scholarly journals The influence of sample distribution on growth model output for a highly-exploited marine fish, the Gulf Corvina (Cynoscion othonopterus)

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5582 ◽  
Author(s):  
Derek G. Bolser ◽  
Arnaud Grüss ◽  
Mark A. Lopez ◽  
Erin M. Reed ◽  
Ismael Mascareñas-Osorio ◽  
...  
Keyword(s):  
Growth Model ◽  
Marine Fish ◽  
Growth Models ◽  
Growth Patterns ◽  
Von Bertalanffy ◽  
Model Output ◽  
Age Classes ◽  
Equal Distribution ◽  
Sample Distribution ◽  
Richards Model

Estimating the growth of fishes is critical to understanding their life history and conducting fisheries assessments. It is imperative to sufficiently sample each size and age class of fishes to construct models that accurately reflect biological growth patterns, but this may be a challenging endeavor for highly-exploited species in which older fish are rare. Here, we use the Gulf Corvina (Cynoscion othonopterus), a vulnerable marine fish that has been persistently overfished for two decades, as a model species to compare the performance of several growth models. We fit the von Bertalanffy, Gompertz, logistic, Schnute, and Schnute–Richards growth models to length-at-age data by nonlinear least squares regression and used simple indicators to reveal biased data and ensure our results were biologically feasible. We then explored the consequences of selecting a biased growth model with a per-recruit model that estimated female spawning-stock-biomass-per-recruit and yield-per-recruit. Based on statistics alone, we found that the Schnute–Richards model described our data best. However, it was evident that our data were biased by a bimodal distribution of samples and underrepresentation of large, old individuals, and we found the Schnute–Richards model output to be biologically implausible. By simulating an equal distribution of samples across all age classes, we found that sample distribution distinctly influenced model output for all growth models tested. Consequently, we determined that the growth pattern of the Gulf Corvina was best described by the von Bertalanffy growth model, which was the most robust to biased data, comparable across studies, and statistically comparable to the Schnute–Richards model. Growth model selection had important consequences for assessment, as the per-recruit model employing the Schnute–Richards model fit to raw data predicted the stock to be in a much healthier state than per-recruit models employing other growth models. Our results serve as a reminder of the importance of complete sampling of all size and age classes when possible and transparent identification of biased data when complete sampling is not possible.

Statistical modeling of baleen and body length at age in bowhead whales (Balaena mysticetus)

2012 ◽  
Vol 90 (8) ◽  
pp. 915-931 ◽  
Author(s):  
S.C. Lubetkin ◽  
J.E. Zeh ◽  
J.C. George
Keyword(s):  
Body Length ◽  
Growth Model ◽  
Growth Models ◽  
Information Criterion ◽  
Single Stage ◽  
Von Bertalanffy ◽  
Balaena Mysticetus ◽  
Bowhead Whales ◽  
Multi Stage ◽  
Age Estimates

We used baleen lengths and age estimates from 175 whales and body lengths and age estimates from 205 whales to test which of several single- and multi-stage growth models best characterized age-specific baleen and body lengths for bowhead whales ( Balaena mysticetus L., 1758) with the goal of determining which would be best for predicting whale age based on baleen or body length. Previous age estimates were compiled from several techniques, each of which is valid over a relatively limited set of physical characteristics. The best fitting single-stage growth model was a variation of the von Bertalanffy growth model for both baleen and body length data. Based on Bayesian information criterion, the two- and three-stage versions of the von Bertalanffy model fit the data better than did the single-stage models for both baleen and body length. The best baleen length models can be used to estimate expected ages for bowhead whales with up to 300–325 cm baleen, depending on sex, which correspond to age estimates approaching 60 years. The best body length models can be used to estimate expected ages for male bowhead whales up to 14 m, and female bowheads up to 15.5 m or ages up to approximately 40 years.

Age and growth estimates of the jumbo flying squid (Dosidicus gigas) off Peru

2019 ◽  
Vol 32 ◽  
pp. 7
Author(s):  
Carlos Goicochea-Vigo ◽  
Enrique Morales-Bojórquez ◽  
Viridiana Y. Zepeda-Benitez ◽  
José Ángel Hidalgo-de-la-Toba ◽  
Hugo Aguirre-Villaseñor ◽  
...  
Keyword(s):  
Growth Model ◽  
Growth Pattern ◽  
Growth Models ◽  
Information Criterion ◽  
Growth Patterns ◽  
Age And Growth ◽  
Dosidicus Gigas ◽  
Asymptotic Growth ◽  
Schnute Model ◽  
Gompertz Growth Model

Mantle length (ML) and age data were analyzed to describe the growth patterns of the flying jumbo squid, Dosidicus gigas, in Peruvian waters. Six non-asymptotic growth models and four asymptotic growth models were fitted. Length-at-age data for males and females were analysed separately to assess the growth pattern. Multi-model inference and Akaike's information criterion were used to identify the best fitting model. For females, the best candidate growth model was the Schnute model with L∞ = 106.96 cm ML (CI 101.23–110.27 cm ML, P < 0.05), age at growth inflection 244.71 days (CI 232.82–284.86 days, P < 0.05), and length at growth inflection 57.26 cm ML (CI 55.42–58.51 cm ML, P < 0.05). The growth pattern in males was best described by a Gompertz growth model with L∞ = 127.58 cm ML (CI 115.27–131.80 cm ML, P < 0.05), t0 = 21.8 (CI 20.06–22.41, P < 0.05), and k = 0.007 (CI 0.006–0.007, P < 0.05). These results contrast with the growth model previously reported for D. gigas in the region, where the growth pattern was identified as non-asymptotic.

scholarly journals A New Flexible Sigmoidal Growth Model

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 204 ◽  
Author(s):  
Liying Cao ◽  
Pei-Jian Shi ◽  
Lin Li ◽  
Guifen Chen
Keyword(s):  
Growth Rate ◽  
Growth Model ◽  
Growth Models ◽  
Differential Evolution Algorithm ◽  
Growth Patterns ◽  
Growth Data ◽  
Ecological Processes ◽  
Maximum Biomass ◽  
Sigmoidal Growth ◽  
Growth Of Animals

Biological growth is driven by numerous functions, such as hormones and mineral nutrients, and is also involved in various ecological processes. Therefore, it is necessary to accurately capture the growth trajectory of various species in ecosystems. A new sigmoidal growth (NSG) model is presented here for describing the growth of animals and plants when the assumption is that the growth rate curve is asymmetric. The NSG model was compared with four classic sigmoidal growth models, including the logistic equation, Richards, Gompertz, and ontogenetic growth models. Results indicated that all models fit well with the empirical growth data of 12 species, except the ontogenetic growth model, which only captures the growth of animals. The estimated maximum asymptotic biomass wmax of plants from the ontogenetic growth model was not reliable. The experiment result shows that the NSG model can more precisely estimate the value and time of reaching maximum biomass when growth rate becomes close to zero near the end of growth. The NSG model contains three other parameters besides the value and time of reaching maximum biomass, and thereby, it can be difficult to assign initial values for parameterization using local optimization methods (e.g., using Gauss–Newton or Levenberg–Marquardt methods). We demonstrate the use of a differential evolution algorithm for resolving this issue efficiently. As such, the NSG model can be applied to describing the growth patterns of a variety of species and estimating the value and time of achieving maximum biomass simultaneously.

Growth and sexual size dimorphism in Alberta populations of the eastern short-horned lizard, Phrynosoma douglassi brevirostre

1985 ◽  
Vol 63 (1) ◽  
pp. 139-154 ◽  
Author(s):  
G. Lawrence Powell ◽  
Anthony P. Russell
Keyword(s):  
Growth Model ◽  
Sexual Difference ◽  
Sexual Size Dimorphism ◽  
Growth Models ◽  
Tail Length ◽  
Growth Patterns ◽  
Head Width ◽  
Adult Size ◽  
Adult Males ◽  
Size Dimorphism

Alberta populations of Phrynosoma douglassi brevirostre display marked sexual size dimorphism, adult females being considerably larger than adult males. Discriminant analyses of whole mensural characters and of scaled mensural characters indicate that this dimorphism is present from birth, although it is more strongly expressed after sexual maturity. Recapture data were used to generate modified logistic by weight growth models for snout–vent length (SVL), and allometric models for each sex were generated for growth in tail length, head length, and head width. The SVL growth model for females indicates delayed maturity leading to greater adult size, an expected feature of a female viviparine. The SVL growth model for males indicates that growth ceases sooner than in females, resulting in a smaller adult size. This is possibly a result of male dispersal competition, an hypothesis further borne out by the results of a preliminary analysis of mobility in the two sexes, and may also be influenced by intersexual dietary competition. Differences in head dimensions between the sexes are a function of the differences in SVL at adulthood, but there is a significant sexual difference in the allometric relationship of tail length to SVL. No difference in the growth patterns and adult size of either sex was found to exist over the range in Alberta.

scholarly journals Growth Patterns of birds, dinosaurs and reptiles: Are differences real or apparent?

2019 ◽  
Author(s):  
Norbert Brunner ◽  
Manfred Kühleitner ◽  
Werner-Georg Nowak ◽  
Katharina Renner-Martin ◽  
Klaus Scheicher
Keyword(s):  
Differential Equation ◽  
Growth Pattern ◽  
Broiler Chicken ◽  
Growth Models ◽  
Growth Patterns ◽  
Von Bertalanffy ◽  
Growth Data ◽  
Quantitative Studies ◽  
Best Fitting

AbstractSystematics of animals was done on their appearance or genetics. One can also ask about similarities or differences in the growth pattern. Quantitative studies of the growth of dinosaurs have made possible comparisons with modern animals, such as the discovery that dinosaurs grew in relation to their size faster than modern reptiles. However, these studies relied on only a few growth models. If these models are false, what about the conclusions? This paper fits growth data to a more comprehensive class of models, defined by the von Bertalanffy-Pütter differential equation. Applied to data about dinosaurs, reptiles and birds, the best fitting models confirmed that dinosaurs may have grown faster than alligators. However, compared to modern broiler chicken, this difference was small.

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