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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 International Council for the Exploration of the Sea. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected] 1014 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 1015 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 1016 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. 1017 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. 1018 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 1019 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 1020 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 1021 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. References Alvarez, I., Lorenzo, M. N., and deCastro, M. 2012. Analysis of chlorophyll a concentration along the Galician coast: seasonal variability and trends. ICES Journal of Marine Science, 69: 728– 738. Araújo, M. B., and New, M. 2007. Ensemble forecasting of species distributions. Trends in Ecology and Evolution, 22: 42– 47. Boyce, D. G., Tittensor, D. P., and Worm, B. 2008. Effects of temperature on global patterns of tuna and billfish richness. 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