Ecological Modelling, 56 (1991) 47-61 47 Elsevier Science Publishers B.V., Amsterdam

An autoregressive model of the temperature-growth relationship for the Western Mediterranean blue

whiting Micromesistius poutassou

Luis E. Ca lderon-Agui le ra

Centro de Investigacidn Cientifica y de Educacidn Superior de Ensenada, Apdo. Postal 2732, C.P. 22830, Ensenada, Baja California, Mexico

(Accepted 3 October 1990)

ABSTRACT

Calderon-Aguilera, L.E., 1991. An autoregressive model of the temperature-growth relation- ship for the Western Mediterranean blue whiting Micromesistius poutassou. Ecol. Model- ling, 56: 47-61.

The relationship between sea surface temperature and blue whiting growth parameters is explored through an autoregressive model. Sea temperatures in April, May and June were used as input because water stratification in the Western Mediterranean and blue whiting recruitment to the fishing ground take place during those months. The otolith nucleus diameter and annulus width from age groups 1-3 were selected as output because the fishery is supported by those age groups. A sample of 4767 individuals was selected for aging from 145685 fishes collected between 1950 and 1987 off the Catalonian coast. Otoliths were embedded in plastic resin and sectioned for measuring the nucleus diameter and annulus width. The best model was obtained using an instrumental variable procedure when sea temperature in May was used as input and annulus width of age group 1 as output. The model states that, if temperature rises, the individual will grow faster; this is supported by the cross-correlation function for CPUE and sea surface temperature in May. Some ecological constrains of the model are presented.

INTRODUCTION

Fish growth is one of the mos t s tudied topics in f ishery science Mos t popu la t ion dynamic studies, fish farming, as well as regula t ion deve lopmen t

depend u p o n g rowth estimates. As a consequence there is m u c h l i terature

available on g rowth models (1138 references indexed in the A S F A be tween 1982 and 1989); however, mos t of these papers are descr ipt ive ra ther than

predict ive and do not take into accoun t env i ronmen ta l factors such as

temperature .

0304-3800/91/$03.50 © 1991 - Elsevier Science Publishers B.V. All rights reserved

48 L.E. CALDERON-AGU1LERA

The use of time series analysis and non-conventional techniques in ecological and fisheries studies is relatively recent. Grant et al. (1981) proposed a general bioeconomic fishery simulation model for annual crop marine fisheries that has proven to be useful. Linder et al. (1987) used Event Tree Risk Analysis to compare management policies for two species (Geo- rges Bank haddock stock Melanogrammus aiglefinus L. and Gulf of Mexico menhaden Brevortia patronus). Simulation techniques have been applied to the Texas brown shrimp (Penaeus aztecus) fishery by Carothers and Grant (1987).

Gutierrez and Morales-Nin (1986) analyzed the relationship between water temperature and otolith daily increments of Dicentrarchus labrax larvae reared ill the laboratory by means of a third-order linear transfer function model. More recently, Campana and Hurley (1989) have proposed a generic larval growth model that considers sea surface temperature. The advantages of autoregressive models have been pointed out by Stevens and Overton (1978) but this may be the first time that they have been applied to temperature-growth modelling of natural populations.

The blue whiting Micromesistius poutassou Risso is one of the most important species ill the Catalonian trawler fishery. Its spawning season lasts for a short period in February. Juveniles remain at midwater depths until they are 15-17 cm long when they migrate to the bottom, usually by May (Bas, 1964). The age groups 1-3 support the fishery and individuals older than 5 are very scarce (Bas and Calderon-Aguilera, 1989).

The references dealing with the growth of this species on the Catalonian coast are those from Bas (1964), Bas and Morales (1966) and Veron-Jane (1986). They are mostly descriptive and with no more than 3 years of sampling.

The goals of the present study are: (a) to develop a model of the temperature-growth relationship in natural populations, (b) to use this kind of model for predicting harvests within the Western Mediterranean blue whiting stock; and (c) to demonstrate the use of alternative techniques in fisheries studies.

MATERIALS AND METHODS

The general research design from specimen collection to the model development is presented in Fig. 1; an explanation is given below.

Data sources

A total of 145 685 individuals of blue whiting were collected between 1950 and 1987 at landing ports on the Catalonian coast (Blanes and Barcelona,

WESTERN MEDITERRANEAN BLUE WHITING 49

size ~ _ ~ frequency distribution

Landings

length, weight recording

n = 145685

otolith removal l

agendetermination= 4767 - -

embedding in plastic resin, cutting, annulus measurement

n = 867

otolith diameter fish length relationship

length/age key and growth equation

/ time series of Itime series of ss

age group growth L temperature

L L

AUTOREGRESSIVE MODEL(ARX)

Fig. 1. Flow diagram of the general sequence of model development.

Spain). All specimens were measured (total length), weighed (total and gutted weight), and sexed. The stage of maturity was identified and the saggita otoliths were removed.

Sea surface temperature data for the period of 1950-1959 are from the Castell6n fishing area (Andreu and Rodriguez-Roda, 1951; Rodriguez-Roda, 1952, 1953, 1955; Rodriguez-Roda and Herrera, 1955; Herrera, 1957, 1958a, b, 1960, 1961); for 1967-1987 the data are from Badalona thermoelectric station area (FECSA); for 1969 to 1982 from L'Estartit; and for 1974-1987 from Medan Islands (Appendix 1).

50 L.E. CALDERON-AGUILERA

Sampling scheme

In order to subsample the number of otoliths for aging, an average of 120 otoli ths/sampled-year were randomly selected from the total which still allowed all year classes to be represented during the study period.

Aging was done by direct observation of the sagittae otoliths following the methods proposed by ICSEF (1983). Otoliths were previously ground and polished.

For building the growth series a sub-sample of 30 otol i ths/year was chosen from the total after they were read. These otoliths were embedded in plastic resin following the Lowestoff Laboratory technique (Bedford, 1983), and were cut at the middle of the nucleus and mounted on glass slides. The total diameter (L 1 ), nucleus diameter (NO) and each of the growth-rings ( H i, Oi; H~ = first winter ring; O 4 = fourth summer ring, etc.) were measured using a calibrated stereoscopic ocular micrometer (30 x ).

In order to obtain comparable values all otoliths were measured along the same axis. Because of the irregular otolith morphology, 300 otoliths were first measured along the three major axes (Fig. 2) to determine the axis with the smallest coefficient of variation and to define an appropriate sample size. The transverse axis presented the least coefficient of variation so all measurements were made along it.

The otolith diameter (L~) and fish total length (TL) were related by a model of the form:

TL = aL b (1)

Using this equation the TL of previous ages can be back-calculated; also the theoretical TL can be inferred from otolith ring width.

The model assumes that: (1) All individuals are born on 1 February of each year, and spawning lasts

a short period with no change from year to year. (2) Individuals recruit to the fishery when they are 0.5 year old. (3) The exploited stock is supported by age groups 1-3. (4) Maximum age in the fishery is 5 years. (5) All organisms of the same cohort are equally affected by the environ-

ment (temperature, food limitation, depredation, fishing, etc.) (6) The annual growth rates of the cohort are normally distributed. (7) Sex ratio is not significantly different from 1 : 1. (8) Temperature is a controlling factor of growth.

Model development

The conceptual model assumes that the input variable (sea temperature) affects the individual growth process. The output variable (otolith size) is an expression of somatic growth.

W E S T E R N M E D I T E R R A N E A N B L U E W H I T I N G 51

Fig. 2. Transverse view of an otolith cut at nucleus level. The coefficient of variation of the measurement is indicated for each structure along the three axes.

The model was determined using the IES Program (Escobet, 1989). This program estimates parameters using an instrumental variable procedure and incorporates some subroutines from the P C - M A T L A B package (Mathworks, 21 Eliot St., Natick, MA 01760, Version 13.3). It basically consists of an iterative search of the least variance model using the AIC Akkaies' test modified by Edmonds (1985). To achieve this the program changes the system order and lag (k ) until the best combinat ion is found.

The adjusted model is of the form:

Yt + a l y t - 1 + . . . + a n Y t - n = bout + b l U t - 1 -q- " '" " ] - b m u t - m - 1 (2)

where Yt is the output at time t, u t is the input at time t, n are the system zeros, m are the system poles, and a and b are parameters.

The model is phrased in terms of a difference equation since data were collected at discrete, regularly spaced time intervals and the formation of the growth rings is a discrete process, so z-transforms of the inputs and outputs were used.

52 L.E. CALDERON-AGUILERA

The input variables considered were mean sea surface temperature (MT) in April, MT in May and MT in June. During these months water stratification and recruitment to the fishing ground takes place. The output variables were nucleus diameter (ND), annulus width of age group 1 (AW 1 = H 1 + O1), annulus width of age group 2 (AW2 = H 2 + 02), annulus width of age group 3 (AW3 = H 3 + 03) and total width (AWt) of annuli from age groups 1-3. Fifteen combinations (3 inputs × 5 outputs) were tested as described to find the best model. Since the model relates data in a time series to previous values in another time series, it is named an autoregressive model with exogenous inputs (ARX) (Box and Jenkins, 1976; Chatfield, 1975).

Even when fish are on the bottom, surface temperature was used because it is (a) an indicator of oceanographic process such as currents, movements of water masses and upwellings, and (b) easily obtained. The latter is an important consideration because one of the objectives of the model is planning the fishing effort given an available input. Sea surface temperature is normally used in most environmental-fisheries studies (e.g. see the compi- lation by Tomczak, 1977).

RESULTS

The irregular morphology of the otoliths resulted in large variance in the measurements. However, the measurements may still be considered repre- sentative; the ratio of the standard deviation to the mean is less than 10% for year classes up to the three (Table 1). Therefore, only these year classes were considered in the model building process. As mentioned before, these age groups support the fishery representing almost 80% of the total landed biomass. The percent distribution by age groups was as follows: age group

TABLE 1

Statistics of all blue whiting otolith measurements (1 : 0.0334 mm), 1952-1987

ND H1 O1 H2 02 H3 03 H4 04 H5

n 563 439 361 303 220 164 97 57 18 16 n*min 304 74 60 44 37 27 28 16 18 32 mean 54.63 5.23 5.79 3.95 4.05 3.18 3.74 2.95 3.28 1.50 SD 4.37 1.95 2.16 1.67 1.53 1.32 1.34 1.07 1.15 1.58 VAR 19.08 3.81 4.68 2.79 2.34 1.75 1.80 1.14 1 . 3 1 2.50 SE 95% 0.36 0.20 0.20 0.19 0.20 0.20 0.27 0.28 0.53 0.77 %sE/mean 0.66 3.81 3.49 4.76 4.99 6.36 7.13 9.39 16.14 51.65

n, sample size; nmi n* minimum sample size for getting a measure within 95% CL (according to Altman, 1982); sE 95%, standar error at 95% CL; % SE/mean, percentage of the sE/mean ratio.

WESTERN MEDITERRANEAN BLUE WHITING 53

0: 12.2%; age group 1: 28.1%; age group 2: 32.4%; age group 3: 18.3% and age group 4 and older: 9%.

Otolith measurements including up to the age group 3 are presented in

TABLE 2

Blue whiting otolith nucleus diameter (ND) and annulus each cohort along the study period

width ( H i, Oi) (1:0.0334 mm) in

Cohort N D HI O1 H2 0 2 H3 0 3

1951 56.60 2.00 3.80 2.80 3.20 2.00 2.00 1952 56.70 4.18 6.00 * 4.66 * 4.33 * 3.00 4.12 *

1953 55.50 4.92 5.12 3.94 4.39 * 3.80 4.25 * 1954 58.00 4.40 5.50 3.34 3.64 2.00 2.75 1955 53.00 5.00 6.00 * 4.00 5.00 * 3.00 3.00

1956 56.70 5.00 5.75 5.00 * 5.16 * 3.00 3.00 1957 a 51.20 5.25 6.75 * 2.25 4.50 * 2.50 3.00 1958 53.80 6.06 6.32 * 4.42 * 3.26 2.92 3.33 1959 54.30 4.80 * 6.13 * 4.00 3.72 3.33 3.66 1960 51.70 4.82 6.61 * 3.71 5.25 * 3.57 * 4.77 * 1961 a 53.00 5.85 * 5.17 2.38 4.69 * 3.83 * 3.00 1962 55.50 5.17 5.40 3.55 3.53 2.85 3.80 1963 53.30 4.81 6.64 * 3.88 4.60 * 3.11 4.00

1964 a 55.30 8.16 a 8.50 * 4.25 * 4.50 * 3.00 3.50 1965 56.00 4.80 6.20 * 3.50 3.50 3.00 4.00 1966 a 56.00 6.00 * 9.00 * 3.34 3.64 2.00 3.00

1967 ~ 54.00 5.23 7.00 * 3.83 3.85 3.34 3.64 1968 57.00 5.00 5.23 3.83 3.85 3.34 3.64

1969 57.30 5.12 7.31 * 4.68 * 4.37 * 3.12 3.88 1970 54.90 4.85 5.64 3.42 3.92 3.16 3.60 1971 56.50 3.00 5.66 3.83 3.85 3.34 3.64 1972 52.00 5.00 5.00 4.00 4.00 4.00 * 3.00 1973 54.60 2.50 5.50 3.33 3.60 2.50 3.50 1974 57.30 4.00 3.85 2.50 4.50 * 3.34 3.64 1975 54.30 5.17 5.36 2.78 4.11 2.00 4.00 * 1976 52.90 5.76 * 6.00 * 3.50 4.16 1.66 5.50 1977 55.50 2.75 7.00 * 3.00 4.50 * 2.50 3.25 1978 58.50 4.00 5.50 2.25 3.50 2.00 3.30 1979 55.20 4.28 5.28 2.25 2.00 1.00 2.00 1980 56.60 4.00 4.00 3.34 3.85 3.00 3.64 1981 a 57.00 4.00 6.00 * 4.20 * 4.94 * 3.67 4.00

1982 a 55.00 5.40 * 6.40 * 3.00 4.00 2.60 2.80 1983 56.00 5.55 * 6.10 * 3.40 4.00 3.20 2.50 1984 55.00 4.90 6.83 * 4.10 4.81 * 3.60 2.75 1985 52.10 5.66 * 6.90 * 5.50 * 2.80 1986 53.70 5.25 6.42 * 1987 50.30

Warm year (Gutierrez, 1986) * Significantly larger ( P < 0.05)

54 L.E. CALDERON-AGUILERA

TABLE 3

Characteristics of the two best autoregressive models of the sea temperature-blue whiting growth process

Model n rn k AIC test

a 1 0 0 36.5 b 2 1 0 71.8

n, system poles; m, system zeros; k, lag.

Table 2. The annulus ring width varied from year to year and decreased with age. significantly larger annuli (P < 0.05) over the study period are also indicated.

The total length (TL) and the otolith diameter (L1) relationship is ex- pressed by the equation:

TL = 63.27L T M (n = 802, r2 = 0.86, SE = 1.07) (3)

The best model was obtained using the MX in May as the input and the annulus width of age group 1 (AWl) as the output. The best two models for this input and output are presented in, Table 3.

Model a has the least residual variance (1.35), so this model was chosen to describe the temperature-growth relationship. Its parameters with their variance are: a = - 0.6379 (0.0514) and b = 0.1761 (0.0323). The model is (time step = 1 (year):

AWl,,,- 0.6379AW1,, , , - 0.1761MT( ° C)May r (4)

1 8 "

1 6 -

1 4 -

1 2 -

~ l O - = 0 c <

8 -

6 -

4 1 9 5 4

II

i I i I I I

I I I / \

/.//x,, "~ V ~

\ 1

= , r , i t

1 9 5 9 1 9 6 4 1 9 6 9 1974 1 9 7 9 1 9 8 4

Fig. 3. Autoregressive model of the relationship between the seawater temperature in May and the annulus size of the year-class I. The dashed line indicates observed data.

W E S T E R N M E D I T E R R A N E A N B L U E W H I T I N G 5 5

2

- 1 i 0 2 4 6 8 10

Log

Fig. 4. Residual distribution of the model. Lines indicate 95% confidence limits.

The adjusted model describes the data reasonably well (Fig. 3). The y-axis is in micrometric units (1:0.0334 mm) so differences are in hundredths of a millimeter. The residual distribution of this model indicates the presence of 'white-noise' only. This means that the residual distribution falls within the confidence limits (P < 0.05; see Fig. 4) (If the residuals were outside the confidence limits then the model would exhibit 'red-noise', i.e the intrinsic variance of the data is too large, and the model would not be valid).

This model was used to simulate the effect on growth of a sudden increase of 5 °C in sea temperature. As shown in Fig. 5, if temperature increased the 1-year-old individuals would grow faster, but the effect would decay ex- ponentially over time.

The increment on TL was calculated using equation (3). On the average those individuals would measure 25.85 cm (TL) instead of a mean TL of 23.99 cm (the mean length at age 1), which is an increase of almost 2 cm/year.

1.00-

0.66

c .. 0.33

0.00 T 4

Time (years)

8 10

Fig. 5. Simulation of growth response to an increase of 5 °C in sea temperature in May.

56 L.E. C A L D E R O N - A G U I L E R A

The change in weight was computed in the same way from the average length-weight relationship for the population:

W = 0.00816TL 2"9 (n = 4777) (5)

On the average individuals would weight 101.82 g instead of 81.99 g, i.e. almost 20% more. Considering that 28% of the exploited population is composed of 1-year individuals, catches would increase 5.6%.

DISCUSSION

The present model states that if temperature rises the individual will grow faster. In Table 2 it can be seen that all years considered as "warm years" (Gutierrez, 1986) are followed by significantly wider rings ( P < 0.05).

Temperature is a growth controlling factor. Optimal growth rate increases from low growth rates at low temperature up to maximum growth rates at high temperatures (under maximum tolerance limits) (Brett, 1979).

However, temperature can increase or decrease the growth rate depending on the maintenance-metabolism-temperature relationship. A metabolic in- crease would cause a higher maintenance charge and in consequence a higher energy demand. This energy comes from the environment which can be affected by temperature changes.

In the Western Mediterranean colder winters are frequently followed by good spring and summer phytoplankton blooms. Primary productivity de- pends, among other factors, on February and March upwellings (Margalef, 1968). If sea temperature increases this could suggest that the upwelling processes were not very vigorous. Supposing a positive correlation (not necessarily linear) between upwelling strength, primary productivity and the available energy, the increasing growth rate at higher temperature would not be supported by enough available energy (food). This can be considered as a clear constraint of the model but it is not at all an invalidation.

The objective of the model was to examine the relationship between an environmental variable which is easy to collect (sea surface temperature) and an expression of growth in order to predict fishery yields. Although it is not intended to describe such a complex process as growth, if a thermal anomaly is detected in the May sea temperature, changes in the growth rate of blue whiting can be expected and reasonably predicted. The cross-correlation function for CPUE ( t /hp) and MT in May (Fig. 6) supports this assertion.

The blue whiting is part of a multi-species fishery. Fishermen shift effort on a certain species according to market demand. Larger individuals are

t, metric tonne = 1000 kg.

WESTERN MEDITERRANEAN BLUE W H I T I N G

1

57

0.8

0 , 6

0 . 4

.~ 0 . 2

8 o

6 u - 0 . 2

u - 0 . 4

- 0 . 6

0.8 t - - 1 5 - 9 - 3 0 3 9 15

L a g s ( y e a r s )

Fig. 6. Estimated Cross Correlations for CPUE ( t /hp; data from Min. Agric. Pesca Alim., Madrid, 1985) and mean sea surface temperature in May in the Western Mediterranean.

more valuable thus weight gain would be economically important. Since temperature would affect the whole cohort, the fishing strategy can be planned ahead.

Some other ecological constraints of the model could be a lack of consideration of the species interaction or an uncoupling of somatic and otolithic growth rates as an effect of differences in temperature response, as has been observed in Arctic char (Salvelinus alpinus) by Mosegaard et al. (1988).

The mathematical drawbacks of the model are shared with most ecologi- cal system models and have been pointed out by Stevens and Overton (1978): (a) the real system is almost surely not linear so that the constant coefficient linear model should be a time varying linear model, and (b) the assumption of a stationary system is difficult to defend. The sources of errors in ecological models presented by Loehle (1987) were also considered and any serious problem was detected in the present model.

On the other hand, the model presents white-noise only, the parameters have a small variance, and it seems to be useful for predictive purposes.

58 L.E. CALDERON-AGU ILERA

CONCLUSION

In the present s tudy a significant re la t ionship be tween the sea t empera - ture in M a y and the otol i th annulus width of age g roup 1 dur ing the last 37 years has been found. In spite of its ecological cons t ra in ts and ma thema t i ca l drawbacks , the p roposed mode l might be useful for p red ic t ing harvests and p lanning explo i ta t ion strategies. It also shows that the use of t ime-series analysis and s imulat ion techniques in ecological and fisheries studies is promising.

ACKNOWLEDGEMENTS

Dr. C. Bas col lected most of the otoli ths. Dr. J. Q u ev ed o and Ms. T. Escofet k indly gave me the IES P rog ram and helpful advice on Co n t ro l Engineer ing Techniques . I also thank Dr. B. Mora l e s -Nin for her assis tance with the otol i th examinat ions . I am grateful to Mr. G. H a m m a n n for his useful comments , as well as to Dr. E. Gut ie r rez , Ms. A. Escofet , Dr . E. Pavia, Mr. J.C. Burguefio and an a n o n y m o u s reviewer. Th e au tho r was part ia l ly suppor t ed by a Na t iona l Counci l for Science and T e c h n o l o g y of Mexico ( C O N A C Y T ) scholarship (Reg. No. 38505). T h a n k s also to C I C T U S - U n i v e r s i d a d de Sonora . This work was done dur ing the au thor ' s s tay at the Inst i tu to de Ciencias del M a r de Barcelona, Spain.

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Veron-Jane, S., 1986. Morfologia, es t ructura del otolito y crecimiento de la bacaladi l la Micromesistius poutassou Risso, 1826 en la costa catalana. Tesis l icenciatura, Facul tad de Biologia, Univ. Barcelona, 135 pp.

A P P E N D I X 1

Sea surface (ss) temperature ( ° C) off the Ca ta lon ian coast

1950 13.0-16.1 13.9-16.0 15.1-19.0 21.9-24.8 1951 12.0-14.0 12.0-13.0 11.7-14.0 11.0-16.1 13.7-18.3 17.0-24.0 1952 11.5-15.4 10.7-14.0 12.7-16.8 14.3-16.7 16.4-21.5 20,8-26.2 1953 11.2-15.0 11.7-15.0 12.2-14.0 15.0-17.2 15.2-21.0 20.6-21.9 1954 13.7-16.0 9.5-13.5 12.5-16.0 14.5-16.8 16.3-19.0 19.0-24.0 1955 14.0-16.0 13.0-15.0 13.0-15.5 14.3-18.0 19.0-21.0 21.0-23.5 1956 14.0-15.0 10.0-14.0 11.8-14.0 14.0-15.5 19.7-20.0 18.5-22.0 1957 11.5-15.3 13.0-15.0 14.0-16.0 14.0-17.0 16.0-18.5 18.5-21.5 1958 13.7-15.0 13.2-14.6 13.1-15.0 13.1-17.0 15.9-20.1 18.2-22.2 1959 13.2-14.4 12.2-13.4 13.1-14.4 14.0-16.1 15.2-19.0 18.9-24.5 1960 . . . . . . 1961 . . . . . . 1962 12.5-15.5 12.0-15.2 10.5-13.0 12.5-14.0 - 1963 10.0-15.0 10.0-13.0 12.0-14.0 12.0-16.0 14.0-19.0 16.0-24.0 1964 12.0-14.5 11.0-14.0 11.0-14.0 12.5-17.0 13.0-22.0 16.0-24.0 1965 12.0-15.2 11.0-15.0 12.0-14.0 13.0-16.0 14.0-19.0 16.0-25.0 1966 12.0-14.0 12.0-14.0 12.0-13.5 13.0-16.0 14.2-18.5 16.0-23.5 1967 12.0-14.0 11.5-31.5 12.5-14.5 12.5-16.0 12.5-18.0 17.0-22.5 1968 . . . . . . 1969 - - - 12.2-15.2 14.6-16.4 16.1-19.2

- - 12.0-12.0 13.0-15.0 15.0-16.0 17.0-18.0 1970 11.9-13.2 13.1-13.1 12.1-13.1 12.9-13.7 13.8-17.1 18.5-21.0

12.0-13.0 13.0-13.0 12.0-13.0 13.0-13.0 14.0-17.0 18.0-21.0 1971 - 12.6-12.8 12.3-12.5 - 14.5-17.3 -

12.0-12.0 12.0-13.0 12.0-13.0 13.0-14.0 15.0-17.0 18.0-20.0 1972 . . . . . . 1973 . . . . . . 1974 12.0-13.0 12.0-12.0 12.0-14.0 13.0-15.0 14.0-18.0 15.0-19.0

12.5-12.5 12.5-12.5 12.5-13.0 13.0-13.5 14.0-16.0 17.0-19.0 1975 12.0-13.0 11.0-12.0 11.0-12.0 12.0-15.0 14.0-17.0 15.0-19.0

12.0-12.5 12.0-12.5 12.0-12.5 12.5-14.0 15.0-15.0 16.0-20.0 1976 12.0-13.0 12.0-12.0 12.0-13.0 13.0-14.0 13.0-19.0 16.0-23.0

12.5-13.0 12.0-12.5 12.0-12.5 13.0-13.5 13.5-17.0 17.0-21.0 1977 11.0-13.0 11.0-12.0 12.0-13.0 13.0-14.0 13.0-16.0 16.0-20.0

12.0-12.5 11.0-12.5 13.0-13.0 13.0-14.0 14.0-15.0 17.0-20.0

WESTERN MEDITERRANEAN BLUE WHITING 61

A P P E N D I X 1 (cont inued)

1978 12.0-13.0 11.0-13.0 11.0-13.0 12.0-13.0 13.0-16.0 16.0-19.0 12.0-13.0 12.0-12.5 11.5-12.5 12.5-13.0 13.5-16.0 17.0-19.0

1979 12.0-13.0 11.0-13.0 11.0-13.0 11.0-14.0 12.0-17.0 17.0-21.0 12.0-1.3.5 12.0-12.5 12.5-13.0 13.0-13.0 13.0-17.0 18.0-20.0

1980 10.0-13.0 10.0-11.0 10.0-12.0 11.0-14.0 13.0-15.0 14.0-19.0 12.0-13.5 12.0-12.0 11.5-11.5 12.0-13.5 13.5-15.0 17.0-19.0

1981 10.0-11.0 10.0-11.0 11.0-12.0 11.0-13.0 13.0-16.0 15.0-19.0 12.0-12.0 12.5-12.5 12.5-13.0 13.0-14.0 15.0-17.0 18.0-20.0

1982 10.0-11.0 11.0-11.0 10.0-12.0 11.0-11.0 13.0-17.0 17.0-21.0 11.5-12.0 11.5-12.0 12.5-12.5 13.0-14.0 13.5-18.0 18.0-20.0

1983 11.0-12.0 10.0-12.0 10.0-11.0 11.0-13.0 12.0-16.0 15.0-21.0 13.0-13.0 11.5-13.0 11.5-12.5 13.0-13.0 13.5-15.0 16.0-19.0

1984 11.0-12.0 10.0-11.0 10.0-12.0 11.0-13.0 12.0-14.0 13.0-19.0 13.0-13.5 12.5-12.5 12.0-12.0 12.0-14.0 14.0-15.0 15.0-20.0

1985 11.0-13.0 11.0-11.0 12.0-12.0 12.0-13.0 13.0-15.0 16.0-19.0 12.5-14.0 12.5-13.0 12.5-13.0 13.0-13.5 14.0-16.0 17.0-20.0

1986 11.0-12.0 10.0-11.0 11.0-11.0 11.0-12.0 12.0-18.0 17.0-18.0 12.5-13.0 12.0-12.5 12.0-12.5 13.0-13.5 13.5-18.0 17.0-20.0

1987 12.7-13.4 11.6-13.0 12.1-12.5 12.1-14.7 13.4-15.2 16.8-18.7

F r o m 1950 to 1959 data are absolute m i n i m um ss t empe ra tu r e - abso lu t e max imum ss tempera ture f rom the Castel lon Fishing Area. F rom 1962 to 1987 data (upper figure) are m i n i m u m mean ss t e m p e r a t u r e - m a x i m u m mean ss tempera ture from Badalona ( F E C S A Thermoelect r ic Station). F rom 1969 to 1987 da ta (lower figure) are m in imum mean ss t e m p e r a t u r e - m a x i m u m mean ss tempera ture f rom Medan Islands.