Simulation of Favorable Habitats for Non-Gregarious Locust Pests in North Kazakhstan Based on Satellite Data for Preventive Measures

• Autorzy: Baibussenov Kurmet , Bekbayeva Aigul , Azhbenov Valery

Identyfikatory

DOI

10.12911/22998993/150043

• Języki publikacji: EN

Abstrakty

EN

The paper considers the approaches and possibilities of using two types of simulation: the species distribution model and the ecological niche model. The study aimed to simulate favorable habitats and the potential spread of non-gregarious locust pests in North Kazakhstan based on satellite and ground data for preventive measures. The MaxEnt software was used to conduct the simulation. According to the species distribution model, high indicators of the habitat are predicted in the Pavlodar and Kostanay regions, on 69.9–100% of the studied territory. With the simulation of ecological niches for non-gregarious locust pests, the following class boundaries were determined for the transition from quantitative to qualitative indicators from I (85–100%) to IV (0–50%), which indicates the zones of the probability of pest attack from a higher indicator to a lower one. According to the fundamental model, high indicators of the area of pest occurrence, that is, zones I and II, are located in the central and northern parts of the Pavlodar region. Here, the probability of non-gregarious locust occurrence of zone I and II with a ratio of 1:1 is observed in a slightly arid, moderately warm agro-climatic zone. In the southern part of the Kostanay region, the simulation predicts the probability of occurrence on zones I and II with a ratio of 1:2 in the moderately arid warm agro-climatic zone of this region. In the southern and southeastern parts of the Akmola region, the model predicts the probability of occurrence in zones I and II with a ratio of 1:3 in a slightly humid, moderately warm agro-climatic zone of the region. The considered species distribution model can be used as a modern tool for long-term forecasting of the spread of non-gregarious locust pests since it takes into account the peculiarities of the agricultural landscape. The fundamental niche model can be used in a long-term population forecast since it focuses more on the theoretical conditions of pest habitats.

Słowa kluczowe

EN

non-gregarious locust; distribution; phytosanitary forecasting; ecological niche; North Kazakhstan

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2022
• Tom: Vol. 23, nr 7
• Strony: 299--311
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Baibussenov Kurmet

• S. Seifullin Kazakh Agro Technical University, 62 Zhenis Ave., 010011, Nur-Sultan, Republic of Kazakhstan

• https://orcid.org/0000-0002-9957-3073

autor

Bekbayeva Aigul

• S. Seifullin Kazakh Agro Technical University, 62 Zhenis Ave., 010011, Nur-Sultan, Republic of Kazakhstan

• https://orcid.org/0000-0002-3477-1888

autor

Azhbenov Valery

• Zh. Zhyembaev Kazakh Scientific Research Institute of Plant Protection and Quarantine, 1 Kultobe Str., 050000, Almaty, Republic of Kazakhstan

• https://orcid.org/0000-0003-0469-9579

Bibliografia

• 1. Adu-Acheampong, S., Samways, M., Landmann, T., Kyerematen, R., Minkah, R., Mukundamago, M., Moshobane, C. 2017. Endemic grasshopper species distribution in an agro-natural landscape of the Cape Floristic Region, South Africa. Ecological Engineering, 105, 133–140.
• 2. Aguilar, M., Lado, C. 2012. Ecological niche models reveal the importance of climate variability for the biogeography of protosteloid amoebae. The ISME Journal, 6, 1506–1514.
• 3. Akmollaeva, A.S. 2004. Species of locusts in different biotopes. In: International scientific conference “Strategy of scientific support of the agro-industrial complex of the Republic of Kazakhstan in the fields of agriculture, plant growing and horticulture: Reality and prospects”. Book 2. Izdatel’stvo “Agrouniversitet”, Almaty 2004, 202–203. (in Kazakh)
• 4. Azhbenov, V.K. 2013. Guidance for monitoring the Italian locust using GPS technology. Astana, 41. (in Russian)
• 5. Azhbenov, V.K., Baibussenov, K.S., Sarbaev, A.T., Harizanova, V.B. 2015. Preventive approach of phytosanitary control of locust pests in Kazakhstan and adjacent areas. Proceedings of Conference IICBE-2015, Penang, Malaysia 2015, 33–37.
• 6. Baibusenov, K.S., Azhbenov, V.K., Suieubaev, O.A., Bekbaeva, A.M., Yatsyuk, S.V. 2020. The phytosanitary condition of agricultural lands of Northern Kazakhstan regarding the development and distribution of harmful non-gregarious locusts. Vestnik nauki KATU im. S. Seifullina, 3(106), 16–24. (in Russian)
• 7. Baibussenov, K.S., Sarbaev, A.T., Azhbenov, V.K., Harizanova, V.B. 2015. Predicting the phase state of the abundance dynamics of harmful non-gregarious locusts in Northern Kazakhstan and substantiation of protective measures. Biosciences Biotechnology Research Asia, 12(2), 1535–1543.
• 8. Booth, T.H., Nix, H.A., Busby, J.R., Hutchinson, M.F. 2013. Bioclim: The first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Diversity and Distributions, 20(1), 1–9.
• 9. Childebaev, M.K. 2002. On the fauna and ecology of locusts (Orthoptera, Acridoidea, Tetrigoidea) of the Irtysh Plateau. Tethys Entomological Reseach, 6, 5–12. (in Russian)
• 10. Colwell, R.K., Rangel, T.F. 2009. Hutchinson’s duality: The once and future niche. Proceedings of the National Academy of Sciences, 106, 19651–19658.
• 11. Cressman, K. 2013. Role of remote sensing in desert locust early warning. Journal of Applied Remote Sensing, 7(1), 075098. https://doi.org/10.1117/1.JRS.7.075098
• 12. Elith, J., Phillipps, S.J., Hastie, T., Dudik, M., Chee, Y.E., Yates, C. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43–57.
• 13. Hutchinson, G.E. 1957. Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22, 415–427.
• 14. Jakob, J. 2001. The Shuttle Radar Topography Mission (SRTM): A breakthrough in remote sensing of topography. Acta Astronautica, 48(5–12), 559–565.
• 15. Kambulin, V.E. 2018. Locusts: Methods for assessing damage, population forecast and technologies for identifying populated areas. Almaty, 300. (in Russian)
• 16. Kambulin, V.E., Niyazbekov, Z.B., Muratova, N.R., Tsychueva, N.Y. 2015. Recommendations for locust monitoring using the GIS technology and remote sensing of the Earth. Izd-vo KazNIIZKR, Almaty, 28. (in Russian)
• 17. Kimathi, E., Tonnang, H., Subramanian, S., Cressman, K., Abdel-Rahman, E., Tesfayohannes, M., Niassy, S., Torto, B., Dubois, T., Tanga, C., Berresaw, M., Ekesi, S., Mwangi, D., Kelemu, S. 2020. Prediction of breeding regions for the desert locust Schistocerca gregaria in East Africa. Scientific Reports, 10, 11937. https://doi.org/10.1038/s41598-020-68895-2
• 18. Klein, I., van der Woude, S., Schwarzenbacher, F., Muratova, N., Slagter, B., Malakhov, D., Oppelt, N., Kuenzer, C. 2022. Predicting suitable breeding areas for different locust species – A multi-scale approach accounting for environmental conditions and current land cover situation. International Journal of Applied Earth Observation and Geoinformation, 107, 102672. https://doi.org/10.1016/j.jag.2021.102672
• 19. Latchininsky, A., Piou, C., Franc, A., Soti, V. 2016. Applications of remote sensing to locust management. In: N. Baghdadi (Ed.), Land surface remote sensing: Environment and risks. Elsevier, 263–293.
• 20. Latchininsky, A.V., Sivanpillai, R. 2010. Locust habitat monitoring and risk assessment using remote sensing and GIS technologies. Integrated Management of Arthropod Pests and Insect Borne Diseases, 5, 163–188.
• 21. Le Gall, M., Overson, R., Cease, A.A. 2019. Global review on locusts (Orthoptera: Acrididae) and their interactions with livestock grazing practices. Frontiers in Ecology and Evolution, 7, 263.
• 22. Malakhov, D.V., Tsychuyeva, N.Y., Kambulin, V.E. 2018. Ecological simulation of Locusta migratoria L. breeding conditions in south-eastern Kazakhstan. Russian Journal of Ecosystem Ecology, 3(1), 14.
• 23. Malakhov, D.V., Zlatanov, B.V. 2020. An ecological niche model for Dociostaurus maroсcanus, Thunberg, 1815 (Orthoptera, Acrididae): The nesting environment and survival of egg-pods. Biosis: Biological Systems, 1(1), 8–24.
• 24. Merow, C., Smith, M.J., Silander, J.A. 2013. A practical guide to Maxent for simulation species’ distributions: What it does, and why inputs and settings matter. Ecography, 36(10), 1058–1069.
• 25. Orlov, M., Sheludkov, A. 2019. Bioclimatic data optimization for spatial distribution models. In: I. Bychkov, V. Voronin (Eds.), Information technologies in the research of biodiversity. Springer proceedings in earth and environmental sciences. Springer, Cham, 86–95.
• 26. Phillips, S.J., Anderson, R.P., Schapire, R.E. 2006. Maximum entropy simulation of species geographic distributions. Ecological Modelling, 190(3–4), 231–259.
• 27. Sagitov, A.O., Duisembekov, B.A. 2016. Phytosanitary monitoring of harmful and especially dangerous harmful organisms (pests, diseases, weeds): A manual. 3rd edition in the Kazakh language. Kazakhskii NIIZiKR, Almaty, 376. (in Kazakh)
• 28. Sergeev, M.G. 2010. Harmful locusts in Russia and neighboring regions: Past, present, future. Zashchita i karantin rastenii, 1, 18–22. (in Russian)
• 29. Spivak, L., Vitkovskaya, I., Batyrbaeva, M., Kauazov, A. 2011. Drought risk assessment for the regions of Kazakhstan based on long-term ERS data. Kosmicheskie issledovaniya i tekhnologii, 1, 33–37. (in Russian)
• 30. Spivak, L.F., Batyrbaeva, M.Zh., Vitkovskaya, I.S., Muratova, N.R., Islamgulova, A.F. 2017. Spatial and temporal features of the change in the state of the steppe vegetation of Kazakhstan according to satellite imagery. Ekosistemy: Ekologiya i dinamika, 1(3), 116–145. (in Russian)
• 31. Van Huis, A., Cressman, K., Magor, J.I. 2007. Preventing desert locust plagues: Optimizing management interventions. Entomologia Experimentalis et Applicata, 122(3), 191–214.
• 32. Zhang, L., Lecoq, M., Latchininsky, A., Hunter, D. 2019. Locust and grasshopper management. Annual Review of Entomology, 64, 15–34.