DOI : 10.4197/Mar. 24-1.4
43
Interannual Variability of the South-eastern
Mediterranean Catch and its Relation to Hydrographical and Air-Temperature Anomalies
T. M. El-Geziry, , I. A. Maiyza, S. Abdel-Hafez, Sh. I. Maiyza1 and M. S. Kamel
National Institute of Oceanography and Fisheries, Alexandria, Egypt
1 General Authority for Fish Resources Develpoment, Alexandria, Egypt [email protected], [email protected], [email protected]
Abstract: Water temperature, salinity and air temperature are among different abiotic factors that affect the fish catch. The present paper aims at drawing the general trend of changes in the catch of the South- eastern Mediterranean Sea, in relation to the trend of hydrographical and air-temperature anomalies. It can be considered as an attempt to get a clue on this relationship, using the physical amount of catch as a tool to assure the results previously obtained on the cyclic behaviour of these natural conditions. The quadratic regression of the present data (1963-2008) of catch shows a general parabola configuration for both the different species and the total catch. This parabola shows general minima in the interval of years 1974-1983, and may reflect a cyclic behaviour in the changes in the catch. The later may extend to cover wider cycles, which may reach 70-years, as concluded in previous research. The period of minimum catch in the present paper coincides with the period of minimum values of anomalies of both hydrographical and air-temperature conditions affecting the area of investigation. The positive correlation to the salinity for the different fish species may reflect a special significance for the impact of salinity on the catch.
Keywords: Mediterranean Sea, temperature anomalies, salinity anomaly, fisheries, Egyptian Mediterranean catch.
Introduction
Water temperature, salinity, and seabed configuration are some abiotic factors affecting fisheries. Climatological conditions may also be considered among the abiotic factors, which impact on the total catch.
The catch along the Egyptian Mediterranean coast is considered for the present study. The catch comprises 15 different specified species and others (CAPMAS 1963-1994; and GAFRD 1995-2008). This is used to examine the trend of changes in the fishery of the area of interest.
Meanwhile, it is used in the present paper as a tool, which serves to evaluate (confirm) results previously obtained (Maiyza and Kamel, 2009
& 2010; and Maiyza et al., 2010 & 2011) on the general trend of anomalies of sea surface temperature, surface salinity and air temperature at the same region.
The study of surface temperature and salinity anomalies is fairly essential for solving many oceanographic problems (Heburn, 1985). In addition, the change in the global climate has become an issue of a special concern since the late 1980s. Many authors were keen to investigate the behaviour of the trend of changes in both: hydrographic conditions (temperature and salinity) in the oceans and in the global climate. This includes: (Maiyza (1984); Fedrouich (1985); Levilus (1995); Maiyza et al. (1995); Maiyza and Kamel (2009; 2010); and Maiyza et al. (2011).
The proposed question is to which extent the changes in the hydrographical and metrological conditions would be related to the fishery of a specified area, and how strong will the catch be tied to the variations in these conditions. Previous research has concluded some kind of cyclic changes in fisheries related to climatic cycles (70-year cycles), e.g. Kawasaki (1994) and Hylen (2002), which coincides with cyclic climatic changes concluded by Maiyza et al. (2011).
The present work aims at modelling the general trend of changes in the catch of the fishery in south-eastern Mediterranean Sea using the quadratic regression technique. The paper also attempts to show the tie between the changes in the catch and those in the anomalies of hydrographical and meteorological conditions at the same area; in order to reflect a more comprehensive picture on the relationship between them.
Data and Methods of Analysis
Figure 1 represents the area of investigation, the South-eastern Mediterranean Sea. This extends between latitudes 30° 00’ - 33° 00’ N and longitudes 25° 00’ - 34° 00’ E. The area is divided to 18 grid-cells of 1° x 1° size. The hydrographic conditions and air-temperature affecting the area were previously studied by Maiyza et al. (2010), Maiyza and Kamel (2009 & 2010) and Maiyza et al. (2011) in order to investigate the trend of changes in the sea surface temperature anomaly (SSTA), sea surface salinity anomaly (SSSA) and air-temperature anomaly (ATA), respectively.
The data of the total catch in the present paper represent the catch along the South-eastern Mediterranean coast. This is derived from the data base of the CAPMAS (1963-1994) and the GAFRD (1995-2008).
The catch comprises 15 specified species and others based on annual records; during the period from 1963 to 2008, i.e. 46 years.
Fig. 1. South-eastern Mediterranean including the Egyptian Mediterranean Coast.
The general trend of variations in the catch of the different species is examined using the quadratic regression technique. The controlling equation, which models the fluctuations in the annual catch of each species, as well as that for the total catch, is generated using the Microsfot Excel®, and charts of configurations are drwan using the same
software. The correlation factors between the changes in the individual species and total catch and that of anomalies in the meteorological and hydrographical conditions are also computed using the Microsoft Excel®.
Discussion
The common commercial names of the different species in the present paper with their mean annual catch are listed in Table 1.
While Figures 2 (a&b) show the general trend of variation of the different species, Figure 3 draws the general trend of variation in the total catch in the area of inetrest. For every species, the equation, which has been produced using the quadratic regression trend, is shown.
Table 1. Common (commercial) names and the mean annual catch of the investigated species in the present Study.
Species
No. Common (commercial) name Mean annual catch (tonnes/year)
1 Cartilagenous fish 875.65
2 Shrimp 2991.52
3 Sardine 8263.00
4 Swordfish 496.82
5 Mullets 1219.33
6 Gurnards 684.33
7 Swimcrabes 852.15
8 Shade-fish 618.91
9 Common seabream 883.20
10 European baracuda 529.33
11 Bogue 1373.61
12 Common sole 526.02
13 Blue fish 207.69
14 Spotted seabass 171.69
15 Golden Groupers 673.06
16 Others 3977.37
Fig. 2(a). Trend of regression and variations in the catch fish species (1-8).
1- Cartilagenous fish y = 2.6489x2 - 10473x + 1E+07
0 500 1000 1500 2000 2500 3000 3500 4000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
2- Shrimps y = 8.7447x2 - 34675x + 3E+07
0 2000 4000 6000 8000 10000 12000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
3- Sardine y = 19.211x2 - 75941x + 8E+07
0 5000 10000 15000 20000 25000 30000 35000 40000 45000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (Tonnes)
4- Swordfish y = 1.6169x2 - 6402.4x + 6E+06
0 200 400 600 800 1000 1200 1400 1600 1800 2000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
5- Mullets
y = 4.1612x2 - 16426x + 2E+07
-1000 0 1000 2000 3000 4000 5000 6000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
6- Gurnards y = -0.079x2 + 332.71x - 348412
0 500 1000 1500 2000 2500
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
7- Swimcrabes y = 2.4714x2 - 9764x + 1E+07
0 500 1000 1500 2000 2500 3000 3500 4000 4500
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
8- shade-fish y = 1.8904x2 - 7491x + 7E+06
0 500 1000 1500 2000 2500
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
Fig. 2(b). Trend of regression and variations in the catch fish species (9-16).
9- Common seabream y = 1.5799x2 - 6245.3x + 6E+06
0 500 1000 1500 2000 2500 3000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
10- European baracuda y = -0.0369x2 + 176.9x - 205328
-500 0 500 1000 1500 2000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
11- Bogue
y = 0.7853x2 - 3055.4x + 3E+06
0 500 1000 1500 2000 2500 3000 3500 4000 4500
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
12- Common sole y = 0.4796x2 - 1886x + 2E+06
-500 0 500 1000 1500 2000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
13- Blue fish y = 0.6057x2 - 2396.2x + 2E+06
0 100 200 300 400 500 600 700 800 900 1000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
14- Spotted seabass y = 0.9893x2 - 3915.1x + 4E+06
-200 0 200 400 600 800 1000 1200
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
15- Golden groupers y = 1.4035x2 - 5552.2x + 5E+06
0 200 400 600 800 1000 1200 1400 1600 1800
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
16- Others y = -4.2888x2 + 17208x - 2E+07
-4000 -2000 0 2000 4000 6000 8000 10000 12000 14000 16000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Years
Catch (tonnes)
Fig. 3. Trend of regression and variation of the total catch in the area of interest.
It can be noticed that the variations in the whole catch show parabola forms except for the Gurnards and the European baracuda. The lowest recorded total catch occurred in 1974. The different species have minimum amount of catch in the interval of 1974 to 1983. This is true except for the Bogue and the Common sole.
Hydrographically, the lowest SSTA over the same area of inetrest for the period 1948-2008 was previously concluded (Maiyza et al., 2010) to be within the year interval 1978-1988 (Fig. 4). This is, thus, included in the same period interval of minimum catch for the different species in the present paper, which reflects a kind of relationship between the cyclic changes in the catch and that in the water temperature.
Fig. 4. Mean annual variation of SSTA of the South Eastern Mediterranean Sea (Maiyza et al., 2010).
Trend of variation in the total catch (16 species) y = 42.184x2 - 166506x + 2E+08
0 20000 40000 60000 80000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Years
Total annual catch (tonnes)
Also, the lowest SSSA concluded by Maiyza and Kamel (2010), for a continous data set from 1948 to 2010, occurred in 1979 (Fig. 5). This, again, lies in the interval of lowest catch driven from the quadratic regression for the different speices in the present paper.
Fig. 5. Trend of variations of the SSSA shows the lowest anomaly in the year 1979 (Maiyza and Kamel, 2010).
Regarding the ATA, the lowest ATA was previously calcultaed, for a continous record (1958-1990), to be in 1981 (Fig. 6, Maiyza et al., 2011), which lies in the interval of lowest catch for the different species in the present study (1974-1983).
Fig. 6. The general trend of variation in the air temperature anomaly previously concluded by Maiyza et al. (2011).
50 100 150 200 250 300 350 400
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
Months (1=January, 1958)
Monthly Air Temperature Anomaly y = 6.5703e-006*x2 - 0.003647*x + 0.37918 Month (1= Apr. 1948)
SSSA
744 720 696 672 648 624 600 576 552 528 504 480 456 432 408 384 360 336 312 288 264 240 216 192 168 144 120 96 72 48 1 24 1.0 0.5 0.0 -0.5 -1.0 -1.5
Yt = 0.0499328 - 0.000927544*t + 1.257733E-06*t**2
Correlation of cath to the hydrographical and air temperature anomalies
The correlation factor between the total catch and the SSTA is 0.31. Fourteen out of the sixteen (87.5%) of examined species has positive correlation to the SSTA. The two negative correlation ( negligible correlation) was only for two species: the Bogue (-0.01) and the Blue fish (-0.03, Fig. 7).
Fig. 7. Correlation between the different species and the sea surface temperature anomaly.
The total catch in the present study is correlated to the SSSA with a factor of 0.21. As shown in Fig. 8, the correlation between the different fish species in the present research and the SSSA is positive for the whole stock. This might physically indicate a special interest for the salinity as a controlling abiotic factor that impacts on the catch from the long term point of view. The highest correlation factor is 0.34 (Shade fish) and the lowest is 0.04 (Common sole).
Fig. 8. Correlation between the different species and the sea surface salinity anomaly.
Correlation between fish species and sea surface temperature anomaly
-0.1 0 0.1 0.2 0.3 0.4 0.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Species
Correlation factor
Correlation between fish species and sea surface salinity anomaly
0 0.1 0.2 0.3 0.4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Species
Correlation factor
The correlation factor is 0.29 between the total catch and the ATA.
The correlation factor between the different species and the ATA (Fig. 9) varies between a minimum of -0.27 for the Swimcrabes and the Blue fish, and a maximum of 0.43 for the Spotted seabass.
Fig. 9. Correlation between the different species and the air temperature anomaly.
Conclusion
Different abiotic, beside biotic, factors affect the fish catch. The present paper is an attempt to get a clue on the relationship between the changes in the catch of different species and the abiotic factors represented by the hydrographical and air temperature conditions of the south-eastern Mediterranean. In general, the cyclic behaviour in the catch of the examined species is shown. This behaviour coincides with the cyclic changes in the previously studied behaviours of anomalies of sea surface temerature, salinity and air temperature. This cyclical relationship may extend to cover the global cyclic changes (70-year cycle) as previously concluded by different researchers worldwide (Kawasaki 1994; Hylen 2002 and Maiyza et al. 2011).
The calculated correlation factors show a special impact for the variation in the sea surface salinity anomaly on the catch of the different recorded species.
More investigations are needed to study the tie between changes in the catch from a fishery and in different physical parameters.
Correlation between fish species and air temperature anomaly
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Species
Correlation factor
References
Fedrouich, L.A. (1985) Regular formation of large scale temperature anomalies of the surface layer in the northern Pacific Ocean. Ph.D. Thesis, Moscow Univ., 24p.
Hylen, A. (2002) Fluctuations in abundance of Northeast Arctic cod during the 20th century.
ICES Marine Science Symposia: 534-550.
Heburn, G.W. (1985) Effect of wind versus hydraulic forcing on the dynamics of the western Mediterranean Sea. Rapp. Comm. Int. Mer Medit., 29: 3.
Kawasaki, T. (1994) A decade of the regime shift of small pelagics. FAO expert consultation (1983) to the PICES III (1994). Bul. Jap. Soci. Fisher. Oceanogr., 58: 321-333.
Levitus, S. (1995) Interannual to decadal scale variability of the world Ocean. IAPSO XXI General Assembly, Honolulu, Hawaii, USA, August 5-12, 1995.
Maiyza, I.A. (1984) Long term variation of water temperature in the eastern part of the Mediterranean Sea, Ph.D. Thesis, Moscow Univ., 240p (in Russian).
Maiyza I.A.; Mohamed, E.E.; Saad, N.N. and Sharaf El-Din, S.H. (1995) Sea surface temperature anomalies in the western Mediterranean. IAPSO XXI General Assembly, Honolulu, Hawaii, USA, August 5-12, 1995.
Maiyza, I.A. and Kamel, M.S. (2009) Climatological Trend of Sea Surface Temperature Anomalies in the South Eastern Mediterranean Sea. JKAU: Mar. Sci., 20: 59-66.
Maiyza I.A. and Kamel, M.S. (2010) Climatological trend of Sea surface salinity anomalies in the South Eastern Mediterranean Sea. JKAU: Mar. Sci., 21(2): 57-63.
Maiyza I.A.; Said, M.A. and Kamel, M.S. (2010) Sea Surface Temperature Anomalies in the South Eastern Mediterranean Sea. JKAU: Mar. Sci., 21(1): 151-159.
Maiyza, I.A.; El-Geziry, T.M.; Maiyza, H.I. and Kamel, M.S. (2011) Climatological of air temperature anomalies in the southeastern Mediterranean Sea. JKAU: Mar. Sci., 22(2): in press.
The Central Agency for Public Mobilization and Statistics (CAPMAS). Year-book of fishery statistics (1963-1994), Cairo, Egypt.
The Genreal Authority for Fish Resources Development (GAFRD). Year-book of fishery statistics (1995-2008), Cairo, Egypt.
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