mapping distributions of marine organisms using environmental niche modelling - aquamaps
DESCRIPTION
Mapping distributions of marine organisms using environmental niche modelling - AquaMaps. K. Kaschner, J. Ready, S. Kullander, R. Froese and many more….INCOFISH, FishBase…. INTRODUCTION. AquaMaps Basic Concept. Environmental envelope type modeling approach. - PowerPoint PPT PresentationTRANSCRIPT
Mapping distributions of marine organisms using environmental niche modelling - AquaMaps
K. Kaschner, J. Ready, S. Kullander, R. Froese and many more….INCOFISH, FishBase…
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
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.
AquaMaps Basic ConceptINTRODUCTION
Pc = PBathymetryc * PSSTc * PSalinityc * PChloroAc *
PIceDistc * PLandDistc
AquaMaps Basic ConceptINTRODUCTION
European flounder
(Platichthys flesus)
AquaMaps Basic ConceptINTRODUCTION
European flounder
(Platichthys flesus)
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
Automated Envelope Generation: 1. Step: Selection of Species Records
ENVELOPES
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
2. Step: Selection of “Good” Records
Cross-check with known FAO areas of occurrence (e.g. FishBase)
ENVELOPES
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
2. Step: Selection of “Good” Records
Cross-check with known FAO areas of occurrence (e.g. FishBase)
ENVELOPES
European flounder
(Platichthys flesus), n = 33
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
4. Step: Calculate Percentile Ranges ENVELOPES
Mean annual SST [C]
Max =16.75 Min =1.65
75% = 15.0925% = 9.06
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
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
4. Step: Calculate Percentile Ranges ENVELOPES
25% -75 % Percentile = “Preferred range”
4. Step: Calculate Percentile Ranges ENVELOPES
25% -75 % Percentile = “Preferred range”
4. Step: Calculate Percentile Ranges ENVELOPES
25% -75 % Percentile = “Preferred range”
4. Step: Calculate Percentile Ranges ENVELOPES
Mean annual SST [C]
Max =16.75 Min = 1.65
90% = 16.23 10% = 7.27
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
4. Step: Calculate Percentile Ranges ENVELOPES
10% -90 % Percentile = “Preferred range”
4. Step: Calculate Percentile Ranges ENVELOPES
10% -90 % Percentile = “Preferred range”
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
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
6. Step: Ensure Minimum Range Width
ENVELOPES
Mean annual SST [C]
ΔMin = 1 °C
ΔMin = 2 °C
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
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
Model Algorithm
Predictor
Preferred min
Preferred max
Min Max
PMax
Rel
ativ
e pr
obab
ilit
y of
oc
curr
ence
MODEL ALGORITHM
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
Model Output ALGORITHM
Model Output ALGORITHM
Effects of Individual PredictorsMODEL
ALGORITHM
Bathymetry
Effects of Individual PredictorsMODEL
ALGORITHM
SST
Effects of Individual PredictorsMODEL
ALGORITHM
Salinity
Effects of Individual PredictorsMODEL
ALGORITHM
Chlorophyll A
Effects of Individual PredictorsMODEL
ALGORITHM
Distance to ice edge
Effects of Individual PredictorsMODEL
ALGORITHM
Distance to land
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
Preliminary ResultsEXAMPLES
Atlantic herring
(Clupea harengus), n = 7500
Preliminary ResultsEXAMPLES
Atlantic herring
(Clupea harengus), n = 7500
Preliminary ResultsEXAMPLES
Atlantic cod
(Gadus morhua), n = 215
Preliminary ResultsEXAMPLES
Atlantic cod
(Gadus morhua), n = 215
Preliminary ResultsEXAMPLES
Tropical two-wing flyingfish
(Exocoetus volitans), n = 330
Preliminary ResultsEXAMPLES
Tropical two-wing flyingfish
(Exocoetus volitans), n = 330
Data cleaning needed
Preliminary ResultsEXAMPLES
Tope shark
(Galeorhinus galeus), n = 110
Preliminary ResultsEXAMPLES
Tope shark
(Galeorhinus galeus), n = 110
Preliminary ResultsEXAMPLES
Orange roughy
(Hoplostethus atlanticus), n = 116
Preliminary ResultsEXAMPLES
Orange roughy
(Hoplostethus atlanticus), n = 116
Preliminary ResultsEXAMPLES
Coelacanth
(Latimeria chalumnae), n = 10
Preliminary ResultsEXAMPLES
Coelacanth
(Latimeria chalumnae), n = 10
Preliminary ResultsEXAMPLES
Coelacanth
(Latimeria chalumnae), n = 10
Preliminary ResultsEXAMPLES
Red lionfish
(Pterois volitans), n = 65
Preliminary ResultsEXAMPLES
Red lionfish
(Pterois volitans), n = 65
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?
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
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
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