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NR 422- Habitat Suitability Models
Jim Graham
Spring 2009
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Habitat Suitability• Predict the potential distribution of a
species based on finding suitable habitat• Also known as:
– Niche modeling– Predicting distributions
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Terminology• Realized Niche – current distribution
– Established species– Late succession (minimal disturbance)
• Potential Niche – future distribution?– Invasive species– Theatened and endangered species
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Approaches• Mechanistic/Experimental
– Based on understanding of a species requirements and experiments
– Can miss the complexity of environmental conditions and genetic plasticity
• Statistical– Based on the existing distribution of a
species– Can miss the “realized niche”
• Observational / Anecdotal– Hard to validate
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Basic Idea• Basic idea is to find a correlation
between a species and a variable we can measure– Temperature– Precipitation– Surface type: Water, Rock, Soil Type– Distance to human activity– Other species!
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Process
Occurrence Data
Parameters and
EquationsResults
Statistical Model
Distribution Map
Environmental Layers
Processing
Model Validation
Experiments
And Observations
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Correlations• Correlations between environmental variables
and species requirements
Responce to Height at Elevation
y = -0.0035x + 23.133
R2 = 0.92150
2
4
6
8
10
12
0 2000 4000 6000 8000
Elevation (meters)
Hei
gh
t (m
eter
s)
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Tamarix and Precipitation
Proportion of Occurances in Precipitation Categories
0
0.2
0.4
0.6
0.8
1
1.2
7 28 49 71 92 113 134 156 177 198 219 240 262 283
Percipitation (cm per year)
Pro
po
tio
n o
f O
cc
ura
nc
es
GODM Tamarix
Continental US
DiGIR Tamarix
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Tamarix and Temperature
Proportion of Occurances in Temperature Categories
0
0.2
0.4
0.6
0.8
1
1.2
0.2 2.2 4.1 6.0 7.9 9.8 11.8 13.7 15.6 17.5 19.4 21.4 23.3
Temperature (degrees C)
Pro
po
rtio
n o
f O
cc
ura
nc
es
GODM Tamarix
Continental US
DiGIR Tamarix
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Box Model
Temperature (degrees C)
Pre
cip
itat
ion
(cm
/yea
r) 50
30
5.6
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Tamarix Potential Habitat
Legend
Tamarix Occurrence
Tamarix Ecoregions
US States
Legend
Tamarix Occurrence
Tamarix Ecoregions
US States
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Vegetation Layers• Minimum temperatures at certain times of
the year• Amount of sun• Precipitation• Soil type• Elevation• Slope• Aspect
www.geography.hunter.cuny.edu
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Herbivore Layers• Vegetation layers• Proximity to cover• Distance to water
www.ministryofpropaganda.co.uk media-2.web.britannica.com
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Carnivore Layers• Herbivore layers• Proximity to cover• Distance to water
www.juneauempire.com
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Proxy Layers• Remotely sensed:
– MODIS– LandSat– Aerial
• Human disturbance• DEMs: Elevation, slope, aspect
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White Tailed Deer• Habitat Suitability Index (HSI) =
Forage * Cover• Log(Deer Density) = a + b (HSI)
Roseberry, J. L., Woolf, A. 1998. Habitat-Population Density Relationships for White-Tailed Deer in Illinois, Wildlife Society Bulletin, Vol. 26, No. 2 (Summer, 1998), pp. 252-258
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Black Bears in Rocky
Baldwin, R.A., L. C. Bender. 2007. Den-Site Characteristics of Black Bears in Rocky Mountain National Park, Colorado, JOURNAL OF WILDLIFE MANAGEMENT 72(8):1717–1724
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Habitat Suitability Index• HIS =
– 0 for least suitable– 1 for most suitable
• HIS = V1 * V2 * V3– Where each VX is a raster scaled from 0 to
1– 0 = unsuitable factor– 1 = suitable factor– In between values for intermediate suitability
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Categories• Assign each category a value from 0 to 1
based on how suitable it is.
Forest Shrub Grassland Alpine0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
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Ranges• Create mask rasters for area below and
above (0 for unsuitable, 1 for suitable)1.0
0.0
Mask (0.0) Mask (0.0)1.0
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Gradients
1.0
0.0
Mask 1.0Gradient
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Envelopes1.0
0.0
Mask Mask1.0Gradient Gradient
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Statistical Approaches• Linear Regression (continuous variables)• Logistic Regression (presence data)• Genetic Algorithm for Rule-set Production
: GARP• Classification and Regression Trees:
CART• MaxEnt (presence)
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Integrating Climate Change
Japanese Honeysuckle
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Where to go from here• Spatial modeling
– Robin’s class• OpenModeler