THE CHALLENGE (S)

1. Filling gaps in VBORNET Vector Maps:The practicalitiesWilliam WintWith apologies to David Morley, Jolyon Medlock, Francis Schaffner, Bulent Alten, Ozge Erizon, Els Ducheyne, Wesley Tack, Neil Alexander

2. THE CHALLENGE (S) Expert network data Five species to start Significant gaps for all species Four are P/A not density at polygon level One is Presence only

3. THE SOLUTIONS (1) Filling small to medium sized holes not a problem though NLDA/RF models may need to be restricted in extent. (Sandfly species) Filling larger gaps also readily possible, provided there are known absences

4. MODELLING (for ordinary mortals) Vector sampling, mapping, and modelling software suite, built over 4 years with support from ESA, with objective of producing commercially viable product. Used modelling module, which can do bootstrapped NLDA, RF, BRT, GLM, etc

5. MODELLING COVARIATES Covariates used: Remote sensing MODIS Suites 2000-2008, (2012) synoptic datasets using Fourier Processing Seasonal indicators for Veg Index, LST (corrected for discontinuity) Precipitation Altitude People Various other Indicators

6. THE SOLUTIONS (2) Masking with habitat preferences (especially unsuitability) feasible, bearing in mind different land cover/use sources, and sometime very small/limited areas of suitable habitats.

7. THE SOLUTIONS (3) If no absences, there are several approaches: 1) Fish around for more data from literature 2) Get experts to do the fishing 3) Generate pseudo absences, These usually set by assuming absence to be a set distance from presence or use something like Maxent, Mahalanobis/Euclidean Distance (which effectively does the same but with parameter distance) • Not applicable here as too many unknown data

8. THE SOLUTIONS (4) 4a) Define suitable habitat, and combine with unsuitability and limiting environmental thresholds to generate some sort of known presence/absence mask, within known present areas (and possibly beyond). More Expert fishing to define habitat suitability in three categories: Primary, Secondary, Unsuitable. Also limiting factor values Model resulting binary P/A 4b) Generate continuous variable of suitability within known presence areas. Means using different continuous variable modelling methods (OK with VECMAP)

9. THE SOLUTIONS (5) Habitats etc Some massaging needed to combine habitat sources.

10. THE SOLUTIONS (6) Thresholds If no absences, also combination of unsuitability and limiting environmental thresholds

11. THE SOLUTIONS (7) Possible Model Inputs All Layers = expert defined habitat, masked by snow cover and vegbut from where?? …. Initially only from “Known Presence”

12. SELECTION OF MODELS Experts provided with range of models and asked to select from models which meet accuracy metric standards. Ensembles also provided in later iterations <> RF/NLDA (<>)…… Primary/Primary and secondary habitat ( )

13. SELECTED MODELS Model selected for each species Need to integrate with VBORNet maps so convert to values for NUTS admin units

14. CONVERSION TO NUTS Possibilities: Binary (present or not) Not discriminatory enough (also small habitats) Needed categories similar to VBORNet results Proportion of pixels that indicate presence Recode model to 1, 0 using 50% Calculate % Reclass % to Predicted Risk Level < 1 Minimal 1-25 Low 25 – 75 Medium > 75 High Produce combined map ? Proportion of suitable habitat a better measure

15. CONVERSION TO NUTS

16. NEXT Rest of vectors Wider range of limiting factors/habitat suitability, Different for different countries if needed, Still iterative expert input and validation, Incorporate Expert defined habitat input into VBORNet routine data entry Finer scale maps, new imagery, formalised feedback

17. ONGOING

18. ONGOING

19. ONGOING

20. Many Thanks