Journal of Oceanography, Vol. 65, pp. 93 to 102, 2009
Habitat Suitability Index of Chub Mackerel (Scomber japonicus) from July to September in the East China Sea X INJUN CHEN1,2*, G ANG LI 1,2, BO FENG1,3 and SIQUAN TIAN1,2 1
College of Marine Sciences of Shanghai Ocean University, Shanghai 201306, China The Key Laboratory of Shanghai Education Commission for Oceanic Fisheries Resources Exploitation, Shanghai 201306, China 3 Fisheries College of Guangdong Ocean University, Zhanjiang 524088, China 2
(Received 21 November 2007; in revised form 1 June 2008; accepted 2 June 2008)
The habitat quality of Chub mackerel (Scomber japonicus) in the East China Sea has been a subject of concern in the last 10 years due to large fluctuations in annual catches of this stock. For example, the Chinese light-purse seine fishery recorded 84000 tons in 1999 compared to 17000 tons in 2006. The fluctuations have been attributed to variability in habitat quality. The habitat suitability Index (HSI) has been widely used to describe fish habitat quality and in fishing ground forecasting. In this paper we use catch data and satellite derived environmental variables to determine habitat suitability indices for Chub mackerel during July to September in the East China Sea. More than 90% of the total catch was found to come from the areas with sea surface temperature of 28.0°–29.4°C, sea surface salinity of 33.6–34.2 psu, chlorophyll-a concentration of 0.15–0.50 mg/m3 and sea surface height anomaly of –0.1– 1.1 m. Of the four conventional models of HSI, the Arithmetic Mean Model (AMM) was found to be most suitable according to Akaike Information Criterion analysis. Based on the estimation of AMM in 2004, the monthly HSIs in the waters of 123°– 125 °E and 27°30′′ –28 °00′′ N were more than 0.6 during July to September, which coincides with the catch distribution in the same time period. This implies that AMM can yield a reliable prediction of the Chub mackerel’s habitat in the East China Sea.
1. Introduction Chub mackerel (Scomber japonicus) is a cosmopolitan coastal pelagic species, widely distributed on the continental shelf of the warm and temperate waters of the Pacific, Atlantic, Indian Oceans and their adjacent seas (Collette and Nauen, 1983; Kiparissis et al., 2000; Tang, 2006). Chub mackerel distributed in the northwest Pacific Ocean is considered to consist of two stocks, the Tsushima Current stock and the Pacific stock (Watanabe et al., 2000). The Tsushima Current stock is distributed in the East China Sea, Yellow Sea and Sea of Japan and is mainly exploited by the light-purse seine fisheries of China, Japan, and South Korea. Its annual total catch fluctuated from 250 to 450 thousand tons during 1998 and 2004 (Yoshiaki et al., 2005; Zhang et al., 2007; Zheng et al., 2008; Li et al., 2008). There is a growing need to adopt ecosystem concepts
Keywords: ⋅ Habitat suitability index, ⋅ Scomber japonicus, ⋅ East China Sea, ⋅ Chinese purse seine fishery.
in fishery resources management and exploitation. Habitat suitability index (HSI) modeling is a valuable tool in ecology. It can be used in combination with GIS technology to provide maps and information upon which managers can make informed decisions (Terrel, 1984; Bovee and Zuboy, 1988). HSI models are based on suitability indices that reflect habitat quality as a function of one or more environmental variables. The HSI modeling method used in this study is based on the U.S. Fish and Wildlife Service Habitat Evaluation Procedures Program (Terrel, 1984; Bovee and Zuboy, 1988), which is primarily used in terrestrial and freshwater environments, but has also been applied to estuaries and offshore systems (Gibson, 1994; Reyes et al., 1994; Brown et al., 2000). Outputs produced from HSI modeling can predict the spatiotemporal variation of a fish’s habitat conditions, and fish habitat models have been routinely developed using combined empirical and GIS-based spatial modeling techniques (Clark et al., 1999; Rubec et al., 1999; Brown et al., 2000; Eastwood et al., 2001). HSI models describe the relations of different eco-
* Corresponding author. E-mail:
[email protected] Copyright©The Oceanographic Society of Japan/TERRAPUB/Springer
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logical variables and estimate habitat suitability for a given species. Brooks (1997) distinguished stages in the HSI modeling: the first is model development, and the second, which is often neglected, should focus on validation. Previous studies indicate great differences in the performance of HSI models (Block et al., 1994; Rothley, 2001; Dettmers et al., 2002). The accuracy of GIS-based fish habitat models is largely dependent on two factors: the quality of the input data and the method used to construct the empirical model (Eastwood and Meaden, 2004). While the former cannot usually be controlled by the modeler, for the latter a number of different techniques are available to construct empirical models to quantify relationships between a fish species and its environments. Thus, we need to conduct a comparative study to evaluate the performance of different approaches to identify an optimum HSI model before such a HSI model can be used in application. One of the most important issues when developing HSI models is the selection of variables for inclusion in the model. An ideal HSI model should be simple and defined by a few key variables. However, it is well known that fish population dynamics are complex and depend on the combined effects of many ecosystem variables. Previous studies have identified that population dynamics and the distribution of fishing grounds of Chub mackerel might be influenced by environmental variables such as sea surface temperature (SST), sea surface height anomaly (SSHA), chlorophyll-a concentration (Chl-a), sea surface salinity (SSS) and currents (Hong et al., 1997; Yatsuia et al., 2002; Hiyama et al., 2002; Yatsu et al., 2005; Sun et al., 2006; Zheng et al., 2008); these environmental variables are commonly used in the fishing ground or fish habitat studies (Mizobata and Saitoh, 2004; Zagaglia et al., 2004; Chen and Shao, 2006). For example, eddies and upwellings are directly visible from the altimetry data (SSHA) as either high or low areas (Zhang et al., 2001). Ladner et al. (1996) found correlations between the chlorophyll concentration and tuna, mackerel, and sardine biomasses depending on the year and month in offshore California waters. S. japonicus usually migrates northwards with feeding during June to September and southwards with wintering or spawning from November to January of the next year. During July to September, this fish is mainly located in the East China Sea and is exploited commercially on a large scale. Therefore, the objective of this study is to produce a simple, yet effective HSI model to predict habitat and distribution for S. japonicus from July to September in the East China Sea. Such a model can contribute greatly to a better understanding of the dynamics of this fish’s population, leading to the development of effective management for the sustainable exploitation of this important fisheries resource.
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Fig. 1. Procedure for estimating habitat suitability index.
2. Material and Methods 2.1 Catch and environment data Commercial catch and effort data for the Chinese mainland large light-purse seine fishery in the East China Sea from 1998 to 2004 were obtained from the purse seine fishery technology group of Shanghai Fisheries University. These data include the names of fishing vessels, fishing locations and fishing time, catch per net and number of sets. Nominal CPUE was calculated as the weight of catch per net (ton/net). Four environmental variables were chosen, i.e. SST, SSHA, SSS and Chl-a (Yatsuia et al., 2002; Hiyama et al., 2002; Yatsu et al., 2005; Sun et al., 2006; Cui and Chen, 2007; Li et al., 2008; Zheng et al., 2008). Monthly SST with a spatial resolution of 0.5° was obtained from the Physical Oceanography Distributed Active Archive Center (PODAAC) of the National Aeronautics and Space Administration (NASA) website (http:// poet.jpl.nasa.gov). Monthly SSS and SSHA datasets, both with a spatial resolution of 0.5°, were downloaded from the IRI/LDEO Climate Data Library (http:// iridl.ldeo.columbia.edu). Monthly Chl-a Level-3 Standard Map Images with a spatial resolution of 9 km, from the Sea viewing Wide Field of view Sensor (SeaWiFS), were obtained from the Goddard Space Flight Center of NASA website (http://oceancolor.gsfc.nasa.gov). 2.2 Data processing The monthly nominal CPUE in the fishing unit of 0.5° × 0.5° was calculated as follows:
CPUE ymij =
Catch ymij Net ymij
,
(1)
where CPUE ymij, Catch ymij and Net ymij are the monthly nominal CPUE, and catch and number of fishing sets, respectively, at longitude i, latitude j in month m, year y. The relative abundance index (RAI) was calculated as follows:
Table 1. Measured range of four variables, sea surface temperature (°C, SST), sea surface salinity (psu, SSS), chlorophyll-a (mg·m–3, Chl-a) and sea surface height anomaly (m, SSHA), and their corresponding optimum range and associated percentage of catch in these areas. Variables SST SSS Chl-a SSHA
RAI ymij =
Range
Optimum range
Percentage of catch in areas with optimum range
25.6–30.0 32.6–34.4 0.05–4.7 –1.5–1.9
28.0–29.4 33.6–34.2 0.15–0.50 –0.1–1.1
92.9% 90.5% 91.5% 97.9%
CPUE ymij CPUE max
,
HSI = (SI1 + SI2 + SI3 + SI4)/4.
(2 )
(5)
Geometric Mean Model (GMM; Lauver et al., 2002):
where RAIymij is relative abundance index at longitude i, latitude j, in month m, year y. CPUEmax is the maximum of CPUE for each month during July to September. The value of RAI was presumed to be an indicator of habitat quality and was therefore assumed to be similar to an actual suitability index (SI). The values 0 and 1 denote a fully non-suitable and fully suitable habitat, respectively (Bayer and Porter, 1988). 2.3 HSI model 2.3.1 Habitat suitability models In general, HSI models compute an HSI of a given species from one or more relevant habitat variables (US Fish and Wildlife Service, 1980a, b). The HSI represents the model output and is a univariate variable having a value between 0 and 1 (Brooks, 1997). The habitat variables can be seen as model inputs, and for marine fishes these inputs typically describe environmental conditions. In this study, four input habitat variables were chosen (Fig. 1), i.e. SST, SSHA, Chl-a and SSS. The relationship between CPUE and each variable was converted into the curves of suitability index (SI), which is continuous, ranging from 0 to 1.0. The SI values derived from each variable were then combined into the empirical HSI model (Fig. 1). The empirical HSI model could take the form of one of the following models: Continued Product Model (CPM; Grebenkov et al., 2006; Chen et al., 2008):
HSI = (SI 1 × SI2 × SI3 × SI4)1/4.
(6)
2.3.2 Model selection and validation For the above four models, the likelihood and the Akaike Information Criterion (AIC) of each set of HSI can be represented by the following equation: i =1
L( data / θ ) = ∏ n
( RAI i − HSI i )2 1 exp − 2δ 2 2πδ
AIC = –2ln(Lmax) + 2m
(7) (8)
where L(data/θ) is the likelihood of observing the data set given parameters θ ; vector θ denotes the vector of all parameters; RAIi and HSIi are the actual HSI and model output value, respectively, at the i-th point of the dataset; n is the number of observations (n = 3367); m is the number of model parameters (m = 4); and L max is the maximum of L(data/θ ). The model which yields the minimum AIC was selected as the best model. This was used for model testing and validation. 2.3.3 Mapping HSI distribution The spatial distributions of HSI and catch data are mapped by the software Marine Explorer (Version 4.0). 3. Results
HSI = SI1 × SI2 × SI 3 × SI4.
(3)
Minimum Model (MINM; Van der Lee et al., 2006): HSI = Min(SI1, SI2, SI3, SI 4).
(4)
Arithmetic Mean Model (AMM; Hess and Bay, 2000):
3.1 Suitability index curves for four different variables This fish species was widely distributed in waters with SST ranging from 25.6 to 30.0°C, SSS from 32.6 to 34.4 psu, Chl-a from 0.05 to 4.7 mg/m3 and SSHA from –1.5 to 1.9 m, but the optimum values of four variables were 28.0–29.4°C SST, 33.6–34.2 psu SSS, Chl-a 0.15 to 0.50 mg/m3 and –0.1 to 1.1 m SSHA. In the waters with optimum variables, the catch accounted for 92.9%, Habitat Suitability Index for Scomber japonicus
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Table 2. Fitted suitability index (SI) models and their parameter estimation. Variable SST SSS Chl-a SSHA
SI models 2
exp(–1.3728(XSST – 28.70) ) exp(–5.5154(XSSS – 33.81)2 ) exp(–0.5517(ln(XCh l-a ) + 1.378)2 ) exp(–1.3260(XSSHA – 0.4712)2 )
(a)
(c)
(b)
(d)
F value
P value
10.51 30.75 15.60 71.48
0.0023 0.0001 0.0001 0.0001
Fig. 2. Suitability index (SI) curve of sea surface temperature (°C, a), salinity (psu, b), chlorophyll-a (mg·m –3, c) and sea surface height anomaly (m, d) for Chub mackerel in summer in the East China Sea.
90.5%, 91.5% and 97.9% of the total catch, respectively (Table 1). Four SI curves between RAI and environmental variables were modeled as shown in Table 2 and plotted in Fig. 2 (P < 0.01). 3.2 HSI model selection The HSI values were estimated from the four empirical models (Eqs. (3)–(6)) respectively, and the goodness of fit was evaluated using Eqs. (7) and (8) (Table 3). For the four models, the lowest HSI value (0.2418) of standard deviation came from AMM, and the highest value (0.3499) was from GMM. The AIC value (321.41) of AMM was the least among the four models, while the AIC value derived from GMM was highest (AIC = 377.38), which indicates that the AMM represents the best estimate of habitat suitability for Chub mackerel. 96
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3.3 HSI model validation Based on the data of SST, SSS, Chl-a and SSHA, the HSI was estimated using the AMM (Table 4). High numbers of fishing sets were found to correspond to high values of HSI. The area with an HSI value less than 0.2 had no fishing sets, which indicates that the areas are not at all suitable for inhabition by Chub mackerel. The area with an HSI value between 0.2 and 0.5 had 10.19% of the total fishing sets and the corresponding catch per fishing set reached 16–27 t. This indicates that these areas may be suitable for Chub mackerel. The area with an HSI value from 0.5 to 0.7 had 10.70% of the total fishing sets and the corresponding catch per fishing set attained 11–16 t. This indicates that these areas are rather suitable for Chub mackerel. The 79.11% of fishing sets occurring in the area with an HSI value equal to or more than 0.7, and the cor-
Table 3. Goodness of fit tests for different HSI models including Continued Product Model (CPM), Minimum Model (MINM), Arithmetic Mean Model (AMM) and Geometric Mean Model (GMM). Variable Number of variables Observations Mean Standard deviation Kurtosis Skewness Minimum Maximum 95% CI AIC
CPM
MINM
AMM
GMM
4 540 0.3037 0.2939 –1.1234 0.5636 0.0000 0.9493 0.0248 340.4
4 540 0.4153 0.3138 –1.3826 0.0014 0.0000 0.9737 0.0265 326.6
4 540 0.6708 0.2418 –0.4486 –0.7502 0.0000 0.9872 0.0204 321.4
4 540 0.5679 0.3499 –1.0275 –0.6840 0.0000 0.9871 0.0296 377.4
Table 4. Habitat Suitability Index (HSI) from Arithmetic Mean Model (AMM) using the number of fishing sets and catch per set for the Chub mackerel fishery in the East China Sea. HSI
Fishing sets
Percentage of the total fishing sets (%)
Average catch per set (t/net)
0–0.1 0.1–0.2 0.2–0.3 0.3–0.4 0.4–0.5 0.5–0.6 0.6–0.7 0.7–0.8 0.8–0.9 0.9–1.0
0 0 45 26 9 8 76 203 418 0
0.00 0.00 5.73 3.31 1.15 1.02 9.68 25.86 53.25 0
0.00 0.00 16.55 20.62 27.00 15.75 11.39 18.82 18.19 0
responding catch per fishing sets was more than 18 t, indicating that these areas are the best habitat for Chub mackerel. According to the HSI value estimated from the AMM in 2004, the spatial distribution of monthly HSI, fishing sets and catch were mapped (Figs. 3 and 4). We found that the main fishing area occurred in the waters of 123°– 125°E and 27°30′–28°N during July to September, in which the monthly HSIs were more than 0.6 (Figs. 3 and 4). This may imply that AMM can give reliable prediction on the Chub mackerel’s habitat. 4. Discussions Fish habitat is a key component in the management and sustainable exploitation of fishery resources. In this study we found that the four environmental factors SST, SSS, SSHA and Chl-a, could be used to predict the Chub mackerel’s habitat suitability index. Previous studies and observations made in the fisheries show that the southern fishing ground of Chub mackerel mainly concentrated in the waters of 122.5°E–124°E and 26.5°N–28°N (Cui and
Chen, 2005), and the HSI value was also more than 0.6 in these waters. CPUE was considered as an index of fish abundance (Bertrand et al., 2002), and fishing efforts were considered as an index of fish occurrence or fishing availability (Andrade and Garcia, 1999). In 2004, the area with an HSI value equal to or more than 0.6 had 88.79% of total fishing sets, and the catch per fishing set was more than 17 t, indicating that the predicted HSI is closely related to the observed distribution of S. japonicus using 2004 data and the model has the ability to effectively estimate the suitable habitat for S. japonicus. However, in September some of the mapped catches and fishing sets fell below the 0.6 HSI contour (Fig. 4), and the highest CPUE (27.0t/net) was located in the area with HSI ranging from 0.4 to 0.5 (Table 4). This may be caused by a limited number of fishing sets, only 9 in total. The HSI model may also need to be improved in the future. The HSI estimated with the four environmental variables provides a reliable prediction of the Chub mackerel’s habitat. This may be for the following reasons: first, Habitat Suitability Index for Scomber japonicus
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(c)
(a)
(b)
Fig. 3. Distributions of habitat suitability index (HSI) in July (a), August (b) and September (c) derived from the arithmetic mean model (AMM) and catch for Chub mackerel fishery in the East China Sea in 2004. Contours and black circle represent the HSI and fishing catch, respectively.
this fish belongs to a pelagic species and inhabits the upper oceanic waters; secondly, the fish makes a seasonal migration each year and concentrates on the fronts or eddies featured with suitable environmental conditions (Deng and Zhao, 1991; Chen, 2004; Zheng et al., 2008); and finally, fish usually prefer inhabiting in an area with a suitable range of environmental variables (Belyaev and Rygalov, 1987; Cui and Chen, 2005; Sun et al., 2006). The fish mainly inhabited waters with 26.5–30°C SST 98
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and 33.3–34.3 psu SSS during July to September (Chen, 2004; Cui and Chen, 2005), which is similar to the index ranges of SI observed in this study. The four important environmental variables are also used in other fisheries studies and successfully show the relationship between fish distribution/habitat and these variables (Gallienne et al., 2004; Zainuddin et al., 2006). We also found that different HSI models tended to yield varying results (Table 3). The mean HSI value var-
(a)
(c)
(b)
Fig. 4. Distribution of habitat suitability index (HSI) in July (a), August (b) and September (c) derived from the arithmetic mean model (AMM) and number of fishing sets for the Chub mackerel fishery in the East China Sea in 2004. Contours and black circle reprensent the HSI and fishing sets, respectively.
ied from 0.3037 (CPM) to 0.6708 (AMM), its standard deviation ranged from 0.2418 (AMM) to 0.3499 (GMM) and the value of skewness is also different varying from –0.7502 (AMM) to 0.5636 (CPM). The most suitable HSI model is estimated by AMM based on the lowest value of ACI (321.4). Therefore, in addition to the selection of environmental variables, a great deal of attention should be paid to the selection of the most suitable model and indicator, especially for the conservation and management
of fisheries. To evaluate the performance of empirical models, their outputs were compared with corresponding abundance density. This approach, however, is not equivalent to testing the HSI model’s accuracy in predicting the quality of habitat for a species (Wakeley, 1988). In the evaluation of habitat quality, it is important to capture both the habitat characteristics and habitat selection in the linkage between physical environments and habitat preference of target species, because only a precise HSI Habitat Suitability Index for Scomber japonicus
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model can make a reliable assessment (Fukuda et al., 2006). In this study, we find that the AMM can perform well in quantifying the habitat of Chub mackerel in the East China Sea. The uncertainty associated with the HSI model prediction usually results from three sources. The first is the reliability of the SI curve. It is important that a reliable, single variable SI curve representing a species’ suitable environmental range is obtained. The reliability of such curves may be influenced by expert knowledge, empirical data and previous studies. In this study, significant (p < 0.01) probability density functions were obtained to describe the fishery data. The second source is the representation of input data. The samples must reflect the overall distribution of variables in the study area. Thus, the model can be tested and validated to improve the goodness of fit for the HSI curve, and the collection of data can be improved to reduce the uncertainty of input data (Van et al., 2001). The third is the HSI model structure. There is a great difference in outputs yielded from different models. We can use information criterion, such as AIC and Bayesian Information Criterion, to select the best model. AIC has the advantage of testing the significance of the differences between the functions of different model specifications (Akaike, 1973). Sakamoto et al. (1986) described an alternative to the AIC, called the BIC (Adkison et al., 1996), which is also a tool for selecting the best model (Wang and Liu, 2006). We may also apply HSI models to identify potential fishing grounds, but this should be done with great caution. In this study, CPM and MINM may not be appropriate to determine potential fishing grounds because high fishing sets occurred in the areas with low HSI levels, which contradicts our assumptions. The GMM can produce relatively good results but large errors exist. For the above models, if one SI value in the four variables is equal to zero in the extreme condition, the HSI is also equal to zero. So the three estimated methods are not suitable for this fish’s habitat. While the AMM is not affected by these two extreme conditions (SI is too low or too high), the estimated HSI value may be reliable. Therefore, a proposed catch optimization strategy for fishermen would be to target areas with high habitat suitability indices. A dynamic, real-time habitat model incorporating more environmental variables, such as bathymetry and current, may further improve the process of identifying potential fishing areas, but more studies are needed to develop such models in the future, which can be used to identify the critical habitats such as spawning and nursery grounds. The identification and protection of these critical habitats is essential for the long-term conservation of commercial fish stocks in these times of ecosystem-based fishery management.
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5. Summary and Conclusion Chub mackerel (Scomber japonicus) is an important coastal pelagic species mainly exploited by the light-purse seine fisheries of China, Japan and South Korea. In the last 10 years, the annual total catch fluctuated from 250 to 450 thousand tons, which might be caused by variability in habitat quality. Thus the habitat quality of Chub mackerel in the East China Sea is a subject of concern. In this study we used catch data and satellite derived environmental variables to determine habitat suitability indices for Chub mackerel. We found that more than 90% of the total catch came from the areas with sea surface temperature 28.0°–29.4°C, sea surface salinity 33.6–34.2 psu, chlorophyll-a concentration 0.15–0.50 mg/m 3 and sea surface height anomaly –0.1–1.1 m, during 1998 to 2003. This means that these optimum environmental variables provide a better foraging habitat for this fish. The AMM used to build the HSI model was found to be most suitable based on the results of AIC analysis. In 2004, the monthly HSI values estimated from AMM in the waters of 123°–125°E and 27°30′–28°00′ N were more than 0.6 during July to September, which is consistent with the distribution of catch. We conclude that the AMM can yield a reliable prediction of the Chub mackerel’s habitat in the East China Sea. We might also use the suitable area with greater HSI (more than 0.6) to predict the growth and spatial distribution of Chub mackerel. Acknowledgements We thank the Shanghai Fisheries University for providing the catch data, and NASA and IRI/LDEO Climate Data Library of Columbia University for providing the environmental data. This study is supported by the Program for New Century Excellent Talents in University (NCET-06-0437), National Science and Technology Program (2006BAD09A05), the Key Laboratory of Shanghai Education Commission for Oceanic Fisheries Resources Exploitation and Shanghai Leading Academic Discipline Project (Project T1101). We also thank Prof. Y. Chen of the University of Maine, USA for helping review the manuscript. Constructive and helpful comments from the two anonymous reviewers have greatly improved an early version of the manuscript, for which we are grateful. References Adkison, M. D., R. M. Peterman, M. F. Laointe, D. M. Gillis and J. Korman (1996): Alternative models of climatic effects on sockeye salmon, Oncorhynchus nerka, productivity in Bristol Bay, Alaska, and the Fraser River, British Columbia. Fish. Oceanogr., 5, 137–152. Akaike, H. (1973): Information theory and an extension of the maximum likelihood principle. p. 267–281. In International Symposium on Information Theory, 2nd ed., ed. by B. N.
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