Mapping distributions of marine organisms using environmental niche modelling - AquaMaps

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