Predictores de diversidad en anfibios de la Amazonía: una aproximación por aprendizaje automático.

dc.contributorPaz Velez, Andrea
dc.contributorCrawford, Andrew Jackson
dc.contributorBIOMICS
dc.creatorDoqueresana Ortega, Yuber Steven
dc.date.accessioned2022-06-06T15:54:07Z
dc.date.available2022-06-06T15:54:07Z
dc.date.created2022-06-06T15:54:07Z
dc.date.issued2022-06-07
dc.identifierhttp://hdl.handle.net/1992/57703
dc.identifierinstname:Universidad de los Andes
dc.identifierreponame:Repositorio Institucional Séneca
dc.identifierrepourl:https://repositorio.uniandes.edu.co/
dc.description.abstractLa 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.
dc.description.abstractThe 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.
dc.languagespa
dc.publisherUniversidad de los Andes
dc.publisherBiología
dc.publisherFacultad de Ciencias
dc.publisherDepartamento de Ciencias Biológicas
dc.relationAleixo, 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.X
dc.relationAmatulli, 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.2018.40
dc.relationAmphibiaWeb. (2021). AmphibiaWeb. University of California. https://amphibiaweb.org/index.html
dc.relationAntonelli, 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.CH24
dc.relationAntonelli, 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.602010
dc.relationAntonelli, 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.1713819115
dc.relationBarratt, 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.12582
dc.relationBayat, 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/F12040461
dc.relationBhattacharya, M. (2013). Machine Learning for Bioclimatic Modelling. International Journal of Advanced Computer Science and Applications, 4(2). https://doi.org/10.14569/IJACSA.2013.040201
dc.relationBlaustein, 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.X
dc.relationBoubli, 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.2014.09.005
dc.relationBreiman, L. (2001). Random Forests. Machine Learning 2001 45:1, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
dc.relationBrown, Hill, Carnaval, & Haywood. (2018). PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Nature - Scientific Data, 5:180254. http://www.paleoclim.org/
dc.relationCastroviejo-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.12208
dc.relationColinvaux, 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.
dc.relationCovarrubias, 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.12851
dc.relationDarriba, 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.2109
dc.relationDavies 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.2011.0058
dc.relationDe 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.0159314
dc.relationDirzo, 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.28.050302.105532
dc.relationFaith, D. P. (1992a). Conservation evaluation and phylogenetic diversity. Biological Conservation, 61(1), 1-10. https://doi.org/10.1016/0006-3207(92)91201-3
dc.relationFaith, 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.X
dc.relationFernandes, 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/1034
dc.relationFerreira, A. S., Lima, A. P., Jehle, R., Ferrão, M., & Stow, A. (2020). The Influence of Environmental Variation on the Genetic Structure of a Poison Frog Distributed Across Continuous Amazonian Rainforest. Journal of Heredity, 111(5), 457-470. https://doi.org/10.1093/JHERED/ESAA034
dc.relationFontes, D., Cordeiro, R. C., Martins, G. S., Behling, H., Turcq, B., Sifeddine, A., Seoane, J. C. S., Moreira, L. S., & Rodrigues, R. A. (2017). Paleoenvironmental dynamics in South Amazonia, Brazil, during the last 35,000 years inferred from pollen and geochemical records of Lago do Saci. Quaternary Science Reviews, 173, 161-180. https://doi.org/10.1016/J.QUASCIREV.2017.08.021
dc.relationFraga, R. de, & Carvalho, V. T. de. (2021). Testing the Wallace's riverine barrier hypothesis based on frog and Squamata reptile assemblages from a tributary of the lower Amazon River. Https://Doi.Org/10.1080/01650521.2020.1870838. https://doi.org/10.1080/01650521.2020.1870838
dc.relationFritz, S. A., & Rahbek, C. (2012). Global patterns of amphibian phylogenetic diversity. Journal of Biogeography, 39(8), 1373-1382. https://doi.org/10.1111/J.1365-2699.2012.02757.X
dc.relationGarzón-Orduña, I. J., Benetti-Longhini, J. E., & Brower, A. V. Z. (2014). Timing the diversification of the Amazonian biota: butterfly divergences are consistent with Pleistocene refugia. Journal of Biogeography, 41(9), 1631-1638. https://doi.org/10.1111/JBI.12330
dc.relationGascon, C., Malcolm, J. R., Patton, J. L., Silva, M. N. F. da, Bogart, J. P., Lougheed, S. C., Peres, C. A., Neckel, S., & Boag, P. T. (2000). Riverine barriers and the geographic distribution of Amazonian species. Proceedings of the National Academy of Sciences, 97(25), 13672-13677. https://doi.org/10.1073/PNAS.230136397
dc.relationGaston, K. J., & Spicer, J. I. (2004). Biodiversity: an introduction.
dc.relationGodinho, M. B. D. C., & Da Silva, F. R. (2018). The influence of riverine barriers, climate, and topography on the biogeographic regionalization of Amazonian anurans. Scientific Reports 2018 8:1, 8(1), 1-11. https://doi.org/10.1038/s41598-018-21879-9
dc.relationGotelli, N. J., & Colwell, R. K. (2001). Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters, 4(4), 379-391. https://doi.org/10.1046/J.1461-0248.2001.00230.X
dc.relationGrant, T., Frost, D., Caldwell, J., Gagliardo, R., Haddad, C., Kok, P., Means, B., Noonan, B., Schargel, W., & Wheeler, W. (2006). Phylogenetic systematics of dart-poison frogs and their relatives (amphibia: athesphatanura: dendrobatidae). Bulletin of the American Museum of Natural History, 299, 1-262. https://bioone.org/journals/bulletin-of-the-american-museum-of-natural-history/volume-2006/issue-299/0003-0090(2006)299%5B1%3APSODFA%5D2.0.CO%3B2/PHYLOGENETIC-SYSTEMATICS-OF-DART-POISON-FROGS-AND-THEIR-RELATIVES-AMPHIBIA/10.1206/0003-0090(2006)299[1:PSODF
dc.relationGreen, J. L., Hastings, A., Arzberger, P., Ayala, F. J., Cottingham, K. L., Cuddington, K., Davis, F., Dunne, J. A., Fortin, M.-J., Gerber, L., & Neubert, M. (2005). Complexity in Ecology and Conservation: Mathematical, Statistical, and Computational Challenges. BioScience, 55(6), 501-510. https://doi.org/10.1641/0006-3568(2005)055[0501:CIEACM]2.0.CO;2
dc.relationGuayasamin, Juan M., Castroviejo-Fisher, S., Ayarzagüena, J., Trueb, L., & Vilà, C. (2008). Phylogenetic relationships of glassfrogs (Centrolenidae) based on mitochondrial and nuclear genes. Molecular Phylogenetics and Evolution, 48(2), 574-595. https://doi.org/10.1016/J.YMPEV.2008.04.012
dc.relationGuayasamin, Juan Manuel., Castroviejo-Fisher, S., Trueb, L., Ayarzagüena, J., Rada, M., & Vilá Carles. (2009). Phylogenetic systematics of Glassfrogs (Amphibia: Centrolenidae) and their sister taxon Allophryne ruthveni. Zootaxa, 2100, 1-97. http://www.mapress.com/zootaxa/2009/f/zt02100p097.pdf
dc.relationHaffer, J. (1969). Speciation in amazonian forest birds. Science, 165(3889), 131-137. https://doi.org/10.1126/SCIENCE.165.3889.131
dc.relationHalliday, T. R. (2008). Why amphibians are important. International Zoo Yearbook, 42(1), 7-14. https://doi.org/10.1111/J.1748-1090.2007.00037.X
dc.relationHawkins, B. A., Field, R., Cornell, H. V., Currie, D. J., Guégan, J. F., Kaufman, D. M., Kerr, J. T., Mittelbach, G. G., Oberdorff, T., O'Brien, E. M., Porter, E. E., & Turner, J. R. G. (2003). Energy, water, and broad-scale geographic patterns of species richness. Ecology, 84(12), 3105-3117. https://doi.org/10.1890/03-8006
dc.relationHaykin, S. (1999). Neural networks: a comprehensive foundation. In Neural Networks - A Comprehensive Foundation (2nd ed.). Prentice Hall.
dc.relationHebert, P. D. N., Cywinska, A., Ball, S. L., & DeWaard, J. R. (2003). Biological identifications through DNA barcodes. Proceedings. Biological Sciences, 270(1512), 313-321. https://doi.org/10.1098/RSPB.2002.2218
dc.relationHiggins, D. G., & Sharp, P. M. (1988). CLUSTAL: a package for performing multiple sequence alignment on a microcomputer. Gene, 73(1), 237-244. https://doi.org/10.1016/0378-1119(88)90330-7
dc.relationHijmans, R., Etten, J. Van, Summer, M., Cheng, J., Baston, D., Bevan, A., Bivand, R., & Busseto, L. (2022). raster: Geographic Data Analysis and Modeling (3.5-15). https://cran.r-project.org/web/packages/raster/index.html
dc.relationHuelsenbeck, J. P., & Rannala, B. (2004). Frequentist Properties of Bayesian Posterior Probabilities of Phylogenetic Trees Under Simple and Complex Substitution Models. Systematic Biology, 53(6), 904-913. https://doi.org/10.1080/10635150490522629
dc.relationITIS. (2019). ITIS - Report: Aromobatidae. www.itis.gov
dc.relationIyer, A. A., & Briggman, K. L. (2021). Amphibian behavioral diversity offers insights into evolutionary neurobiology. Current Opinion in Neurobiology, 71, 19-28. https://doi.org/10.1016/J.CONB.2021.07.015
dc.relationKalamandeen, M., Gloor, E., Mitchard, E., Quincey, D., Ziv, G., Spracklen, D., Spracklen, B., Adami, M., Aragaõ, L. E. O. C., & Galbraith, D. (2018). Pervasive Rise of Small-scale Deforestation in Amazonia. Scientific Reports, 8(1). https://doi.org/10.1038/S41598-018-19358-2
dc.relationKaratzoglou, A., Hornik, K., Smola, A., & Zeileis, A. (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software, 11, 1-20. https://doi.org/10.18637/JSS.V011.I09
dc.relationKarger, D. N, Nobis, M. P., Normand, S., Graham, C. H., & Zimmermann, N. E. (2021). CHELSA-TraCE21k v1. 0. Downscaled transient temperature and precipitation data since the last glacial maximum. Climate of the Past Discussions, 1-27. https://chelsa-climate.org/last-glacial-maximum-climate/
dc.relationKarger, Dirk Nikolaus, Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the earth's land surface areas. Scientific Data, 4. https://doi.org/10.1038/SDATA.2017.122
dc.relationKopuchian, C., Campagna, L., Lijtmaer, D. A., Cabanne, G. S., García, N. C., Lavinia, P. D., Tubaro, P. L., Lovette, I., & Giacomo, A. S. Di. (2020). A test of the riverine barrier hypothesis in the largest subtropical river basin in the Neotropics. Molecular Ecology, 29(12), 2137-2149. https://doi.org/10.1111/MEC.15384
dc.relationKuhn, M. (2022). Caret: Classification and Regression Training. CRAN. https://github.com/topepo/caret/
dc.relationKumar Verma, A. (2016). Biodiversity: Its Different Levels and Values. International Journal on Environmental Sciences, 7(2), 143-145. https://www.researchgate.net/publication/342638063
dc.relationLarsson, A. (2014). AliView: A fast and lightweight alignment viewer and editor for large datasets. Bioinformatics, 30(22), 3276-3278. https://doi.org/10.1093/BIOINFORMATICS/BTU531
dc.relationLecocq, T., Vereecken, N. J., Michez, D., Dellicour, S., Lhomme, P., Valterová, I., Rasplus, J. Y., & Rasmont, P. (2013). Patterns of Genetic and Reproductive Traits Differentiation in Mainland vs. Corsican Populations of Bumblebees. PLOS ONE, 8(6), e65642. https://doi.org/10.1371/JOURNAL.PONE.0065642
dc.relationLehner, B., & Grill, G. (2013). Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrological Processes, 27(15), 2171-2186. https://www.hydrosheds.org/page/hydrorivers
dc.relationLiaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. 2(3). http://www.stat.berkeley.edu/
dc.relationLiedtke, H. C., Gower, D. J., Wilkinson, M., & Gomez-Mestre, I. (2018). Macroevolutionary shift in the size of amphibian genomes and the role of life history and climate. Nature Ecology & Evolution 2018 2:11, 2(11), 1792-1799. https://doi.org/10.1038/s41559-018-0674-4
dc.relationLus Nunes-De.Alameida, C., Batista Haddad, C., & Toledo, L. F. (2021). A revised classification of the amphibian reproductive modes. SALAMANDRA, 57(3), 413-427. https://www.salamandra-journal.com/index.php/home/contents/2021-vol-57/2054-nunes-de-almeida-c-h-l-c-f-b-haddad-l-f-toledo-1/file
dc.relationLyra, M. L., Haddad, C. F. B., & de Azeredo-Espin, A. M. L. (2017). Meeting the challenge of DNA barcoding Neotropical amphibians: polymerase chain reaction optimization and new COI primers. Molecular Ecology Resources, 17(5), 966-980. https://doi.org/10.1111/1755-0998.12648
dc.relationMaddison, W. P., & Maddison, D. (2021). Mesquite: a modular system for evolutionary analysis (3.70). http://www.mesquiteproject.org
dc.relationMartín-Regalado, C. N., Briones-Salas, M., Manríquez-Morán, N., Sánchez-Rojas, G., Cornejo-Latorre, C., Lavariega, M. C., & Moreno, C. E. (2020). Assembly mechanisms and environmental predictors of the phylogenetic diversity of cricetid rodents in southern Mexico. Evolutionary Ecology 2020 34:2, 34(2), 175-191. https://doi.org/10.1007/S10682-020-10034-4
dc.relationMcCullagh, P., & Nelder, J. A. (1989). Generalized Linear Models. https://doi.org/10.1007/978-1-4899-3242-6
dc.relationMenin, M., Waldez, F., Lima, & A. P., & Menin, M. (2011). Effects of environmental and spatial factors on the distribution of anuran species with aquatic reproduction in central Amazonia. HERPETOLOGICAL JOURNAL, 21, 255-261. http://ppbio.inpa.gov.br/Eng/inventarios/ducke/anuros
dc.relationMiller, M. A., Pfeiffer, W., & Schwartz, T. (2010). Creating the CIPRES Science Gateway for inference of large phylogenetic trees. 2010 Gateway Computing Environments Workshop, GCE 2010. https://doi.org/10.1109/GCE.2010.5676129
dc.relationMorlon, H., Schwilk, D. W., Bryant, J. A., Marquet, P. A., Rebelo, A. G., Tauss, C., Bohannan, B. J. M., & Green, J. L. (2011). Spatial patterns of phylogenetic diversity. Ecology Letters, 14(2), 141. https://doi.org/10.1111/J.1461-0248.2010.01563.X
dc.relationMountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247-259. https://doi.org/10.1016/J.ISPRSJPRS.2010.11.001
dc.relationMusher, L. J., Ferreira, M., Auerbach, A. L., McKay, J., & Cracraft, J. (2019). Why is Amazonia a source of biodiversity Climate-mediated dispersal and synchronous speciation across the Andes in an avian group (Tityrinae). Proceedings of the Royal Society B, 286(1900). https://doi.org/10.1098/RSPB.2018.2343
dc.relationMyers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B., & Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature 2000 403:6772, 403(6772), 853-858. https://doi.org/10.1038/35002501
dc.relationNaimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K., & Toxopeus, A. G. (2014). Where is positional uncertainty a problem for species distribution modelling Ecography, 37(2), 191-203. https://doi.org/10.1111/J.1600-0587.2013.00205.X
dc.relationNazareno, A. G., Dick, C. W., & Lohmann, L. G. (2017). Wide but not impermeable: Testing the riverine barrier hypothesis for an Amazonian plant species. Molecular Ecology, 26(14), 3636-3648. https://doi.org/10.1111/MEC.14142
dc.relationNazareno, A. G., Dick, C. W., & Lohmann, L. G. (2019). Tangled banks: A landscape genomic evaluation of Wallace's Riverine barrier hypothesis for three Amazon plant species. Molecular Ecology, 28(5), 980-997. https://doi.org/10.1111/MEC.14948
dc.relationOtt, R. F. (2020). How Lithology Impacts Global Topography, Vegetation, and Animal Biodiversity: A Global-Scale Analysis of Mountainous Regions. Geophysical Research Letters, 47(20), e2020GL088649. https://doi.org/10.1029/2020GL088649
dc.relationPatton, J. L., Silva, M. N. F. da, & Malcolm, J. R. (1994). Gene genealogy and differentiation among arboreal spiny rats (rodentia: echimyidae) of the amazon basin: a test of the riverine barrier hypothesis. Evolution, 48(4), 1314-1323. https://doi.org/10.1111/J.1558-5646.1994.TB05315.X
dc.relationPaz, A., Spanos, Z., Brown, J. L., Lyra, M., Haddad, C., Rodrigues, M., & Carnaval, A. (2018). Phylogeography of Atlantic Forest glassfrogs (Vitreorana): when geography, climate dynamics and rivers matter. Heredity 2018 122:5, 122(5), 545-557. https://doi.org/10.1038/s41437-018-0155-1
dc.relationPaz, 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
dc.relationPelletier, 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.
dc.relationPeters, 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
dc.relationPillay, 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
dc.relationPoelchau, 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
dc.relationPoggio, 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
dc.relationProyecto 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/
dc.relationQGIS Geographic Information System. (2021). QGIS Geographic Information System (3.24). Open Source Geospatial Foundation Project. http://qgis.osgeo.org/
dc.relationQian, 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
dc.relationRéjaud, 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
dc.relationRivera 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/
dc.relationRocha, 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
dc.relationSaatchi, 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
dc.relationStuart, S. N., Hoffmann, M., Chanson, J. ., Cox, N. ., Berridge, R. ., Ramani, P., & Young, B. . (2008). Threatened Amphibians of the World (Frist edit). IUCN, Conservation International and Lynx Edicions. https://www.amphibians.org/wp-content/uploads/2018/12/1-TAW-intro.pdf
dc.relationTamura, K., Stecher, G., & Kumar, S. (2021). MEGA11: Molecular Evolutionary Genetics Analysis Version 11. Molecular Biology and Evolution, 38(7), 3022-3027. https://doi.org/10.1093/molbev/msab120
dc.relationTautz, D., Arctander, P., Minelli, A., Thomas, R. H., & Vogler, A. P. (2003). A plea for DNA taxonomy. Trends in Ecology & Evolution, 18(2), 70-74. https://doi.org/10.1016/S0169-5347(02)00041-1
dc.relationThomas, E., van Zonneveld, M., Loo, J., Hodgkin, T., Galluzzi, G., & van Etten, J. (2012). Present Spatial Diversity Patterns of Theobroma cacao L. in the Neotropics Reflect Genetic Differentiation in Pleistocene Refugia Followed by Human-Influenced Dispersal. PLoS ONE, 7(10). https://doi.org/10.1371/JOURNAL.PONE.0047676
dc.relationTrabucco, A., & Zomer, R. (2010). Global Soil Water Balance Geospatial Database. CGIAR Consortium for Spatial Information.
dc.relationTuanmu, M. N., & Jetz, W. (2015). A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling. Global Ecology and Biogeography, 24(11), 1329-1339. https://doi.org/10.1111/GEB.12365
dc.relationVane-Wright, R. I., Humphries, C. J., & Williams, P. H. (1991). What to protect Systematics and the agony of choice. Biological Conservation, 55(3), 235-254. https://doi.org/10.1016/0006-3207(91)90030-D
dc.relationVapnik, V. (1979). Estimation of Dependences Based on Empirical Data. The Statistician, 33(3), 324. https://doi.org/10.2307/2988246
dc.relationVasconcelos, T. D. S., Santos, T. G. Dos, Haddad, C. F. B., & Rossa-Feres, D. D. C. (2010). Climatic variables and altitude as predictors of anuran species richness and number of reproductive modes in Brazil. Journal of Tropical Ecology, 26(4), 423-432. https://doi.org/10.1017/S0266467410000167
dc.relationVenables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S. https://doi.org/10.1007/978-0-387-21706-2
dc.relationVences, M., Nagy, Z. T., Sonet, G., & Verheyen, E. (2012). DNA barcoding amphibians and reptiles. Methods in Molecular Biology (Clifton, N.J.), 858, 79-107. https://doi.org/10.1007/978-1-61779-591-6_5
dc.relationVitt, L. J., & Caldwell, J. P. (2014). An introductory biology of amphibians and reptiles. Herpetology: An Introductory Biology of Amphibians and Reptiles, 4, 1-720. https://doi.org/10.1016/B978-0-12-374346-6.X0001-6
dc.relationWallace, A. R. (1852). On the Monkeys of the Amazon. Http://Dx.Doi.Org/10.1080/037454809494374, 14(84), 451-454. https://doi.org/10.1080/037454809494374
dc.relationZeisset, I., & Beebee, T. (2008). Amphibian phylogeography: a model for understanding historical aspects of species distributions. Heredity, 101(2), 109-119. https://doi.org/10.1038/HDY.2008.30
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titlePredictores de diversidad en anfibios de la Amazonía: una aproximación por aprendizaje automático.
dc.typeTrabajo de grado - Pregrado


Este ítem pertenece a la siguiente institución