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Potential distribution model of Leontochir ovallei using remote sensing data

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Potential distribution model of Leontochir ovallei using remote sensing data

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Payacán, S.; Alfaro, F.; Pérez-Martínez, W.; Briceño-De-Urbaneja, I. (2019). Potential distribution model of Leontochir ovallei using remote sensing data. Revista de Teledetección. 0(54):59-69. https://doi.org/10.4995/raet.2019.12792

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Título: Potential distribution model of Leontochir ovallei using remote sensing data
Otro titulo: Modelo de distribución potencial de Leontochir ovallei con datos de sensores remotos
Autor: Payacán, Sergio Alfaro, F.D. Pérez-Martínez, Waldo Briceño-de-Urbaneja, Idania
Fecha difusión:
Resumen:
[EN] Predicting the potential distribution of short-lived species with a narrow natural distribution range is a difficult task, especially when there is limited field data. The possible distribution of L. ovallei was modeled ...[+]


[ES] Predecir la distribución potencial de especies de vida corta con un rango de distribución natural restringido es una tarea compleja, especialmente cuando los datos de campo son limitados. La posible distribución de ...[+]
Palabras clave: Leontochir ovallei , Potential distribution , Machine learning techniques , Maximum entropy , Environmental factors , Modelo de distribución de especies , Aprendizaje automático , Máxima entropía , Factores ambientales
Derechos de uso: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Fuente:
Revista de Teledetección. (issn: 1133-0953 ) (eissn: 1988-8740 )
DOI: 10.4995/raet.2019.12792
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.4995/raet.2019.12792
Agradecimientos:
The Master supported this work in Remote Sensing program, Earth Observation Center Hémera and Centre for Genomics, Ecology and Environment (GEMA), Faculty of Sciences, Universidad Mayor.
Tipo: Artículo

References

Austin, M. 2007. Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modellling, 200(1-2), 1-19. https://doi.org/10.1016/j. ecolmodel.2006.07.005

Baldwin, R.A. 2009. Use of Maximum Entropy Modeling in Wildlife Research. Entropy, 11(4), 854-866. https://doi.org/10.3390/e11040854.

Bartel, R.A., Sexton, J.O. 2009. Monitoring habitat dynamics for rare and endangered species using satellite images and niche-based models. Ecography, 32(5), 888-896. https://doi.org/10.1111/ j.1600-0587.2009.05797.x. [+]
Austin, M. 2007. Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modellling, 200(1-2), 1-19. https://doi.org/10.1016/j. ecolmodel.2006.07.005

Baldwin, R.A. 2009. Use of Maximum Entropy Modeling in Wildlife Research. Entropy, 11(4), 854-866. https://doi.org/10.3390/e11040854.

Bartel, R.A., Sexton, J.O. 2009. Monitoring habitat dynamics for rare and endangered species using satellite images and niche-based models. Ecography, 32(5), 888-896. https://doi.org/10.1111/ j.1600-0587.2009.05797.x.

Barragán-Barrera, D.C., do Amaral, K.B., ChávezCarreño, P.A., Farías-Curtidor, N., LancherosNeva, R., Botero-Acosta, N., Bueno, P., Moreno, I.B., Bolaños-Jiménez, J., Bouveret, L., Castelblanco-Martínez, D.N., Luksenburg, J.A., Melliger, J., Mesa-Gutiérrez R., de Montgolfier, B., Ramos, E.A., Ridoux, V., Palacios, D.M. 2019. Ecological Niche Modeling of Three Species of Stenella Dolphins in the Caribbean Basin, With Application to the Seaflower Biosphere Reserve. Frontiers in Marine Science, 6(10). https://doi. org/10.3389/fmars.2019.00010.

Carvajal, D.E., Loayza, A.P., López, R.P., Toro, P.J., Squeo, F.A. 2014. Growth and early seedling survival of four Atacama Desert shrub species under experimental light and water availability regimes. Revista Chilena de Historia Natural, 87(1), 28. https://doi.org/10.1186/S40693-014- 0028-9.

Cereceda, P, Larraín, H., Osses, P., Lázaro, P., García, J.L., Hernández, V. 2000. El factor clima en la floración del desierto en los años "El Niño" 1991 y 1997". Revista de Geografía Norte Grande, 27, 37-52. Accesible at https://repositorio.uc.cl/ bitstream/handle/11534/10433/000313720. pdf?sequence=1&isAllowed=y

CONAF. 1997. Corporación Nacional Forestal, Plan de Manejo Parque Nacional Llanos de Challe. Documento de Trabajo Nº 250. Mieres, G. (Ed.) Santiago, 129 pp.

Ćorović, J, Popović, M., Cogălniceanu, D., Carretero, M.A., Crnobrnja-Isailović, J. 2018. Distribution of the meadow lizard in Europe and its realized ecological niche model. Journal of Natural History, 52(29-30), 1909-1925. https://doi.org/10.1080/002 22933.2018.1502829.

Chavez, P.S. 1996. Image-based atmospheric corrections: Revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025-1036.

Chávez, R.O., Moreira-Muñoz, A., Galleguillos, M., Olea, M., Aguayo, J., Latín, A., Aguilera-Betti, I., Muñoz, A.A., Manríquez H. 2019. GIMMS NDVI time series reveal the extent, duration, and intensity of "blooming desert" events in the hyper-arid Atacama Desert, Northern Chile. International Journal Applied Earth Observation and Geoinformation, 76, 193-203. https://doi.org/10.1016/j.jag.2018.11.013.

Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carls, G., Carré, G., Marquéz, J.R., Gruber, B., Lafourcade, B., Leitão, P.J., Münkemüller, T., McClean, C., Osborne, P.E., Reineking, B., Schröder, B., Skidmore, A.K., Zurell, D., Lautenbach, S. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27-46. https://doi.org/10.1111/j.1600- 0587.2012.07348.x.

Errázuriz, A.M., Hanisch, M. 1995. Horizonte 7: Historia y Geografía. Editorial Andrés Bello. https:// books.google.com.br/books?id=fB2TTLzH_5IC (accessed 07 February 2017).

Franklin, J. 2010. Ecology, Biodiversity and Conservation. In Mapping Species Distributions: Spatial Inference and Prediction, Frontmatter, pp. I-Viii, Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511810602.

Garreaud, R., Rutllant, J.A., Fuenzalida, H. 2002. Coastal Lows along the Subtropical West Coast of South America: Mean Structure and Evolution. Monthly Weather Review, 130, 75-88. https://doi. org/10.1175/1520-0493(2002)1302.0.CO;2.

Garreaud, R., Barichivich, J., Christie, D.A. and Maldonado, A. 2008. Interannual variability of the coastal fog at Fray Jorge relict forests in semiarid Chile. Journal of Geophysical Research, 113, G04011. https://doi.org/10.1029/2008JG000709.

Giannakopoulos, A., Vasileiou, N.G.C., Gougoulis, D.A., Cripps, P.J., Ioannidi, K.S., Chatzopoulos, D.C., Billinis, C., Mavrogianni, V.S., Petinaki, E., Fthenakis, G.C. 2019. Use of geographical information system and ecological niche modelling for predicting potential space distribution of subclinical mastitis in ewes. Veterinary Microbiology, 228, 119-128. https://doi.org/10.1016/j.vetmic.2018.11.021.

González, B.A., Samaniego, H., Marín, J.C., Estades, C.F. 2013. Unveiling Current Guanaco Distribution in Chile Based upon Niche Structure of Phylogeographic Lineages: Andean Puna to Subpolar Forests. PLoS ONE, 8(11), e78894. https://doi.org/10.1371/journal.pone.0078894.

Haughian, S.R., Clayden, S.R., Cameron, R. 2018. On the distribution and habitat of Fuscopannaria leucosticta in New Brunswick, Canada. Écoscience, 26(2), 99-112. https://doi.org/10.1080/11956860.2 018.1526997.

Hawk, A.M. 2017. Habitat modeling of a rare endemic Trillium Species (Trillium Simile Gleason): a comparison of the methods maxent and domain for modeling rare species-rich habitat. In Biology Department. Vol. Master of Science in Biology (Dissertation). Western Carolina University, USA.

Hernández, P.A., Graham, C.H., Master, L.L., Albert, D.L. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773-785. https://doi.org/10.1111/j.0906- 7590.2006.04700.x.

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jaarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal Climatology, 25(15), 1965-1978. https://doi.org/10.1002/joc.1276.

Jafari, A., Zamani-Ahmadmahmoodi, R., Mirzaei, R. 2018. Persian leopard and wild sheep distribution modeling using the Maxent model in the Tang-eSayad protected area, Iran. Mammalia, 83(1), 84- 96. https://doi.org/10.1515/mammalia-2016-0155.

Juliá, C., Montecinos, S., Maldonado, A. 2008. Características climáticas de la Región de Atacama, In Libro Rojo de la Flora Nativa y de Los Sitios Prioritarios para su Conservación: Región de Atacama, Squeo, F.A., Arancio, G., Gutiérrez, J.R., (Eds.), pp. 3: 25-42, Ediciones Universidad de La Serena, La Serena, Chile.

Kafley, H., Khadka, M., Sharma, M. 2009. Habitat Evaluation and Suitability Modeling of Rhinoceros unicornis in Chitwan National Park, Nepal: A Geospatial Approach, In XII World Forestry Congress, 18-23 October, Buenos Aires, Argentina.

Kottek, M., Grieser, J., Beck, C., Rudolf, B., Rubel, F. 2006. World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259-263. https://doi.org/10.1127/0941- 2948/2006/0130.

Kumar, S., Stohlgren, T.J. 2009. Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. Journal of Ecology and The Natural Environment, 1(4), 94-98. Accesible at https://pdfs. semanticscholar.org/66b8/8f62b73226934f6f7216 efbcf31ac1e7ef61.pdf.

Larraín, H., Velásquez, F., Cereceda, P., Espejo, R., Pinto, R., Osses, P., Schemenauer, R.S. 2002. Fog measurements at the site "Falda Verde" north of Chañaral compared with other fog stations of Chile. Atmospheric Research, 64(1-4), 273-284. https://doi.org/10.1016/S0169-8095(02)00098-4.

Latorre, C., González, A.L., Quade, J., Fariña, J.M., Pinto, R., Marquet, P.A. 2011. Establishment and formation of fog-dependent Tillandsia landbeckii dunes in the Atacama Desert: Evidence from radiocarbon and stable isotopes. Journal of Geophysical Research, 116, G03033. https://doi.org/10.1029/2010JG001521.

Marini, M.Á., Barbet-Massin, M., Martínez, J., Prestes, N.P., Jiguet, F. 2010. Applying ecological niche modelling to plan conservation actions for the Red-spectacled Amazon (Amazona pretrei). Biological Conservation, 143(1), 102-112. https://doi.org/10.1016/j.biocon.2009.09.009.

MINSEGPRES (Ministerio Secretaría General de la Presidencia). 2008. Decreto Supremo N° 50/2008. Aprueba y oficializa nómina para el segundo proceso de clasificación de especies según su estado de conservación, Santiago, Chile.

Morales, N. 2012. Modelos de distribución de especies: Software Maxent y sus aplicaciones en Conservación. Revista Conservación Ambiental, 2(1), 1-5. Accesible at https://issuu.com/ fundacionecomabi/docs/revista_conservaci__n_ ambiental_mas.

Muñoz, C. 1973. Chile: Plantas en extinción. Editorial Universitaria, Santiago, Chile. 248 p.

Muñoz-Schick, M., Sierra, T. 2006. Ficha de antecedentes de especie: Leontochir ovallei, in Documento de trabajo, Proceso Nacional de Clasificación de Especies. Comisión Nacional del Medio Ambiente (CONAMA).

Phillips, S.J., Dudík, M., Schapire, R.E. 2004. A maximum entropy approach to species distribution modeling. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML), 4-8 July, Banff, Alberta Canada, p.83. https://doi.org/10.1145/1015330.1015412.

Phillips, S.B., Aneja, V.P., Kang, D., Arya, S.P. 2006. Modelling and analysis of the atmospheric nitrogen deposition in North Carolina. International Journal of Global Environmental Issues, 6(2-3), 231-252. https://doi.org/10.1504/IJGENVI.2006.010156

Pliscoff, P., Fuentes-Castillo, T. 2011. Modelación de la distribución de especies y ecosistemas en el tiempo y en el espacio: una revisión de las nuevas herramientas y enfoques disponible. Revista de Geografía Norte Grande, 48, 61-79. https://doi.org/10.4067/S0718-34022011000100005.

QGIS Development Team. 2017. QGIS Geographic Information System, Version 2.18 Brighton. Open Source Geospatial Foundation Project. Accesible at http://www.qgis.org.

R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/.

Rundel, P.W., Dillon, M.O., Palam, B., Mooney, H.A., Gulmon, S.L., Ehleringer, J.R. 1991. The Phytogeography and Ecology of the Coastal Atacama and Peruvian Deserts. Aliso: A Journal of Systematic and Evolutionary Botany, 13(1/2), 1-49. https://doi.org/10.5642/aliso.19911301.02.

Sarma, B., Baruah, P., Tanti B. 2018. Habitat distribution modeling for reintroduction and conservation of Aristolochia indica L. - a threatened medicinal plant in Assam, India. Journal Threatened Taxa, 10(11), 12531-12537. https://doi.org/10.11609/jott.3600.10.11.12531-12537.

Sarricolea, P., Herrera-Ossandon, M.J., MeseguerRuiz, Ó. 2017. Climatic regionalisation of continental Chile. Journal of Maps, 13(2), 66-73.https://doi.org/10.1080/17445647.2016.1259592.

Shiv, P., Samant, S.S., Lal, M., Ram J. 2019. Population Assessment and Habitat Distribution Modelling of High Value Corylus jacquemontii for in situ Conservation in the State of Himachal Pradesh, India. Proceedings of the Indian National Science Academy, 85(1), 275-289. https://doi.org/10.16943/ ptinsa/2018/49507.

Squeo, F.A., Arancio, G., Gutiérrez, J.R. 2008. Libro rojo de la Flora Nativa y de los Sitios Prioritarios para su Conservación: Región de Atacama. Editorial Universidad de La Serena, La Serena, Chile, pp. 25-42.

Tang, J., Li, J., Lu, H., Lu, F.,d Lu, B. 2018. Potential distribution of an invasive pest, Euplatypus parallelus, in China as predicted by Maxent. Pest Management Science, 75, 1630-1637. https://doi. org/10.1002/ps.5280.

Thapa, A., Wu, R., Hu, Y., Nie, Y., Sing, P.B., Khatiwada, J.R., Yan, L., Gu, X., Wei, F. 2018. Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecology and Evolution, 8(21), 10542-10554. https://doi.org/10.1002/ece3.4526.

Thompson, M.V., Palma, B., Knowles, J.T., Holbrook, N.M. 2003. Multi-annual climate in Parque Nacional Pan de Azúcar, Atacama Desert, Chile. Revista Chilena de Historia Natural, 76(2), 235-254. https://doi.org/10.4067/S0716- 078X2003000200009.

Urbina-Cardona, J.N., Flores-Villela, O. 2010. Ecological-Niche Modeling and Prioritization of Conservation-Area Networks for Mexican Herpetofauna. Conservation Biology, 24(4), 1031-1041. https://doi.org/10.1111/j.1523- 1739.2009.01432.x.

Wang, W.C., Lo, N.J., Chang, W.I., Huang, K.Y. 2012. Modeling spatial distribution of a rare and endangered plant species (Brainea insignis) in Central Taiwan", in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B7, 241-246. https://doi.org/10.5194/isprsarchivesXXXIX-B7-241-2012.

Wilson, A.M., Jetz, W. 2016. Remotely Sensed HighResolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions. PLOS Biology, 14(3), e1002415. https://doi.org/10.1371/ journal.pbio.1002415.

Wisz, M.S., Hijmans, R.J., Li, J., Peterson, A.T., Graham, C.H., Guisan, A., NCEAS Predicting Species Distributions Working Group. 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14(5), 763-773. https://doi.org/10.1111/j.1472- 4642.2008.00482.x.

Zimmermann, N.E., Edwards, T.C., Moisen G.G., Frescino, T.S., Blackard, J.A. 2007. Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah. Journal of Applied Ecology, 44(5), 1057-1067. https://doi.org/10.1111%2Fj.1365- 2664.2007.01348.x.

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