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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…. INTRODUCTION. AquaMaps Basic Concept. Environmental envelope type modeling approach.

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Mapping distributions of marine organisms using environmental niche modelling - AquaMaps

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  1. Mapping distributions of marine organisms using environmental niche modelling - AquaMaps K. Kaschner, J. Ready, S. Kullander, R. Froese and many more….INCOFISH, FishBase…

  2. INTRODUCTION AquaMaps Basic Concept • Environmental envelope type modeling approach Species-specific environmental envelopes PMax (HSPEN) Relative probability of occurrence (HSPEC) Predictor (HCAF) Min Preferred min Preferred max Max

  3. 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.

  4. INTRODUCTION AquaMaps Basic Concept Pc = PBathymetryc*PSSTc *PSalinityc*PChloroAc* PIceDistc*PLandDistc

  5. INTRODUCTION AquaMaps Basic Concept European flounder (Platichthys flesus)

  6. INTRODUCTION AquaMaps Basic Concept European flounder (Platichthys flesus)

  7. ENVELOPES 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)

  8. ENVELOPES Automated Envelope Generation:1. Step: Selection of Species Records

  9. ENVELOPES Automated Envelope Generation:1. Step: Selection of Species Records Minimum: n = 10 records with reliable species ID & location information European flounder (Platichthys flesus), n = 65

  10. ENVELOPES 2. Step: Selection of “Good” Records Cross-check with known FAO areas of occurrence (e.g. FishBase)

  11. 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)

  12. ENVELOPES 2. Step: Selection of “Good” Records Cross-check with known FAO areas of occurrence (e.g. FishBase) European flounder (Platichthys flesus), n = 33

  13. ENVELOPES 3. Step: Grouping over “Good” Cells Non-grouped records (n = 33) Records grouped over cells (n = 20) Mean annual SST [C] Frequency Mean annual SST [C] Minimum: n = 10 cells

  14. 75% = 15.09 25% = 9.06 Max =16.75 Min =1.65 ENVELOPES 4. Step: Calculate Percentile Ranges Mean annual SST [C]

  15. Mean = 11.85 + SD = 15.73 - SD = 7.97 + 2SD = 19.51 - 2SD = 4.09 ENVELOPES 4. Step: Calculate Percentile Ranges Mean annual SST [C]

  16. ENVELOPES 4. Step: Calculate Percentile Ranges

  17. ENVELOPES 4. Step: Calculate Percentile Ranges 25% -75 % Percentile = “Preferred range”

  18. ENVELOPES 4. Step: Calculate Percentile Ranges 25% -75 % Percentile = “Preferred range”

  19. ENVELOPES 4. Step: Calculate Percentile Ranges 25% -75 % Percentile = “Preferred range”

  20. 90% = 16.23 10% = 7.27 Max =16.75 Min = 1.65 ENVELOPES 4. Step: Calculate Percentile Ranges Mean annual SST [C]

  21. ENVELOPES 4. Step: Calculate Percentile Ranges

  22. ENVELOPES 4. Step: Calculate Percentile Ranges 10% -90 % Percentile = “Preferred range”

  23. ENVELOPES 4. Step: Calculate Percentile Ranges 10% -90 % Percentile = “Preferred range”

  24. ENVELOPES 5. Step: Broadening of Min-Max Ranges Mean annual SST [C] Note that if true value is more extreme then this is kept 90% = 16.23 10% = 7.27 Max =1.5 * Interquartile = 24.34 Min =1.5 * Interquartile = - 0.21

  25. 5. Step: Broadening of Min-Max Ranges ENVELOPES

  26. ENVELOPES 6. Step: Ensure Minimum Range Width Mean annual SST [C] ΔMin = 1 °C ΔMin = 2 °C

  27. 6. Step: Ensure Minimum Range Width ENVELOPES 1 °C 2 °C 1 ppu 2 ppu 10 20 2 km 4 km 2 km 4 km

  28. 7. Step: Store Envelope in HSPEN ENVELOPES

  29. MODEL ALGORITHM Model Algorithm PMax Relative probability of occurrence Predictor Min Preferred min Preferred max Max

  30. MODEL ALGORITHM Model 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

  31. ALGORITHM Model Output

  32. ALGORITHM Model Output

  33. MODEL ALGORITHM Effects of Individual Predictors Bathymetry

  34. MODEL ALGORITHM Effects of Individual Predictors SST

  35. MODEL ALGORITHM Effects of Individual Predictors Salinity

  36. MODEL ALGORITHM Effects of Individual Predictors Chlorophyll A

  37. MODEL ALGORITHM Effects of Individual Predictors Distance to ice edge

  38. MODEL ALGORITHM Effects of Individual Predictors Distance to land

  39. MODEL ALGORITHM 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)

  40. EXAMPLES Preliminary Results Atlantic herring (Clupea harengus), n = 7500

  41. EXAMPLES Preliminary Results Atlantic herring (Clupea harengus), n = 7500

  42. EXAMPLES Preliminary Results Atlantic cod (Gadus morhua), n = 215

  43. EXAMPLES Preliminary Results Atlantic cod (Gadus morhua), n = 215

  44. EXAMPLES Preliminary Results Tropical two-wing flyingfish (Exocoetus volitans), n = 330

  45. EXAMPLES Preliminary Results Tropical two-wing flyingfish (Exocoetus volitans), n = 330 Data cleaning needed

  46. EXAMPLES Preliminary Results Tope shark (Galeorhinus galeus), n = 110

  47. EXAMPLES Preliminary Results Tope shark (Galeorhinus galeus), n = 110

  48. EXAMPLES Preliminary Results Orange roughy (Hoplostethus atlanticus), n = 116

  49. EXAMPLES Preliminary Results Orange roughy (Hoplostethus atlanticus), n = 116

  50. EXAMPLES Preliminary Results Coelacanth (Latimeria chalumnae), n = 10

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