Content uploaded by Simon Nicol
Author content
All content in this area was uploaded by Simon Nicol on Apr 23, 2015
Content may be subject to copyright.
1 23
Climatic Change
An Interdisciplinary, International
Journal Devoted to the Description,
Causes and Implications of Climatic
Change
ISSN 0165-0009
Climatic Change
DOI 10.1007/s10584-012-0595-1
Modelling the impact of climate change
on Pacific skipjack tuna population and
fisheries
Patrick Lehodey, Inna Senina, Beatriz
Calmettes, John Hampton & Simon
Nicol
1 23
Your article is protected by copyright and all
rights are held exclusively by Springer Science
+Business Media Dordrecht. This e-offprint
is for personal use only and shall not be self-
archived in electronic repositories. If you
wish to self-archive your work, please use the
accepted author’s version for posting to your
own website or your institution’s repository.
You may further deposit the accepted author’s
version on a funder’s repository at a funder’s
request, provided it is not made publicly
available until 12 months after publication.
Modelling the impact of climate change on Pacific
skipjack tuna population and fisheries
Patrick Lehodey &Inna Senina &Beatriz Calmettes &
John Hampton &Simon Nicol
Received: 23 January 2012 /Accepted: 17 September 2012
#Springer Science+Business Media Dordrecht 2012
Abstract IPCC-type climate models have produced simulations of the oceanic environment
that can be used to drive models of upper trophic levels to explore the impact of climate
change on marine resources. We use the Spatial Ecosystem And Population Dynamics
Model (SEAPODYM) to investigate the potential impact of Climate change under IPCC
A2 scenario on Pacific skipjack tuna (Katsuwonus pelamis). IPCC-type models are still
coarse in resolution and can produce significant anomalies, e.g., in water temperature. These
limitations have direct and strong effects when modeling the dynamics of marine species.
Therefore, parameter estimation experiments based on assimilation of historical fishing data
are necessary to calibrate the model to these conditions before exploring the future scenarios.
A new simulation based on corrected temperature fields of the A2 simulation from one
climate model (IPSL-CM4) is presented. The corrected fields led to a new parameterization
close to the one achieved with more realistic environment from an ocean reanalysis and
satellite-derived primary production. Projected changes in skipjack population under simple
fishing effort scenarios are presented. The skipjack catch and biomass is predicted to slightly
increase in the Western Central Pacific Ocean until 2050 then the biomass stabilizes and
starts to decrease after 2060 while the catch reaches a plateau. Both feeding and spawning
habitat become progressively more favourable in the eastern Pacific Ocean and also extend
to higher latitudes, while the western equatorial warm pool is predicted to become less
favorable for skipjack spawning.
Climatic Change
DOI 10.1007/s10584-012-0595-1
Electronic supplementary material The online version of this article (doi:10.1007/s10584-012-0595-1)
contains supplementary material, which is available to authorized users.
This article is part of the Special Issue on "Climate and Oceanic Fisheries" with Guest Editor James Salinger.
P. Lehodey (*):I. Senina :B. Calmettes
CLS, Space Oceanography Division, 8-10 rue Hermes, 31520 Ramonville Saint-Agne, France
e-mail: PLehodey@cls.fr
J. Hampton :S. Nicol
Secretariat of the Pacific Community, BP D5 Noumea cedex, New Caledonia
Author's personal copy
1 Introduction
The oceanic fisheries of the tropical Pacific Ocean are dominated by skipjack Katsuwonus
pelamis, yellowfin Thunnus albacares, bigeye tuna T. obesus and albacore T. alalunga,
which together represent >90 % of the world catch taken by industrial fleets, and 70 % of the
estimated global tuna catch, just above 4 million tonnes in 2010 (Lehodey et al. 2011a).
Skipjack dominates the total catch of tuna and almost all the catch of this species is taken by
surface fishery (especially purse seine) together with young yellowfin tuna. The subsurface
longline fishery targets mature bigeye and yellowfin tuna in equatorial waters. According to
the Western and Central Pacific Fisheries Commission (WCPFC) and the Inter-American
Tropical Tuna Commission (IATTC), these tuna resources are approaching maximum
sustainable yield and in some cases (e.g., bigeye) exceeded this limit.
Even if they have developed elaborated thermoregulation mechanisms (Brill 1994) tuna
are strongly influenced by their temperature environment. Other variables like dissolved
oxygen, currents and prey concentration are also of key importance. For instance, the El
Niño Southern Oscillation (ENSO), known to change drastically the oceanic conditions of
the tropical Pacific Ocean, strongly affects migration, abundance and catches of tuna
(Lehodey et al. 1997; Lehodey 2001; Senina et al. 2008). The projection of the future
climate in the tropical Pacific Ocean, based on a multi-model mean, indicates a“weak shift
towards conditions which may be described as ‘El Niño-like’, with SSTs in the central and
eastern equatorial Pacific warming more than those in the west”(Solomon et al. 2007).
Thus, while the fisheries are setting the level of exploitation to reach the maximum
sustainable yield, it becomes urgent to assess the likely effects that climate change itself
can have on tuna populations.
Projected changes under one of the worst (A2) emission scenario defined for the IPCC
fourth assessment report (Nakicenovic et al. 2000) forecast an overall temperature increase
in the surface tropical Pacific Ocean of 0.7–0.8 °C by 2035 relative to 1980–1999; and by
2.5–3.0 °C in 2100 (Ganachaud et al. 2011). Sea surface temperatures in the central and east
equatorial Pacific are expected to warm more than those in the west. The size of the western
equatorial Warm Pool is projected to increase by ~250 % by 2035 and by 800 % under A2 in
2100. ENSO events are projected to continue for the remainder of the twenty-first century at
least, although there is little agreement among models about the frequency or amplitude of
El Niño and La Niña episodes in the future (Ganachaud et al. 2011). With climate warming,
productivity at the primary (phytoplankton) level is projected to decrease in the tropical
Pacific Ocean (Steinacher et al. 2010) and to propagate in the food web through zooplankton
(prey of tuna larvae and juveniles) and micronekton (prey of adult tuna). Changes in ocean
circulation, vertical stratification and mesoscale activity will also likely affect spawning and
feeding grounds of tuna (Lehodey et al. 2011a,b).
Due to the strong interactions between environment and tuna physiology and behavior,
complex responses should result from these numerous environmental changes. To investi-
gate them, we use the SEAPODYM (Spatial Ecosystem and Population Dynamics Model)
modeling framework that describes the spatial dynamics of tuna and tuna-like species under
the influence of both fishing and environmental effects (Lehodey et al. 2008). This model
includes a definition of habitat indices, spawning, movements in the responses to the habitat
quality and basin-scale seasonal migrations, accessibility of forage for tunas within different
vertical layers (Lehodey et al. 2010a), predation and senescence mortality and its change due
to environmental (food competition) conditions. Thus, taken together these mechanisms
describe most of the recognized interactions between tuna and the oceanic environment.
However, ocean acidification associated with increasing atmospheric CO2 are not yet
Climatic Change
Author's personal copy
included in the model due to the lack of knowledge on its potential impacts on tuna
physiology. A data assimilation technique based on adjoint code and maximum likelihood
estimation is implemented to assist in parameterization using historical fishing data (Senina
et al. 2008).
A preliminary study simulating IPCC climate impact on Pacific bigeye tuna (Lehodey et al.
2010b) demonstrated that the approach was possible but highlighted important sources of
uncertainty and gaps in knowledge which limited the confidence in these first results. A major
issue was a large anomaly detected in temperature fields which may prevent the model to reach
convergence or produce spurious correlations. In this second study devoted to the use of IPCC—
type models to explore the impact of future climate change on tuna populations, we corrected the
temperature fields before running optimization experiments with an application to the most
tropical tuna species, skipjack. The results are compared to those achieved without temperature
correction and to an optimization experiment using more realistic environmental forcing based
on an ocean reanalysis and satellite derived primary production.
2 Material and method
2.1 Modeling approach
The main features of the SEAPODYM model (Suppl. Inf. S1) include i) environmental
forcing such as temperature, currents, euphotic depth, primary production and dissolved
oxygen concentration, ii) prediction of the temporal and spatial distributions of functional
groups of prey, iii) prediction of the spatial dynamics of age-structured predator (tuna)
populations, iv) prediction of the total catch and the size-frequency of catch by fleet, and
v) parameter optimization based on fishing data assimilation techniques.
The mid-trophic level model (Lehodey et al. 2010a) describes vertical and horizontal
dynamics of prey groups. Dynamics of tuna populations are predicted using habitat indices,
movements, growth and mortality. The feeding habitat is based on the accessibility of prey
groups to tuna. The spawning habitat combines temperature preference and coincidence of
spawning with presence or absence of predators and food for larvae. Successful larval
recruitment is linked to spawning stock biomass and mortality during the drift with currents.
Older tuna can swim in addition to being advected by currents. Food requirement and
intraspecific competition indices are computed to adjust locally the natural mortality of
cohorts, based on food demand, accessibility to available forage components and biomass of
other tuna cohorts (Lehodey et al. 2008,2010b; Senina et al. 2008).
The first optimization experiment used physical variables (temperature and currents)
extracted from the 1980–2008 SODA reanalysis
1
(Carton et al. 2000) and primary produc-
tion and euphotic depth derived from satellite data (Behrenfeld and Falkowski 1997)
2
.
Dissolved oxygen concentration is a climatology from the World Ocean Atlas (Garcia et
al. 2010). The simulation covered the time period 1998–2008 for which satellite ocean color
data were available. Then IPCC simulation experiments are conducted using environmental
forcing predicted from a biogeochemical model (PISCES) coupled to a climate model
(IPSL-CM4) as previously described in Lehodey et al. (2010b). Present experiments use a
correction of temperature fields. The scenario used was the SRES A2 IPCC scenario for the
21th century, i.e., atmospheric CO
2
concentrations reaching 850 ppm in the year 2100, and
1
Available at http://www.atmos.umd.edu/~ocean/
2
Available at http://www.science.oregonstate.edu/ocean.productivity/index.php
Climatic Change
Author's personal copy
historical data between 1860 and 2000. In the following text, we will refer to these three
different configurations by their physical forcing SODA, IPSL and IPSLc respectively.
Projecting the dynamics of an exploited species inevitably raises the issue of projecting
the future fishing effort. Any change in fish distribution will certainly redistribute also the
fishing effort based on available resource by region and various criteria like management
measures, access to fishing zones, cost of fuel, etc. Thus, predicting fleet dynamics under
environmental changes and fish population redistribution is a challenging modeling task by
itself. As a first approximation of projected fishing effort, we simply used an average of
previous years. We computed the average effort of a relatively long period (1980–2000) in
order to limit the bias due to interannual variability (ENSO) that is known to have strong
spatial impact on fisheries for this species (Lehodey et al. 1997). The projected catch and
resulting fish biomass remain a crude estimate in absence of more elaborate scenario of
future fishing effort and management but is of interest to assess the implications of climate
change on the future spatial distribution of the stock and catch.
2.2 Fishing data for parameter optimization
The definition of fisheries was based from the best available fishing data sets that have been
provided by the WCPFC (through the Secretariat of the Pacific Community), and the IATTC
(public domain data) at a resolution of 1° × month or 5° × month. A total of 13 fisheries (S2;
Lehodey et al. 2011b) weredefined based on fishing gears (pole-and-line, purse seine, longline)
and strategies (sets on free school, floating object, dolphin associated…). These data were used
to run optimization experiments at Pacific basin-scale and to take into account the fishing
mortality due to all fisheries. The optimization approach included both spatially disaggregated
catch by fishery (monthly 1° or 5° square) and associated size frequency of catch (quarterly 5° x
5° or 10° x 20° or other). Fishing data of the domestic Philippines-Indonesia fishery were not
used for optimization due to too low accuracy. Nevertheless, total catch in this region was used
for computation of fishing mortality. Details on the optimization approach can be found in
previous studies (Senina et al. 2008; Lehodey et al. 2010b). Compared to the first optimization
study of skipjack (Senina et al. 2008), the definition of fisheries has been improved in both
western andeastern Pacific Ocean (13 fisheries instead of 6) and new data added, especially size
frequency data for the eastern Pacific fisheries. Each fishery is defined by a constant catchability
coefficient allowed to change linearly with time, and a size-selectivity function characterizing
the efficiency of the gear to catch the species at different sizes. Thus, the optimization approach
estimates 3 to 4 parameters by fishery, with 18 other parameters used to describes the spatial
population dynamics of the species, including the definition of habitats, movements, larval
recruitment and natural mortality.
Model results are evaluated using the fit between prediction and observation and comparison
with independent estimates from the last stock assessment analysis conducted by the WCPFC
(Hoyle et al. 2011). The fit to catch, catch per unit of effort (CPUE) and size frequency of catch
is checked for all fisheries as well as their residuals (Lehodey et al. 2011b). Overall spatial fit
between predicted and observed catch for all the fleets over the whole time series is provided by
the standard R-squared goodness of fit (Eq. 1). A value of 1 means a perfect fit.
Rij2¼1P
tf
Cobs
ijtf Cpred
ijtf
2
P
tf
Cobs
ijtf Cobs
ijf
2ð1Þ
Climatic Change
Author's personal copy
where Cis catch, pred and obs mean predicted and observed respectively, iand jare the
coordinates of the grid cell, tis the time step and fthe fishery.
We also compute the relative error (e) in % of predicted catch (Eq. 2).
e¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
P
tf
Cobs
ijtf Cpred
ijtf
2
s
P
tf
Cobs
ijtf
ð2Þ
2.3 Correction of IPSL-CM4 temperature fields
The IPSL-CM4 A2 climate simulation used in a previous analysis (Lehodey et al. 2010b)
demonstrated a good seasonal cycle at high latitudes, internally generated interannual
variability close to observed ENSO variability in the tropics, a good coherence between
changes in the vertical structure, primary production (Schneider et al. 2008; Steinacher et al.
2010) and dissolved oxygen concentration. However, as other complex climate models it
includes some biases and anomalies, and in particular a strong cold temperature anomaly
was detected in the mid to high latitudes. Such anomalies are partly linked to too coarse
resolution in the atmospheric component of the climate model system (Hourdin et al. 2012)
which can create a latitudinal shift in the atmospheric circulation that is propagated to the
ocean dynamics.
The temperature anomaly of the IPSL-CM4 A2 simulation has been corrected using a
climatology (Locarnini et al. 2010) and a weighting function based on latitude, with the
objective of keeping unchanged the original prediction in the tropical region but correcting
the temperature in higher latitude according to the climatology from observation (Eq. 3). An
illustration of the change due to correction can be seen in the supplementary information
(S3).
Tc¼f latðÞTcl obs þ1f latðÞðÞTcl mod þTanom ð3Þ
where
T
c
: Corrected temperature (IPSL corrected)
T
cl_obs
: Temperature climatology from observation (WOA)
T
cl_mod
: Temperature climatology from biased model (average IPSL 1900–2000)
T
anom
: Temperature anomaly (difference between IPSL temperature and T
cl_mod
)
and
f latðÞ¼1aebjlatþ5j
ðÞ
þ1
hi.ð4Þ
with a02e11 and b00.95
3 Results
All optimization experiments use an identical population structure. It is defined by age
(cohorts) with variable time unit allowing us to save computation time. There is 1- month
cohort for larvae life stage, one 2-month cohort for juvenile stage, four 2-month cohorts for
young fish (before age at maturity) and 12 cohorts for adult stages (one of 2 months, eight of
Climatic Change
Author's personal copy
3 months, two of 4 months and one of 6 months). The last one is a “+ cohort”accumulating
older fish. First age of maturity is set to 10 months. All parameters cannot be optimized
simultaneously due to antagonistic and correlated processes, e.g., mortality and larval
recruitment. Thus, for each configuration a series of simulations was carried out to achieve
the optimal parameterization.
3.1 Optimization based on historical fishing period
3.1.1 Optimization with ocean reanalysis (SODA)
This first optimization experiment using the ocean reanalysis SODA and satellite derived
primary production for the environmental forcing was carried with a relatively short time
series (1998–2007). Nevertheless, it was possible to achieve convergence thanks to the large
fishing dataset and this realistic environment.
Optimal spawning temperature (SST) was not estimated and fixed at 29.5 °C, with
standard error being 3.0 °C. The optimal temperature for the oldest cohort was estimated
to 20.6 °C, however the standard error parameter was large and reached the upper boundary
value 4.5 (Table 1). This parameterization agrees well with current knowledge on the species
(S4). Skipjack are known to have spawning activity peaking between 26° and 30 °C
(Schaefer 2001), and an overall species distribution identified from all historical occurrences
between 17–30 °C (Sund et al. 1981). Like skipjack, yellowfin tuna spawns in warm waters
(Schaefer 1998) and it is worth to note that laboratory experiments on this latter species
allowed identifying an optimal range of 26–31 °C for rapid growth and moderate to high
survival in first feeding larvae (Wexler et al. 2011). Estimation of the optimal temperature for
the oldest cohort is a key progress compared to previous optimization and is due in large part
to the use of a better fishing dataset with data from regions with contrasted oceanographic
conditions (e.g., the eastern Pacific) and to the use of a more realistic environmental forcing.
For the oxygen function, the threshold value was estimated close to 2.5 mL/L (Table 1),
above the lethal levels of 1.87 mL/L (50 cm Fork Length) defined from laboratory
Table 1 Estimates of habitats and movement parameters of previous simulations and the new one using
environmental forcings from SODA and IPSL-CM4 before and after correction of temperature fields
Parameters estimated by the model Unit Senina et al. (2008) SODA-1° IPSL IPSL-c
Habitats
T
s
Optimum of the spawning temperature
function
°C 30.5 29.5
a
29.48 30
a
σ
s
Std. Err. of the spawning temperature
function
°C 3.5
a
3
a
3.5
a
3
a
αLarvae food-predator trade-off
coefficient
–3.67 1.03 0.001
a
0.498
T
a
Optimum of the adult temperature
function at maximum age
°C 26
a
20.64 25
a
20.1
σ
a
Std. Err. of the adult temperature
function at maximum age
°C 1.62 4.5] 1.1 4.09]
ÔOxygen threshold value at Ψ
O
00.5 mL·L
−1
3.86 2.47 5.46 2.07
Movements
V
M
Maximum sustainable speed B.L. s
−1
1.3 [1 0.949 0.723
a
Fixed; [val0value close to minimum boundary value; val] 0value close to maximum boundary value
Climatic Change
Author's personal copy
experiments and below the value of 3.8 mL/L below which skipjack spend less than 10 % of
their time (Brill 1994). Estimates of movement parameters are not yet fully satisfactory since
the value for maximum sustainable speed (V
M
) reached the fixed lower boundary (1 body
length s
−1
) while diffusion coefficient for fish movements reached the upper boundary
leading to a theoretical maximum diffusion rate of 1,000 nmi
−2
d
−1
. Nevertheless, the fixed
value for V
M
seems reasonable in the view of direct observations from electronic tagging
experiments for other tunas (e.g., Lutcavage et al. 2000). Indeed, it is predicted to decrease
with size (S4) due to increasing accessibility to forage biomass of deeper layers, and thus
decreasing of the horizontal gradients of the feeding habitat index controlling the movement.
Average predicted maximum diffusion rates (S4) also remain in the range of average values
estimated from conventional tagging data (Sibert et al. 1999).
Reasonable estimates of catchability and selectivity coefficients were obtained when
allowing a linear temporal increase in catchability. Overall fit to predicted catch was
generally good (S5) with a mean R
2
of 0.7 and relative errors in catch below 10 % in all
major fishing grounds, this error being high only in few places where catch is really
occasional and represents a very small volume compared to the total.
With this new parameterization, the total skipjack biomass for the Western Central Pacific
Ocean (WCPO) is estimated to be around 7 million tonnes (Fig. 1), about one million tonnes
above the biomass estimates from the last stock assessment study with the model
MULTIFAN-CL used by the WCPFC (Hoyle et al. 2011), but below some of the previous
highest estimates (Langley and Hampton 2008). The uncertainty in biomass estimates is
inherent to all population dynamics models. As this study is focused on the change of spatial
distributions and long term trends, the estimates of absolute biomass is less important. In the
case of SEAPODYM, overestimation of biomass estimates may be due to the spatial
structure of the model and its numerical treatment, as it was generally observed that coarse
spatial resolution and/or fishing data used for optimization can lead to too high diffusion
especially in the margin of the core habitat of the species. Another source of uncertainty
concerns the Philippine-Indonesia region where only poor quality fishing data are available
and cannot be used in the optimization (though accounted for in fishing mortality), despite it
being a major spawning ground of this species together with the western equatorial warm
pool. Indeed, when the central equatorial region (region 3: 20°N-20°S; 170°E −150°W) is
considered alone, both model estimates are much closer (Fig. 1).
3.1.2 Optimization with climate model (IPSL and IPSLc)
Despite the use of a longer data time series (1976–2000), the first optimization carried out
with the IPSL-CM4 climate model forcing was not satisfactory as several key parameters
could not be estimated and had to be fixed and some other estimated at values with weak
biological significance (Table 1). In particular, spawning success was predicted to be only
controlled by temperature (α~0), while the adult feeding habitat seemed constrained by a
spurious anti-correlation between temperature preference and oxygen leading to an adult
thermal habitat too restricted to warm temperature (T
a
025 °C; σ
a
01.1 °C) and a sensitivity
to dissolved oxygen concentration too high (Ô05.46 mLL
−1
) in disagreement with current
knowledge on the species as discussed in previous section.
The correction of temperature bias in IPSL-CM4 climate outputs resulted in a better fit to
fishing data (S5) and more coherent parameterization. This latter is much closer to the one
achieved with the ocean reanalysis (SODA), though as in all other experiments all param-
eters cannot be estimated together, likely because there is not enough realism in the forcing
and a lack of critical data for some life stage (e.g., larvae) or mechanisms (e.g., movement) to
Climatic Change
Author's personal copy
achieve optimization of all parameters. However an encouraging result of using the corrected
temperature forcing is a successful and realistic estimate of the adult thermal habitat with
parameters similar to those obtained with the ocean reanalysis (Table 1).
The overall fit to catch and CPUE data of all fisheries showed reasonable spatial
correlation with this corrected forcing (S5). The skipjack total biomass estimate for the
Fig. 1 Total skipjack biomass and catch estimates in the WCPO. aestimates with SODA (red line), and
corrected IPSL (blue and black lines) forcing with actual fishing effort for the historical period and either no
fishing (continuous blue line) or fishing effort scenario based on 1980–2000 period (dotted lines), and
comparison for the historical period with the last WCPFC stock assessment estimate (MFCL; dotted purple
line). bcomparison by region (shown on figure S5) between biomass estimated with SEAPODYM using
ocean reanalysis SODA and the last stock assessment (MFCL 2011) of the WCPFC (Hoyle et al. 2011) region
1 = 40°N‐20°N, 120°E‐150°W; region 2 = 20°N‐20°S, 120°E‐170°E; region 3 = 20°N‐20°S, 170°E‐150°W. c
total monthly catch (metric tonnes) for historical and projected periods according to medium (average 1980–
2000) and high (1.5 times the 1980–2000 average) fishing effort scenarios;12 months moving averages (thick
lines) are superimposed
Climatic Change
Author's personal copy
WCPO is around 6 million tonnes, i.e. between estimates of the SODA optimization
experiment and the MULTIFAN-CL stock assessment model (Fig. 1).
The modeled average spatial distribution of larvae for the recent decade in both simu-
lations using climate model outputs show a large favorable region in the western equatorial
Pacific extending eastward in a narrow north subequatorial band. This overall pattern is
similar to the one resulting from the experiment using SODA reanalysis (Fig. 2). In the latter
however, the distribution is less homogeneously distributed due to the higher resolution but
also because of a much narrower standard error of the temperature spawning function
(Table 1). For the same period, there are much more marked differences in spatial dynamics
and distributions of young and adult fish, and thus total biomass (Fig. 3), though in all cases
the core habitat is in the western and central tropical Pacific. The biomass distribution
predicted with the first climate model experiment (IPSL) is less convincing given the very
large catch occurring west of 165°E between Philippines, Indonesia, Papua New Guinea and
Solomon Islands (S5).
3.2 Projections under IPCC A2 scenario
The IPCC A2 projection with both uncorrected (IPSL) and corrected (IPSLc) temperature
forcing fields provided strongly divergent trends in the spatial distribution of all life stages.
This is clearly illustrated in Figs. 2and 3with the average larvae density and total biomass
distributions predicted for the last decade of the 21
st
Century. For larvae, both simulations
predicted an extension toward higher latitudes but in the second simulation (IPSLc) the
favorable spawning ground is also shifted to the central and eastern Pacific (Fig. 2). The
average density of skipjack larvae is projected to decrease in the present-day spawning
grounds (western equatorial warm pool and Philippines-Indonesia) with the exception of a
narrow equatorial band. This trend is essentially driven by the increasing temperature, e.g.,
while the optimal spawning temperature is 29.5 °C (std. err. 3 °C), the average SST remains
permanently above 32 °C after 2075 in the area defined by latitudes 10°N-10°S and
longitudes 110°E-180°E.
The resulting distribution in total biomass is also an extension to higher latitudes, and either a
strong contrast between an increasingly favorable central-eastern region and the less and less
favorable western region (IPSL), or a more diffuse expansion to the eastern Pacific with a shift
of the core habitat from the western to the central equatorial region (IPSLc). Given the analysis
of optimization results described above, this second simulation seems more reasonable. The
sensitivity to projected change in dissolved oxygen concentration was tested with a simulation
replacing predicted oxygen concentrations by the climatology. The results did not change,
indicating a lack of sensitivity. Thus, the predicted changes in skipjack dynamics over this
climate simulation are driven by changes in temperature, productivity and currents.
When projecting the fishing effort based on the average period 1980–2000, the predicted
catch was lower than observed for the recent years. This is logically due to the continuously
increasing effort since the 1980s that resulted in an average moderate fishing effort com-
pared to the effort deployed in the last years (Fig. 1). Thus, a second projection was
conducted based on the same fishing effort data set and a factor of multiplication set to
1.5. In both projections of fishing effort under this A2 scenario, estimated skipjack tuna
catches in the WCPO would increase and the stock biomass remains stable until the 2060s
(Fig. 1). After this date, the catch remains stable but the biomass starts to decrease, reaching
at the end of the Century about one million below the level in 2000 when starting the
projection. As previously noted, the fishing scenario is rather crude and this projection
cannot be used as a stock status evaluation. However, since the projected fishing effort is
Climatic Change
Author's personal copy
constant, it is clear that the biomass decreases in the western equatorial region (Fig. 3)in
relation with environmental changes described above.
4 Discussion
Understanding the impact of climate change on tuna populations is linked to our capacity to
explain, model and predict the effect of natural variability for the historical period for which
Fig. 2 Average spatial distributions of Pacific skipjack larvae density. Comparison of larvae density (ind.
m
−2
) averaged over 10 years at the beginning and the end of 21st Century for simulations based on ocean
reanalysis (SODA-Psat), and climate model (IPSL-CM4-PISCES) before (IPSL) and after (IPSLc) correction
of temperature fields
Climatic Change
Author's personal copy
we have data. With realistic environmental forcing, the SEAPODYM model used in this
study seems relatively robust with a good fit to observed fishing data under the influence of
natural variability, especially the ENSO variability. Thus, the parameter optimization ap-
proach provides a good framework to measure the progress and evaluate the outputs.
However, even though the modeling of spatial population dynamics is based on a limited
number of parameters, the task is challenging, especially when using environmental forcings
from Earth Climate models.
Fig. 3 Average spatial distributions of Pacific skipjack total biomass. Comparison of total biomass (gm
−2
)
averaged over 10 years at the beginning and the end of 21st Century for simulations based on ocean reanalysis
(SODA-Psat), and climate model (IPSL-CM4 –PISCES) before (IPSL) and after (IPSLc) correction of
temperature fields
Climatic Change
Author's personal copy
Ocean prediction from Earth Climate models are not fully comparable for historical
periods to results achieved from ocean reanalysis. These models are still coarse in resolution
and can produce significant anomalies, e.g., in water temperature as in the IPSL-CM4 model
used here. Consequently, an ecosystem or fish population dynamics model parameterized
from ocean observation or ocean reanalyses needs to be adjusted to the climate model
environment. However, the parameter optimization approach relies on the distribution of
historical fishing data under the influence of actual variability, which does not necessarily
coincide with the internal variability of the climate model, despite the fact that this variabil-
ity is close in frequency to the natural variability (e.g., ENSO, PDO). Though these
limitations have no consequence when describing projections of climate, they can have
direct and strong effects when modeling the dynamics of marine poïkilotherm species.
A constant bias for a given variable over the whole domainof the model can be compensated
by a shift in the new estimated values of the associated parameters. However, when this bias
occurs only in part of the domain, there is no other choice than to attempt to correct the bias, as it
has been demonstrated in this analysis. On the other hand, bias adjustment of models may
remove systematic errors in the mean state but assumes the mean state and climate signal are
independent of each other. Since each model has its own bias, another approach could be to use
ocean forcing from multi-model averaging. This would likely result in a better simulation of
current and future climate than any individual model by cancelling non-systematic errors.
Despite all these uncertainties on the Earth Climate modeling, it is encouraging to see that
fairly good results were obtained from optimization experiments after correction of temper-
ature fields. Skipjack is a typical tropical tuna species, and not too surprisingly the species is
predicted to thrive of climate change at least in the first half of the Century, through a general
expansion of its habitat both in the central and eastern Pacific and towards higher latitudes.
Besides, this type of habitat expansion is typically observed during strong El Niño events.
The positive trend however is stopped in the western central tropical Pacific in the middle of
the second half of the Century and biomass then starts to decrease even under a moderate
fishing effort scenario, indicating that the environmental conditions are becoming much less
favorable in this region (too warm for spawning and decreasing productivity). Given its
thermal preference, this species is mainly associated to the epipelagic layer above the
thermocline, a tendency that is reinforced with projected increasing vertical stratification.
Therefore, it is not surprising to verify that projected change in dissolved oxygen has little
impact on skipjack dynamics, since the decrease in oxygen is predicted to occur mainly in or
below the thermocline and is limited in the upper layer (Lehodey et al. 2010b).
In the case of a fishing effort equivalent to the effort of recent years, the decrease would
certainly become still more striking with a biomass in the WCPO substantially lower at the
end of the Century than today. Of course this scenario is linked to the skills of the model to
predict a correct biomass for the population. Compared to the WCPFC stock assessment, the
biomass estimate with the climate model experiment is only slightly above but show less
amplitude in its variability. It is possible that this estimate still decrease in future experi-
ments, especially with the use of higher resolution and more realistic environment. Com-
pared to previous lower resolution simulations (e.g., Senina et al. 2008), the estimates are
roughly 20 % and 30 % lower for total and adult biomasses respectively in the SODA
experiment. One mechanism to explain this decrease in biomass estimate is linked to a
reduced diffusion of biomass and more patchy spatial density distribution, due to a better
match between positions of high and low CPUE with fish density.
Therefore, while efforts need to be continued to increase the realism and resolution of
environmental forcing, the use of higher resolution fishing data should be envisaged in the
optimization approach to improve the parameterization. The access to these data may
Climatic Change
Author's personal copy
become problematic however, due to the usual problem of confidentiality of fishing
data. Other key data exist that can be added in the optimization process. In particular,
assimilation of existing large datasets from conventional and electronic tagging should
provide key information for improving estimation of movement and habitat parameters
of the species (by cohort). A rapid improvement of fishing data collection in the
Philippines and Indonesian regions is also highly desirable both for current manage-
ment and future experiments with SEAPODYM. This model itself should continue to
be enhanced in the definition of its mechanisms (e.g., spawning migration, food
competition), and multi-species optimization experiments need to be conducted to
explore possible mechanisms in food competition. New developments are also re-
quired to add bioenergetic rules to simulate physiological effects associated with
climate change impacts. Finally, the sub-model describing the functional groups of
micronektonic prey organisms is a key component in SEAPODYM that requires
further validation and development, especially by assimilation of acoustic data.
In conclusion, though the model and its predictions will be continuously upgraded,
results from past and present studies as well as those coming from regular updates for
evaluating the effects of climate change are an important achievement that can be already
used, particularly by the Pacific Island Countries to learn to adapt to the inevitable changes
that will occur to their fisheries resources in response to global warming. A fundamental
challenge for these Countries will be to develop coherent policies for fisheries management
at both regional and national levels. The spatially explicit aspect of SEAPODYM allows the
evaluation of the impacts of national level policies on the regional scale and vice versa (Bell
et al. 2011). With climate change, the fishing impact will definitely remain a key driver of
tuna stocks. Simulations with management scenarios based on multi-decadal projections
including climate change are needed to assist in the timely provision of information on the
sustainability of current and future harvest strategies and their potential impact on tuna
populations and oceanic ecosystems. In addition, it would be beneficial in further studies to
define more realistic future fishing scenarios based on economical requirements and ac-
counting for spatial change in the biomass.
Acknowledgments The authors wish to thank the Ocean Productivity team for providing the SeaWiFS-
derived primary production, Peter Williams (SPC) and Michael Hinton (IATTC) for preparing and supplying
catch and size composition data, and Laurent Bopp for assisting in the use of IPSL-CM4 and PISCES outputs.
This work was funded partly by the 10th European Development Fund project SCICOFISH (Scientific
Support to coastal and Oceanic Fisheries Management in the western and Central Pacific Ocean), the
Australian Department of Climate Change and Energy Efficiency and by the Deutsche Gesellschaft für
Internationale Zusammenarbeit (GIZ) project (Enhanced estimates of climate change impacts on WCPO
tuna). The views expressed herein are those of the authors and do not necessarily reflect the views of their
organizations or funding Agencies.
References
Behrenfeld MJ, Falkowski PG (1997) A consumer’s guide to phytoplankton primary productivity models.
Limnol Oceanogr 42(7):1479–1491
Bell JD, Reid C, Batty MJ, Allison EH, Lehodey P, Rodwell L, Pickering TD, Gillett R, Johnson JE, Hobday
AJ, Demmke A (2011) Economic and social implications of climate change for contributions by fisheries
and aquaculture to the Pacific Community. In: Bell JD, Johnson JE, Hobday AJ (eds) Vulnerability of
tropical Pacific fisheries and aquaculture to climate change. Secretariat of the Pacific Community,
Noumea, pp 733–801
Climatic Change
Author's personal copy
Brill RW (1994) A review of temperature and O2 tolerance studies of tunas pertinent to fisheries oceanog-
raphy, movement models and stock assessments. Fish Oceanogr 3:204–216
Carton JA, Chepurin G, Cao X, Giese BS (2000) A simple ocean data assimilation analysis of the global upper
ocean 1950–1995, part 1: methodology. J Phys Oceanogr 30:294–309
Ganachaud AS, Sen Gupta A, Orr JC, Wijffels SE, Ridgway KR, Hemer MA, Maes C, Steinberg CR, Tribollet
AD, Qiu B, Kruger JC (2011) Observed and expected changes to the tropical Pacific Ocean. In: Bell J,
Johnson JE, Hobday AJ (eds) Vulnerability of tropical pacific fisheries and aquaculture to climate change.
Secretariat of the Pacific Community, Noumea, pp 115–202
Garcia HE, Locarnini RA, Boyer TP, Antonov JI, Baranova OK, Zweng MM, Johnson DR (2010)
World Ocean Atlas 2009, volume 3: dissolved oxygen, apparent oxygen utilization, and oxygen
saturation. In: Levitus S (ed) NOAA Atlas NESDIS 70. U.S. Government Printing Office,
Washington, p 344
Hourdin F, Foujols MJ, Codron F, Guemas V, Dufresne JL, Bony S, Denvil S, Guez L, Lott F, Ghattas J,
Braconnot P, Marti O, Meurdesoif Y, Bopp L (2012) Impact of the LMDZ atmospheric grid configuration
on the climate and sensitivity of the IPSL-CM5A coupled model. Clim Dyn
Hoyle S, Kleiber P, Davies N, Langley A, Hampton J (2011) Stock assessment of skipjack tuna in the western
and central Pacific Ocean. Seventh Regular Session of the Scientific Committee, 9–17 August 2011,
Pohnpei, Federated States of Micronesia, WCPFC-SC7-2011/SA-WP-04: p 134
Langley A, Hampton J (2008) Stock assessment of skipjack tuna in the western and central Pacific Ocean.
Fourth Regular Session of the Scientific Committee, 11–22 August 2008 Port Moresby, Papua New
Guinea, WCPFC-SC4-2008/SA-WP-4: p 75
Lehodey P (2001) The pelagic ecosystem of the tropical Pacific Ocean: dynamic spatial modelling and
biological consequences of ENSO. Prog Oceanogr 49:439–468
Lehodey P, Bertignac M, Hampton J, Lewis T, Picaut J (1997) El Niño Southern Oscillation and tuna in the
western Pacific. Nature 389:715–718
Lehodey P, Senina I, Murtugudde R (2008) A spatial ecosystem and populations dynamics model
(SEAPODYM)—modelling of tuna and tuna-like populations. Prog Oceanogr 78:304–318
Lehodey P, Murtugudde R, Senina I (2010a) Bridging the gap from ocean models to population dynamics of
large marine predators: a model of mid-trophic functional groups. Prog Oceanogr 84:69–84
Lehodey P, Senina I, Sibert J, Bopp L, Calmettes B, Hampton J, Murtugudde R (2010b) Preliminary
forecasts of population trends for Pacific bigeye tuna under the A2 IPCC scenario. Prog Oceanogr
86:302–315
Lehodey P, Hampton J, Brill RW, Nicol S, Senina I, Calmettes B, Pörtner HO, Bopp L, Ilyina T, Bell JD,
Sibert J (2011a) Vulnerability of oceanic fisheries in the tropical Pacific to climate change. In: Bell J,
Johnson JE, Hobday AJ (eds) Vulnerability of tropical pacific fisheries and aquaculture to climate change.
Secretariat of the Pacific Community, Noumea, pp 447–506
Lehodey P, Senina I, Calmettes B, Hampton J, Nicol S, Williams P, Jurado Molina J, Ogura M, Kiyofuji H, Okamoto
S (2011b) SEAPODYM working progress and applications to Pacific skipjack tuna population and fisheries. 7th
regular session of the Scientific Steering Committee, 8–17 August 2011, Pohnpei, Federate States of Micronesia.
WCPFC-SC7-2011/EB- WP 06. http://www.wcpfc.int/meetings/2011/7th-regular-session-scientific-committee
Locarnini RA, Mishonov AV, Antonov JI, Boyer TP, Garcia HE, Baranova OK, Zweng MM, Johnson DR
(2010) World Ocean Atlas 2009, volume 1: temperature. In: Levitus S (ed) NOAA Atlas NESDIS 68.
U.S. Government Printing Office, Washington, p 184
Lutcavage ME, Brill RW, Skomal GB, Chase BC, Goldstein JL, Tutein J (2000) Tracking adult North Atlantic
bluefin tuna (Thunnus thynnus) in the northwestern Atlantic using ultrasonic telemetry. Mar Biol
137:347–358
Nakicenovic N, Alcamo J, Davis G, De Vries B et al (2000) Special report on emissions scenarios: a special
report of the working group III of the intergovernmental panel on climate change. PNNL-SA-39650.
Cambridge University Press, New York
Schaefer KM (1998) Reproductive biology of yellowfin tuna (Thunnus albacares) in the eastern Pacific
Ocean. IATTC Bull 21(5):205–272
Schaefer KM (2001) Assessment of skipjack tuna (Katsuwonus pelamis) spawning activity in the eastern
Pacific Ocean. Fish Bull 99:343–350
Schneider B, Bopp L, Gehlen M, Segschneider TL, Frölicher J, Cadule P, Friedlingstein P, Doney SC,
Behrenfeld MJ, Joos F (2008) Climate-induced interannual variability of marine primary and export
production in three global coupled climate carbon cycle models. Biogeosci 5:597–614
Senina I, Sibert J, Lehodey P (2008) Parameter estimation for basin-scale ecosystem-linked population models
of large pelagic predators: application to skipjack tuna. Prog Oceanogr 78:319–335, 4
Climatic Change
Author's personal copy
Sibert JR, Hampton J, Fournier DA, Bills PJ (1999) An advection–diffusion reaction model for the estimation
of fish movement parameters from tagging data, with application to skipjack tuna (Katsuwonus pelamis).
Can J Fish Aquat Sci 56:925–938
Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) (2007)
Contribution of working group I to the fourth assessment report of the intergovernmental panel on
climate change, 2007. Cambridge University Press, Cambridge
Steinacher M, Joos F, Frölicher TL, Bopp L, Cadule P, Cocco V, Doney SC, Gehlen M, Lindsay K, Moore JK,
Schneider B, Segschneider J (2010) Projected 21st century decrease in marine productivity: a multi-model
analysis. Biogeosci 7:979–1005
Sund PN, Blackburn M, Williams F (1981) Tunas and their environment in the Pacific Ocean: a review.
Oceanogr Mar Biol Ann Rev 19:443–512
Wexler J, Margulies D, Scholey V (2011) Temperature and dissolved oxygen requirements for survival of
yellowfin tuna, Thunnus albacares, larvae. J Exp Mar Biol Ecol 404:63–72
Climatic Change
Author's personal copy