Predicting Nature's Demand for Bees based on Time Series Models
DOI:
https://doi.org/10.54097/hset.v35i.7051Keywords:
Bee Populations; Principal Component Analysis; Time Series Forecasting; Grey Relation Analysis.Abstract
Bees play a very important role in the entire ecosystem, as their pollination ensures the genetic diversity of flowering plants. Therefore, we need to study the factors influencing bee population size and its pollination. In this paper, we build a mathematical model and solve it to obtain the influence of different factors on the number of bee populations, and calculate the number of bees required for pollination in a certain space. Finally, we analyze the advantages and disadvantages of the model, as well as some more discussion.
Downloads
References
Yang, XS. (2012). Flower Pollination Algorithm for Global Optimization. In: Durand-Lose, J., Jonoska, N. (eds) Unconventional Computation and Natural Computation. UCNC 2012. Lecture Notes in Computer Science, vol 7445. Springer, Berlin, Heidel- berg. https://doi.org/10. 1007/978-3-642-32894-7-27.
Eickwort, G.C., Matthews, R.W., & Carpenter, J.E. (1981). Observations on the NestingBehavior of Megachile rubi and M. texana with a Discussion of the Significance of Soil Nesting in the Evolution of Megachilid Bees (Hymenoptera: Megachilidae). Journal of the Kansas Entomological Society, 54, 557-570.
Bascompte, J., Jordano, P., & Olesen, J. M. (2006). Asymmetric coevolutionary net- works facilitate biodiversity maintenance. Science (New York, N.Y.), 312(5772), 431-433. https://doi. org/10. 1126/ science. 1123412.
Kaiser-Bunbury, C.N., Muff, S., Memmott, J., Müller, C.B., & Caflisch, A. (2010). The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecology letters, 13 4, 442-52 .
Vázquez, D.P., Blüthgen, N., Cagnolo, L., & Chacoff, N. (2009). Uniting pattern and process in plant-animal mutualistic networks: a review. Annals of botany, 103 9, 1445-57.
Jeger, M. J., Madden, L. V., & van den Bosch, F. (2009). The effect of transmission route on plant virus epidemic development and disease control. Journal of theoretical biology, 258(2), 198-207. https:// doi. org/ 10. 1016/j.jtbi.2009.01.012.
Huang, Haomin and Mingxin Wang. The reaction-diffusion system for an SIR epidemic model with a free boundary. Discrete and Continuous Dynamical Systems-series B 20 (2015): 2039-2050.
Ngonghala, C.N., Teboh-Ewungkem, M.I., & Ngwa, G.A. (2016). Observance of period-doubling bifurcation and chaos in an autonomous ODE model for malaria with vector demography. Theoretical Ecology, 9, 337-351.
Dalal, N., Greenhalgh, D., & Mao, X. (2008). A stochastic model for internal HIV dynamics. Journal of Mathematical Analysis and Applications, 341, 1084- 1101.
Dafni, A. (1984). Mimicry and Deception in Pollination. Annual Review of Ecology, Evolution, and Systematics, 15, 259-27.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







