Review on mathematical modeling of honeybee population dynamics

Honeybees have an irreplaceable position in agricultural production and the stabilization of natural ecosystems. Unfortunately, honeybee populations have been declining globally. Parasites, diseases, poor nutrition, pesticides, and climate changes contribute greatly to the global crisis of honeybee...

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Autores principales:	Jun Chen, Gloria DeGrandi-Hoffman, Vardayani Ratti, Yun Kang
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	honeybee parasites pathogens pesticides dynamical systems seasonality mathematical models Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/f0cfb72a7bfa4db39945d9103b524b89
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spelling	oai:doaj.org-article:f0cfb72a7bfa4db39945d9103b524b892021-11-29T06:28:53ZReview on mathematical modeling of honeybee population dynamics10.3934/mbe.20214711551-0018https://doaj.org/article/f0cfb72a7bfa4db39945d9103b524b892021-11-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021471?viewType=HTMLhttps://doaj.org/toc/1551-0018Honeybees have an irreplaceable position in agricultural production and the stabilization of natural ecosystems. Unfortunately, honeybee populations have been declining globally. Parasites, diseases, poor nutrition, pesticides, and climate changes contribute greatly to the global crisis of honeybee colony losses. Mathematical models have been used to provide useful insights on potential factors and important processes for improving the survival rate of colonies. In this review, we present various mathematical tractable models from different aspects: 1) simple bee-only models with features such as age segmentation, food collection, and nutrient absorption; 2) models of bees with other species such as parasites and/or pathogens; and 3) models of bees affected by pesticide exposure. We aim to review those mathematical models to emphasize the power of mathematical modeling in helping us understand honeybee population dynamics and its related ecological communities. We also provide a review of computational models such as VARROAPOP and BEEHAVE that describe the bee population dynamics in environments that include factors such as temperature, rainfall, light, distance and quality of food, and their effects on colony growth and survival. In addition, we propose a future outlook on important directions regarding mathematical modeling of honeybees. We particularly encourage collaborations between mathematicians and biologists so that mathematical models could be more useful through validation with experimental data.Jun ChenGloria DeGrandi-HoffmanVardayani RattiYun KangAIMS Pressarticlehoneybeeparasitespathogenspesticidesdynamical systemsseasonalitymathematical modelsBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 9606-9650 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	honeybee parasites pathogens pesticides dynamical systems seasonality mathematical models Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	honeybee parasites pathogens pesticides dynamical systems seasonality mathematical models Biotechnology TP248.13-248.65 Mathematics QA1-939 Jun Chen Gloria DeGrandi-Hoffman Vardayani Ratti Yun Kang Review on mathematical modeling of honeybee population dynamics
description	Honeybees have an irreplaceable position in agricultural production and the stabilization of natural ecosystems. Unfortunately, honeybee populations have been declining globally. Parasites, diseases, poor nutrition, pesticides, and climate changes contribute greatly to the global crisis of honeybee colony losses. Mathematical models have been used to provide useful insights on potential factors and important processes for improving the survival rate of colonies. In this review, we present various mathematical tractable models from different aspects: 1) simple bee-only models with features such as age segmentation, food collection, and nutrient absorption; 2) models of bees with other species such as parasites and/or pathogens; and 3) models of bees affected by pesticide exposure. We aim to review those mathematical models to emphasize the power of mathematical modeling in helping us understand honeybee population dynamics and its related ecological communities. We also provide a review of computational models such as VARROAPOP and BEEHAVE that describe the bee population dynamics in environments that include factors such as temperature, rainfall, light, distance and quality of food, and their effects on colony growth and survival. In addition, we propose a future outlook on important directions regarding mathematical modeling of honeybees. We particularly encourage collaborations between mathematicians and biologists so that mathematical models could be more useful through validation with experimental data.
format	article
author	Jun Chen Gloria DeGrandi-Hoffman Vardayani Ratti Yun Kang
author_facet	Jun Chen Gloria DeGrandi-Hoffman Vardayani Ratti Yun Kang
author_sort	Jun Chen
title	Review on mathematical modeling of honeybee population dynamics
title_short	Review on mathematical modeling of honeybee population dynamics
title_full	Review on mathematical modeling of honeybee population dynamics
title_fullStr	Review on mathematical modeling of honeybee population dynamics
title_full_unstemmed	Review on mathematical modeling of honeybee population dynamics
title_sort	review on mathematical modeling of honeybee population dynamics
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/f0cfb72a7bfa4db39945d9103b524b89
work_keys_str_mv	AT junchen reviewonmathematicalmodelingofhoneybeepopulationdynamics AT gloriadegrandihoffman reviewonmathematicalmodelingofhoneybeepopulationdynamics AT vardayaniratti reviewonmathematicalmodelingofhoneybeepopulationdynamics AT yunkang reviewonmathematicalmodelingofhoneybeepopulationdynamics
_version_	1718407560257601536

Review on mathematical modeling of honeybee population dynamics

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