Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective PDF

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FISHERIES OCEANOGRAPHY Fish. Oceanogr. 19:5, 382–396, 2010 Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective ROBINSON MUGO,1,2,* SEI-ICHI SAITOH,1 anomalies (surface height anomalies 0–50 cm), and AKIRA NIHIRA3 AND TADAAKI low to...

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Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing per... Sei-ichi Saitoh Fisheries Oceanography

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FISHERIES OCEANOGRAPHY

Fish. Oceanogr. 19:5, 382–396, 2010

Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective

ROBINSON MUGO,1,2,* SEI-ICHI SAITOH,1 AKIRA NIHIRA3 AND TADAAKI KUROYAMA3 1

Laboratory of Marine Environment and Resource Sensing, Graduate School of Fisheries Sciences, Hokkaido University, 3-1-1 Minato-cho, Hakodate, 041-8611, Hokkaido, Japan 2 Kenya Marine and Fisheries Research Institute, P.O. Box 81651, Mombasa, Kenya 3 Ibaraki Prefecture Fisheries Research Station, Hitachinaka, Ibaraki, Japan

ABSTRACT Skipjack tuna habitat in the western North Pacific was studied from satellite remotely sensed environment and catch data, using generalized additive models and geographic information systems. Weekly resolved remotely sensed sea surface temperature, surface chlorophyll, sea surface height anomalies and eddy kinetic energy data were used for the year 2004. Fifteen generalized additive models were constructed with skipjack catch per unit effort as a response variable, and sea surface temperature, sea surface height anomalies and eddy kinetic energy as model covariates to assess the effect of environment on catch per unit effort (skipjack tuna abundance). Model selection was based on significance of model terms, reduction in Akaike’s Information Criterion, and increase in cumulative deviance explained. The model selected was used to predict skipjack tuna catch per unit effort using monthly resolved environmental data for assessing model performance and to visualize the basin scale distribution of skipjack tuna habitat. Predicted values were validated using a linear model. Based on the fourparameter model, skipjack tuna habitat selection was significantly (P < 0.01) influenced by sea surface temperatures ranging from 20.5 to 26C, relatively oligotrophic waters (surface chlorophyll 0.08–0.18, 0.22–0.27 and 0.3–0.37 mg m)3), zero to positive

*Correspondence. e-mail: [email protected]. ac.jp ⁄ [email protected] Received 16 May 2009 Revised version accepted 18 May 2010 382

anomalies (surface height anomalies 0–50 cm), and low to moderate eddy kinetic energy (0–200 and 700– 2500 cm2 s–2). Predicted catch per unit effort showed a trend consistent with the north–south migration of skipjack tuna. Validation of predicted catch per unit effort with that observed, pooled monthly, was significant (P < 0.01, r2 = 0.64). Sea surface temperature explained the highest deviance in generalized additive models and was therefore considered the best habitat predictor. Key words: generalized additive models, geographic information systems, habitat characterization, remote sensing, skipjack tuna, western North Pacific

INTRODUCTION Skipjack tuna (Katsuwonus pelamis) is a highly migratory pelagic species inhabiting all tropical and subtropical waters of the world’s oceans (Matsumoto, 1975; Arai et al., 2005). The species is commercially important, ranked among the first 10 species that have contributed highly to global catches in previous years (FAO, 2009). A significant portion of these catches are from the Pacific Ocean, which has one of the most productive fisheries in the world, particularly the western North Pacific. Skipjack tuna catches from the Pacific Ocean have increased consistently since the 1980s (Miyake et al., 2004). Despite the high catches and exploitation rates, the western Pacific stock is said to be capable of sustaining even larger catches (Lehodey et al., 1998). Catches are highest from May to August off Japan (Wild and Hampton, 1993). Katsuwonus pelamis are caught almost entirely by surface gears such as pole and line (Langley et al., 2005) and purse seines, although other miscellaneous gears are also used (Miyake et al., 2004). Distribution in sub-tropical waters is confined to the 15C surface temperature isotherm (Wild and Hampton, 1993). In the western Pacific, skipjack tuna have been captured as far as 44N off Japan (Wild and Hampton, 1993; Langley et al., 2005). Migration patterns in the western North Pacific follow a north– south seasonal cycle where the poleward movement occurs in the fall–summer season (Kawai and Sasaki,

doi:10.1111/j.1365-2419.2010.00552.x

 2010 Blackwell Publishing Ltd.

Skipjack tuna habitat from RS & GIS in western NP

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Figure 1. Schematic illustration of the northern migration of skipjack tuna in the western North Pacific, off the southeast and east coasts of Japan. The northern migration routes of different groups of skipjack tuna are shown with green lines (1–4). The typical path of the Kuroshio Current is indicated by the continuous red line; the Oyashio Current is shown in blue. Figure modified from Nihira (1996).

1962; Matsumoto, 1975; Watanabe et al., 1995; Ogura, 2003). This migration is also influenced by ocean currents and the fish move along prevailing currents, utilizing them as foraging habitats (Uda and Ishino, 1958; Uda, 1973). The westernmost groups comprise one originating from the Philippine islands and a second group from the Marianna–Marshall islands. These groups migrate northwards along the Japanese coastal waters (Fig. 1). The third group originates east of the Marshall Islands and moves northwest into Japanese offshore waters (Matsumoto, 1975). Part of this group could move farther downstream of the Kuroshio to the east of Midway Island. In late summer and early autumn, the fish begin their southward migration. Skipjack tuna are known to associate with fronts, warm-water streamers and eddies during their northward migration from sub-tropical to temperate waters (Tameishi and Shinomiya, 1989; Sugimoto and Tameishi, 1992). We hypothesized that skipjack tuna were utilizing oceanographic features recognizable from satellite remotely sensed data, and that such information could be used in a multivariate model to derive habitat signatures for the species in the western North Pacific. Skipjack tuna physiology and morphology play a major role in determination of habitat and, by extension, distribution (Wild and Hampton, 1993). They lack a swim bladder, which allows for rapid vertical movements within the near surface habitat.

They also have high oxygen demands (3–3.5 mL L–1) (Barkley et al., 1978) due to high metabolic rates. Oxygen concentration usually approaches saturation (4.5 mL L–1) in surface waters, but is often less than the minimum requirement for skipjack (2.45 mL L)1) in waters below the thermocline (Wild and Hampton, 1993), restricting skipjack tuna mainly to the mixed layer above the thermocline. This makes oceanographic satellite observations ideal for habitat studies for such a species. Nihira (1996) explained a ‘size screening’ mechanism for migration of skipjack tunas across the Kuroshio Front, a phenomenon where only fish above 45 cm in fork length are able to move from the Transitional Zone, across the front, and into the southern area of the Sub-tropical Counter Current during the southward migration. This was due to their ability to raise their body temperature while in the transitional area. Those smaller than 45 cm were incapable of raising their body temperature and thus remained in the Transitional Zone. Global demand for fish is exerting more pressure on fish stocks, in addition to climate change-induced impacts (changes in species distributions and disruption of marine ecosystems) (Cheung et al., 2009). Developing robust tools for near-real time habitat assessments will facilitate prediction of stocks’ responses to externalities such as climate change and fishing pressure. In many studies (Saitoh et al., 1986; Sugimoto and Tameishi, 1992; Nihira, 1996; Andrade

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and Garcia, 1999; Andrade, 2003) temperature has been the main environmental variable used to explain skipjack tuna occurrence and abundance. Although temperature is important, other factors such as chlorophyll concentration and ocean mesoscale variability could have direct or indirect effects on forage distribution and hence on the distribution of apex predators. Chlorophyll concentration is good indicator of albacore tuna habitats (Laurs et al., 1984; Zainuddin et al., 2008), and mesoscale variability is known to influence catch per unit efforts (CPUEs) of albacore tuna (Domokos et al., 2007) and foraging habitat for seabirds (Nel et al., 2001). Some of these variables may synergistically form suitable habitats for pelagic species. Generalized additive models (GAMs) were used to model skipjack tuna habitats from catch data and satellite remotely sensed oceanographic data. A GAM (Hastie and Tibshirani, 1990) is a semi-parametric extension of a generalized linear model, with an assumption that the functions are additive and that the components are smooth (Guisan et al., 2002). It uses a link function to establish a relationship between the mean of the response variable and a ‘smoothed’ function of the explanatory variable(s). The strength of GAMs lies in their ability to deal with highly nonlinear and non-monotonic relationships between the response and the set of explanatory variables. This makes them ideal for expressing underlying relationships in ecological systems. Statistical models and GIS (Valavanis et al., 2008) are tools with the potential to enhance species habitat research. The objective of this work was to study skipjack tuna habitat from multisensor satellite remotely sensed environment and fishery data, using GAMs and GIS.

ders twice after leaving the coast of Hokkaido, generating the first and second intrusions (Kawai, 1972). The meanders are separated by a warm core ring (WCR) originating from the northward movement of the ring produced by the Kuroshio (Yasuda et al., 1992). The southern limit of sub-polar waters is often referred to as the Oyashio Front (Talley et al., 1995). The Oyashio ecosystem is an important fishing ground for several sub-arctic species and sub-tropical migrants (Saitoh et al., 1986). The Kuroshio originates from the sub-tropical gyre and is distinguished by low density, nutrient-poor, warm and high salinity surface waters (Kawai, 1972; Talley et al., 1995). The Kuroshio Extension is an eastward-flowing inertial jet characterized by large-amplitude meanders and energetic pinched-off eddies, with high eddy kinetic energies (Qiu, 2002). Confluence of the two currents results in a mixed region, the Kuroshio–Oyashio Transition Zone (Yasuda, 2003). The behavior of the Kuroshio Extension, warm streamers and WCRs in the Transition Zone is important to the fishing industry (e.g., Saitoh et al., 1986; Sugimoto and Tameishi, 1992). Fishery data Skipjack tuna daily catch data obtained from the Ibaraki Prefecture Fisheries Research Station, for the period March to November (2004) were digitized from fishing logs of a pole and line fishery (173 vessels) and compiled into a database. These data comprised daily geo-referenced fishing positions (latitude and longitude), catch in tonnes and effort, from which catch per unit effort (CPUE) was determined in tonnes per boat day. The data were mapped using ARCGIS 9.2 ( ESRI, Redlands, CA, USA) and further compiled into weekly and monthly resolved datasets. Remotely sensed environmental data

MATERIALS AND METHODS Study area This study was conducted in the western North Pacific (18–50N and 125–180E) (Fig. 1), an area where Japanese skipjack tuna fishing vessels operate off the east coast of Japan. It is a productive ecosystem influenced mainly by the Tsugaru Warm Current, the Oyashio Current and the Kuroshio Current (Talley et al., 1995). The Tsugaru Warm Current originates from the Tsushima Current and flows with warm and saline water from the Sea of Japan (Talley et al., 1995). The Oyashio waters, formed from the Okhotsk Sea and the Western Sub-arctic Gyre (Yasuda, 2003), flow southward, transporting low temperature, low salinity and nutrient-rich waters to the sub-tropical gyre (Sakurai, 2007). The Oyashio commonly mean-

Weekly and monthly environment databases were compiled for sea surface temperature (SST), sea surface chlorophyll (SSC), sea surface height anomaly (SSHA) and eddy kinetic energy (EKE). Daily SST and SSC, Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua standard mapped images (SMI) for the year 2004, with a spatial resolution of approximately 4.63 km were downloaded from the PO.DAAC (http://podaac.jpl.nasa.gov) and the Ocean Color (http://oceancolor.gsfc.nasa.gov) sites respectively, and composited into 7-day images using SEADAS version 5.3 (NASA, Greenbelt, MD, USA). Given that it is extremely challenging to match daily fishery data to daily chlorophyll images (which usually have very sparse data due to clouds), we used 7-day composite images. The 7-day MODIS SST and SSC

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Skipjack tuna habitat from RS & GIS in western NP

images also matched the temporal scale for SSHA and geostrophic velocities from AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic data) which have a weekly temporal resolution. Ninemonthly (March–November) SST and SSC SMI were also downloaded from the PO.DAAC and Ocean Color sites, respectively. Weekly mean sea level anomaly data were downloaded from the AVISO (http://www.aviso.oceanobs.com), into ARCGIS 9.2 using the Marine Geo-spatial Ecology Tool (MGET) (Roberts et al., in press), a custom made add-in program for marine-GIS applications. We downloaded the delayed time, updated and merged product of mean sea level anomaly. The data are global images with a 1 ⁄ 3 resolution. They were re-sampled to the SST and SSC resolution and subset to the study area, using ARCGIS 9.2. The weekly SSHA images were averaged to monthly images in SEADAS 5.3. Weekly global geostrophic current velocity images, 1 ⁄ 3 (u and v components) were downloaded as ARCGIS rasters from AVISO using the MGET. The u and v weekly rasters were used to calculate EKE with the Raster Calculator function in Spatial Analyst extension (ARCGIS 9.2) using Eqn 1 (Robinson, 2004). The calculated EKE rasters were subset to the study area. The weekly images were averaged to monthly EKE in SEADAS 5.3. EKE ¼ 1=2ðu2 þ v2 Þ

ð1Þ

Matching fishery data to remotely sensed environment data Weekly resolved skipjack tuna data were matched to corresponding images for SST, SSC, SSHA and EKE using a C-shell script. The ship-track function in SEADAS 5.3 was used to extract values corresponding to latitude and longitude positions from the fishery dataset. The result was a full matrix of CPUEs and the respective environmental variables (Valavanis et al., 2008). This matrix was used to fit GAMs. Generalized additive models GAMs were constructed in R (version 2.7.2) software, using the gam function of the mgcv package (Wood, 2006), with CPUE as the response variable and SST, SSC, SSHA and EKE as predictor variables. A model of the form shown in Eqn 2 was applied gðui Þ ¼ a0 þs1 ðx1i Þþs2 ðx2i Þþs3 ðx3i Þþ:::sn ðxni Þ;

ð2Þ

where g is the link function, ui is the expected value of the dependent variable (CPUE), a0 is the model constant, and sn is a smoothing function for each of the model covariates xn (Wood, 2006). The CPUE follows a continuous distribution; therefore, we chose the

385

Gaussian family which is associated with the identity link function. We used a logarithmic transformation on CPUEs to normalize the asymmetrical distribution (Zainuddin et al., 2008). A factor of 0.1 was added before log-transformation to account for zero CPUEs (Howell and Kobayashi, 2006). Models were constructed from the simplest form using one independent variable, e.g., SST only, with subsequent addition of predictor variables. Model selection was based on significance of predictor terms, deviance explained, and reduction in Akaike Information Criterion (AIC) value (Johnson and Omland, 2004). Constructed GAMs can be used to predict skipjack tuna CPUEs using the predict.gam function in mgcv package, given a set of covariates similar to those used to build the model. Such an approach was employed by Howell and Kobayashi (2006) and Zagaglia et al. (2004). We made predictions from the best model selected from a set of 15 models. Due to sparseness of data on SST and SSC weekly images, we made predictions from monthly composites of the four environmental predictors. Spatial mapping and validation of predicted CPUEs Predicted CPUEs were mapped using GENERIC MAPPING TOOLS (Wessel and Smith, 1998) (GMT 4.4.0) and subsequently overlain with observed CPUEs. The mapped grids were sampled using the latitude and longitude positions of the observed CPUEs, thus creating a matrix of observed versus predicted CPUEs. We further compared observed and predicted CPUEs, pooled monthly, using a linear model. RESULTS Temporal variability of CPUE and environmental variables The monthly spatial distribution of fishing sets from March to November 2004 relative to oceanographic variables is shown in Fig. 2a–d. Figure 3 exemplifies the weekly fishing ground-oceanographic environment relationship in September (week 38) where the association with SST and SSC gradients and eddies is more apparent. The fishing fleet ‘moved’ north from March to August, and south from September to November. The latitudinal displacement is shown in Fig. 4a. Figure 4b–f presents weekly time series plot of mean CPUE, SST, SSC, SSHA and EKE. The mean CPUE (Fig. 4b) increased gradually until week 27, after which there was a decline. The lowest mean CPUE values were recorded in the last weeks of the fishing season (October–November). Mean SST rose to 27.8C (week 29) (Fig. 4c) and thereafter declined to 17.5C in the last week. The

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Figure 2. Spatial distribution of skipjack tuna fishing locations overlaid on 9-monthly images for each of the four environmental variables (a) SST, (b) SSC, (c) SSHA and (d) EKE). Fishing locations are shown as red dots. (a)

(b)

mean SSC concentration was lowest in the 12th week (0.09), after which it increased to about 0.4 mg m–3 (week 17), with sharp rises in November (Fig. 4d). Mean SSHA (Fig. 4e) shows a pattern where anoma-

(c)

(d)

lies were positive (week 11–33). During this period (March–June), the fishing fleet was at or south of the Kuroshio Front. Anomalies from week 34 to 44 were all negative, a period when the fleet had sailed beyond

 2010 Blackwell Publishing Ltd, Fish. Oceanogr., 19:5, 382–396.

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Figure 3. Fishing positions (gray dots) in one of the weeks in September 2004 (week 38) overlaid on weekly averaged SST, SSC, SSHA and EKE. The 20C SST and a 0.3 mg m–3 SSC contour (red line) are plotted on the respective images to emphasize SST and SSC gradients. Eddies are observable on the SSHA and EKE images.

the Kuroshio area. The mean EKE (Fig. 4f) ranged from 110 to 2315.7 cm2 s–2. All mean EKE values were below 1000 cm...