Aquamaps predictive distribution maps for marine organisms
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AquaMaps. AquaMaps Predictive distribution maps for marine organisms. K. Kaschner, J. S. Ready, E. Agbayani, J. Rius, K. Kesner-Reyes, P. D. Eastwood, A. B. South, S. O. Kullander, T. Rees, C. H. Close, R. Watson, D. Pauly, and R. Froese. EC project PL003739. INTRODUCTION.

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AquaMaps Predictive distribution maps for marine organisms

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Aquamaps predictive distribution maps for marine organisms

AquaMaps

AquaMapsPredictive distribution maps for marine organisms

K. Kaschner, J. S. Ready, E. Agbayani, J. Rius,

K. Kesner-Reyes, P. D. Eastwood, A. B. South,

S. O. Kullander, T. Rees, C. H. Close,

R. Watson, D. Pauly, and R. Froese.

EC project PL003739


Niche models basic concept

INTRODUCTION

Niche models: Basic Concept

Various algorithms exist for presence only data: GARP, Maxent, Bioclim

AquaMaps designed specifically to deal with the 3D aspect of the marine environment, to incorporate expert review and to be automated, so usable with all available species data


Aquamaps basic concept

INTRODUCTION

AquaMaps Basic Concept

  • Environmental envelope based modeling (Habitat Suitability Index style approach)

Species-specific environmental envelopes

PMax

(HSPEN)

Relative probability of occurrence

(HSPEC)

Predictor

(HCAF)

Min

Preferred

min

Preferred

max

Max


Hcaf table

HCAF table

  • Environmental data per 0.5 degree latitude / longitude square

  • Contents

    • Bathymetry (min, mean, max)

    • Mean annual Temperature (surface and bottom)

    • Mean annual Salinity (surface and bottom)

    • Mean annual Primary productivity

    • Mean annual Sea ice concentration

    • Distance to land

    • Many others…

    • …including C-squares


C squares

ENVELOPES

C-squares

  • Provides a unique spatial identification system for each half degree square allowing:

  • Easy database queries

  • Fast online map production

  • Rees, Tony. 2003. "C-Squares", a New Spatial Indexing System and its Applicability to the Description of Oceanographic Datasets. Oceanography 16 (1), pp. 11-19.


Automated envelope generation selection of species records

ENVELOPES

Automated Envelope Generation:Selection of Species Records

Minimum: n = 10 records with reliable species ID & location information

European flounder

(Platichthys flesus), n = 65


Selection of good records

ENVELOPES

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)


Aquamaps predictive distribution maps for marine organisms

Store Envelope in HSPEN

ENVELOPES


Aquamaps predictive distribution maps for marine organisms

Store Envelope in HSPEN

ENVELOPES


Model algorithm

MODEL

ALGORITHM

Model Algorithm

Pc = PBathymetryc*PTempc *PSalinityc*PPriProdc*

PIceConcc

  • = Multiplicative approach:

    • Each parameter can act as “knock-out” criterion

    • Redundant parameters have no effect on distribution

    • Geometric mean now implemented


Model output

MODEL

OUTPUT

Model Output


Model output1

MODEL

OUTPUT

Model Output


Model output2

MODEL

OUTPUT

Model Output


Model output3

MODEL

OUTPUT

Model Output


Expert review

EXPERT

REVIEW

Expert review

  • Expert knowledge is important - the automated system provides the base from which to refine species distribution maps

  • Performed through the ”Create your own map” link from any species distribution map

  • Reviewed maps should be used in preference to un-reviewed maps in all further analysis


Aquamaps predictive distribution maps for marine organisms

Create Your Own Map


Aquamaps predictive distribution maps for marine organisms

Key areas

(parameter values are different compared to surrounding waters or other areas of known occurrence)


Aquamaps predictive distribution maps for marine organisms

Saving Expert-reviewed Map


Aquamaps predictive distribution maps for marine organisms

Activity password: please ask us if you want it


Recommended format for expert remarks

Recommended format for Expert Remarks

  • State problem with prediction (e.g., salinity min too high resulting low probability in a given area, missing distribution, etc).

  • Cite reference(s) if possible.

  • What actions were taken (e.g., changed value in salinity envelope, adjusted bounding box, added “good cells”, etc.).

  • Other comments affecting map prediction (e.g., bias of occurrence data, artifact of bounding box on producing linear edges to distributions).


Summary by group

SUMMED

OUTPUTS

Summary by group

Current options to display by: species richness; mean length; mean trophic level; and mean resilience


Summary by personal list

SUMMED

OUTPUTS

Summary by personal list

E.g. Where is the suitable habitat for a particular species assemblage?


Summary by personal list1

SUMMED

OUTPUTS

Summary by personal list

E.g. Where is the suitable habitat for a particular species assemblage?

To right is a summary of the suitable habitat for a list of 83 species observed on an eastern pacific rocky reef

(Must provide species list to AquaMaps staff at this point)


Future functionality options

FUTURE

OPTIONS

Future functionality options

  • Area/environment delimited species checklists

  • Use of predictions of distributions from climate model data


Future functionality options1

FUTURE

OPTIONS

Future functionality options

  • Area/environment delimited species checklists

  • Use of predictions of distributions from climate model data

Map showing differences in modelled sea surface temperature from 1990’s to 2040’s under a ’middle of the road’ scenario

Red = heating

Blue = cooling


Acknowledgements

Acknowledgements

  • EC funding: project PL003739

  • PEW Charitable Trust

  • FishBase

  • OBIS

  • Sea Around Us Project

  • CSIRO Marine and Atmospheric Research

  • CEFAS, U.K.

  • Max Planck Institute for Meteorology


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