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Knowledge of the population dynamics and changes in the abundance of Pacific sardine fishery is relevant for fishery management. In this study, an integrated catch-at-age model (ASAP) was performed on 47 fishing seasons (1971−2018) of Sardinops sagax in the Gulf of California, including five indices of relative abundance (survey catch rate, acoustic surveys, egg and larvae, spawning probability, and bird diet index). The ASAP model showed the importance of age group 0 (recruits); this is the most important age group in the Pacific sardine fishery, and its abundance has consequences for the variability of the total abundance of the species. In this study, the spawning and acoustic indices denoted the changes in abundance of Pacific sardine population better than the survey catch rate and bird diet ratio. Historically, the total biomass is estimated to have varied from 461 thousand to 4.82 million t. In the 1970s and 1980s, a peak biomass close to three million tons was reached, and in the second peak from the mid-1990s to the late 2000s, the total biomass varied between 3.26 and 4.82 million t. When the spawning biomass was at least 750 x 103 t, the recruitment (age group 0) showed high values, promoting high catches in the subsequent years. The annual fishing mortality estimates did not show a rise related to the periods of high harvestable biomass; therefore, the catch records were low in comparison to the harvestable biomass estimated from ASAP, and the annual fishing mortalities were less than 0.22 during periods of high abundance. The changes in biomass and the variability observed in the Pacific sardine fishery were validated for the model diagnostics; the retrospective patterns did not identify poor fits to data, therefore the robustness of the model can be used for management purposes. According to the Kobe phase plot, the Pacific sardine fishery in the Gulf of California has been fully exploited with an apparent stable level of harvest.

Growth variability has been a common feature within different Panopea species, affecting the accurate estimation of growth parameters and their theoretical trajectories. Recently, the interest in solving this problem has increased; however, the methods used for individual growth modeling of Panopea spp. have not been effective in identifying the variability within the shell length-at-age composition. In the current study, a novel approach is proposed to analyze the effect of shell length-at-age variability on the individual growth and growth parameters of Panopea globosa. The shell length-at-age data were fitted to six candidate growth models: von Bertalanffy, Gompertz, Johnson, Logistic, Generalized von Bertalanffy growth model (GVBGM), and Richards using a negative log-likelihood function. The variance for each age observed () in the population was estimated for each growth model. The best candidate growth model was selected based on corrected Akaike information criterion (AICc). The results in...

In size-based stock assessment models, the stochastic growth of individuals is expressed through a transition matrix representing the growth variability as probability of shift from one length class to another during a time period. This process is important because it describes the changes in population size structure brought about by the increase in length or weight of organisms over time. In this study, four stochastic growth matrices were developed within an integrated catch-at-length assessment model (ICLAM), and the changes in the population dynamics and quantities relevant to fishery management of Panopea globosa were analyzed. The growth increments were estimated through von Bertalanffy, Gompertz, Logistic, and Schnute models using a gamma probabilistic density function. A corrected Akaike information criterion was used to select the best performance among the four ICLAMs. The ICLAM associated with the von Bertalanffy matrix was the best for describing the catch-at-length data, providing conservative estimates on the condition of the stock; while the ICLAM for the Logistic matrix showed low performance, exhibiting the highest estimates for all the components of population dynamics, such as an increase of 75.3% in the recruitment and 58.5% in the total abundance with regard to the von Bertalanffy matrix. These differences emphasize the importance of determining a suitable stochastic growth matrix because it has serious implications in biasing stock assessments, resulting in inadequate management strategies.

In this study, a length-structured population model was implemented to describe changes in the abundance of Panopea globosa over time, given parameters for growth and natural mortality rate. The following modules described population dynamics: stochastic individual model growth, survival, selectivity, recruitment, and estimates of initial numbers. In addition, the harvest rate, total biomass, and vulnerable biomass (VB) were calculated to advise management quantities. Spatial serial depletion patterns were detected for P. globosa and evident from the expansion of the fishing grounds over time. These patterns of geographic expansion are common in developing fisheries and are a problem for stock assessment. The densest patches available are usually harvested or even depleted, with fishers switching to new beds and potentially rotating back over time. If the changes in the harvestable area are ignored, the increases in VB and recruitment may mask the population dynamics of several beds, including those that are depleted and have low yield.