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Estimating polymorphic growth curve sets with nonchronological data.

Kai Moriguchi¹

¹Faculty of Agriculture and Marine Science Kochi University Nankoku City Japan.

Ecology and Evolution

|September 21, 2020

Summary

This summary is machine-generated.

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This study introduces a new method for estimating polymorphic growth curve sets using nonchronological data. The developed likelihood function successfully distinguished between polymorphic cypress and anamorphic larch tree growth patterns.

Area of Science:

Ecology
Biostatistics
Mathematical Biology

Background:

Individual growth curves often show orderly variation, not random mixtures.
Standardizing growth curves by asymptotes reveals anamorphic (identical) or polymorphic (non-identical) sets.
Estimating polymorphic growth curve sets from nonchronological data has been challenging.

Purpose of the Study:

To develop a novel estimation method for polymorphic growth curve sets using nonchronological data.
To derive a likelihood function applicable to polymorphic growth curve estimation.
To apply the method to real-world tree growth data.

Main Methods:

Developed a likelihood function for polymorphic growth curve sets.
Employed simple maximum likelihood estimation.

Keywords:

anamorphic asymptote growth model maximum likelihood estimation polymorphic underdispersion

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Included weighted nonlinear regression and log-transformed least-squares as special cases for anamorphic sets.

Main Results:

The growth curve sets for cypress (Chamaecyparis obtusa) and larch (Larix kaempferi) trees were estimated.
Model selection (AIC, likelihood ratio test) indicated cypress growth is polymorphic and larch growth is anamorphic.
The polymorphic model improved fitting for cypress by addressing underdispersion.

Conclusions:

The developed method enables estimation of polymorphic growth curve sets from nonchronological data.
The distinction between anamorphic and polymorphic growth is crucial for accurate modeling.
Future work may incorporate environmental variables and random effects into the likelihood function.