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Academic Journal of Environmental Biology, 2021, 2(1); doi: 10.38007/AJEB.2021.020101.

Environmental Biology and Conservation Genetics Research in the Context of Artificial Intelligence

Author(s)

Vareun Verman

Corresponding Author:
Vareun Verman
Affiliation(s)

Chandigarh University, India

Abstract

Due to the deterioration of the environment, many species are in an endangered state. Conservation genetics has emerged for the protection of endangered species. So far, it has a great application in the protection of animals and plants. Conservation genetics is mainly devoted to protecting the genetic diversity and evolutionary potential of populations and providing a theoretical basis for the formulation of conservation strategies. However, for some endangered plants lacking basic genetic information, it is difficult to formulate effective management measures. DNA sequence analysis can provide highly repetitive and informative data, which plays a huge role in population genetics, phylogenetics, environmental biology, and more. In this paper, artificial intelligence technologies such as microsatellite marker technology and DNA sequencing technology were used to explore golden camellia, to explore the genetic diversity of golden camellia, and to put forward suggestions to guide future protection strategies for the survival and evolution of golden camellia.

Keywords

Artificial Intelligence Technology, Environmental Biology, Conservation Genetics, DNA Sequence

Cite This Paper

Vareun Verman. Environmental Biology and Conservation Genetics Research in the Context of Artificial Intelligence. Academic Journal of Environmental Biology (2021), Vol. 2, Issue 1: 1-9. https://doi.org/10.38007/AJEB.2021.020101.

References

[1] Rahman M J , Amin S , Nahiduzzaman M , et al. Influence of seasons, habitat sanctuaries, gears and environmental variables on the catches of hilsa shad (Tenualosa ilisha) in Bangladesh waters. Journal of environmental biology, 2018, 39(5SPEC.):767-776. https://doi.org/10.22438/jeb/39/5(SI)/21

[2] Kardos M . Conservation genetics. Current Biology, 2021, 31(19):R1185-R1190. https://doi.org/10.1016/j.cub.2021.08.047

[3] Aristizabal, Duque, Sandra, et al. Conservation genetics of otters: Review about the use of non-invasive samples.. Therya, 2018, 9(1):85-93. https://doi.org/10.12933/therya-18-515

[4] Ella, Kelly, Ben, et al. Assisted gene flow%conservation genetics%evolutionary rescue%population modelling%population viability analysis. Ecology letters, 2019, 22(3):447-457.

[5] Goodall-Copestake, William, Paul. nrDNA:mtDNA copy number ratios as a comparative metric for evolutionary and conservation genetics. Heredity: An International Journal of Genetics, 2018, 121(2):105-111. https://doi.org/10.1038/s41437-018-0088-8

[6] Sarasola J H , Grande J M , Negro J J . Birds of Prey || Conservation Genetics in Raptors.  2018, 10.1007/978-3-319-73745-4(Chapter 15):339-371.

[7] Koch K , Pink C , Hamilton N , et al. A population genetic study of feral cats on Christmas Island. Australian Journal of Zoology Aust. J. Zool.  2021, 68(3):120-125. https://doi.org/10.1071/ZO20081

[8] Ekici B , Kazanasmaz Z T , Turrin M , et al. Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 1: Background, methodology, setup, and machine learning results. Solar Energy, 2021, 224(2):373-389.

[9] Ilker, Ercanli. Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height. Forest Ecosystems, 2020, v.7(02):3-20. https://doi.org/10.1186/s40663-020-00226-3

[10] Jeste D V , Graham S A , Nguyen T T , et al. Beyond artificial intelligence: exploring artificial wisdom. International Psychogeriatrics, 2020, 32(8):1-9.

[11] Murakami Y . Trends in Ethics of Artificial Intelligence: Significance of Philosophy of Technology in Research and Development. Ieice Ess Fundamentals Review, 2018, 11(3):155-163. https://doi.org/10.1587/essfr.11.3_155

[12] Santos,  R. O , Rehage, et al. Combining data sources to elucidate spatial patterns in recreational catch and effort: fisheries-dependent data and local ecological knowledge applied to the South Florida bonefish fishery. Environmental Biology of Fishes, 2019, 102(2):299-317.

[13] M Fakhar, AN Sabri. Alleviation of nickel stress by halophilic bacteria in mungbean (Vigna radiata). Chinese Journal of Applied and Environmental Biology, 2018, 24(3):465-469.

[14] Trevelline B K , Stephenson J F ,  Kohl K D . Two's company, three's a crowd: Exploring how host–parasite–microbiota interactions may influence disease susceptibility and conservation of wildlife. Molecular Ecology, 2020, 29(8):1402-1405. https://doi.org/10.1111/mec.15397

[15] Thum R A , Chorak G M , Newman R M , et al. Genetic diversity and differentiation in populations of invasive Eurasian (Myriophyllum spicatum) and hybrid (Myriophyllum spicatum × Myriophyllum sibiricum) watermilfoil. Invasive Plant Science and Management, 2020, 13(2):59-67. https://doi.org/10.1017/inp.2020.12

[16] Odzer M N , Brooks A M L , Whitman E R , et al. Effects of environmental factors on the detection of subsurface green turtles in aerial drone surveys. Wildlife Research Wildl. Res.  2021, 49(1):79-88. https://doi.org/10.1071/WR20207

[17] Stern S . Introduction: Artificial intelligence, technology, and the law. University of Toronto Law Journal, 2018, 68(supplement 1):1-11. https://doi.org/10.3138/utlj.2017-0102

[18] Sohn K , Sung C E , Koo G , et al. Artificial Intelligence in the Fashion Industry: Consumer Responses to GAN Technology. International Journal of Retail & Distribution Management, 2021, 49(1):61-80.

[19] Zeba G , Dabic M , Iak M , et al. Technology mining: Artificial intelligence in manufacturing. Technological Forecasting and Social Change, 2021, 171(October 2021):1-18. https://doi.org/10.1016/j.techfore.2021.120971