Geomatics and Environmental Engineering, vol. 18, no. 3

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Vol. 18 No. 3 (2024): Geomatics and Environmental Engineering

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Dec 25, 2023
Accepted
Mar 22, 2024
Published
Apr 18, 2024
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Assessing Potential Distributions of Bird Endemic Species: Case Studies of Macrocephalon maleo and Rhyticeros cassidix and Their Threats

https://doi.org/10.7494/geom.2024.18.3.45
Septianto Aldiansyah
niversity of Indonesia, Faculty of Mathematics and Natural Sciences, Department of Geography, Depok, Indonesia
https://orcid.org/0000-0002-5432-8322
Risna
PB University, Faculty of Mathematics and Natural Sciences, Department of Microbiology, Bogor, Indonesia
https://orcid.org/0009-0001-6401-1302
Randi Adrian Saputra
Halu Oleo University, Faculty of Teacher Training and Education, Department of Geography Education, Kendari, Indonesia
https://orcid.org/0009-0001-2108-4392

Corresponding Author(s) : Septianto Aldiansyah

septianto.aldiansyah@ui.ac.id

Geomatics and Environmental Engineering, Vol. 18 No. 3 (2024): Geomatics and Environmental Engineering

Abstract

Maleo and knobbed hornbill are bird species that are endemic on the island of Sulawesi, which is highly threatened by forest fires. Fires tend to destroy any affected species; however, it is not possible to survey the entire range of the original distribution of the two endemic bird species that are affected by forest fires due to practical constraints. Species distribution modeling using maximum entropy is considered to be an alternative to understanding the potential distribution area of species against the threat of forest fires. The prediction model from MaxEnt all have AUC values of greater than 0.70, which means that the model is good enough to classify the records of the presence of M. maleo and R. cassidix along with the past forest fires. The environmental variables that affect the distribution of M. maleo are its distance from hot water, rivers, and roads, while the distribution of R. cassidix is strongly influenced by its distance from roads, settlements, and rivers. Forest fire distribution is mostly influenced by soil type, land-use land cover, and rainfall. It is predicted that around 238,690 and 677,070 ha of the potential distribution of M. maleo and R. cassidix, respectively, are potentially disturbed and affected by forest fires. However, this number much greater outside conservation areas. The results of this study can be used by the government of the Republic of Indonesia (especially the Ministry of Environment and Forestry) for determining conservation actions for both species in the future.

Keywords

potential distribution Macrocephalon maleo Rhyticeros cassidix forest fire maximum entropy
Aldiansyah, S., Risna, & Saputra, R. A. (2024). Assessing Potential Distributions of Bird Endemic Species: Case Studies of Macrocephalon maleo and Rhyticeros cassidix and Their Threats. Geomatics and Environmental Engineering, 18(3), 45–61. https://doi.org/10.7494/geom.2024.18.3.45
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References
  1. Mustari I.A.H.: Manual Identifikasi dan Bio-Ekologi Spesies Kunci di Sulawesi. PT Penerbit IPB Press, Bogor 2021.
  2. Coates B.J., Bishop. K.D.: A guide to the birds of Wallacea Alderley. Dove Publications, Australia 1997.
  3. Bashari H., Rahmanita D., Hunowu I.: Ekologi dan Teknik Survei Populasi Maleo Senkawor Macrocephalon maleo di Taman Nasional Bogani Nani Wartabone. Kotamobagu. Balai Taman Nasional Bogani Nani Wartabone, Kotamobagu 2021.
  4. Sugiarto D.P., Budi P., Efi P., Handry M., Darystin: Keanekaragaman Hayati Taman Nasional Rawa Aopa Watumohai. DIPA, Tatangge 2010.
  5. Sari R.K., Nugroho A., Hadiprakarsa Y.Y., Siregar H. (eds.): Strategi dan Rencana Aksi Konservasi Rangkong Gading (Rhinoplax vigil) Indonesia 2018–2028. Direktur Jenderal Konservasi Sumber Daya Alam dan Ekosistem – Kementarian Lingkungan Hidup dan Kehutanan Republik Indonesia, Jakarta 2018. https://ksdae.menlhk.go.id/assets/publikasi/SRAK%20Rangkong%20Gading_Published.pdf [access: 8.01.2023].
  6. Del Hoyo J., Elliott A., Sargatal J.: Handbook of the Birds of the World. Volume 6: Mousebirds to Hornbills. Lynx Edicions, Barcelona 2001.
  7. Kinnaird M.F., O’Brien T.G.: The Ecology and Conservation of Asian Hornbills: Farmers of the Forest. University of Chicago Press, Chicago 2007.
  8. Kinnaird M.F., O’Brien T.G.: Breeding ecology of the Sulawesi Red-knobbed Hornbill Aceros cassidix. Ibis, vol. 141(1), 1999, pp. 60–69. https://doi.org/10.1111/j.1474-919X.1999.tb04263.x.
  9. Bird J.P., Martin R., Akçakaya H.R., Gilroy J., Burfield I.J., Garnett S.T., Symes A., Taylor J., Şekercioğlu Ç.H., Butchart S.H.M.: Generation lengths of the world’s birds and their implications for extinction risk. Conservation Biology, vol. 34(5), 2020, pp. 1252–1261. https://doi.org/10.1111/cobi.13486.
  10. Global Forest Watch, Interactive Forest Change Mapping Tool. http://www.globalforestwatch.org/ [access: 2.01.2023].
  11. Voigt M., Supriatna J., Deere N.J., Kastanya A., Mitchell S.L., Rosa I.M.D., Santika T., Siregar R., Tasirin J.S., Widyanto A., Winarni N.L., Zakaria Z., Mumbunan S., Davies Z.G., Struebig M.J.: Emerging threats from deforestation and forest fragmentation in the Wallacea centre of endemism. Environmental Research Letters, vol. 16(9), 2021, 094048. https://doi.org/10.1088/1748-9326/ac15cd.
  12. Froese G.Z.L., Mustari A.H.: Assessments of Maleo Macrocephalon maleo nesting grounds in South-east Sulawesi reveal severely threatened populations. Bird Conservation International, vol. 29(4), 2019, pp. 497–502. https://doi.org/10.1017/S0959270918000333.
  13. van As J.: Maleo nesting ground project in Tangkoko, North-Sulawesi, Indonesia. Megapode Newsletter, vol. 20(1), 2007, pp. 6–7.
  14. Semwal R.L., Mehta J.P.: Ecology of forest fires in chir pine (Pinus roxburghii Sarg.) forests of Garhwal Himalaya. Current Science, vol. 70(6), 1996, pp. 426–427.
  15. Roy P.S.: Forest fire and degradation assessment using satellite remote sensing and geographic information system. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, vol. 361, 2003, pp. 361–400.
  16. Kittur B.H., Swamy S.L., Bargali S.S., Jhariya M.K.: Wildland fires and moist deciduous forests of Chhattisgarh, India: divergent component assessment. Journal of Forestry Research, vol. 25, 2014, pp. 857–866. https://doi.org/10.1007/s11676-014-0471-0.
  17. North M.P., Stephens S.L., Collins B.M., Agee J.K., Aplet G., Franklin J.F., Fulé P.Z.: Reform forest fire management. Science, vol. 349(6254), 2015, pp. 1280–1281. https://doi.org/10.1126/science.aab2356.
  18. Murthy K.K., Sinha S.K., Kaul R., Vaidyanathan S.: A fine-scale state-space model to understand drivers of forest fires in the Himalayan foothills. Forest Ecology and Management, vol. 432, 2019, pp. 902–911. https://doi.org/10.1016/j.foreco.2018.10.009.
  19. DeBano L.F., Neary D.G., Ffolliott P.F.: Fire Effects on Ecosystems. John Wiley & Sons, New York 1998.
  20. Ferry Slik J., Verburg R.W., Keßler P.J.: Effects of fire and selective logging on the tree species composition of lowland dipterocarp forest in East Kalimantan, Indonesia. Biodiversity and Conservation, vol. 11(1), 2002, pp. 85–98. https://doi.org/10.1023/A:1014036129075.
  21. Phillips S.J., Anderson R.P., Schapire R.E.: Maximum entropy modeling of species geographic distributions. Ecological Modelling, vol. 190(3–4), 2006, pp. 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026.
  22. Chitale V., Behera M.D.: How will forest fires impact the distribution of endemic plants in the Himalayan biodiversity hotspot? Biodiversity and Conservation, vol. 28(8–9), 2019, pp. 2259–2273. https://doi.org/10.1007/s10531-019-01733-8.
  23. Devineau J.L., Fournier A., Nignan S.: Savanna fire regimes assessment with MODIS fire data: Their relationship to land cover and plant species distribution in western Burkina Faso (West Africa). Journal of Arid Environments, vol. 74(9), 2010, pp. 1092–1101. https://doi.org/10.1016/j.jaridenv.2010.03.009.
  24. Ambagau Y.: Analisis kesesuaian habitat burung maleo (Macrocephalon maleo) di taman nasional Bogani Nani Wartabone. Fakultas Kehutanan, Institut Pertanian Bogor, Bogor 2010 [thesis].
  25. Gazi R.: Analisis kondisi lokasi bertelur maleo senkawor (macrocephalon maleo) di kabupaten Mamuju provinsi Sulawesi Barat. Fakultas Kehutanan, Institut Pertanian Bogor, Bogor 2008 [thesis].
  26. Laban L.M.: Pendugaan Populasi, Preferensi Habitat Peneluran dan Pola Sebaran Maleo (Macrocephalon maleo Sal Muller 1846) Berdasarkan Keberadaan Sarang di Kawasan Taman Nasional Lore Lindu Kabupaten Donggala Propinsi Sulawesi Tengah. Fakultas Kehutanan, Institut Pertanian Bogor, Bogor 2007 [thesis].
  27. Mangi H., Ningsih S., Ihsan M.: Asosiasi Burung Julang Sulawesi (Rhyticeros cassidix) dengan Pohon Eboni (Diospyros celebica Bakh) di Cagar Alam Pangi Binangga Desa Pangi Kabupaten Parigi Moutong. Jurnal Warta Rimba, vol. 1(1), 2013, pp. 1–8.
  28. Nur R.F.: Kelimpahan dan Distribusi Burung Rangkong (Famili Bucerotidae) di Kawasan PT. Kencana Sawit Indonesia (KSI), Solok Selatan, Sumatera Barat. Prosiding SEMIRATA, vol. 1(1), 2013, pp. 231–236.
  29. Asrianny A., Saputra H., Achmad A.: Identifikasi Keanekaragaman dan Sebaran Jenis Burung untuk Pengembangan Ekowisata Bird Watching di Taman Nasional Bantimurung Bulusaraung. Perennial, vol. 14(1), 2018, pp. 17–23. https://doi.org/10.24259/perennial.v14i1.4999.
  30. GBIF.org (6 January 2023a): GBIF Occurrence Download. https://doi.org/10.15468/dl.yqmj2p [access: 6.01.2023].
  31. GBIF.org (6 January 2023b): GBIF Occurrence Download. https://doi.org/10.15468/dl.z4ydfp [access: 6.01.2023].
  32. FIRMS: Active Fire Data Download. https://firms.modaps.eosdis.nasa.gov [access: 6.01.2023].
  33. Badan Informasi Geospasial (BIG): Shapefile RBI Seluruh Indonesia (Perwilayah) Download. https://www.indonesia-geospasial.com [access: 20.11.2022].
  34. Congalton R.G., Green K.: Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC Press, Boca Raton 2019.
  35. Global Wind Atlas (GWA): https://globalwindatlas.info [access: 19.11.2023].
  36. Aldiansyah S., Risna R.: Assessing potential habitat suitability of vulnerable endemic species: A case study of Diospyros celebica Bakh and Rhyticeros cassidix. Forum Geografic, vol. 22(2), 2023, pp. 159–169. http://doi.org/10.5775/fg.2023.2.3596.
  37. Benítez-Badillo G., Lascurain-Rangel M., Álvarez-Palacios J.L., GómezDíaz J.A., Avendaño-Reyes S., Dávalos-Sotelo R., López-Acosta J.C.: Influence of land-use changes (1993 and 2013) in the distribution of wild edible fruits from Veracruz (Mexico). Tropical Conservation Science, vol. 11(1), 2018. https://doi.org/10.1177/1940082918758662.
  38. Saputra M.H., Lee H.S. Evaluation of climate change impacts on the potential distribution of Styrax sumatrana in North Sumatra, Indonesia. Sustainability, vol. 13(2), 2021, 462. https://doi.org/10.3390/su13020462.
  39. Condro A.A., Prasetyo L.B., Rushayati S.B., Santikayasa I.P., Iskandar E.: Predicting hotspots and prioritizing protected areas for endangered primate species in Indonesia under changing climate. Biology, vol. 10(2), 2021, 154. https://doi.org/10.3390/biology10020154.
  40. Aldiansyah S., Wahid K.A.: Species Distribution Modelling Using Bioclimatic Variables on Endangered Endemic Species (Bubalus depressicornis and Bubalus quarlesi). Geosfera Indonesia, vol. 8(1), 2023, pp. 1–18. https://doi.org/10.19184/geosi.v8i1.31862.
  41. Aldiansyah S., Wardani F.: Evaluation of flood susceptibility prediction based on a resampling method using machine learning. Journal of Water and Climate Change, vol. 14(3), 2023, pp. 937–961. https://doi.org/10.2166/wcc.2023.494.
  42. Javidan N., Kavian A., Pourghasemi H.R., Conoscenti C., Jafarian Z.: Data mining technique (maximum entropy model) for mapping gully erosion susceptibility in the gorganrood watershed, Iran. [in:] Shit P., Pourghasemi H., Bhunia G. (eds.), Gully Erosion Studies from India and Surrounding Regions, Springer, Cham 2020, pp. 427–448. https://doi.org/10.1007/978-3-030-23243-6_29.
  43. Aldiansyah S., Wardani F.: Assessment of resampling methods on performance of landslide susceptibility predictions using machine learning in Kendari City, Indonesia. Water Practice & Technology, vol. 19(1), 2024, pp. 52–81. https://doi.org/10.2166/wpt.2024.002.
  44. Yudaputra A.: Modelling potential current distribution and future dispersal of an invasive species Calliandra calothyrsus in Bali Island, Indonesia. Biodiversitas, vol. 21(2), 2020, pp. 674–682. https://doi.org/10.13057/biodiv/d210233.
  45. Pearson R.G., Raxworthy C.J., Nakamura M., Townsend, Peterson A.: Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. Journal of Biogeography, vol. 34(1), 2007, pp. 102–117. https://doi.org/10.1111/j.1365-2699.2006.01594.x.
  46. Barbet-Massin M., Thuiller W., Jiguet F.: How much do we overestimate future local extinction rates when restricting the range of occurrence data in climate suitability models? Ecography, vol. 33(5), 2010, pp. 878–886. https://doi.org/10.1111/j.1600-0587.2010.06181.x.
  47. Araújo M.B., Anderson R.P., Márcia Barbosa A., Beale C.M., Dormann C.F., Early R., Garcia R.A., Guisan A., Maiorano L., Naimi B., O’Hara R.B., Zimmermann N.E., Rahbek C.: Standards for distribution models in biodiversity assessments. Science Advances, vol. 5(1), 2019, eaat4858. https://doi.org/10.1126/sciadv.aat4858.
  48. Petitpierre B., Broennimann O., Kueffer C., Daehler C., Guisan A.: Selecting predictors to maximize the transferability of species distribution models: Lessons from cross-continental plant invasions. Global Ecology and Biogeography, vol. 26(3), 2017, pp. 275–287. https://doi.org/10.1111/geb.12530.
  49. Hosmer D.W., Lemeshow S.: Applied Logistic Regression. 2nd ed. John Wiley & Sons, New York 2000.
  50. Krzanowski W.J., Hand D.J.: ROC Curves for Continuous Data. 1st ed. CRC Press, Boca Raton 2009.
  51. Lobo J.M., Jiménez-Valverde A., Real R.: AUC: A misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, vol. 17(2), 2008, pp. 145–151. https://doi.org/10.1111/j.1466-8238.2007.00358.x.
  52. Gorog A.J., Pamungkas B., Lee R.J.: Nesting ground abandonment by the Maleo (Macrocephalon maleo) in North Sulawesi: Identifying conservation priorities for Indonesia’s endemic megapode. Biological Conservation, vol. 126(4), 2005, pp. 548–555. https://doi.org/10.1016/j.biocon.2005.07.006.
  53. Dekker R.W.R.J.: The distribution and status of nesting grounds of the maleo Macrocephalon maleo in Sulawesi, Indonesia. Biological Conservation, vol. 51(2), 1990, pp. 139–150. https://doi.org/10.1016/0006-3207(90)90108-2.
  54. Butchart S.H., Baker G.C.: Priority sites for conservation of maleos (Macrocephalon maleo) in Central Sulawesi. Biological Conservation, vol. 94(1), 2000, pp. 79–91. https://doi.org/10.1016/S0006-3207(99)00161-5.
  55. Winarni N.L., Jones M.: Effect of anthropogenic disturbance on the abundance and habitat occupancy of two endemic hornbill species in Buton island, Sulawesi. Bird Conservation International, vol. 22(2), 2012, pp. 222–233. https://doi.org/10.1017/S0959270911000141.
  56. Mayasari A., Kinho J., Suryawan A.: Asosiasi eboni (Diospyros spp.) dengan jenis-jenis pohon dominan di Cagar Alam Tangkoko Sulawesi Utara. Info Balai Penelitian Kehutanan Manado, vol. 2(1), 2012, pp. 15–72.
  57. Buringh P.: Introduction to Study of Soils in Tropical and Sub-Tropical Regian. 3rd ed. John Wiley & Sons, New York 1970.
  58. Ministry of Environment and Forestry: Kerhutla Monitoring Sistem. https://sipongi.menlhk.go.id/ [access: 8.01.2023].
  59. Peterson A.T., Soberón J., Pearson R.G., Anderson R.P., Martínez-Meyer E., Nakamura M., Araújo M.B.: Ecological Niches and Geographic Distributions. Princeton University Press, Princeton 2011.
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