3. Loarie et al. 2009 Geophys Res Lett 36:L14810 Bradshaw et al. 2009 Front Ecol Environ 7:79-87
Hansen et al. 2010 PNAS 107:8650-8655
Bradshaw et al. 2009 Trends Ecol Evol 24:541-548
5. N Stork
X Giam D Fordham
B Brook C Sekerçioglu
S Gregory
LP Koh
C Bradshaw FL He
L Gibson
C Bradshaw
S Gregory N Stork D Fordham S Williams
LP Koh X Giam
B Brook
15. log relative frequency log relative frequency
-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -3.5 -3.0 -2.5 -2.0 -1.5
0
0
200
50
400
species
species
100
600
0.0000
0.0500
0.1000
0.1500
0.2000
800
-10
150
log relative frequency log relative frequency
-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -5 -4 -3 -2 -1
-8
0
0
10
-6
50
20
-4
species
species
30
100
-2
40
150
0
log relative frequency log relative frequency
-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 -5 -4 -3 -2 -1
0
0
2
20
4
50
40
60
species
species
6
100
80
100
8
150
120
log relative frequency log relative frequency
10
-8 -7 -6 -5 -4 -3 -2 -1 -3.5 -3.0 -2.5 -2.0 -1.5
0
0
12
50
100
100
200
species
species
150
300
200
400
16. • habitat destruction pattern little influence on SAR
predictions
• species aggregation also small influence
• species abundance distribution (SAD) large effect
• degree of lag as indicated by MVPp most important
modifier of SAR predictions
• potential to predict z partially (and extinction risk)
based on SAD
• matrix sensitivity next step
17. • Barry W. Brook, The University of Adelaide
• Damien A. Fordham, The University of Adelaide
• Stephen D. Gregory, The University of Adelaide
• Lian Pin Koh, ETH Switzerland
• Xingli Giam, Princeton University
• Nigel E. Stork, Griffith University Corey Bradshaw
• Luke Gibson, National University of Singapore corey.bradshaw@adelaide.edu.au
ConservationBytes.com
• Cagan Sekerçioglu, University of Utah
• Stephen E. Williams, James Cook University
• Fangliang He, University of Alberta; Sun-yat Sen University