Mapping distributions of marine organisms using environmental niche modelling - AquaMaps
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Transcript of Mapping distributions of marine organisms using environmental niche modelling - AquaMaps
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Mapping distributions of marine organisms using environmental niche modelling - AquaMaps
K. Kaschner, J. Ready, S. Kullander, R. Froese and many more….INCOFISH, FishBase…
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AquaMaps Basic Concept
• Environmental envelope type modeling 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 – Mean annual SST (Sea surface temperature) – Mean annual Salinity– Mean annual Chlorophyll A (now primary production)– Mean annual Sea ice concentration (replacing distance to ice edge)– Mean annual distance to land – Etc.
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AquaMaps Basic ConceptINTRODUCTION
Pc = PBathymetryc * PSSTc * PSalinityc * PChloroAc *
PIceDistc * PLandDistc
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AquaMaps Basic ConceptINTRODUCTION
European flounder
(Platichthys flesus)
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AquaMaps Basic ConceptINTRODUCTION
European flounder
(Platichthys flesus)
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Environmental Envelopes: Sources of Information
Envelopes can be defined based on • expert knowledge / published information
– E.g. depth ranges for fishes -> FishBase• automatically generated based on species
records (point data)
ENVELOPES
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Automated Envelope Generation: 1. Step: Selection of Species Records
ENVELOPES
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Automated Envelope Generation: 1. Step: 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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2. Step: Selection of “Good” Records
Cross-check with known FAO areas of occurrence (e.g. FishBase)
ENVELOPES
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2. Step: 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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2. Step: Selection of “Good” Records
Cross-check with known FAO areas of occurrence (e.g. FishBase)
ENVELOPES
European flounder
(Platichthys flesus), n = 33
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3. Step: Grouping over “Good” Cells
Mean annual SST [C]
ENVELOPES
Mean annual SST [C]
Fre
quen
cy
Non-grouped records
(n = 33)
Records grouped over cells
(n = 20)
Minimum: n = 10 cells
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4. Step: Calculate Percentile Ranges ENVELOPES
Mean annual SST [C]
Max =16.75 Min =1.65
75% = 15.0925% = 9.06
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4. Step: Calculate Percentile Ranges ENVELOPES
Mean annual SST [C]
- 2SD = 4.09
Mean = 11.85- SD = 7.97+ SD = 15.73
+ 2SD = 19.51
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4. Step: Calculate Percentile Ranges ENVELOPES
Min 25% 75% Max
Depth 1 11 50 100
SST [C] 1.65 9.06 15.09 16.75
Salinity [ppu] 6.13 18.02 35.07 38.00
ChloroA [?] 111.56 143.01 175.94 190
IceDist [km] 733 1816 2974 3443
LandDist [km] 1 5 19.25 328
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4. Step: Calculate Percentile Ranges ENVELOPES
25% -75 % Percentile = “Preferred range”
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4. Step: Calculate Percentile Ranges ENVELOPES
25% -75 % Percentile = “Preferred range”
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4. Step: Calculate Percentile Ranges ENVELOPES
25% -75 % Percentile = “Preferred range”
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4. Step: Calculate Percentile Ranges ENVELOPES
Mean annual SST [C]
Max =16.75 Min = 1.65
90% = 16.23 10% = 7.27
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4. Step: Calculate Percentile Ranges ENVELOPES
Min 10% 90% Max
Depth 1 11 50 100
SST [C] 7.27 7.27 16.23 16.5
Salinity [ppu] 6.09 6.53 37.88 38
ChloroA [?] 111.56 113.60 188 195
IceDist [km] 1574 1574 3233 3434
LandDist [km] 1 2 146 328
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4. Step: Calculate Percentile Ranges ENVELOPES
10% -90 % Percentile = “Preferred range”
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4. Step: Calculate Percentile Ranges ENVELOPES
10% -90 % Percentile = “Preferred range”
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5. Step: Broadening of Min-Max Ranges
ENVELOPES
Mean annual SST [C]
Max =1.5 * Interquartile = 24.34
90% = 16.23 10% = 7.27
Min =1.5 * Interquartile = - 0.21
Note that if true value is more extreme then this is kept
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ENVELOPES
Min 10% 90% Max
Depth 1 11 50 100
SST [C] -0.21 7.27 16.27 24.35
Salinity [ppu] 6.13 6.53 37.88 38.00
ChloroA [?] 70.74 113.60 188 190
IceDist [km] 733 1574 3233 4852
LandDist [km] 1 2 146 328
5. Step: Broadening of Min-Max Ranges
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6. Step: Ensure Minimum Range Width
ENVELOPES
Mean annual SST [C]
ΔMin = 1 °C
ΔMin = 2 °C
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ENVELOPES 6. Step: Ensure Minimum Range Width
1 °C2 °C
1 ppu2 ppu
10 20
2 km4 km
2 km4 km
Min 10% 90% Max
Depth 1 11 50 100
SST [C] -0.21 7.27 16.27 24.35
Salinity [ppu] 6.13 6.53 37.88 38.00
ChloroA [?] 70.74 113.60 188 190
IceDist [km] 733 1574 3233 4852
LandDist [km] 1 2 146 328
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ENVELOPES
7. Step: Store Envelope in HSPEN
Min 10% 90% Max
Depth 1 11 50 100
SST [C] -0.21 7.27 16.27 24.35
Salinity [ppu] 6.13 6.53 37.88 38.00
ChloroA [?] 70.74 113.60 188 190
IceDist [km] 733 1574 3233 4852
LandDist [km] 1 2 146 328
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Model Algorithm
Predictor
Preferred min
Preferred max
Min Max
PMax
Rel
ativ
e pr
obab
ilit
y of
oc
curr
ence
MODEL ALGORITHM
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Model AlgorithmMODEL
ALGORITHM
Pc = PBathymetryc * PSSTc * PSalinityc * PChloroAc *
PIceDistc * PLandDistc
– Multiplicative approach:
• Each parameter can act as “knock-out” criterion
• Redundant parameters have no effect on distribution
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Model Output ALGORITHM
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Model Output ALGORITHM
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Effects of Individual PredictorsMODEL
ALGORITHM
Bathymetry
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Effects of Individual PredictorsMODEL
ALGORITHM
SST
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Effects of Individual PredictorsMODEL
ALGORITHM
Salinity
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Effects of Individual PredictorsMODEL
ALGORITHM
Chlorophyll A
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Effects of Individual PredictorsMODEL
ALGORITHM
Distance to ice edge
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Effects of Individual PredictorsMODEL
ALGORITHM
Distance to land
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Additional Rules
• If MinIceEdgeDist > 1000 km then ignore parameter (Rethinking – data changing to ice concentration)
• If MaxLandDist > 1000 km then MaxLandDist = maximum distance (4000 km)
MODEL ALGORITHM
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Preliminary ResultsEXAMPLES
Atlantic herring
(Clupea harengus), n = 7500
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Preliminary ResultsEXAMPLES
Atlantic herring
(Clupea harengus), n = 7500
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Preliminary ResultsEXAMPLES
Atlantic cod
(Gadus morhua), n = 215
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Preliminary ResultsEXAMPLES
Atlantic cod
(Gadus morhua), n = 215
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Preliminary ResultsEXAMPLES
Tropical two-wing flyingfish
(Exocoetus volitans), n = 330
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Preliminary ResultsEXAMPLES
Tropical two-wing flyingfish
(Exocoetus volitans), n = 330
Data cleaning needed
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Preliminary ResultsEXAMPLES
Tope shark
(Galeorhinus galeus), n = 110
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Preliminary ResultsEXAMPLES
Tope shark
(Galeorhinus galeus), n = 110
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Preliminary ResultsEXAMPLES
Orange roughy
(Hoplostethus atlanticus), n = 116
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Preliminary ResultsEXAMPLES
Orange roughy
(Hoplostethus atlanticus), n = 116
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Preliminary ResultsEXAMPLES
Coelacanth
(Latimeria chalumnae), n = 10
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Preliminary ResultsEXAMPLES
Coelacanth
(Latimeria chalumnae), n = 10
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Preliminary ResultsEXAMPLES
Coelacanth
(Latimeria chalumnae), n = 10
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Preliminary ResultsEXAMPLES
Red lionfish
(Pterois volitans), n = 65
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Preliminary ResultsEXAMPLES
Red lionfish
(Pterois volitans), n = 65
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Points for InvestigationDISCUSSION
• Advantages/disadvantages of envelope modeling in comparison to other habitat suitability modeling / mapping approaches (GARP, Maxent, Bioclim etc.)
• Minimum number of records required?• Environmental data
– Seasonal data– Historical and predicted future data– Categorical data? E.g. habitat types
• Multiplicative model (Geometric mean)? • Weighting factors (e.g. known forcing factors)?• Effects of effort biases?• Others?
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Existing modellingDISCUSSION
• Other presence only modelling– GARP (Genetic Algorithm for Rule-Set Parsimony)
• The ‘industry standard’ but a bit of a ‘black box’
– Maxent (Maximum entropy) – latest popular method
• A machine learning method, iterating algorithm
• Computationally quite fast (but not as fast as AquaMaps)
– Bioclim – early simplistic method
• Uses similar approach to envelopes
• Moderately fast computation
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AquaMaps comparedDISCUSSION
• Advantages– Speed
• Simple calculations take very little time• Can be done on-the fly over the internet (www.fishbase.se
Tools/AquaMaps)– FAO area use to block out areas of known absence
• Can be switched off to allow prediction of areas that could be invaded– Batch processing
• runs the whole database in one go – many species
• Potential Disadvantages– Accuracy?
• As yet unknown – testing underway but looks good at this scale– Resolution?
• 0.5 degree scale • difficult to reapply at local scales without remaking HCAF
But - Other methods also require the environmental data sets to be provided at the correct scale
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Acknowledgements• FishBase – Provision of data and interface
– Occurrence records, depth data, FAO area assignment
• BADC (British Atmospheric Data Centre) – Provision of data from global climate models– Future and past environmental data (just beginning)
– Plan to predict the effects of climate change of fish distributions using:
• Historical data - 100yrs ago and 50yrs ago
• Future modelled data - 20yrs time, 50yrs time, 100yrs time
• INCOFISH partners