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. 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. 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. Requisitos
Variables Registros
ambientales Georreferenciados
de la especie
Software
modelamiento
Procesamiento en
Software GIS
Modelo de
Dist. potencial

5. Variables ambientales
19 variables bioclimáticas
http://worldclim.org/

6. Variables ambientales
Variables edafológicas
http://www.isric.org/UK/About+ISRIC/Projects/Track+Record/SOTERLAC.htm

7. Variables ambientales
Variables topográficas
http://srtm.csi.cgiar.org/

8. Variables ambientales
Otras variables (i.e. regiones ecológicas, suelos)
http://www.fao.org/geonetwork

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. 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. Registros especies
Calidad de datos crucial!!
Ej.: Bases de datos GBIF
CURRENT STATUS OF
THE Plantae RECORDS

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. • Using a random sample of 950.000
occurrences with coordinates

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. • 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. 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. RESULTING DATABASE
TOTAL EVALUATED RECORDS: 950.000
Good records: 840.449 88.47% of total

18. Registros especies
Verificación de coordenadas / módulo en DIVA-GIS
Verificación de coordenadas

19. Registros especies
Verificación de coordenadas
Points outside all polygons Points do not match relations

20. Registros especies
Georreferenciación: Asignación de coordenadas
http://bg.berkeley.edu/

21. Software
Elith et al., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29: 129-151

22. © Proaves
Barker et al., n.d. Modeling the South American Range of the Cerulean Warbler. Presented at the ESRI International User Conference

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. Modelos en acción!
Modelo 41
Modelo
Caso: Annona cherimola

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. 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. Tool to visualize likelihood of gene flow and
introgression
Five categories:
Very high
High
Moderate
Low
Very low

28. Slide 27
ed1 Perhaps i can merge this slide with the barley one
Ehsan Dulloo, 3/27/2008

29. CASE STUDY
Barley
(Hordeum vulgare ssp. vulgare)

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. 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. Likelihood of gene flow and introgression in
Barley

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. 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. Book
Publication

36. Targeting Cassava Pest and Disease Problems
Environment
Characterization
Climate change

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. Phaseolus acutifolius var. tenuifolius

39. Phaseolus acutifolius var. acutifolius

41. 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…

42. 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

43. Validación modelos
• Parámetros estadísticos
– Area under the receiver Operating
Characteristic curve (AUC)
– Receiver Operating Characteristic curve
(ROC)
– Correlation (COR)
– Kappa

44. Validación modelos
• Modelo basado en conocimiento de expertos
• Validación y re-parametrización
• KMLs de Google Earth + plugin + encuesta electrónica

45. Gracias
Esta presentación está disponible en:
http://www.slideshare.net/laguanegna