Have a personal or library account? Click to login
Comparing the effectiveness of random forest and generalized linear models in predicting ungulate browsing impact on Kyiv Polissya’s young pine forests Cover

Comparing the effectiveness of random forest and generalized linear models in predicting ungulate browsing impact on Kyiv Polissya’s young pine forests

Open Access
|Mar 2025

References

  1. Bergqvist, G., Bergström, R. Edenius, L. 2001. Patterns of stem damage by moose (Alces alces) in young Pinus sylvestris stands in Sweden. Scandinavian Journal of Forest Research, 16 (4), 363–370.
  2. Bergvall, U.A., Leimar, O. 2017. Directional associational plant defense from red deer (Cervus elaphus) foraging decisions. Ecosphere, 8 (3), e01714.
  3. Bondar, O., Rumiantsev, M., Tkach, L., Obolonyk, I. 2020. Prevailing forest types in the river catchments within the Left-Bank Forest-Steppe zone, Ukraine. Folia Forestalia Polonica, Series A – Forestry, 62 (2), 100–113.
  4. Champagne, E., Raymond, P., Royo, A.A., Speed, J.D., Tremblay, J.P., Côté, S.D. 2021. A review of ungulate impacts on the success of climate-adapted forest management strategies. Current Forestry Reports, 1–16.
  5. D’Aprile, D. et al. 2020. Effects of twenty years of ungulate browsing on forest regeneration at Paneveggio Reserve, Italy. Forests, 11 (6), 612.
  6. Ecological passport of Kyiv region (in Ukrainian). 2021. Regional State Administration, Kyiv. Available at https://mepr.gov.ua/news/37742.html (access on 1 March 2011).
  7. Harris, G., Nielson, R.M., Rinaldi, T., Lohuis, T. 2014. Effects of winter recreation on northern ungulates with focus on moose (Alces alces) and snowmobiles. European Journal of Wildlife Research, 60, 45–58.
  8. Hurley, M.A., Hebblewhite, M., Lukacs, P.M., Nowak, J.J., Gaillard, J.M., Bonenfant, C. 2017. Regional-scale models for predicting overwinter survival of juvenile ungulates. The Journal of Wildlife Management, 81 (3), 364–378.
  9. Hofmann, R.R. 1989. Evolutionary steps of ecophysiological adaptation and diversification of ruminants: a comparative view of their digestive system. Oecologia, 78 (4), 443–457.
  10. Illanas, S. et al. 2022. New models for wild ungulates occurrence and hunting yield abundance at European scale. EFSA Supporting Publications, 19, 7631E.
  11. James, G., Witten, D., Hastie, T., Tibshirani, R., Taylor, J. 2023. An introduction to statistical learning: With applications in Python. Springer Nature.
  12. Kupferschmid, A.D. 2018. Selective browsing behavior of ungulates influences the growth of Abies alba differently depending on forest type. Forest Ecology and Management, 429, 317–326.
  13. Lindmark, M., Sunnerheim, K., Jonsson, B.G. 2020. Natural browsing repellent to protect Scots pine Pinus sylvestris from European moose Alces alces. Forest Ecology and Management, 474 (1), 118347.
  14. Long, Z.T., Pendergast, T.H., Carson, W.P. 2007. The impact of deer on relationships between tree growth and mortality in an old-growth beech-maple forest. Forest Ecology and Management, 252, 230–238.
  15. Månsson, J., Kalén, C., Kjellander, P., Andrén, H., Smith, H. 2007. Quantitative estimates of tree species selectivity by moose (Alces alces) in a forest landscape. Scandinavian Journal of Forest Research, 22 (5), 407–414.
  16. Migunova, Ye.S. 1993. Forests and forest lands (Quantitative evaluation of interactions) (in Russian). Ecology, Moscow.
  17. Moezzi, F., Poorbagher, H., Eagderi, S., Feghhi, J. 2023. Comparing the performance of generalized linear model (GLM) and random forest (RF) models in predicting catch distribution of Caspian Kutum (Rutilus frisii). Journal of Fisheries, 76 (1), 27–38.
  18. Niyogi, R., Sarkar, M.S., Hazra, P., Rahman, M., Banerjee, S., John, R. 2021. Habitat connectivity for the conservation of small ungulates in a humandominated landscape. ISPRS International Journal of Geo-Information, 10 (3), 180.
  19. Ostapenko, B.F., Tkach, V.P. 2002. Forest typology (in Ukrainian). Pleyada, Kharkiv.
  20. Pascual-Rico, R. et al. 2021. Usually hated, sometimes loved: A review of wild ungulates’ contributions to people. Science of The Total Environment, 801, 149652.
  21. Pedregosa, F. et al. 2011 Scikit-learn: machine learning in Python. The Journal of Machine Learning Research, 12, 2825–2830.
  22. Perea, R., Gil, L. 2014. Tree regeneration under high levels of wild ungulates: The use of chemically vs. physically-defended shrubs. Forest Ecology and Management, 312, 47–54.
  23. Pfeffer, S.E., Singh, N.J., Cromsigt, J.P.G.M., Kalén, C., Widemo, F. 2021. Predictors of browsing damage on commercial forests – A study linking nationwide management data. Forest Ecology and Management, 479, 118597.
  24. Python Software Foundation. Python Language Reference. Available at http://www. python.org.
  25. Ramirez, J.I., Jansen, P.A., den Ouden, J., Goudzwaard, L., Poorter, L. 2019. Longterm effects of wild ungulates on the structure, composition and succession of temperate forests. Forest Ecology and Management, 432, 478–488.
  26. Spathelf, P., Lavnyy, V., Matysevych, O., Danchuk, O. 2024. German-Ukrainian efforts towards building climate-resilient forests in Western Ukraine – first results of alternative regeneration systems. South-East European Forestry, 15 (1), 81–89.
  27. Shadura, M.V., Gulyk, I.T., Shadura, A.M. 2004. Damage of forest plantations by wild boar (Sus scrofa L.) and roe-deer (Capreolus capreolus L.) on Polissya of Ukraine (in Ukrainian). Scientific Bulletin of UNFU, 14 (8), 426–433.
  28. Shanley, C.S., Eacker, D.R., Reynolds, C.P., Bennetsen, B.M., Gilbert, S.L. 2021. Using LiDAR and Random Forest to improve deer habitat models in a managed forest landscape. Forest Ecology and Management, 499, 119580.
  29. Skorski, M. 2025. Handy formulas for binomial moments. Modern Stochastics: Theory and Applications, 12 (1), 27–41.
  30. Song, L., Langfelder, P., Horvath, S. 2013. Random generalized linear model: a highly accurate and interpretable ensemble predictor. BMC Bioinformatics, 14 (1), 1–22.
  31. Spitzer, R., Felton, A., Landman, M., Singh, N.J., Widemo, F., Cromsigt, J.P.G.M. 2020. Fifty years of European ungulate dietary studies: a synthesis. Oikos, 129, 1668–1680.
  32. Stankowich, T. 2008. Ungulate flight responses to human disturbance: A review and meta-analysis. Biological Conservation, 141, 2159–2173.
  33. Stutz, R.S. et al. 2019. Efficient application of a browsing repellent: Can associational effects within and between plants be exploited? European Journal of Forest Research, 138, 253–262.
  34. Traill, L.W., Plard, F., Gaillard, J.M., Coulson, T. 2021. Can we use a functional trait to construct a generalized model for ungulate populations? Ecology, 102 (4), e03289.
  35. Vehvilainen, H., Koricheva, J. 2006. Moose and vole browsing patterns in experimentally assembled pure and mixed forest stands. Ecography, 29, 497–506.
  36. Velamazán, M., San Miguel, A., Escribano, R., Perea, R. 2017. Threatened woody flora as an ecological indicator of large herbivore introductions. Biodiversity and Conservation, 26, 917–930.
  37. Yevtushevskyi, M.M. 2008. The influence of deer on forest plantations (in Ukrainian). Bulletin of Zaporizhzhya National University. Biological Sciences, 2, 59–63.
  38. Zweifel-Schielly, B., Kreuzer, M., Ewald, K.C., Suter, W. 2009. Habitat selection by an Alpine ungulate: the significance of forage characteristics varies with scale and season. Ecography, 32, 103–113.
DOI: https://doi.org/10.2478/ffp-2025-0001 | Journal eISSN: 2199-5907 | Journal ISSN: 0071-6677
Language: English
Page range: 1 - 11
Submitted on: Mar 9, 2024
|
Accepted on: Jan 2, 2025
|
Published on: Mar 7, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Natalia Vysotska, Oleksandr Khromuliak, Oleksandr Borysenko, Maksym Rumiantsev, Iryna Yashchuk, Oleksandr Kipran, published by Forest Research Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.