Castaneda2009 Modelamiento Distribucion Especies

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Castaneda2009 Modelamiento Distribucion Especies

  1. 1. Introducción al modelamiento de la distribución de especies Nora P. Castañeda n.p.castaneda@cgiar.org © Neil Palmer (CIAT) Biosafety in LAC, 10 Nov 2009 CIAT, Cali, Colombia
  2. 2. Contenido • Por qué modelar especies? • Requisitos • Software • Usos • Validación modelos Distribución actual de Vasconcellea quercifolia en Bolivia Distribución potencial de Vasconcellea quercifolia en Bolivia Distribución potencial corregida de Vasconcellea quercifolia en Bolivia
  3. 3. Modelos de distribución Estimar nicho ecológico de las especies de interés Ampliar áreas de presencia potencial de la especie para análisis en SIG Especies con pocos registros georreferenciados mín.10 registros Distribución real de Distribución potencial de Cordia trichotoma Cordia trichotoma Cordia trichotoma © karenblixen @flickr.com
  4. 4. Requisitos Variables Registros ambientales Georreferenciados de la especie Software modelamiento Procesamiento en Software GIS Modelo de Dist. potencial
  5. 5. Variables ambientales 19 variables bioclimáticas http://worldclim.org/
  6. 6. Variables ambientales Variables edafológicas http://www.isric.org/UK/About+ISRIC/Projects/Track+Record/SOTERLAC.htm
  7. 7. Variables ambientales Variables topográficas http://srtm.csi.cgiar.org/
  8. 8. Variables ambientales Otras variables (i.e. regiones ecológicas, suelos) http://www.fao.org/geonetwork
  9. 9. Registros especies IABIN GBIF – 4 redes temáticas con – 189.471.323 registros vínculos a diversos biodiversidad (9 Nov tipos de información 2009) – Énfasis: América – Global – Acceso libre al público – Acceso libre al público http://www.iabin.net / http://www.gbif.org/
  10. 10. Registros especies SINGER GapAnalysis – Registros de – 13 acervos genéticos accesiones en bancos (7 en camino) de germoplasma del – Datos totalmente CGIAR georreferenciados – Acceso libre al público – Acceso libre al público http://www.singer.cgiar.org/ http://gisweb.ciat.cgiar.org/gapanalysis/
  11. 11. Registros especies Calidad de datos crucial!! Ej.: Bases de datos GBIF CURRENT STATUS OF THE Plantae RECORDS
  12. 12. Registros especies • How to make the terrestrial data reliable enough? – Verify coordinates at different levels • Are the records where they say they are? • Are the records inside land areas (for terrestrial plant species only) • Are all the records within the environmental niche of the taxon? – Correct wrong references – Add coordinates to those that do not have – Cross-check with curators and feedback to the database
  13. 13. • Using a random sample of 950.000 occurrences with coordinates
  14. 14. • Are the records where they say they are?: country-level verification Records with null country: 58.051 6,11% of total Records with incorrect country: 6.918 0,72% of total Total excluded by country 64.969 6,83% of total Records mostly located Inaccuracies in in country coordinates boundaries
  15. 15. • Are the terrestrial plant species in land?: Coastal verification Records in the ocean: 9.866 1,03% of total Records near land (range 5km): 34.347 3,61% of total Records outside of mask: 369 0,04% of total Total excluded by mask 44.582 4.69% of total Errors, and more errors
  16. 16. Not so bad at all… stats • 44’706.505 plant records • 33’340.008 (74,57%) with coordinates • From those – 88.5% are geographically correct at two levels – 6.8% have null or incorrect country (incl. sea plant species) – 4.7% are near the coasts but not in-land Summary of errors or misrepresented data
  17. 17. RESULTING DATABASE TOTAL EVALUATED RECORDS: 950.000 Good records: 840.449 88.47% of total
  18. 18. Registros especies Verificación de coordenadas / módulo en DIVA-GIS Verificación de coordenadas
  19. 19. Registros especies Verificación de coordenadas Points outside all polygons Points do not match relations
  20. 20. Registros especies Georreferenciación: Asignación de coordenadas http://bg.berkeley.edu/
  21. 21. Software Elith et al., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29: 129-151
  22. 22. © Proaves Barker et al., n.d. Modeling the South American Range of the Cerulean Warbler. Presented at the ESRI International User Conference
  23. 23. Software ANN - Artificial Neural Networks AquaMaps Bioclim CSM - Climate Space Model Envelope Score Environmental Distance GARP - Genetic Algorithm for Rule-set Production GARP Best Subsets SVM - Support Vector Machines http://openmodeller.sourceforge.net/
  24. 24. Modelos en acción! Modelo 41 Modelo Caso: Annona cherimola
  25. 25. How likely is geneflow from GM crops to their wild relatives in centres of origin and diversity? Meike Andersson, Carmen de Vicente, Diego F. Alvarez, Andy Jarvis, Glenn Hyman, Ehsan Dulloo http://gisweb.ciat.cgiar.org/geneflow/
  26. 26. 1. Wheat Study crops 2. Rice 3. Maize 4. Soybean Criteria for selection 5. Barley 6. Sorghum Global importance; 7. Finger Millet Worldwide production area; 8. Pearl Millet 9. Cotton Advancement of transgenic 10. Oilseed rape technology; and 11. Common bean Contribution to food security 12. Groundnut (crop species listed in the 13. Cassava 14. Potato Annex I of the ITPGRFA and 15. Oat CGIAR mandate crops) 16. Chickpea 17. Cowpea 18. Sweet potato 19. Banana & plantain 20. Pigeon pea
  27. 27. Tool to visualize likelihood of gene flow and introgression Five categories: Very high High Moderate Low Very low
  28. 28. Slide 27 ed1 Perhaps i can merge this slide with the barley one Ehsan Dulloo, 3/27/2008
  29. 29. CASE STUDY Barley (Hordeum vulgare ssp. vulgare)
  30. 30. Barley (H. vulgare ssp. vulgare) Biological information Annual, cool season crop, highly autogamous (98%) Seed dispersal: water, animals Volunteers frequent, weedy, but not invasive Pollen Flow Mainly wind-pollinated, pollen viability a few hours Outcrossing 50 m GM technology Transformation protocols available GM traits: pest/disease; malting & brewing Field trials in Australia, Canada, Finland, Germany, Hungary, Iceland, N/Zealand, UK and USA To date, no reported commercial production of GM barley
  31. 31. Barley Wild relatives 30 annual species in 4 sections Compatible wild relatives Wild progenitor ssp. spontaneum Closest wild relative: H. bulbosum Most Hordeum have limited geographical distribution Some spp. widespread (H. bulbosum) and weedy in many parts of the world (e.g., H. murinum, H. marinum, and H. jubatum) Hybridization potential GP1: domesticated barley and its wild ancestor H. vulgare ssp. spontaneum GP2: H. bulbosum GP3: all other Hordeum species
  32. 32. Likelihood of gene flow and introgression in Barley
  33. 33. Barley: Management recommendations Barriers with male-sterile bait plants around the area planted with barley to capture any escaped pollen; separation distance for seed production: • USA and Canada: 3 m; OECD and EU 25-50 m; Control volunteer cereals through crop rotation; perform shallow tilling of the soil surface several days post-harvest. Special measures should be taken when transporting barley seeds to avoid seed spill out of harvesting vehicles; control volunteer plants in road sides At regional scale, segregation of crop types may be implemented to avoid contamination of seed production fields
  34. 34. Barley Conclusions Introgression within barley crop-wild- weedy complex possible Probability of introgression between barley and H. bulbosum is low Spontaneous hybridisation with other wild relatives is unlikely Research gaps Dynamics of barley pollen flow; frequencies of outcrossing at various distances
  35. 35. Book Publication
  36. 36. Targeting Cassava Pest and Disease Problems Environment Characterization Climate change
  37. 37. GapAnalysis 13 crop genepools analyzed, 7 analyses in the pipeline Recommendations on which taxa are priority to conserve Maps indicating what and where to collect Results publicly available at: http://gisweb.ciat.cgiar.org/GapAnalysis/
  38. 38. Phaseolus acutifolius var. tenuifolius
  39. 39. Phaseolus acutifolius var. acutifolius
  40. 40. Modelos en acción! • Identificación de vacíos de colección de bancos de germoplasma • Análisis de cambios de riqueza bajo diferentes escenarios cambio climático • Análisis estado de conservación y amenazas de especies silvestres • Identificación ambientes para la prueba de nuevos materiales. • Entre otros…
  41. 41. Validación modelos • ¿Son las variables usadas para generar el modelo, las más adecuadas? Caso: Bertholletia excelsa Climático Climático + Climático + ecoregiones 1 suelos 1 Climático + Climático + Climático + suelos 2 ecoregiones 2 ecoregiones 3
  42. 42. Validación modelos • Parámetros estadísticos – Area under the receiver Operating Characteristic curve (AUC) – Receiver Operating Characteristic curve (ROC) – Correlation (COR) – Kappa
  43. 43. Validación modelos • Modelo basado en conocimiento de expertos • Validación y re-parametrización • KMLs de Google Earth + plugin + encuesta electrónica
  44. 44. Gracias Esta presentación está disponible en: http://www.slideshare.net/laguanegna

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