EDP Sciences logo
Submit your paper
Sciengine logo
Search
Menu
All issues Volume 38 (2025) Aquat. Living Resour., 38 (2025) 14 References
Open Access
Issue
Aquat. Living Resour.
Volume 38, 2025
Article Number 14
Number of page(s) 14
DOI https://doi.org/10.1051/alr/2025011
Published online 17 September 2025
  • Acha EM, Simionato CG, Carozza C, Mianzan H. 2012. Climate‐induced year‐class fluctuations of whitemouth croaker Micropogonias furnieri (Pisces, Sciaenidae) in the Río de la Plata estuary, Argentina-Uruguay. Fish Oceanogr 2: 58–77. [Google Scholar]
  • Ali RAS, Elawad AN, Khalifa MM, El-Mor M. 2016. Length-weight relationship and condition factor of Liza ramada from Eastern coast of Libya. Int J Fish Aquat Res 2: 1–9. [Google Scholar]
  • Aragón-Noriega EA, Alcántara-Razo E, Valenzuela-Quiñónez W, Rodríguez-Quiroz G. 2015. Multi-model inference for growth parameter estimation of the Bigeye Croaker Micropogonias megalops in the Upper Gulf of California. Rev Biol Mar Oceanogr 50: 25–38. [Google Scholar]
  • Aragón-Noriega EA, Valenzuela-Quiñones W, Esparza-Leal H, Ortega-Rubio A, Rodríguez-Quiroz G. 2010. Analysis of management options for artisanal fishing of the bigeye croaker Micropogonias megalops (Gilbert, 1890) in the upper Gulf of California. Int J Biodivers Sci Manage 5: 208–214. [Google Scholar]
  • Aragón-Noriega EA. 2014. Modeling the individual growth of the Gulf corvina, Cynoscion othonopterus (Pisces: Sciaenidae), using a multi-model approach. Cienc Mar 40: 149–161. [Google Scholar]
  • Arzola-Sotelo EA, López-Martínez J, Nevárez-Martínez MO, Morales-Bojórquez E, García-Morales R, Herrera-Cervantes H. 2022. Variation in population dynamics and abundance of the bigeye croaker Micropogonias megalops in the Northern Gulf of California. Reg Stud Mar Sci 52: 102301. [Google Scholar]
  • Arzola-Sotelo EA, López-Martínez J, Rábago-Quiroz CH, Padilla-Serrato JG, Morales-Bojórquez E. 2018. Population dynamics of the Bigeye croaker Micropogonias megalops in the Northern Gulf of California. Calif Coop Ocean Fish Invest Rep 59: 86–101. [Google Scholar]
  • Arzola-Sotelo EA. 2014. Aplicación del enfoque multimodelo para la evaluación del crecimiento individual de la curvina golfina Cynoscion othonopterus en el Alto Golfo de California. Cienc Pesq 22: 79–88. [Google Scholar]
  • Arzola-Sotelo EA. 2024. El chano norteño del Golfo de California y su relación con la industria del surimi. Rec Nat Soc 10: 133–152. [Google Scholar]
  • Bagenal TB, Tesch FW. Age and growth, in: T. Begenal (Ed.), Methods for Assessment of Fish Production in Fresh Waters, 3rd edn, IBP Handbook No. V 3, Blackwell Science Publications, Oxford, 1978, pp. 101-136. [Google Scholar]
  • Beverton RJH, Holt SJ. 1957. On the dynamics of exploited fish populations. Fish Invest 19: 1–533. [Google Scholar]
  • Blackhart K, Stanton DG, Shimada AM. NOAA fisheries glossary. United States Department of Commerce, National Oceanic and Atmospheric Administration. Technical Memorandum NMFS-F/SPO-69, 2006, pp. 61. [Google Scholar]
  • Britten GL, Dowd M, Worm B. 2016. Changing recruitment capacity in global fish stocks. Proc Natl Acad Sci USA 113: 134–139. [Google Scholar]
  • Brusca RC, Álvarez-Borrego S, Hastings PA, Findley LT. 2017. Colorado River flow and biological productivity in the Northern Gulf of California, Mexico. Earth-Sci Rev 164: 1–30. [Google Scholar]
  • Burnham KP, Anderson DR. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd edn, Springer, New York, N.Y., 2002, pp. 488. [Google Scholar]
  • Burnham KP, Anderson DR, Huyvaert KP. 2011. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav Ecol Sociobiol 65: 23–35. [Google Scholar]
  • Curiel-Bernal MV, Cisneros-Mata MÁ, Rodríguez-Domínguez G, Sánchez-Velasco L, Jiménez-Rosenberg SPA, Parés-Sierra A, Aragón-Noriega EA. 2023. Modelling Early Growth of Totoaba macdonaldi (Teleostei: Sciaenidae) under Laboratory Conditions. Fishes 8: 155. [Google Scholar]
  • Cury PM, Fromentin JM, Figuet S, Bonhommeau S. 2014. Resolving hjort's dilemma: how is recruitment related to spawning stock biomass in marine fish? Oceanography 27: 42–47. [Google Scholar]
  • Doll JC, Jacquemin SJ. 2019. Bayesian model selection in fisheries management and ecology. Fish Wildl Manag 10: 691–707. [Google Scholar]
  • Erisman B, Mascareñas-Osorio I, López-Ságastegui C, Moreno-Báez M, Jiménez-Esquivel V, Aburto-Oropeza O. 2015. A comparison of fishing activities between two coastal communities within a biosphere reserve in the Upper Gulf of California. Fish Res 164: 254–265. [Google Scholar]
  • Froese R, Demirel N, Coro G, Kleisner KM, Winker H. 2017. Estimating fisheries reference points from catch and resilience. Fish Fish 18: 506–526. [Google Scholar]
  • Froese R, Pauly D. 2023. FishBase. World Wide Web electronic publication. www.fishbase.org, version (06/2023). [Google Scholar]
  • Galindo-Cortes G, De Anda-Montañez JA, Arreguín-Sánchez F, Salas S, Balart EF. 2010. How do environmental factors affect the stock–recruitment relationship? The case of the Pacific sardine (Sardinops sagax) of the northeastern Pacific Ocean. Fish Res 102: 173–183. [Google Scholar]
  • Gilbert CH. 1890. Scientific results of explorations by the US Fish Commission steamer Albatross. No. XII. A preliminary report on the fishes collected by the steamer Albatross on the Pacific coast of North America during the year 1889, with descriptions of twelve new genera and ninety-two new species. Proc Natl Mus 8: 49–126. [Google Scholar]
  • Haddon M. 2011. Modelling and Quantitative Methods in Fisheries, Chapman and Hall/CRC, Boca Raton, FL, 2011, pp. 433. [Google Scholar]
  • Haimovici M, Cardoso LG. 2017. Long-term changes in the fisheries in the Patos Lagoon estuary and adjacent coastal waters in Southern Brazil. Mar Biol Res 13: 135–150. [CrossRef] [Google Scholar]
  • Haimovici M, Cavole LM, Cope JM, Cardoso LG. 2021. Long-term changes in population dynamics and life history contribute to explain the resilience of a stock of Micropogonias furnieri (Sciaenidae, Teleostei) in the SW Atlantic. Fish Res 237: 105878. [CrossRef] [Google Scholar]
  • Hilborn R, Mangel M. 2013. The Ecological Detective: Confronting Models with Data (MPB-28). Princeton University Press, 2013. [Google Scholar]
  • Hilborn R, Walters CJ. 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty, Chapman and Hall, New York, 1992. [Google Scholar]
  • Jisr N, Younes G, Sukhn C, El-Dakdouki MH. 2018. Length-weight relationships and relative condition factor of fish inhabiting the marine area of the Eastern Mediterranean city, Tripoli-Lebanon. Egypt J Aquat Res 44: 299–305. [Google Scholar]
  • Jones R. 1981. The use of length composition data in fish stock assessments (with notes on VPA and cohort analysis). FAO Fish Circ, pp. 734. [Google Scholar]
  • Jordan DS, Gilbert CH. 1882. Synopsis of the fishes of North America (No. 16). US Government Printing Office. [Google Scholar]
  • Katsanevakis S, Maravelias CD. 2008. Modelling fish growth: multi‐model inference as a better alternative to a priori using von Bertalanffy equation. Fish Fish 9: 178–187. [CrossRef] [Google Scholar]
  • Lee HH, Maunder MN, Piner KR, Methot RD. 2012. Can steepness of the stock–recruitment relationship be estimated in fishery stock assessment models? Fish Res 125: 254–261. [Google Scholar]
  • López-Martínez J, Farach-Espinoza EB, Herrera-Cervantes H, García-Morales R. 2023. Long-term variability in sea surface temperature and chlorophyll a concentration in the Gulf of California. Remote Sens 15: 4088. [Google Scholar]
  • López‐Martínez J, Cabanilla‐Carpio C, Ruiz Choez W, Arzola‐Sotelo EA. 2020. Interannual variability of distribution, abundance and population dynamics of the smooth hammerhead Sphyrna zygaena (Linnaeus, 1758) in the central‐southeast Pacific Ocean. J Fish Biol 97: 341–353. [Google Scholar]
  • Lowerre‐Barbieri S, DeCelles G, Pepin P, Catalán IA, Muhling B, Erisman B, Cadrin SX, Alós J, Ospina-Alvarez A, Stachura MM, Tringali MD, Walters-Burnsed S, Paris CB. 2017. Reproductive resilience: a paradigm shift in understanding spawner‐recruit systems in exploited marine fish. Fish Fish 18: 285–312. [Google Scholar]
  • Ludwig D, Walters CJ. 1981. Measurement errors and uncertainty in parameter estimates for stock and recruitment. Can J Fish Aquat Sci 38: 711–720. [Google Scholar]
  • Mackay D, Moreau J. 1990. A note on the inverse function of the von Bertalanffy growth function. FishByte 1: 28–31. [Google Scholar]
  • Magnusson A, Hilborn R. 2007. What makes fisheries data informative? Fish Fish 8: 337–358. [Google Scholar]
  • Mangel M, Brodziak J, DiNardo G. 2010. Reproductive ecology and scientific inference of steepness: a fundamental metric of population dynamics and strategic fisheries management. Fish Fish 11: 89–104. [Google Scholar]
  • Mangel M, MacCall AD, Brodziak J, Dick EJ, Forrest RE, Pourzand R, Ralston S. 2013. A perspective on steepness, reference points, and stock assessment. Can J Fish Aquat Sci 70: 930–940. [Google Scholar]
  • Maunder MN, Thorson JT. 2019. Modeling temporal variation in recruitment in fisheries stock assessment: a review of theory and practice. Fish Res 217: 71–86. [Google Scholar]
  • Norris KS, McFarland WN. 1958. A new harbor porpoise of the genus Phocoena from the Gulf of California. J Mammal 39: 22–39. [Google Scholar]
  • Olsen EM, Ottersen G, Llope M, Chan KS, Beaugrand G, Stenseth NC. 2011. Spawning stock and recruitment in North Sea cod shaped by food and climate. P Roy Soc B-Biol Sci 278: 504–510. [Google Scholar]
  • Ouyang M, Zhang Q, Chen Z. 2023. Balanced harvesting of dynamical discrete Ricker & Beverton–Holt system. Chaos, Solitons Fractals 170: 113384. [Google Scholar]
  • Quinn JT, Deriso RB. 1999. Quantitative Fish Dynamics, Oxford University Press, New York. pp. 452. [Google Scholar]
  • Ramírez-Rodríguez M. 2017. A profitability analysis of catch quotas for the pacific hake fishery in the Gulf of California. N Am J Fish Manag 37: 23–29. [Google Scholar]
  • Ricard D, Minto C, Jensen OP, Baum JK. 2012. Examining the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database. Fish Fish 13: 380–398. [Google Scholar]
  • Ricker WE. 1975. Computation and interpretation of biological statistics of fish populations. Fish Res Board Can Bull 191: 1–382. [Google Scholar]
  • Rodríguez-Quiroz G, Valenzuela-Quiñonez W, González-Ocampo HA, Ortega-Rubio A. 2019. Can the vaquita be saved from extinction? Human–Wildlife Interactions 12: 284–290. [Google Scholar]
  • Rodríguez-Quiroz GR, Aragón-Noriega EA, Valenzuela-Quiñónez W, Esparza-Leal HM. 2010. Artisanal fisheries in the conservation zones of the Upper Gulf of California. Rev Biol Mar Oceanogr 45: 89–98. [Google Scholar]
  • Román-Rodríguez MJ. 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. Instituto del Medio Ambiente y Desarrollo Sustentable del Estado de Sonora. Informe final SNIB-CONABIO proyecto No. L298. México, DF. [Google Scholar]
  • Shertzer KW, Conn PB. 2012. Spawner-recruit relationships of demersal marine fishes: prior distribution of steepness. Bull Mar Sci 88: 39–50. [Google Scholar]
  • Shimoyama S, Sakuramoto K, Suzuki N. 2007. Proposal for stock-recruitment relationship for Japanese sardine Sardinops melanostictus in North-western Pacific. Fish Sci 73: 1035–1041. [Google Scholar]
  • Skoglund S, Whitlock R, Petersson E, Palm S, Leonardsson K. 2022. From spawner habitat selection to stock‐recruitment: Implications for assessment. Ecol Evol 12: e9679. [Google Scholar]
  • Tirado-Pineda CA. 2019. Pescadores de El Golfo de Santa Clara y Puerto Peñasco, Sonora. Manifestación de Impacto Ambiental, pp. 386. [Google Scholar]
  • Urías-Sotomayor R, Rodríguez-Domínguez G, Félix-Ortiz JA, Ortega-Lizárraga GG, Muñoz-Rubí HA, Aragón-Noriega EA. 2022. Stock reduction analysis of bigeye croaker Micropogonias megalops in the Upper Gulf of California, Mexico. Fishes 7: 15. [Google Scholar]
  • Van Poorten B, Korman J, Walters C. 2018. Revisiting Beverton–Holt recruitment in the presence of variation in food availability. Rev Fish Biol Fish 28: 607–624. [Google Scholar]
  • Walters CJ. 1985. Bias in the estimation of functional relationships from time series data. Can J Fish Aquat Sci 42: 147–149. [Google Scholar]
  • Yang Y, Yamakawa T. 2022. Re-examination of stock–recruitment relationships: a meta-analysis. ICES J Mar Sci 79: 1380–1393. [Google Scholar]
  • Zar JH. 1999. Biostatistical Analysis. Prentice Hall, Upper Saddle River, NJ, pp 663. [Google Scholar]
  • Zhu J, Chen Y, Dai X, Harley SJ, Hoyle SD, Maunder MN, Aires-da-Silva AM. 2012. Implications of uncertainty in the spawner–recruitment relationship for fisheries management: An illustration using bigeye tuna (Thunnus obesus) in the eastern Pacific Ocean. Fish Res 119: 89–93. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.