Interannual variation of growth of the brown shrimp Farfantepenaeus californiensis and its relation to temperature

Fisheries Research 61 (2003) 95–105

Interannual variation of growth of the brown shrimp Farfantepenaeus californiensis and its relation to temperature Juana López-Mart´ınez a,b,∗ , Francisco Arregu´ın-Sánchez b , Sergio Hernández-Vázquez c , Alma Rosa Garc´ıa-Juárez d , Wenceslao Valenzuela-Quiñonez e a

d

Centro de Investigaciones Biológicas del Noroeste, S.C. (CIBNOR) Unidad Guaymas, Km. 2.5 Camino al, Tular Estero de Bacochibampo, Apdo. Postal 349, Guaymas, Sonora 85465, Mexico b Centro Interdisciplinario de Ciencias Marinas del IPN, Apartado Postal 592 La Paz, B.C.S. 23000, Mexico c Centro de Investigaciones Biológicas del Noroeste, S.C. Apartado Postal 128 La Paz, B.C.S. 23000, Mexico Centro Regional de Investigación Pesquera Guaymas, Calle 20 Sur 605 Col. La Cantera, Guaymas, Sonora 85400, Mexico e Escuela de Ciencias del Mar UAS, Paseo Claussen s/n, Apartado Postal 610 Mazatlán, Sinaloa, Mexico Received 17 July 2001; received in revised form 11 September 2002; accepted 11 September 2002

Abstract We evaluated the interannual variation in the growth of brown shrimp Farfantepenaeus californiensis and the relation of this variation with sea surface temperature in the Gulf of California, Mexico. We used daily samplings of commercial captures at the packing plant at Guaymas, Sonora, Mexico from 1978 to 1994, samplings of commercial captures on board trawlers of the commercial fleet that operated along the coast of Sonora during the fishing seasons 1989–1995, and the monthly average of the sea surface temperature recorded by the oceanographic station of the Institute of Geophysics of the National University of Mexico, in Guaymas, Sonora from 1978 to 1995. The growth in the brown shrimp showed clear interannual variations. The relation between the temperature and the φ  and temperature and the growth coefficient K could be represented by a gaussian function that was significant (P < 0.05). We found the optimum temperature for growth was 25 ◦ C with decreases in growth efficiency on both sides of this value. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Brown shrimp; Farfantepenaeus californiensis; Growth; Interannual variability; Environment

1. Introduction In short-lived marine species, such as crustaceans and specifically penaeid shrimp, interannual variations of yields are common (Mendo and Tam, 1993; ∗ Corresponding author. Present address: Centro de Investigaciones Biol´ogicas del Noroeste, S.C. (CIBNOR) Unidad Guaymas, Km. 2.5 Camino al Tular Estero de Bacochibampo, Apdo. Postal 349, Guaymas, Sonora 85465, Mexico. Tel.: +52-62212237; fax: +52-62212238. E-mail address: [email protected] (J. L´opez-Mart´ınez).

Sheridan, 1996). These variations can have their origin in changes in fishing effort, variations of abundance, biomass or availability of resource. The latter variations can be caused by density-dependent or environmental factors that could affect recruitment, growth, production and distribution (Hannah, 1993; Criales and Lee, 1995). The catch from the shrimp fishery in Sonora, Mexico, has shown high interannual variations of yields (Fig. 1) and at the moment, the management of this resource is based on control of fishing effort

0165-7836/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 - 7 8 3 6 ( 0 2 ) 0 0 2 3 9 - 4

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Fig. 1. Annual yields of the shrimp fishery in Sonora, Mexico.

(Arenas-Fuentes and D´ıaz de León, 1997). However, any modification of the effort or the shrimp biomass can cause variations in the yields. Further, biomass is the result of processes such as individual growth, mortality and recruitment, each of which are highly variable (Garc´ıa, 1990). If growth variability is not taken account when applying yield by recruit models, projections of the yields may be wrong. The present work aims to show the relation between the interannual variation of growth of the individual brown shrimp, Farfantepenaeus californiensis and the seawater temperature along the eastern central coast of the Gulf of California.

2. Materials and methods The information used for the present work came from four sources: 1. The 1982 daily packing-plant samplings of commercial captures at Guaymas, Sonora, Mexico, with a total of 124,185 organisms sampled (Table 1) during the fishing season (September–May, 1978– 1995). These sample data were grouped in monthly periods to obtain length frequency distributions covering the fishing season of each year.

2. Samplings of commercial captures on board trawlers of the commercial fleet that operated along the coast of Sonora during the fishing seasons 1989– 1995. These samplings were pooled in monthly periods (Table 2). 3. Seasonal prices by commercial size of brown shrimp to 1985–1995 period (Table 3). Table 1 Number of brown shrimp sampled by fishing season in Sonora, Mexico Season

No. of organisms sampled

1978–1979 1979–1980 1980–1981 1981–1982 1982–1983 1983–1984 1984–1985 1985–1986 1986–1987 1987–1988 1988–1989 1989–1990 1990–1991 1991–1992 1992–1993 1993–1994 1994–1995

3,725 8,550 9,790 10,340 6,200 7,650 5,550 6,690 8,960 4,160 5,480 5,820 5,660 7,520 8,960 12,460 6,670

J. L´opez-Mart´ınez et al. / Fisheries Research 61 (2003) 95–105 Table 2 Number of organisms of brown shrimp sampled on board trawlers of the commercial fleet in Sonora, Mexico Season

No. of organisms sampled

1989–1990 1990–1991 1991–1992 1992–1993 1993–1994 1994–1995

34,609 29,307 23,900 18,978 11,464 12,224

4. Monthly average of the sea surface temperature recorded by the oceanographic station of the Institute of Geophysics of the National University of Mexico, at Guaymas, Sonora 1978–1995. These data were considered representative of the geographic location of the central part of distribution of the stock (Fig. 2), besides being the only relevant long-term seawater temperature data series. Considering that (1) the seawater temperature is the variable most important to the growth of brown shrimp (Ocampo et al., 1999), (2) that most organisms caught in the fishing season born during the main spawning period, May–October (López-Mart´ınez, 2000) and (3) that the period from May to February is the period of maximum growth of the brown shrimp (Snyder and Brusca, 1975), we decided that to relate the growth of brown shrimp with the temperature of the water, the average seawater temperature for the period of

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May–February should be used, and was estimated by
m=Februaryi+1 Tl =

m=Mayi

tm

n

where Tl is the mean seawater temperature for the season l, tm the mean seawater temperature for the month m, i the index for year, n the total number of months. The landed shrimp are subjected to a primary selection by size. Later, in the packing plant, there is a second finer selection for commercial size classification. To determine the possible bias of this process in the catch-structure of sizes, and therefore the effect on the growth estimate, an exploratory analysis of the information coming from the samplings made in port was done. For this, yearly growth estimates were obtained for samplings on board the commercial fleet and for samplings on packing-plant data-series, using as a reference the size-structure taken from the samplings on board the commercial fleet from 1989 at 1995 (assumed as unbiased). Bias was obtained, as the average of the percent difference between the estimated K and L∞ of sampling in port with respect to the values obtained from sampling on board
i=1995 (100 − (Kpi × 100)/Kfi ) A = i=1989 n where A is the bias, i the index for year, Kpi the growth coefficient from sampling in port in the year i, Kfi the

Table 3 Average prices by size for each fishing season to 1985–1995 of brown shrimp Category

Under 10 Under 12 Under 15 16–20 21–25 26–30 31–35 36–40 41–50 51–60 61–70 71–80 Above 80

Season 1985–1986

1986–1987

1987–1988

1988–1989

1989–1990

1990–1991

1991–1992

1992–1993

1994–1995

7.98 7.09 6.56 6.29 6.02 5.39 5.05 4.36 3.91 3.20 2.58 2.25 1.96

8.41 8.07 7.99 7.65 6.56 5.60 4.95 4.60 4.25 3.78 3.06 2.48 2.10

9.52 9.10 8.50 7.85 7.00 5.48 4.56 3.80 3.13 2.78 2.35 2.19 1.97

10.22 9.56 8.92 8.31 7.17 5.98 4.83 4.20 3.83 3.20 2.72 2.41 2.18

9.70 8.52 7.51 6.60 5.47 5.00 4.63 4.08 3.62 3.26 2.89 2.58 2.35

9.03 8.77 8.51 7.78 6.87 5.93 4.88 4.40 3.89 3.51 3.11 2.90 2.41

10.44 9.64 8.68 7.23 6.18 5.06 4.57 4.17 3.57 3.25 3.08 2.76 2.28

9.67 8.60 7.08 6.88 6.48 5.62 4.94 5.98 3.70 3.28 2.81 2.53 2.22

10.82 10.30 9.40 7.79 6.86 6.66 6.12 5.55 4.79 4.23 4.00 3.64 3.34

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Fig. 2. Area of study with shrimp catch zones.

growth coefficient from sampling on board in the year i, n the number of years. To take account of changes in the captured shrimp sizes due to changes in the selection practices, induced by modifications in the prices of the market, we used an analysis of variance of the average prices by size of shrimp of each fishing season for 1985–1995 period (the only period for which this information was available). Month length frequency distribution for the months of the fishing seasons of every year was used to obtain year growth estimates incorporating an oscillatory function in the von Bertalanffy model following Pauly and Gaschutz (1979). Estimates of growth parameters for each year were obtained by the use of ELEFAN I (Pauly and David, 1981) describing growth with the following equation: Lt = L∞ (1 − e−[K(t−t0 )+C(K/2π)sin 2π(t−ts )] ) where Lt is the total length at age t, L∞ the asymptotic total length, K the growth coefficient (per year),

t0 the total length for the hypothetical age t = 0, ts = WP+0.5, with WP the time of the year where the biggest delay in the growth occurred, C the intensity of the growth oscillation. Yearly initial estimates for L∞ and K were obtained following Powell (1979) and Wetherall et al. (1987) and using NSLCA (Shepherd, 1987; Pauly and Arregu´ın-Sánchez, 1995). To compare interannual variation of growth with sea surface temperature, we used (a) the year K value following Francis (1996), and (b) the φ  value (Pauly and Munro, 1984), which represents an index for growth performance with the form φ  = log10 K + 2 log10 L∞ The relations between the temperature and K, temperature and the φ  for the growth obtained with on board samplings, and the estimated obtained with in port samplings, were fitted by a nonlinear estimation, using the least squares estimation procedure to minimizing the sum of squared deviations of the observed

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data for the K or φ  , from those predicted by gaussian model. To test the appropriateness of the gaussian model we used the proportion of variance of the data explained by the model.

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Table 4 ANOVA results of the average prices by size of brown shrimp in the Gulf of California of each fishing season to 1985–1995 period

Effect Error

Sum of squares

d.f.

Mean square

F

P-level

20.5423 670.5920

8 108

2.567 6.20

0.4135

0.9106

3. Results The main bias in the length–frequency distributions came from packing plants because of the absence of small shrimp, which are not classified, and in some cases are reported separately as “pacotilla”. These shrimp represent individuals from 2 to 4 months of age (Fig. 3). Growth estimates obtained from samplings in port showed an average underestimation factor (A) of 30% in K compared to estimates based on samples taken on board (Kpi = 1.48 ± 0.15 and Kfi = 2.23 × 0.74), and an average overestimation factor (A) of 2% in the asymptotic length L∞ (L∞pi = 24.11 ± 0.64 and L∞fi = 23.65 ± 0.48). The results of the variance analysis (Table 4) showed that there were not significant changes (P > 0.05) in the shrimp prices for size that could induce preferential selection of sizes for the period of study. The time-series of length–frequency for two fishing season show interannual changes in the structure size of the brown shrimp (Fig. 4). The annual parameters of growth from 1978 to 1994 (Table 5) in the brown shrimp showed clear interannual variations with values for the growth coefficient ranging from 1.2 ≤ K (per year) ≤ 1.7, for the asymptotic length 23.3 ≤ L∞ (cm) ≤ 26.1 cm, and for growth performance index 2.85 ≤ φ  ≤ 3.06.

Table 5 Parameters for the von Bertalanffy growth equation (K as growth coefficient, and L∞ asymptotic length) for the brown shrimp in the Gulf of California, Mexicoa Year

K (per year)

L∞ (cm, TL)

C

Ts

φ

T

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994

1.3 1.3 1.3 1.4 1.6 1.2 1.5 1.6 1.5 1.6 1.5 1.7 1.5 1.5 1.6 1.6 1.5

24.3 24.1 23.3 26.0 23.3 24.3 23.3 23.4 26.1 25.1 24.0 23.6 24.7 24.7 24.7 23.8 23.4

0.33 0.53 0.40 0.15 0.78 0.38 0.33 0.30 0.30 0.30 0.15 0.60 0.25 0.60 0.33 0.68 0.68

0.18 0.98 0.05 0.90 0.98 0.90 0.15 0.10 0.10 0.20 0.90 0.90 0.85 0.85 0.95 0.85 0.80

2.89 2.88 2.85 2.98 2.94 2.85 2.91 2.94 3.06 3.03 2.94 3.02 2.96 2.96 2.99 2.96 2.91

26.8 27.0 27.1 25.9 26.9 27.3 25.9 26.2 24.5 24.5 24.3 25.6 25.6 25.8 26.2 24.6 24.0

a T is the mean surface temperature for Guaymas, Sonora (May–February, see text for explanation). C and Ts are parameters representing oscillatory growth, φ  is the growth performance index.

The average temperature from May to February decreased and a clear interannual variation was observed, particularly changes associated with El Niño in 1982–1983 (Table 5, Fig. 5). There were also

Fig. 3. Relative age of the brown shrimp cohorts sampled on board ship and in port in Sonora, Mexico.

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increases in temperature during El Niño 1986–1987 and 1991–1993. The tendency of the growth performance index φ  to change with temperature is illustrated using a gaussian structural functions (Fig. 6) that was significant (P < 0.05) and it explains 81 and 57% (on board and in port sampling estimated) of

2 = 0.81 and the total variance for the estimated (Rfi 2 Rpi = 0.57), with parameters    1 2 2 φfi = 31.53 √ e(temp−25) /(2×4.19 ) 2 × π × 4.192

Fig. 4. Monthly length frequency of the brown shrimp sampled in port in Sonora, Mexico: (a) 1982–1983 and (b) 1988–1989 season.

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Fig. 4 (Continued ).

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Fig. 5. Anomaly of growth performance index (φ  ) of brown shrimp and mean monthly sea surface temperature anomaly (from May to February) at Guaymas, Sonora, Mexico.

Fig. 6. Relation between mean monthly sea surface temperature from May to February and annual growth performance index (φ  ) of brown shrimp estimated by on board (A) and in port (B) sampling in Sonora, Mexico.

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Fig. 7. Relation between mean monthly sea surface temperature from May to February and growth coefficient (K) of brown shrimp estimated by on board (A) and in port (B) sampling in Sonora, Mexico.

and  φpi = 54.08

 √



1 2×π

× 7.202

e(temp−25)

2 /(2×7.202 )



Also the relationship between the temperature and the K was represented by a gaussian function (Fig. 7) that was significant (P < 0.05) and it explains 66 and 51% (on board and in port sampling estimated) 2 = 0.66 of the total variance for the estimated (Rfi 2 = 0.51), with parameters and Rpi  Kfi =8.91 and

√

1 2 × π × 1.742



Kpi =13.79



√

1 2 × π × 3.462

e

(temp−25)2 /(2×1.742 )

 e



(temp−25)2 /(2×3.462 )



In accordance with this, the optimum temperature for growth was between 24.5 and 25.5 ◦ C with decreases in growth efficiency on both sides of this value, the adverse effect of the seawater temperature being more obvious above 26.5 ◦ C (Figs. 6 and 7). Mean temperatures below the 24 ◦ C did not occur during the period of this study (1978–1995). 4. Discussion The absence of small sizes in the samples taken in port because of the previous selection on board causes an underestimated value for the growth coefficient, K. If this is one of the population parameters in analytical models used for predictive purposes, the bias can be important for management purposes (Defeo et al., 1992).

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Results suggest that the growth rate of F. californiensis is affected by changes in temperature, agreeing with observations by Del Valle Lucero (1989) for this same species, and for other species of shrimp in other regions (Villela et al., 1997). In particular, temperature during the period from spawning until shrimp are part of the exploited population determines the rate at which they grow. Temperature is a decisive factor for growth (Fry, 1971), because it controls the rate of metabolism, which is particularly evident in short-lived species such as shrimp, squid (Longurst and Pauly, 1987), and octopus (Arregu´ın-Sánchez, 1992). The relationship between growth and temperature suggests brown shrimp growth has a dome-shaped response to temperature, like the environmental window proposed by Cury and Roy (1989). The consistency in the response of the estimated growth with the structures of sizes obtained by samplings made in port, and for on board to the temperature, establishes the relationship between the temperature in the Gulf of California and the growth of the brown shrimp. The temperature optimum values obtained here are coincident with observed data in the laboratory for this species (Ocampo et al., 1999) in which, under temperatures of 19 ◦ C or less, shrimp were lethargic and reduce feeding. At temperatures of 27 ◦ C or higher, there was an increase was observed in food consumption, but the molt was incomplete. In this work, values of less than 24 ◦ C of mean seawater temperature were not testable and their effects were not evaluated, but they probably cause a decrease in growth. During El Niño events, growth was favorable only when the events were of moderate or weak intensity (1986 and 1992), whereas during more intense events, as in 1982–1983, the effect on growth was adverse (Fig. 5, Table 5). This behavior reflects the relation observed between temperature and growth rate shown in Figs. 6 and 7. The traditional practice of predictive models used for fish resources is to consider population parameters such as growth, natural mortality, and recruitment, as averages (stationary models) (Jayakody, 1988; Tabash and Palacios, 1996). Results and conclusions obtained from such predictions can be unrealistic or result in undesirable situations generated by management actions based on such assumptions. Consideration of the sources of variation of growth in fishery mod-

els can be excellent in terms of the uncertainty in the predicted estimates of biomass, and the risk of management decisions.

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