Random variation in model parameters: A comprehensive review of stochastic logistic growth equation

Md Aktar Ul Karim, Vikram Aithal and Amiya Ranjan Bhowmick

Ecological Modelling, 2023, vol. 484, issue C

Abstract: Biological growth curves are central to interdisciplinary research that seeks to improve our understanding of natural processes. Applications of growth equations are widespread across domains, with one particular growth equation being the dominant one: the logistic growth model. Nonlinear models that use parameters to vary with time are essential mathematical tools for analyzing growth patterns in different research areas. Recent research has investigated continuous parameter variation and an empirical method of detecting parameter variation has been proposed (Karim et al., 2022). In the literature, instances are abundant where the parameters vary randomly with time. This review article is focused on the stochastic version of the logistic equation, where researchers introduced stochasticity through random variation of one or more parameters in the model. Our survey reports a segmentation of articles based on the logistic growth equations into harvesting and non-harvesting equations and points out some key future study directions that may demand attention from researchers. This study also identifies the need for data-driven research in stochastic growth equations to improve the applicability of these models for real data application.

Keywords: Intrinsic growth rate; Carrying capacity; Environmental noise; Demographic noise; Multiphasic model (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304380023002053
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:484:y:2023:i:c:s0304380023002053

DOI: 10.1016/j.ecolmodel.2023.110475

Access Statistics for this article

Ecological Modelling is currently edited by Brian D. Fath

More articles in Ecological Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().