NR 422- Habitat Suitability Models
37
NR 422- Habitat Suitability Models Jim Graham Spring 2009
description
NR 422- Habitat Suitability Models. Jim Graham Spring 2009. Habitat Suitability. Predict the potential distribution of a species based on finding suitable habitat Also known as: Niche modeling Predicting distributions. Terminology. Realized Niche – current distribution - PowerPoint PPT Presentation
Transcript of NR 422- Habitat Suitability Models
NR 422- Habitat Suitability Models
Jim GrahamSpring 2009
Habitat Suitability• Predict the potential distribution of a
species based on finding suitable habitat• Also known as:
– Niche modeling– Predicting distributions
Terminology• Realized Niche – current distribution
– Established species– Late succession (minimal disturbance)
• Potential Niche – future distribution?– Invasive species– Theatened and endangered species
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
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!
Process
Occurrence Data
Parameters andEquationsResults
Statistical Model
Distribution Map
Environmental Layers
Processing
Model Validation
ExperimentsAnd
Observations
Correlations• Correlations between environmental variables
and species requirements
Responce to Height at Elevation
y = -0.0035x + 23.133R2 = 0.9215
0
2
4
6
8
10
12
0 2000 4000 6000 8000
Elevation (meters)
Heig
ht (m
eter
s)
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)
Prop
otio
n of
Occ
uran
ces
GODM TamarixContinental USDiGIR Tamarix
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)
Prop
ortio
n of
Occ
uran
ces
GODM TamarixContinental USDiGIR Tamarix
Box Model
Temperature (degrees C)
Prec
ipita
tion
(cm
/yea
r) 50
30
5.6
Tamarix Potential Habitat
LegendTamarix OccurrenceTamarix EcoregionsUS States
LegendTamarix OccurrenceTamarix EcoregionsUS States
Vegetation Layers• Minimum temperatures at certain times of
the year• Amount of sun• Precipitation• Soil type• Elevation• Slope• Aspect
www.geography.hunter.cuny.edu
Herbivore Layers• Vegetation layers• Proximity to cover• Distance to water
www.ministryofpropaganda.co.uk media-2.web.britannica.com
Carnivore Layers• Herbivore layers• Proximity to cover• Distance to water
www.juneauempire.com
Proxy Layers• Remotely sensed:
– MODIS– LandSat– Aerial
• Human disturbance• DEMs: Elevation, slope, aspect
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
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
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
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
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
Gradients
1.0
0.0
Mask 1.0Gradient
Envelopes1.0
0.0
Mask Mask1.0Gradient Gradient
Statistical Approaches• Linear Regression (continuous variables)• Logistic Regression (presence data)• Genetic Algorithm for Rule-set Production
: GARP• Classification and Regression Trees:
CART• MaxEnt (presence)
Integrating Climate Change
Japanese Honeysuckle
Where to go from here• Spatial modeling
– Robin’s class• OpenModeler
