Simon A. Levin Mathematical and Computational Modeling Sciences Center, Arizona State University, 1031 Palm Walk, Tempe AZ 85281, USA.
Carl Hayden Bee Research Center, United States Department of Agriculture-Agricultural Research Service, 2000 East Allen Road, Tucson AZ 85719, USA.
Math Biosci Eng. 2021 Nov 4;18(6):9606-9650. doi: 10.3934/mbe.2021471.
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.
蜜蜂在农业生产和自然生态系统的稳定中具有不可替代的地位。不幸的是,全球范围内蜜蜂数量正在减少。寄生虫、疾病、营养不良、农药和气候变化是导致蜜蜂种群全球危机的主要因素。数学模型被用来提供关于潜在因素和提高蜂群存活率的重要过程的有用见解。在这篇综述中,我们从不同方面介绍了各种数学可处理模型:1)具有年龄分段、食物收集和营养吸收等特征的简单蜜蜂模型;2)具有寄生虫和/或病原体等其他物种的蜜蜂模型;3)受农药暴露影响的蜜蜂模型。我们旨在通过这些数学模型的综述,强调数学建模在帮助我们理解蜜蜂种群动态及其相关生态群落方面的强大功能。我们还回顾了描述蜜蜂种群动态的计算模型,如 VARROAPOP 和 BEEHAVE,这些模型考虑了温度、降雨、光照、距离和食物质量等因素对蜂群生长和生存的影响。此外,我们提出了关于蜜蜂数学建模的重要方向的未来展望。我们特别鼓励数学家和生物学家之间的合作,以使数学模型通过与实验数据的验证更加有用。