Acknowledgements
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-2 0 5 10 15 20 25 30 0 2 4 6 8 10 Mapping world-wide distributions of marine mammals using a Relative Environmental Suitability (RES) model Acknowledgements This work was supported- by The Pew Charitable Trusts of Philadelphia, USA as part of the ‘Sea Around Us’ Project, NSERC and a Li-Tze-Fong Graduate Fellowship Sea Around Us Project, Fisheries Centre, UBC, Vancouver Contact: [email protected] K. Kaschner, R. Watson, A.W. Trites & D. Pauly Fig. 2 a – global bathymetry b – sighting frequency per depth predictor category & assumed environmental envelope c – predicted RES for species based on depth preferences alone C I. Introduction Delineation of large-scale geographic ranges of marine mammals is difficult and often subjective (Fig. 1) We developed a generic approach to map global distributions of 115 marine mammal species based on species-specific habitat preferences using a GIS- based environmental envelope model II. Methods Model input 1: Qualitative & quantitative information about marine mammal habitat preferences Model input 2: Global 0.5 degree lat/long raster data sets of bathymetry, annual mean sea surface temperature (SST), annual mean distance to ice edge (Fig. 1a, 2a , 3a) III. Results & Validation Predicted RES maps: match traditional outlines of max. range extent closely in most cases (Fig. 4c) provide information about likely heterogeneous patterns of species’ occurrence (Fig. 4c) V. Conclusions Relative Environmental Suitability modeling Fig. 3 a – mean annual SST b - sighting frequency per temperature predictor category & assumed environmental envelope c - predicted RES based on depth & temperature preferences Fig. 1 – Standard outline of max. range extent of Sowerby’s beaked whale (Jefferson et al, 1993) & known sightings We assigned species to broad habitat predictor categories (environmental envelopes) in terms of depth, SST and ice edge association (Figs. 1b, 2b, 3b) We calculated the combined relative environmental suitability of each cell based on local environmental conditions (Illustrated step-by-step in Figs. 2, 3, 4) 2a 2c 3a SST [° C] sightin gs 3b 3c Fig. 4 a – mean annual distance from the ice edge b - sighting frequency per ice- edge distance predictor category & assumed environmental envelope c - predicted RES based on depth, temperature & distance from ice edge preferences STEP I FINAL MODEL OUTPUT 4c 4a 0 -200 -1000 -2000 -3000 -4000 -5000 -6000 -7000 -8000 0 2 4 6 8 10 P max ‘Mainly continental slope’ Depth [m] sightings 2b probability distribution Expert knowledge & other source of habitat preference information -1 0 1 500 1000 2000 8000 0 2 4 6 8 10 ‘No association with ice edge‘ Distance from ice edge [km] sighting s 4b Sighting records Low High STEP II STEP III Low High Low High Table 1 Validation results utilizes expert knowledge & is independent of point data for input allows visualization of hypotheses about species distribution represents a more objective approach than the standard outlines of maximum range extents is useful to address ‘Big Picture’ questions relating to biodiversity, marine mammal-fisheries interactions, speciation, historic ranges etc. correlate significantly with observed patterns of species’ occurrence based on independent sighting data sets for all species tested (Table 1) 1 ‘Subpolar – warm temperate’ Species % of random data sets w ith significant correlations rho p N orthern fur seal 0.54 < 0.0001 0 H arbour porpoise 0.59 < 0.0001 0 K iller w hale 0.56 < 0.0001 0.54 A ntarctic m inke w hale 0.71 < 0.0001 0 Spearm an's non-param etric rank correlation
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Mapping world-wide distributions of marine mammals using a Relative Environmental Suitability (RES) model. I. Introduction Delineation of large-scale geographic ranges of marine mammals is difficult and often subjective (Fig. 1) - PowerPoint PPT Presentation
Transcript of Acknowledgements
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Mapping world-wide distributions of marine mammals using a
Relative Environmental Suitability (RES) model
AcknowledgementsThis work was supported- by The Pew Charitable Trusts of Philadelphia, USA as part of the ‘Sea Around Us’ Project, NSERC and a Li-Tze-Fong Graduate Fellowship
Sea Around Us Project, Fisheries Centre, UBC, Vancouver Contact: [email protected]
K. Kaschner, R. Watson,A.W. Trites & D. Pauly
Fig. 2 a – global bathymetry
b – sighting frequency per depth predictor category & assumed environmental envelope
c – predicted RES for species based on depth preferences alone
C
I. Introduction
Delineation of large-scale geographic ranges of marine mammals is difficult and often subjective (Fig. 1)
We developed a generic approach to map global distributions of 115 marine mammal species based on species-specific habitat preferences using a GIS-based environmental envelope model
II. Methods
Model input 1: Qualitative & quantitative information about marine mammal habitat preferences
Model input 2: Global 0.5 degree lat/long raster data sets of bathymetry, annual mean sea surface temperature (SST), annual mean distance to ice edge (Fig. 1a, 2a , 3a)
III. Results & Validation
Predicted RES maps:
match traditional outlines of max. range extent closely in most cases (Fig. 4c)
provide information about likely heterogeneous patterns of species’ occurrence (Fig. 4c)
V. Conclusions
Relative Environmental Suitability modeling
Fig. 3 a – mean annual SST
b - sighting frequency per temperature predictor category & assumed environmental envelope
c - predicted RES based on depth & temperature preferences
Fig. 1 – Standard outline of max. range extent of Sowerby’s beaked whale (Jefferson et al, 1993) & known sightings
We assigned species to broad habitat predictor categories (environmental envelopes) in terms of depth, SST and ice edge association (Figs. 1b, 2b, 3b)
We calculated the combined relative environmental suitability of each cell based on local environmental conditions (Illustrated step-by-step in Figs. 2, 3, 4)
2a
2c
3a
SST [° C]
sig
htin
gs
3b
3c
Fig. 4 a – mean annual distance from the ice edge
b - sighting frequency per ice- edge distance predictor category & assumed environmental envelope
c - predicted RES based on depth, temperature & distance from ice edge preferences
STEP I
FINAL MODEL OUTPUT
4c
4a
0 -200 -1000 -2000 -3000 -4000 -5000 -6000 -7000 -8000
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‘Mainly continental slope’
Depth [m]
sig
htin
gs
2b probability distribution
Expert knowledge & other source of
habitat preference information
-1 0 1 500 1000 2000 8000
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0
‘No association with ice edge‘
Distance from ice edge [km]
sig
htin
gs
4b
Sighting records
Low
High
STEP II
STEP III
Low
High
Low
High
Table 1 Validation results
utilizes expert knowledge & is independent of point data for input
allows visualization of hypotheses about species distribution
represents a more objective approach than the standard outlines of maximum range extents
is useful to address ‘Big Picture’ questions relating to biodiversity, marine mammal-fisheries interactions, speciation, historic ranges etc.
correlate significantly with observed patterns of species’ occurrence based on independent sighting data sets for all species tested (Table 1)
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‘Subpolar – warm temperate’
Species
% of random data sets with significant correlations
rho p
Northern fur seal 0.54 < 0.0001 0
Harbour porpoise 0.59 < 0.0001 0Killer whale 0.56 < 0.0001 0.54Antarctic minke whale 0.71 < 0.0001 0
Spearman's non-parametric rank correlation
