Aqua Maps Predictive distribution maps for marine organisms

Aqua. Maps 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 PL 003739

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

INTRODUCTION Aqua. Maps Basic Concept • Environmental envelope based modeling (Habitat Suitability Index style approach) (HSPEC) Relative probability of occurrence PMax Species-specific environmental envelopes (HSPEN) Min Preferred max Max Predictor (HCAF)

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

ENVELOPES C-squares 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.

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

ENVELOPES Selection of “Good” Records Cross-check with known FAO areas of occurrence (e. g. Fish. Base) (N. B. Chilean e. g. dealt with by nonnative status exclusion)

ENVELOPES Store Envelope in HSPEN Min Depth Temperature [C] Salinity [ppu] Pri. Prod [mg. C per time] Ice. Conc Land. Dist [km] 10% 90% Max 1 11 50 100 -0. 21 7. 27 16. 27 24. 35 6. 13 6. 53 37. 88 38. 00 70. 74 113. 60 188 190 733 1574 3233 4852 1 2 146 328

MODEL ALGORITHM Model Algorithm P = PBathymetry * PTemp * PSalinity * PPri. Prod * PIce. Conc c = Multiplicative approach: Each parameter can act as “knock-out” criterion Redundant parameters have no effect on distribution Geometric mean now implemented c

MODEL OUTPUT Model Output

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 unreviewed maps in all further analysis

Create Your Own Map

Black Sea Mean Values Depth (m) 49. 28 SST (ºC) 15. 11 lower temp in the NW of basin Salinity (psu) 18. 1 lower compared to adjacent waters but higher in Sea of Azov Primary production or chl a 1135 lower compared to adjacent waters Distance to land (km) 3457 15 Distance to ice edge (km) Notes Mediterranean western Mediterranean higher productivity eastern Mediterranean lower productivity North America western coast 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 max productivity to remove from plot Key areas (parameter values are different compared to surrounding waters or other areas of known occurrence)

Saving Expert-reviewed Map

Activity password: please ask us if you want it

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).

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

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

SUMMED OUTPUTS Summary by personal list 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 Aqua. Maps staff at this point)

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

FUTURE OPTIONS Future functionality 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

Acknowledgements • • EC funding: project PL 003739 PEW Charitable Trust Fish. Base OBIS Sea Around Us Project CSIRO Marine and Atmospheric Research CEFAS, U. K. Max Planck Institute for Meteorology