castaneda2009 modelamiento distribucion especies
TRANSCRIPT
Introducción al modelamiento de la distribución de especies
Nora P. Castañ[email protected]
Biosafety in LAC, 10 Nov 2009CIAT, Cali, Colombia
© Neil Palmer (CIAT)
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
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
© karenblixen @flickr.com
Distribución real de
Cordia trichotoma
Distribución potencial de Cordia trichotoma
Cordia trichotoma
Requisitos
Softwaremodelamiento
Variablesambientales
Procesamiento en Software GIS
RegistrosGeorreferenciados
de la especie
Modelo de Dist. potencial
Variables ambientales
� 19 variables bioclimáticas
http://worldclim.org/
Variables ambientales
� Variables edafológicas
http://www.isric.org/UK/About+ISRIC/Projects/Track+Record/SOTERLAC.htm
Variables ambientales
� Variables topográficas
http://srtm.csi.cgiar.org/
Variables ambientales
� Otras variables (i.e. regiones ecológicas, suelos)
http://www.fao.org/geonetwork
Registros especies
� IABIN– 4 redes temáticas con
vínculos a diversos tipos de información
– Énfasis: América– Acceso libre al público
�GBIF– 189.471.323 registros
biodiversidad (9 Nov 2009)
– Global– Acceso libre al público
http://www.gbif.org/http://www.iabin.net /
Registros especies
�SINGER– Registros de
accesiones en bancos de germoplasma del CGIAR
– Acceso libre al público
�GapAnalysis– 13 acervos genéticos
(7 en camino)– Datos totalmente
georreferenciados– Acceso libre al público
http://gisweb.ciat.cgiar.org/gapanalysis/http://www.singer.cgiar.org/
Registros especies
� Calidad de datos � crucial!!� Ej.: Bases de datos GBIF
CURRENT STATUS OFTHE Plantae RECORDS
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
• Using a random sample of 950.000 occurrences with coordinates
• Are the records where they say they are?: country-level verification
Records mostly locatedin country boundaries
Inaccuracies incoordinates
Records with null country: 58.051 � 6,11% of total Records with incorrect country: 6.918 � 0,72% of totalTotal excluded by country 64.969 ���� 6,83% of total
• Are the terrestrial plant species in land?: Coastal verification
Errors, and more errors
Records in the ocean: 9.866 � 1,03% of total Records near land (range 5km): 34.347 � 3,61% of totalRecords outside of mask: 369 � 0,04% of totalTotal excluded by mask 44.582 ���� 4.69% of total
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
TOTAL EVALUATED RECORDS: 950.000
Good records: 840.449 ���� 88.47% of total
RESULTING DATABASE
Verificación de coordenadas
� Verificación de coordenadas / módulo en DIVA-GIS
Registros especies
� Verificación de coordenadas
Points outside all polygons Points do not match relations
Registros especies
� Georreferenciación: Asignación de coordenadas
Registros especies
http://bg.berkeley.edu/
Software
Elith et al., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29: 129-151
Barker et al., n.d. Modeling the South American Range of the Cerulean Warbler. Presented at the ESRI International User Conference
© Proaves
Software
http://openmodeller.sourceforge.net/
ANN - Artificial Neural NetworksAquaMapsBioclimCSM - Climate Space ModelEnvelope ScoreEnvironmental DistanceGARP - Genetic Algorithm for Rule-set ProductionGARP Best SubsetsSVM - Support Vector Machines
Modelo 1Modelo 4
Cas
o:A
nnon
ach
erim
ola
Modelos en acción!
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/
Study crops1. Wheat2. Rice3. Maize4. Soybean5. Barley6. Sorghum7. Finger Millet8. Pearl Millet9. Cotton10. Oilseed rape11. Common bean12. Groundnut13. Cassava14. Potato15. Oat16. Chickpea17. Cowpea18. Sweet potato19. Banana & plantain20. Pigeon pea
� Global importance;� Worldwide production area; � Advancement of transgenic
technology; and � Contribution to food security
(crop species listed in the Annex I of the ITPGRFA and CGIAR mandate crops)
Criteria for selection
Tool to visualize likelihood of gene flow and introgression
Five categories:
� Very high
� High
� Moderate
� Low
� Very low
Slide 27
ed1 Perhaps i can merge this slide with the barley one Ehsan Dulloo, 3/27/2008
CASE STUDY
Barley(Hordeum vulgare ssp. vulgare)
Barley (H. vulgare ssp. vulgare)
� Annual, cool season crop, highly autogamous (98%)� Seed dispersal: water, animals� Volunteers frequent, weedy, but not invasive
Biological information
Pollen Flow
GM technology
� Mainly wind-pollinated, pollen viability a few hours
� Outcrossing 50 m
� 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
Barley
� 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)
Wild relatives
Hybridization potential
� GP1: domesticated barley and its wild ancestor H. vulgare ssp. spontaneum
� GP2: H. bulbosum
� GP3: all other Hordeum species
Likelihood of gene flow and introgression in Barley
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
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
� Dynamics of barley pollen flow; frequencies of outcrossing at various distances
Research gaps
Book Publication
Targeting Cassava Pest and Disease ProblemsTargeting Cassava Pest and Disease Problems
Climate change
EnvironmentCharacterization
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/
Phaseolus acutifolius var. tenuifolius
Phaseolus acutifolius var. acutifolius
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…
Validación modelos• ¿Son las variables usadas para generar el modelo, las más
adecuadas?C
aso:
Ber
thol
letia
exce
lsa
Climático Climático + ecoregiones 1
Climático + suelos 1
Climático + suelos 2
Climático + ecoregiones 2
Climático + ecoregiones 3
Validación modelos
• Parámetros estadísticos– Area under the receiver Operating
Characteristic curve (AUC)– Receiver Operating Characteristic curve
(ROC)
– Correlation (COR)– Kappa
Validación modelos
• Modelo basado en conocimiento de expertos• Validación y re-parametrización• KMLs de Google Earth + plugin + encuesta electrónica
Gracias
Esta presentación está disponible en:
http://www.slideshare.net/laguanegna