Introducción al modelamiento de la distribución de especies

Nora P. Castañedan.p.castaneda@cgiar.org

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