Species Distribution Modelling Using Bioclimatic Variables on Endangered Endemic Species (Bubalus depressicornis and Bubalus quarlesi)
Abstract
Sulawesi Island is an island located in the Wallacea area. Most of the fauna on the island of Sulawesi is a transitional fauna from Australia and Asia. This study aims to model the potential distribution of the species Bubalus depressicornis and Bubalus quarlesi using famous models in the present and in the future as a result of climate change phenomena throughout the island of Sulawesi and beyond their natural habitat. The parameters used are bioclimatic variables and in-situ presence data. The method used is Maximum Entropy by comparing the GLM, SVM, and RF algorithms. The model is evaluated with reference to the values of AUC, COR, TSS, Deviance, and observation data. The RF model is quite good in modeling the distribution of B. depressicornis and B. quarlesi species with AUC values of 0.92 and 1, COR values of 0.59 and 0.84, TSS values of 0.87 and 1, and Deviance values of 0.37 and 0.08, respectively, while the results of data observations show values of 80% and 84%. B. depressicornis was most affected by bio14=0.665, while B. quarlesi was most affected by bio2=0.525, which means that this endemic species is suitable to live in a tropical climate with a warm and wet climate throughout the year, where the difference in temperature at night and during the day is very large. In the future, B. depressicornis and B. quarlesi are estimated to be compatible in an area of 143,281.78 km2 (81%) and 136,892.89 km2 (77%) of the Sulawesi.
References
Agresti, A. (2018). An introduction to categorical data analysis. John Wiley & Sons.
Ahmadi, K., Alavi, S. J., Amiri, G. Z., Hosseini, S. M., Serra-Diaz, J. M., & Svenning, J. C. (2020). The potential impact of future climate on the distribution of European yew (Taxus baccata L.) in the Hyrcanian Forest region (Iran). International Journal of Biometeorology, 64(9), 1451–1462. https://doi.org/10.1007/s00484-020-01922-z.
Ahmed, N., Atzberger, C., & Zewdie, W. (2020). Integration of remote sensing and bioclimatic data for prediction of invasive species distribution in data-poor regions: a review on challenges and opportunities. Environmental Systems Research, 9(1), 1-18. https://doi.org/10.1186/s40068-020-00195-0.
Aldiansyah, A. (2022). Kajian Spasial Konservasi Anoa (Bubalus Quarlesi Ouwens 1910) Pada Skala Lanskap Di Taman Nasional Gandang Dewata, Sulawesi Barat. Tesis. Universitas Indonesia.
Amiri, M., Tarkesh, M., Jafari, R., & Jetschke, G. (2020). Bioclimatic variables from precipitation and temperature records vs. remote sensing-based bioclimatic variables: Which side can perform better in species distribution modeling?. Ecological Informatics, 57, 101060. https://doi.org/10.1016/j.ecoinf.2020.101060.
Andersen, D., Litvinchuk, S. N., Jang, H. J., Jiang, J., Koo, K. S., Maslova, I., ... & Borzée, A. (2022). Incorporation of latitude-adjusted bioclimatic variables increases accuracy in species distribution models. Ecological Modelling, 469, 109986. https://doi.org/10.1016/j.ecolmodel.2022.109986.
Arini, D.I.D., Christita, M., Sheherazade, N., Mayasari, A., Suryaningsih, R., & Simamora, A. T.A.J. (2020). A review of anoa conservation efforts in Sulawesi, Indonesia. IOP Conference Series: Earth and Environmental Science, 533(1), 012003. https://doi.org/10.1088/1755-1315/533/1/012003.
Bandara, A. M. J., Madurapperuma, B. D., Edirisinghe, G., Gabadage, D., Botejue, M., & Surasinghe, T. D. (2022). Bioclimatic Envelopes for Two Bat Species from a Tropical Island: Insights on Current and Future Distribution from Ecological Niche Modeling. Diversity, 14(7), 506. https://doi.org/10.3390/d14070506.
Booth, T.H. (2018). Why understanding the pioneering and continuing contributions of BIOCLIM to species distribution modelling is important. Austral Ecology, 43(8), 852-860.https://doi.org/10.1111/aec.12628.
Broto, B.W. (2015). Struktur dan komposisi vegetasi habitat anoa (Bubalus spp.) di Hutan Lindung Pegunungan Mekongga, Kolaka, Sulawesi Tenggara. Seminar Nasional Masyarakat Biodiversitas Indonesia, 1 (3), https://doi.org/10.13057/psnmbi/m010339.
Burton, J.A., Hedges, S., & Mustari, A.H. (2005). The taxonomic status, distribution and conservation of the lowland anoa Bubalus depressicornis and mountain anoa Bubalus quarlesi. Mammal Review, 35(1), 25-50.
Burton, J., Wheeler, P., & Mustari, A.H. (2016a). Bubalus depressicornis. The IUCN Red List of Threatened Species, 2016 (e.T3126A46364222), 14. https://doi.org/http://dx.doi.org/ 10.2305/IUCN.UK.2016-2.RLTS.T3126A46364222.en.
Burton, J., Wheeler, P., & Mustari, A.H. (2016b). Bubalus quarlesi. The IUCN Red List of Threatened Species 2016: e.T3128A46364433. The IUCN Red List of Threatened Species 2016, 8235, 14. https://doi.org/10.2305/IUCN.UK.2016-2.RLTS.T3128A46364433.en.
Byeon, D. hyeon, Jung, S., & Lee, W. H. (2018). Review of CLIMEX and MaxEnt for studying species distribution in South Korea. Journal of Asia-Pacific Biodiversity, 11(3), 325–333. https://doi.org/10.1016/j.japb.2018.06.002.
Collins, S.D., & McIntyre, N.E. (2015). Modeling the distribution of odonates: a review. Freshwater Science, 34(3), 1144-1158. https://doi.org/10.1086/682688.
Dyderski, M. K., Paź, S., Frelich, L. E., & Jagodziński, A. M. (2018). How much does climate change threaten European forest tree species distributions?. Global Change Biology, 24(3), 1150-1163. https://doi.org/10.1111/gcb.13925.
Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A Statistical Explanation of MaxEnt for Ecologists. Diversity and Distributions, 17(1), 43-57. https://doi.org/10.1111/j.1472-4642.2010.00725.x.
Foden, W.B., Butchart, S.H., Stuart, S. N., Vié, J.C., Akçakaya, H.R., Angulo, A., ... & Mace, G.M. (2013). Identifying the world's most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals. PloS One, 8(6), e65427. https://doi.org/10.1371/journal.pone.0065427.
Fourcade, Y., Besnard, A. G., & Secondi, J. (2018). Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Global Ecology and Biogeography, 27(2), 245-256. https://doi.org/10.1111/geb.12684.
Froese, G.Z., Contasti, A. L., Mustari, A.H., & Brodie, J.F. (2015). Disturbance impacts on large rain-forest vertebrates differ with edge type and regional context in Sulawesi, Indonesia. Journal of Tropical Ecology, 31(6), 509-517. DOI: https://doi.org/10.1017/S0266467415000450.
González-Suárez, M., Gómez, A., & Revilla, E. (2013). Which intrinsic traits predict vulnerability to extinction depends on the actual threatening processes. Ecosphere, 4(6), 1-16. https://doi.org/10.1890/ES12-00380.1.
Groves, C.P. (1969). Systematics of the anoa (Mammalia, Bovidae). Beaufortia, 17, 1-12.
Guo, Q., & Liu, Y. (2010). ModEco: an integrated software package for ecological niche modeling. Ecography, 33(4), 637-642. https://doi.org/10.1111/j.1600-0587.2010.06416.x.
Hamidi, O., Tapak, L., Abbasi, H., & Maryanaji, Z. (2018). Application of random forest time series, support vector regression and multivariate adaptive regression splines models in prediction of snowfall (a case study of Alvand in the middle Zagros, Iran). Theoretical and Applied Climatology, 134(3), 769-776. https://doi.org/10.1007/s00704-017-2300-9.
Hill, L., Hector, A., Hemery, G., Smart, S., Tanadini, M., & Brown, N. (2017). Abundance distributions for tree species in Great Britain: A two‐stage approach to modeling abundance using species distribution modeling and random forest. Ecology and Evolution, 7(4), 1043-1056.. https://doi.org/10.1002/ece3.2661.
Hosni, E. M., Al-Khalaf, A. A., Nasser, M. G., Abou-Shaara, H. F., & Radwan, M. H. (2022). Modeling the potential global distribution of honeybee pest, Galleria mellonella under changing climate. Insects, 13(5), 484. https://doi.org/10.3390/insects13050484.
Howard, C., P.A. Stephens, J.W. Pearce‐Higgins, R.D. Gregory & S.G. Willis (2014). Improving species distribution models: the value of data on abundance. Methods in Ecology and Evolution 5(6): 506–513. https://doi.org/10.1111/2041-210X.12184.
Irawati, D., & Arini, D. (2012). Anoa dan Habitatnya di Sulawesi Utara. Balai Penelitian Kehutanan Manado. www.Bpk-Manado.Litbang.Dephut.Go.Id. [19 Jan 2022].
Jahidin. 2003. Populasi dan Perilaku Anoa Pegunungan (Bubalus (Anoa) quarlesi Ouwens) di Taman Nasional Lore Lindu. Tesis. Institut Pertanian Bogor. Bogor.
Jung, J. M., Byeon, D. hyeon, Jung, S., & Lee, W. H. (2019). Effect of climate change on the potential distribution of the common cutworm (Spodoptera litura) in South Korea. Entomological Research, 49(12), 519–528. https://doi.org/10.1111/1748-5967.12398.
Kamruzzaman, M., Shahid, S., Islam, A.R.M., Hwang, S., Cho, J., Zaman, M., ... & Hossain, M. (2021). Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study. Theoretical and Applied Climatology, 145(3), 1385-1406. https://doi.org/10.1007/s00704-021-03691-0.
Kasim, K. 2002. Potensi anoa (Bubalus depressicornis dan Bubalus quarlesi) sebagai alternatif satwa budidaya dalam mengatasi kepunahannya. Disertasi. Program Pascasarjana Institut Pertanian Bogor, Bogor.
Leclerc, C., Courchamp, F., & Bellard, C. (2018). Insular threat associations within taxa worldwide. Scientific reports, 8(1), 1-8. DOI: https://doi.org/10.1038/s41598-018-24733-0.
Leclerc, C., Courchamp, F., & Bellard, C. (2020). Future climate change vulnerability of endemic island mammals. Nature communications, 11(1), 1-9. DOI: https://doi.org/10.1038/s41467-020-18740-x.
Mahmud, W. (2009). Kelimpahan Anoa Dataran Rendah (Bubalus depressicornis) dan Faktor yang Mempengaruhinya di Hutan Lambusango Pulau Buton, Sulawesi Tenggara. Skripsi. Universitas Gadjah Mada.
Martin, T.E., Kelly, D.J., Keogh, N. T., Heriyadi, D., Singer, H.A., & Blackburn, G.A. (2012). The avifauna of Lambusango Forest Reserve, Buton Island, south-east Sulawesi, with additional sightings from southern Buton. Forktail, 28, 107-112.
Mayasari, A., Arini, D.I.D., Suryawan, A., Suryaningsih, R., Vernia, R.E., Abinawanto, A., Suryanda, A., & Bowolaksono, A. (2018). Sexual behaviour of lowland anoa (Bubalus depressicornis) in the captivity of anoa breeding centre (ABC) Manado. MATEC Web of Conferences, 197(620), 1–5. https://doi.org/10.1051/matecconf/201819706007.
McCullagh, P. & J.A. Nelder (1989). Generalized Linear Models. Chapman and Hall, London, 511pp.
McKnight, T.L. (2000). Climate zones and types. Physical geography: a landscape appreciation.
Morales-Barbero, J., & Vega-Álvarez, J. (2019). Input matters matter: Bioclimatic consistency to map more reliable species distribution models. Methods in Ecology and Evolution, 10(2), 212–224. https://doi.org/10.1111/2041-210X.13124.
Mustari, A.H. (2003). Ecology and Conservation of Lowland Anoa, Bubalus depressicornis, in Sulawesi, Indonesia. University of New England.
Mustari, A.H. (2019). Ekologi, Perilaku, dan Konservasi Anoa. PT Penerbit IPB Press.
Nugraha, A. M. S., & Hall, R. (2018). Late cenozoic palaeogeography of Sulawesi, Indonesia. Palaeogeography, Palaeoclimatology, Palaeoecology, 490, 191-209. https://doi.org/10.1016/j.palaeo.2017.10.033.
O'Brien, T. G., & Kinnaird, M. F. (1996). Changing populations of birds and mammals in North Sulawesi. Oryx, 30(2), 150-156. https://doi.org/10.1017/S0030605300021530.
Pacifici, M., Visconti, P., & Rondinini, C. (2018). A framework for the identification of hotspots of climate change risk for mammals. Global Change Biology, 24(4), 1626-1636. https://doi.org/10.1111/gcb.13942.
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.
Priyono, D.S., Solihin, D.D., Farajallah, A., & Purwantara, B. (2020). The first complete mitochondrial genome sequence of the endangered mountain anoa (Bubalus quarlesi) (Artiodactyla: Bovidae) and phylogenetic analysis. Journal of Asia-Pacific Biodiversity, 13(2), 123–133. https://doi.org/10.1016/J.JAPB.2020.01.006.
Ranuntu, R., & Mallombasang, S. (2015). Studi Populasi Dan Habitat Anoa (Bubalus sp) di Kawasan Hutan Lindung Desa Sangginora Kabupaten Poso. Mitra Sains, 3(2), 81-94. Retrieved from http://mrtg.untad.ac.id/index.php/MitraSains/article/view/129.
Ren, S., Cao, X., Wei, Y., & Sun, J. (2015). Global refinement of random forest. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 723-730).
Ravand, H., & Baghaei, P. (2016). Partial least squares structural equation modeling with R. Practical Assessment, Research, and Evaluation, 21(1), 11. https://doi.org/10.7275/d2fa-qv48.
RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/.
Schmidt, A. F., & Finan, C. (2018). Linear regression and the normality assumption. Journal of Clinical Epidemiology, 98, 146-151. https://doi.org/10.1016/j.jclinepi.2017.12.006.
Shabani, F., Kumar, L., & Ahmadi, M. (2018). Assessing accuracy methods of species distribution models: AUC, specificity, sensitivity and the true skill statistic. Global Journal of Human Social Science, 18(1), 6-18.
Shabani, F., Kumar, L., & Ahmadi, M. (2016). A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area. Ecology and Evolution, 6(16), 5973-5986. https://doi.org/10.1002/ece3.2332.
Steinmetz, R., Srirattanaporn, S., Mor‐Tip, J., & Seuaturien, N. (2014). Can community outreach alleviate poaching pressure and recover wildlife in South‐East Asian protected areas?. Journal of Applied Ecology, 51(6), 1469-1478. https://doi.org/10.1111/1365-2664.12239.
Taylor, K.E. (2009). A summary of the CMIP5 experiment design. Retrieved from http://cmip-pcmdi. llnl. gov/cmip5/docs/Taylor_CMIP5_design. pdf.
Truong, T. T. A., Hardy, G. E. S. J., & Andrew, M. E. (2017). Contemporary remotely sensed data products refine invasive plants risk mapping in data poor regions. Frontiers in Plant Science, 8(May). https://doi.org/10.3389/fpls.2017.00770.
Wardah, W., Labiro, E., Massiri, S. D., Sustri, S., & Mursidin, M. (2012). Vegetasi kunci habitat Anoa di Cagar Alam Pangi Binangga, Sulawesi Tengah. Jurnal Penelitian Kehutanan Wallacea, 1(1), 1-12. http://dx.doi.org/10.18330/jwallacea.2012.vol1iss1pp1-12.
Whitten, T., & Henderson, G. S. (2012). Ecology of Sulawesi. Tuttle Publishing.
Wilby, R.L., Charles, S.P., Zorita, E., Timbal, B., Whetton, P., & Mearns, L.O. (2004). Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change, available from the DDC of IPCC TGCIA, 27.
Worldclim. (2020). Bioclimatic Variable. Retrieved from https://www.worldclim.org/data/bioclim.html. [25 Jan 2022].
Yoon, S., & Lee, W. H. (2021). Methodological analysis of bioclimatic variable selection in species distribution modeling with application to agricultural pests (Metcalfa pruinosa and Spodoptera litura). Computers and Electronics in Agriculture, 190, 106430. https://doi.org/10.1016/j.compag.2021.106430.
Yunus, S.R. (2022). Habitat Terganggu, Anoa Berkeliaran di Lokasi Perusahaan Tambang di Konawe. Kompas ID Online News June 7th 2022. https://www.kompas.id/baca/nusantara/2022/06/07/habitat-terganggu-anoa-muncul-di-lokasi-perusahaan-tambang-di-konawe. [20 June 2022].
Zhang, H., Sun, X., Zhang, G., Zhang, X., Miao, Y., Zhang, M., ... & Huang, L. (2023). Potential Global Distribution of the Habitat of Endangered Gentiana rhodantha Franch: Predictions Based on MaxEnt Ecological Niche Modeling. Sustainability, 15(1), 631. https://doi.org/10.3390/su15010631.
Ahmadi, K., Alavi, S. J., Amiri, G. Z., Hosseini, S. M., Serra-Diaz, J. M., & Svenning, J. C. (2020). The potential impact of future climate on the distribution of European yew (Taxus baccata L.) in the Hyrcanian Forest region (Iran). International Journal of Biometeorology, 64(9), 1451–1462. https://doi.org/10.1007/s00484-020-01922-z.
Ahmed, N., Atzberger, C., & Zewdie, W. (2020). Integration of remote sensing and bioclimatic data for prediction of invasive species distribution in data-poor regions: a review on challenges and opportunities. Environmental Systems Research, 9(1), 1-18. https://doi.org/10.1186/s40068-020-00195-0.
Aldiansyah, A. (2022). Kajian Spasial Konservasi Anoa (Bubalus Quarlesi Ouwens 1910) Pada Skala Lanskap Di Taman Nasional Gandang Dewata, Sulawesi Barat. Tesis. Universitas Indonesia.
Amiri, M., Tarkesh, M., Jafari, R., & Jetschke, G. (2020). Bioclimatic variables from precipitation and temperature records vs. remote sensing-based bioclimatic variables: Which side can perform better in species distribution modeling?. Ecological Informatics, 57, 101060. https://doi.org/10.1016/j.ecoinf.2020.101060.
Andersen, D., Litvinchuk, S. N., Jang, H. J., Jiang, J., Koo, K. S., Maslova, I., ... & Borzée, A. (2022). Incorporation of latitude-adjusted bioclimatic variables increases accuracy in species distribution models. Ecological Modelling, 469, 109986. https://doi.org/10.1016/j.ecolmodel.2022.109986.
Arini, D.I.D., Christita, M., Sheherazade, N., Mayasari, A., Suryaningsih, R., & Simamora, A. T.A.J. (2020). A review of anoa conservation efforts in Sulawesi, Indonesia. IOP Conference Series: Earth and Environmental Science, 533(1), 012003. https://doi.org/10.1088/1755-1315/533/1/012003.
Bandara, A. M. J., Madurapperuma, B. D., Edirisinghe, G., Gabadage, D., Botejue, M., & Surasinghe, T. D. (2022). Bioclimatic Envelopes for Two Bat Species from a Tropical Island: Insights on Current and Future Distribution from Ecological Niche Modeling. Diversity, 14(7), 506. https://doi.org/10.3390/d14070506.
Booth, T.H. (2018). Why understanding the pioneering and continuing contributions of BIOCLIM to species distribution modelling is important. Austral Ecology, 43(8), 852-860.https://doi.org/10.1111/aec.12628.
Broto, B.W. (2015). Struktur dan komposisi vegetasi habitat anoa (Bubalus spp.) di Hutan Lindung Pegunungan Mekongga, Kolaka, Sulawesi Tenggara. Seminar Nasional Masyarakat Biodiversitas Indonesia, 1 (3), https://doi.org/10.13057/psnmbi/m010339.
Burton, J.A., Hedges, S., & Mustari, A.H. (2005). The taxonomic status, distribution and conservation of the lowland anoa Bubalus depressicornis and mountain anoa Bubalus quarlesi. Mammal Review, 35(1), 25-50.
Burton, J., Wheeler, P., & Mustari, A.H. (2016a). Bubalus depressicornis. The IUCN Red List of Threatened Species, 2016 (e.T3126A46364222), 14. https://doi.org/http://dx.doi.org/ 10.2305/IUCN.UK.2016-2.RLTS.T3126A46364222.en.
Burton, J., Wheeler, P., & Mustari, A.H. (2016b). Bubalus quarlesi. The IUCN Red List of Threatened Species 2016: e.T3128A46364433. The IUCN Red List of Threatened Species 2016, 8235, 14. https://doi.org/10.2305/IUCN.UK.2016-2.RLTS.T3128A46364433.en.
Byeon, D. hyeon, Jung, S., & Lee, W. H. (2018). Review of CLIMEX and MaxEnt for studying species distribution in South Korea. Journal of Asia-Pacific Biodiversity, 11(3), 325–333. https://doi.org/10.1016/j.japb.2018.06.002.
Collins, S.D., & McIntyre, N.E. (2015). Modeling the distribution of odonates: a review. Freshwater Science, 34(3), 1144-1158. https://doi.org/10.1086/682688.
Dyderski, M. K., Paź, S., Frelich, L. E., & Jagodziński, A. M. (2018). How much does climate change threaten European forest tree species distributions?. Global Change Biology, 24(3), 1150-1163. https://doi.org/10.1111/gcb.13925.
Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A Statistical Explanation of MaxEnt for Ecologists. Diversity and Distributions, 17(1), 43-57. https://doi.org/10.1111/j.1472-4642.2010.00725.x.
Foden, W.B., Butchart, S.H., Stuart, S. N., Vié, J.C., Akçakaya, H.R., Angulo, A., ... & Mace, G.M. (2013). Identifying the world's most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals. PloS One, 8(6), e65427. https://doi.org/10.1371/journal.pone.0065427.
Fourcade, Y., Besnard, A. G., & Secondi, J. (2018). Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Global Ecology and Biogeography, 27(2), 245-256. https://doi.org/10.1111/geb.12684.
Froese, G.Z., Contasti, A. L., Mustari, A.H., & Brodie, J.F. (2015). Disturbance impacts on large rain-forest vertebrates differ with edge type and regional context in Sulawesi, Indonesia. Journal of Tropical Ecology, 31(6), 509-517. DOI: https://doi.org/10.1017/S0266467415000450.
González-Suárez, M., Gómez, A., & Revilla, E. (2013). Which intrinsic traits predict vulnerability to extinction depends on the actual threatening processes. Ecosphere, 4(6), 1-16. https://doi.org/10.1890/ES12-00380.1.
Groves, C.P. (1969). Systematics of the anoa (Mammalia, Bovidae). Beaufortia, 17, 1-12.
Guo, Q., & Liu, Y. (2010). ModEco: an integrated software package for ecological niche modeling. Ecography, 33(4), 637-642. https://doi.org/10.1111/j.1600-0587.2010.06416.x.
Hamidi, O., Tapak, L., Abbasi, H., & Maryanaji, Z. (2018). Application of random forest time series, support vector regression and multivariate adaptive regression splines models in prediction of snowfall (a case study of Alvand in the middle Zagros, Iran). Theoretical and Applied Climatology, 134(3), 769-776. https://doi.org/10.1007/s00704-017-2300-9.
Hill, L., Hector, A., Hemery, G., Smart, S., Tanadini, M., & Brown, N. (2017). Abundance distributions for tree species in Great Britain: A two‐stage approach to modeling abundance using species distribution modeling and random forest. Ecology and Evolution, 7(4), 1043-1056.. https://doi.org/10.1002/ece3.2661.
Hosni, E. M., Al-Khalaf, A. A., Nasser, M. G., Abou-Shaara, H. F., & Radwan, M. H. (2022). Modeling the potential global distribution of honeybee pest, Galleria mellonella under changing climate. Insects, 13(5), 484. https://doi.org/10.3390/insects13050484.
Howard, C., P.A. Stephens, J.W. Pearce‐Higgins, R.D. Gregory & S.G. Willis (2014). Improving species distribution models: the value of data on abundance. Methods in Ecology and Evolution 5(6): 506–513. https://doi.org/10.1111/2041-210X.12184.
Irawati, D., & Arini, D. (2012). Anoa dan Habitatnya di Sulawesi Utara. Balai Penelitian Kehutanan Manado. www.Bpk-Manado.Litbang.Dephut.Go.Id. [19 Jan 2022].
Jahidin. 2003. Populasi dan Perilaku Anoa Pegunungan (Bubalus (Anoa) quarlesi Ouwens) di Taman Nasional Lore Lindu. Tesis. Institut Pertanian Bogor. Bogor.
Jung, J. M., Byeon, D. hyeon, Jung, S., & Lee, W. H. (2019). Effect of climate change on the potential distribution of the common cutworm (Spodoptera litura) in South Korea. Entomological Research, 49(12), 519–528. https://doi.org/10.1111/1748-5967.12398.
Kamruzzaman, M., Shahid, S., Islam, A.R.M., Hwang, S., Cho, J., Zaman, M., ... & Hossain, M. (2021). Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study. Theoretical and Applied Climatology, 145(3), 1385-1406. https://doi.org/10.1007/s00704-021-03691-0.
Kasim, K. 2002. Potensi anoa (Bubalus depressicornis dan Bubalus quarlesi) sebagai alternatif satwa budidaya dalam mengatasi kepunahannya. Disertasi. Program Pascasarjana Institut Pertanian Bogor, Bogor.
Leclerc, C., Courchamp, F., & Bellard, C. (2018). Insular threat associations within taxa worldwide. Scientific reports, 8(1), 1-8. DOI: https://doi.org/10.1038/s41598-018-24733-0.
Leclerc, C., Courchamp, F., & Bellard, C. (2020). Future climate change vulnerability of endemic island mammals. Nature communications, 11(1), 1-9. DOI: https://doi.org/10.1038/s41467-020-18740-x.
Mahmud, W. (2009). Kelimpahan Anoa Dataran Rendah (Bubalus depressicornis) dan Faktor yang Mempengaruhinya di Hutan Lambusango Pulau Buton, Sulawesi Tenggara. Skripsi. Universitas Gadjah Mada.
Martin, T.E., Kelly, D.J., Keogh, N. T., Heriyadi, D., Singer, H.A., & Blackburn, G.A. (2012). The avifauna of Lambusango Forest Reserve, Buton Island, south-east Sulawesi, with additional sightings from southern Buton. Forktail, 28, 107-112.
Mayasari, A., Arini, D.I.D., Suryawan, A., Suryaningsih, R., Vernia, R.E., Abinawanto, A., Suryanda, A., & Bowolaksono, A. (2018). Sexual behaviour of lowland anoa (Bubalus depressicornis) in the captivity of anoa breeding centre (ABC) Manado. MATEC Web of Conferences, 197(620), 1–5. https://doi.org/10.1051/matecconf/201819706007.
McCullagh, P. & J.A. Nelder (1989). Generalized Linear Models. Chapman and Hall, London, 511pp.
McKnight, T.L. (2000). Climate zones and types. Physical geography: a landscape appreciation.
Morales-Barbero, J., & Vega-Álvarez, J. (2019). Input matters matter: Bioclimatic consistency to map more reliable species distribution models. Methods in Ecology and Evolution, 10(2), 212–224. https://doi.org/10.1111/2041-210X.13124.
Mustari, A.H. (2003). Ecology and Conservation of Lowland Anoa, Bubalus depressicornis, in Sulawesi, Indonesia. University of New England.
Mustari, A.H. (2019). Ekologi, Perilaku, dan Konservasi Anoa. PT Penerbit IPB Press.
Nugraha, A. M. S., & Hall, R. (2018). Late cenozoic palaeogeography of Sulawesi, Indonesia. Palaeogeography, Palaeoclimatology, Palaeoecology, 490, 191-209. https://doi.org/10.1016/j.palaeo.2017.10.033.
O'Brien, T. G., & Kinnaird, M. F. (1996). Changing populations of birds and mammals in North Sulawesi. Oryx, 30(2), 150-156. https://doi.org/10.1017/S0030605300021530.
Pacifici, M., Visconti, P., & Rondinini, C. (2018). A framework for the identification of hotspots of climate change risk for mammals. Global Change Biology, 24(4), 1626-1636. https://doi.org/10.1111/gcb.13942.
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.
Priyono, D.S., Solihin, D.D., Farajallah, A., & Purwantara, B. (2020). The first complete mitochondrial genome sequence of the endangered mountain anoa (Bubalus quarlesi) (Artiodactyla: Bovidae) and phylogenetic analysis. Journal of Asia-Pacific Biodiversity, 13(2), 123–133. https://doi.org/10.1016/J.JAPB.2020.01.006.
Ranuntu, R., & Mallombasang, S. (2015). Studi Populasi Dan Habitat Anoa (Bubalus sp) di Kawasan Hutan Lindung Desa Sangginora Kabupaten Poso. Mitra Sains, 3(2), 81-94. Retrieved from http://mrtg.untad.ac.id/index.php/MitraSains/article/view/129.
Ren, S., Cao, X., Wei, Y., & Sun, J. (2015). Global refinement of random forest. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 723-730).
Ravand, H., & Baghaei, P. (2016). Partial least squares structural equation modeling with R. Practical Assessment, Research, and Evaluation, 21(1), 11. https://doi.org/10.7275/d2fa-qv48.
RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/.
Schmidt, A. F., & Finan, C. (2018). Linear regression and the normality assumption. Journal of Clinical Epidemiology, 98, 146-151. https://doi.org/10.1016/j.jclinepi.2017.12.006.
Shabani, F., Kumar, L., & Ahmadi, M. (2018). Assessing accuracy methods of species distribution models: AUC, specificity, sensitivity and the true skill statistic. Global Journal of Human Social Science, 18(1), 6-18.
Shabani, F., Kumar, L., & Ahmadi, M. (2016). A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area. Ecology and Evolution, 6(16), 5973-5986. https://doi.org/10.1002/ece3.2332.
Steinmetz, R., Srirattanaporn, S., Mor‐Tip, J., & Seuaturien, N. (2014). Can community outreach alleviate poaching pressure and recover wildlife in South‐East Asian protected areas?. Journal of Applied Ecology, 51(6), 1469-1478. https://doi.org/10.1111/1365-2664.12239.
Taylor, K.E. (2009). A summary of the CMIP5 experiment design. Retrieved from http://cmip-pcmdi. llnl. gov/cmip5/docs/Taylor_CMIP5_design. pdf.
Truong, T. T. A., Hardy, G. E. S. J., & Andrew, M. E. (2017). Contemporary remotely sensed data products refine invasive plants risk mapping in data poor regions. Frontiers in Plant Science, 8(May). https://doi.org/10.3389/fpls.2017.00770.
Wardah, W., Labiro, E., Massiri, S. D., Sustri, S., & Mursidin, M. (2012). Vegetasi kunci habitat Anoa di Cagar Alam Pangi Binangga, Sulawesi Tengah. Jurnal Penelitian Kehutanan Wallacea, 1(1), 1-12. http://dx.doi.org/10.18330/jwallacea.2012.vol1iss1pp1-12.
Whitten, T., & Henderson, G. S. (2012). Ecology of Sulawesi. Tuttle Publishing.
Wilby, R.L., Charles, S.P., Zorita, E., Timbal, B., Whetton, P., & Mearns, L.O. (2004). Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change, available from the DDC of IPCC TGCIA, 27.
Worldclim. (2020). Bioclimatic Variable. Retrieved from https://www.worldclim.org/data/bioclim.html. [25 Jan 2022].
Yoon, S., & Lee, W. H. (2021). Methodological analysis of bioclimatic variable selection in species distribution modeling with application to agricultural pests (Metcalfa pruinosa and Spodoptera litura). Computers and Electronics in Agriculture, 190, 106430. https://doi.org/10.1016/j.compag.2021.106430.
Yunus, S.R. (2022). Habitat Terganggu, Anoa Berkeliaran di Lokasi Perusahaan Tambang di Konawe. Kompas ID Online News June 7th 2022. https://www.kompas.id/baca/nusantara/2022/06/07/habitat-terganggu-anoa-muncul-di-lokasi-perusahaan-tambang-di-konawe. [20 June 2022].
Zhang, H., Sun, X., Zhang, G., Zhang, X., Miao, Y., Zhang, M., ... & Huang, L. (2023). Potential Global Distribution of the Habitat of Endangered Gentiana rhodantha Franch: Predictions Based on MaxEnt Ecological Niche Modeling. Sustainability, 15(1), 631. https://doi.org/10.3390/su15010631.
Published
2023-04-18
How to Cite
ALDIANSYAH, Septianto; WAHID, Khalil Abdul.
Species Distribution Modelling Using Bioclimatic Variables on Endangered Endemic Species (Bubalus depressicornis and Bubalus quarlesi).
Geosfera Indonesia, [S.l.], v. 8, n. 1, p. 1-18, apr. 2023.
ISSN 2614-8528.
Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/31862>. Date accessed: 25 apr. 2024.
doi: https://doi.org/10.19184/geosi.v8i1.31862.
Section
Original Research Articles
