Analysis of two models for picarel (Spicara smaris L.) growth using Schnute's micro-simplex nonlinear estimation procedure

Tsangridis, A.; Filippousis, N.

doi:10.1016/0165-7836(94)90082-5

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…Although there are few instances of the usage of AIC for selection of fish growth models (e.g. Tsangridis and Filippousis, 1994;Imai et al, 2002), there is no instance, to my knowledge, of MMI based on information theory in this field.…”

Section: Introductionmentioning

confidence: 93%

Modelling fish growth: Model selection, multi-model inference and model selection uncertainty

Katsanevakis

2006

Fisheries Research

275

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Section: Introductionmentioning

confidence: 93%

Modelling fish growth: Model selection, multi-model inference and model selection uncertainty

Katsanevakis

2006

Fisheries Research

275

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“…The Gompertz and Von Bertalanffy models showed the best fit in Morkaraman and Awassi lambs (Topal et al, 2004); Gompertz model in Suffolk sheep . In fish population, the Gompertz model has been considered as the best fit according to AIC lowest value (Tsangridis and Filippousis, 1994;Imai et al, 2002).…”

Section: Model Parametersmentioning

confidence: 99%

Comparison of non-linear growth models to describe the growth curve in West African Dwarf sheep

Gbangboche

Kakaï

Salifou

et al. 2008

Animal

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The objectives of this study were to compare the goodness of fit of four non-linear growth models, i.e. Brody, Gompertz, Logistic and Von Bertalanffy, in West African Dwarf (WAD) sheep. A total of 5274 monthly weight records from birth up to 180 days of age from 889 lambs, collected during 2001 to 2004 in Betecoucou breeding farm in Benin were used. In the preliminary analysis, the General Linear Model Procedure of the Statistical Analysis Systems Institute was applied to the dataset to identify the significant effects of the sex of lamb (male and female), type of birth (single and twin), season of birth (rainy season and dry season), parity of dam (1, 2 and 3) and year of birth (2001, 2002, 2003 and 2004) on the observed birth weight and monthly weight up to 6 months of age. The models parameters (A, B and k), coefficient of determination (R 2 ), mean square error (MSE) were calculated using language of technical computing package Matlab R , 2006. The mean values of A, B and k were substituted into each model to calculate the corresponding Akaike's Information Criterion (AIC). Among the four growth functions, the Brody model has been selected for its accuracy of fit according to the higher R 2 , lower MSE and AIC. Finally, the parameters A, B and k were adjusted in Matlab R , 2006 for the sex of lamb, year of birth, season of birth, birth type and the parity of ewe, providing a specific slope of the Brody growth curve. The results of this study suggest that Brody model can be useful for WAD sheep breeding in Betecoucou farm conditions through growth monitoring.

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“…AIC takes into consideration sample size and number of parameters upon which a model is based (Akaike 1973(Akaike , 1978, and has been used in a prior study investigating the size-symmetry of competition (Stoll et al 1994). In our study, we calculated AIC using to the equation presented in Tsangridis and Filippousis (1994):…”

Section: Nonlinear Regressionmentioning

confidence: 99%

Investigating the relationship between neighbor root biomass and belowground competition: field evidence for symmetric competition belowground

Cahill¹,

Casper²

2000

Oikos

135

100

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2000. Investigating the relationship between neighbor root biomass and belowground competition: field evidence for symmetric competition belowground. -Oikos 90: 311 -320.Little is known about how small-scale variation in neighbor biomass can influence the strength of root competition experienced by an individual plant. In this study, modified root exclusion tubes were used to vary the accessibility of the soil space surrounding Amaranthus retroflexus target plants to the neighboring plants. A gradient of root accessibility was created by drilling varying numbers of holes into standard root exclusion tubes, made of 15 cm diameter PVC pipe. Belowground competitive intensity, defined as biomass reduction due to root interactions alone, relative to growth in the absence of neighbors, was then measured along the resulting gradient of neighbor root densities. At low neighbor root abundances the strength of belowground competition was proportional to neighbor root biomass, consistent with prior evidence that belowground competition is symmetric. If belowground competition were asymmetric, neighbor roots should have had little effect on target plants when they are rare relative to those of the target plant. At higher neighbor root abundances, belowground competitive intensity should increase rapidly. The strong relationship found between neighbor root biomass and belowground competitive intensity suggests relatively small variations in root biomass could lead to large variations in belowground competition. Reduced belowground competition in areas with low root biomass could have important implications for the establishment and growth of poor belowground competitors, suggesting a mechanism by which species coexistence may occur despite extremely intense root competition.

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Analysis of two models for picarel (Spicara smaris L.) growth using Schnute's micro-simplex nonlinear estimation procedure

Cited by 6 publications

References 5 publications

Modelling fish growth: Model selection, multi-model inference and model selection uncertainty

Modelling fish growth: Model selection, multi-model inference and model selection uncertainty

Comparison of non-linear growth models to describe the growth curve in West African Dwarf sheep

Investigating the relationship between neighbor root biomass and belowground competition: field evidence for symmetric competition belowground

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