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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

AquaMaps Predictive distribution maps for marine organisms

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AquaMaps. 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. INTRODUCTION. - PowerPoint PPT Presentation

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Page 1: AquaMaps Predictive distribution maps for marine organisms

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

Page 2: AquaMaps Predictive distribution maps for marine organisms

Niche models: Basic ConceptINTRODUCTION

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

Page 3: AquaMaps Predictive distribution maps for marine organisms

AquaMaps Basic Concept• Environmental envelope based modeling

(Habitat Suitability Index style approach)

Predictor

Preferred min

Preferred max

Min Max

PMax

Species-specific environmental envelopes

Rel

ativ

e pr

obab

ilit

y of

oc

curr

ence

(HSPEN)

(HCAF)

(HS

PE

C)

INTRODUCTION

Page 4: AquaMaps Predictive distribution maps for marine organisms

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

Page 5: AquaMaps Predictive distribution maps for marine organisms

C-squaresENVELOPES

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.

Page 6: AquaMaps Predictive distribution maps for marine organisms

Automated Envelope Generation: Selection of Species Records

Minimum: n = 10 records with reliable species ID & location

information

ENVELOPES

European flounder

(Platichthys flesus), n = 65

Page 7: AquaMaps Predictive distribution maps for marine organisms

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

Page 8: AquaMaps Predictive distribution maps for marine organisms

ENVELOPES

Store Envelope in HSPENMin 10% 90% Max

Depth 1 11 50 100

Temperature [C] -0.21 7.27 16.27 24.35

Salinity [ppu] 6.13 6.53 37.88 38.00

PriProd [mgC per time]

70.74 113.60 188 190

IceConc 733 1574 3233 4852

LandDist [km] 1 2 146 328

Page 9: AquaMaps Predictive distribution maps for marine organisms

ENVELOPES

Store Envelope in HSPENMin 10% 90% Max

Depth 1 11 50 100

Temperature [C] -0.21 7.27 16.27 24.35

Salinity [ppu] 6.13 6.53 37.88 38.00

PriProd [mgC per time]

70.74 113.60 188 190

IceConc 733 1574 3233 4852

LandDist [km] 1 2 146 328

Page 10: AquaMaps Predictive distribution maps for marine organisms

Model AlgorithmMODEL

ALGORITHM

Pc = PBathymetryc * PTempc * PSalinityc * PPriProdc *

PIceConcc

= Multiplicative approach: Each parameter can act as “knock-out” criterionRedundant parameters have no effect on distribution

Geometric mean now implemented

Page 11: AquaMaps Predictive distribution maps for marine organisms

Model OutputMODEL

OUTPUT

Page 12: AquaMaps Predictive distribution maps for marine organisms

Model OutputMODEL

OUTPUT

Page 13: AquaMaps Predictive distribution maps for marine organisms

Model OutputMODEL

OUTPUT

Page 14: AquaMaps Predictive distribution maps for marine organisms

Model OutputMODEL

OUTPUT

Page 15: AquaMaps Predictive distribution maps for marine organisms

Expert reviewEXPERTREVIEW

•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

Page 17: AquaMaps Predictive distribution maps for marine organisms

Create Your Own Map

Page 18: AquaMaps Predictive distribution maps for marine organisms

Key areas (parameter values are different compared to surrounding waters or other areas of known occurrence)

Black SeaMean Values Notes

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  

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

Page 19: AquaMaps Predictive distribution maps for marine organisms

Saving Expert-reviewed Map

Page 24: AquaMaps Predictive distribution maps for marine organisms

Activity password: please ask us if you want it

Page 27: AquaMaps Predictive distribution maps for marine organisms

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).

Page 31: AquaMaps Predictive distribution maps for marine organisms

Summary by groupSUMMEDOUTPUTS

Current options to display by: species richness; mean length; mean trophic level; and mean resilience

Page 32: AquaMaps Predictive distribution maps for marine organisms

Summary by personal listSUMMEDOUTPUTS

E.g. Where is the suitable habitat for a particular species assemblage?

Page 33: AquaMaps Predictive distribution maps for marine organisms

Summary by personal listSUMMEDOUTPUTS

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)

Page 34: AquaMaps Predictive distribution maps for marine organisms

Future functionality optionsFUTUREOPTIONS

• Area/environment delimited species checklists• Use of predictions of distributions from climate model data

Page 35: AquaMaps Predictive distribution maps for marine organisms

Future functionality optionsFUTUREOPTIONS

• 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

Page 36: AquaMaps Predictive distribution maps for marine organisms

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