Journal of biological and health sciences

Universidad de Sonora

ISSN: 1665-1456

Bionomic modelling of hyperstable fish populations. The gulf corvina,

Cynoscion othonopterus, fishery as case study

Modelización bionómica de poblaciones de peces hiperestables. La pesquería de curvina golfina,

Cynoscion othonopterus, como estudio de caso

Ricardo Urías-Sotomayor1,2, Eugenio A. Aragón-Noriega1, Jorge Payán-Alejo2, Miguel Á. Cisneros-Mata3*, Guillermo Rodríguez-Domínguez2

1 Unidad Guaymas del Centro de Investigaciones Biológicas del Noroeste, S. C. Km 2.35 Camino al Tular, Estero de Bacochibampo, Guaymas, Sonora 85454, México.

2 Facultad de Ciencias del Mar, Universidad Autónoma de Sinaloa. Paseo Claussen S/N, Mazatlán, Sinaloa 82000, México.

3 Instituto Mexicano de Investigación en Pesca y Acuacultura Sustentables. Calle 20 No. 605-Sur. Guaymas, Sonora 85400, México.


Catch and fishing effort data are generally available, hence surplus production models are commonly used to conduct assessments. However, hyperstability resulting from spaw- ning aggregations (SA) pose challenges to determine status and inform management of many fisheries resources. Using data from 1991 to 2019, we develop a method to study hy- perstable fished stocks relaxing the assumption of constant catchability, hence direct dependence of catch-per-unit- effort and biomass. Information criterion was used to choose the best model including a Cobb-Douglas function for gulf corvina (Cynoscion othonopterus), a sciaenid fish endemic to the gulf of California, managed through annual quotas. Bionomic stock-reduction models were fit using catch, effort, published natural mortality, virgin biomass, and economic structure. Models were solved using maximum likelihood and the best model chosen with Akaike information crite- rion. Current fishing effort is beyond bionomic optimum. This deserves a precautionary approach to protect this endemic species and sustain the fishery.

Key words: hyperstability, Cobb-Douglas, stock reduction, gulf corvina


Generalmente se disponen de datos de captura y esfuer- zo para evaluar stocks usando modelos de producción excedente. Sin embargo, la hiperestabilidad resultante de agregaciones de desove (AD) genera retos en muchos recur- sos pesqueros; entonces se recomienda usar funciones de producción no lineal. Utilizando datos de 1991 a 2019, desar- rollamos un método para evaluar pesquerías de recursos hip- erestables, relajando el supuesto de capturabilidad constan- te y dependencia directa de captura-por-unidad-de-esfuerzo y biomasa. Usamos criterios de información para determinar el mejor modelo usando una función Cobb-Douglas. Ejem- plificamos con la pesquería de corvina "golfina" (Cynoscion othonopterus), un pez endémico al golfo de California con un sistema de cuotas anuales. Los modelos bionómicos se ajustaron utilizando captura y esfuerzo anuales, tasas de mortalidad natural, biomasa virgen y estructura económica.

*Author for correspondence: Miguel Á. Cisneros-Mata e.mail:

Received: June 13, 2023

Accepted: February 19, 2024

Published: April, 1, 2024

Ajustando por máxima verosimilitud, el mejor modelo se eligió con el criterio de Akaike. El esfuerzo de pesca actual so- brepasa el óptimo bionómico. Esto implica adoptar enfoques precautorios para proteger esta especie endémica y sostener la pesquería.

Palabras clave: hiperestabilidad, Cobb-Douglas, reduc- ción de stock, corvina del golfo.


Sound assessments of fishing resources are critical for ma- nagement (Melnychuk et al., 2017). However, a pervasive challenge is scarcity of data (Hilborn et al., 2020). Often, catch-effort series are used to fit models because catch- per-unit-effort (CPUE) indicates abundance, despite caveats (Harley et al., 2001; Haggarty and King, 2006). However, CPUE may remain high even if abundance decreased; this is known as hyperstability and typically happens when dioecius fish aggregate to reproduce (Erisman et al., 2011).

The gulf corvina (Cynoscion othonopterus) is a marine fish endemic to the gulf of California (henceforth, the gulf), commercially fished within a biosphere reserve under an annual quota regime. Assessing this species remains a chal- lenge because it does not lend itself to standard assessments due to hyperstability, that results in spawning aggregations which increases its vulnerability to fishing. This might indeed be resulting in biased estimates, crucial to allocate quotas.

From January to May, corvina forms spawning aggrega- tions (SA) in shallow (< 50 m) waters at the upper gulf, resulting in increased vulnerability to fishing. Over 4,000 ton per year are taken by ~700 small-scale vessels. Management based on a total annual catch is done since 2012 (Mendívil-Mendoza et al., 2018). Corvina grows relatively fast, reaching maturity in two years (Gherard et al., 2013). An average female lays

~1´670,000 eggs per spawn (Román-Rodríguez, 2000; Cote- ro-Altamirano et al., 2018), associated with high productivity of the area (Gherard et al., 2013).


Hyperstability challenges stock assessments and management (Mackinson et al., 1997). A good example was the collapse of northern cod (Gadus morhua) stock off Newfoundland and Labrador (Rose and Kulka, 1999). One way to overcome this problem is using ancillary, fisheries-in-

Volume XXVI

DOI: 10.18633/biotecnia.v26.2065

dependent information; another desirable way to improve modelling is relaxing assumption of direct proportionality of CPUE and abundance (Ulltang, 1996; Walter and Porch, 2012). The latter can be done by replacing the production function where Y is yield, q is constant catchability, f is fishing effort and B is biomass of the fishing resource. An alternative pro- duction function (Cobb and Douglas, 1928) is (i.e., Y/fµ = Bγ) where μ and γ reflect relation between CPUE and abundance (Harley et al., 2001). When µ and γ <1, either yield is affected as effort increases, or catchability increases as biomass de- creases, respectively. When both µ and γ = 1, the production function is equivalent to Schaefer’s function, widely used to assess fished stocks. It is also useful to conduct stock reduc- tion analysis (SRA) (Kimura and Tagart, 1982) which, based on annual catches, seeks to solve Baranov’s catch equations sys- tem (Baranov, 1918) linked to natural mortality M, biomass, recruitment, and a biomass reduction ratios.

The gulf corvina has high economic value: flesh, swim bladder and gonads are commercialized; this strongly increases market demand. In open access (de facto or de



Catch (tons)






1990 2000 2010 2020


Figure 1. Catch series of Cynoscion othonopterus at the upper gulf of California.

Figura 1. Serie de capturas de Cynoscion othonopterus en el alto golfo de California.

C =(B F(1–exp(–F–M ) ⁄ (F+M)

jure) regimes, economic value can stimulate fishing effort

i i i

i i i

and stock depletion to risky levels; this is particularly true for stocks, such as corvina, which form SA. In such cases, bionomic models can provide information useful for man- agement (Pascoe et al., 2016). Some types of models consider age or size structure (Jensen, 1974; Schnute, 1987; Martell et al., 2008; Methot and Wetzel, 2013) which, in comparison to aggregated models, allow more realistic analyses, for exam- ple the influence of natural or fishery-induced variability at different ages. Such models are highly demanding in terms of data and information, while biomass-aggregated models are widely used for stock management, particularly in data poor situations (Haddon, 2011). Here, we follow an approach of information theory and test six different model scenarios, all based on the assumption of aggregated biomass. We assess the gulf corvina (henceforth, corvina) through a bion- omic evaluation of its fishery using SRA with a Cobb-Douglas function (Hannesson, 1983). This allowed consideration of catchability as a function of stock biomass, hence accounting for hyperstability.


Annual corvina catch data (1991 to 2019) were obtained from research centers (IMIPAS, CIBNOR) and effort (2013 to 2019) from Mexico’s federal fishing authority (CONAPESCA) (Fig. 1). Effort is mean number of boats operating during a fishing season. Corvina beach price and effort costs were collected through interviews with fishers and by Environmental Defen- se Fund of Mexico (EDF). All prices refer to year 2019.

The SRA (Kimura and Tagart, 1982) was used to assess the corvina. Data consisted of annual catch series, two esti- mates of natural mortality rates M = 0.26 and 0.38year-1 (Eris- man et al., 2014) and virgin biomass ranging between 15,000 to 30,000 tons. SRA solves n+1 (n = # of years) simultaneous nonlinear equations for catch (Ci), biomass (Bi) and biomass depletion rate (P) observed in n years of fishing:

Bi=Bi–1 exp(–Fi–M)+R

P=Bn+1 ⁄ B1 (1)

where, for year i, Fi = instantaneous fishing natural mor- tality rate, and R=constant recruitment. We used fixed values of M, B1 and P, and solved for R, F1, … , Fn restricting solution to values of B1 and P that intersected virgin biomass (B1), recruitment and exploitation rate (E̅ = F/(Fi+M) = 0.825; 0.794, 0.858 95% CI) for years 2013 to 2015 (Erisman et al., 2014; 2020). Biomass was then projected after 2019, changing Fi to test catch quota scenarios.

For the bionomic analysis, the Cobb-Douglas function was fitted to annual biomass data (2014-2019) estimated with SRA using M = 0.26, restricted to E̅ = 2013-2015 = 0.825, and observed catch-effort. Six production function were fitted:

Case 1) - ∞ < μ < +∞ and - ∞ < γ < + ∞; Case 2) μ = 1 and- ∞ < γ < +∞;

Case 3) - ∞ < μ < +∞ and γ = 1; Case 4) μ = 0 and- ∞ < γ < +∞; Case 5) - ∞ < μ < +∞ and γ = 0; and Case 6) μ = 1 and γ = 1

Note that case 6 is Schaefer’s model which could a priori be discarded in the case of corvina, due to hyperstability. However, it was included because in case of being a “loser” model, it should be disregarded in fisheries management decisions. Models were fitted using maximum likelihood assuming lognormal residuals; the “best” model was chosen using the Akaike criterion for small sample size (AICc) (Burn- ham and Anderson, 2002).

The Cobb-Douglas function was equated to biological SRA yield function and solved for f to estimate fishing effort:

B[e−F−M − e−F] + R = A ∗ f μB𝛾𝛾 (2)

B1−γ[e−F−M − e−F] + B−γR μ

fPEC = ( A )



Stock Reduction Analysis (SRA)

With M = 0.26 y-1, solutions to the catch equation system were

fPEC is equilibrium effort to obtain a sustainable yield, B is biomass in equilibrium.

Total income was obtained multiplying corvina beach price by sustainable biological yield; costs were estimated multiplying cost per unit effort by total effort. Total net income (πt) was obtained subtracting cost income: πt=p[A*fμ Bγ]–cf. Solving for f and equating total net income to zero, effort in economic equilibrium fEEC was estimated as:

γ (  1  )

found for a biomass decline ratio ≥ 0.24 (Table 1) for constra- int E̅ = 2013-2015 = 0.825 and 0.794; no solutions where possible when E̅ = 2013-2015 = 0.858. With M = 0.26 and restrictions of E̅ = 2013-2015, solutions were found for biomass depletion ratio between 0.24-0.276; initial biomass ranged between 19,465- 20,814 ton, and annual recruitment 4,444-4,768 ton. With M = 0.38, solutions were limited to biomass depletion rate between 0.330-0.345.

Table 1. Results of the SRA model for gulf corvina (Cynoscion othonopterus) con-


pAB 1−μ

= [ ]



sidering two values of annual natural mortality rate (M) restricted to E̅

Tabla 1.


fEBE occurs when fPEC = fEEC. Effort for maximum economic

Resultados del modelo SRA para curvina golfina (Cynoscion otho- nopterus) considerando dos valores de tasa de mortalidad natural anual (M)

yield f


was obtained equating the income function to zero

restringida por E̅ 2013-2015.

M P B E̅ R

and obtaining the first derivative as a function of f:

( 1 )

                                                  1                        2013-2015                        

0.26 0.235 No solutions



= [pμABγ



° (5)

0.240 19,465 0.847 4,444

0.253 20,000 0.825 4,579











Biomass (tons)






1990 1995 2000 2005 2010 2015 2020













1990 1995 2000 2005 2010 2015 2020


Figure 2. Total (A) and relative (B) biomass of Cynoscion othonopterus using SRA considering two natural mortality rates and restrictions in exploitation

rates E2013-2015.

Figura 2. Biomasa total (A) y relativa (B) de Cynoscion othonopterus usando

SRA considerando dos tasas de mortalidad natural y restricciones en las ta- sas de explotación E2013-2015.

0.276 20,814 0.794 4,768

0.38 0.250 No solutions

0.300 No solutions

0.330 14,881 0.824 4,678

0.345 15,632 0.794 4,884

Estimates of B1 differed for both M values (Fig. 2A). Final biomass varied slightly regardless of M: 4,500 - 5,700 tons. Current biomass relative to B1 for both M values showed a similar pattern of decline; at the end of the series relative biomass estimates with M = 0.26 were nearly 0.25*B1. Solu- tions with M = 0.38 suggest declining biomass between 0.33

- 0.345 of B1 (Fig. 2B).

With M = 0.38 y-1 and both restrictions of E̅ 2013-2015, SRA yielded F estimates higher than with M = 0.26. However, at the end of 2019, with restrictions in either M and E̅ 2013-2015 resulted in F to vary between 1.5 - 2.7 (Fig. 3), higher than F0.5B1,M 0.26 = 0.35 or F0.5B1, M 0.38 = 0.61. F increased sharply from 2012-2016 and dropped in 2017, coinciding with decreased fishing due to a “payment for not fishing” policy implemen- ted to which most fishers subscribed. The highest F was for 2016, when the largest catch was recorded.

Relative biomass from 2019 - 2025 using M = 0.26 y-1 increased under scenario of 3,000 and 3,500 tons of annual catch quota (Fig. 4A); for a 4,000 tons quota scenario, relative biomass increased less. A catch quota greater than 4,200 ton yielded a biomass depletion of 25 % B1 by 2025. Using M =

0.38 yielded more conservative biomass depletion. Relative biomass increased with annual catch quotas ≤ 4,000 ton.

Production Function

Function # 5 was a “clear winner”, having the lowest AICc and weight Wi greater than 90 % (Table 2). This function was:

Table 2. Parameters and model selection of six Cobb-Douglas production function cases. A in case 6 is catchability q. Bold numbers are predetermined values. The winning model parameters are in bold in the Wi.





E2013-2015 0.794










F0.5B1, M= 0.38

F0.5B1, M= 0.26

Tabla 2. Parámetros y selección de modelo entre seis casos de la función de

producción Cobb-Douglas. A en el caso 6 es capturabilidad q. Los números en negritas son valores predeterminados. Los parámetros del modelo gana- dor están en negrita en el Wi.

Case Parameters A µ ν AIC ΔAIC W


1990 2000 2010 2020


Figure 3. Estimated instantaneous fishing mortality rate of Cynoscion oth- onopterus at the upper gulf of California from 1991 to 2019. The number above the dashed line is an F value out of scale. Horizontal lines represent the F value required for a constant stock of 0.5K for two values of M.

Figure 3. Tasa instantánea de mortalidad por pesca estimada de Cynoscion othonopterus en el alto golfo de California desde 1991 a 2019. El número so- bre la línea discontinua es un valor F fuera de escala. Las líneas horizontales representan el valor de F requerido para un stock constante de 0.5K para dos valores de M.


                                                                                               c            c         i      









































6          2        0.0019      1        1      113  18.3    0.0  


Yt=1,099.08f 0.226 (6)

Yield is related to effort but not to stock biomass: μ =

0.226 implies existence of congested fishing effort, indicating that an increase unit effort decreases mean CPUE.

Bionomic Analysis

Price per kilogram of corvina between 2011 and 2019 was MXN 12.5 and remained almost stable during that period, mean price of gas bladder was MXN 279 per kg and fluctua- ted slightly, and mean price of gonad was MXN 87 per kg and showed a slight increase over time (Licón-González et al., 2023). The average catch per trip of a fishing boat is 1,135 kg and over 90 % of the cost structure belongs to variable costs. Out of a total of MXN 2,895 per kg, 40 % and 35 % correspond, respectively, to gasoline and gutting (EDF, 2016).


M = 0.26






Annual catch quot

3000 t

3500 t

4000 t






Since catch is independent of biomass, fPEC

and f


do not

converge, which reflects lack of bionomic equilibrium (Fig. 5).


constant = 2,379 fishing units, never reached equilibrium

2018 2020 2022 2024 2026

because fPEC approaches 557 when biomass approaches zero. In a free access scenario, effort would grow until the corvina


is exhausted, before reaching bionomic equilibrium. fMEY is


M = 0.38

3000 t

3500 t

4000 t

4900 t








Annual catch quot

reached with 348 small boats (Fig. 6). A remarkable result is

2018 2020 2022 2024 2026


Figure 4. Projected relative biomass of gulf corvina (Cynoscion othonopterus) from 2019 to 2025 using parameter estimates of SRA and assuming different annual catch quota scenarios.

Figura 4. Biomasa relativa proyectada de curvina golfina (Cynoscion oth- onopterus) de 2019 a 2025 usando estimaciones de parámetros de SRA y asumiendo diferentes escenarios de cuotas anuales de captura.

Figure 5. Population equilibrium (PEC) and economic equilibrium (EEC) curves for the gulf corvina (Cynoscion othonopterus) fishery in the upper gulf of California.

Figura 5. Curva de esfuerzo en equilibrio poblacional (PEC) y de equilibrio económico EEC para la pesquería de curvina golfina (Cynoscion othonopter- us) en el alto golfo de California.

Figure 6. Total revenues and costs in the gulf corvina (Cynoscion othonopterus) fishery in the upper gulf of California.

Figura 6. Ingresos y costos totales en la pesquería de curvina golfina (Cynoscion othonopterus) del alto golfo de California.

study were estimated by Erisman et al. (2014) from empir- ical formulas of Pauly (1980) and Jensen (1996). Both were the best of 30 formulas for assessments with limited data (Kenchington, 2014). With M = 0.26, solutions to SRA catch equations were restricted to the range of exploitation rates estimated by Erisman et al. (2014) for 2013 - 2015 (from 25 % to 75 %). In contrast, with M = 0.38, no solutions were possi- ble for the entire range of E̅ 2013-2015 used. Solutions were only found when mean value and lowest of these exploitation rates were used. The only viable solution with M = 0.38 was found using as constraint the lowest estimated exploitation rate during the 2013 - 2015 seasons. Consequently, the value of M for this species is closer to 0.26 than 0.38 y-1. Because of this, true biomass at the end of 2019 may be closer to 0.25Bv than 0.35Bv.

that f


at 0.25B1

with only 477 small boats, meaning that

The gulf corvina stock is in worst condition than previ-

ously estimated by Enciso-Enciso (2014) who considered to

increased fishing effort compromises sustainability of the fishery.


Despite problems of CPUE known since decades ago (Coppola and Pascoe, 1998; Maunder et al., 2006), surplus production models will likely continue in use because catch- effort are easy to collect (e.g., Urías-Sotomayor et al., 2018; Alam et al., 2021; Angelini et al., 2021; Meissa et al., 2021). We highlighted how assessments can be done with ancillary fishery-independent data. When fish form aggregations that increase catchability and CPUE, use of a Cobb-Douglas production function can overcome problems associated with hyperstability. For our case study, we were able to derive meaningful management information for the gulf corvina. This is important because official annual assessments use Schaefer’s production function (e.g., Ruelas-Peña et al., 2013). Our approach captured interannual dynamics of corvi-

na. Fluctuations of ca. eight years seem to indicate cyclical events. Within a fishing season, tidal cycles also influence availability of corvina (Román-Rodríguez, 2000); hence mid- term cycles and lunar effects could be linked and influence corvina. The only lunar cycle with a period of 8.85 y is displa- cement to the east of the lunar perigee; this produces intense spring tides approximately every 4.4 y (Haigh et al., 2011). Such variability should be considered in modelling to inform management of corvina.

Although SRA is a deterministic procedure, using two estimates of M and an interval of 25 % and 75 % of 2013-2015 (see below) allowed to address uncertainty of assessments. B1 es- timates in this work might be considered “virgin” biomass (Bv) because initial catch represents a relatively low fishing effort. Thus, estimates of ratio of final to initial biomass means that stock biomass of corvina at the upper gulf of California is between 25 % and 34.5 % of Bv, being both lower than 0.5Bv estimated by Enciso-Enciso (2014).

Lower stock size is associated with M = 0.26 y-1 and higher wit M = 0.38 y-1; thus, a good M estimate is important to assess this fishery. The two M values used in the present

be optimal, close to 0.5Bv. A condition of the gulf corvina between 0.25Bv and 0.35 Bv is of concern since the lowest has been a biomass level where all common functions of stock-recruitment predict depensation or reduced recruit- ment (Beverton and Holt, 1957; Ricker, 1975; Barrowman and Myers, 2000), which could be defined as limit reference point for management (Caddy and Mahon, 1996).

Our results agree with Mendívil-Mendoza et al. (2018): the fishery is unsustainable because it is based on mega-breed- ers. In other words, this fishery’s profitability and continuity relies heavily on aggregated fish and therefore cannot be considered as a fishery where random distribution of fish plays a key role. Results of Erisman et al. (2020) coincide with ours. They estimated that yield per recruit was 73 % of maxi- mum possible for this population. Erisman et al. (2014) found contradictory results regarding stock status in 2012. The low abundance of mega-spawners and an analysis of spawning potential indicated overexploitation; but increasing average corvina sizes in catches related to regulations of mesh size indicated sustainability. From 2009 to 2019, however, catch size gradually decreased at a rate of 1 cm y-1 (INAPESCA, 2021). This fishery has been managed with annual quotas be- tween 4,000 and 4,500 tons between 2015 and 2020; in 2021 this was increased to 5,128 tons. Our projections with SRA show that a < 4,000 tons quota recover the stock. Catches above 4,000 tons are only possible if M = 0.38 y-1 but the stock

status remains close to the limit point 0.25Bv.

The best Cobb-Douglas production function indicated

that as effort increases fishers compete, reducing their over- all catchabilities. The zero-exponent obtained for biomass indicates that catch is independent of abundance; it depends on effort, which supports the hypothesis of hyperstability. For the same reason, fPEC and fEEC do not converge, indicating absence of a bionomic equilibrium, and that under open access fishing effort can deplete the stock.

Spawning aggregations can withstand increased catch rates despite stock reduction until the fishery collapses. Al- though catch is independent of biomass, a reduction lower than 0.25B1 compromises sustainability of the stock, achieved

with a fishing effort of 477 boats. In 2019, 450 boats oper- ated close to the limit. Currently, 737 boats possess fishing permits for gulf corvina; this threatens the fishery because all too often quotas are not enforced (Mendívil-Mendoza et al., 2018). Even if quotas were not exceeded, excess fishing units beyond economic optimum signifies reduced catch rates and, most likely profits, due to effort crowding.

Stimulus to increase catch follows a concomitant in- crease in market prices. When catches increase early in the fishing season, market saturates and price plummets. Recent interest to market gonads and crops increases income, which increase catches (EDF, 2016).

In 2016, catches concentrated in the first month of the season, saturating the market and lowering the price from

$15,000.00 MXN/ton (USD 779.22) to $8,000.00 MXN/ton

(USD 415.58); in the following month catches decreased by a third (EDF, 2016). This indicates that the price reduction observed in 2015 can reduce effort due to low profitability.

Our economic analysis assumed constant prices and costs; an elastic price could change these results. Market saturation in the first month causes reduction in price (EDF, 2016). However, in recent years the price of gutted product seems to follow a supply-demand relationship, but price stabilizes complementing with sale of swim bladders and gonads.

Annual quota disregards hyperstability, which can be overcome using nonlinear production functions and fisher- ies-independent data. With this approach, we found that the gulf corvina biomass is close to 25 % of virgin stock, which calls for precautionary management to avoid economic collapse to the fishery and reduced viability of this species. Quotas need be precautionary strict enforcement is needed.


Disregarding spawning aggregations can prevent accurate assessments that may cause stocks to collapse.

Hyperstability can be included using non-linear produc- tion functions such as the Cobb-Douglas. Ancillary informa- tion can also be used in data-poor situations.

The gulf corvina (C. othonopterus) forms seasonal spaw- ning aggregations which increase its vulnerability. Currently, assessment and estimation of annual catch quotas are done disregarding hyperstability of the stock. This can be over- come using nonlinear production functions and ancillary, fisheries-independent data.

We found that the gulf corvina biomass is close to 25% of virgin stock, which requires precautionary measures to avoid economic fishery collapse compromising viability of this endemic species.

The gulf corvina fishery continues to occur and yields revenues not because of the randomness of common fishe- ries, but because of the spawning aggregations it relies upon. Costs of fishing are reduced and, therefore, profits are positi- vely influenced by the gregarious nature of mega spawners in a relatively small space. Therefore, fishers and managers should reduce current fishing mortality and determine a

maximum size limit so that mega spawners can continue to contribute to annual recruitment. A maximum size limit in combination with an annual quota need to strictly be enfor- ced so that this endemic species can continue to be the basis of an important social and economic activity.


R.U.S. thanks support by CONAHCYT, for a postdoctoral leave at CIBNOR, S.C. M.Á.C.M. thanks partial support from CONAH- CYT.


Alam, M.S., Liu, Q., Nabi, M.R.U. and Al-Mamun, M.A. 2021. Fish stock assessment for data-poor fisheries, with a case study of tropical Hilsa shad (Tenualosa ilisha) in the water of Bangladesh. Sustainability. 13(7): 1-23.

Angelini, S., Armelloni, E.N., Costantini, I., De Felice, A., Isajlović, I., Leonori, I., Manfredi, C., Masnadi, F., Scarcella, G., Tičina,

V. and Santojanni, A. 2021. Understanding the dinamics of ancillary pelagic species in the Adriatic Sea. Front. Mar. Sci. 8:728948.

Baranov, F.I. 1918. On the question of the biological basis of fisheries. Nauchn. Issled. Ikhtiologicheskii Inst. Izv. 1: 81-128. Barrowman, N.J. and Myers, R.A. 2000. Still more spawner– recruitment curves: the hockey stick and its generalizations.

Can. J. Fish. Aq. Sci. 57: 665-676.

Beverton, R.J.H. and Holt, S.J. 1957. On the Dynamics of Exploited Fish Populations. London: Great Britain Ministry of Agriculture, Fisheries and Food. 533 pp.

Burnham, K.P. and Anderson, D.R. 2002. Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.), New York: Springer. 488 pp.

Caddy, J.F. and Mahon, R. 1996. Puntos de referencia para la ordenación pesquera = Reference points for fisheries management. Rome: FAO 347 pp.

Cobb, C.W. and Douglas, P.H. 1928. A theory of production. Am.

Econ. Rev. 18: 139-165.

Coppola, G. and Pascoe, S. 1998. A Surplus Production Model with a nonlinear catch-effort relationship. Mar. Res. Econ. 13: 37-50.

Cotero-Altamirano, C.E., Enciso-Enciso, C., Hernández-Escalante, L., Zobeyda-Brasil, L., Valles-Ríos, H. and Venegas, B. 2018. Reproducción de la curvina golfina Cynoscion othonopterus en el Golfo de California [Reproduction of the gulf corvina Cynoscion othonopterus in the Gulf of California]. Cienc. Pesq. 26(2): 37-46.

Enciso-Enciso, C. 2014. Evaluación de la pesquería de curvina golfina Cynoscion othonopterus (Jordan & Gilbert, 1882) en el alto golfo de California [Assessment of the gulf corvina fishery Cynoscion othonopterus (Jordan & Gilbert, 1882) in the upper Gulf of California]. [MSc Thesis]. [Sinaloa (Mexico)]: Universidad Autónoma de Sinaloa. 65 pp.

[EDF] Environmental Defense Fund de México. 2016. Resultados económicos de la temporada de curvina golfina, Golfo de Santa Clara 2016 [Economic results of the gulf corvina season, Gulf of Santa Clara 2016. 29 pp.]. resulteconcurvina2016_4comunidades_0.pdf (Accesed 10

January 2022).

Erisman, B.E., Allen, L.G., Claisse, J.T., Pondella, D.J., Miller, E.F. and Murray, J.H. 2011. The illusion of plenty: hyperstability masks collapses in two recreational fisheries that target fish spawning aggregations. Can. J. Fish. Aquat. Sci. 68: 1705- 1716.

Erisman, B.E., Appel, A.M., MaCall, A.D., Román, M.J. and Fujita,

R. 2014. The influence of gear selectivity and spawning behavior on a data-poor assessment of a spawning aggregation fishery. Fish. Res. 159: 75-87.

Erisman, B.E., Grüss, A., Mascareñas-Osorio, I., Lícon-González, H., Johnson, A.F. and López-Sagástegui, C. 2020. Balancing conservation and utilization in spawning aggregation fisheries: a trade-off analysis of an overexploited marine fish. ICES J. Mar. Sci. 77: 148-161.


Gherard, K.E., Erisman, B.E., Aburto-Oropeza, O., Rowell, K. and Allen, L.G. 2013. Growth, development, and reproduction in gulf corvina (Cynoscion othonopterus). Bull. South. Cal. Acad. Sci. 112(1): 1-18.

Haddon, M. 2011. Modelling and quantitative methods in fisheries, 2nd ed. Boca Raton, FLA: Chapman & Hall/CRC 406 pp.

Haggarty, D.R. and King, J.R. 2006. CPUE as an index of relative abundance for nearshore reef fishes. Fish. Res. 81(1): 89-93.

Haigh, I.D., Elio, M. and Pattiaratchi, C. 2011. Global influences of the 18.61 year nodal cycle and 8.85 year cycle of lunar perigee on high tidal levels. J. Geophys. Res. 116, C06025. https://doi:10.1029/201 0JC006645.

Hannesson, R. 1983. Bioeconomic production function in fisheries: theoretical and ernpirical analysis. Can. J. Fish. Aquat. Sci. 40: 968-982.

Harley, S.J., Myers, R.A. and Dunn, A. 2001. Is catch-per-unit- effort proportional to abundance? Can. J. Fish. Aquat. Sci. 58: 1760-1772.

Hilborn, R., Amoroso, R.O, Anderson, C.M., Baum, J.K., Branch, T.A., Costello, C., de Moor, C.L., Faraj, A., Hively, D., Jensen,

O.P., Kurota, H., Richard, L., Mace, P., McClanahan, T.,

Melnychuk, M.C., Minto, C., Osiol, G.Ch., Parma, A.M., Pons, M., Segurado, S., Szuwalski, C.S., Wilson, J.R. and Ye, Y. 2020. Effective fisheries management instrumental in improving fish stock status. Proc. Nat. Acad. Sci. 17: 2218-2224.

[Inapesca] Instituto Nacional de Pesca y Acuacultura. 2021. Acuerdo por el que se establece el volumen de captura permisible para el aprovechamiento de curvina golfina (Cynoscion othonopterus), en aguas de jurisdicción federal del Alto Golfo de California y delta del río Colorado para la temporada de pesca 2021 [Agreement establishing the volume of allowable catch for the use of Gulf curvina (Cynoscion othonopterus), in waters of federal jurisdiction of the Upper Gulf of California and Delta of the Colorado River for the 2021 fishing season]. Dictamen Técnico RJL/ INAPESCA/DGAIPP/0006/2021. Mexico City: Secretaría de Agricultura y Desarrollo Rural.

Jensen, A.L. 1974. Leslie matrix models for fisheries studies.

Biometrics 30(3): 547-551. https://doi:10.2307/2529208.

Jensen, A.L. 1996. Beverton and Holt life history invariants result fromo ptimal trade-off of reproduction and survival. Can. J. Fish. Aquat. Sci. 53: 820-822.

Katsanevakis, S. 2006. Modelling fish growth: Model selection, multi-model inference and model selection uncertainty. Fish. Res. 81: 229-235.

Kenchington, T.J. 2014. Natural mortality estimators for information-limited fisheries. Fish and Fisheries. 15(4): 533- 562.

Kimura, D. and Tagart, J. 1982. Stock reduction analysis, another solution to the catch equations. Can. J. Fish. Aquat. Sci. 39: 1467-1472.

Licón González, H.A., Sanjurjo-Rivera, E., Olivares-Bañuelos, N.C., Vázquez-Vera, W.L. and Ortiz-Rodríguez, R. 2023. Participative management experiences in the corvina fishery at the upper Gulf of California. Reg. y Soc. 35, e1678.

Mackinson, S., Sumaila, U.R. and Pitcher, T.J. 1997. Bioeconomics and catchability: fish and fishers behavior during stock collapse. Fish. Res. 31: 11-17.

Martell, S.J.D.; Pine, W.E., Walters, C.J. 2008. Parameterizing age-structured models from a fisheries management perspective. Can. J. Fish. Aquat. Sci. 65(8): 1586-1600. https:// doi:10.1139/f08-055.

Maunder, M.N., Sibert, J.R., Fonteneau, A., Hampton. J., Kleiber, P. and Harley, S.J. 2006. Interpreting catch per unit effort data to assess the status of individual stocks and communities. ICES J. Mar. Sci. 63: 1373-1385.

Meissa, B., Dia, M., Baye, B.C., Bouzouma, M., Beibou, E. and Roa-Ureta, R.H. 2021. A comparison of three data-poor stock assessment methods for the pink spiny lobster fishery in Mauritania. Front. Mar. Sci. 8.

Melnychuk, M.A., Peterson, E., Elliott, M. and Hilborn, R. 2017. Fisheries management impacts on target species status. Proc. Nat. Acad. Sci. 114: 178-183.

Mendívil-Mendoza, J.E., Aragón-Noriega, E.A., Arreola-Lizárraga, J.A., Rodríguez-Domínguez, G., Castillo-Vargasmachuca,

S.G. and Ortega-Lizárraga, G.G. 2018. Indicadores de sustentabilidad para la pesquería de curvina golfina Cynoscion othonopterus en el Alto Golfo de California = Sustainability fishery indicator for Gulf corvina Cynoscion othonopterus in the Upper Gulf of California. Rev. Biol. Mar. Oceanogr. 53(1): 119-130.

Methot, R.D. and Wetzel, C.R. 2013. Stock synthesis: A biological and statistical framework for fish stock assessment and fishery management. Fish. Res. 142: 86-99. https:// doi:10.1016/j.fishres.2012.10.012.

Pauly, D. 1980. On the interrelationships between natural mortality, growth parameters, and mean environmental temperature in 175 fish stocks. ICES J. Mar. Sci. 39: 175-192.

Pascoe, S., Kahui, V., Hutton, T. and Dichmont, C. 2016. Experiences with the use of bioeconomic models in the

management of Australian and New Zealand fisheries. Fish. Res. 183: 539-548. doi:10.1016/j.fishres.2016.01.008.

Ricker, W.E. 1975. Computation and interpretation of biological statistics of fish populations. Bull. Fish. Res. Board Can. 191: 1-382.

Román-Rodríguez, M.J. 2000. Estudio poblacional del chano norteño, Micropogonias megalops y la curvina golfina Cynoscion othonopterus (Gilbert) (Pisces: Sciaenidae), especies endémicas del Alto Golfo de California, México [Population study of the northern chano, Micropogonias megalops and the gulf curvina Cynoscion othonopterus (Gilbert) (Pisces: Sciaenidae), endemic species of the Upper Gulf of California, Mexico]. [Sonora (México)] Instituto del Medio Ambiente y Desarrollo Sustentable del Estado de Sonora. Hoja de cálculo SNIB-CONABIO proyecto No. L298. CONABIO. Mexico. Also available on iptconabio/resource?r=SNIB-L298 (Accesed 10 January 2022).

Rose, G.A and Kulka, D.W. 1999. Hyperaggregation of fish and fisheries: how catch-per-unit-effort increased as the northern cod (Gadus morhua) declined. Can. J. Fish. Aquat. Res. 56(Suppl. 1): 118-127.

Ruelas-Peña, J.H., Valdez-Muñoz, C. and Aragón-Noriega, E.A. 2013. La pesquería de la corvina golfina y las acciones de manejo en el Alto Golfo de California, México = Analysis of the corvina gulf fishery as a function of management actions in the Upper Gulf of California, Mexico. Lat. Amer. J. Aquat. Res. 41(3): 498-505.


Schnute, J. 1987. A General Fishery Model for a Size-Structured Fish Population. Can. J. Fish. Aquat. Sci. 44(5): 924-940. https://doi:10.1139/f87-111.

Ulltang, Ø. 1996. Stock assessment and biological knowledge: can prediction uncertainty be reduced? ICES J. Mar. Sci. 53: 659-675.

Urías-Sotomayor, R., Rivera-Parra, G.I., Martínez-Cordero, F.J., Castañeda-Lomas, N., Pérez-González, R. and Rodríguez- Domínguez, G. 2018. Stock assessment of jumbo squid Dosidicus gigas in northwest Mexico. Lat. Am. J. Aquat. Res. 46(2): 330-336.

Walter, J.F. and Porch, C.E. 2012. Modeling terminal-year fishing mortality rates in western Atlantic bluefin tuna virtual population analyses. Aquat. Living Resour. 25: 333-340.