How well do environmentbased models predict species
abundances at a coarse scale?
Volker Bahn and Brian McGill
McGill University
CSEE, Toronto, May, 2007
www.volkerbahn.com
Distribution Map
Rose-breasted Grosbeak
http://www.natureserve.org/
Distribution Map
Rose-breasted Grosbeak
http://www.mbr-pwrc.usgs.gov/bbs/bbs.html
Species Distributions
• Central to ecology
– Krebs, C. J. 1972. Ecology: The
experimental analysis of distribution and
abundance.
– Andrewartha, H. G., and L. C. Birch. 1984.
The ecological web: More on the
distribution and abundance of animals.
• Conservation of species
Distribution Modelling
• How does distribution modelling work?
– Occurrence or abundance data at some
locations
– Record environmental conditions
– Build statistical model relating sample data
to environmental predictors
– Predict occurrence for non-surveyed areas
Absent
<4
4-8
8-12
12-16
16-20
>20
Research Questions
• How well does niche-based distribution
modelling work?
• How can one assess the predictive
ability of distribution models?
• Which influence does the evaluation
scheme have on the assessment of the
models?
Methods
• Breeding Bird Survey
• 1996-2000
• 1293 locations
• 79 - 190 species
• Environmental data
• Regression trees/
Random forests
Contagion
Results
Dependent
Bird abundance
Bird abundance
Sim. Ranges
Independent
Environment
Contagion
Environment
R2*
0.32
0.43
0.24
*Averaged over 190 species
Bahn, V and McGill, B.J. (2007) Can niche-based
distribution models outperform spatial interpolation?
Global Ecology and Biogeography: online early.
Resubstitution – no split
Training set
Test set
Out of range
Random split
Training set
Test set
Out of range
Strips
Training set
Test set
Out of range
Halves
Training set
Test set
Out of range
1.0
0.8
0.6
0.4
0.2
AUC
Somer's D
2
rbinom
0.0
2
r
2
R
None
Random
Strips
Halves
Conclusion
• When training and test data are
interspersed, interpolation does the job
just as well as niche-based models
• Niche-based models predict poorly into
new areas
• Evaluations are dependent on
information content and testing scheme
Outlook
• If environmental conditions are not a
good predictor then what are we
missing?
• We don’t get the right information from
remotely sensed data
• Processes are not stationary
• Spatial processes: dispersal and
population dynamics
Acknowledgements
• Thousands of volunteers, CWS & USGS
for BBS data
• Grad students, friends and collaborators
in the lab and beyond
• Family
• Funding from NSERC
Discussion?
Species Peak at Optimum?
• Typically not
• Mueller-Dombois, D. & Ellenberg, H. (1974)
Aims and methods of vegetation ecology.
Wiley, New York.
• Rehfeldt, G.E., Ying, C.C., Spittlehouse, D.L. &
Hamilton, D.A., Jr. (1999) Genetic responses
to climate in Pinus contorta: Niche breadth,
climate change, and reforestation. Ecological
Monographs, 69(3), 375-407.
Species Peak at Optimum?
• Wang et al. 2006. Use of response functions in
selecting lodgepole pine populations for future
climates. J Global Change Biology
12(12):2404-2416
• Frazier, M., R.B. Huey, and D. Berrigan. 2005.
Thermodynamics constrains the evolution of
insect population growth rates: "warmer is
better." American Naturalist 168:512-520.
Mueller-Dombois and Ellenberg (1974)
Dispersal
1.000
260
0.800
240
0.007
200
180
160
140
120
100
80
Proportion of Population
220
0.006
0.005
0.004
0.003
0.002
0.001
60
40
0.000
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17
20
0
Bahn, V., W.B. Krohn, and R.J. O'Connor. Under review. Dispersal leads to
autocorrelation in animal distributions: a simulation model. Submitted to Journal
of Applied Ecology.
Before/after Dispersal
260
240
220
200
180
160
140
120
100
80
60
40
20
0
250
Without dispersal
With dispersal
Population size
200
150
100
50
0
0
50
100
150
Carrying capacity K
200
250
Conditional Autoregressive
Y = Xβ + ρC(Y – Xβ) + ε
Temp max ≥ 28.6
|
Temp min ≥ 3.2
Yearly var precip ≥ 0.2
Seasonal var precip ≥ 0.3
0.7
1.2
n = 115 n = 145
1.0
n = 66
Precip ≥ 66.1
1.7
n = 273
1.6
n = 24
2.8
n = 54
5
4
3
Sqrt transformed counts
2
1
20
25
30
Temperature maximum (C)
5
4
3
Sqrt transformed counts
2
1
20
25
30
Temperature maximum (C)