presentation at 2012 Entomological Society of America

1. Range-wide, species-specific,
ecological and historical
biogeography:
Getting the concepts right in
ecological niche modeling and
species distribution modeling
A. Townsend Peterson
University of Kansas

9. It Is Not That Simple
1. Spatial Autocorrelation
2. Study Design – M
3. Study Design – BAM
4. MESS and MOP

15. It Is Not That Simple
1. Spatial Autocorrelation
2. Study Design – M
3. Study Design – BAM
4. MESS and MOP

16. The Area of Distribution
G Physiological
requirements
(Abiotic)
A
Favorable biotic
environment
(Biotic)
B
Accessible to
dispersal
(Movements)
M

17. Test Arena: The Lawrence Species

18. Model Evaluation

19. Model Comparison

21. It Is Not That Simple
1. Spatial Autocorrelation
2. Study Design – M
3. Study Design – BAM
4. MESS and MOP

22. The Area of Distribution
G Physiological
requirements
(Abiotic)
A
Favorable biotic
environment
(Biotic)
B
Accessible to
dispersal
(Movements)
M

23. BAM II
Classic
BAM
Hutchinson’s
Dream
Wallace’s
Dream
All OK

24. Effect of BAM Scenarios
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CB
EF
WD
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HD
KGo
KAKA
KGo
KGoKGo
KA KA
Figure 3. CB = Classic BAM scenario; WD = Wallace’s Dream; EF = Elith’s Fantasy; HD = Hutchinson’s Dream
Blue = Virtual Species 1; Green = Virtual Species 3; Orange = Virtual Species 4
Circles = GARP; Squares = Maxent; Triangles = GAM

26. It Is Not That Simple
1. Spatial Autocorrelation
2. Study Design – M
3. Study Design – BAM
4. MESS and MOP

31. Assess levels of spatial
autocorrelation in
environmental data,
adjust input point data
accordingly
Estimate ecological
niche (various
algorithms)
Evaluation reality of
model transfer results,
when possible
Transfer to other
situations—time and
space
Project niche
model to
geographic
space
Model calibration,
adjusting parameters to
maximize quality
Collate primary
biodiversity data
documenting
occurrences
Process environmental
layers to be maximally
relevant to distributional
ecology of species in
question
Collate GIS database of
relevant environmental
data layers
Assess BAM scenario for
species in question; avoid
M-limited situations
Saupe et al. 2012. Variation in niche and distribution model performance: The need
for a priori assessment of key causal factors. Ecological Modelling, 237–238, 11-22.
Estimate M as
area of analysis
in study
Barve et al. 2011. The crucial role of the
accessible area in ecological niche modeling and
species distribution modeling. Ecological
Modelling, 222, 1810-1819.
Assess extrapolation
(MESS and MOP)
KU Ecological Niche Modeling Group. 2013.
Constraints on interpretation of ecological niche
models by limited environmental ranges on
calibration areas. In preparation.
Model evaluation
Peterson et al. 2008. Rethinking receiver operating
characteristic analysis applications in ecological niche
modelling. Ecological Modelling, 213, 63-72.
Model thresholding
Peterson et al. 2007. Transferability and
model evaluation in ecological niche
modeling: A comparison of GARP and
Maxent. Ecography, 30, 550-560.
Assess spatial precision of
occurrence data, adjust
inclusion of data (obs and
env) accordingly
General Methodological Summary:
Peterson et al. (2011) Ecological Niches
and Geographic Distributions, Princeton
University Press, Princeton.
Refine
estimate of
current
distribution via
land use, etc.
Reduce dimensionality
Compare present and
future to assess
effects of change

32. Atlas of Brazilian Triatomines I
Panstrongylus geniculatus
Panstrongylus lutzi
Panstrongylus megistus
Psammolestes tertius
Rhodnius nasutus
Rhodnius neglectus
Rhodnius pictipes
Rhodnius robustus
Triatoma melanocephala
Triatoma pseudomaculata

33. Factor Comparisons
No geographic structuring with respect to
phylogeny, such that evolutionary origin or
“geographic inertia” appears to have little
explanatory power for the distributions of
Leishmania clades in Brazil.
Close coincidence
between distributions
of vectors and
Leismania clades and
among clades with
respect to environment.

34. Assess levels of spatial
autocorrelation in
environmental data,
adjust input point data
accordingly
Estimate ecological
niche (various
algorithms)
Evaluation reality of
model transfer results,
when possible
Transfer to other
situations—time and
space
Project niche
model to
geographic
space
Model calibration,
adjusting parameters to
maximize quality
Collate primary
biodiversity data
documenting
occurrences
Process environmental
layers to be maximally
relevant to distributional
ecology of species in
question
Collate GIS database of
relevant environmental
data layers
Assess BAM scenario for
species in question; avoid
M-limited situations
Saupe et al. 2012. Variation in niche and distribution model performance: The need
for a priori assessment of key causal factors. Ecological Modelling, 237–238, 11-22.
Estimate M as
area of analysis
in study
Barve et al. 2011. The crucial role of the
accessible area in ecological niche modeling and
species distribution modeling. Ecological
Modelling, 222, 1810-1819.
Assess extrapolation
(MESS and MOP)
KU Ecological Niche Modeling Group. 2013.
Constraints on interpretation of ecological niche
models by limited environmental ranges on
calibration areas. In preparation.
Model evaluation
Peterson et al. 2008. Rethinking receiver operating
characteristic analysis applications in ecological niche
modelling. Ecological Modelling, 213, 63-72.
Model thresholding
Peterson et al. 2007. Transferability and
model evaluation in ecological niche
modeling: A comparison of GARP and
Maxent. Ecography, 30, 550-560.
Assess spatial precision of
occurrence data, adjust
inclusion of data (obs and
env) accordingly
General Methodological Summary:
Peterson et al. (2011) Ecological Niches
and Geographic Distributions, Princeton
University Press, Princeton.
Refine
estimate of
current
distribution via
land use, etc.
Reduce dimensionality
Compare present and
future to assess
effects of change

35. Thanks Much!
town@ku.edu
Biodiversity Informatics journal:
https://journals.ku.edu/index.php/jbi
Biodiversity Informatics Training Curriculum:
http://www.facebook.com/groups/BiodiversityInfo
rmatics/