Spatial Distribution and Suitability of the Endemic Babirusa Habitat (Babyrousa babyrussa) on Buru Island, Maluku using Maximum Entropy
Abstract
Buru Island is the endemic habitat of the Babirusa (Babyrousa babyrussa), facing pressures from human activities and habitat fragmentation. This study used the Maximum Entropy (MaxEnt) modeling method to map the spatial distribution and assess the habitat suitability of Babirusa based on environmental variables including elevation, slope, temperature, land cover, distance to water, and distance from built-up areas. The results show that the habitat is divided into four main classes: Very Low at 24.95%, Low at 31.67%, Moderate at 29.71%, and High at 13.68% of the total island area, which requires more intensive management and protection. Elevation and distance from settlements have an influence but with relatively small contributions, indicating the species’ tolerance to elevation variation. This model provides a scientific basis for integrated conservation strategies, including habitat management, reduction of anthropogenic pressures, and sustainable spatial planning based on habitat suitability to ensure the long-term survival of Babirusa on Buru Island.
Downloads
References
Abdul Haris Mustari, James Burton, Muhamad Basri, Tony R. Soehartono, Siti Chadidjah Kaniawati, Ikeu Sri Rejeki, Phil Wheeler, G. S. (2013). Strategy and Action Plan for Conservation of Babirusa (Babyrousa babyrussa) 2013 - 2022. Directorate of Conservation of Biodiversity and Ecosystems, Ministry of Environment and Forestry, Indonesia.
Ahmadi, M., Hemami, M., Kaboli, M., & Shabani, F. (2023). MaxEnt brings comparable results when the input data are being completed; Model parameterization of four species distribution models. Ecology and Evolution, 13(2). https://doi.org/10.1002/ece3.9827
Astudillo, P. X., Barros, S., Mejía, D., Villegas, F. R., Siddons, D. C., & Latta, S. C. (2024). Using surrogate species and MaxEnt modeling to prioritize areas for conservation of a páramo bird community in a tropical high Andean biosphere reserve. Arctic, Antarctic, and Alpine Research, 56(1). https://doi.org/10.1080/15230430.2023.2299362
Ayu Diyah Setiyani, Bayu Wisnu Broto, & Tri H. Kuswoyo. (2023). Suaka Alam Masbait: Secercah harapan pelestarian babirusa di Kepulauan Maluku. In Mengenal Lebih Dekat Satwa Langka Indonesia dan Memahami Pelestariannya. Penerbit BRIN. https://doi.org/10.55981/brin.602.c636
Christopel Paino. (2021). Tidak Hanya di Sulawesi, Babirusa Ditemukan juga di Pulau Ini. Mongabay. https://mongabay.co.id/2021/07/26/tidak-hanya-di-sulawesi-babirusa-ditemukan-juga-di-pulau-ini/
Clayton, L. M., Milner‐Gulland, E. J., Sinaga, D. W., & Mustari, A. H. (2000). Effects of a Proposed Ex Situ Conservation Program on In Situ Conservation of the Babirusa, an Endangered Suid. Conservation Biology, 14(2), 382–385. https://doi.org/10.1046/j.1523-1739.2000.99123.x
de Oliveira Sousa, J. B., de Sousa Conceição Benathar, I., Ebling, A. A., Kohler, S. V., de Oliveira, X. M., de Paula Protásio, T., Rodrigues, M., & Goulart, S. L. (2025). Using maximum entropy modeling to predict the potential distributions of genus Copaifera L. in a conservation unit in the Brazilian Amazon. Plant Ecology, 226(2), 185–196. https://doi.org/10.1007/s11258-024-01484-9
Ferretti, F., Lazzeri, L., Mori, E., Cesaretti, G., Calosi, M., Burrini, L., & Fattorini, N. (2021). Habitat correlates of wild boar density and rooting along an environmental gradient. Journal of Mammalogy, 102(6), 1536 – 1547. https://doi.org/10.1093/jmammal/gyab095
González, E. M., Henríquez, W. A., & Armenteras-Pascual, D. (2021). Mineral lick distribution modeling and NW Amazon conservation planning alternatives. Biodiversity and Conservation, 30(12), 3409–3432. https://doi.org/10.1007/s10531-021-02253-0
Jati, A. S., Broto, B. W., Dri, G. F., Latifiana, K., Fraver, S., Rejeki, I. S., Bustang, & Mortelliti, A. (2024). Conserving large mammals on small islands: A case study on one of the world’s most understudied pigs, the Togean islands babirusa. Biodiversity and Conservation, 33(3), 1207 - 1223. https://doi.org/10.1007/s10531-024-02800-5
Jeong, A., Kim, M., & Lee, S. (2024). Analysis of Priority Conservation Areas Using Habitat Quality Models and MaxEnt Models. Animals, 14(11), 1680. https://doi.org/10.3390/ani14111680
Luo, J., Ma, Y., Liu, Y., Zhu, D., & Guo, X. (2024). Predicting Polygonum capitatum distribution in China across climate scenarios using MaxEnt modeling. Scientific Reports, 14(1), 20020. https://doi.org/10.1038/s41598-024-71104-z
Macdonald, A.A., Burton, J., Leus, K. (2008). Babyrousa babyrussa. The IUCN Red List of Threatened Species. https://doi.org/https://dx.doi.org/10.2305/IUCN.UK.2008.RLTS.T2461A9441445
Macdonald, A. A. (2017). Sulawesi Babirusa Babyrousa celebensis (Deninger, 1909). In M. Melletti & E. Meijaard (Eds.), Ecology, Conservation and Management of Wild Pigs and Peccaries (pp. 59–69). Cambridge University Press. https://doi.org/ 10.1017/9781316941232.008
Macdonald, A. A. (2021). Anomalous erosion patterns on the cheek teeth of babirusa (Genus babyrousa). Canadian Journal of Zoology, 99(1), 1 – 8. https://doi.org/10.1139/cjz-2020-0173
Martha Fischer. (2002). Husbandry Guidelines for the Babirusa (Babyrousa babyrussa) Species Survival Plan. http://azaungulates.squarespace.com/s/Babirusa-Husbandry-Manual-2002.pdf
Ngarega, B. K., Masocha, V. F., & Schneider, H. (2021). Forecasting the effects of bioclimatic characteristics and climate change on the potential distribution of Colophospermum mopane in southern Africa using Maximum Entropy (Maxent). Ecological Informatics, 65, 101419. https://doi.org/10.1016/j.ecoinf.2021.101419
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3 - 4), 231 - 259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Rosyidy, M. K., & Wibowo, A. (2020). GIS-Based Spatial Model for Habitat Suitability of Babirusa (Babyrousa celebensis), in Gorontalo Province. Jurnal Geografi Lingkungan Tropik, 4(1). https://doi.org/10.7454/jglitrop.v4i1.77
Su, Q., Du, Z., Luo, Y., Zhou, B., Xiao, Y., & Zou, Z. (2024). MaxEnt Modeling for Predicting the Potential Geographical Distribution of Hydrocera triflora since the Last Interglacial and under Future Climate Scenarios. Biology, 13(9), 745. https://doi.org/10.3390/biology13090745
Wibowo, A. (2021). Modeling habitat suitability of vulnerable Mollucan Babirusa Babyrousa babyrussa in small island of Buru, Indonesia1-6. https://doi.org/10.1101/2021.07.28.454166
Zhang, J., Li, X., Li, S., Yang, Q., Li, Y., Xiang, Y., & Yao, B. (2025). MaxEnt Modeling of Future Habitat Shifts of Itea yunnanensis in China Under Climate Change Scenarios. Biology, 14(7), 1-12. https://doi.org/10.3390/biology14070899
Zhang, K., Yao, L., Meng, J., & Tao, J. (2018). Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Science of The Total Environment, 634, 1326–1334. https://doi.org/10.1016/j.scitotenv.2018.04.112
Copyright (c) 2026 Heinrich Rakuasa, Vadim V Khromykh, Philia Christi Latue, Susan E Manakane, Glendy Somae, Benson Joshua
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works
2
