DOI QR코드

DOI QR Code

Assessing the Carrying Capacity of Wild Boars in the Bukhansan National Park using MaxEnt and HexSim Models

  • Tae Geun Kim (National Park Research Institute, Korea National Park Service)
  • Received : 2023.06.12
  • Accepted : 2023.07.12
  • Published : 2023.08.01

Abstract

Understanding the carrying capacity of a habitat is crucial for effectively managing populations of wild boars (Sus scrofa), which are designated as harmful wild animal species in national parks. Carrying capacity refers to the maximum population size supported by a park's environmental conditions. This study aimed to estimate the appropriate wild boar population size by integrating population characteristics and habitat suitability for wild boars in the Bukhansan National Park using the HexSim program. Population characteristics included age, survival, reproduction, and movement. Habitat suitability, which reflects prospecting and resource acquisition, was determined using the Maximum Entropy model. This study found that the optimal population size for wild boar ranged from 217 to 254 individuals. The population size varied depending on the amount of resources available within the home range, indicating fewer individuals in a larger home range. The estimated wild boar population size was 217 individuals for the minimum amount of resources (50% minimum convex polygon [MCP] home range), 225 individuals for the average amount of resources (95% MCP home range), and 254 individuals for the maximum amount of resources (100% MCP home range). The results of one-way analysis of variance revealed a significant difference in wild boar population size based on the amount of resources within the home range. These findings provide a basis for the development and implementation of effective management strategies for wild boar populations.

Keywords

Acknowledgement

This study was supported by the National Park Research Institute's Natural Resource Survey and Research on the Population Survey and Management of Harmful Wildlife in Urban National Parks (NPRI 2018-09).

References

  1. Acevedo, P., Quiros-Fernandez, F., Casal, J., and Vicente, J. (2014). Spatial distribution of wild boar population abundance: basic information for spatial epidemiology and wildlife management. Ecological Indicators, 36, 594-600. https://doi.org/10.1016/j.ecolind.2013.09.019
  2. Alsamadisi, A.G., Tran, L.T., and Papes, M. (2020). Employing inferences across scales: integrating spatial data with different resolutions to enhance Maxent models. Ecological Modelling, 415, 108857. https://doi.org/10.1016/j.ecolmodel.2019.108857
  3. Baldwin, R.A. (2009). Use of maximum entropy modeling in wildlife research. Entropy, 11, 854-866. https://doi.org/10.3390/e11040854
  4. Ballari, S.A., Cuevas, M.F., Cirignoli, S., and Valenzuela, A.E.J. (2015). Invasive wild boar in Argentina: using protected areas as a research platform to determine distribution, impacts and management. Biological Invasions, 17, 1595-1602. https://doi.org/10.1007/s10530-014-0818-7
  5. Bevans, R. (2020). ANOVA in R | a complete step-by-step guide with examples. Retrieved Jun 22, 2023 from https://www.scribbr.com/statistics/anova-in-r/.
  6. Bieber, C., and Ruf, T. (2005). Population dynamics in wild boar Sus scrofa: ecology, elasticity of growth rate and implications for the management of pulsed resource consumers. Journal of Applied Ecology, 42, 1203-1213. https://doi.org/10.1111/j.1365-2664.2005.01094.x
  7. Bobek, B., Furtek, J., Bobek, J., Merta, D., and Wojciuch-Ploskonka, M. (2017). Spatio-temporal characteristics of crop damage caused by wild boar in north-eastern Poland. Crop Protection, 93, 106-112. https://doi.org/10.1016/j.cropro.2016.11.030
  8. Bosch, J., Mardones, F., Perez, A., de la Torre, A., and Munoz, M.J. (2014). A maximum entropy model for predicting wild boar distribution in Spain. Spanish Journal of Agricultural Research, 12, 984-999. https://doi.org/10.5424/sjar/2014124-5717
  9. Brotons, L., Thuiller, W., Araujo, M.B., and Hirzel, A.H. (2004). Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography, 27, 437-448. https://doi.org/10.1111/j.0906-7590.2004.03764.x
  10. Bruinderink, G.W.T.A.G., and Hazebroek, E. (1995). Modelling carrying capacity for wild boar Sus scrofa scrofa in a forest/heathland ecosystem. Wildlife Biology, 1, 81-87. https://doi.org/10.2981/wlb.1995.0013
  11. Burgman, M.A., and Fox, J.C. (2003). Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning. Animal Conservation Forum, 6, 19-28. https://doi.org/10.1017/S1367943003003044
  12. Cadenas-Fernandez, E., Ito, S., Aguilar-Vega, C., Sanchez-Vizcaino, J.M., and Bosch, J. (2022). The role of the wild boar spreading African swine fever virus in Asia: another underestimated problem. Frontiers in Veterinary Science, 9, 844209. https://doi.org/10.3389/fvets.2022.844209
  13. Cho, I.C., Han, S.H., Fang, M., Lee, S.S., Ko, M.S., Lee, H., et al. (2009). The robust phylogeny of Korean wild boar (Sus scrofa coreanus) using partial D-loop sequence of mtDNA. Molecules and Cells, 28, 423-430. https://doi.org/10.1007/s10059-009-0139-3
  14. Choi, T.Y., Lee, Y.S., and Park, C.H. (2006). Home-range of wild boar, Sos scrofa living in the Jirisan National Park, Korea. Journal of Ecology and Environment, 29, 253-257.
  15. Croft, S., Franzetti, B., Gill, R., and Massei, G. (2020). Too many wild boar? Modelling fertility control and culling to reduce wild boar numbers in isolated populations. PLoS One, 15, e0238429. https://doi.org/10.1371/journal.pone.0238429
  16. Ditchkoff, S.S., Jolley, D.B., Sparklin, B.D., Hanson, L.B., Mitchell, M.S., and Grand, J.B. (2012). Reproduction in a population of wild pigs (Sus scrofa) subjected to lethal control. Journal of Wildlife Management, 76, 1235-1240. https://doi.org/10.1002/jwmg.356
  17. Dudzinska, M., and Dawidowicz, A. (2021). Detecting the severity of socio-spatial conflicts involving wild boars in the city using social media data. Sensors, 21, 8215. https://doi.org/10.3390/s21248215
  18. Elith, J., Phillips, S.J., Hastie, T., Dudik, M., Chee, Y.E., and Yates, C.J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43-57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
  19. ESRI. (2011). ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute.
  20. Fernandez-Llario, P. (2004). Environmental correlates of nest site selection by wild boar Sus scrofa. Acta Theriologica, 49, 383-392. https://doi.org/10.1007/BF03192536
  21. Ferretti, F., Lazzeri, L., Mori, E., Cesaretti, G., Calosi, M., Burrini, L., et al. (2021). Habitat correlates of wild boar density and rooting along an environmental gradient. Journal of Mammalogy, 102, 1536-1547. https://doi.org/10.1093/jmammal/gyab095
  22. Franklin, J. (2010). Mapping species Distributions: Spatial Inference and Prediction. Cambridge University Press.
  23. Guberti, V., Khomenko, S., Masiulis, M., and Kerba, S. (2019). African Swine Fever in Wild Boar Ecology and Biosecurity. FAO Animal Production and Health Manual No. 22. Food and Agriculture Organization.
  24. Han, S.H., Oh, J.G., Cho, I.C., Ko, M.S., Kim T.W., Chang, M.H., et al. (2011). A molecular genetic analysis of the introduced wild boar species (Sus scrofa coreanus) on Mount Halla, Jeju Island, Korea. Korean Journal of Environment and Ecology, 25, 658-665.
  25. Heinrichs, J.A., Aldridge, C.L., O'Donnell, M.S., and Schumaker, N.H. (2017). Using dynamic population simulations to extend resource selection analyses and prioritize habitats for conservation. Ecological Modelling, 359, 449-459. https://doi.org/10.1016/j.ecolmodel.2017.05.017
  26. Huber, P.R., Greco, S.E., Schumaker, N.H., and Hobbs, J. (2014). A priori assessment of reintroduction strategies for a native ungulate: using HexSim to guide release site selection. Landscape Ecology, 29, 689-701. https://doi.org/10.1007/s10980-014-0006-2
  27. Kim, H.R., Kim, T.G., Hong, G.P., Kim, J.M., and Kim, E.K. (2017). Home range analysis of wild boars (Sus scrofa) in Heuimangbong (peak) of Hallyeohaesang National Park. Journal of National Park Research, 8, 133-137.
  28. Kim, M., Park, H., and Lee, S. (2021). Analysis of roadkill on the Korean expressways from 2004 to 2019. International Journal of Environmental Research and Public Health, 18, 10252. https://doi.org/10.3390/ijerph181910252
  29. Kim, S.O., Kwon, K.I., Kim, T.S., Ko, H.S., and Jang, G.S. (2014). An analysis on aspects of farm lands damaged by the wild boar (Sus scrofa) in Gyeongnam province, Korea. Journal of the Korean Society of Environmental Restoration Technology, 17, 17-27. https://doi.org/10.13087/kosert.2014.17.6.17
  30. Kim, T.G., Cho, Y., and Oh, J.G. (2015). Prediction model of pine forests' distribution change according to climate change. Korean Journal of Ecology and Environment, 48, 229-237. https://doi.org/10.11614/KSL.2015.48.4.229
  31. Kim, Y., Cho, S., and Choung, Y. (2019). Habitat preference of wild boar (Sus scrofa) for feeding in cool-temperate forests. Journal of Ecology and Environment, 43, 30. https://doi.org/10.1186/s41610-019-0126-3
  32. Korea National Park Service (KNPS). (2004). Natural Resources Monitoring in Mt. Bukhan National Park: 3rd Year. Korea National Park Service.
  33. Kramer-Schadt, S., Niedballa, J., Pilgrim, J.D., Schroder, B., Lindenborn, J., Reinfelder, V., et al. (2013). The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions, 19, 1366-1379. https://doi.org/10.1111/ddi.12096
  34. Lee, O., Schlichting, P.E., and Jo, Y.S. (2022). Habitat model for wild boar (Sus scrofa) in Bukhansan National Park, Seoul. Journal of Urban Ecology, 8, juac027. https://doi.org/10.1093/jue/juac027
  35. Lee, S.M., and Lee, E.J. (2019). Diet of the wild boar (Sus scrofa): implications for management in forest-agricultural and urban environments in South Korea. PeerJ, 7, e7835. https://doi.org/10.7717/peerj.7835
  36. Lee, S.M., Lee, E.J., Park, H.B., and Seo, C.W. (2018a). Factors affecting crop damage by the wild boar (Sus scrofa): a case study in Geochang county, Gyeongnam province, Korea. Korean Journal of Environment and Ecology, 32, 140-146. https://doi.org/10.13047/KJEE.2018.32.2.140
  37. Lee, W.S., Kim, S.O., Kim, Y., Kim, J.H., and Jang, G.S. (2018b). Maximum entropy modeling of farmland damage caused by the wild boar (Sus scrofa). Applied Ecology and Environmental Research, 16, 1101-1117. https://doi.org/10.15666/aeer/1602_11011117
  38. Lombardini, M., Meriggi, A., and Fozzi, A. (2017). Factors influencing wild boar damage to agricultural crops in Sardinia (Italy). Current Zoology, 63, 507-514. https://doi.org/10.1093/cz/zow099
  39. Markov, N., Economov, A., Hjeljord, O., Rolandsen, C.M., Bergqvist, G., Danilov, P., et al. (2022). The wild boar Sus scrofa in northern Eurasia: a review of range expansion history, current distribution, factors affecting the northern distributional limit, and management strategies. Mammal Review, 52, 519-537. https://doi.org/10.1111/mam.12301
  40. Massei, G., Genov, P.V., Staines, B.W., and Gorman, M.L. (1997). Factors influencing home range and activity of wild boar (Sus scrofa) in a Mediterranean coastal area. Journal of Zoology, 242, 411-423. https://doi.org/10.1111/j.1469-7998.1997.tb03845.x
  41. McGinley, M. (2013). Carrying capacity. Retrieved Jul 24, 2023 from http://editors.eol.org/eoearth/wiki/carrying_capacity.
  42. Meriggi, A., and Sacchi, O. (2001). Habitat requirements of wild boars in the northern Apennines (N Italy): a multi-level approach. Italian Journal of Zoology, 68, 47-55. https://doi.org/10.1080/11250000109356382
  43. Ministry of Environment (MOE). (2023a). Yearly report on damages caused by harmful wildlife. Retrieved Jul 24, 2023 from https://www.me.go.kr/home/file/readFile.do?fileId=224882&fileSeq=2.
  44. Ministry of Environment (MOE). (2023b). Current Status of African swine fever (ASF) outbreak in domestic wild boar (as of July 3rd, 23.7.3.). Retrieved Jul 24, 2023 from https://me.go.kr/home/file/readFile.do?fileId=230124&fileSeq=1.
  45. Morales, N.S., Fernandez, I.C., and Baca-Gonzalez, V. (2017). MaxEnt's parameter configuration and small samples: are we paying attention to recommendations? A systematic review. PeerJ, 5, e3093. https://doi.org/10.7717/peerj.3093
  46. Morelle, K., and Lejeune, P. (2015). Seasonal variations of wild boar Sus scrofa distribution in agricultural landscapes: a species distribution modelling approach. European Journal of Wildlife Research, 61, 45-56. https://doi.org/10.1007/s10344-014-0872-6
  47. National Institute of Biological Resources (NIBR). (2017). Wildlife Survey. National Institute of Biological Resources.
  48. National Institute of Biological Resources (NIBR). (2021). Wildlife Survey. National Institute of Biological Resources.
  49. National Park Research Institute (NPRI). (2017). Research on Management Strategies for Wild Boar in Bukhansan National Park. National Park Research Institute.
  50. National Park Research Institute (NPRI). (2018). Research on the Population Survey and Management of Harmful Wildlife in Urban National Parks. National Park Research Institute.
  51. National Park Research Institute (NPRI). (2019). Research on Habitat Assessment of Wild Boar in Urban National Parks: Year 2. National Park Research Institute.
  52. Nahlik, A., and Sandor, G. (2003). Birth rate and offspring survival in a free-ranging wild boar Sus scrofa population. Wildlife Biology, 9, 37-42. https://doi.org/10.2981/wlb.2003.062
  53. Oh, H.S., Chang, M.H., and Kim, B.S. (2007). Current stains of mammals in Hallasan National Park. Korean Journal of Environment and Ecology, 21, 235-242.
  54. Oh, K.K., Kim, D.G., and Kim, C.E. (2008). Distribution of actual vegetation and management of Bukhansan National Park. Korean Journal of Environment and Ecology, 22, 83-97.
  55. Pagany, R. (2020). Wildlife-vehicle collisions - influencing factors, data collection and research methods. Biological Conservation, 251, 108758. https://doi.org/10.1016/j.biocon.2020.108758
  56. Pandey, P., Shaner, PJ.L., and Sharma, H.P. (2016). The wild boar as a driver of human-wildlife conflict in the protected park lands of Nepal. European Journal of Wildlife Research, 62, 103-108. https://doi.org/10.1007/s10344-015-0978-5
  57. Park, J.M., Do, M.R., Sim, W.D., and Lee, J.S. (2019). A study on the improvement of guideline in digital forest type map. Journal of the Korean Association of Geographic Information Studies, 22, 168-182. https://doi.org/10.11108/kagis.2019.22.1.168
  58. Perlman, K.R. (2017). Using a two-species individual-based model to examine the population responses of northern Spotted Owls to experimental removals of Barred Owls in the Pacific Northwest. (Master's thesis). Oregon State University, USA.
  59. Peterson, T.A., Papes, M., and Eaton, M. (2007). Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography, 30, 550-560. https://doi.org/10.1111/j.0906-7590.2007.05102.x
  60. Phillips, S.J. (2017). A brief tutorial on Maxent. Retrieved July 7, 2023 from http://biodiversityinformatics.amnh.org/open_source/maxent/.
  61. Phillips, S.J., and Dudik, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161-175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
  62. Phillips, S.J., Anderson, R.P., and Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  63. Plhal, R., Kamler, J., Homolka, M., and Adamec, Z. (2011). An assessment of the applicability of photo trapping to estimate wild boar population density in a forest environment. Folia Zoologica, 60, 237-246. https://doi.org/10.25225/fozo.v60.i3.a8.2011
  64. Radosavljevic, A., and Anderson, R.P. (2014). Making better MAXENT models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41, 629-643. https://doi.org/10.1111/jbi.12227
  65. Rho, P. (2015). Using habitat suitability model for the wild boar (Sus scrofa Linnaeus) to select wildlife passage sites in extensively disturbed temperate forests. Journal of Ecology and Environment, 38, 163-173. https://doi.org/10.5141/ecoenv.2015.018
  66. Schley, L., Dufrene, M., Krier, A., and Frantz, A.C. (2008). Patterns of crop damage by wild boar (Sus scrofa) in Luxembourg over a 10-year period. European Journal of Wildlife Research, 54, 589-599. https://doi.org/10.1007/s10344-008-0183-x
  67. Schumaker, N.H. (2015). HexSim (version 4.0.20). Retrieved Jul 24, 2023 from www.hexsim.net.
  68. Schumaker, N.H., and Brookes, A. (2018). HexSim: a modeling environment for ecology and conservation. Landscape Ecology, 33, 197-211. https://doi.org/10.1007/s10980-017-0605-9
  69. Schumaker, N.H., Brookes, A., Dunk, J.R., Woodbridge, B., Heinrichs, J.A., Lawler, J.J., et al. (2014). Mapping sources, sinks, and connectivity using a simulation model of northern spotted owls. Landscape Ecology, 29, 579-592. https://doi.org/10.1007/s10980-014-0004-4
  70. Seo, C.W., and Park, C.H. (2000). Wild boar (Sus scrofa corranus Heude) habitat modeling using GIS and logistic regression. Journal of GIS Association of Korea, 8, 85-99.
  71. Singer, F.J., Otto, D.K., Tipton, A.R., and Hable, C.P. (1981). Home ranges, movements, and habitat use of European wild boar in Tennessee. Journal of Wildlife Management, 45, 343-353. https://doi.org/10.2307/3807917
  72. Sjarmidi, A., Spitz, F., and Valet, G. (1992). Food resource used by wild boar in southern France. In F. Spitz, G. Janeau, G. Gonzalez, and S. Aulagnier (Eds.), Ongules/Ungulates 91. Proceedings of the International Symposium (pp. 171-173). Societe Francaise pour l'Etude et la Protection des Mammiferes.
  73. Snow, N.P., Jarzyna, M.A., and VerCauteren, K.C. (2017). Interpreting and predicting the spread of invasive wild pigs. Journal of Applied Ecology, 54, 2022-2032. https://doi.org/10.1111/1365-2664.12866
  74. Spencer, W., Rustigian-Romsos, H., Strittholt, J., Scheller, R., Zielinski, W., and Truex, R. (2011). Using occupancy and population models to assess habitat conservation opportunities for an isolated carnivore population. Biological Conservation, 144, 788-803. https://doi.org/10.1016/j.biocon.2010.10.027
  75. Squires, J.R., DeCesare, N.J., Olson, L.E., Kolbe, J.A., Hebblewhite, M., and Parks, S.A. (2013). Combining resource selection and movement behavior to predict corridors for Canada lynx at their southern range periphery. Biological Conservation, 157, 187-195. https://doi.org/10.1016/j.biocon.2012.07.018
  76. Stockwell, D., and Peters, D. (1999). The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science, 13, 143-158. https://doi.org/10.1080/136588199241391
  77. Ward, G., Hastie, T., Barry, S., Elith, J., and Leathwick, J.R. (2009). Presence-only data and the EM algorithm. Biometrics, 65, 554-563. https://doi.org/10.1111/j.1541-0420.2008.01116.x
  78. Yackulic, C.B., Chandler, R., Zipkin, E.F., Royle, J.A., Nichols, J.D., Campbell Grant, E.H., et al. (2013). Presence-only modelling using MAXENT: when can we trust the inferences? Methods in Ecology and Evolution, 4, 236-243. https://doi.org/10.1111/2041-210x.12004