1. AquaMaps Predictive distribution maps for marine organisms K. Kaschner, J. S. Ready, E. Agbayani, J. Rius, K. Kesner-Reyes, P. D. Eastwood, A. B. South, S. O. Kullander, T. Rees, C. H. Close, R. Watson, D. Pauly, and R. Froese. EC project PL003739 AquaMaps

2. Niche models: Basic Concept INTRODUCTION Various algorithms exist for presence only data: GARP, Maxent, Bioclim AquaMaps designed specifically to deal with the 3D aspect of the marine environment, to incorporate expert review and to be automated, so usable with all available species data

3. AquaMaps Basic Concept Environmental envelope based modeling (Habitat Suitability Index style approach) Predictor Preferred min Preferred max MinMax P Max Species-specific environmental envelopes Relative probability of occurrence (HSPEN) (HCAF) (HSPEC) INTRODUCTION

4. HCAF table Environmental data per 0.5 degree latitude / longitude square Contents –Bathymetry (min, mean, max) –Mean annual Temperature (surface and bottom) –Mean annual Salinity (surface and bottom) –Mean annual Primary productivity –Mean annual Sea ice concentration –Distance to land –Many others… –…including C-squares

5. C-squares ENVELOPES Provides a unique spatial identification system for each half degree square allowing: Easy database queries Fast online map production Rees, Tony. 2003. "C-Squares", a New Spatial Indexing System and its Applicability to the Description of Oceanographic Datasets. Oceanography 16 (1), pp. 11-19.

6. Automated Envelope Generation: Selection of Species Records Minimum: n = 10 records with reliable species ID & location information ENVELOPES European flounder (Platichthys flesus), n = 65

7. Selection of “Good” Records Cross-check with known FAO areas of occurrence (e.g. FishBase) (N.B. Chilean e.g. dealt with by non- native status exclusion) ENVELOPES

8. Store Envelope in HSPEN Min10%90%Max Depth11150100 Temperature [C]-0.217.2716.2724.35 Salinity [ppu]6.136.5337.8838.00 PriProd [mgC per time] 70.74113.60188190 IceConc733157432334852 LandDist [km]12146328

9. ENVELOPES Store Envelope in HSPEN Min10%90%Max Depth11150100 Temperature [C]-0.217.2716.2724.35 Salinity [ppu]6.136.5337.8838.00 PriProd [mgC per time] 70.74113.60188190 IceConc733157432334852 LandDist [km]12146328

10. Model Algorithm MODEL ALGORITHM P c = P Bathymetry c * P Temp c * P Salinity c * P PriProd c * P IceConc c = Multiplicative approach: Each parameter can act as “knock-out” criterion Redundant parameters have no effect on distribution Geometric mean now implemented

11. Model Output MODEL OUTPUT

12. Model Output MODEL OUTPUT

13. Model Output MODEL OUTPUT

14. Model Output MODEL OUTPUT

15. Expert review EXPERT REVIEW Expert knowledge is important - the automated system provides the base from which to refine species distribution maps Performed through the ”Create your own map” link from any species distribution map Reviewed maps should be used in preference to un-reviewed maps in all further analysis

17. Create Your Own Map

18. Key areas (parameter values are different compared to surrounding waters or other areas of known occurrence) Black Sea Mean ValuesNotes Depth (m)49.28 SST (ºC)15.11lower temp in the NW of basin Salinity (psu)18.1lower compared to adjacent waters but higher in Sea of Azov Primary production or chl a1135lower compared to adjacent waters Distance to land (km)3457 Distance to ice edge (km)15 Mediterranean western Mediterranean higher productivity eastern Mediterranean lower productivity North America western coast lower max productivity to remove from plot Red Sea Depth shallow Temperature warm Salinity high Persian Gulf Depth shallow Temperature warm Salinity high Primary production or chl a low Yellow Sea and Kamchatka Depth shallow SST (ºC) lower compared to surrounding waters Salinity (psu) lower Primary production or chl a lower

19. Saving Expert-reviewed Map

24. Activity password: please ask us if you want it

27. Recommended format for Expert Remarks State problem with prediction (e.g., salinity min too high resulting low probability in a given area, missing distribution, etc). Cite reference(s) if possible. What actions were taken (e.g., changed value in salinity envelope, adjusted bounding box, added “good cells”, etc.). Other comments affecting map prediction (e.g., bias of occurrence data, artifact of bounding box on producing linear edges to distributions).

31. Summary by group SUMMED OUTPUTS Current options to display by: species richness; mean length; mean trophic level; and mean resilience

32. Summary by personal list SUMMED OUTPUTS E.g. Where is the suitable habitat for a particular species assemblage?

33. Summary by personal list SUMMED OUTPUTS E.g. Where is the suitable habitat for a particular species assemblage? To right is a summary of the suitable habitat for a list of 83 species observed on an eastern pacific rocky reef (Must provide species list to AquaMaps staff at this point)

34. Future functionality options FUTURE OPTIONS Area/environment delimited species checklists Use of predictions of distributions from climate model data

35. Future functionality options FUTURE OPTIONS Area/environment delimited species checklists Use of predictions of distributions from climate model data Map showing differences in modelled sea surface temperature from 1990’s to 2040’s under a ’middle of the road’ scenario Red = heating Blue = cooling

36. Acknowledgements EC funding: project PL003739 PEW Charitable Trust FishBase OBIS Sea Around Us Project CSIRO Marine and Atmospheric Research CEFAS, U.K. Max Planck Institute for Meteorology