Modeling of Growth Depensation of Geoduck Clam<i>Panopea globosa</i>Based on a Multimodel Inference Approach

Luquin-Covarrubias, Marlene Anaid; Morales-Bojórquez, Enrique; González-Peláez, Sergio Scarry; Hidalgo-de-la-Toba, José Ángel; Lluch‐Cota, Daniel B.

doi:10.2983/035.035.0212

Cited by 15 publications

(15 citation statements)

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“…However, it was also suggested [22] that "many factors may contribute to size variation among fish of one age", and it is also feasible that individual variability increases with age. This type of individual variability was named "growth depensation" [25,26]. The problem was originally tackled [25] for the vB growth model developing an equation that solved the growth depensation which was later extended to six asymptotic models [26].…”

Section: Discussionmentioning

confidence: 99%

“…This type of individual variability was named "growth depensation" [25,26]. The problem was originally tackled [25] for the vB growth model developing an equation that solved the growth depensation which was later extended to six asymptotic models [26]. On the other hand, equations were developed to compute the growth compensation in five asymptotic models [39].…”

Section: Discussionmentioning

confidence: 99%

“…86-87), (b) Multiplicative: Variance increases/decreases with length [20] (pp. 86-86), (c) Depensatory: Variance grows with length and reaches an asymptote [25,26], (d) Compensatory: Variance decreases asymptotically with length [27], and (e) Observed variance: the variance structure used is obtained from the original data [20]. Based on the assumed variance structure, the adequate method can be selected, for example, loglikelihood.…”

Section: Introductionmentioning

confidence: 99%

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Using Observed Residual Error Structure Yields the Best Estimates of Individual Growth Parameters

Curiel-Bernal¹,

Aragón-Noriega²,

Cisneros-Mata³

et al. 2021

Fishes

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Obtaining the best possible estimates of individual growth parameters is essential in studies of physiology, fisheries management, and conservation of natural resources since growth is a key component of population dynamics. In the present work, we use data of an endangered fish species to demonstrate the importance of selecting the right data error structure when fitting growth models in multimodel inference. The totoaba (Totoaba macdonaldi) is a fish species endemic to the Gulf of California increasingly studied in recent times due to a perceived threat of extinction. Previous works estimated individual growth using the von Bertalanffy model assuming a constant variance of length-at-age. Here, we reanalyze the same data under five different variance assumptions to fit the von Bertalanffy and Gompertz models. We found consistent significant differences between the constant and nonconstant error structure scenarios and provide an example of the consequences using the growth performance index ϕ′ to show how using the wrong error structure can produce growth parameter values that can lead to biased conclusions. Based on these results, for totoaba and other related species, we recommend using the observed error structure to obtain the individual growth parameters.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Observed Residual Error Structure Yields the Best Estimates of Individual Growth Parameters

Curiel-Bernal¹,

Aragón-Noriega²,

Cisneros-Mata³

et al. 2021

Fishes

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show abstract

“…These differences in the maturity size among populations of P. globosa should be taken into account when establishing management strategies, mainly because the maturity size at 50% is positively correlated with its longevity; therefore, individuals mature at a specific fraction of their maximum length (Jensen, 1996;(Law, 2000;Nadon & Ault, 2016). This aspect is relevant for P. globosa, its individual growth is highly variable; in Bahía Magdalena (Pacific coast), geoduck clams exhibited an asymptotic shell length of 179.85 mm (Luquin-Covarrubias et al, 2016a;Luquin-Covarrubias et al, 2016b); the geoduck population from Guaymas (Central Gulf of California) had an asymptotic length of 122 mm (Cortez-Lucero et al, 2011;Cruz-Vázquez et al, 2012), while in Puerto Peñasco and San Felipe (Upper Gulf of California), the asymptotic length values were 161.79 mm and 190.84 mm, respectively (Aragón-Noriega, Calderon-Aguilera & Pérez-Valencia, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…The optimization was performed in phases; this procedure consists of estimating only a subset of parameters through the optimization of the objective function and adding more parameters in each sequential phase until all parameters are estimated. An advantage is that to the extent that new parameters are included into a new phase, the previously estimated parameters are still estimated, and they are not fixed as prespecified values, allowing the progressive improvement of the goodness of fit of the RSS (Legault & Restrepo, 1998;Luquin-Covarrubias et al, 2016a;Luquin-Covarrubias et al, 2016b). A non-linear fit in phases is an statistical procedure accepted when there is high uncertainty in seed values and where the number of parameters to be estimated is greater than 20 (Fournier et al, 2012;Punt, Huang & Maunder, 2013;Canales, Company & Arana, 2016;Cao, Chen & Richards, 2016;Fisch et al, 2019).…”

Section: Parameter Estimationmentioning

confidence: 99%

Evidence of overfishing of geoduck clam Panopea globosa from a length-based stock assessment approach

Luquin-Covarrubias

Morales-Bojórquez

García-Borbón³

et al. 2020

PeerJ

Self Cite

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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.

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Modeling the biological growth with a random logistic differential equation

et al. 2023

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We modeled biological growth using a random differential equation (RDE), where the initial condition is a random variable, and the growth rate is a suitable stochastic process. These assumptions let us obtain a model that represents well the random growth process observed in nature, where only a few individuals of the population reach the maximal size of the species, and the growth curve for every individual behaves randomly. Since we assumed that the initial condition is a random variable, we assigned a priori density, and we performed Bayesian inference to update the initial condition’s density of the RDE. The Karhunen–Loeve expansion was then used to approximate the random coefficient of the RDE. Then, using the RDE’s approximations, we estimated the density f(p, t). Finally, we fitted this model to the biological growth of the giant electric ray (or Cortez electric ray) Narcine entemedor. Simulations of the solution of the random logistic equation were performed to construct a curve that describes the solutions’ mean for each time. As a result, we estimated confidence intervals for the mean growth that described reasonably well the observed data. We fit the proposed model with a training dataset, and the model is tested with a different dataset. The model selection is performed with the square of the errors.

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Modeling of Growth Depensation of Geoduck ClamPanopea globosaBased on a Multimodel Inference Approach

Cited by 15 publications

References 48 publications

Using Observed Residual Error Structure Yields the Best Estimates of Individual Growth Parameters

Using Observed Residual Error Structure Yields the Best Estimates of Individual Growth Parameters

Evidence of overfishing of geoduck clam Panopea globosa from a length-based stock assessment approach

Modeling the biological growth with a random logistic differential equation

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