The Bertalanffy-Pütter (BP) five-parameter growth model provides a versatile framework for the modeling of growth. Using data from a growth experiment in literature about the average size-at-age of 24 species of ...The Bertalanffy-Pütter (BP) five-parameter growth model provides a versatile framework for the modeling of growth. Using data from a growth experiment in literature about the average size-at-age of 24 species of tropical trees over ten years in the same area, we identified their best-fit BP-model parameters. While different species had different best-fit exponent-pairs, there was a model with a good fit to 21 (87.5%) of the data </span><span style="font-family:Verdana;">(</span><span style="font-family:""><span style="font-family:Verdana;">“Good fit” means a </span><span style="font-family:Verdana;">normalized root-mean-squared-error <i></span><i><span style="font-family:Verdana;">NRMSE</span></i><span style="font-family:Verdana;"></i> below 2.5%. This threshold was the 95% quantile of the lognormal distribution that was fitted to the <i></span><i><span style="font-family:Verdana;">NRMSE</span></i><span style="font-family:Verdana;"></i> values for the best-fit models for the data)</span></span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;"> In view of the sigmoidal character of this model despite the early stand we discuss </span><span style="font-family:Verdana;">whether </span><span style="font-family:Verdana;">the setting of the growth experiment may have impeded growth.展开更多
The paper searched for raw data about wild-caught fish, where a sigmoidal growth function described the mass growth significantly better than non-sigmoidal functions. Specifically, von Bertalanffy’s sigmoidal growth ...The paper searched for raw data about wild-caught fish, where a sigmoidal growth function described the mass growth significantly better than non-sigmoidal functions. Specifically, von Bertalanffy’s sigmoidal growth function (metabolic exponent-pair a = 2/3, b = 1) was compared with unbounded linear growth and with bounded exponential growth using the Akaike information criterion. Thereby the maximum likelihood fits were compared, assuming a lognormal distribution of mass (i.e. a higher variance for heavier animals). Starting from 70+ size-at-age data, the paper focused on 15 data coming from large datasets. Of them, six data with 400 - 20,000 data-points were suitable for sigmoidal growth modeling. For these, a custom-made optimization tool identified the best fitting growth function from the general von Bertalanffy-Pütter class of models. This class generalizes the well-known models of Verhulst (logistic growth), Gompertz and von Bertalanffy. Whereas the best-fitting models varied widely, their exponent-pairs displayed a remarkable pattern, as their difference was close to 1/3 (example: von Bertalanffy exponent-pair). This defined a new class of models, for which the paper provided a biological motivation that relates growth to food consumption.展开更多
文摘The Bertalanffy-Pütter (BP) five-parameter growth model provides a versatile framework for the modeling of growth. Using data from a growth experiment in literature about the average size-at-age of 24 species of tropical trees over ten years in the same area, we identified their best-fit BP-model parameters. While different species had different best-fit exponent-pairs, there was a model with a good fit to 21 (87.5%) of the data </span><span style="font-family:Verdana;">(</span><span style="font-family:""><span style="font-family:Verdana;">“Good fit” means a </span><span style="font-family:Verdana;">normalized root-mean-squared-error <i></span><i><span style="font-family:Verdana;">NRMSE</span></i><span style="font-family:Verdana;"></i> below 2.5%. This threshold was the 95% quantile of the lognormal distribution that was fitted to the <i></span><i><span style="font-family:Verdana;">NRMSE</span></i><span style="font-family:Verdana;"></i> values for the best-fit models for the data)</span></span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;"> In view of the sigmoidal character of this model despite the early stand we discuss </span><span style="font-family:Verdana;">whether </span><span style="font-family:Verdana;">the setting of the growth experiment may have impeded growth.
文摘The paper searched for raw data about wild-caught fish, where a sigmoidal growth function described the mass growth significantly better than non-sigmoidal functions. Specifically, von Bertalanffy’s sigmoidal growth function (metabolic exponent-pair a = 2/3, b = 1) was compared with unbounded linear growth and with bounded exponential growth using the Akaike information criterion. Thereby the maximum likelihood fits were compared, assuming a lognormal distribution of mass (i.e. a higher variance for heavier animals). Starting from 70+ size-at-age data, the paper focused on 15 data coming from large datasets. Of them, six data with 400 - 20,000 data-points were suitable for sigmoidal growth modeling. For these, a custom-made optimization tool identified the best fitting growth function from the general von Bertalanffy-Pütter class of models. This class generalizes the well-known models of Verhulst (logistic growth), Gompertz and von Bertalanffy. Whereas the best-fitting models varied widely, their exponent-pairs displayed a remarkable pattern, as their difference was close to 1/3 (example: von Bertalanffy exponent-pair). This defined a new class of models, for which the paper provided a biological motivation that relates growth to food consumption.