Predictores de diversidad en anfibios de la Amazonía: una aproximación por aprendizaje automático.
La amazonia es una zona megadiversa de gran importancia en la biología evolutiva, esta alberga más de 500 especies de anfibios. Para estimar los patrones de biodiversidad se pueden utilizar índices de biodiversidad, los cuales pueden estar relacionados con las diferencias ambientales. En este anális...
- Autores:
-
Doqueresana Ortega, Yuber Steven
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2022
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/57703
- Acceso en línea:
- http://hdl.handle.net/1992/57703
- Palabra clave:
- Aprendizaje automatizado
Amazonía
Biodiversidad
Filogeografía
Anfibios
Diversidad biológica
Genética de anfibios
Biología
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Predictores de diversidad en anfibios de la Amazonía: una aproximación por aprendizaje automático. |
| title |
Predictores de diversidad en anfibios de la Amazonía: una aproximación por aprendizaje automático. |
| spellingShingle |
Predictores de diversidad en anfibios de la Amazonía: una aproximación por aprendizaje automático. Aprendizaje automatizado Amazonía Biodiversidad Filogeografía Anfibios Diversidad biológica Genética de anfibios Biología |
| title_short |
Predictores de diversidad en anfibios de la Amazonía: una aproximación por aprendizaje automático. |
| title_full |
Predictores de diversidad en anfibios de la Amazonía: una aproximación por aprendizaje automático. |
| title_fullStr |
Predictores de diversidad en anfibios de la Amazonía: una aproximación por aprendizaje automático. |
| title_full_unstemmed |
Predictores de diversidad en anfibios de la Amazonía: una aproximación por aprendizaje automático. |
| title_sort |
Predictores de diversidad en anfibios de la Amazonía: una aproximación por aprendizaje automático. |
| dc.creator.fl_str_mv |
Doqueresana Ortega, Yuber Steven |
| dc.contributor.advisor.none.fl_str_mv |
Paz Velez, Andrea Crawford, Andrew Jackson |
| dc.contributor.author.none.fl_str_mv |
Doqueresana Ortega, Yuber Steven |
| dc.contributor.researchgroup.es_CO.fl_str_mv |
BIOMICS |
| dc.subject.keyword.none.fl_str_mv |
Aprendizaje automatizado Amazonía Biodiversidad Filogeografía |
| topic |
Aprendizaje automatizado Amazonía Biodiversidad Filogeografía Anfibios Diversidad biológica Genética de anfibios Biología |
| dc.subject.armarc.none.fl_str_mv |
Anfibios Diversidad biológica Genética de anfibios |
| dc.subject.themes.es_CO.fl_str_mv |
Biología |
| description |
La amazonia es una zona megadiversa de gran importancia en la biología evolutiva, esta alberga más de 500 especies de anfibios. Para estimar los patrones de biodiversidad se pueden utilizar índices de biodiversidad, los cuales pueden estar relacionados con las diferencias ambientales. En este análisis se estimó como la influencia de la heterogeneidad del ambiente afecta la diversidad de dos familias de anuros, Centrolenidae y Aromobatidae, usando 33 diferentes variables ambientales como predictores: topografía, ríos, propiedades de suelos, el clima actual y pasado. Se realizó un modelo de ensamblaje de aprendizaje automatizado que incluye 4 algoritmos supervisados, que miden la importancia de las variables como predictores de los índices de riqueza de especies y diversidad filogenética. Las variables ambientales predijeron el 23% de la riqueza de especies para centrolenidos y un 16% de la diversidad filogenética en los aromobatidos. Cuando se unieron ambas familias por índice de biodiversidad el poder predictivo del modelo fue del 15% del conjunto de datos para ambos índices. La isotermalidad fue la variable con mayor importancia dentro de los modelos, la evapotranspiración y la cobertura vegetal también tuvieron un rol importante. Se pretende incluir más grupos taxonómicos como familias para mejorar el poder predictivo del modelo. |
| publishDate |
2022 |
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2022-06-06T15:54:07Z |
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2022-06-06T15:54:07Z |
| dc.date.issued.none.fl_str_mv |
2022-06-07 |
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Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_7a1f |
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http://purl.org/redcol/resource_type/TP |
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Heredity 2018 122:5, 122(5), 545-557. https://doi.org/10.1038/s41437-018-0155-1 Paz, Andrea, Brown, J. L., Cordeiro, C. L. O., Aguirre-Santoro, J., Assis, C., Amaro, R. C., Raposo do Amaral, F., Bochorny, T., Bacci, L. F., Caddah, M. K., d'Horta, F., Kaehler, M., Lyra, M., Grohmann, C. H., Reginato, M., Silva-Brandão, K. L., Freitas, A. V. L., Goldenberg, R., Lohmann, L. G., Carnaval, A. C. (2021). Environmental correlates of taxonomic and phylogenetic diversity in the Atlantic Forest. Journal of Biogeography, 48(6), 1377-1391. https://doi.org/10.1111/JBI.14083 Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G., Williams, Z. C., Brunke, M. A., & Gochis, D. (2016). Global 1-km Gridded Thickness of Soil, Regolith, and Sedimentary Deposit Layers. ORNL DAAC, Oak Ridge, Tennessee, USA. Peters, M. K., Hemp, A., Appelhans, T., Behler, C., Classen, A., Detsch, F., Ensslin, A., Ferger, S. W., Frederiksen, S. B., Gebert, F., Haas, M., Helbig-Bonitz, M., Hemp, C., Kindeketa, W. J., Mwangomo, E., Ngereza, C., Otte, I., Röder, J., Rutten, G., Steffan-Dewenter, I. (2016). Predictors of elevational biodiversity gradients change from single taxa to the multi-taxa community level. Nature Communications 2016 7:1, 7(1), 1-11. https://doi.org/10.1038/ncomms13736 Pillay, R., Venter, M., Aragon-Osejo, J., González-del-Pliego, P., Hansen, A. J., Watson, J. E. M., & Venter, O. (2021). Tropical forests are home to over half of the world's vertebrate species. Frontiers in Ecology and the Environment. https://doi.org/10.1002/FEE.2420 Poelchau, M. F., & Hamrick, J. L. (2013). Palaeodistribution modelling does not support disjunct Pleistocene refugia in several Central American plant taxa. Journal of Biogeography, 40(4), 662-675. https://doi.org/10.1111/J.1365-2699.2011.02648.X Poggio, L., De Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217-240. https://doi.org/10.5194/SOIL-7-217-2021 Proyecto MapBiomas Amazonía- Colección 3.0 de la Serie Anual de Mapas de Cobertura y Uso del Suelo de la Amazonía, adquirido en 01/2022 a través del enlace: https://plataforma.panamazonia.mapbiomas.org/ QGIS Geographic Information System. (2021). QGIS Geographic Information System (3.24). Open Source Geospatial Foundation Project. http://qgis.osgeo.org/ Qian, H. (2009). Global tests of regional effect on species richness of vascular plants and terrestrial vertebrates. Ecography, 32(3), 553-560. https://doi.org/10.1111/J.1600-0587.2008.05755.X Réjaud, A., Rodrigues, M. T., Crawford, A. J., Castroviejo-Fisher, S., Jaramillo, A. F., Chaparro, J. C., Glaw, F., Gagliardi-Urrutia, G., Moravec, J., De la Riva, I. J., Perez, P., Lima, A. P., Werneck, F. P., Hrbek, T., Ron, S. R., Ernst, R., Kok, P. J. R., Driskell, A., Chave, J., & Fouquet, A. (2020). Historical biogeography identifies a possible role of Miocene wetlands in the diversification of the Amazonian rocket frogs (Aromobatidae: Allobates). Journal of Biogeography, 47(11), 2472-2482. https://doi.org/10.1111/JBI.13937 Rivera Martínez, R. (2020). Diversidad filogenética: una forma de medir la historia evolutiva de la biodiversidad. Desde El Herbario CICY, 12, 270¿275. http://www.cicy.mx/sitios/desde_herbario/ Rocha, D. G. da, & Kaefer, I. L. (2019). What has become of the refugia hypothesis to explain biological diversity in Amazonia Ecology and Evolution, 9(7), 4302-4309. https://doi.org/10.1002/ECE3.5051 Saatchi, S. S. (2013). LBA-ECO LC-15 SRTM30 Digital Elevation Model Data, Amazon Basin: 2000. ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/1181 Stuart, S. N., Hoffmann, M., Chanson, J. ., Cox, N. ., Berridge, R. ., Ramani, P., & Young, B. . (2008). 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Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Paz Velez, AndreaCrawford, Andrew Jacksonvirtual::21039-1Doqueresana Ortega, Yuber Stevenecba8a8d-f13a-4dc5-8c8d-dacf1687e930600BIOMICS2022-06-06T15:54:07Z2022-06-06T15:54:07Z2022-06-07http://hdl.handle.net/1992/57703instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/La amazonia es una zona megadiversa de gran importancia en la biología evolutiva, esta alberga más de 500 especies de anfibios. Para estimar los patrones de biodiversidad se pueden utilizar índices de biodiversidad, los cuales pueden estar relacionados con las diferencias ambientales. En este análisis se estimó como la influencia de la heterogeneidad del ambiente afecta la diversidad de dos familias de anuros, Centrolenidae y Aromobatidae, usando 33 diferentes variables ambientales como predictores: topografía, ríos, propiedades de suelos, el clima actual y pasado. Se realizó un modelo de ensamblaje de aprendizaje automatizado que incluye 4 algoritmos supervisados, que miden la importancia de las variables como predictores de los índices de riqueza de especies y diversidad filogenética. Las variables ambientales predijeron el 23% de la riqueza de especies para centrolenidos y un 16% de la diversidad filogenética en los aromobatidos. Cuando se unieron ambas familias por índice de biodiversidad el poder predictivo del modelo fue del 15% del conjunto de datos para ambos índices. La isotermalidad fue la variable con mayor importancia dentro de los modelos, la evapotranspiración y la cobertura vegetal también tuvieron un rol importante. Se pretende incluir más grupos taxonómicos como familias para mejorar el poder predictivo del modelo.The Amazon is a megadiverse area of great importance in evolutionary biology, home to more than 500 species of amphibians. Biodiversity indices, which can be related to environmental differences, can be used to estimate biodiversity patterns. In this analysis we estimated how the influence of environmental heterogeneity affects the diversity of two families of anurans, Centrolenidae and Aromobatidae, using 33 different environmental variables as predictors: topography, rivers, soil properties, current and past climate. An automated learning ensemble model including 4 supervised algorithms was performed, measuring the importance of variables as predictors of species richness and phylogenetic diversity indices. Environmental variables predicted 23% of species richness for centrolenids and 16% of phylogenetic diversity in aromobatids. When both families were pooled by biodiversity index the predictive power of the model was 15% of the data set for both indices. The isothermality was the variable with the highest importance within the models. Evapotranspiration and vegetation cover also played an important role. It is intended to include more taxonomic groups as families to improve the predictive power of the model.BiólogoPregrado45 páginasapplication/pdfspaUniversidad de los AndesBiologíaFacultad de CienciasDepartamento de Ciencias BiológicasPredictores de diversidad en anfibios de la Amazonía: una aproximación por aprendizaje automático.Trabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPAprendizaje automatizadoAmazoníaBiodiversidadFilogeografíaAnfibiosDiversidad biológicaGenética de anfibiosBiologíaAleixo, A. (2004). Historical diversification of a terra-firme forest bird superspecies: a phylogeographic perspective on the role of different hypotheses of amazonian diversification. Evolution, 58(6), 1303-1317. https://doi.org/10.1111/J.0014-3820.2004.TB01709.XAmatulli, G., Domisch, S., Tuanmu, M. N., Parmentier, B., Ranipeta, A., Malczyk, J., & Jetz, W. (2018). Data Descriptor: A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data, 5. https://doi.org/10.1038/SDATA.2018.40AmphibiaWeb. (2021). AmphibiaWeb. University of California. https://amphibiaweb.org/index.htmlAntonelli, A., Quijada-Mascareñas, A., Crawford, A. J., Bates, J. M., Velazco, P. M., & Wüster, W. (2010). Molecular Studies and Phylogeography of Amazonian Tetrapods and their Relation to Geological and Climatic Models. In Amazonia, Landscape and Species Evolution: A Look into the Past. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781444306408.CH24Antonelli, A., & Sanmartín, I. (2011). Why are there so many plant species in the Neotropics TAXON, 60(2), 403-414. https://doi.org/10.1002/TAX.602010Antonelli, A., Zizka, A., Carvalho, F. A., Scharn, R., Bacon, C. D., Silvestro, D., & Condamine, F. L. (2018). Amazonia is the primary source of Neotropical biodiversity. Proceedings of the National Academy of Sciences, 115(23), 6034-6039. https://doi.org/10.1073/PNAS.1713819115Barratt, C. D., Bwong, B. A., Onstein, R. E., Rosauer, D. F., Menegon, M., Doggart, N., Nagel, P., Kissling, W. D., & Loader, S. P. (2017). Environmental correlates of phylogenetic endemism in amphibians and the conservation of refugia in the Coastal Forests of Eastern Africa. Diversity and Distributions, 23(8), 875-887. https://doi.org/10.1111/DDI.12582Bayat, M., Burkhart, H., Namiranian, M., Hamidi, S. K., Heidari, S., & Hassani, M. (2021). Assessing Biotic and Abiotic Effects on Biodiversity Index Using Machine Learning. Forests 2021, Vol. 12, Page 461, 12(4), 461. https://doi.org/10.3390/F12040461Bhattacharya, M. (2013). Machine Learning for Bioclimatic Modelling. International Journal of Advanced Computer Science and Applications, 4(2). https://doi.org/10.14569/IJACSA.2013.040201Blaustein, A. R., Wake, D. B., & Sousa, W. P. (1994). Amphibian Declines: Judging Stability, Persistence, and Susceptibility of Populations to Local and Global Extinctions. Conservation Biology, 8(1), 60-71. https://doi.org/10.1046/J.1523-1739.1994.08010060.XBoubli, J. P., Ribas, C., Lynch Alfaro, J. W., Alfaro, M. E., da Silva, M. N. F., Pinho, G. M., & Farias, I. P. (2015). Spatial and temporal patterns of diversification on the Amazon: A test of the riverine hypothesis for all diurnal primates of Rio Negro and Rio Branco in Brazil. Molecular Phylogenetics and Evolution, 82(PB), 400-412. https://doi.org/10.1016/J.YMPEV.2014.09.005Breiman, L. (2001). Random Forests. Machine Learning 2001 45:1, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324Brown, Hill, Carnaval, & Haywood. (2018). PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Nature - Scientific Data, 5:180254. http://www.paleoclim.org/Castroviejo-Fisher, S., Guayasamin, J. M., Gonzalez-Voyer, A., & Vilà, C. (2014). Neotropical diversification seen through glassfrogs. Journal of Biogeography, 41(1), 66-80. https://doi.org/10.1111/JBI.12208Colinvaux, P. A., Irion, G., Räsänen, M. E., Bush, M. B., & Nunes de Mello, J. A. S. (2001). A paradigm to be discarded: Geological and paleoecological data falsify the HAFFER & PRANCE refuge hypothesis of Amazonian speciation. Amazoniana: Limnologia et Oecologia Regionalis Systematis Fluminis Amazonas, 16(3/4), 609-646.Covarrubias, S., González, C., & Gutiérrez-Rodríguez, C. (2021). Effects of natural and anthropogenic features on functional connectivity of anurans: a review of landscape genetics studies in temperate, subtropical and tropical species. Journal of Zoology, 313(3), 159-171. https://doi.org/10.1111/JZO.12851Darriba, D., Taboada, G. L., Doallo, R., & Posada, D. (2012). jModelTest 2: more models, new heuristics and parallel computing. Nature Methods 2012 9:8, 9(8), 772-772. https://doi.org/10.1038/nmeth.2109Davies Jonathan, T., & Buckley, L. B. (2011). Phylogenetic diversity as a window into the evolutionary and biogeographic histories of present-day richness gradients for mammals. Philosophical Transactions of the Royal Society B: Biological Sciences, 366(1576), 2414-2425. https://doi.org/10.1098/RSTB.2011.0058De Melo, W. A., Lima-Ribeiro, M. S., Terribile, L. C., & Collevatti, R. G. (2016). Coalescent Simulation and Paleodistribution Modeling for Tabebuia rosealba Do Not Support South American Dry Forest Refugia Hypothesis. PloS One, 11(7). https://doi.org/10.1371/JOURNAL.PONE.0159314Dirzo, R., & Raven, P. H. (2003). Global State of Biodiversity and Loss. Https://Doi.Org/10.1146/Annurev.Energy.28.050302.105532, 28, 137¿167. https://doi.org/10.1146/ANNUREV.ENERGY.28.050302.105532Faith, D. P. (1992a). Conservation evaluation and phylogenetic diversity. Biological Conservation, 61(1), 1-10. https://doi.org/10.1016/0006-3207(92)91201-3Faith, D. P. (1992b). Systematics and conservation: on predicting the feature diversity of subsets of taxa. Cladistics, 8(4), 361-373. https://doi.org/10.1111/J.1096-0031.1992.TB00078.XFernandes, A., Cohn-Haft, M., Hrbek, T., & Farias, I. (2015). Rivers acting as barriers for bird dispersal in the Amazon. Revista Brasileira de Ornitologia - Brazilian Journal of Ornithology, 22(4), 363-373. http://www.revbrasilornitol.com.br/BJO/article/view/1034Ferreira, A. S., Lima, A. P., Jehle, R., Ferrão, M., & Stow, A. (2020). 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