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The Linear Interval Method for Determining Habitat Selection
of Riparian Wildlife Species 1
Kerry M. Christensen
2
Since this technique (originally developed for river
otters) can be used in highly heterogeneous habitats, incorporates both categorical and continuous data, yields a physiugnomic representation of habitat structure, and facilitates
the use of multivariate statistics in data analysis, it is
inherently superior to those techniques typically employed
by wildlife ecologists in studies of habitat selection.
INTRODUCTION
Many of the recent studies of habitat selection
have used either the random point method (Andrew
and Mosher 1982, Irwin and Peek 1983, Pierce and
Peek 1984, Servheen 1983, Tilton and Willard 1982,
Witmer and deCalesta 1983) as described by Marcum
and Loftsgaarden (1980) or some type of mapping
method (Collins et al. 1978, Johnson and Montalbano
1984, Kaminski and Prince 1984, Lokemoen et al.
1984, Maxon 1978, Pietz and Tester 1983) as described
by Neu et al. (1974) to determine relative availabilities of habitat categories. Those studies
employing the mapping method frequently use a
planimeter to determine the area of well defined
habitat types delineated on a map or aerial photograph. The relative area of each habitat type yields
a measure of relative availability. Using the
random point method, a random distribution of points
overlaying a map (or aerial photo) of the study
area determines habitat sampling locations. Habitat
variables are then simultaneously categorized or
measured at each location, and the relative frequency of each category represents the relative availability. Both of these techniques typically employ
chi-square analysis to test the null hypothesis
that habitat components are used in direct proportion to their availability.
Here I describe the "linear interval" method
of determining habitat availabilities in riparian
environments. In addition to determining availabilities, this method yields a physiognomic
representation of habitat structure, corresponding
with topographic map locations, for the riparian
area under consideration. This representation
facilitates the use of multivariate statistics to
determine habitat selection of riparian wildlife
species as described below. The applicability of
Paper presented at the First North American
Riparian Ecosystems Conference. University of
Arizona, Tuscon, Arizona April 16-18, 1985.
Kerry M. Christensen, Department of Biological
Sciences, Northern Arizona University, Flagstaff,
Arizona
86011.
101
multivariate statistical methods in studying
wildlife habitat is well documented (Shugart 1981,
Williams 1983), and these techniques are currently
being used extensively (Brown and Batzli 1984,
Mannan and Meslow 1984, Munro and Rounds 1985,
Pierce and Peek 1984, Rice et al. 1983, Ryan et al.
1984, Van Horne 1982).
PROCEDURE
Using the linear interval method of riparian
habitat characterization, important habitat variables
are measured or categorized at some regular interval
along an imaginary line parallel to the water's
edge for the entire length of riparian area under
consideration.
In macroscale investigations, or
studies involving wide-ranging species over great
distances, an alternative to examining the entire
length of stream is to sample only portions of the
riparian area (see Rice et al. 1983). Interval
distance is dictated by habitat heterogeneity and by
the degree of resolution desired by the investigator.
In a study of river otters (Lutra canadensis), I used
an interval distance of 12 m. in an effort to examine
microhabitat influences on otter habitat use. The
habitat on each bank can be characterized either
simultaneously or independently, depending mainly
on the width of the stream in question, and the
habitat variables being examined.
In my study,
examining each bank independently, I chose to measure
water depth, bank slope, and percent canopy cover,
and I categorized the river type (four categories),
bank type (five categories), and bank vegetation
type (four categories) at each location. By plotting
these interval locations on a topographic map
(scale 1:24,000 or larger) a complete picture of
habitat structure was obtained (fig. 1).
To determine habitat preferences of riparian
wildlife species, observed locations of individuals
are first plotted on a topographic map (fig.2), and
the habitat is characterized as the interval locations were.
Then using discriminant analysis (Klecka
1975), these location characteristics are compared to
the interval location characteristics (availability)
to determine habitat differences between used locations and the locations available.
Similarly,
entire areas of apparent heavy usage (concentrations
Bull Pen Ranch
WEST CLEAR CREEK
•
USED
D
o -
UNUSED
LOCATION OF OBSERVED
INDIVIDUALS
gcm
Figure 1. Sample physiognomic representation of
riparian habitat using the linear interval
method.
of species observations; fig.3) can be compared to
unused areas, again using discriminant analysis, to
determine habitat differences between used and unused areas. The habitat selection of one or several
species can be dete~mined using this methodology.
The effect of season can also be examined.
Although no hard and fast rules exist for
determining the sample size (number of intervals,
and number of species observations) necessary for
the statistical analysis, sample characteristics
are very important to the validity and interpretation of the results (see Morrison 1984, and
Williams 1983). Ideally, the number of interval
locations used in the analysis should approximate
the number of observations of the species in question,
and both should be as large as possible (greater than
50 at least). Therefore when dealing with species
that are sparsely distributed, and/or wide-ranging
(i.e. where few observations are possible), a
Figure 3. Hypothetical designation of used and
unused areas for comparison using discriminant
analysis.
number of interval locations equal to the number of
observations can be randomly chosen from the entire
set of interval locations, or chosen randomly from
unused areas when comparing used to unused stretches.
In general, Capen (1981), Green (1974,1979~, .
Morrison (1984), Rice et al. (1983), and Wlillams
(1983) can be consulted for discussions of the
assumptions and interpretation of disriminant
analysis prior to collection of data.
WEST CLEAR CREEK
Bull Pen Ranch
Camp Verde
<-
13 Km
2 1
2
Figure 2. Locations of observed individuals plotted on a
topographic map.
102
Results obtained from the discriminant analysis
can be represented in a number of ways.
I chose to
create histograms of the correlation (tabulated in
the analysis) between the canonical variates and the
original variables (fig.4). The use of these
correlations is supported by Williams (1981) and
Morrison (1984).
I ordered these from the largest
positive correlation to the largest negative one,
yielding and easily interpretable figure.
Additionally, I labeled the habitat categories as to their
inclusion in the step-wise procedure.
GROUP
An example of the form of data entry used for
the analysis is given in Table 1.
Categorical
variables are given a one(l) if present at the
location, or a zero(O) if absent. Under the
category "group", a one(l) indicates a used location,
and a two(2) indicates an unused location in this
example.
with delineated patches of different plant community
types. A main drawback of this method is that habitat variables such as water depth, temperature,
bank slope etc. cannot be examined and these may
be of significant importance in determining whether
an area is used by an animal.
The mapping method of quantifying habitat
availability for determining the habitat selection
of wildlife species is most useful in areas of
gentle topography where habitat components form
discrete entities. This usually applies to areas
80
VERDE R.
40
20
o
1&1
en
::»
a::
-40
-60
......o
-80
"~
80
Z
40
o
t:
...
o
-20
1&1
-40
o
(.)
CLEAR Cr.
20
c
a::
a::
w.
60
-60
(.)
a::
1&1
a.
I
RJ£f. .!.l g
1
0
a
ROCK
I
SAND
0
VEGETATlON TYPE
GCI
~
0
NOGCI NOGCI GCI
NOCAN Qlli NO CAN
1
0
MAX.
WATER
DEPTH
~
3.1
SANK
SLOPE
(degrees)
CROWN
~
37
I
0
1.0
61
7
0
0
2.7
14
47
0
0
4.2
72
-80
These within site comparisons lend themselves
readily to analysis using multivariate statistical
methods. As previously mentioned, a drawback of the
statistical procedure advocated by Marcum and
Loftsgaarden (1980; Chi-square test of homogeneity,
Mendenhall 1971) is that the habitat components
examined must be categorical variables. Discriminant analysis accomodates both categorical and
continuous data (table 1.).
...Z
III
I
POOL
ROCKI
~ ~
0
The linear interval method of habitat characterization has the same advantages as the random
point method, but has inherent qualities which make
it a superior technique especially in riparian
environments.
The random point method yields
availabilities only,
In addition to overall availabilities, the linear interval method gives a representation of habitat structure corresponding with
topographic map locations for the entire area of
study (or for samples of the area as mentioned
previously).
This facilitates the comparison of
habitat composition at different locations within
the study site. Thus it is possible to compare the
habitat characteristics of used and unused locations,
or denning and foraging areas for example. This is
not possible using the random point or mapping
methods.
-20
1&1
BANK TYPE
RIVER TYPE
The random point method has greater applicability
than the mapping method especially in areas of rugged
terrain with a relatively heterogeneous interspersion
of habitat components.
Since classification of
habitat components can occur on the ground, it is
possible to examine habitat parameters like those
listed above, although only categorical variables
can be considered when using the statistical methods
promoted by Marcum and Loftsgaarden (1980; Chi-square
test of homogeneity, Mendenhall 1971). Using this
method, several habitat parameters can be handled
simultaneously, whereas each parameter must be
treated seperately using the mapping method
(Marcum and Loftsgaarden 1980).
DISCUSSION
60
Table 1. Sample data entry format used with the
linear interval method and discriminant analysis.
80
E. VERDE R.
60
40
6
20
o
-20
RIFF
RV
2
VEG GCC GCN
SV
12
11
NN
ROCK
NC POOL SAND
4
-40
The linear interval method of examining habitat
structure can be applied to studies of any riparian
wildlife species. Although this method is most
applicable to riparian environments, it can also be
used in other linear habitats such as forest edges,
or coastal and lentic shorelines. Mo~ifications of
this procedure(such as characterization of grid points
in a square design) may increase the applicability of
this technique.
-60
-80
Figure 4. An easily interpretable representation
of the results from the discriminant analysis
using data from my otter study.
103
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104