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Stock assessment of the geoduck clam Panopea globosa in Mexico has been based on data-poor without consideration of the biological traits of the species, promoting a passive management strategy without biological reference points for its harvest and conservation, which results in limited advice regarding the sustainability of the fishery. The stock assessment was supported on an integrated catch-at-size assessment model. The model described the population changes, including recruitment, selectivity, fishing mortality, individual growth patterns and survival over time, providing management quantities for the geoduck clam fishery, such as biomass-at-length (total and vulnerable) and harvest rate-at-length. The results indicated overfishing of the geoduck clam population; the harvest rate exceeded the management tactics established for this fishery, even the individuals smaller than the minimum legal size (130 mm) were harvested. Thus, declines in the total biomass (from 3,262 to 1,130 t) and recruitment (representing an 86% decrease) were observed from 2010 to 2012. Although the results showed a recovery trend in recruitment and total biomass from 2014 to 2016, this trend may have been due to the spatial relocation of fishing mortality.

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.