ICES Journal of Marine Science: Journal du Conseil Advance Access originally published online on July 11, 2007
ICES Journal of Marine Science: Journal du Conseil 2007 64(6):1152-1160; doi:10.1093/icesjms/fsm103
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Influence of El Niño/La Niña on the western winter–spring cohort of neon flying squid (Ommastrephes bartramii) in the northwestern Pacific Ocean
1 College of Marine Science and Technology, Shanghai Fisheries University, PO Box 67, Jungong Road 334, Shanghai 200090, People's Republic of China
2 School of Marine Sciences, 218 Libby Hall, University of Maine, Orono, ME 04469, USA
Correspondence to X. J. Chen: tel: +86-21-65711303; fax: +86-21-65710389, e-mail: xjchen{at}shfu.edu.cn
Chen, X. J., Zhao, X. H., and Chen, Y. 2007. Influence of El Niño/La Niña on the western winter–spring cohort of neon flying squid (Ommastrephes bartramii) in the northwestern Pacific Ocean. – ICES Journal of Marine Science, 64: 1152–1160.Ommastrephes bartramii is an oceanic squid distributed widely in the North Pacific, and its western winter–spring cohort is the target of a traditional squid fishery. Commercial fisheries data from the Chinese mainland squid-jigging fleet for the period 1995–2004 were analysed with respect to environmental variables. Sea surface temperature anomalies (SSTA) in the Niño 3.4 region had the strongest negative correlation with the SSTA of feeding and spawning grounds of the squid, with a time-lag of three and eight months (p < 0.05), respectively. A La Niña event would result in a decrease in squid recruitment through variability in environmental conditions on the spawning grounds, whereas an El Niño event would lead to environmental conditions favourable to squid recruitment. El Niño/La Niña events also influenced squid distribution on the feeding grounds, resulting in a northward shift of the fishing grounds in La Niña years and a southward shift in El Niño years. A multiple linear regression equation was derived to describe the dependence of the squid abundance index on environmental variables.
Keywords: abundance index, El Niño, La Niña, northwestern Pacific, Ommastrephes bartramii, sea surface temperature anomaly
Received 5 September 2006; accepted 7 June 2007; advance access publication 11 July 2007.
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Introduction |
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 Top Introduction Material and methodsResultsDiscussionReferences |
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The neon flying squid, Ommastrephes bartramii (Lesueur), is an oceanic cephalopod widely distributed in subtropical and temperate waters of the world (Roper et al., 1984). In the North Pacific, a commercial fishery has targeted it since 1974, when the fishery for Todarodes pacificus around Japan declined (Araya, 1983). Later, South Korea and Taiwan entered the jig fishery. Estimated annual production (including high seas driftnetting) was 250 000–350 000 t in the 1980s and 1990s (FAO, 2001). In 1993, Mainland China started surveying the resource in the northwestern Pacific using squid-jigging vessels, and subsequently started a small-scale commercial fishery in 1994. Since then, the fishery has expanded greatly, and the total annual catch by China has been >80 000 t since 1996 (Figure 1), >90% of the total North Pacific catch of the species.
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The neon flying squid spawns an autumn cohort from September to February and a winter–spring cohort from January to May. Both cohorts have a 1-year lifespan (Yatsu et al., 1997, 1998). The population can be further subdivided into four stocks: a central and an eastern stock of the autumn cohort, and a western and a central-eastern stock of the winter–spring cohort (Nagasawa et al., 1998; Yatsu et al., 1998). Of the four stocks, the western stock of the winter–spring cohort has been the main target of the Chinese squid-jigging fleet since 1995, yielding up to 70 000 t annually. The western winter–spring cohort of O. bartramii spawns mainly in the area between 20–30°N and 130–170°E (Figure 2; Bower, 1996; Nagasawa et al., 1998; Yatsu et al., 1998). They can be found from subtropical waters to the boundary of the Subarctic during the first half of summer, then migrating north into the Subarctic Domain, which is the main fishing ground for the species (38–46°N, 150–165°E), from August to November (Figure 2; Murata and Nakamura, 1998).
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Fluctuations in marine fish production have been linked to climatic processes and events (Cushing, 1982; Beamish, 1995). Ommatrephid squid, being short-lived ecological opportunists, are also typically subject to large fluctuations in abundance, responding rapidly to changes in environmental conditions (Brandt, 1983; Wadley and Lu, 1983; Sugimoto and Tameishi, 1992; Yatsu et al., 2000; Anderson and Rodhouse, 2001; Rodhouse, 2001; Bazzino et al., 2005; Waluda et al., 2006). Recent research has suggested that variations in the oceanographic environment can play an important role in determining both the distribution and the abundance of squid populations (Waluda et al., 1999, 2001; Sakurai et al., 2000). Anderson and Rodhouse (2001) suggested that the El Niño–Southern Oscillation (ENSO) phenomenon might influence the variability in abundance of paralarvae of the squid Dosidicus gigas, with favourable retention during El Niño conditions and unfavourable offshore transportation during La Niña and normal conditions. Further, Ichii et al. (2002) suggested links between the ENSO and adult D. gigas fished close to the Costa Rica Dome. Waluda et al. (2001) analysed sea surface temperature (SST) data and showed that some 55% of the variability in recruitment to the Falkland Islands Illex argentinus fishery could be explained by variation in the total area of surface water of putative optimum water temperature for larval development on the spawning grounds during the spawning season. Recruitment to the Falkland Islands fishery was also influenced by the ENSO phenomenon through its impact on the environment at the spawning grounds (Waluda et al., 1999). Finally, variability in the abundance of T. pacificus in the Sea of Japan is driven by changes in optimum SST for larval development (Sakurai et al., 2000).
As are most ommastrephid squid, O. bartramii is short-lived and exhibits great interannual variability in abundance and distribution (Chen and Chiu, 1999; Yatsu et al., 2000; Chen et al., 2003; Bower and Ichii, 2005). Chen and Chiu (1999) suggested that its distribution and abundance are strongly affected by environmental conditions such as water temperature and salinity. The optimal SST for squid schooling differs in different areas. It is usually higher west of 165°E than east of 165°E, and varies with season (Gong and Kim, 1990; Chen, 1995, 1997, 1999; Yatsu and Watanabe, 1996; Murata and Nakamura, 1998; Chen and Tian, 2005). Yatsu et al. (2000), on the basis of an analysis of variability in catch per unit effort (cpue) from 1979 to 1998, reported that recruitment rates of the autumn cohort were lower in El Niño years when water temperatures from winter to summer were less than in normal years in the North Pacific. However, little information was available on the variability in abundance and distribution of the winter–spring cohort, especially variability resulting from El Niño/La Niña events.
This study aims to evaluate how the western winter–spring cohort of squid O. bartramii in the northwestern Pacific is related to environmental variables such as sea surface temperature anomalies (SSTA) in the Niño 3.4 region (120–170°W, 5°N–5°S), SST and SSTA on the spawning and feeding grounds (Figure 2), and to develop a forecasting model for recruitment.
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Material and methods |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Data on daily catch (t), effort (days fished), and fishing locations were obtained from the Chinese commercial jigging fleet operating in the area 150–165°E, 38–46°N in the northwestern Pacific during August and November from 1995 to 2004. One unit of fishing area is defined as 1° latitude by 1° longitude. Cpue was quantified as t of squid caught per fishing day. Chinese squid-jigging vessels are all modified from the same type of inshore bottom trawler, and have a main engine power of 120 kW x 2, squid-attracting lamp power of 112 kW, and 16 squid-jigging machines, and are almost identical in fishing power. The fishery is operated at night and there is no bycatch. Cpue therefore tends to be a reliable index of fish stock abundance because the sampling units (fishing vessels) are homogeneous in their characteristics and operating behaviour (Richards and Schnute, 1986; Waluda et al., 1999, 2001).
Data on monthly SST and SSTA on the spawning grounds (20–30°N, 130–170°E) and feeding grounds (150–165°E, 38–46°N) were obtained at the resolution of 1° latitude by 1° longitude from the Joint WMO/IOC Technical Commission for Oceanography and Marine Meteorology Products Bulletin Data Products (http://iridl.ldeo.columbia.edu/sources/.igoss/.data_products.html). The SSTA for the Niño 3.4 region was obtained from the NOAA Climate Prediction Center (http://www.cpc.ncep.noaa.gov/), and calculated as three-month averages of SST departure from normal for a critical region of the equatorial Pacific (Niño 3.4 region; Figure 2).
Based on the index of SSTA for the Niño 3.4 region, NOAA's operational definitions for El Niño and La Niña (http://www.noaanews.noaa.gov/stories2005/s2394.htm) are as follows: El Niño, a phenomenon in the equatorial Pacific Ocean characterized by a positive SST departure from normal in the Niño 3.4 region 
0.5°C, averaged over three consecutive months; La Niña, a phenomenon in the equatorial Pacific Ocean characterized by a negative SST departure from normal in the Niño 3.4 region 0.5°C, averaged over three consecutive months.
Correlation coefficients between SSTs of spawning grounds and feeding grounds vs. the Niño 3.4 SSTA were calculated with a cross-correlation function (George et al., 2005). According to the results of this cross-correlation analysis, we selected the time-lag that yielded the strongest correlation in analysing SSTs of spawning and feeding grounds influenced by El Niño/La Niña events. Multiple linear regression analyses were conducted to establish a cpue forecast model, with the SSTAs of spawning and feeding grounds as environmental variables, and to evaluate whether the SSTAs of spawning and feeding grounds had a positive or negative influence on the cpue of neon flying squid. The regression model is:
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| (1) |
where cpuei is the catch per fishing day from August to November in year i (t d–1), Ti1 the average SSTA during the spawning period (January–May) in year i on the spawning grounds (20–30°N, 130–170°E) (°C), Ti2 the average SSTA during the feeding period (August–November) in year i on the feeding grounds (38–46°N, 150–165°E) (°C), Ti3 the value of Ti1 multiplied by Ti2, and a0, a1, a2, and a3 are model parameters to be estimated (George et al., 2005). The variable Ti3 was included for testing if there was an interactive effect between Ti1 and Ti2 on the cpue of squid. We used a least squares method to estimate the model parameters. The spatial distribution of the squid catch was mapped using the software Marine Explorer 4.0 (Environment Simulation Laboratory Co. Ltd., Japan).
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Results |
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El Niño/La Niña events
Based on the definitions for El Niño and La Niña, there were four El Niño events (January–March 1995, May 1997–April 1998, May 2002–March 2003, and July 2004–December 2004) and three La Niña events (September 1995–March 1996, July 1998–June 2000, and October 2000–February 2001) between January 1995 and December 2004 (Figure 3).
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SST on the spawning grounds
On the spawning grounds, the average monthly SSTs between January and May from 1995 to 2004 varied from 22.9 to 25.5°C (Figure 4a). However, the average monthly SSTs rose to 27.5°C in June, and ranged between 28 and 29°C from July to October, then fell to 26.9°C in November and to 25.4°C in December (Figure 4a). Of the significant cross-correlation analyses (p < 0.05), the SSTA of the Niño 3.4 region preceding the SSTA of the spawning grounds by eight months yielded the strongest negative correlation (Figure 5a). During the spawning season (January–May), monthly SST on the spawning grounds varied from 22.5 to 23.8°C in normal years and from 22.4 to 26.0°C in all other years. The average monthly SSTA ranged from –0.15 to 1.12°C and changed greatly under different climate conditions (Figure 6a). The average SSTAs were 0.84, 0.18, and 0.15°C, respectively, for La Niña, El Niño, and normal years.
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SST on the feeding grounds
On the feeding grounds, the average monthly SST between August and November from 1995 to 2004 varied from 12.3 to 18.2°C (Figure 4b). However, it fell to 9.2°C in December, then ranged between 5.5 and 8.3°C between January and May, before rising to 12.1°C in June and to 15.1°C in July (Figure 4b). Of the significant cross-correlation analyses for time-lag from –5 months to 8 months (p < 0.05), the SSTA of the Niño 3.4 region preceding the SSTA of the feeding grounds by three months yielded the strongest negative correlation (Figure 5b). During the feeding season (August–November), the monthly SST on the feeding grounds varied from 11.8 to 19.7°C for all years, and from 12.0 to 18.0°C in normal years. The monthly SSTA ranged from –1.23 to 1.33°C and changed under different climate conditions (Figure 6b). The average SSTAs for the La Niña, normal, and El Niño years were 1.16, 0.27, and –0.47°C, respectively.
Squid distribution
Between 150°E and 165°E, fishing grounds were generally confined to the area between 153°E and 165°E and 40°N and 44°N from 1995 to 2004 (Figure 7a–c). However, the fishing areas (1° latitude by 1° longitude) with a yield >1500 t were spread all over the fishing grounds. In 1999, the temperature environment of the feeding grounds was affected by a La Niña, and the average SSTA on the feeding grounds rose to 1.16°C. At the same time, the area where squid were being caught moved north to 43–44°N, and the catch was concentrated, with the yield in many fishing areas being >1500 t (Figure 7a). In 2001, the SST on the feeding grounds fell into the range characteristic of a normal year, the distribution of the fishing grounds was more widely dispersed, and the catch in each area dropped, generally to <1500 t (Figure 7b). In 2002, the feeding grounds of squid were influenced by an El Niño, and the average SSTA on the feeding grounds fell to –0.47°C. Concurrently, the fishing area moved south to 43°N, and the catch was highly concentrated so that the yield in six fishing areas was >1500 t (Figure 7c).
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The squid distribution was analysed in terms of spatial variation in SST. In normal years, squid were distributed extensively across large areas, and the optimum SST (defined as the SST associated with a catch >5000 t; Figure 8a) was 14–19°C. When the feeding grounds were under the influence of an El Niño or a La Niña, the squid distribution was more converged, and the corresponding optimum SSTs were 14–17 and 16–19°C, respectively (Figure 8b and c).
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Variability in squid abundance
The monthly catch per unit fishing area for each year ranged from 0 to 6890 t, though a large part of the catch (44.7 and 49.4% of the effort) was in the range 100–800 t. The average cpue per fishing area was 0–25 t d–1, but most catch rates (and 60% of the effort) fell in the range 1.5–5.0 t d–1. Monthly cpue series for all fishing areas combined from August to November of the years 1995–2004 ranged from 0.94 t d–1 (in November 2000) to 5.07 t d–1 (in August 2004), and most catch rates exceeded 2.0 t d–1 (Figure 9).
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El Niño and La Niña events tended to influence the recruitment of squid through variability in the environmental conditions on the spawning grounds. In 2002 and 2004, temperatures on the spawning grounds were normal, with an SSTA of 0.15°C, favourable for development of squid larvae. The monthly mean cpue during August and October varied from 2.97 to 5.07 t d–1 in 2004 and from 1.71 to 2.02 t d–1 in 2002 (Figure 9), compared with the average annual cpue of 3.16 t d–1 in 2003 and 1.50 t d–1 in 2001 (Figure 1).
In 1999 and 2001, squid recruitment was lower, probably the consequence of the abnormal environmental conditions, with a high SSTA of 1.45°C in 1999 and 0.88°C in 2001 on the spawning grounds as a result of the La Niña influence. Therefore, monthly cpue from August to October was just 1.7–2.4 t d–1 in 1999 and 1.26–1.64 t d–1 in 2001 (Figure 9), compared with the average annual cpue of 2.89 t d–1 in 1998 and 2.36 t d–1 in 2000 (Figure 1).
In 2003, an El Niño year, the SST on the spawning grounds was normal, with an SSTA of 0.16°C, favourable for development of squid larvae and hence recruitment. As a result, the monthly cpue was relatively high, ranging from 1.94 to 4.06 t d–1 (Figure 9), compared with the average annual cpue of 1.91 t d–1 in 2002, a normal year (Figure 1).
Regressions of cpue against environmental variables
Regression analyses revealed that cpue, an index of squid abundance, was closely related to the SST on the spawning and feeding grounds. Because an SSTA can represent the interannual variability of environmental conditions better than SST, the former was used as an independent variable in a multiple linear regression analysis of the cpue of squid and environmental variables (Table 1). The resultant empirical model was significant (p < 0.05), and was estimated as
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The multiple linear model suggested that cpue was negatively correlated with SSTA on the spawning grounds and positively correlated with SSTA on the feeding grounds. The parameter a1 in the model had larger values than parameter a2 [see Equation (1)], so we conclude that SSTA variability on the spawning grounds tended to have a greater impact on squid recruitment than the same variability on the feeding grounds. Parameter a2 was not significant (p > 0.05; Table 1), suggesting that the SSTA on the feeding grounds was not significant in explaining the cpue. Therefore, the SSTA on the spawning grounds was more important than SSTA on the feeding grounds in explaining the variation in cpue. Parameter a3 was significant (p < 0.05; Table 1), suggesting that there was a significant interaction between Ti1 and Ti2 in explaining the squid cpue.
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Discussion |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Influence of SST on the spawning grounds on recruitment of squid
Ommastrephes bartramii is a short-lived (generally one year of life) ecologically opportunistic species, so its population and recruitment are vulnerable to variability in the environment (Rodhouse, 2001). Recent studies suggest that variability in the abundance and recruitment of ommastrephid squid can be explained by environmental variables (Waluda et al., 1999; Sakurai et al., 2000). Ichii and Mahapatra (2004) considered that fluctuations in the autumn cohort of neon flying squid were caused mainly by massive changes in the environment. Their study also showed that El Niño/La Niña events could be related to recruitment, this effect being realized via SST changes on the spawning grounds. We found that if the spawning area of O. bartramii was influenced by a La Niña event, the SST would often rise >1°C compared with the normal level, resulting in unfavourable conditions for squid recruitment. In El Niño years, the SST is almost the same as in normal years, leading to favourable conditions for squid recruitment.
Influence on SST on the feeding grounds and the distribution of fishing grounds
Many studies have shown that the monthly optimal SST for O. bartramii is 15–19, 14–18, 10–13, and 12–15°C from August to November, respectively, in the waters between 150°E and 165°E (Chen, 1997, 1999; Chen and Tian, 2005). This study has shown that El Niño/La Niña events have a significant and negative lag effect on the SST of O. bartramii feeding grounds in the northwest Pacific, which subsequently influences the spatial distribution of the fishing grounds. When the feeding area is affected by a La Niña event, the SST generally rises, the Subarctic front moves north, the high-yield fishing grounds are located farther north, and the optimum SST for squid schooling ranges from 16 to 19°C. If the feeding grounds are influenced by an El Niño event, the SST generally falls, the Subarctic front moves south, and the fishing grounds are more to the south and also more aggregated. The fishing areas of high yield tend to be more aggregated and the optimum SST ranges from 14 to 17°C, lower than in the years of a La Niña. Hanawa (1991) reported that westerly winds are strong in the North Pacific between 40°N and 50°N during an El Niño year, resulting in cold water rising from the deep and SST falling. This would explain why the fishing grounds of squid move south and the optimum SST for squid schooling is lower. In normal years, the fishing grounds are distributed more sporadically over large areas, with optimum SST ranging from 14 to 19°C, and the catch in each fishing area often being <1500 t.
Cpue and environmental conditions on spawning and feeding grounds
Recent studies have shown that statistically significant variability in recruitment and abundance of squid can be explained by variability in the environment (Roberts and Sauer, 1994; Robin and Denis, 1999; Agnew et al., 2000; Sakurai et al., 2000; Roberts, 2005). Robin and Denis (1999) reported variability in Loligo vulgaris vulgaris in the English Channel related to interannual changes in SST. Off South Africa, variability in the abundance of chokka squid L. vulgaris reynaudii is apparently driven by storm events on the spawning grounds, which reduces breeding success (Roberts and Sauer, 1994). Roberts (2005) also reported that chokka squid biomass and catch were highly variable, likely reflecting changes in the local ecosystem, which affects squid spawning and recruitment. Negative linear correlations between maximum summer SST (monthly average) and chokka squid biomass in the following autumn (r2 = 0.94) and annual catch (r2 = 0.69) support the link between a "cold ridge–copepod maximum" and the early life cycle of chokka squid. Agnew et al. (2000) showed that 66% of the variance in recruitment of L. gahi in the Southwest Atlantic could be explained by SST six months prior to recruitment.
Chen and Chiu (1999) reported that the distribution and abundance of O. bartramii in the eastern North Pacific was strongly affected by water temperature and salinity, temperature having the higher predictive power for abundance. As we describe in our model, the cpue of O. bartramii is closely related to temperatures on the spawning and feeding grounds, but changes in environmental variables such as SST and SSTA can be influenced by El Niño/La Niña events. An abnormally high SSTA on the spawning grounds is not conducive to squid abundance, a fact further confirmed by our multiple regression analysis. However, higher or lower SSTA on the feeding grounds (averaged at 1.16 and –0.47°C in La Niña and El Niño years, respectively) makes the spatial distribution of squid more aggregated, and is therefore favourable to squid schooling and hence fishing (high catch per fishing area).
The results presented above could assist in forecasting squid recruitment strength from environmental conditions and El Niño/La Niña effects on spawning and feeding grounds. Such forecasts can result in better management, perhaps better catch rates, and considerable reductions in searching time and hence costs for the fishing fleet. Another factor that can influence the abundance and distribution of O. bartramii is the Kuroshio Current. The western winter–spring cohort of O. bartramii moves north along with the Kuroshio during summer (Bower and Ichii, 2005). Therefore, it is important to analyse squid growth, distribution, and recruitment with respect to the Kuroshio. Previous studies have shown that the Kuroshio meander is closely related to the occurrence of El Niño/La Niña events (Hjdeo, 1975; Yamagata et al., 1985; Sun, 1990; Wang et al., 1993; Yuan et al., 2001). Although we acknowledge its importance, we did not include the Kuroshio in our analysis because quantification of the impact of the Kuroshio Current on the abundance and distribution of squid can only be done through establishing a dynamic ecosystem model, which is beyond the scope of this study. This, however, will be the focus of future research.
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Acknowledgements |
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We thank the Chinese squid-jigging technology group for providing the catch data, and the Joint WMO/IOC Technical Commission for Oceanography and Marine Meteorology Products Bulletin and NOAA Climate Prediction Center for providing the environmental data. The study was funded by the Shanghai Municipal Education Commission and Shanghai Education Development Foundation (Shu Guang project), supported by the Programme for New Century Excellent Talents in University and Shanghai Leading Academic Discipline Project (Project #T1101). Finally, we acknowledge the constructive comments of two anonymous reviewers and editor Panayiota Apostolaki, which helped us improve the manuscript considerably.
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References |
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