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Providing means for a better understanding of biodiversity Hector Tobón, Daniel Amariles, Julián Ramírez, Louis Reymondin, Jorge Camacho, Jhon Tello and Andy Jarvis improving primary data and using it for threat assessment and in situ conservation planning in South America International Centre for Tropical Agriculture, CIAT Cali, Colombia February 16, 2011
Introducing working mates The old crew... Louis, Hector, Julian, Daniel + Andy Jorge Roles: ,[object Object]
Louis: programming supervision
Julian: modelling supervision
Hector/Daniel: primary java developers
JhonJairo/Jorge: web developers
Johannes: biodiversity modelerJohannes New faces...         Jhon Jairo
Deliverables Data quality checking and improvement Cross-checking of coordinates Georreferencing Niche modeling Training of niche models script Threat and conservation analysis over South America Google-maps based navigation tool
 Developed scripts and documentation for data cleansing Keywords ,[object Object]
Coordinate verification (error detection)
Georeferencingprocess (error correction) - Biogeomancer,[object Object]
@Data quality: Average status of a large dataset The IABIN TNs databases holds a number of occurrences, but not all of them have coordinates or are free of geospatial issue SSTN 3,866,145 occurrences, 3,452,938 (89.31%) with coordinates PTN 1,144,678 occurrences, 583,753 (50.99%) with coordinates I3N 19,663 occurrences, 2,991 (15.21%) with coordinates How many of them are correct, and reliable? How many new georreferences could we get?
@Data quality: Our approach How to make the data reliable enough? Verify coordinates at different levels Are the records where they say they are? [Country Level] Are the records inside land areas (for terrestrial plant species only) [Continental Level] Are all the records within the environmental niche of the taxon? [Environmental Level] Sea records: not verifiable Correct wrong references Add references to those that do not have Cross-check with curators and feedback the database
Progress made: filtering Cross checking of coordinates and Georreferencing script Record to verify Shape condition: The country in the  record is not null and match with the country calculated from coordinates. yes Coordinates condition: The record has coordinates and no spatial issues yes no no Mask condition: The record coordinates fall within land areas. yes no Environmental outliers condition: The  record is within the niche of the taxon (80% of the environmental space) no yes Record to exclude Reliable record
SSTN Evaluation Results Evaluated records: 3,441,589 Reliable vs Excluded
PTN Evaluation Results Evaluated records: 583,753 Reliable vs Excluded
I3N Evaluation Results Evaluated records: 2.913
‘Georeferenciability’ The occurrences from the different datasets that have no coordinates, or have geospatial issues or any detected problem to make it excludable might be ‘georeferenciables’ if these occurrences records contains the enough administrative information to find a possible geospatial location an include them for our analysis.
Biogeomancer certainty analysis BG uncertainty We took some reliable points to compare with Biogeomancer results and check its certainty. Biogeomancer result Dataset  coordinate Sometimes  both points are very close Record 6 (UNITED STATES, Alabama, Tuscaloosa) Biogeomancer estimated uncertainty: 6468m Real gap: 100m Sometimes  not Record 5 (UNITED STATES, Louisiana, Cameron) Biogeomancer estimated uncertainty: 29m Real gap: 873000m
@Niche modeling: Training of niche models script Aplying the maximum entropy algorithm Macoubea guianensis Aubl.: food for rural indigenous communities in the Amazon Data harvesting Building the presence model
@Niche modeling: Assessing threats and current conservation actions ,[object Object]
Asses the distribution of each species  in relation to:
Different threat scenarios.
Existing protected areas.,[object Object]
@Niche modelling: Task ,[object Object]
Asses short and midterms threats for each species within their potential range.
Asses the conservation status (percentage of potential habitat protected for each species),[object Object]
Big issue: quality of SDMs
Where are species rich areas? ,[object Object]
Andes: important diversity  spot for all species and greatest in relation to all other spots
Amazon: lack of data impedes from realising the existent richness
Guyana shield and Brazilian Atlantic forest: less important than andes but still highly diverse, particularly for insects.,[object Object]

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IABIN Threat Assessment Project Presentation (Costa Rica) by Andy Jarvis from CIAT

  • 1. Providing means for a better understanding of biodiversity Hector Tobón, Daniel Amariles, Julián Ramírez, Louis Reymondin, Jorge Camacho, Jhon Tello and Andy Jarvis improving primary data and using it for threat assessment and in situ conservation planning in South America International Centre for Tropical Agriculture, CIAT Cali, Colombia February 16, 2011
  • 2.
  • 7. Johannes: biodiversity modelerJohannes New faces... Jhon Jairo
  • 8. Deliverables Data quality checking and improvement Cross-checking of coordinates Georreferencing Niche modeling Training of niche models script Threat and conservation analysis over South America Google-maps based navigation tool
  • 9.
  • 11.
  • 12. @Data quality: Average status of a large dataset The IABIN TNs databases holds a number of occurrences, but not all of them have coordinates or are free of geospatial issue SSTN 3,866,145 occurrences, 3,452,938 (89.31%) with coordinates PTN 1,144,678 occurrences, 583,753 (50.99%) with coordinates I3N 19,663 occurrences, 2,991 (15.21%) with coordinates How many of them are correct, and reliable? How many new georreferences could we get?
  • 13. @Data quality: Our approach How to make the data reliable enough? Verify coordinates at different levels Are the records where they say they are? [Country Level] Are the records inside land areas (for terrestrial plant species only) [Continental Level] Are all the records within the environmental niche of the taxon? [Environmental Level] Sea records: not verifiable Correct wrong references Add references to those that do not have Cross-check with curators and feedback the database
  • 14. Progress made: filtering Cross checking of coordinates and Georreferencing script Record to verify Shape condition: The country in the record is not null and match with the country calculated from coordinates. yes Coordinates condition: The record has coordinates and no spatial issues yes no no Mask condition: The record coordinates fall within land areas. yes no Environmental outliers condition: The record is within the niche of the taxon (80% of the environmental space) no yes Record to exclude Reliable record
  • 15. SSTN Evaluation Results Evaluated records: 3,441,589 Reliable vs Excluded
  • 16. PTN Evaluation Results Evaluated records: 583,753 Reliable vs Excluded
  • 17. I3N Evaluation Results Evaluated records: 2.913
  • 18. ‘Georeferenciability’ The occurrences from the different datasets that have no coordinates, or have geospatial issues or any detected problem to make it excludable might be ‘georeferenciables’ if these occurrences records contains the enough administrative information to find a possible geospatial location an include them for our analysis.
  • 19. Biogeomancer certainty analysis BG uncertainty We took some reliable points to compare with Biogeomancer results and check its certainty. Biogeomancer result Dataset coordinate Sometimes both points are very close Record 6 (UNITED STATES, Alabama, Tuscaloosa) Biogeomancer estimated uncertainty: 6468m Real gap: 100m Sometimes not Record 5 (UNITED STATES, Louisiana, Cameron) Biogeomancer estimated uncertainty: 29m Real gap: 873000m
  • 20. @Niche modeling: Training of niche models script Aplying the maximum entropy algorithm Macoubea guianensis Aubl.: food for rural indigenous communities in the Amazon Data harvesting Building the presence model
  • 21.
  • 22. Asses the distribution of each species in relation to:
  • 24.
  • 25.
  • 26. Asses short and midterms threats for each species within their potential range.
  • 27.
  • 29.
  • 30. Andes: important diversity spot for all species and greatest in relation to all other spots
  • 31. Amazon: lack of data impedes from realising the existent richness
  • 32.
  • 33.
  • 34. ~20% difference between groups at 10% protection Plants and birds better protected No species is protected in more than 50% of the distributional range… Less difference (<10%), but insects and mammals look better!
  • 35.
  • 36. Main threat to biodiversity are accessibility by population, recent conversion (deforestation) and fire.
  • 37. Protected areas are located in places with an above average species richness under current conditions.
  • 38.
  • 39. @Navigation Tool Live Demo Avaliable in http://gisweb.ciat.cgiar.org/ita/menumap1.html Currently in development
  • 40.
  • 41.
  • 43.
  • 44.
  • 45. Summarising, in terms of data We have: Assessed all three databases and delivered them to CBI Developed automated scripts for cross-checking, georeferencing and species distribution modelling Modelled ~19,000 species from ~3,900 genera Assessed all species with regards of threat distributions and conservation and found that population accessibility, fires and recent conversion are the most important threats to species Started and advanced with the development of a web visualisation interface.