Download An ensemble analysis to predict future habitats of striped marlin

ICES Journal of
Marine Science
ICES Journal of Marine Science (2013), 70(5), 1013–1022. doi:10.1093/icesjms/fss191
An ensemble analysis to predict future habitats of striped marlin
(Kajikia audax) in the North Pacific Ocean
Nan-Jay Su 1, Chi-Lu Sun 1*, André E. Punt 2, Su-Zan Yeh 1, Gerard DiNardo 3, and Yi-Jay Chang 1
1
Institute of Oceanography, National Taiwan University, Taipei 10617, Taiwan
School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, USA
3
NOAA Fisheries, Pacific Islands Fisheries Science Center, Honolulu, HI 96822, USA
2
*Corresponding author: tel: +886 2 2362 9842; fax: +886 2 2362 9842; e-mail: [email protected]
Su, N.-J., Sun, C.-L., Punt, A. E., Yeh, S.-Z., DiNardo, G., and Chang, Y.-J. 2013. An ensemble analysis to predict future habitats of striped marlin
(Kajikia audax) in the North Pacific Ocean. – ICES Journal of Marine Science, 70: 1013– 1022.
Received 26 August 2013; accepted 28 November 2012; advance access publication 8 January 2013.
Striped marlin is a highly migratory species distributed throughout the North Pacific Ocean, which shows considerable variation in
spatial distribution as a consequence of habitat preference. This species may therefore shift its range in response to future changes
in the marine environment driven by climate change. It is important to understand the factors determining the distribution of
striped marlin and the influence of climate change on these factors, to develop effective fisheries management policies given the economic importance of the species and the impact of fishing. We examined the spatial patterns and habitat preferences of striped marlin
using generalized additive models fitted to data from longline fisheries. Future distributions were predicted using an ensemble analysis,
which represents the uncertainty due to several global climate models and greenhouse gas emission scenarios. The increase in water
temperature driven by climate change is predicted to lead to a northward displacement of striped marlin in the North Pacific Ocean.
Use of a simple predictor of water temperature to describe future distribution, as in several previous studies, may not be robust, which
emphasizes that variables other than sea surface temperatures from bioclimatic models are needed to understand future changes in
the distribution of large pelagic species.
Keywords: global climate models, sea surface temperature, spatial distribution, thermal preferences.
Introduction
Striped marlin, Kajikia audax, is a highly migratory species distributed throughout tropical, subtropical, and temperate waters of the
Pacific Ocean (Nakamura, 1985). However, they are found in more
temperate waters compared with other billfishes, such as blue
marlin (Makaira nigricans; Molony, 2008). The distribution of
striped marlin in the Pacific Ocean has been inferred from commercial longline catch-rates and conventional tagging data, and
characterized as a horseshoe-like pattern, primarily between 20
and 308 north and south of the equator in the central Pacific
Ocean, with a continuous distribution across the equator along
the west coast of Central America (McDowell and Graves, 2008).
Very few trans-equatorial movements in the western Pacific
Ocean have been reported from tagging experiments (Ortiz
et al., 2003; Domeier, 2006).
Several genetic subdivisions have been hypothesized for striped
marlin in the Pacific Ocean. For example, a hypothesis of
# 2013
northern –southern Pacific stocks was proposed by Kamimura
and Honma (1958), while McDowell and Graves (2008) suggested
a North Pacific stock (Japan –Taiwan–Hawaii–California) and a
Mexican stock based on significant genetic differentiation
between these two regions. More recently, Purcell and Edmands
(2011) also proposed two stocks of striped marlin in the
North Pacific Ocean, Japan –Hawaii–Southern California, and
Mexico–Central America, based on pairwise microsatellite analyses from more representative samples of the species’ range.
Several studies have highlighted the importance of temperature
as a determinant of the spatial distribution of striped marlin.
For example, Howard and Ueyanagi (1965) indicated that the
20 –258C sea surface temperature (SST) isotherm generally
formed a boundary for the distribution of striped marlin in the
western Pacific Ocean. Ortega-Garcia et al. (2003) showed that
highest catch-rates of striped marlin in the recreational fishery
were recorded between 22 and 248C SST in waters off California
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and Mexico. Inferences from archival tagging data for striped
marlin in the southwest Pacific Ocean suggest that they prefer
waters where the temperature is between 20 and 248C (Sippel
et al., 2007), while a thermal preference of 20 –268C was reported
based on captures from longline fisheries (Hobday, 2010).
Striped marlin is a surface-dwelling species, and spend the majority of their time in surface waters, as revealed by studies based
on ultrasonic telemetry (e.g. Brill et al., 1993) and pop-up archival
satellite tags (e.g. Domeier et al., 2003; Sippel et al., 2007).
However, dives to deeper than 50 m at night for feeding were
observed by Sippel et al. (2011). Holts and Bedford (1990)
showed that the thermocline may hinder the vertical movement
of striped marlin, and inferred that the depth of the thermocline
would influence the catch per unit effort (cpue) of this species.
For example, catch-rates are higher in areas where vertical
habitat is compressed by shallower thermoclines than in areas
with deeper thermoclines (Ward and Myers, 2005), further highlighting the impact of thermal preference on the distribution of
striped marlin.
Climate change due to anthropogenic effects is impacting the
global oceans, particularly water temperature (IPCC, 2007).
Several analyses have reported a global warming trend in the
oceans and a negative impact on marine ecosystems (Alvarez
et al., 2012). Large pelagic species, such as tunas and billfishes,
are likely to change their distribution to response to
climate-related environmental variability before other fish
species due to their highly migratory nature (e.g. Lehodey et al.,
2003; Su et al., 2011). Thus, a reasonable assumption is that
future increases in SST will impact the geographic distribution
of striped marlin because they inhabit the thermally mixed layer
in the upper ocean and have been proposed to be one of the
species that will be most impacted by climate change (Hobday,
2010).
The objectives of this study were to identify the thermal preferences of striped marlin, then to use this information to develop
spatially explicit representations of seasonal thermal habitats for
this species based on predictive models of geographic distribution
using outputs from global climate models (GCMs). The future distribution of striped marlin is an appropriate way to assess some of
the potential impacts of global climate change, and could greatly
improve biological and ecological understanding, as well as fisheries management for this species.
Material and methods
Data used
Catch and effort data for the Taiwanese distant-water longline
fishery for striped marlin in the North Pacific Ocean grouped by
month and 58 × 58 grid cell (Figure 1) were available from the
Overseas Fisheries Development Council of Taiwan (http://www.
ofdc.org.tw/). Catches were expressed as the number of fish
caught, while effort was the number of hooks deployed. Fishery
data for 1998–2009 were used for the analyses because only a
small amount of data was available before 1998.
Monthly satellite remotely sensed SST data for 1998–2009
averaged to 58 × 58 latitude/longitude to match the scale of the
fishery data were sourced from the online POET tool at Physical
Oceanography Distributed Active Archive Center, National
Aeronautics and Space Administration (PO.DAAC, NASA; http://
poet.jpl.nasa.gov/). Monthly simulated SST (mean surface temperature) data from the Fourth Assessment Report (AR4) of the
N-J. Su et al.
Figure 1. (a) Distribution of fishing effort (in 106 hooks) and (b)
nominal cpue (number of fish caught per 1000 hooks) for striped
marlin in the Taiwanese longline fisheries in the North Pacific Ocean
(1998 – 2009).
Intergovernmental Panel on Climate Change (IPCC) were obtained
from the website of the Program for Climate Model Diagnosis and
Intercomparison (PCMDI; http://www-pcmdi.llnl.gov/) for three
warming scenarios: lower (SRES B1), medium (SRES A1B), and
higher (SRES A2) greenhouse gas emissions. The scenarios represent
combinations of different rates of change in economic growth,
human population size, land use, and the introduction of new
and more efficient technology, among others, but can be generally
characterized by maximum atmospheric CO2 concentrations
(IPCC, 2007). For example, scenario SRES B1 is characterized by
rapid changes in economic structures towards a service and
information economy, with the introduction of clean and
resource-efficient technology and a reduction in materials intensity.
Approximate atmospheric CO2 equivalent concentrations corresponding to the estimated radiative forcing due to anthropogenic
greenhouse gases and aerosols in 2100 for the SRES B1, A1B, and
A2 scenarios are about 600, 850, and 1250 ppm, respectively
(IPCC, 2007). Further details on the greenhouse gas emissions scenarios can be found in Nakicenovic et al. (2000).
All of current GCMs that differ in model structure and parameterization provide simulated future values for oceanographic
variables for various climate change scenarios, except for few
cases (see Table 1). The model names, approximate spatial resolutions, and sources of the GCMs used in this study are listed in
Table 1. The spatial resolution of GCM-predicted SST (the only
variable which was found to be highly related to catch-rates of
striped marlin) varied from 0.56258 × 1.1258 to 48 × 58 (latitude
by longitude). The simulated SST data from GCMs were interpolated to a 58 × 58 resolution for use in the forecasts of the spatial
distribution of striped marlin.
Habitat preferences from statistical modelling
The spatial pattern of North Pacific striped marlin abundance was
modelled using generalized additive models (GAMs). GAMs are
extensions of commonly used generalized linear models (Hastie
and Tibshirani, 1990). The underlying assumption of a GAM is
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An ensemble analysis to predict future habitats
Table 1. GCMs and their resolution (latitude × longitude) used in
this study.
Model
BCCR:BCM2
Resolution
2.88 × 2.88
CCCma:CGCM-T47
3.758 × 3.758
CCCma:CGCM-T63
2.88 × 2.88
CNRM:CM3
2.88 × 2.88
CSIRO:MK3.0
1.98 × 1.98
CSIRO:MK3.5
1.98 × 1.98
GFDL:CM2
2.08 × 2.58
GFDL:CM2_1
2.08 × 2.58
NASA:GISS-AOM
3.08 × 4.08
NASA:GISS-EH
4.08 × 5.08
NASA:GISS-ER
3.98 × 5.08
LASG:FGOALS
2.88 × 2.88
INGV:SXG
1.1258 × 1.1258
INM:CM3
4.08 × 5.08
IPSL:CM4
2.58 × 3.758
NIES:MIROC-HI
0.56258 × 1.1258
NIES:MIROC-MED
2.88 × 2.88
CONS:ECHO-G
3.758 × 3.758
MPIM:ECHAM5
1.98 × 1.98
MRI:CGCM
2.88 × 2.88
NCAR:CCSM3
1.48 × 1.48
NCAR:PCM
2.88 × 2.88
UKMO:HADCM3
2.758 × 3.758
UKMO:HADGEM1
1.258 × 1.8758
Source
Bjerknes Centre for Climate
Research, Norway
Canadian Centre for Climate
Modelling & Analysis,
Canada
Canadian Centre for Climate
Modelling & Analysis,
Canada
Centre National de
Recherches
Météorologiques, France
Commonwealth Scientific and
Industrial Research
Organisation, Australia
Commonwealth Scientific and
Industrial Research
Organisation, Australia
Geophysical Fluid Dynamics
Laboratory, USA
Geophysical Fluid Dynamics
Laboratory, USA
NASA Goddard Institute for
Space Studies, USA
NASA Goddard Institute for
Space Studies, USA
NASA Goddard Institute for
Space Studies, USA
Institute of Atmospheric
Physics, China
Istituto Nazionale di Geofisica
e Vulcanologia, Italy
Institute of Numerical
Mathematics, Russia
Institut Pierre Simon Laplace,
France
National Institute for
Environmental Studies,
Japan
National Institute for
Environmental Studies,
Japan
Meteorological Institute of
the University of Bonn,
Germany
Max Planck Institute for
Meteorology, Germany
Meteorological Research
Institute, Japan
National Center for
Atmospheric Research, USA
National Center for
Atmospheric Research, USA
Hadley Centre for Climate
Prediction and Research,
Met Office, UK
Hadley Centre for Climate
Prediction and Research,
Met Office, UK
GFDL:CM2, NASA:GISS-EH, INGV:SXG, and UKMO:HADGEM1 did not
forecast SST for the B1 scenario, and CCCma:CGCM-T63, NASA:GISS-AOM,
NASA:GISS-EH, LASG:FGOALS, and NIES:MIROC-HI for the A2 scenario.
that functions of the predictors are additive and smooth. GAMs
can be used to represent non-linear relationships between a response variable and predictor variables, including environmental
variables such as temperature (Su et al., 2008).
Two GAMs were considered. SST was the only environmental
variable used to describe habitats (i.e. the “SST” model) owing
to the limited number of variables useful for predicting fish distribution produced by current GCMs at the mesoscale and because
initial analyses did not support the inclusion of other environmental variables such as chlorophyll-a concentration (http://
oceancolor.gsfc.nasa.gov/), mixed layer depth (http://www.
science.oregonstate.edu/ocean.productivity/), dissolved oxygen
(http://www.nodc.noaa.gov/General/oxygen.html), and sea
surface height anomaly (http://coastwatch.pfeg.noaa.gov/data.
html) in a predictive model. Oceanographic variables are not the
sole determinant of striped marlin habitat. Thus, another model
(the “Full” model) was constructed including spatial and temporal
factors as well as SST as predictor variables in a stepwise addition
(Table 2). The models can be written as
SST model : g(CPUE) s(SST),
Fullmodel : g(CPUE) s(SST) + s(Year) + s(Month) + s(Latitude)
+ s(Longitude) + s(Latitude : Longitude),
where g is the link function, and s() is a smoothing function for the
explanatory covariates. Cpue is the log-transformed catch-rate of
striped marlin, with a small constant (10% of the grand mean)
added to avoid log-transformation problems (Howell and
Kobayashi, 2006; Mugo et al., 2010).
The effects of the explanatory variables, the fit of the models,
and the assumption of a lognormal error model were evaluated
using standard diagnostics: changes in the residual deviance, the
Akaike Information Criterion (AIC), the distribution of residuals
and quantile–quantile (Q –Q) plots, as well as p-values derived
from the x 2 test.
Predicting habitat maps
Two types of habitat maps were produced for North Pacific striped
marlin using each GAM. The “current” habitats of striped marlin
in the North Pacific Ocean for each month during 1998–2009
were predicted based on existing satellite remote-sensed data,
while “future” habitats were projected by month using the simulated outputs from the GCMs for each greenhouse gas emissions
scenario over 2001– 2099 (this period was selected because all
the GCMs provide SST outputs for these years, except for
NASA:GISS-ER which is projected from 2004; Table 1).
Although interannual variation in simulated SST is present in
the GCM outputs, the years do not correspond to particular calendar years. It is also not reasonable to forecast the year effect in the
Full model owing to the high variance associated with extrapolating the full model into the future. Therefore, the forecasts of the
Full model were based on the value of the year factor estimated
for 2001.
The set of cells that may be considered preferred habitats were
defined as those for which predicted relative density from the
GAM is in the top 10% (Goodyear, 2003). GCMs can provide
insight into possible future climate conditions. However, there is
considerable uncertainty surrounding the predictions of ocean
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N-J. Su et al.
Table 2. The changes in residual deviance and AIC due to adding each of the factors, and the p-values derived from the x 2 tests between
models that differ by one factor.
Predictor
NULL
+SST
+Year
+Month
+Latitude
+Longitude
+Latitude:Longitude
Total deviance explained
Residual deviance
3245.4
3005.3
2778.8
2745.3
2696.6
2617.1
2324.7
28.4%
Deviance explained
Percentage of total deviance explained (%)
240.1
226.5
33.5
48.7
79.5
292.4
26.1
24.6
3.6
5.3
8.6
31.8
AIC
8226
8031
7839
7827
7794
7766
7700
p (x 2)
,0.01
,0.01
,0.01
,0.01
,0.01
,0.01
Figure 2. Q– Q plots and residual distributions for the two GAMs: (a) “Full” model and (b) SST model.
conditions from GCMs given differences in model structure and
assumptions regarding population size and economic growth, as
well as changes to initial conditions and model parameterizations.
A solution to deal with model uncertainty is to conduct an ensemble analysis that includes all GCMs and all scenarios to explore the
resulting range of projections (Hobday, 2010). Several approaches
have been used for ensemble forecasting of species distributions,
including bounding box and consensus techniques, although
probabilistic forecasting is considered one of the most appropriate
methods for a large ensemble analysis (see Araújo and New, 2007).
The future habitat maps predicted based on simulated environmental variables were thus integrated through an ensemble analysis using probabilistic techniques, to express some of the
uncertainty. This involved computing the likelihood of the presence of preferred habitats from each GCM and expressing this in
the form of a probability map. For each month, there are 63
future habitat distribution predictions (24 GCMs under 3 scenarios; see details in Table 1), which were treated as independent
in the analysis. Thus, the probability of preferred habitats at
each 58 × 58 grid cell was based on the percentage of the 63
model –scenario combinations for which the predicted relative
density at that location is considered preferred (i.e. in the top
10% of predicted catch-rates). This ensemble analysis can
account for the variation over multiple models and scenarios,
and can be used to examine potential changes in spatial distribution. Predicted habitat areas for February and August are presented as examples of those in winter and summer, respectively.
The computational and presentational tasks were conducted
using R version 2.15.1 (R Development Core Team, 2012).
The gam function of the mgcv package was used to construct
the GAMs.
Results
The Taiwanese distant-water longline fleets operated throughout
the North Pacific Ocean, with two major fishing grounds
(Figure 1a). However, striped marlin appears more abundant in
subtropical waters near the Hawaiian Islands and in tropical
waters in the eastern Pacific Ocean (Figure 1b).
An ensemble analysis to predict future habitats
The residuals from the Full model conform well with the assumption of lognormality according to Q –Q plots, and because
the residuals (in log-space) appear normal (Figure 2a). In contrast,
the residuals for the SST model did not confirm with the lognormal distribution (Figure 2b). Other error models did not
Figure 3. The effect of SST on the cpue of striped marlin in the
North Pacific Ocean inferred from the nominal data, the Full model,
and the SST model. The vertical dashed line indicates an SST of
24.58C.
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lead to increased agreement for the SST model (results not
shown). All the factors, and the interaction between latitude and
longitude, were highly statistically significant (p , 0.01) based
on x 2 tests between models that differed by one factor (Table 2).
SST explained 7.4% of the total deviance, while 28.4% of the
total deviance can be explained by the Full model, with latitude
and longitude accounting for a large proportion of explained
deviance (Table 2).
The effects of SST on relative density of striped marlin differed
slightly between the two models, both of which were consistent
with the results inferred from the observed nominal data
(Figure 3). The abundance of striped marlin was predicted to be
high in waters where SST was around 23– 268C, with a peak occurring at 24.58C (Figure 3).
Striped marlin are abundant in temperate waters of the central
Pacific and tropical waters of the eastern Pacific Ocean based on
nominal cpue (see, for example, February and August in
Figure 4a). These two areas could be considered preferred
habitat for striped marlin in the North Pacific Ocean. The
spatial distribution predicted using the Full model was similar to
that implied by the nominal cpue data (Figure 4a and b). The
habitat of striped marlin predicted using the SST model shifted
northward during summer (August in Figure 4c), while a southward displacement was evident during winter (February in
Figure 4. Distributions of cpue for striped marlin in the North Pacific Ocean during February (left column) and August (right column) for
1998 –2009, based on (a) the observed (nominal) data and predicted values from (b) the Full model and (c) the SST model.
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N-J. Su et al.
Figure 5. SST isotherms (24.58C) in the North Pacific Ocean for February (left column) and August (right column) for 2001 to the 2080s
obtained from each of the 63 climate models under the SRES A1B, SRES A2, and SRES B1 scenarios.
Figure 4c). In contrast, there is no obvious evidence of seasonal
movement from the Full model (Figure 4b).
24.58C SST isotherms from each GCM under the three greenhouse gas emission scenarios are shown for February and August
(as examples) in Figure 5. The initial conditions (2001) and predictions for three 30-year periods corresponding to the 2020s
(2010– 2039), the 2050s (2040– 2069), and the 2080s (2070 –
2099) allow an evaluation of the changes in the simulated SST
outputs. There is a poleward shift in the 24.58C isotherm for
both summer and winter (Figure 5).
Probability maps for striped marlin habitat based on the ensemble analysis over multiple GCMs and different scenarios
(SRES B1, A1B, and A2) were constructed for the four periods
in Figure 5 to evaluate the potential changes in spatial distribution
(Figures 6 and 7). The habitats predicted by the SST model show
an obvious northward shift. Specifically, the core habitats (red
areas in Figures 6 and 7) during February moved north of 208N
by the 2080s, and the habitat ranges (blue areas) north of 208N
increased 133%, compared with those in 2001. Similar northward
displacements were also evident for August, with an increase of
53% to the habitat ranges north of 208N by the 2080s
(Figure 7). However, only minor changes to the spatial distribution of striped marlin are predicted by the Full model because of
the effects of the time-invariant variables, latitude and longitude.
There is, however, a predicted reduction of 27.6% in core
habitat from the Full model from 2001 to the 2080 due to future
predicted changes in SST (Figure 7).
Discussion
Data presented in this study support published evidence of preferred temperature ranges for North Pacific striped marlin (23 –
268C; Figure 3). Two preferred habitat areas in the North Pacific
An ensemble analysis to predict future habitats
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Figure 6. Ensemble probability maps for preferred habitats of striped marlin in the North Pacific Ocean for February for 2001 and three
30-year periods, from the SST model (left column) and the Full model (right column).
Ocean, separated at about 140 – 1458W (Figure 4b), were identified. These areas are consistent with previous results based on analyses using data from other fisheries (Japanese longline catch-rates;
Hinton et al., 2010), and of the outcomes from tagging experiments (Domeier, 2006).
The analysis presented here provides a new approach for exploring potential impacts of climate change on the habitat of a
cosmopolitan species which is widely distributed throughout an
ocean basin. In previous research, preferred thermal ranges for
fish species were mostly chosen subjectively using published data
because there were no clear guidelines to define them (e.g. Boyce
et al., 2008). In contrast, the relationship between habitat preference and SST was derived from GAMs in this study. GAMs are
used extensively to model spatial distributions because of their
ability to consider non-linear relationships between covariates
and response variables. Strengths and weaknesses of habitat modelling techniques for GAMs can be found in Elith et al. (2006).
Catch and effort data for a bycatch species often contain observations with zero catches, and these observations would lead to
computational problems if the data were log-transformed. This
problem has been overcome using the “delta approach” that
models the cpue data in two steps (the probability of catching
the fish and their relative abundance, e.g. Su et al., 2011), while
a small constant was added to address this issue in some studies
(e.g. Howell and Kobayashi, 2006; Mugo et al., 2010), including
the present study. Thermal preferences and spatial predictions of
striped marlin were robust to whether the delta method was
applied or a constant was added to all cpues (results not
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N-J. Su et al.
Figure 7. As for Figure 6, but for August.
shown), which implies that the predictions of the potential
impacts of climate change are robust to how zero observations
are treated.
A range of observed or satellite-based oceanographic and biological variables has been used to describe fish –environment associations, including sea surface chlorophyll-a concentration, sea
surface height, eddies, and fronts (Hobday, 2010). We included
chlorophyll-a, depth of the mixed layer, and sea surface height
anomaly as predictor variables in initial analyses to predict the
spatial pattern of striped marlin. However, the deviance explained
by these environmental variables was small (3%). Availability of
dissolved oxygen has been shown to influence the vertical distribution of billfishes and tunas (Hoolihan et al., 2011; Stramma et al.,
2012). For example, Goodyear et al. (2008) and Prince et al. (2010)
argue that the shoaling of the oxygen minimum zone in the
tropical Atlantic Ocean could restrict the usable habitat of blue
marlin and sailfish. However, the inclusion of climatological dissolved oxygen concentration in the Full model hardly increased
the explained deviance (,2%).
Further extensions of this study could include other oceanographic and biological variables that have been demonstrated to
relate to spatial dynamics of striped marlin (e.g. forage abundance;
Sippel et al., 2011), but are not available currently, as well as the
information on operational targeting practices and fishing strategies, such as how the gear is set and the number of hooks
employed per basket. It is likely that the use of logbook data for
each operation at a finer spatial scale would increase the explained
deviance and improve the precision of predictions of spatial distributions. This is because, although striped marlin spend most of
their time in the surface layer above the thermocline, their
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An ensemble analysis to predict future habitats
temperature preferences might be subject to local oceanographic
conditions (Sippel et al., 2007). Unfortunately, the lack of data
in a finer spatial scale prevented such analysis for this paper.
Climate change is likely to affect community structure, the diversity and function of marine ecosystems as well as the distribution, abundance, phenology, and physiology of individual species.
However, past studies have focused largely on changes in the distribution of coastal and benthic species (Hobday, 2010). One of
the limitations of current GCM outputs for predicting fish distribution at a broader geographic extent is that few of potentially
useful oceanographic variables to determine distribution are modelled (Pearson and Dawson, 2003). Although latitude and longitude can improve the fit to the data (Table 2), these variables are
time-invariant and arbitrary units on grids that describe location.
However, this emphasizes the importance of other oceanographic
and biological variables derived from GCMs for predicting fish
distribution in the future, to capture the oceanographic factors
that latitude and longitude may be currently capturing.
SST is the predominant environmental predictor of the distribution of striped marlin (Domeier et al., 2003; Ortega-Garcia
et al., 2003; Sippel et al., 2007), but it is not the only determinant
variable for striped marlin habitats (as more deviance is explained
by other factors; Table 2). However, SST could relate directly to
physiological limitations or indirectly to prey abundance, and
thus other biological and oceanographic variables are likely to correlate with SST, such as sea surface chlorophyll-a concentration
(Brill and Lutcavage, 2001; Watters and Maunder, 2001). The
SST model only includes a single, but important, predictor variable, in contrast to traditional fisheries habitat models which
include several environmental variables (Hobday, 2010). SST has
been considered the single best descriptor of habitat suitability
for pelagic fish, and outputs from the analysis based on SST may
be relatively robust (Boyce et al., 2008). However, this study
showed that predicting future spatial distributions using only
SST leads to markedly different predictions than those using a
model which explained more of the variance in the data.
The uncertainty among the predictions from the various GCMs
and the different future climate scenarios that represent a range of
plausible emission pathways was taken into account in this study
through the ensemble analysis. Running ensembles for different
models and scenarios and combining the results using probabilistic approaches do not eliminate model uncertainty. However, this
approach could substantially reduce the likelihood of making
management decisions based on predictions that are far from
the truth (Araújo and New, 2007).
Understanding of future distribution and potential impacts
driven by climate change is important in planning adaptation
strategies for harvested marine species (Hobday, 2010). Results
derived in this study for an economically important and highly migratory species can contribute valuable information to natural resource policy-making and management. As suggested by Hobday
and Hartmann (2006) and Su et al. (2012), habitat characteristics
and spatial distribution of a species should be accounted for to
improve management and conservation efforts, particularly for
pelagic migratory species. For example, the discontinuity in distribution between the eastern and central North Pacific Ocean is
worth considering for spatial stratification of future stock assessments. Local time-area closures have been shown to be a useful
tool to reduce fishing mortality of striped marlin (Jensen et al.,
2010). Spatially explicit fisheries management measures such as
marine protected areas could be developed based on the preferred
habitats and how they are likely to change over time.
Acknowledgements
We thank the Overseas Fisheries Development Council, Taiwan,
for providing the Taiwanese longline fisheries data. We also
thank two anonymous reviewers and the editor for their thoughtful comments and suggestions.
Funding
This study was funded partially by the National Science Council and
the Fisheries Agency of Council of Agriculture, Taiwan, through the
research grants NSC98-2611-M-002-002, NSC99-2611-M-002-013,
NSC099-2811-M-002-201, 99AS-10.1.1-FA-F4(1), and 100AS10.1.1-FA-F3(1) to C-L.S.
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Handling editor: Howard Browman