Mostrar el registro sencillo del documento

dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.contributor.authorParra Quijano, Mauricio
dc.contributor.authorIriondo, José María
dc.contributor.authorTorres, María Elena
dc.contributor.authorLópez, Francisco
dc.contributor.authorPhillips, Jade
dc.contributor.authorKell, Shelagh
dc.date.accessioned2024-03-11T03:25:34Z
dc.date.available2024-03-11T03:25:34Z
dc.date.issued2021
dc.identifier.isbn9789585050389
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/85787
dc.descriptionilustraciones, fotografías, mapas
dc.description.abstractCAPFITOGEN tools and their evolution, CAPFITOGEN3, are the result of continuous work since 2012 when the first two tools were conceived and designed. These tools did not come out overnight but have been under a constant pro- cess of development since 2005 when the first ELC map was obtained. Then, other useful ecogeographic applications were developed for the conservation and use of plant genetic resources for food and agriculture (PGRFA). Since 2012, there have been great achievements, but also several mistakes have been made; we have encountered some obstacles and difficulties, but we have also come across wonderful people who have contributed to make CAPFITOGEN a dream come true. I talk about a dream because these tools were literally that, a dream I had when I finished my PhD thesis. At that time, I thought that some of these methodological advances should be available to everyone and not only to a small group of future researchers who would cite my papers. Based on that dream, I assumed the premise that the effort of working on the scientific field was only compensated when progress reached people to help them improve or make their lives easier. CAPFITOGEN has been able to reach a high number of technicians and researchers who consistently conserve and use agrobiodiversity. The program has been successful at supporting all these people by allowing them to perform analyses and tasks that would not have been possible before. (texto tomado de la fuente)
dc.format.extent303 páginas
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherUniversidad Nacional de Colombia. Facultad de Ciencias Agrarias
dc.rightsUniversidad Nacional de Colombia, 2021
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc630 - Agricultura y tecnologías relacionadas
dc.titleCapfitogen 3 : a toolbox for the conservation and promotion of the use of agricultural biodiversity
dc.typeLibro
dc.type.driverinfo:eu-repo/semantics/book
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.contributor.photographerParra Quijano, Mauricio
dc.contributor.translatorDíaz, Ana María
dc.publisher.placeBogotá
dc.relation.referencesAkinwande, M. O., Dikko, H. G., Samson, A. 2015. Variance inflation factor: as a condition for the inclusion of suppressor variable (s) in regression analysis. Open Journal of Statistics, 5(07), 754-767.
dc.relation.referencesAllouche, O., Tsor, A., Kadmon, R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 5(7): 1223-1232.
dc.relation.referencesAustin, M.P., Van Niel, K.P. 2010. Improving species distribution models for climate change studies: variable selection and scale. Journal of Biogeography 38(1): 1-8.
dc.relation.referencesBarbet-Massin, M., Jiguet, F., Albert, C. H., Thuiller, W. 2012. Selecting pseudo-absences for species distribution models: how, where and how many?. Methods in Ecology and Evolution, 3(2), 327-338.
dc.relation.referencesBarnes, L. R., Gruntfest, E. C., Hayden, M. H., Schultz, D. M., Benight, C. 2007. False alarms and close calls: A conceptual model of warning accuracy. Weather and Forecasting, 22(5), 1140-1147.
dc.relation.referencesBarnes, L. R., Schultz, D. M., Gruntfest, E. C., Hayden, M. H., Benight, C. C. 2009. Corrigendum: False alarm rate or false alarm ratio?. Weather and Forecasting, 24(5), 1452-1454.
dc.relation.referencesBates, C. G. 1930. The frost hardiness of geographic strains of Norway pine. Journal of Forestry, 28(3), 327-333.
dc.relation.referencesBooth, T. H., Nix, H. A., Busby, J. R., Hutchinson, M. F. 2014. BIOCLIM: the first species distribution modelling
dc.relation.referencesBower, A. D., Clair, J. B. S., Erickson, V. 2014. Generalized provisional seed zones for native plants. Ecological Applications, 24(5), 913-919.
dc.relation.referencesBradter, U., Kunin, W. E., Altringham, J. D., Thom, T. J., Benton, T. G. 2013. Identifying appropriate spatial scales of predictors in species distribution models with the random forest algorithm. Methods in Ecology and Evolution, 4(2), 167-174.
dc.relation.referencesBrown, A.H.D. 1989. The case for core collections. In: Brown, A.H.D., Frankel, O.H., Marshall, D.R., Williams, J.T. (ed.) The use of plant genetic resources. Cambridge University Press, Cambridge, UK.
dc.relation.referencesBrown, A.H.D. 1995. The core collection at the crossroads. p. 3–19. En Hodgkin, T., Brown, A.H.D., Hintum, T.J.L., Morales, E.A.V. (ed.) Core collections of plant genetic resources. John Wiley & Sons, New York, NY.
dc.relation.referencesCalinski, T., Harabasz, J. 1974. A dendrite method for cluster analysis. Communications in Statistics. 3(1): 1-27.
dc.relation.referencesCeballos-Silva, A., López-Blanco, J. 2003. Evaluating biophysical variables to identify suitable areas for oat in Central Mexico: a multi-criteria and GIS approach. Agriculture, Ecosystems and Environment 95 (2003) 371–377.
dc.relation.referencesCevallos, D., Bede-Fazekas, Á., Tanács, E., Szitár, K., Halassy, M., Kövendi-Jakó, A., Török, K. 2020. Seed transfer zones based on environmental variables better reflect variability in vegetation than administrative units: evidence from Hungary. Restoration Ecology, 28(4), 911-918.
dc.relation.referencesChapman, A.D. 2005. Principles of data quality, version 1.0. Report of the Global Biodiversity Information Facility, Copenhagen.
dc.relation.referencesChefaoui, R. M., Lobo, J. M. 2008. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecological modelling, 210(4), 478-486.
dc.relation.referencesChrisman, N.R. 1983. The role of quality information in the long-term functioning of a GIS. Proceedings of AUTOCART06, 2: 303-321. Falls Church, VA: ASPRS.
dc.relation.referencesChuine, I. 2010. Why does phenology drive species distribution? Philosophical transactions of the royal society B. 365, 3149–3160
dc.relation.referencesCohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37-40.
dc.relation.referencesContreras-Toledo, A. R., Cortés-Cruz, M., Costich, D. E., Rico-Arce, M., Magos Brehm, J., Maxted, N. 2019. Diversity and conservation priorities of crop wild relatives in Mexico. Plant Genetic Resources Characterisation and Utilisation, 17, 140-150.
dc.relation.referencesCrossa, J., Vencovsky, R. 1994. Implications of the variance effective population size on the genetic conservation of monoecious species. Theoretical and Applied Genetics 89:936–942
dc.relation.referencesCrossa, J., Vencovsky, R. 1997. Variance effective population size for two-stage sampling of monoecious species. Crop Science 37:14–26
dc.relation.referencesCrossa, J., Vencovsky, R. 2011 Chapter 5: Basic sampling strategies: theory and practice. In: Guarino, L., Ramanatha Rao, V., Goldberg, E. (ed.) Collecting Plant Genetic Diversity: Technical Guidelines – 2011 Update. Bioversity International. Available online (accessed 6 November 2013) http://cropgenebank.sgrp.cgiar.org/index.php?option=com_content&view=article&id=671
dc.relation.referencesCutler, D. R., Edwards Jr, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., Lawler, J. J. 2007. Random forests for classification in ecology. Ecology, 88(11): 2783-2792.
dc.relation.referencesCutler, D. R., Edwards Jr, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., Lawler, J. J. 2007. Random forests for classification in ecology. Ecology, 88(11), 2783-2792.
dc.relation.referencesDamme, P., Garcia, W., Tapia, C., Romero, J., Manuel Sigueñas, M., Hormaza, J.I. 2012. Mapping Genetic Diversity of Cherimoya (Annona cherimola Mill.): Application of Spatial Analysis for Conservation and Use of Plant Genetic Resources. PLoS ONE 7(1): e29845. doi:10.1371/journal.pone.0029845
dc.relation.referencesDean, N., Raftery, A.E., Scrucca, L. 2015. Package ‘clustvarsel’, variable selection for Model-Based clustering. http:// cran.r-project.org/web/packages/clustvarsel/clustvarsel.pdf
dc.relation.referencesDice, L.R. 1945. Measures of the Amount of Ecologic Association Between Species. Ecology 26:297–302.
dc.relation.referencesFAO, BIOVERSITY. 2015. FAO/Bioversity multi-crop Passport descriptors V.2. URL: https://bioversityinternational.
dc.relation.referencesDinerstein, E. D., Olson, A., Joshi, C., Vynne, N., Burgess, E., Wikramanayake, N., Hahn, S., Palminteri, P., Hedao, R., Noss, M., Hansen, H., Locke, E., Ellis, B., Jones, C., Barber, V., Hayes, R., Kormos, C., Martin, V., Crist, E., Sechrest, W., Price, L., Baille, J., Weeden, D., Suckling, K., Davis, C., Sizer, N., Moore, R., Thau, D., Birch, T., Potapov, P., Turubanova, S., Tyukavina, A., Souza, N., Pintea, L., Brito, J., Llewellyn, O., Miller, A., Patzelt, A., Ghazanfar, S., Timberlake, J., Klozer, H., Shenan-Farpón, Y., Kindt, R.Barnekow, J., van Breugel, P., Graudal, L., Voge, M., Al- Shammari, K., Saleem, M. 2017. An ecoregion-based approach to protecting half the terrestrial realm. BioScience, 67(6), 534-545.
dc.relation.referencesDinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N. D., Wikramanayake, E., Hahn, N., Palminteri, S., Hansen, M. 2017. An ecoregion-based approach to protecting half the terrestrial realm. BioScience, 67(6), 534-545.
dc.relation.referencesDoherty, K. D., Butterfield, B. J., Wood, T. E. 2017. Matching seed to site by climate similarity: techniques to prioritize plant materials development and use in restoration. Ecological Applications, 27(3), 1010-1023.
dc.relation.referencesDolnicar, S., Grabler, K., Mazanec, J. A. 1999. A tale of three cities: perceptual charting for analyzing destination images. Pp. 39-62 in: Woodside, A. et al. (eds) Consumer psychology of tourism, hospitality and leisure. CAB International, New York.
dc.relation.referencesDurka, W., Michalski, S. G., Berendzen, K. W., Bossdorf, O., Bucharova, A., Hermann, J. M., Holzel, N., Kollmann, J. 2017. Genetic differentiation within multiple common grassland plants supports seed transfer zones for ecological restoration. Journal of Applied Ecology, 54(1), 116-126.
dc.relation.referencesEl Bouhssini, M. E., Street, K., Joubi, A., Ibrahim, Z., Rihawi, F. 2009. Sources of wheat resistance to Sunn pest, Eurygaster integriceps Puton, in Syria. Genetic Resources and Crop Evolution 56: 1065– 1069.
dc.relation.referencesElith, J., Leathwick, J. R. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40(1), 677.
dc.relation.referencesElith, J., Leathwick, J. R., Hastie, T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802-813.
dc.relation.referencesElith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., Yates, C. J. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43-57.
dc.relation.referencesEndresen, D.T.F. 2010. Predictive association between trait data and ecogeographic data for Nordic barley landraces. Crop Science 50: 2418-2430.
dc.relation.referencesEndresen, D.T.F., Street, K., Mackay, M., Bari, A., Amri, A., De Pauw, E., Nazari, K., Yahyaoui, A. 2012. Sources of resistance to stem rust (Ug99) in bread wheat and durum wheat identified using Focused Identification of Germplasm Strategy. Crop Science 52: 764-773.
dc.relation.referencesErickson, V. J., Mandel, N. L., Sorensen, F. C. 2004. Landscape patterns of phenotypic variation and population structuring in a selfing grass, Elymus glaucus (blue wildrye). Canadian Journal of Botany, 82(12), 1776-1789.
dc.relation.referencesFAO 2010. The Second Report on the State of the World’s Plant Genetic Resources for Food and Agriculture. Rome
dc.relation.referencesFAO, BIOVERSITY. 2015. FAO/Bioversity multi-crop Passport descriptors V.2. URL: https://bioversityinternational. org/e-library/publications/detail/faobioversity-multi-crop-passport-descriptors-v21-mcpd-v21/
dc.relation.referencesFAO, IPGRI. 2001.Lista de descriptores de pasaporte para cultivos múltiples desarrollada por la FAO y el IPGRI.
dc.relation.referencesFAO. 1997. Plan de Acción Mundial para la Conservación y Utilización Sostenible de los Recursos Fitogenéticos para la Alimentación y la Agricultura y la Declaración de Leipzig. Rome, Italy. 64p.
dc.relation.referencesFAO. 2012. Segundo Plan de Acción Mundial para los Recursos Fitogenéticos para la Alimentación y la Agricultura. Rome, Italy. 104p.
dc.relation.referencesFawcett, T. 2004. ROC graphs: Notes and practical considerations for researchers. Machine learning, 31, 1-38.
dc.relation.referencesFeeley, K. J., Silman, M. R. 2009. Extinction risks of Amazonian plant species. Proceedings of the National Academy of Sciences, 106(30), 12382-12387.
dc.relation.referencesFielding, A. H., Bell, J. F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental conservation, 24(01), 38-49.
dc.relation.referencesFitzgerald, H., Palmé, A., Asdal, Å., Endresen, D., Kiviharju, E., Lund, B., Rasmussen, M., Thorbjornsson, H., Weibull, J. 2019. A regional approach to Nordic crop wild relative in situ conservation planning. Plant genetic resources, 17(2), 196-207.
dc.relation.referencesFoley, D.H., Wilkerson, R.C., Rueda, L.M. 2009. Importance of the “what,” “when,” and “where” of mosquito collection events. J Med Entomol. 2009 Jul;46(4):717-22.
dc.relation.referencesFowells, H. A. 1949. Cork oak planting tests in California. Journal of Forestry, 47(5), 357-365.
dc.relation.referencesFraley C., Raftery, A.E. 2007. Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification 24:155-181.
dc.relation.referencesGarcía, R. M., Parra-Quijano, M., Iriondo, J. M. 2017. A multispecies collecting strategy for crop wild relatives based on complementary areas with a high density of ecogeographical gaps. Crop Science, 57(3), 1059-1069.
dc.relation.referencesGarcía, R. M., Parra-Quijano, M., Iriondo, J. M. 2017. A multispecies collecting strategy for crop wild relatives based on complementary areas with a high density of ecogeographical gaps. Crop Science, 57(3), 1059-1069.
dc.relation.referencesGermino, M. J., Moser, A. M., Sands, A. R. 2019. Adaptive variation, including local adaptation, requires decades to become evident in common gardens. Ecological Applications, 29(2), e01842.
dc.relation.referencesGhamkhar, K., R. Snowball, B.J. Wintle, Brown, A.H.D. 2008. Strategies for developing a core collection of bladder clover (Trifolium spumosum L.) using ecological and agro-morphological data. Aust. J. Agric. Res. 59:1103–1112.
dc.relation.referencesGibson, A., Nelson, C. R. 2017. Comparing provisional seed transfer zone strategies for a commonly seeded grass, Pseudoroegneria spicata. Natural Areas Journal, 37(2), 188-199.
dc.relation.referencesGower, J.C. 1971. A general coefficient of similarity and some of its properties. Biometrics 27: 85774.
dc.relation.referencesGrenier, C., Hamon, P., Bramel-Cox, P.J.. 2001. Core collection of sorghum: II. Comparison of three random sampling strategies. Crop Science. 41:241–246.
dc.relation.referencesGuisan, A., Edwards, T. C., Hastie, T. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological modelling, 157(2), 89-100.
dc.relation.referencesGuisan, A., Thuiller, W. 2005. Predicting species distribution: offering more than simple habitat models. Ecology letters, 8(9), 993-1009.
dc.relation.referencesGuisan, A., Zimmermann, N. E. 2000. Predictive habitat distribution models in ecology. Ecological modelling, 135(2), 147-186.
dc.relation.referencesHamann, A., Gylander, T., Chen, P. Y. 2011. Developing seed zones and transfer guidelines with multivariate regression trees. Tree Genetics & Genomes, 7(2), 399-408.
dc.relation.referencesHanson, J. O., Rhodes, J. R., Riginos, C., Fuller, R. A. 2017. Environmental and geographic variables are effective surrogates for genetic variation in conservation planning. Proceedings of the National Academy of Sciences, 114(48), 12755-12760.
dc.relation.referencesHarris, J. A., Hobbs, R. J., Higgs, E., Aronson, J. 2006. Ecological Restoration and Global Climate Change. Restoration Ecology, 14 (2), 170–176.
dc.relation.referencesHavens, K., Vitt, P., Still, S., Kramer, A. T., Fant, J. B., Schatz, K. 2015. Seed sourcing for restoration in an era of climate change. Natural Areas Journal, 35(1), 122-133.
dc.relation.referencesHiggs, E. S. 1997. What is Good Ecological Restoration?. Conservation biology, 11(2), 338-348. IPCC. 2013. IPCC Fifth Assessment Report (AR5). IPCC s. 10-12
dc.relation.referencesHijmans, R., Elith, J., 2015. Species distribution modeling with R. https://goo.gl/p8beyk
dc.relation.referencesHill, A.W., Guralnick, R., Flemons, P., Beaman, R., Wieczorek, J., Ranipeta, A., Chavan, V., Remsen, D. 2009. Location, location, location: utilizing pipelines and services to more effectively georeference the world’s biodiversity data. BMC Bioinformatics. 2009 Nov 10;10 Suppl 14:S3. doi: 10.1186/1471-2105-10-S14-S3.
dc.relation.referencesHoffmann, M.H., Glaß, A.S., Tomiuk, J., Schmuths, H., Fritsch, R.M., Bachmann, K. 2003. Analysis of molecular data of Arabidopsis thaliana (L.) Heynh. (Brassicaceae) with Geographical Information Systems (GIS). Molecular Ecology, 12: 1007–1019
dc.relation.referencesIPCC, 2013. Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In:Stocker,T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
dc.relation.referencesJarvis, A., Lane, A., Hijmans, R. J. 2008. The effect of climate change on crop wild relatives. Agriculture, ecosystems & environment, 126(1), 13-23.
dc.relation.referencesJarvis, A.; Williams, K.; Williams, D.; Guarino, L.; Caballero, P.J. Mottram, G. 2005. Use of GIS for optimizing a collecting mission for a rare wild pepper (Capsicum flexuosum Sendtn.) in Paraguay. Genet. Resour. Crop Evol. 52:671-682.
dc.relation.referencesJohnson, G. R., Sorensen, F. C., St Clair, J. B., Cronn, R. C. 2004. Pacific northwest Forest tree seed zones a template for native plants?. Native Plants Journal, 5(2), 131-140.
dc.relation.referencesJohnson, R. C., Erickson, V. J., Mandel, N. L., St Clair, J. B., Vance-Borland, K. W. 2010. Mapping genetic variation and seed zones for Bromus carinatus in the Blue Mountains of eastern Oregon, USA. Botany, 88(8), 725-736.
dc.relation.referencesJombart, T., Collins, C. 2015. A tutorial for discriminant analysis of principal components (DAPC) using adegenet 2.0. 0. London: Imperial College London, MRC Centre for Outbreak Analysis and Modelling.
dc.relation.referencesJustus, J., Sarkar, S. 2002. The principle of complementarity in the design of reserve networks to conserve biodiversity: a preliminary history. Journal of Biosciences, 27(4): 421-435.
dc.relation.referencesKati, V., Devillers, P., Dufrêne, M., Legakis, A., Vokou, D., Lebrun, P. 2004. Hotspots, complementarity or representativeness? Designing optimal small-scale reserves for biodiversity conservation. Biological Conservation, 120(4): 471-480.
dc.relation.referencesKaufman, L., Rousseeuw, P.J. 1987, Clustering by means of Medoids, in Statistical Data Analysis Based on the L1– Norm and Related Methods. Y. Dodge (eds), North-Holland, 405–416.
dc.relation.referencesKebede, A. S., Nicholls, R. J., Allan, A., Arto, I., Cazcarro, I., Fernandes, J. A., Hill, C.T., Hutton, C.W., Kay, S., Lázár, A.N., Macadam, I., Palmer, M., Suckall, N., Tompkins, E.L., Vincent,K., Whitehead, P. W. (2018). Applying the global RCP–SSP–SPA scenario framework at sub-national scale: A multi-scale and participatory scenario approach. Science of the Total Environment, 635, 659-672.
dc.relation.referencesKetchen, D. J., Shook, C. L. 1996. The application of cluster analysis in Strategic Management Research: An analysis and critique. Strategic Management Journal 17(6): 441–458.
dc.relation.referencesKhazaei, H., Street, K., Bari, A., Mackay, M., Stoddard, F.L. 2013. The FIGS (Focused Identification of Germplasm Strategy) approach identifies traits related to drought adaptation in Vicia faba genetic resources. PLoS ONE 8(5): e63107. doi:10.1371/journal.pone.0063107
dc.relation.referencesKing, J. R., Jackson, D. A. 1999. Variable selection in large environmental data sets using principal components analysis. Environmetrics, 10(1): 67-77.
dc.relation.referencesKramer, A. T., Havens, K. 2009. Plant conservation genetics in a changing world. Trends in plant science, 14(11), 599-607.
dc.relation.referencesKramer, A. T., Larkin, D. J., Fant, J. B. 2015. Assessing potential seed transfer zones for five forb species from the Great Basin Floristic Region, USA. Natural Areas Journal, 35(1), 174-188.
dc.relation.referencesLiu, C., White, M., Newell, G. 2009. Measuring the accuracy of species distribution models: a review. In Proceedings 18th World IMACs/MODSIM Congress. Cairns, Australia (pp. 4241-4247).
dc.relation.referencesLobo, J. M., Jiménez-Valverde, A., Real, R. 2008. AUC: a misleading measure of the performance of predictive distribution models. Global ecology and Biogeography, 17(2), 145-151.
dc.relation.referencesMackay, M. C., Street, K. 2004. Focused identification of germplasm strategy – FIGS. p. 138-141. En: Black, C.K., Panozzo, J.F., Rebetzke, G.J. (eds). Cereals 2004. Proceedings of the 54th Australian Cereal Chemistry Conference and the 11th Wheat Breeders’ Assembly, 21-24 September 2004, Canberra, Australian Capital Territory (ACT). Cereal Chemistry Division, Royal Australian Chemical Institute, Melbourne, Australia.
dc.relation.referencesMackay, M.C. 1990. Strategic planning for effective evaluation of plant germplasm. p. 21-25 En: Srivastava, J.P., Damania, A.B. (eds). Wheat genetic resources: Meeting diverse needs. John Wiley & Sons, Chichester, UK.
dc.relation.referencesMaiorano, L., Cheddadi, R., Zimmermann, N. E., Pellissier, L., Petitpierre, B., Pottier, J., Guisan, A. 2013. Building the niche through time: using 13,000 years of data to predict the effects of climate change on three tree species in Europe. Global Ecology and Biogeography, 22(3), 302-317.
dc.relation.referencesMantel, N. (1967) The detection of disease clustering and a generalized regression approach. Cancer Res. 27: 209- 220.
dc.relation.referencesMarinoni, L., Parra Quijano, M., Zabala, J.M., Pensiero, J.F., Iriondo, J.M. 2021. Spatio-temporal seed transfer zones as an efficient restoration strategy in response to climate change. Ecosphere, in press.
dc.relation.referencesMarmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K., Thuiller, W. 2009. Evaluation of consensus methods in predictive species distribution modelling. Diversity and distributions, 15(1), 59-69.
dc.relation.referencesMateo, R. G., Croat, T. B., Felicísimo, A. M., Munoz, J. 2010. Profile or group discriminative techniques? Generating reliable species distribution models using pseudo-absences and target-group absences from natural history collections. Diversity and Distributions, 16(1), 84-94.
dc.relation.referencesMcKay, J. K., Christian, C. E., Harrison, S., Rice, K. J. 2005. “How local is local?”—a review of practical and conceptual issues in the genetics of restoration. Restoration Ecology, 13(3), 432-440.
dc.relation.referencesMeinshausen, M., Nicholls, Z. R., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J.G., Daniel, J.S., John, A., Krummel, P.B., Luderer, G., Meinshausen, N., Montzka, S.A., Rayner, P.J., Reimann, S., Smith, S.J., van den Berg, M., Velders, G.J.M., Vollmer, M.K., Wang, R. H. 2020. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geoscientific Model Development, 13(8), 3571-3605.
dc.relation.referencesMiller, S. A., Bartow, A., Gisler, M., Ward, K., Young, A. S., Kaye, T. N. 2011. Can an ecoregion serve as a seed transfer zone? Evidence from a common garden study with five native species. Restoration Ecology, 19(201), 268-276.
dc.relation.referencesMponya, N. K., Chanyenga, T., Brehm, J. M., Maxted, N. 2020. In situ and ex situ conservation gap analyses of crop wild relatives from Malawi. Genetic Resources and Crop Evolution, 68: 759-771.
dc.relation.referencesOmernik, J. M., Griffith, G. E. 2014. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environmental management, 54(6), 1249-1266. org/e-library/publications/detail/faobioversity-multi-crop-passport-descriptors-v21-mcpd-v21/
dc.relation.referencesOtegui, J., Ariño, A.H., Encinas, M.A., Pando, F. 2013. Assessing the primary data hosted by the Spanish node of the Global Biodiversity Information Facility (GBIF). PLoS One. 2013;8(1), e55144. doi: 10.1371/journal.pone.0055144.
dc.relation.referencesÖzesmi, S. L., Özesmi, U. 1999. An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecological modelling, 116(1), 15-31. package, its early applications and relevance to most current MAXENT studies. Diversity and Distributions, 20(1), 1-9.
dc.relation.referencesParra Quijano, M. 2016. Tools CAPFITOGEN Program to Strengthen Capabilities in National Plant Genetic Resources Programs in Latin America.
dc.relation.referencesParra Quijano, M., Iriondo, J. M. Torres, E. 2012A. Ecogeographical land characterization maps as a tool for assessing plant adaptation and their implications in agrobiodiversity studies. Genetic Resources Crop Evolution, 59, 205–217
dc.relation.referencesParra Quijano, M., Iriondo, J. M., Torres, E. 2012B. Applications of ecogeography and geographic information systems in conservation and utilization of plant genetic resources. Spanish Journal of Agricultural Research, 10(2), 419-429.
dc.relation.referencesParra Quijano, M., Iriondo, J.M., Frese, L., Torres, E. 2012C. Spatial and ecogeographic approaches for selecting genetic reserves in Europe. In: N. Maxted, M.E. Dulloo, B.V. Ford-Lloyd, L. Frese, J. Iriondo y M.A.A. Pinheiro de Carvalho (ed.) Agrobiodiversity Conservation: securing the diversity of crop wild relatives and landraces. CABI, Wallingford, UK.
dc.relation.referencesParra Quijano, M., Iriondo, J.M., Torres, M.E., De la Rosa, L. 2011a. Evaluation and validation of ecogeographical core collections using phenotypic data. Crop Science 51:694-703.
dc.relation.referencesParra-Quijano, M. Iriondo, J.M., De la Cruz, M., Torres, M.E. 2011 A. Strategies for the development of core collections based on ecogeographical data. Crop Science 51:656-666
dc.relation.referencesParra-Quijano, M. Iriondo, J.M., Frese, L., Torres, M.E. 2012 C. Spatial and ecogeographic approaches for selecting genetic reserves in Europe. En: N. Maxted, M.E. Dulloo, B.V. Ford-Lloyd, L. Frese, J. Iriondo, M.A.A. Pinheiro de Carvalho (ed.) Agrobiodiversity Conservation: securing the diversity of crop wild relatives and landraces. CABI, Wallingford, UK.
dc.relation.referencesParra-Quijano, M. Iriondo, J.M., Torres, M.E. 2012 A. Ecogeographical land characterization maps as a tool for assessing plant adaptation and their implications in agrobiodiversity studies. Genetic Resources and Crop Evolution 59(2):205-217 DOI 10.1007/s10722-011-9676-7 89
dc.relation.referencesParra-Quijano, M. Iriondo, J.M., Torres, M.E. 2012 B. Improving representativeness of genebank collections through species distribution models, gap analysis and ecogeographical maps. Biodiversity and Conservation 21:79-96 DOI 10.1007/s10531-011-0167-0
dc.relation.referencesParra-Quijano, M. Iriondo, J.M., Torres, M.E., De la Rosa, L. 2011 B. Evaluation and validation of ecogeographical core collections using phenotypic data. Crop Science 51:694-703
dc.relation.referencesParra-Quijano, M., Draper, D., Iriondo, J. 2003. Assessing in situ conservation of Lupinus spp. In Spain through GIS. Crop Wild Relative, 1: 8-9.
dc.relation.referencesParra-Quijano, M., Iriondo, J. M., Torres, E. 2012b. Applications of ecogeography and geographic information systems in conservation and utilization of plant genetic resources. Spanish journal of agricultural research. 2: 419- 429.
dc.relation.referencesParra-Quijano, M., Iriondo, J. M., Torres, E. 2012c. Improving representativeness of genebank collections through species distribution models, gap analysis and ecogeographical maps. Biodiversity and Conservation, 21(1), 79-96.
dc.relation.referencesParra-Quijano, M., Iriondo, J.M., de la Cruz, M., Torres, M.E. 2011b. Strategies for the development of core collections based on ecogeographical data. Crop Science 51:656-666.
dc.relation.referencesParra-Quijano, M., Iriondo, J.M., Frese, L., Torres, E.. 2012a. Spatial and ecogeographic approaches for selecting genetic reserves in Europe. En: Maxted, N., Dulloo, M.E., Ford-Lloyd, B.V., Frese, L., Iriondo, J., Pinheiro de Carvalho, M.A.A. (ed.) Agrobiodiversity Conservation: securing the diversity of crop wild relatives and landraces. CABI, Wallingford, UK
dc.relation.referencesParra-Quijano, M.; Draper, D.; Torres, E., Iriondo, J.M. 2008. Ecogeographical representativeness in crop wild relative ex situ collections. p. 249-273. In Maxted, N.; Ford-Lloyd, B.V.; Kell, S.P.; Iriondo, J.M.; Dulloo, M.E., Turok, J. (ed.) Crop wild relative conservation and use. CAB International, Wallingford.
dc.relation.referencesParra-Quijano, M.; Draper, D.; Torres, E., Iriondo, J.M. 2008. Ecogeographical representativeness in crop wild relative ex situ collections. p. 249-273. In Maxted, N.; Ford-Lloyd, B.V.; Kell, S.P.; Iriondo, J.M.; Dulloo, M.E., Turok, J. (ed.) Crop wild relative conservation and use. CAB International, Wallingford.
dc.relation.referencesPearce, J., Ferrier, S. 2000. Evaluating the predictive performance of habitat models developed using logistic regression. Ecological modelling, 133(3), 225-245.
dc.relation.referencesPeeters, J. P., Wilkes, H. G., Galwey, N. W. 1990. The use of ecogeographical data in the exploitation of variation from gene banks. Theoretical and applied genetics, 80(1), 110-112.
dc.relation.referencesPhillips, J., Asdal, Å., Magos Brehm, J., Rasmussen, M., Maxted, N. 2016. In situ and ex situ diversity analysis of priority crop wild relatives in Norway. Diversity and Distributions, 22(11), 1112-1126.
dc.relation.referencesPhillips, J., Asdal, Å., Magos Brehm, J., Rasmussen, M., Maxted, N. 2016. In situ and ex situ diversity analysis of priority crop wild relatives in Norway. Diversity and Distributions, 22(11), 1112-1126.
dc.relation.referencesPhillips, S. J., Dudík, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick, J., Ferrier, S. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications, 19(1), 181-197.
dc.relation.referencesPliscoff, P., Luebert, F., Hilger, H. H., Guisan, A. 2014. Effects of alternative sets of climatic predictors on species distribution models and associated estimates of extinction risk: A test with plants in an arid environment. Ecological Modelling, 288, 166-177.
dc.relation.referencesPotter, K. M., Hargrove, W. W. 2012. Determining suitable locations for seed transfer under climate change: a global quantitative method. New Forests, 43, 581–599.
dc.relation.referencesRaftery, A. E., Dean, N. 2006. Variable selection for model-based clustering. Journal of the American Statistical Association, 101(473):168-178.
dc.relation.referencesRamirez-Villegas, J., Jarvis, A., Läderach, P. 2013. Empirical approaches for assessing impacts of climate change on agriculture: The EcoCrop model and a case study with grain sorghum. Agricultural and Forest Meteorology, 170, 67-78.
dc.relation.referencesRamirez-Villegas, J., Khoury, C., Jarvis, A., Debouck, D., Guarino, L. 2010. A gap analysis methodology for collecting crop genepools: a case study with Phaseolus beans. PLoS ONE 5(10), e13497. doi:10.1371/journal.pone.0013497.
dc.relation.referencesRebelo, A. G., Siegfried, W. R. 1990. Protection of fynbos vegetation: ideal and real-world options. Biological Conservation, 54(1): 15-31.
dc.relation.referencesReddy, L.J., H.D. Upadhyaya, C.L.L. Gowda, S. Singh. 2005. Development of core collection in pigeonpea (Cajanus cajan (L.) Millspaugh) using geographic and qualitative morphological descriptors. Genetic Resources and Crop Evolution 52:1049–1056.
dc.relation.referencesRichardson, B. A., Chaney, L. 2018. Climate-based seed transfer of a widespread shrub: population shifts, restoration strategies, and the trailing edge. Ecological Applications, 28(8), 2165-2174.
dc.relation.referencesRoebber, P. J. 2009. Visualizing multiple measures of forecast quality. Weather and Forecasting, 24(2), 601-608.
dc.relation.referencesRousseeuw, P.J. 1987. Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis. Computational and Applied Mathematics 20: 53–65. doi:10.1016/0377-0427(87)90125-7.
dc.relation.referencesRubio Teso, M. L., Iriondo, J. M. 2019. In situ Conservation Assessment of Forage and Fodder CWR in Spain Using Phytosociological Associations. Sustainability, 11(21), 5882.
dc.relation.referencesRussell, J., van Zonneveld, M., Dawson, I. K., Booth, A., Waugh, R., Steffenson, B. 2014. Genetic diversity and ecological niche modelling of wild barley: refugia, large-scale post-lgm range expansion and limited mid-future climate threats. PloS one, 9(2), e86021.
dc.relation.referencesScheldeman, X., van Zonneveld, M. 2011. Manual de Capacitación en Análisis Espacial de Diversidad y Distribución de Plantas. Bioversity International, Roma, Italia.
dc.relation.referencesScheldeman, X., van Zonneveld, M. 2011. Manual de Capacitación en Análisis Espacial de Diversidad y Distribución de Plantas. Bioversity International, Roma, Italia.
dc.relation.referencesShryock, D., Defalco, L. A., Esque, T. C. 2018. Spatial decision-support tools to guide restoration and seed-sourcing in the Desert Southwest. Ecosphere, 9 (10), 1-19
dc.relation.referencesSillero, N., Barbosa, A. M. 2021. Common mistakes in ecological niche models. International Journal of Geographical Information Science, 35(2):213-226.
dc.relation.referencesSoberón, J., Peterson, T. 2004. Biodiversity informatics: managing and applying primary biodiversity data. Phil. Trans. R. Soc. Lond. B. 359, 689-698.
dc.relation.referencesTapia, C., Paredes, N., Lima, L. (2019). Representatividad de la diversidad del género musa en el ecuador. Revista Científica Ecuatoriana, 6(1).
dc.relation.referencesTaylor, N. G., Kell, S. P., Holubec, V., Parra-Quijano, M., Chobot, K., Maxted, N. (2017). A systematic conservation strategy for crop wild relatives in the Czech Republic. Diversity and Distributions, 23(4), 448-462.
dc.relation.referencesThomas, E., Alcazar, C., Moscoso L. G., Vásquez A., Osorio L. F., Salgado-Negrete, B., Gonzalez, M., Parra-Quijano, M., Bozzano, M., Loo, J., Jalonen, R., Ramírez, W. 2017. The importance of species selection and seed sourcing in forest restoration for enhancing adaptive capacity to climate change: Colombian tropical dry forest as a model. The Lima declaration on biodiversity and climate change: contributions from science to policy for sustainable development, (89), 122-132
dc.relation.referencesThomas, 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): e47676.doi:10.1371/journal.pone.0047676
dc.relation.referencesThormann, I. 2012. Applying FIGS to crop wild relatives and landraces in Europe. Crop Wild Relative 8 14:16.
dc.relation.referencesThuiller, W. 2004. Patterns and uncertainties of species’ range shifts under climate change. Global Change Biology, 10(12), 2020-2027.
dc.relation.referencesThuiller, W., Araújo, M. B., Lavorel, S. 2003. Generalized models vs. classification tree analysis: predicting spatial distributions of plant species at different scales. Journal of Vegetation Science, 14(5), 669-680.
dc.relation.referencesThuiller, W., Lafourcade, B., Engler, R., Araújo, M. B. 2009. BIOMOD–a platform for ensemble forecasting of species distributions. Ecography, 32(3), 369-373.
dc.relation.referencesTohme, J., Jones, P., Beebe, S., Iwanaga, M. 1995. The combined use of agroecological and characterisation data to establish the CIAT Phaseolus vulgaris core collection. p. 95-107. In Hodgkin, T., Brown, A.H.D., van Hintum, Th.J.L., Morales, E.A.V. (eds.) Core collections of plant genetic resources. IPGRI, Rome.
dc.relation.referencesTohme, J., Jones, P., Beebe, S., Iwanaga, M. 1995. The combined use of agroecological and characterisation data to establish the CIAT Phaseolus vulgaris core collection. p. 95–107. In Hodgkin, T., Brown, A.H.D., Hintum, T.J.L., Morales, E.A.V. (ed.) Core collections of plant genetic resources. John Wiley & Sons, New York, NY.
dc.relation.referencesUpadhyaya, H.D., Ortiz, R., Bramel, P.J., S. Singh, S. 2003. Development of a groundnut core collection using taxonomical, geographical and morphological descriptors. Genet. Resour. Crop Evol. 50:139–148.
dc.relation.referencesvan Zonneveld M, Scheldeman X, Escribano P, Viruel MA, Van Damme P, et al. (2012) Mapping Genetic Diversity of Cherimoya (Annona cherimola Mill.): Application of Spatial Analysis for Conservation and Use of Plant Genetic Resources. PLoS ONE 7(1): e29845. doi:10.1371/journal.pone.0029845
dc.relation.referencesVanDerWal, J., Shoo, L. P., Graham, C., Williams, S. E. 2009. Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know?. Ecological modelling, 220(4), 589-594.
dc.relation.referencesWilliams, C.L., Hargrove, W.W., Liebman, M., James, D.E. 2008. Agro-ecoregionalization of Iowa using multivariate geographical clustering. Agriculture, Ecosystems and Environment 123 (2008) 161–174
dc.relation.referencesWilliams, M. I., Dumroese, R. K. 2013. Preparing for Climate Change: Forestry and Assisted Migration. Journal of Forestry, 111 (4), 287–297
dc.relation.referencesWithrow-Robinson, B. A., Johnson, R. 2006. Selecting native plant materials for restoration projects: ensuring local adaptation and maintaining genetic diversity Oregon State University. URL: https://ir.library.oregonstate.edu/ downloads/g732d9349
dc.relation.referencesWood, J. M. 2007. Understanding and Computing Cohen’s Kappa: A Tutorial. WebPsychEmpiricist. URL: Journal at http://wpe.info/.
dc.relation.referencesXiurong, Z., Yingzhong, Z., Yong, C., Xiangyun, F., Qingyuan, G., Mingde, Z., Hodgkin, T. 2000. Establishment of sesame germplasm core collection in China. Genet. Resour. Crop Evol. 47:273– 279.
dc.relation.referencesYonezawa, K., Nomura, T., Morishima, H. 1995. Sampling strategies for use in stratified germplasm collections. p. 35–53. In Hodgkin, T., Brown, A.H.D., Hintum, T.J.L., Morales, E.A.V. (ed.) Core collections of plant genetic resources. John Wiley & Sons, New York, NY.
dc.relation.referencesYonezawa, K.; Nomura, T., Morishima, H. 1995. Sampling strategies for use in stratified germplasm collections. P. 35-53. In: Hodgkin, T., Brown, A.H.D., van Hintum, Th.J.L., Morales, E.A.V. (ed.) Core collections of plant genetic resources. John Willey & sons, Chichester, UK.
dc.relation.referencesZair, W., Maxted, N., Brehm, J. M., Amri, A. 2020. Ex situ and in situ conservation gap analysis of crop wild relative diversity in the Fertile Crescent of the Middle East. Genetic Resources and Crop Evolution, 1-17.
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.lembConservación de los recursos agrícolas
dc.subject.lembConservación de la abriobiodiversidad
dc.subject.lembEcología agrícola
dc.subject.lembProgramación automática (Informática)
dc.type.coarhttp://purl.org/coar/resource_type/c_2f33
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.contentText
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2
dcterms.audience.professionaldevelopmentEstudiantes
dcterms.audience.professionaldevelopmentInvestigadores
dcterms.audience.professionaldevelopmentPúblico general


Archivos en el documento

Thumbnail

Este documento aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del documento

Atribución-NoComercial-SinDerivadas 4.0 InternacionalEsta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial 4.0.Este documento ha sido depositado por parte de el(los) autor(es) bajo la siguiente constancia de depósito