AquaMaps Predictive distribution maps for marine organisms K. Kaschner, J. S. Ready, E. Agbayani, J....
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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
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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
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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
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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
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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.
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Automated Envelope Generation: Selection of Species Records
Minimum: n = 10 records with reliable species ID & location
information
ENVELOPES
European flounder
(Platichthys flesus), n = 65
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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
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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
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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
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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
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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
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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
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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).
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Summary by groupSUMMEDOUTPUTS
Current options to display by: species richness; mean length; mean trophic level; and mean resilience
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Summary by personal listSUMMEDOUTPUTS
E.g. Where is the suitable habitat for a particular species assemblage?
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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)
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Future functionality optionsFUTUREOPTIONS
• Area/environment delimited species checklists• Use of predictions of distributions from climate model data
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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
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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