Spatial patterns of aquatic habitat in Oregon
Kim K. Jones, Rebecca L. Flitcroft, and Barry A. Thom
Two held projects were designed to describe the spatial variation in aquatic habitat in Oregon and
assess the influence of historic and current habitat character on species composition, life tristories,
survival, and production of salmonids. The basin-wide census surveys provided information on the
quality of local aquatic habitat throughout a stream or watershed. Sample surveys selected sites
randomly across the landscape to monitor status and spatial distribution of aquatic habitat, and to
assess temporal change. Field surveys for both survey designs coliected information on channel
morphology, riparian condition, and instream physical habitat using a hierarchically organized
survey method incorporating habitat units and larger stream reaches. Each survey design had
strengths and weaknesses in landscape-level analysis at micro and macro scales. The survey data
were integrated onto a geographic information system through the process of dynamic segmentation
This allowed for the use of individual stream routes that were made spatially explicit through
calibration. Two scales of analysis were maintained in the GIS by the creation of two separate route
events that contain reach or habitat unit level information. Hierarchical organization of the data and
geographic information systems (GIS) integration permitted flexibility in data manipulation,
stratification by ecological or geographical criteria, and multiple scales ofanalysrs.
O 2001 Fishery GIS Research Group
Key words: GIS, landscape, salmonids, suweys.
Kim K. Jones I/, and Rebecca L. Ftitcroft
Oregon Department of Fish and Wildlife,
28655 Highway 34, Corvallis, OR,97333, U.S.A.
P
hone
:
I ( U SA) -5 4 I -7 5 7 -426 3 ( ext. 260
)
;
Fax
:
I ( U SA) - 5 4 1 -7 57 -4 I 02 ;
1/
Barry A. Thom
E-mail: ionesk@fsL.orst.edtt
National Marine Fisheries Service, 1315 East-West Hwy,
Silver Spring, MD,20910, U.S.A.
Phone : I ( USA)-30 I -7 I 3 - 140 I ; Fax: I ( USA)-3 0 I -7 I 3 -0376 ;
E - mail
:
Bar
lt. Tho rn@
no aa.
R
ov
1. Introduction
Methods to characterize aquatic habitat and its relationship to survival, production, and life history
of
fishes have traditionally been limited
to descriptive, statistical, or graphical
analyses. One
challenge has been to provide an understandable template to view and analyze the spatial complexity
of features in a stream network interactively with the life history diversity of mrgratory salmonids or
other fishes. Recent developments in the applications of geographic information systems (GIS)
technology have improved the display and analysis of fisheries information. GIS has been an
underused tool in fishenes management (Isaak and Hubert, 1997), but may be very useful in
in aquatic habitat at multiple scales in relation to the ecology of Pacific
salmon. Two aquatic survey designs coupled with GlS-based analysis provided a powerful
combination to describe spatial patterns in aquatic habitat. We will describe the two survey
descnbing the variation
approaches, the method
of GIS integration, and the importance of GIS to the
presentation of the results.
266
analysis and
The ability to comprehensively survey aquatic habitat in streams improved in 1984 with
the publication of the Hankin and Reeves survey methodology (Hankin, 1984; Hankin and Reeves,
1988). While the primary objective of the methodology was to estimate the number of fish in a
stream, it has been adapted as a survey design to efficiently collect.information on aquatic habitat
throughout a stream or watershed. The methodology permitted surveyors to collect information
continuously from the stream mouth to headwaters. This census survey design is referred to as a
basin survey (Dotloff et al., 1997), and was a departure from the more traditional representative
(Dolloff et al., 1991). The major advantage to census surveys was the continuous
record of geomorphic reaches, habitat units, and associated features. It provided process information
reach survey
in addition to status, such as hydrologic processes, distribution of large wood debns or sediment, and
the life history of anadromous, fluvial, or resident fishes in a stream or
watershed. However, a representative reach survey based on a probability sample design can provide
a statistically robust representation of a large geographic area (Firman and Jacobs, this issue). The
hmrtations of each survey design led Oregon Department of Fish and Wildlife to initiate both types
of aquatic surveys: basin (complete census) surveys and monitoring (sample) surueys. Each survey
strategy was designed to meet a unique set of objectives. The objectives and design of each survey
determined the method of GIS integration and interpretation and utility of results.
features that influence
GIS technology has been used to describe patterns in aquatic habitat at the reach, stream,
and regional scale. Bottom et al. (1997) used a GIS to integrate biological, physical, climatic and
cultural datasets across the North Pacific Basin ecosystem to evaluate how environmental factors
influenced the life history, geographic patterns, and status of Pacific salmon species. Techniques to
into a GIS were developed and described by Martischang (1993), McKinney
(1997), Radko (1991), and Hupperts (1998) using Arclnfo (ESRI, l99Z). Martischange (1993) used
an "address matching" or "geocoding" system to attach habitat unit data to a watercourse. McKinney
(1997) displayed reach data from 17 l2I km of stream on a GIS and summarized habitat information
across three ecological scales: interior Columbia River Basin, province, and river basin. Lang (1998)
used reach-level habitat information to demonstrate the use of GIS to descnbe coho salmon
)nchorynchus kisutch habitat in the Umpqua basin. Radko (1997) used dynamic segmentation to
spatially integrate habitat-unit-level data for a few streams in Idaho. These technological advances rn
GIS methodology have been critical steps in the broad-scale application of GIS to aquatic habitat.
integrate survey data
Fishenes biologists' purpose for integrating survey and landscape data into a GIS format
to display spatial patterns in aquatic habitat to further their understanding of the distribution,
survival and production, and life history diversity of fish species. Whittier and Hughes (1988)
suggested that coupling the ecoregion classification (Omemick, 1987) with a hierarchical stream
was
classification system (Frissell et al., 1986) would provide a useful spatial classification system.
Incorporating a life history theme adds a temporal dimension to the analysis. Salmon life
history is intertwined with habitat at the scale of channel unit to river network (Lichatowich et al.,
A life history approach has the advantage of incorporating spatial structure and connectivity
of the habitat with the survival of fish at each life stage (Mobrand et aI., 1997; Nickelson and
Lawson, 1998). Each approach had limitations in terms of scale, spatial explicitness, or modeling
1995).
connectivity within a drainage.
267
A primary impediment to the universal use of survey results was the limitation to statistical
or multivariate analyses, and to tabular and graphical displays of the results. Integration of the
survey data in GIS provided a technique not only to display the survey data in an easily
understandable format, but also to allow additional analysis in relation to other spatial data. The
ability to use a GIS to create spatially explicit coverages of the results of a stream survey provides a
powerful tool to analyze data at multiple scales, and to present complex information to resource
managers in an understandable format.
2. Methods
2.1 Basin (census) surveys
The objectives of the basin surveys were to describe important stream and watershed components
and processes at different spatial scales, develop habitat protection and restoration strategies, and
estimate salmonid survival and production based on habitat characteristics. Stream selection was
based on status of fish population(s), proposed management activities within the basin, or priority for
restoration. Approximately 10 000 km of streams have been surveyed since 1990 (Map
l).
b
.$r
.s..
,
\r"
\
f-F*;
hq
"B
*
-
1:100k Hydrography
Stlems Surveyed &
Available on GIS
m0 KlbreleF
100
m
2OOMitos
Map 1. ODFW Aquatic Inventories Project census stream surveys available on GIS. Map showing the distribution of
--
stream habitat surveys that have been completed and attached
to
I : I 00 000
hydrography for the state of Oregon.
Field surveys emphasized channel and valley morphology (stream and reach data), riparian
characteristics and condition (reach data), and instream habitat (habitat unit data) (Moore et al.,
1997; Jones and Moore, 1999). The smallest unit of measure was channel habitat units, such as pools,
riffles, and glides, measured on the scale of meters. Channel units were recorded within reaches.
268
n to statistical
gration
of
in
an
tial
dah,
the
easily
The
Reaches were up to 10 km in length, and were composed of a group of continuous habitat units that
were analyzed together due to similar geomorphology, hydrology, land use, or riparian
characteristics. Information on type, size, and character of riparian vegetation within 30 m of the
channel was collected every ll2 to I km alone the stream.
'ey provides a
n to
resource
components
ategies,
and
lection
was
priority for
2.2 Monitoring (sample) surveys
Monitoring sample surveys were designed to assess the status and trends in habitat across five
coastal gene conservation areas (GCA). The survey also described associations ofgeographic trends
in habitat quality with geographic range and life-history diversity of salmonids. A GIS was used to
randomly select sites in a spatially balanced manner in each geographic unit from all lst though 3rd
order streams on a 1:100 000 USGS hydrologic stream coverage (Firman and Jacobs, this issue). The
sample selection process prevented clumping of sites, while meeting probability sampling
assumptions. Forty sites were sampled in each geographic area (Map 2). Each site represented 64176 km of stream depending on geographic unit, providing a sample weighting for statistical
analysis. The number and distribution of sample sites located across the landscape was intended to
provide enough statistical power for the detection of trends and landscape-scale habitat
charucteization (Firman and Jacobs, this issue). The design of the sample selection and the number
of sites allows for post-stratification (Cochran, 1977), provided a minimum of 20 sites are included
in each new stratum and the weights of the sample are known.
Coho Gene Conservation Groups
(GCG) Monitoring Sites:
o North Coast
r Mid Coast
r Mid-South Coast
r Umpqua
a South Coast
a. li.
'tl
ution
'.'.'
I
of
n.
parian
'.'l:t i.; '-:
**'t
att
t
ltttt
t
pools,
Map 2. Coho salmon Oncorhynchus kisutch gene conservation group habitat monitoring sites. About 40 sites were
surveyed in each gene conservation group as part of a habitat-monitoring survey. The survey sites were randomly
distributed across the landscape and are intended to provide both temporal and spatial bases for present and future
rches.
analysis.
et al.,
269
Even though the sample or stream selection criteria for monitoring surveys differed, the
field method remained the same. Survey crews collect information on channel morphoiogy, npanan
characteristics, and instream habitat. We surveyed 500-1000 m at each sample site, depending on
stream size, which allowed data to be collected at 2o-/;0 habitat units at each site. A site length
of
500-1000 m was sufficient to sample features that tended to be patchy in nature, such as wood
debns jams and deep pools.
2.3 GIS integrarion
The data from the basin and monitoring surveys were maintained separately, although the method
of
analysis and database storage was similar for each set. The detailed information associated with each
habitat unit was maintained in a habitat unit database for each stream. Each site or stream had 20 to
1000 habitat unit records. Secondly, the unit information was summarized within a reach ro creare
a
reach database. Each monitoring site had one or two reaches, while a stream had from one to ten
reaches. To transform the dataset into a spatially explicit database, we calculated a distance from
the
beginning of the survey to the end based on the length of each habitat unit, creating a ',TO" and
"FROM" field in the database. The "TO" and "FROM" field provided an exact locarion along
the
for each habitat unit. A LLID field was also appended ro the database. The LLID is the
latitude and longitude of the starting arc of the stream and has been calculated at the l00k level to
stream
ensure uniqueness and compatibility in stream identification among all user groups (Hupperts,
l99g).
The habitat unit databases were then appended to create a single large database of all streams within
a glven geographic area.
A simrlar procedure was performed on the reach data and combined into
a
single database.
Data were related spatially to a digitized stream layer in a GIS through the process of
dynamic segmentation. Dynamic segmentation is a process available using Arc/INFO software
(ESRI' 1992) that allows for the attachment of data to a route layer without changing the line work.
A route layer overlays the arc layer (digitized line cover with spatial relationships) and spatially
integrates the distance and direction of the underlying arcs. Routes also allow for the malntenance
and spatial manipulation of multiple datasets without altering the underlyin g arc Iayer.In our process
of dynamrc segmentation, the route layer is altered to represent the extent and direction of the stream
survey.
The procedures to itttegrate the survey data onto the GIS are outlined in Figure 1. The
databases were integrated onto fourlh-scale hydrologic unit coverages (HUC) of streams at
the 1:100
000 scale.
A
standardized and routed set of coverages (Hupperts, 1998) was obtained through the
State of Oregon GIS center to use as a base. The routes were edited to match the location
and
direction of the field survey. A calibration point coverage was created to match known features
such
as bridges and tnbutary junctions observed dunng the survey
to locations on the HUC. Dynamrc
segmentation of the calibrated route placed each reach or habitat unit onto a spatially correct location.
Habitat unit and reach-level information were linked to the routes based on the LLID, and
FROMDIST and TODIST attributes. The dynamic segmentation was checked and corrections made
if
An arc coverage was created from the corrected routes. Two scales of analysrs were
maintained in the GIS by the creation of two separate route events that contained reach or
necessary.
habitat-unit-level information.
210
e
n
Aquatic [nventories Project
Dynamic segmentation flow chart
n
)f
HUC editing:
,d
.
.
.
o
te reach.dbf
habunit.dbf
move endpoint of route
remeasure route
rerun route if necessary
add arcs ifnecessary
Edit 4fr field huc
rf
rh
Generate calibration
to
Make calibration coverase:
point coverage
,a
<calcover>
.
.
3n
he
nd
coPY parameters
add attributes (measure and
stream_id)
Calibrate huc
.
.
he
he
to
Dynamically segment huc
to .dbf files to make
)8).
separate unit and reach events
from huc
add points ("labels" in ArcTools)
check the label id # and values
t1n
la
Check dynamic segmentation:
Check dynarnic segmentation
Use
r
of
Make appropriate corections
and redo events:
are
everything is in the correct location
o
rlly
nce
view newly created events with
calibration coverages to make sure
create arc coverages
rrk.
ArcView:
view newly created events using
the offset option in
Build and projectcopy covers
AV
3.1 to
check arc segment direction
)ESS
)am
Make export files (.e00 )
Key
The
100
the
C
n
Denotesprocess.
!-_l
Represents database work
Represents coverage creatlon.
and
;uch
rmlc
rtlon.
and
nade
were
nor
Figure 1. Dynamic segmentation schematic using reach and habitat level datasets. The attachment of habitat unit and
reach-level datasets to 1:100 000 hydrography was completed using a combination of database management and
coverage manipulation in Arclnfo. The series of steps ensured an accurate spatial representation of habitat
distribution.
271
The monitoring survey datasets were linked to GIS
through the LLID, and the polnt
coverage from the original site selection. The
information was dynamically segmented with
fourth-scale hydrologic unit coverages (1:100 000)
in the same manner as the census survey
information' Reach-level route events were maintained
as with the basin surveys. However, due to
the finite nature of the survey lengths of monitoring
suryeys there was no need to calibrate the
underlying route structure.
3. Results
The two survey designs chatacteized aquatic habitat
at a site, reach, stream, and landscape level.
The different designs of the census and monitoring
suryeys determined how GIS was used to
lnterpret the results' Basin' or census, surveys
collected data contrnuously on the entrre sream
to
inventory specific features or stream processes. The
results were limited to the surveyed streams
because of the non-random selection of streams.
GIS was used primarily as a tool for data
resummary' display, and analysis' The monitoring,
or sample, surveys rncorporated GIS from the
onset' selecting sites based on a spatially explicit
random sampling protocol on a GIS (Firman and
Jacobs' this issue)' Because of the random selection
of sites, the surveys permitted charactenzation
of aquatic habitat within the larger geographic extent,
such as a gene conseryation unit. Additionally,
each site represented a length of stream within
its geomorphic context and the GIS could be
used to
post-stratify the samples for additional analysis.
The hierarchically organized, survey technique of
habitat units and reaches that was
utilized in both census and monitoring surveys allowed
for micro and macro scales of analysis. For
the purpose of this paper, micro and macro will
refer to habitat-unit-level informatron and
reach-level information respectively, rather than
to the geographic extent of the analysis. At a
micro
scale' habitat units were analyzed, to reflect distnbution
within a watershed, or at broader geographic
scales' The macro scale of data analysis included
comparisons between different reaches within
a
watershed or at a broader scale. The monitoring
surveys were designed to detect changes at the
meso-scale-comparison of sites across a large geographic
area, such as a GCA, suite of watersheds,
or ecoregions.
3.1 Census survey results
Census survey results were sulnmaized
at the scale of the reach, or as a summary of multiple
reaches (Table 1)' and stored in a reach database.
summaries of conditions within a reach included
descriptors of channel type, pool character
and amount, large wood debris, substrate, bank
condition,
and riparian characteristics' Many of the habitat
features were expressed as a percentage, number
per
unit distance' or relative to the width of the channel
for comparative purposes. Habitat variables in
a
stream' watershed, or basin were displayed
in tabular form or as fiequency distributions.
comparisons were made with adjacent reaches
in a stream, or between streams and watersheds.
standards were set based on features important
to salmon survival at each life history stage, or
with
historical conditions (Jones and Moore 1999).
272
)int
/irh
Table 1' Portion of middte Rogue and Applegate reach summary table. Summary statistics are
calculated at the reach
scale for all census surveys. Reaches represent portions of a stream that may be assessed
group
vey
geomorphology, land use, vegetation or ownership.
as a
due to
)to
Stream
Reach
the
Length
(m)
Land
2ud
Channei
Slope
use
Open
u
^1..,
5NJ
qa
Hog Creek
2
3
Bloody Run
I
2
I
I
Cent Gulch
to
to
NS
Stratton Cr.
2
Ltl Stratton
I
I
Slate Creek
z
rta
3
he
rd
4
Waters Cr
1
)n
v,
2
uland
use codes:
1416
1648
3 573
7'74
7"t2
2 753
3 962
3 105
2 r25
1 865
5 388
3 749
3 010
2 383
863
LT
ST
(180)
Bank
erosl0n
Fines
Gravel
riffles
riffles
(vo)
vo)
7o
4.7
17.0
27.0
6.0
0.9
5.0
4.6
23.0
24.0
1.5
4.3
0.4
3.8
2.0
0.0
5.3 ST
15.9 YT
18.',1 ST
8.5 LT
11.5 LT
4.8 ST
5.2 ST
0.5 RR
0.8 RR
I.O RR
1.8 RR
1.1 RR
13.0
3.9
r7.0
30.0
2.8
1.5
2.0
1.0
I 1.0
29.0
0.0
0.0
2.3
3.3
1.8
1.6
5.8
1.5
3.8
2.8
RR
7.0
30.0
39.0
22.0
21.0
16.0
0.0
9.0
25.0
0.0
19.0
10.0
0.7
15.0
58.0
9.0
0.2
24.0
55.0
r 1.0
1.5
23.0
47.0
9.0
8.2
I 1.0
21.0
8.7
31.0
35.0
16.0
4.8
15.0
28.0
11.0
3.9
9.0
12.0
0.0
12.0
2r.0
2r.0
34.0
RR--rural residential, sr--second growth timber, LT--large timber YT--young timber
to
We used two methods of reach-level analysis. The first was using simple queries of
1S
)r
,d
'o
.c
a
i,
predefined reaches' The reaches were identified at the time of the original
stream survey analysis and
were based on features including geomorphology, hydrology, land use, and
substrate composrtion.
By querying the reach coverage for the Tillamook River watershed on the north
Oregon coast (Map
3), we distinguished stream sections that met criteria based on coho salmon life
history requlrements.
The query of the reach coverage was performed based on three criteria: pool
habitat > 30Vo habitat
area, gravel
in spawning habitat comprised
greater than 1 m
>30Vo
of the substrate area, and the number of pools
in depth > 2 per km (Map 4.2). The query results showed limited reaches within this
watershed that could be characterized as containing good habitat
reaches available, 5 were chosen that met the querv criteria.
for all coho life stases. of the 53
The second method required using the unit-level data to redefine reaches to target
research
or management questions. This was accomplished by identifying areas of interest
based on quenes
of the unit-level GIS coverage. we redefined the reaches in the Tillamook River watershed
into
sections that met ecological criteria for coho salmon spawning and rearing
habitat.
r
1
we performed two sets of queries on the habitat unit dataset to determine the distribution
of rearing and spawning habitat in the Tillamook River watershed. The first set identified
the
location of potential rearing habitat including all pools, deep pools (> 1 m in
depth), and slow water
pool habitats such as alcoves, backwaters and beaver dam pools. A second
set of queries identified
important spawning habitat including nffle habitats with at least 30vo gravel
substrate and less than
l5vo fine sediments (Map 4.1). A visual assessment of the distribution showed that
spawning and
reanng habitat for coho was located throughout the basin, but not all
streams possessed both
spawning and rearing habitat.
zt-7
:i:r',rll
:iil
l
Map 3. Tillamook River watershed. The Tillamook River watershed is located on the north Oreson coast and covers
about 200 km2.
r
*tep,l2:
3{P/o
map sho*'ing reaches mnraining: pmls >
habitat ae4 rillle gravel > 3CP/o substrate and
pools I
Mlp,l.t:
m&prh>?perkn-
criteria
Mep,Ll
Flabirat UniLs
}lap4J
xith Seleaion
)Vwly Cre*ed
Statisti6
Criteria
uiis > 1"0 m in deprh
r
r
Riffte mits
grel
o
Pool Mits (?o habitat units)
Poolmits
-*--
RiI*e rmits w/>
-
Rifre rmfu r',rt 3{F/" gravel
and< l5?ifim
-
P@l
Reach Srnmat_v
30e/o
Slo*'waterhabitaLs
r
Fims in rifffe unils (% sub6tate)
{s/s
habilar mirs)
Deep pool rmirs {>l-0 m in deprh)
AE
,
fr
-.:-
}{rp-Lmd Orncrchip
r Stal€ Et Small Privac
- Federal a TimberCmrpsiex
llep 4J: nrry s}cwing reaclns identlfed b]'
$,ery ofmit lwel datBset
Map 4. Tillamook River watershed habitat distribution at reach and unit scales. The Tillamook River watershed was
queried at the census survey reach (4.2) and habitat (4.1) level in order to distinguish spawning and rearing habitat for
coho salmon, Oncorhynchus kisutch. The Bewley Creek dataset was resummarized based on the spatial distribution
ofhabitat unit queries and 7 new reaches were generated (4.3).
274
T
g(
dt
we reclassified the reaches on Bewley creek based on the distribution
of coho habitat.
Three reaches were identified in the original survey
based on geomorphic criteria. Following the GIS
analysis, seven reaches were visually identified
using both land ownership and the spatial
distnbution of spawning and rearing habitat as guides (Map
4.3). Reaches I and,2showed potential
differences in pool composition and were identified
as rearing reaches. Reaches 3 and 5 stood
out as
potentially important spawning areas due to their
concentration of low-silt riffles. Reaches 4 and 6
appeared to have many deep pools. Reach 7 was
differentiated because of an apparent decrease in
deep pool habitat. we resummaizedtheunit
level information into these seven new.reaches.
The newly generated summary statistics for Bewley Creek
corroborate the
assessment of habitat differentiation (Table 2).
Reaches
visual
L and,2 were separated by a land-use change
from light grazing to young timber. Both reaches were
pool dominated, but reach I contained more
pool area as well as higher numbers of deep pools per
km than reach 2. Reaches 3 and 5 have high
contents of riffle gravel with lower amounts of fine
sediments than all other reaches. The number of
deep pools is lowest in reaches 3 and 5. Reach
4 is dominated by pool habitats and has a high
number of deep pools per km' Reaches 6 and 7
are also dominated by pools and both have high
numbers of slow-water habitats for rearing. The gradient
in reach 7 is higher than in the other
reaches that is characteristic of the headwaters
of a stream. The original survey subdivided Bewley
creek into 3 geomorphic reaches. Restratification by
biological criteria identified 7 reaches as
high-quality adult spawning or juvenile rearing areas
and displayed their location in relation to land
use
patterns.
Table 2' Bewley Creek reach summary table. Reach
surnmary statistics were recalculated for Bewley
creek after a
GIS assessment of the distribution of spawning
and rearing habitat for coho salmon oncorhynchus
kisutch.
seven
reaches were identifred as distinctly important
areas for coho.
2nd
Stream Reach
Bewley I
2
u
"
Length
(m)
Channel
Gradient
Length
Bank
Erosion
Deep
Slow
Riffles
Riffle
Riffle
pools/
water
(vo
frnes
gravel
(Vo area) l<m"
(Vo area)b
area)
(vo)
(vo)
Pools
Vo)
|
635
3 442
5
0.3
7t.o
35.6
18.3
0.0
12.3
40
s9.o
28.0
0.3
36.0
24.6
5.5
0.1
27.r
40.0
36.0
40.0
J
665
0
0.3
22.6
21.1
0.0
961
l4
0.0
r7.2
10.0
^
0.3
3.2
10.3
2.2
5
l 090
66.r
t2.5
43.0
62
54.0
0.9
0.0
45.8
"t9
2.8
6
|
1.7
J).J
20.0
0.9
50.0
0.0
75.8
6.0
28.5
7
r45
22.9
1 872
36.0
2.2
40.0
1.9
55.4
4.0
15.0
38.7
40.0
56.0
262
Deep pools defined as pools with depth greater
than 1.0 meter.
Slow water habitats incrude dammed and backwater
areas, alcoves and beaver poors.
3.2
Monitoring survey results
The results of monitoring surveys described the
status and distribution of habitat within large
geographic areas' Given additional years
of surveys, the monitonng surveys will be used to
determine temporal trends in status and distribution
of habitat. Gene conservation area (GCA) was
275
(
lj
l
the primary stratum for site selection. Results of the monitoring surveys were summanzed into the
reach database. Because the sites were randomly selected, we calculated means and variances for
each key feature, and graphically displayed the results as cumulative distribution frequencies (CDR
for each GCA.
We post-sffatified the sample sites according to geology and land ownership pattems. The
site GIS coverage was overlain on the ownership coverage. The ownership attributes were joined to
the site attribute table, permitting a site selection based on ownership. Cumulative distribution
frequency (CDD graphs were generated for comparative analysis. The median and first and third
quartiles descnbed the range and central tendencies of the frequency distnbutions of the habitat
attributes used in the analysis of current habitat conditions (Zar, 1996). Confidence intervals of each
CDF were calculated based on a probability distnbution. Statistical comparisons were made using a
Kolmogorov-Smimov goodness
of fit
test
for
continuous data (Zar, 1996). Four graphs were
selected to show differences with respect to land ownership. The three ownership classes were small
privately owned parcels (urban, rural residential, agnculture, and small woodlot), large parcels
owned by private industrial forest companies or Oregon Department of Forestry, and federally
owned property in US Bureau of Land Management or Forest Service management. The variables
compared were channel exposure, percent areal extent of fine sediments (< 2 mm diameter) in riffle
units, number of nparian conifers
> 3 cm diameter, and volume of large woody debris (>
15 cm
diameter) (Figure 2).
100
100
15
75
50
.E
a
25
25
0
0
20
40
60
20
80
Fine Sediments (Ea area)
100
r00
75
75
E
50
40
60
80
Channel exposure (%))
.g
25
to
25
0
0
20
40
50 100 150 200 250 300 350 400
60
Volume of Large Wood
Riparian conifers
Figure 2. Cumulative frequency distribution graphs of post-stratification analysis of monitoring surveys with land
ownerstrip
Cumulative frequency distribution graphs are a useful way to represent monitoring survey information. posrstratiflcation
of
the monitoring survey database was based on land ownership and assessed in relation to a variety of physical featues.
Presented here are the graphs
for (a) percentage of areal extent of fine sediments (< 2mm diameter) in riffle uilts, (b)
of 180 degrees, (c) volume of large woody debris greater than 15 cm dbh per 100 m of
channel exposure as a percentage
stream, and (d) number of conifers larger than 50 cm dbh in a 30 m wide riparian zone per 305 m of stream.
276
Private land in small individually owned parcels tended to have the highest amount
of
channel exposure' the lowest volume of large woody debris, fewest riparian
conifers, and the highest
amount of fine sediments in
riffle units compared with the other land ownership classifications. It
fine sediments than reference
large parcels owned by corporations or the state of oregon was
also had higher channel exposure, fewer riparian conifers, and more
conditions. Private land
in
chatactenzed by more channel exposure than reference conditions, with large
woody debris volumes
and percentage of fine sediments in riffles similar to federally owned
land. The number of riparian
conifers was more similar to small private land parcels than to federal ownership.
Federal ownership
had more riparian conifers than the other land use classifications, but
was still lower than reference
conditions (Jones and Moore. 1999).
We also post-stratified the monitoring sites in relation to geology (Figure
3). To prevent
sffeam size from biasing the results, we selected only sites in basin areas
of 4-z0krrf . The selected
sites were divided into volcanic and sedimentary and plotted in relation
to volume of large woody
debns, number of deep pools per km, percent areal extent of fine sediments (<
2 mm diameter) in
nffles, and number of riparian conifers > 50 cm diameter per 305 m of stream length.
100
100
(b)
/)
t)
i'sn
50
a
.=
h
25
25
0
20
40
0
246
60
Fine Sediments (Vo area)
Pools >1.0 m depth/km
100
100
75
75
i'so
g--
.:
a
,5
25
,<
0
0
100 150 200 250 300 350
400
Riparian conifers
Figure 3' Cumulative frequency distribution graphs of post-stratification
analysis of monitoring surveys with geology.
The monitoring survey dataset was post-stratified on geology. Underlying geology
was classified as volcanic or
for each geology
in riffle units, (b) number of pools >
1'0 m in depth per km, (c) volume of large woody debris greater
than 15 cm dbh per 100 m of stream, and (d) number
of riparian conifers larger than 50 cm dbh in a 30 m wide riparian
zone per 305 m of stream
sedimentary rock strata, and the dataset was assessed in relation
to a variety of physical features
classification' Presented here are (a) percentage of areal extent of fine
sediments
277
zone was detected between
No difference in the number of large conifers in the riparian
was apparent between the two types of geology
volcanic and sedimentary geology' Little difference
withrespecttothevolumeoflargewoodydebris.Thereisadetectabledifferenceintheamountof
regions have a higher proportion of fine
fine sediments in riffle units. Sites in sedimentary geology
above the 50m percentile' More deep pools are
sediments than in regions with volcanic geology
geology after the 75m percentile'
present in the volcanic geology type than in sedimentary
4. Discussion
reach,
The basin surveys coupled with a GIS presentation described conditions and process within the
stream, and watershed, providing a spatial representation of the habitat a fish encounters during the
stream phase of its life cycle (anadromous or fluvial) or entire life cycle (resident). The GIS template
was an analytical and visual complement to the design of the basin surveys. Hierarchical
organization of the basin survey and GIS integration facilitated the analysis at multiple scales, and
the map-based view (GIS) provided a perspective that was more comprehensive than a tabular or
graphical presentation @gure 4). Compaisons of habitat conditions by site, reach, stream, or
wafershed allowed corresponding comparisons to life histories, survival, and production of salmon
populations, or to land management.
The census surveys were amenable to micro and macro scale analysis and provided a
comprehensive picture of the condition of aquatic habitat within the stream surveyed. The survey
extent covered all of the area in a stream and made extrapolation of information within that stream
unnecessary. The continuous-survey approach provided estimates of habitat conditions throughout a
stream (Dolloff et al., 1997), supplied a complete inventory of barriers to fish passage (e.g., falls or
culverts), described habitat and hydrologic relationships among streams or landscape features, and
estimated potential fish distribution by life stage. By attaching stream survey information to GIS, we
were also able to view and analyze the information in a watershed context. For example, the spatial
pattern of reaches and the quality of habitat determined the importance of selected reaches for adult
holding, spawning, and juvenile rearing habitat of coho salmon O. kisutch.
The census surveys characteized the surveyed stream, but the extrapolation of
the
information to a broader extent may not be appropriate. The primary limitation to basin surveys was
the difficulty of extrapolating results to unsurveyed streams because of the non-random selection of
streams.
White characteization of broader geographic extent is relatively difficult with basin
surveys, such analysis is the foundation for the monitoring survey. The monitoring surveys were
developed specifically with the goal of assessing the condition of streams in large geographic areas,
rather than determining the conditions in specific streams. This was reflected in the monitoring
surveys sample strategy that incorporated small portions of habitat selected from a random selection
of stream reaches. The resulting data described an area rather than characterizing the condition of a
specific sffeam system. Therefore, it was inappropriate to characterize the condition of a stream with
an individual monitorine site.
278
:ogy
tof
line
The monitoring dataset facilitated a time series analysis of habitat conditions (Firman
and
Jacobs, this issue). Enough sites were selected in each coastal GCA every
year to provide
a sample
size that ailowed
for trend determination and aquatic habitat characteization. The sample survey
provided the most statistical power to descnbe conditions across a
broad geographic area (Stevens
and Olsen, 1999; Firman and Jacobs, this issue). Post-stratification of
the sample sites provided
additional opportunities for comparing conditions with other landscape features.
GIS played
ch,
he
a very important role in the monitoring survey. Sites were selected with the
GIS and sample weights were assigned based on the length of streams in the
stream"coverage. GIS
played a critical role in locating the sites for field sampling, analyzing
the data in the a priora srata,
reselecting samples for post-stratification, and will be used to conduct
spatial analyses on the
distnbution of site features across the landscaoe.
rte
aI
rd
)r
The integration of basin and monitoring surveys into a GIS system has many
benefits to a
research and monitoring program. One advantage is the ease with which
other aquatic datasets can
be spatially linked to the stream survey coverage for analyses. The
most important
benefit was the
willingness of resource managers and the public to use technical information
when presented rn
n
map-based view.
a
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280