Transcript Lots of Ways to Measure Landscape Pattern

Lots of Ways to Measure
Landscape Pattern
• Amount of each class
– Critical probability at point of
percolation
• 50-65% of landscape
depending on pattern
• Aggregation of classes into patches
– Patch size, shape, P/A, edge,
density
• Frequency distribution of patch
aggregation metrics
– Gives landscape its texture
• Spatial distribution of patches
– Distances between patches,
exact placement on landscape,
distance
to important features.
(Hargis et
al. 1997)
Fig 9.1 here
Relating Wildlife Populations to
Landscape Variables
• We selected 4
variables that were
minimally correlated
– Number of Patches
– Contrast-weighted
Edge Density
– Juxtaposition
– Mean Shape Index
(Marzluff et al. 2003)
High
Low
Create UD
Utilization distribution
Relate UD
to various
resource
metrics
Quantify Resources
Within UD
Distribution of vegetation
at each pixel in UD
Surface depicting meters
of high contrast edge within
200m of each pixel in UD
Multiple Regression Produces
a Resource Utilization
Function
• Relative Use = 1.32
Use is a
CONTINUOUS
MEASURE
Use:Availability Can Be
Incorporated to Any
Degree Desired
- 0.14 (Mature Forest)
- 0.29 (Clear cut)
+ 0.09 (Number of Patches)
+ 0.005 (Contrast-weighted Edge)
- 0.002 (Patch Juxtaposition)
+ 0.14 (Patch Shape)
Relative Importance of Resources at
the Population Level (n = 25)
Resource Attribute
Mean
Standardize
d

95%
Confidenc
e Interval
P
( = 0)
# of jays with use
significantly
associated with
attribute
+
-
Number of Patches
+0.11*
-0.57 –
0.28
0.19
14*
9
Contrast-weighted Edge
+0.06*
-0.13 –
0.26
0.50
10*
9
Mature Forest
-0.05
-0.18 –
0.08
0.45
12
8
Clear cut
-0.04
-0.17 –
0.09
0.51
6
9
Interspersion –
Juxtaposition
-0.01*
-0.14 –
0.16
0.87
11
8*
-0.11 –
0.84
9*
12
0.14fragmented areas within their home range
* Use in direction predicted if jays select for edgy,
Patch Shape Index
+0.01*
Relative Importance of Resources at
the Population Level
(n = 25)
Pattern more
Resource Attribute
Number of Patches
Mean
Standardize
d

+0.11*
important
than
95%
P
type
of ( = 0)
Confidenc
e Interval
vegetation
-0.57 –
0.28
+0.06*
-0.13 –
0.26
Mature Forest
-0.05
-0.18 –
0.08
Clear cut
-0.04
-0.17 –
0.09
Interspersion –
Juxtaposition
-0.01*
-0.14 –
0.16
Contrast-weighted Edge
0.19
# of jays with use
significantly
associated with
attribute
+
-
14*
Greater use of
0.50
10*
areas with
many patches
0.45
12
and edge as
0.51
expected6
0.87
11
9
9
8
9
8*
-0.11 –
0.84
9*
12
0.14fragmented areas within their home range
* Use in direction predicted if jays select for edgy,
Patch Shape Index
+0.01*
Relative Importance of Resources at
the Population Level (n = 25)
Resource Attribute
Mean
Standardize
d

95%
Confidenc
e Interval
P
( = 0)
# of jays with use
significantly
associated with
attribute
+
-
Number of Patches
+0.11*
-0.57 –
0.28
0.19
14*
9
Contrast-weighted Edge
+0.06*
-0.13 –
0.26
0.50
10*
9
Mature Forest
-0.05
-0.18 –
0.08
0.45
12
8
Clear cut
-0.04
-0.17 –
0.09
0.51
6
9
0.87
11
8*
Interspersion –
-0.14 –
-0.01*
Population
Not
Consistent
in
Use
of
Resources
Juxtaposition
0.16
-0.11 –
0.84
9*
12
0.14fragmented areas within their home range
* Use in direction predicted if jays select for edgy,
Patch Shape Index
+0.01*
Another way to Relate Use to
Habitat
• Map the similarity between habitat at point
Z and habitat used
• Vector of habitat attributes measured at
used sites and all points in landscape
• Calculate the similarity between point in
landscape and average vector of used
habitat
– Mahalanobis distance
Mahalanobis Distance
d = ( x  u ) ( x  u )
1
x is a vectorof habitatcharact erist ics
associat edwith each cell
u is a mean vect ro of habitatcharact erist ics
at all used locations
Measure of dissimilarity between sample habitat
characters (x) and ideal habitat represented by u.
Example of Mahalanobis
Distance Probabilities
• Clark et al. 1993
• Black Bears in
Arkansas
• Yellow depicts areas
where habitat
characters approach
the ideal mean habitat
vector
Another Example
• Black-tailed
Jackrabbits in
Southwestern Idaho
• Managers Know
where to focus
restoration efforts and
where to reduce
chance of wildfire
• Knick and Dyer 1997