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Growth model of the reared sea urchin Paracentrotus ... - SciViews

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have <strong>the</strong> basic von Bertalanffy 1 <strong>model</strong> that was designed for linear<br />

measurements <strong>of</strong> size (1938, his eq. 26). With d = 3, we have <strong>the</strong> cube <strong>of</strong> a<br />

linear measurement, that is, a volume or a weight (not considering possible<br />

allometries) and Richards <strong>model</strong> reduces to von Bertalanffy <strong>model</strong> 2,<br />

designed for weight measurements. Weibull's <strong>model</strong> (Weibull, 1951)<br />

belongs probably to this category too, though <strong>the</strong> exponent c applies only<br />

to time t. When c = 1, it also reduces to <strong>the</strong> basic von Bertalanffy 1 <strong>model</strong>,<br />

with a slightly different parameterization. The effect <strong>of</strong> <strong>the</strong> exponent,<br />

being d in Richards or c in Weibull, is to transform <strong>the</strong> von Bertalanffy 1<br />

curve into a S-shaped one, or sigmoid, by means <strong>of</strong> a power<br />

transformation.<br />

'Transitional' <strong>model</strong>s fit <strong>the</strong> S-shape as a transition between two states.<br />

The logistic function describes a transition between two constant states<br />

corresponding to its two horizontal asymptotes: D = 0 and D = a. In regard<br />

to <strong>the</strong> results obtained in Table 12, this <strong>model</strong> is not adapted here and it<br />

has no affinity with <strong>the</strong> von Bertalanffy 1 <strong>model</strong>. This <strong>model</strong> was initially<br />

designed to <strong>model</strong> population growth, not individual growth (Verhulst,<br />

1838). The 4-parameter logistic is ano<strong>the</strong>r 'transitional' <strong>model</strong> and we will<br />

demonstrate later its relation with von Bertalanffy 1. With <strong>the</strong> current<br />

parameterization, it also represents a transition between two constant states<br />

materialized by two horizontal asymptotes at D = a and D = d. It fits P.<br />

lividus data very well.<br />

The original growth <strong>model</strong> <strong>of</strong> eq. 29 is a third 'transitional' <strong>model</strong> and<br />

is our missing link as a general equivalent for 'transitional' <strong>model</strong>s to <strong>the</strong><br />

Richards' curve for 'dimensional' <strong>model</strong> (if we except d = -1 and d → ∞<br />

that are physically and biologically meaningless, and thus probably<br />

ma<strong>the</strong>matic artifacts in this context). When l = 0, it reduces to <strong>the</strong> von<br />

Bertalanffy 1 <strong>model</strong>. We now have to demonstrate it is a generalization <strong>of</strong><br />

<strong>the</strong> 4-parameter logistic function. If, in D = d + (a - d)/(1+e -b(t-c) ) we<br />

perform <strong>the</strong> following replacements: t ⇒ t', a ⇒ D0 + ∆D∞, b ⇒ k,<br />

c ⇒ ln(l)/k and d ⇒ D0 – ∆D∞/l, we obtain:<br />

Part IV: A growth <strong>model</strong> with intraspecific competition<br />

173

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