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


European flounder

(Platichthys flesus)


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


Cross-check with known FAO areas of occurrence (e.g. FishBase)(N.B. Chilean e.g. dealt with by non-native status exclusion)

ENVELOPES


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


Mean annual SST [C]

- 2SD = 4.09

Mean = 11.85- SD = 7.97+ SD = 15.73

+ 2SD = 19.51


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


25% -75 % Percentile = “Preferred range”






Mean annual SST [C]

Max =16.75 Min = 1.65

90% = 16.23 10% = 7.27


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





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


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


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


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


ALGORITHM

SST


ALGORITHM

Salinity


ALGORITHM

Chlorophyll A


ALGORITHM

Distance to ice edge


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

http://www.fishbase.se/Photos/ThumbnailsSummary.php?ID=24


Atlantic herring

(Clupea harengus), n = 7500


Atlantic cod

(Gadus morhua), n = 215



Atlantic cod

(Gadus morhua), n = 215


Tropical two-wing flyingfish

(Exocoetus volitans), n = 330



Tropical two-wing flyingfish

(Exocoetus volitans), n = 330

Data cleaning needed


Tope shark

(Galeorhinus galeus), n = 110

http://www.fishbase.se/Photos/PicturesSummary.cfm?ID=4642&what=species


Tope shark

(Galeorhinus galeus), n = 110


Orange roughy

(Hoplostethus atlanticus), n = 116

http://www.fishbase.se/Photos/PicturesSummary.cfm?ID=334&what=species


Orange roughy

(Hoplostethus atlanticus), n = 116


Coelacanth

(Latimeria chalumnae), n = 10

http://www.fishbase.se/Photos/PicturesSummary.cfm?StartRow=1&ID=2063&what=species


Coelacanth



Coelacanth



Red lionfish

(Pterois volitans), n = 65



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

http://www.fishbase.se/

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

Documents

Mapping distributions of marine organisms using environmental niche modelling - AquaMaps