蜂群动态和蜂巢安置的数学模型

Zixuan Zhang, Dongyi He, Hanwen Zhang
{"title":"蜂群动态和蜂巢安置的数学模型","authors":"Zixuan Zhang, Dongyi He, Hanwen Zhang","doi":"10.1145/3590003.3590070","DOIUrl":null,"url":null,"abstract":"Animal pollinators have been supporting the lives of human beings on Earth. Bee pollinators are the biggest contributors to the pollination of crops, providing humans with food. Given such circumstances, this paper investigates the population of honeybee colonies and the processes of bee pollination. We constructed Honeybee Colony Population Model (BCPM) to predict the population of a honeybee colony over time. We first outlined the life cycle of a honeybee, including eggs, larval stage, pupal stage, and adult bee stage. Within the adult bee stage, bees transition back and forth between foragers and hive bees depending on the number of available resources and the workload of nursing tasks. By listing out factors that affect the population in each stage, we established equations representing the rate of change in each of the stages of a honeybee's life cycle, as well as an equation describing the change in resource storage. We also evaluated the death rate and the resources in each month of the year and calculated each group's typical maximum, minimum, and mean population in a honeybee colony: 3 years after the establishment of the colony, the total adult population follows a seasonal change with recurring patterns each year, giving a maximum of 100862 bees and a minimum of 35676 bees. The annual average population is found to be 64877 bees. We then conducted a sensitivity analysis on BCPM and found that the initial number of bee hives and the initial amount of available resources have the most significant impact on the population of the colony. We also observed an unusual pattern in the cross-analysis of the two factors and constructed Simplified Colony Collapse Disorder Model (SCCDM) to predict whether a colony will collapse using only one equation. In response to estimate the number of hives needed to support the pollination of a specific land area, we constructed Hive Deployment Model (HDM). We first divided the land into 20 nodes and then found the most appropriate locations to place the hives. After establishing the equations for movements between nodes per day per forager group, we developed an iterating algorithm to find the number of hives needed to pollinate crops on 20 acres of land. We collected data for 9 typical bee-pollinated plants and found the number of hives needed for each type of plant based on the algorithm, with blueberries being the most demanding, requiring 83 hives, whilst apples and roses only required 2 hives at the other end of the spectrum. Then, we established a sensitivity analysis to ensure the stability of the model by changing two arbitrary parameters. Finally, we discussed the potential advantages and disadvantages of our model. We have also created a non-technical blog that summarizes our investigation, presenting our results in a simplified way","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mathematical models of colony population dynamics and hive placement\",\"authors\":\"Zixuan Zhang, Dongyi He, Hanwen Zhang\",\"doi\":\"10.1145/3590003.3590070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Animal pollinators have been supporting the lives of human beings on Earth. Bee pollinators are the biggest contributors to the pollination of crops, providing humans with food. Given such circumstances, this paper investigates the population of honeybee colonies and the processes of bee pollination. We constructed Honeybee Colony Population Model (BCPM) to predict the population of a honeybee colony over time. We first outlined the life cycle of a honeybee, including eggs, larval stage, pupal stage, and adult bee stage. Within the adult bee stage, bees transition back and forth between foragers and hive bees depending on the number of available resources and the workload of nursing tasks. By listing out factors that affect the population in each stage, we established equations representing the rate of change in each of the stages of a honeybee's life cycle, as well as an equation describing the change in resource storage. We also evaluated the death rate and the resources in each month of the year and calculated each group's typical maximum, minimum, and mean population in a honeybee colony: 3 years after the establishment of the colony, the total adult population follows a seasonal change with recurring patterns each year, giving a maximum of 100862 bees and a minimum of 35676 bees. The annual average population is found to be 64877 bees. We then conducted a sensitivity analysis on BCPM and found that the initial number of bee hives and the initial amount of available resources have the most significant impact on the population of the colony. We also observed an unusual pattern in the cross-analysis of the two factors and constructed Simplified Colony Collapse Disorder Model (SCCDM) to predict whether a colony will collapse using only one equation. In response to estimate the number of hives needed to support the pollination of a specific land area, we constructed Hive Deployment Model (HDM). We first divided the land into 20 nodes and then found the most appropriate locations to place the hives. After establishing the equations for movements between nodes per day per forager group, we developed an iterating algorithm to find the number of hives needed to pollinate crops on 20 acres of land. We collected data for 9 typical bee-pollinated plants and found the number of hives needed for each type of plant based on the algorithm, with blueberries being the most demanding, requiring 83 hives, whilst apples and roses only required 2 hives at the other end of the spectrum. Then, we established a sensitivity analysis to ensure the stability of the model by changing two arbitrary parameters. Finally, we discussed the potential advantages and disadvantages of our model. We have also created a non-technical blog that summarizes our investigation, presenting our results in a simplified way\",\"PeriodicalId\":340225,\"journal\":{\"name\":\"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3590003.3590070\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3590003.3590070","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

动物传粉者一直支持着地球上人类的生命。蜜蜂授粉者是农作物授粉的最大贡献者,为人类提供食物。在这种情况下,本文研究了蜂群的种群和蜜蜂授粉的过程。我们建立了蜂群种群模型(BCPM)来预测一个蜂群随时间的种群数量。我们首先概述了蜜蜂的生命周期,包括卵、幼虫期、蛹期和成虫期。在成年蜜蜂阶段,蜜蜂根据可用资源的数量和护理任务的工作量在觅食蜂和蜂群之间来回转换。通过列出每个阶段影响种群的因素,我们建立了代表蜜蜂生命周期每个阶段变化率的方程,以及描述资源储存变化的方程。我们还评估了一年中每个月的死亡率和资源,并计算了每个蜂群的典型最大值、最小值和平均种群数量:蜂群建立3年后,每年的成年总种群数量遵循季节性变化,并重复出现模式,最大值为100862只,最小值为35676只。年平均蜜蜂数量为64877只。然后,我们对BCPM进行了敏感性分析,发现初始蜂箱数量和初始可用资源数量对蜂群种群的影响最为显著。我们还观察到这两个因素在交叉分析中的不寻常模式,并构建了简化的群体崩溃失调模型(SCCDM),仅使用一个方程来预测群体是否会崩溃。为了估计支持特定土地区域授粉所需的蜂箱数量,我们构建了蜂箱部署模型(Hive Deployment Model, HDM)。我们首先将土地划分为20个节点,然后找到最合适的位置放置蜂箱。在建立了每个采集者群体每天在节点之间移动的方程之后,我们开发了一个迭代算法来计算20英亩土地上为作物授粉所需的蜂箱数量。我们收集了9种典型的蜜蜂授粉植物的数据,并根据算法找到了每种植物所需的蜂箱数量,其中蓝莓要求最高,需要83个蜂箱,而苹果和玫瑰只需要2个蜂箱。然后,通过改变任意两个参数,建立敏感性分析,保证模型的稳定性。最后,我们讨论了该模型的潜在优点和缺点。我们还创建了一个非技术博客来总结我们的调查,以一种简化的方式展示我们的结果
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mathematical models of colony population dynamics and hive placement
Animal pollinators have been supporting the lives of human beings on Earth. Bee pollinators are the biggest contributors to the pollination of crops, providing humans with food. Given such circumstances, this paper investigates the population of honeybee colonies and the processes of bee pollination. We constructed Honeybee Colony Population Model (BCPM) to predict the population of a honeybee colony over time. We first outlined the life cycle of a honeybee, including eggs, larval stage, pupal stage, and adult bee stage. Within the adult bee stage, bees transition back and forth between foragers and hive bees depending on the number of available resources and the workload of nursing tasks. By listing out factors that affect the population in each stage, we established equations representing the rate of change in each of the stages of a honeybee's life cycle, as well as an equation describing the change in resource storage. We also evaluated the death rate and the resources in each month of the year and calculated each group's typical maximum, minimum, and mean population in a honeybee colony: 3 years after the establishment of the colony, the total adult population follows a seasonal change with recurring patterns each year, giving a maximum of 100862 bees and a minimum of 35676 bees. The annual average population is found to be 64877 bees. We then conducted a sensitivity analysis on BCPM and found that the initial number of bee hives and the initial amount of available resources have the most significant impact on the population of the colony. We also observed an unusual pattern in the cross-analysis of the two factors and constructed Simplified Colony Collapse Disorder Model (SCCDM) to predict whether a colony will collapse using only one equation. In response to estimate the number of hives needed to support the pollination of a specific land area, we constructed Hive Deployment Model (HDM). We first divided the land into 20 nodes and then found the most appropriate locations to place the hives. After establishing the equations for movements between nodes per day per forager group, we developed an iterating algorithm to find the number of hives needed to pollinate crops on 20 acres of land. We collected data for 9 typical bee-pollinated plants and found the number of hives needed for each type of plant based on the algorithm, with blueberries being the most demanding, requiring 83 hives, whilst apples and roses only required 2 hives at the other end of the spectrum. Then, we established a sensitivity analysis to ensure the stability of the model by changing two arbitrary parameters. Finally, we discussed the potential advantages and disadvantages of our model. We have also created a non-technical blog that summarizes our investigation, presenting our results in a simplified way
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信