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A novel model to quantify ages of organisms and predict development time distribution of their growth stages

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  • Jian, Fuji

Abstract

A basic model structure was suggested to correlate the biological age of an organism with its aging, development, and multiplication. This simple model had the similar basic assumptions as the degree-day model, but was more theoretical comprehensive and approachable. The proposed methodological and mathematical framework was based on the same shape concept of various distributions of an organism. A new term, Physi-Biological time, was introduced to calculate the Physi-Biological age which was the product of the chronological time, maximum lifespan, and development rate of the organism at the given environmental condition. The Physi-Biological time was used to normalize the distribution of an organism development under different environmental conditions, so the development of the organism at one environmental condition with the maximum lifespan could be mapped to the development of the same organism at other environmental conditions. The ageing, mortality, oviposition of adults of Cryptolestes ferrugineus were used as an example to demonstrate the modeling method. The prediction was consistent with the measured data published by different researchers.

Suggested Citation

  • Jian, Fuji, 2021. "A novel model to quantify ages of organisms and predict development time distribution of their growth stages," Ecological Modelling, Elsevier, vol. 440(C).
    • Handle: RePEc:eee:ecomod:v:440:y:2021:i:c:s0304380020304555
    DOI: 10.1016/j.ecolmodel.2020.109391
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    1. Jian, Fuji & Jayas, Digvir S. & White, Noel D.G. & Fields, Paul G., 2007. "A distributed-delay model to predict ageing and survival rates of adults of Cryptolestes ferrugineus (Stephens) (Coleoptera: Laemophloeidae) in granaries filled with wheat," Ecological Modelling, Elsevier, vol. 200(3), pages 412-420.
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    Cited by:

    1. Li, Shangge & Jian, Jinfeng & Poopal, Rama Krishnan & Chen, Xinyu & He, Yaqi & Xu, Hongbin & Yu, Huimin & Ren, Zongming, 2022. "Mathematical modeling in behavior responses: The tendency-prediction based on a persistence model on real-time data," Ecological Modelling, Elsevier, vol. 464(C).

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