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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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dc.contributor.author Payacán, Sergio es_ES
dc.contributor.author Alfaro, F.D. es_ES
dc.contributor.author Pérez-Martínez, Waldo es_ES
dc.contributor.author Briceño-de-Urbaneja, Idania es_ES
dc.date.accessioned 2020-03-06T10:15:04Z
dc.date.available 2020-03-06T10:15:04Z
dc.date.issued 2019-12-23
dc.identifier.issn 1133-0953
dc.identifier.uri http://hdl.handle.net/10251/138443
dc.description.abstract [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 using the maximum entropy approach. This species has a very restricted distribution along the hyperarid coastal desert in northern Chile. Our results showed that local and regional environmental factors define its distribution. Changes in altitude and microhabitat related to the landforms are of critical importance at the local scale, whereas cloud cover variations associated with coastal fog was the principal factor determining the presence of L. ovallei at the regional level. This study verified the value of the maximum entropy in understanding the factors that influence the distribution of plant species with restricted distribution ranges. es_ES
dc.description.abstract [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 L. ovallei se modeló utilizando la técnica de máxima entropía. Esta especie tiene una distribución muy restringida a lo largo del desierto costero hiperárido del norte de Chile. Nuestros resultados mostraron que los factores ambientales locales y regionales definen su distribución. Los cambios de altitud y el microhábitat relacionados con la forma del terreno son de importancia crítica a escala local, mientras que las variaciones en la cobertura nubosa asociadas con la niebla costera fueron el principal factor que determinó la presencia de L. ovallei a nivel regional. Este estudio verificó el valor de la técnica de máxima entropía en la comprensión de los factores que influyen en la distribución de las especies de plantas con rangos de distribución restringidos. es_ES
dc.description.sponsorship 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. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista de Teledetección es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Leontochir ovallei es_ES
dc.subject Potential distribution es_ES
dc.subject Machine learning techniques es_ES
dc.subject Maximum entropy es_ES
dc.subject Environmental factors es_ES
dc.subject Modelo de distribución de especies es_ES
dc.subject Aprendizaje automático es_ES
dc.subject Máxima entropía es_ES
dc.subject Factores ambientales es_ES
dc.title Potential distribution model of Leontochir ovallei using remote sensing data es_ES
dc.title.alternative Modelo de distribución potencial de Leontochir ovallei con datos de sensores remotos es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/raet.2019.12792
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation 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 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/raet.2019.12792 es_ES
dc.description.upvformatpinicio 59 es_ES
dc.description.upvformatpfin 69 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 0 es_ES
dc.description.issue 54 es_ES
dc.identifier.eissn 1988-8740
dc.relation.pasarela OJS\12792 es_ES
dc.description.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 es_ES
dc.description.references Baldwin, R.A. 2009. Use of Maximum Entropy Modeling in Wildlife Research. Entropy, 11(4), 854-866. https://doi.org/10.3390/e11040854. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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 es_ES
dc.description.references CONAF. 1997. Corporación Nacional Forestal, Plan de Manejo Parque Nacional Llanos de Challe. Documento de Trabajo Nº 250. Mieres, G. (Ed.) Santiago, 129 pp. es_ES
dc.description.references 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. es_ES
dc.description.references Chavez, P.S. 1996. Image-based atmospheric corrections: Revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025-1036. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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). es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references Muñoz, C. 1973. Chile: Plantas en extinción. Editorial Universitaria, Santiago, Chile. 248 p. es_ES
dc.description.references 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). es_ES
dc.description.references 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. es_ES
dc.description.references 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 es_ES
dc.description.references 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. es_ES
dc.description.references QGIS Development Team. 2017. QGIS Geographic Information System, Version 2.18 Brighton. Open Source Geospatial Foundation Project. Accesible at http://www.qgis.org. es_ES
dc.description.references R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES
dc.description.references 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. es_ES


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