eﬀects on local ﬁsheries [72]. Changes in ﬁsh species density and biomass, and catch rates
of target species, are common after such events [23,56,79,84,89]. For
example, signiﬁcant decreases have been reported [84] in ﬁsh species
richness, density and biomass on reefs in southern New Caledonia following Cyclone Erica, a Category 5 tropical cyclone (TC). Such changes
can result in reductions or loss of critical ecosystem functions, leading
to regime shifts to less desirable benthic assemblage types (e.g., fromstructurally complex coral-dominated systems to rubble-, turf- or
macro-algal-dominance) [23,84]. In addition, increased runoﬀ resulting
from extreme rainfall during cyclones or stand-alone heavy rainfall
events can add to the physical destruction of coral and seagrass habitats
caused by storm surge [46,89]. Prolonged turbid conditions reduce thelight needed by the symbiotic dinoﬂagellates that provide much of thefood for corals, and needed by seagrasses for growth. In addition, thehigher nutrient loads associated with increased runoﬀ cause problems
for coral reefs by stimulating the growth of epiphytic algae, which also
reduces light levels for corals. The time required for reefs to recoverfrom disturbances, including cyclones and coral bleaching, can temporally increase suitable habitat for ciguatera-causing dinoﬂagellates,
resulting in increased incidence of ciguatera ﬁsh poisoning [72,73].
Tropical cyclones and severe storms have direct economic implications for coastal ﬁsheries and aquaculture in small island states by reducing ﬁshing time and destroying or damaging ﬁshery assets and infrastructure, such as landing sites, boats and equipment [5,26]. For
example, in Vanuatu, the estimated physical damage to the ﬁsheries
sector following the Category 5 TC Pam in 2015 was valued at VT 268
million (~USD$2.6 million), with half of the damage aﬀecting artisanalﬁsheries, and the other half distributed between commercial ﬁsheries(24%), loss of ﬁsheries infrastructure (17%), community aquaculture(5%), ﬁsheries market facilities (3%) and subsistence ﬁsheries (1%)[34]. Similarly, the estimated physical damage to wild-capture, coastalﬁsheries after TC Winston, an extremely destructive Category 5 cyclonethat struck Fiji in February 2016, was valued at F$33 million (~USD$16 million), with coastal subsistence ﬁsheries sustaining the majorityof damage (F$27.7 million) [32]. In Samoa, an estimated 27% of canoes
owned by artisanal ﬁshers were damaged during TC Evan in December
2012, while it was estimated that artisanal ﬁshers would experience aloss of ﬁshing income for two months [33].
5. Climate variability prediction skill across diﬀerent time scales
Seasonal to inter-annual prediction has been applied, with moderate
success, to generate predictions of the climate state (e.g., ENSO phase)[6,57] and the potential impacts from changes in climate state (e.g.,
rainfall probabilities) and/or tropical cyclone activity [21,51] acrossthe globe. In particular, recent developments in our understanding of
the processes underpinning ENSO, improvements in model resolution
and parameterisations, and observing and analysis/assimilation systems, are seen as important in improving ENSO predictions [38]. In the
1990s, real-time ENSO prediction capability was moderate, with 6month lead predictions (hindcasts) of 3-month mean SST conditions
being correlated at about r = 0.6 with observations [7]. At the time,
dynamical models were viewed as not performing any better than statistical models, e.g., predictions of the 1997/98 El Niño [6,53].
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been proposed for many years as an integral part of eﬀective coastalzone management [4,27] and for mitigating longer-term eﬀects of climate change on ﬁsheries and aquaculture in PICTs [14,15,17]. Following [17], our identiﬁcation of adaptation options included consideration of potential social and ﬁnancial barriers to the uptake ofadaptations, such as cultural norms and gender issues that could limit
broad-based community participation, rather than being based solely
on the availability of technology and projected future responses of theresources underpinning coastal ﬁsheries production.
In addition, we focus on adaptation mechanisms that promote
greater resilience of coastal ecosystems and small-scale coastal ﬁsheries
to the longer-term impacts of climate change (i.e., adaptations underthe ‘win-win’ and ‘lose-win’ scenarios of [17]. We speciﬁcally excluded‘win-lose’ investments, such as support to increase coastal ﬁshing eﬀort
or capacity through construction of larger vessels or more eﬀective gear
types [17], because they represent maladaptation to the longer-terms
eﬀects of climate change for small-scale coastal ﬁsheries.

Nevertheless, ENSO predictability has gradually improved, and prediction skill from dynamical models has now exceeded analogous skill
from statistical models [7]. However, inter-seasonal prediction systems
continue to face challenges, for example, the predicted strong El Niño
event for 2014/15 did not eventuate [59]. Ultimately, improved predictability of ENSO and its impacts will require better understanding of
the diversity of ENSO patterns and their time evolution [22].
On sub-seasonal time scales, a major source of predictability in the
climate system, particularly in the tropics, resides in the Madden-Julian
Oscillation (MJO). The MJO is the dominant mode of intra-seasonalvariability (approx. 30–90 days) in the tropical atmosphere. The MJO
inﬂuences ocean variability in the Indo-Paciﬁc region through changes
in tropical cyclone activity [50], sea level [66], ocean currents [48],
SST [28] and primary productivity [49]. The MJO can be predicted ontime scales of 2–4 weeks [67], providing enhanced skill for long-range
predictions that bridge the gap between weather forecasts and seasonal
climate predictions [92]. There are numerous potential applications forskillful forecasts on subseasonal-to-seasonal (S2S) time scales [87].
However, our knowledge of the potential for S2S applications to ocean
temperatures and ﬁsheries is in its infancy.
Skill for climate predictions on all time scales is still very moderate,
due to the infancy of forecast work and due to the inherent limitationsof making forecasts over longer time scales [60]. On seasonal to multiyear scales, predictions for temperature are already better than using
climatological estimates because of the persistence of ENSO and its
impact on temperature in diﬀerent regions. Seasonal to multiyear predictions of rainfall are currently of very low skill (not much better, if atall, than climatology), but oﬀer scope for improvement where the ENSO
signal is strongest [60]. One advantage for ﬁsheries applications is thatskill will generally be higher for the ocean than for the atmosphere andland because the processes responsible for multiyear predictability arein the ocean [61].
Traditionally, climate forecasts have focused on seasonal time scales
and have exploited the persistence of ENSO events. Extending climate
forecasts to multiyear and decadal time scales is a newer developmentthat has been enabled by the availability of a longer record of subsurface ocean data, where processes reside for longer time scales. At thisstage, it is still unclear how much predictability exists on multiyear timescales, but this will depend in part on how well slower processes like thePaciﬁc Decadal Oscillation (PDO) can be forecast [64]. Climate models
initialised to the current phase of the PDO already display some skill for
multiyear temperature forecasts [60].

6.2. Predictions to assist adaptations for coastal ﬁsheries
A prime example of adaptation to assist small-scale ﬁshers catch
more ﬁsh on a day to day basis to help feed rapidly-growing populations would be to develop eﬀective tools for predicting when yellowﬁn
and skipjack tuna, and other associated large pelagic ﬁsh like wahooand mahi mahi, are likely to be available in good numbers in nearshore
waters [18]. When combined with programs to expand the number ofnearshore ﬁsh aggregating devices (FADs) [16], which will require installation and maintenance of FAD infrastructure [16] and training insafe and eﬀective FAD-ﬁshing methods [18], eﬀective prediction tools
will assist communities to schedule their various other livelihood activities and adaptations to climatic variability and climate change, e.g.,
production of subsistence food crops [10,78]. Governments might also
provide supports for alternative livelihood programs and set up social
safety nets or insurance programs for small-scale ﬁshers and communities [3]. Better predictions of tuna abundance should also confer
beneﬁts on industrial tuna ﬁsheries in the region (see Supplementary
Material).
Being able to extend predictions regarding extremes, such as marine
heatwaves and tropical cyclones, will justify disaster preparedness
programs and enable better decision making on the part of coastalﬁshers, communities and governments, potentially leading to fasterrecovery of aﬀected ecosystems. For example, it will allow communities
to be notiﬁed in advance of the risks to public health associated with 1)
eating dead and decaying ﬁsh that wash up on beaches following ﬁshkills, or 2) ﬁsh harvested from reefs that are likely to have a higher
incidence of ciguatera ﬁsh poisoning. Predictions will also allow plans
to be made to transfer ﬁshing eﬀort away from coral reefs predicted tobe aﬀected to nearshore pelagic ﬁsh, or from reef ﬁsh species worst
aﬀected by extreme weather events to species that typically increase inlocal abundance following such events. Such predictions will help to 1)
maintain the overall catch of ﬁsh, and 2) promote restoration of theecological function of coral reefs. However, caution will be needed tolimit harvests of ﬁsh species that have important ecological functions,e.g., herbivores [19], or have an inherent vulnerability to overﬁshing[36]. Similarly, planned harvests of ecologically important invertebrates, such as sea cucumbers, trochus or giant clams, could bepostponed to allow these groups to perform their key ecological functions.
Predicting climatic extremes, such as cyclones or marine heatwaves,
well in advance may also allow suﬃcient time to mobilise available
resources and stockpile ﬁsh and food products to support coastal
communities to endure these diﬃcult events and to mitigate their impacts afterwards. Speciﬁc actions might include, for example, accumulating alternate ﬁsh protein resources with a longer shelf life (e.g.,canned tuna, smoked and dried ﬁsh), or placing ﬁsh in cold storageprior to a natural disaster and to reduce ﬁshing pressure on aﬀected

6. Application of climate predictions to policy and management
6.1. Implications for Paciﬁc Island governments and communities
Our understanding of the patterns of variability and impact suggestthat the most important current gap for climate predictions to respondto the impacts listed in Section 4 is between 6 months and 10 years.
Predictions over that timeframe will enable more proactive mitigation,
adaptation and avoidance by governments and communities on shorter
timescales than are currently possible. For example, there would be thepotential better to mitigate impacts on small-scale ﬁshers and coastal
community infrastructure, to stockpile resources at the most appropriate times when cyclones can be predicted at longer time scales, or toalter ﬁshing patterns in anticipation of climate-induced movements or
mortality events in future years. We highlight three speciﬁc events thatpredictions will be able to highlight: a) Increased El Niño-like conditions; b) Short- to medium-term (on the scale of months to years)
marine heatwaves; and c) Increased severity of cyclones and storms.
Several opportunities exist to assist Paciﬁc Island governments,
small scale ﬁshers and communities to adapt to the eﬀects of shorterterm and mid-range climatic variability by extending regional and national predicting capability. Many of these interventions, which are
outlined below and summarised in Table 1, are not new. They have298

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Table 1
Summary of potential management and policy adaptation options for coastal ﬁsheries in small-island developing states under various climate prediction scenarios.
Event

Impacts

Adaptation options

Supporting policies

Increasing El Niñolike conditions

Movement of tuna eastward along theequator and at higher latitudes, withdeclines in catches occurring in western
PICTs.

Installation / maintenance of ﬁsh aggregating devices(FADs), especially to help aggregate available tunacloser to coastal populations in western PICTs, includingﬁsheries agency budget planning to ensure funds areavailable for maintenance and repair of FADs.
Flexible arrangements to allocate more of the tuna
resources to local food security.
Increase allocation of area of the EEZ available to smallscale ﬁshers
Improved post-harvest methods and food storage
systems to stockpile tuna and small pelagics when goodcatches are made.
Implementation of alternative livelihood programs forsmall-scale coastal communities
Creation of social safety nets – e.g., insurance programsfor SSF, community insurance banks - for communities.
Awareness raising to avoid health implications of eating
dead and decaying ﬁsh, and ﬁsh with ciguatera
poisoning.
Awareness raising of event, likely impacts, andimportance of herbivorous ﬁsh in facilitating reef
resilience and recovery.
Flexible management practices to allow establishmentof temporary ‘no-take’ areas or gear restrictions (e.g.,bans on night spearﬁshing with torches).
Installation / maintenance of FADs to aggregate tunaand other small pelagics near coastal communities.
Fuel and gear subsidies to encourage ﬁshers to ﬁsh on
FADs to transfer ﬁshing eﬀort away from reefs.

Include nearshore FADs as part of the national
infrastructure for food security.
Transfer some access rights and allocations fromindustrial tuna ﬁsheries to small-scale ﬁsheries.

Short-medium term
marineheatwaves

Fish kills under persistently warmer
conditions (e.g. due to low dissolved
O2)
Coral bleaching and subsequent
overgrowth by macroalgae andincrease in ciguatoxic microalgae.

Changes in ﬁsh community
composition and species’ abundance.
Declines in extent and quality ofhabitats and loss/contraction of ﬁshinggrounds.
Depressed catch rates and declines inoverall catches of key target species.

Increased severity ofcyclones andstormsDeclines in extent and quality ofhabitats and loss/contraction of ﬁshinggrounds
Overgrowth of dead coral bymacroalgae and increase in ciguatoxic
microalgae.
Changes in ﬁsh community
composition and species’ abundance.

Depressed catch rates and declines inoverall catches of key target species.

Damage to ﬁshing ﬂeet.

Flexible arrangements to allocate more of the tuna
resources to local food security.
Improved post-harvest methods and food storage
systems to stockpile tuna and small pelagics when goodcatches are madeAwareness raising to avoid health implications of eatingﬁsh with ciguatera poisoning.
Awareness raising of event, likely impacts, andimportance of maintaining stocks of herbivorous ﬁsh in
facilitating reef resilience and recovery.
Flexible management practices to allow establishmentof temporary ‘no-take’ areas or gear restrictions (e.g.,bans on night spearﬁshing with torches).
Installation /maintenance of FADs to aggregate tunaand other small pelagics near coastal communities.
Fuel and gear subsidies to encourage ﬁshers to ﬁsh on
FADs to transfer ﬁshing eﬀort away from reefs.
Flexible arrangements to allocate more of the tuna
resources to local food security.
Improved post-harvest methods and food storage
systems to stockpile tuna and small pelagics when goodcatches are made.
Flexible licensing provisions (where relevant) and gear
subsidies to allow ﬁshers to target other species/areas.
Emergency preparedness training programs in coastal
communities and early warning systems to ensure
preparedness (e.g., move ﬂeet, secure gear).

Apply targeted subsidy and training programs to
support key adaptations.
Collaborative monitoring and decision-making
processes to ensure that proposed interventions areappropriate and eﬀective

Strengthen ﬁsheries legislation to apply communitybased management.
Promote access to nearshore pelagics such as tunaand ﬁsh expected to increase in abundance; include
nearshore FADs as part of the national infrastructure
for food security.

Apply targeted subsidy and training programs to
support key adaptations.
Collaborative monitoring and decision-making
processes to ensure that proposed interventions areappropriate and eﬀective

Strengthen ﬁsheries legislation to apply communitybased management.
Promote access to nearshore pelagics such as tunaand ﬁsh expected to increase in abundance; include
nearshore FADs as part of the national infrastructure
for food security.
Apply targeted subsidy and training programs to
support emergency preparedness and key
adaptations.

Collaborative monitoring and decision-making
processes to ensure that proposed interventions areappropriate and eﬀective.

throughout PICTs now provides the opportunity for government agencies involved in disaster risk management to issue bulletins alerting
small-scale ﬁshers to impending risks with suﬃcient notice to enable tothem to store boats and ﬁshing gear in cyclone-proof storage [18]. It is
not practical to use predictions of adverse weather condition to climateproof FADs by removing them from the water in the lead-up to storm
events due to the diﬃculties in doing so from small craft. Instead, spare
FADs materials can also be stored in cyclone-proof containers on eachisland and installed once the storm has abated (Bell et al. this volumeb). Reliable predictions will enable PICTs to plan annual budgets toensure funds are available for FAD maintenance and repair (Table 1).

reefs in the weeks to months following the event. In the lead-up to a
predicted event, coordination of increased ﬁshing on FADs at a local
scale, or increased landings of tuna by industrial ﬂeets for domestic
food security at a national scale, may be required. In either case, investment in storage facilities may be required. Predicting destructive
weather and climate events over extended time scales may also provide
suﬃcient warning to move vessels onto land above the reach of storm
surges, and secure vessels and other community infrastructure against
wind damage. Similarly, loss of ﬁshing gear could be minimized if
suﬃcient time is provided to retrieve and securely store the equipment
before the onset of a cyclone. The widespread use of mobile phones299

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P.K. Dunstan et al.

periods, there are a number of options for adaption (outlined in [17]).
In the six month to 10 years’ time period, improving an understandingof frequency, trend and location of events are critical areas for research,but many decisions are not so “sophisticated” that a speciﬁc temperature threshold is needed. This timeframe corresponds neatly with theplanning horizons for many development activities. The availability ofclimate projections has raised awareness of the need to take long-term
action to limit the magnitude and impacts of greenhouse gas emissions,but tactical decision-making about investments over these intermediatetime scales remains limited. Improved prediction of climatic variability
will help address this situation and allow both policy makers and ﬁshers
to adapt to climate events. However, to harness the potential beneﬁts ofimproved predictions, stakeholders in coastal ﬁsheries will need to
become more ﬂexible. In particular, policy and management responses
will need to vary over the same time scales as climatic variability. In
many Paciﬁc Island countries, support will be needed to make thistransition.

Predictions over intermediate time scales may also allow communities to adopt ﬂexible management measures, including temporaryﬁshing closures on areas predicted to be worst-aﬀected, appropriate
size limits, regulations on ﬁshing gear and bans on certain ﬁshingmethods (such as night-time spearﬁshing with torches), prior to andfollowing the event to reduce ﬁshing pressure on coastal habitats andvulnerable ﬁsh species. Such measures should help to maintain andpromote reef resilience and recovery. On the other hand, the combination of predictions and monitoring of species movements might also
allow communities to take advantage of emerging stocks and resources.
Strengthening the role of collaborative governance and communitybased management will be a vital part of this process [2]. Some PICTs
have invested in aquaculture technology to culture species such as giant
clams and sea cucumbers (in particular sandﬁsh, Holothuria scabra) tohelp restore wild stocks [11]. Extending climate prediction over the
scale of six months to 10 years would inform operations of optimal
times for release of cultured juveniles, and what remedial action to taketo counter or avoid poor conditions. Improved climate predictions
would also be of beneﬁt to the aquaculture industry (Supplementary
Materials).

Appendix A. Supplementary material
Supplementary data associated with this article can be found in the
online version at http://dx.doi.org/10.1016/j.marpol.2017.09.033.

6.3. Communication of climate predictions

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Eﬀective communication is critical to the application of climate
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