Citation:
Bezymennyi M., Tarasov O. Ecological niche modeling of bacillus anthracis in Ukraine using the maxent algorithm. Veterinary Biotechnology, 48, 9−31. https://doi.org/10.31073/vet_biotech48-01
BEZYMENNYI M., Postgraduate Student, This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0000-0001-8042-0061
TARASOV O., Cand. Sci. (Vet. Med.), Sr. Researcher, This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0000-0003-1481-5529
Institute of Veterinary Medicine of the NAAS
ECOLOGICAL NICHE MODELING OF BACILLUS ANTHRACIS IN UKRAINE USING THE MAXENT ALGORITHM
Introduction. Ukraine is known for its numerous anthrax outbreaks in the past and the large number of burial sites for dead animals. The ability of the pathogen’s spores to remain viable in the soil for an indefinite period of time poses a risk of new outbreaks in the country. It is known that the ability of the anthrax pathogen to persist in the soil of certain areas depends on the interaction of biotic and abiotic environmental factors. Modeling the pathogen’s ecological niche can help identify such areas and assist in determining the risks of new outbreaks.
The goal of the work. To identify the regions of Ukraine that have the most favorable environmental conditions for the long-term survival of the anthrax pathogen.
Materials and methods. The model was developed using the Maxent algorithm based on georeferenced data on anthrax outbreaks and a set of environmental predictors, including climatic, edaphic, and vegetation-related variables transformed through principal component analysis. The model performance was evaluated using partial ROC analysis, omission rates, and the Akaike information criterion. Prediction uncertainty was estimated using bootstrap replicates, and an independent dataset (2001–2022) was used for validation.
Results of research and discussion. The areas most suitable for B. anthracis cover a significant portion of Ukraine. The highest suitability was found in the Central, Southern, and Eastern regions. The Northern and mountainous areas of the country proved to be less suitable. Soil-related predictors made the greatest contribution to the model. The analysis of uncertainty revealed spatial variability in prediction reliability and allowed identification of the most stable suitable areas. The use of independent temporal validation confirmed the model’s ability to reproduce the spatial patterns of pathogen spread beyond the calibration sample.
Conclusions and prospects for further research. The results obtained may have practical significance for the anthrax epidemiological surveillance system and can be used to optimize sampling plans, monitor soil reservoirs of infection, and assess the risk of disease outbreaks. A promising direction for future research could be the integration of data on land use, livestock density, and anthropogenic pressure into the model, as well as the consideration of climate change and model projections in future climate scenarios.
Keywords: anthrax, Bacillus anthracis, risk assessment, zoonosis, ecological niche, Maxent.
REFERENCES
1. Carlson, C. J., Getz, W. M., Kausrud, K. L., Cizauskas, C. A., Blackburn, J. K., Bustos Carrillo, F. A., Colwell, R., Easterday, W. R., Ganz, H. H., Kamath, P. L., Økstad, O. A., Turner, W. C., Kolstø, A.-B., & Stenseth, N. C. (2018). Spores and soil from six sides: interdisciplinarity and the environmental biology of anthrax (Bacillus anthracis). Biological Reviews, 93(4), 1813–1831. https://doi.org/10.1111/brv.12420
2. Книш, О., & Мартинов, А. (2022). Особливості дослідження процесів споруляції та гермінації у представників роду Bacillus. Annals of Mechnikov’s Institute, 2, Article 2. https://doi.org/10.5281/zenodo.6634727
3. Driciru, M., Rwego, I. B., Ndimuligo, S. A., Travis, D. A., Mwakapeje, E. R., Craft, M., Asiimwe, B., Alvarez, J., Ayebare, S., & Pelican, K. (2020). Environmental determinants influencing anthrax distribution in Queen Elizabeth Protected Area, Western Uganda. PLOS ONE, 15(8), e0237223. https://doi.org/10.1371/journal.pone.0237223
4. Arotolu, T., Wang, H., Lv, J., Shi, K., Gils, H., & Wang, X.-L. (2022). Modeling the environmental suitability for Bacillus anthracis in the Qinghai Lake Basin, China. PLoS ONE, 17(10), e0275261. https://doi.org/10.1371/journal.pone.0275261
5. Deka, M., R Vieira, A., & Bower, W. (2022). Modelling the ecological niche of naturally occurring anthrax at global and circumpolar extents using an ensemble modelling framework. Transboundary and Emerging Diseases. https://doi.org/10.1111/tbed.14602
6. Hugh-Jones, M., & Blackburn, J. (2009). The ecology of Bacillus anthracis. Molecular Aspects of Medicine, 30(6), 356. https://doi.org/10.1016/j.mam.2009.08.003
7. Carlson, C. J., Kracalik, I. T., Ross, N., Alexander, K. A., Hugh-Jones, M. E., Fegan, M., Elkin, B. T., Epp, T., Shury, T. K., Zhang, W., Bagirova, M., Getz, W. M., & Blackburn, J. K. (2019). The global distribution of Bacillus anthracis and associated anthrax risk to humans, livestock and wildlife. Nature Microbiology, 4(8), 1337–1343. https://doi.org/10.1038/s41564-019-0435-4
8. Khalaf, S. M. H., Alqahtani, M. S. M., Selim, Y. A., Elsayed, K. O., & Bendary, H. A. (2025). Spatiotemporal dynamics of Bacillus anthracis under climate change: a machine learning approach. Frontiers in Microbiology, 16. https://doi.org/10.3389/fmicb.2025.1659876
9. Gachohi, J., Bett, B., Karanja, S., Kikuvi, G., Nyamai, M., Mwangi, T., & Njenga, K. (2019). A systematic mapping protocol of methods and practices employed in ecological niche modelling of anthrax. Global Epidemiology, 1, 100014. https://doi.org/10.1016/j.gloepi.2019.100014
10. Romero-Alvarez, D., Peterson, A. T., Salzer, J. S., Pittiglio, C., Shadomy, S., Traxler, R., Vieira, A. R., Bower, W. A., Walke, H., & Campbell, L. P. (2020). Potential distributions of Bacillus anthracis and Bacillus cereus biovar anthracis causing anthrax in Africa. PLOS Neglected Tropical Diseases, 14(3), e0008131. https://doi.org/10.1371/journal.pntd.0008131
11. John, L., Shekede, M. D., Gwitira, I., Mazhindu, A. N., Pfukenyi, D. M., & Chikerema, S. (2024). Modelling climate change impacts on the spatial distribution of anthrax in Zimbabwe. BMC Public Health, 24(1), Article 1. https://doi.org/10.1186/s12889-024-17856-9
12. Walsh, M. G., de Smalen, A. W., & Mor, S. M. (2018). Climatic influence on anthrax suitability in warming northern latitudes. Scientific Reports, 8(1), Article 1. https://doi.org/10.1038/s41598-018-27604-w
13. Otieno, F. T., Gachohi, J., Gikuma-Njuru, P., Kariuki, P., Oyas, H., Canfield, S. A., Bett, B., Njenga, M. K., & Blackburn, J. K. (2021). Modeling the Potential Future Distribution of Anthrax Outbreaks under Multiple Climate Change Scenarios for Kenya. International Journal of Environmental Research and Public Health, 18(8), Article 8. https://doi.org/10.3390/ijerph18084176
14. Escobar, L. E. (2020). Ecological Niche Modeling: An Introduction for Veterinarians and Epidemiologists. Frontiers in Veterinary Science, 7. https://doi.org/10.3389/fvets.2020.519059
15. Islam, S., Castaneda-Guzman, M., Soler-Tovar, D., & Escobar, L. (2025). Ecological Niche Modeling Applications to Infectious Diseases. Biodiversity Informatics, 19. https://doi.org/10.17161/bi.v19i.23725
16. Blackburn, J., Matakarimov, S., Kozhokeeva, S., Tagaeva, Z., Bell, L., Kracalik, I., & Zhunushov, A. (2017). Modeling the Ecological Niche of Bacillus anthracis to Map Anthrax Risk in Kyrgyzstan. The American Journal of Tropical Medicine and Hygiene, 96, 16–0758. https://doi.org/10.4269/ajtmh.16-0758
17. Gachohi, J., Bett, B., Otieno, F., Mogoa, E., Njoki, P., Muturi, M., Mwatondo, A., Osoro, E., Ngere, I., Dawa, J., Nasimiyu, C., Oyas, H., Njagi, O., Canfield, S., Blackburn, J., & Njenga, K. (2022). Anthrax hotspot mapping in Kenya support establishing a sustainable two-phase elimination program targeting less than 6% of the country landmass. Scientific Reports, 12(1), 21670. https://doi.org/10.1038/s41598-022-24000-3
18. Blackburn, J., Mcnyset, K., Curtis, A., & Hugh-Jones, M. (2007). Modeling the Geographic Distribution of Bacillus anthracis, the Causative Agent of Anthrax Disease, for the Contiguous United States using Predictive Ecologic Niche Modeling. The American Journal of Tropical Medicine and Hygiene, 77(6), 1103–1110. https://doi.org/10.4269/ajtmh.2007.77.1103
19. Joyner, T. A., Lukhnova, L., Pazilov, Y., Temiralyeva, G., Hugh-Jones, M. E., Aikimbayev, A., & Blackburn, J. K. (2010). Modeling the Potential Distribution of Bacillus anthracis under Multiple Climate Change Scenarios for Kazakhstan. PLOS ONE, 5(3), e9596. https://doi.org/10.1371/journal.pone.0009596
20. Mullins, J., Lukhnova, L., Aikimbayev, A., Pazilov, Y., Van Ert, M., & Blackburn, J. K. (2011). Ecological Niche Modelling of the Bacillus anthracis A1.a sub-lineage in Kazakhstan. BMC Ecology, 11(1), 32. https://doi.org/10.1186/1472-6785-11-32
21. Barro, A., Moloney, B., Porter, K., Muller, J., & Blackburn, J. (2016). Redefining the Australian Anthrax Belt: Modeling the Ecological Niche and Predicting the Geographic Distribution of Bacillus anthracis. PLOS Neglected Tropical Diseases, 10, e0004689. https://doi.org/10.1371/journal.pntd.0004689
22. Kracalik, I. T., Kenu, E., Ayamdooh, E. N., Allegye-Cudjoe, E., Polkuu, P. N., Frimpong, J. A., Nyarko, K. M., Bower, W. A., Traxler, R., & Blackburn, J. K. (2017). Modeling the environmental suitability of anthrax in Ghana and estimating populations at risk: Implications for vaccination and control. PLOS Neglected Tropical Diseases, 11(10), e0005885. https://doi.org/10.1371/journal.pntd.0005885
23. Pittiglio, C., Shadomy, S., Idrissi, A., Soumare, B., Lubroth, J., & Makonnen, Y. (2022). Seasonality and Ecological Suitability Modelling for Anthrax (Bacillus anthracis) in Western Africa. Animals, 12, 1146. https://doi.org/10.3390/ani12091146
24. Arede, M., Allepuz, A., Beltran-Alcrudo, D., Casal, J., & Romero-Alvarez, D. (2024). Suitability of anthrax (Bacillus anthracis) in the Black Sea basin through the scope of distribution modelling. PLOS ONE, 19(11), e0303413. https://doi.org/10.1371/journal.pone.0303413
25. Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
26. Valavi, R., Guillera-Arroita, G., Lahoz-Monfort, J. J., & Elith, J. (2022). Predictive performance of presence-only species distribution models: a benchmark study with reproducible code. Ecological Monographs, 92(1), e01486. https://doi.org/10.1002/ecm.1486
27. Bezymennyi, M., Bagamian, K. H., Barro, A., Skrypnyk, A., Skrypnyk, V., & Blackburn, J. K. (2014). Spatio-temporal patterns of livestock anthrax in Ukraine during the past century (1913–2012). Applied Geography, 54, 129–138. https://doi.org/10.1016/j.apgeog.2014.07.016
28. Korniienko, L. Y., Ukhovskyi, V. V., Moroz, O. A., Chechet, O. M., Haidei, O. S., Tsarenko, T. M., Bondarenko, T. M., Karpulenko, M. S., & Nenych, N. P. (2022). Epizootological and epidemiological situation of anthrax in Ukraine in the context of mandatory specific prevention in susceptible animals. Regulatory Mechanisms in Biosystems, 13(4), Article 4. https://doi.org/10.15421/022245
29. Kozytska, T., Bassiouny, M., Chechet, O., Ordynska, D., Galante, D., Neubauer, H., & Wareth, G. (2023). Retrospective Analysis of Official Data on Anthrax in Europe with a Special Reference to Ukraine. Microorganisms, 11(5), Article 5. https://doi.org/10.3390/microorganisms11051294
30. Безименний, М., & Тарасов, О. (2024). Просторовий розподіл та активність стаціонарно неблагополучних за сибіркою пунктів в Україні. Бюлетень «Ветеринарна біотехнологія», 44, 9–28. https://doi.org/10.31073/vet_biotech44-01
31. Hay, S. I., Tatem, A. J., Graham, A. J., Goetz, S. J., & Rogers, D. J. (2006). Global environmental data for mapping infectious disease distribution. Advances in Parasitology, 62, 37–77. https://doi.org/10.1016/S0065-308X(05)62002-7
32. Runfola, D., Anderson, A., Baier, H., Crittenden, M., Dowker, E., Fuhrig, S., Goodman, S., Grimsley, G., Layko, R., Melville, G., Mulder, M., Oberman, R., Panganiban, J., Peck, A., Seitz, L., Shea, S., Slevin, H., Youngerman, R., & Hobbs, L. (2020). geoBoundaries: A global database of political administrative boundaries. PLOS ONE, 15(4), e0231866. https://doi.org/10.1371/journal.pone.0231866
33. Cobos, M. E., Peterson, A. T., Barve, N., & Osorio-Olvera, L. (2019). kuenm: an R package for detailed development of ecological niche models using Maxent. PeerJ, 7, e6281. https://doi.org/10.7717/peerj.6281
