RIVER RESEARCH AND APPLICATIONS
River Res. Applic. 26: 835–847 (2010)
Published online 30 July 2009 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/rra.1297
SPATIAL DISTRIBUTION OF LARGE WOOD JAMS IN STREAMS RELATED TO
STREAM-VALLEY GEOMORPHOLOGY AND FOREST AGE IN
NORTHERN MICHIGAN
ARTHUR E. L. MORRIS,a P. CHARLES GOEBEL a* and BRIAN J. PALIK by
a
School of Environment and Natural Resources, Ohio Agricultural Research and Development Center (OARDC), The Ohio State University,
1680 Madison Avenue, Wooster, OH 44691, USA
b
Northern Research Station, USDA Forest Service, 1831 Highway 169E, Grand Rapids, MN 55744, USA
ABSTRACT
Geomorphology at the scale of stream valleys influences smaller scale processes that give rise to spatially distributed patches,
including large wood jams (LWJ) in streams. Understanding the spatial distribution of LWJ along streams with reference to
large-scale geomorphology is valuable for understanding stream and riparian interactions, and may be critical for effective
stream management and restoration. We surveyed the locations of LWJ along 18 stream segments within study areas defined by
stream-valley geomorphology. The objective of this study was to test the prediction that LWJ in this system will be aggregated in
areas defined by stream-valley geomorphology, but be randomly distributed at smaller scales. The spatial distribution of LWJ
was analysed by a one-dimensional K-function analysis capable of detecting aggregated, random and segregated patterns at
different scales. The prediction that LWJ aggregate in areas defined by stream-valley geomorphology was supported: LWJ
aggregated at scales up to several kilometres in three streams. LWJ also was segregated at smaller scales in two of these streams;
this was detectable when several stream valley segments were considered together. The prediction that LWJ would be
randomly distributed at smaller scales was supported at most smaller scales for most streams. In fact, 40% of individual stream
valley segments contained LWJ that were randomly distributed at all scales. Twenty-seven per cent of individual stream valley
segments showed segregated LWJ distributions. Large-scale aggregation of LWJ evidences the need to select reference reaches
that encompass several geomorphic patches at the scale of the stream valley. Segregated patterns of LWJ distributions evidence
opportunities to better understand the relationships between hydraulic systems, material transport dynamics and riparian forests.
Copyright # 2009 John Wiley & Sons, Ltd.
key words: K-function; linear distribution; ecological restoration; woody debris
Received 2 March 2009; Revised 6 June 2009; Accepted 24 June 2009
INTRODUCTION
Spatial patterns at small scales are often controlled by landscape structure at larger scales (Chen et al., 2006). In a
watershed, geomorphology at different scales imposes limits on the variability of small-scale interactions between
stream flow, channel morphology and riparian variables that organize stream and riparian structure (Frissell et al.,
1986), including large wood jams (LWJ) in streams. The formation of LWJ depends on the size of wood pieces
relative to stream channel width and depth, the size of wood pieces relative to stream flow, and the presence of
trapping structures (Abbe and Montgomery, 2003); all of these parameters are controlled in part by the
geomorphology of the drainage area. Geomorphic variability at relatively large scales may therefore correspond
with patterns of LWJ distribution along stream channels. Random and aggregated (clumped) LWJ spatial patterns
may develop (Kraft and Warren, 2003; Swanson, 2003). LWJ may also form segregated (uniform; i.e. regularly
spaced) patterns analogous to the spatially rhythmic patterns of alluvial deposition and scouring (Montgomery
et al., 1995; Chin, 2002; Kraft and Warren, 2003); identifying these patterns has theoretical and practical value.
Identifying the spatial distribution of LWJ along streams with reference to large-scale geomorphology has value
for effective stream management because the spacing between LWJ has the potential for controlling fluvial
*Correspondence to: P. Charles Goebel, School of Environment and Natural Resources, Ohio Agricultural Research and Development Center
(OARDC), The Ohio State University, 1680 Madison Avenue, Wooster, OH 44691. E-mail: [email protected]
y
The contribution of Brian J. Palik was prepared as part of his official duties as a US Government employee.
Copyright # 2009 John Wiley & Sons, Ltd.
836
A. E. L. MORRIS ET AL.
processes and structuring communities of aquatic organisms (Montgomery et al., 1995; Schlosser, 1995; Palmer
et al., 2000; Montgomery et al., 2003; Mutz, 2003). Identifying the spatial distribution of LWJ will also be
important for direct restoration of LWJ to streams. For example, identifying the distribution of LWJ in a large-scale
context is key for the establishment of appropriate reference reaches (Young et al., 2006). Quantifying spatial
distribution of LWJ may further help to provide clear goals and objective reference for management and restoration.
Quantitative analysis of LWJ spatial distribution patterns is needed for most landscapes and ecoregions.
Although others have recognized the need to evaluate the spatial distribution of both LW and LWJ at large scales
in streams (e.g. Martin and Benda, 2001; Benda et al., 2003; Swanson, 2003; Young et al., 2006; Meleason et al.,
2007), relatively little work has been done to evaluate patterns of spatial distribution of LWJ, especially to quantify
spatial distributions (but see Montgomery et al., 1995; Gurnell and Sweet, 1998; Wing et al., 1999; Keim et al.,
2000; Kraft and Warren, 2003). Furthermore, only one study of which we are aware has attempted to identify
segregated distribution patterns of LWJ in streams (Kraft and Warren, 2003) although the development of regularly
spaced patterns seems probable in some circumstances. In most studies, LWJ distribution is represented by a mean
number of LWJ per some distance (e.g. 0.5 to about 170 LWJ 1000 m1 in the Queets River, coastal Washington,
USA; Abbe and Montgomery, 2003), or rarely by average spacing between LWJ (e.g. median LWJ spacing of 6.4–
9.9 stream widths and mean LWJ spacing of 7.8–8.5 stream widths in the Highland Water catchment, UK; Gurnell
and Sweet, 1998). The average quantity of LWJ per distance provides valuable information about LW or LWJ loads
in streams, but it provides no information about spatial arrangement and distribution patterns within the increment
of measurement. Similarly, while average spacing between LWJ has been used to advantage in studies (e.g. Gurnell
and Sweet, 1998), it implicitly represents only a uniform distribution pattern (equally spaced LWJ; segregated
LWJ) within the area of interest.
Quantifying linear spatial distribution patterns will allow testing of conceptual frameworks for LWJ structure,
which may in turn provide insight to the causes of LWJ spatial pattern. Swanson (2003) suggested a typology of
controls on LW amounts and arrangement in streams, based on interactions between recruitment of wood from
riparian areas, transport of wood in the stream, and trapping of wood in the stream (Table I). Because LWJ
integrates LW movement patterns in the stream, we suggest that the typology of Swanson (2003) may also be used
to make generalized predictions about LWJ distribution. Kraft and Warren (2003) predicted that a segregated
pattern of wood jams would be likely to occur in mature stream systems that are capable of transporting much of the
recruited wood. Based on the typology of Swanson (2003), the work of Kraft and Warren (2003), and our own
observations while measuring LWJ in other studies (Palik et al., 1998; Morris et al., 2007), we developed testable
predictions for LWJ spatial distribution within a geomorphic hierarchy in the Porcupine Mountains in Northern
Michigan. Assuming a relatively uniform recruitment of wood from consistently forested riparia, we predicted that
LWJ distribution would reflect trapping-site control of pattern, but with dispersed input and relatively effective
transport capacities for most pieces of wood. Therefore, we predicted that: (1) at the scale at which major stream
valley geomorphology patches occur (medium to large scale) within the watershed, LWJ will be aggregated in some
geomorphically defined areas, and (2) at the smaller scale within patches of similar stream valley geomorphology,
wood will be randomly distributed. Because these predictions are general under the assumption of uniform wood
inputs, we predicted that similar patterns of distribution would occur in both unlogged (old-growth) and logged
(second-growth) forests of the area.
Table I. Typology for predicting the distribution of wood in streams
Control type
Supporting characteristics
Distribution of wood pieces
Discrete-source-area
Transport distances are much shorter than the
spacing between source areas
Transport distances are much longer than the
spacing between source areas
No discrete trapping sites. Transport distances
are long relative to source area spacing
Limited transport capacity
Aggregated at source areas (aggregations
reflect the spatial distribution of source areas)
Aggregated at trapping sites (aggregations
reflect the spatial distribution of trapping sites)
Randomly distributed
Trapping-site
Transport
Dispersed source
Randomly distributed
This table summarizes information from Swanson (2003). The predictions in this table should also apply to LWJ.
Copyright # 2009 John Wiley & Sons, Ltd.
River Res. Applic. 26: 835–847 (2010)
DOI: 10.1002/rra
SPATIAL DISTRIBUTION OF LWJ IN STREAMS
837
METHODS
Study site
We studied stream segments defined by stream valley geomorphology (Frissell et al., 1986) in six streams in the
Porcupine Mountains along the south shore of Lake Superior in Upper Michigan, USA (Figure 1). The Porcupine
Mountains provide a historically valuable area for evaluating LWJ because they support the largest virgin forest
extant in the Great Lakes region. In addition, the Porcupine Mountains provide a geomorphically diverse area in
which LWJ may be studied with relationship to several different stream corridor geomorphologies. Several streams
flow through the forests of the Porcupine Mountains into Lake Superior, yet as far as we know, LWJ spatial
distribution has never been evaluated for this region. Streams in the study area form in the low-gradient upland areas
of the Porcupine Mountains, descend relatively steeply as they cut through bedrock that comprises the Porcupine
Mountains, and then flow through moderate to low-gradient, clay-lake plains before entering Lake Superior
(Figure 2).
Most of the study segments were in the Porcupine Mountains Wilderness State Park (PMWSP) which contains
one of the largest (13 000 ha) contiguous, old-growth, hardwood-hemlock forests in the Lake States (Frelich and
Lorimer, 1991). We studied four streams in old-growth forest in the PMWSP: the Little Carp River, the Big Carp
River, the Upper Carp River and Scott Creek. We also studied segments of two streams in second-growth forest
landscape near the northeastern boundary of the PMWSP: the Union River and the Little Iron River.
The upper reaches of the streams in this study were first order, and the lower reaches were second or third order.
Few records of streamflow exist for these rivers. Goebel et al. (2003) reported discharge in the Little Carp River
during annual floods ranging from 4.7 m3 sec1 in the low gradient sections of the river to 9.4 m3 sec1 in the lower,
high gradient portions of the river. The discharge associated with 50-year flood events was estimated to range from
17.5 to 38.1 m3 sec1 (Goebel, 2001). Discharge of the Little Carp River is thought to be similar to discharge in
the other streams of this study. Most extreme flow events occur in the spring as dense snowpacks (ranging from 1 to
3 m thick) melt, often very rapidly (Goebel, 2001).
Figure 1. Study areas in the Porcupine Mountains. Dotted line represents the boundary between old- and second-growth forest. The northern
second-growth forest along the shore of Lake Superior was logged prior to 1910. Stream segment classification is explained in the text
Copyright # 2009 John Wiley & Sons, Ltd.
River Res. Applic. 26: 835–847 (2010)
DOI: 10.1002/rra
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A. E. L. MORRIS ET AL.
Figure 2. Schematic diagram of geomorphically defined sections of streams in the study area. This diagram is vertically exaggerated and
stylized to emphasize geomorphic features of the stream valley
Old-growth forests of the Porcupine Mountains are dominated by eastern hemlock (Tsuga canadensis, (L.)
Carr.), northern white cedar (Thuja occidentalis, L.), yellow birch (Betula alleghaniensis, Britt.) and sugar maple
(Acer saccharum, Hook). The furthest upstream portions of the Big and Little Carp Rivers, and the segments of
Upper Carp River we studied contained a relatively higher proportion of hardwood species than the downstream
portions of the Little and Big Carp Rivers (Morris et al., 2007). Maximum tree height in the old-growth study area is
approximately 40 m, with mean canopy tree height of 25 m and 60 cm mean diameter at breast height. Segments of
streams we studied were forested to the edge of the bankfull channel. Human influence to study-streams in oldgrowth forest has been limited to recreational hiking, camping and fishing in some areas. Beaver activity was
apparent in all streams we studied in old-growth forest; however, no well-established beaver dams occurred in
segments of stream that we studied during the period of data collection.
Second-growth forests in the study area of the Porcupine Mountains have been logged within the last 50 years.
Species composition is similar to the old-growth forests, although second-growth forests contain a high proportion
of hardwood species and smaller trees in some areas, especially maple (Acer spp), birch (Betula spp.) and ash
(Fraxinus spp.). Dense patches of small balsam fir (Abies balsamea (L.) Mill.) also occur in second-growth riparian
areas. Most of the Union River and Little Iron River forests grew to the edge of the stream channels in segments we
studied, and narrow strips (<10 m) of uncut trees remained in the clay-lake plain of the Little Iron River where
timber harvest has occurred within the last 5 years. Unlike the old-growth areas, second-growth forests we studied
have experienced a variety of human-related disturbances including timber harvesting, limited mining, camping
and road construction. Beaver dams in various states of repair were located in the upstream portions of the Union
River and Little Iron River, although no active beaver dams occurred in streams segments we studied.
Individual mortality (especially associated with bank erosion and windthrow) appeared to be the most common
sources of LW recruitment to streams in both old- and second-growth forests of the Porcupine Mountains. Seasonal
precipitation can be heavy with 800–900 mm precipitation, up to 7 m of which occurs as snow (McNab and Avers,
1994). The topography is, however, not conducive to avalanches, landslides or other forms of mass wasting
(significant factors associated with large wood dynamics in the Pacific Northwest, Benda et al., 2003), except
localized bankslope failures. Fires occur infrequently in these forests. However, windstorms are common and are
responsible for mortality of individual trees and stands. The last major windstorm responsible for mass mortality in
the study area occurred in 1953, affecting 1800 ha near the centre of the PMWSP, around the northern, upstream end
of the Little Carp River.
Copyright # 2009 John Wiley & Sons, Ltd.
River Res. Applic. 26: 835–847 (2010)
DOI: 10.1002/rra
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SPATIAL DISTRIBUTION OF LWJ IN STREAMS
Study segments
Based on initial observations of geomorphology and streams in the Porcupine Mountains, we identified four
major stream segment classes to study; these classes were given three-letter codes (Table II). We surveyed LWJ in a
total of four CLP, four HRT, four MRT and six LRP segments. Individual study segments ranged from 480 to
4820 m. We surveyed LWJ in portions of streams encompassing three contiguous segment classes in each of four
streams for total distances that were longer than individual study segments: Little Carp River 8650 m; Big Carp
River 2900 m; Upper Carp River 1980 m and Union River 1965 m.
To specifically characterize the study segments, we measured defining-factors in each segment. We measured
stream valley gradient and constraint, channel sinuosity, channel width and characterized channel bedding. We
estimated valley gradient by measuring elevation at the upstream and downstream ends of each study section from
1:24 000, 30-m digital elevation models (DEM), or topographic maps. We classified valley constraint from the
width of the valley approximately 10-m above the valley floor; this measurement was chosen because it provided a
consistent measure from topographic maps that could be used to compare valley constraint between segments. We
measured valley width at the contour line at higher elevation (10 m) from the point where a stream line crossed a
contour line. We measured width for every stream-contour that crossed the stream in the valley segments. We
classified stream valleys with widths less than 210 m as high constraint, 211–400 m as medium constraint, and
greater than 400 m as low constraint; this classification was only intended to allow us to classify stream valley
constraints within the range of variability in this study. We computed the channel sinuosity index from GIS maps as
the total channel distance between the furthest upstream and downstream points in a study section divided by the
straight line distance between those two points. We computed mean wet channel width from five or more
measurements in each section, including the upstream end and downstream end of each section. Although LWJ
characteristics probably reflect bankfull channel width more than wet channel width at the time of our observations,
wet channel width was clear for the field crew to measure, and we considered wet channel width a decent measure
for comparing channel width between study segments. We noted channel bedding as the visually most predominant
material: boulder, cobble, gravel or sediment; if rock-plane bedding comprised a substantial portion of a
geomorphic section we noted bedding as rock-plane. The geomorphology measures and channel sinuosity measure
we used to represent valley segments could be obtained at the desk using GIS, while stream channel width and
bedding can be quickly determined in the field. We chose these measures deliberately because they represent the
kinds of measurements that are useful in rapid assessments of stream valley geomorphology.
LWJ measurements
We considered LWJ to be two or more pieces of wood each with length greater than 1 m and diameter greater than
10 cm contacting each other and extending into the bankfull channel. We only evaluated the spatial distribution of
LWJ that spanned 50% or more of the channel because we considered these to be most reciprocally involved with
Table II. Geomorphic characteristics of studied stream segment classes in the Porcupine Mountains
Class Order Gradient
Valley Channel
constraint bedding
Channel
sinuosity
LRP
1–2
Low
Low-med Cobble, Gravel,
sediment
High
HRT
1–2
High
MRT
1–3
Med
CLP
1–3
Low
Channel
width
Description
Inland high in the Porcupine Mountains
parallel to the bedrock structures
that form the Mountains
High
Rock-plane, Boulder, Low
Low
Steep bedrock-controlled traversing
Cobble
the mountain-forming bedrock
Med
Cobble, Gravel
High
Med
Intermediate gradient and elevation
between uplands and the clay-lake
plain
Low-med Rock-plane,
Med-high Med-high Clay-lake plain
Cobble
Copyright # 2009 John Wiley & Sons, Ltd.
Low
River Res. Applic. 26: 835–847 (2010)
DOI: 10.1002/rra
A. E. L. MORRIS ET AL.
840
stream processes (hereafter LWJ refers to large wood jams that span 50% or more of the channel). We measured the
distance along the channel to the approximate midpoint of each LWJ using a nylon tape or hip chain (Fremaco
Fieldranger1). We obtained geospatial coordinates for most points using a global positioning system (Satloc
SLXg31 backpack; Trimble1 and Eagle Explorer1 handheld) and entered points into a geographic information
system (GIS) to generate distribution maps using ARCGIS (v. 9.0, ESRI, Redlands, CA). We represented one very
long, loose aggregation of LW in the Little Carp River MRT segment with four points; all other LWJ were shorter
and were represented by single points. We also noted the proportion of wetted channel spanned by LWJ as well as
the channel feature (rock, live tree, bank or bed) that appeared most likely to have trapped and anchored large wood
at that location.
Statistical analysis
To evaluate the distribution of LWJ along the length of the stream channels, we represented streams as onedimensional lines with LWJ as points on these lines. We used a (one-dimensional) K-function to detect differences
in LWJ distribution. This K-function analysis provides a sensitive quantification of linear pattern including both
aggregation and segregation for situations when more information than the frequency or size of points (LWJ) is
desired (Cressie NAC, 1993). In comparison, the two-dimensional nearest neighbour approach used by Wing et al.
(1999) and Keim et al. (2000) to identify random and aggregated LW patterns (up to 20 m) in streams in southern
Oregon required more detailed data acquisition and was not as good as a one-dimensional analysis for identifying
the distribution of LWJ along the line of the stream channel. The linear K-function summarizes the number of LWJ
occurring within a given distance of every LWJ along a stream reach. We used a variable-width edge correction to
remove unknown effects of LWJ near the ends of sections (Cressie NAC, 1993). Computations were made using a
spatial version of the temporally linear, edge-corrected equation for K shown by Cressie NAC (1993) as follows:
KðdÞ ¼
D=N
PN1 PN
i¼1
j¼iþ1 Ið0 < dj di
PNs1
i¼1 Iðdt > TÞ
T; dt > TÞ
(1)
where D is the total distance of the surveyed stream (m), N the number of LWJ in the reach, di the distance (m) along
the channel to the ith LWJ, dj is the distance (m) along the channel to the jth LWJ, T the distance (m) or scale of
consideration and dt is the distance from the end of the reach to di (i.e. D di). The term I takes a value of one when
the conditions in parentheses are met, or 0 if they are violated (e.g. if dj di T and positive, and dt > T, then I ¼ 1).
All distances are in meters, and we defined distances only in one direction. Specifically, the jth distances were
upstream from the ith distances. We computed K for a range of T-values corresponding to the length of surveyed
stream, in 5-m increments.
For a random distribution of points, K equals T (when computed with with Equation (1)). The value of K will be
higher for clustered points than for points from a random distribution (K > T) because each aggregated point will
have more points within distance T than would occur randomly. Conversely, segregated points will show a lower Kvalue than randomly distributed points (K < T) because each segregated point will have fewer points within
distance T than would occur randomly.
To test the likelihood that empirical LWJ distributions arose randomly, we compared empirical K-values to those
of known random distributions, using Monte Carlo simulations with 1000 random distributions of points.
Distributions represented the number of LWJ over the length of each particular reach. Each random distribution of
LWJ formed the basis for computing K at the same T-values as the empirical distribution. For every T-value, we
therefore had 1001 K-values: 1000 simulated K-values representing known random distributions, and one empirical
K-value representing the actual distribution. At each scale (T), all 1001 K-values were ranked from smallest to
largest, allowing us to identify the proportional rank (PR) of the empirical K-value (PR ¼ rank of empirical-K/
1001).
The proportional rank provided a simple way to represent scales of aggregation or segregation that were most
likely not to have accidentally appeared from random distributions. Therefore, we presented K-analysis data in
charts of proportional rank. We reported aggregation or segregation if, at a given T-value, empirical K-values
exceeded 97.5% (aggregation) or were less than 2.5% (segregation) of the Monte Carlo simulated K-values,
Copyright # 2009 John Wiley & Sons, Ltd.
River Res. Applic. 26: 835–847 (2010)
DOI: 10.1002/rra
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SPATIAL DISTRIBUTION OF LWJ IN STREAMS
respectively. To provide the most conservative identification of nonrandom spatial pattern, we also reported
K-values where the empirical-K-value was greater than the largest random-K-value (PR ¼ 1.00, aggregation) or
smaller than the smallest random-K-value (PR ¼ 0.00, segregation).
At the largest scales relative to an evaluated stream length, some points did not meet the edge-correction criteria
for inclusion in K-function analysis. We therefore only compared empirical and simulated K distributions for
scales (T) where all 1001 distributions contributed. We performed K-function computations and simulations using
SAS/STAT (v. 9.1, SAS, Cary, NC).
RESULTS
Characteristics of LWJ
The set of parameters we recorded to characterize each study segment is given in Table III. Streams in all
segments transported substantial amounts of recruited LW. Only about 8% of the LWJ for which we noted anchors
were anchored on large rocks. A large proportion (45%) of the LWJ we observed were anchored by LW pieces
trapped by standing riparian trees. Forty-seven per cent of the LWJ anchored by LW interacting with the bank or bed
of the channel itself with no apparent anchoring boulder or live tree.
Differences were apparent in LWJ abundance among segments within streams (Figure 3). MRT or LRP segments
contained more abundant wood than was contained by other geomorphically defined segments in each stream. No
LWJ occurred in the HRT segment of the Union River or in the CLP segment of the Little Iron River, and only two
LWJ occurred in the CLP segment of the Big Carp River.
Table III. Characteristics of stream segments surveyed in the Porcupine Mountains
Valley
gradient
Old growth
CLP
Big Carp R
Little Carp R
HRT
Big Carp R
Little Carp R
Upper Carp R
LRP
Big Carp R
Little Carp R
Upper Carp R
Scott Creek
MRT
Big Carp R
Little Carp R
Upper Carp R
Second Growth
CLP
Little iron R
Union R
HRT
Union R
LRP
Little iron R
Union R
MRT
Union R
Valley
constraint
Channel wetted
width (m)
Channel
sinuosity
Channel
bedding
Distance
surveyed (m)
0.013
0.017
Med
Med
9
7
1.2
1.2
Rock-plane/Cobble
Rock-plane/Cobble
1199
3151
0.051
0.037
0.063
High
High
High
8
7
6
1.1
1.2
1.2
Rock-plane/Cobble
Rock-plane/Boulder
Rock-plane
680
1412
718
0.005
0.008
0.024
0.018
Low
Low
Low
Med
7
4
4
3
1.5
1.3
1.2
1.8
Cobble/Gravel
Cobble/Gravel
Cobble/Gravel
Cobble/Gravel
1000
582
459
1027
0.018
0.023
0.042
Med
Med
Med
7
5
5
1.5
1.4
1.7
Cobble/Gravel
Cobble/Gravel
Cobble/Gravel
2018
4823
1167
0.007
0.030
Med
Low
12
3
1.1
1.8
Rock-plane
Rock-plane/Cobble
900
1050
0.030
High
4
1.1
Rock-plane
0.007
0.011
Low
Med
5
3
1.7
1.8
Cobble/Sediment
Cobble/Gravel
1020
980
Med
3
1.4
Cobble/Gravel
1140
0.030
Copyright # 2009 John Wiley & Sons, Ltd.
480
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DOI: 10.1002/rra
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A. E. L. MORRIS ET AL.
Figure 3. Distribution of LWJ in four streams in the Porcupine Mountains. (a) Big Carp River, (b) Little Carp River, (c) Upper Carp River,
(d) Union River. In this and all other figures, LWJ refers to LWJ spanning at least 50% of the channel. Contiguous study segments are labelled.
Distance is 0 at furthest downstream point
Distribution patterns among segments
LWJ aggregated at many scales from about 50 m to more than 1 km in three of four cases in which
several contiguous segments were evaluated (Big Carp River, Little Carp River and the Union River; Table IV;
Figures 4–5). The LRP and MRT segment classes were areas of large-scale LWJ aggregation. Segregation was
evident at several scales in two of four cases in which contiguous study segments were considered (Little Carp
River and Big Carp River; Table IV; Figure 4a and b). The exception was the Upper Carp River in which LWJ
showed only random patterns at all scales when three contiguous segments were considered together (Table IVc).
The Big Carp River showed aggregation of LWJ at scales from about 25–1000 m, due to a cluster of LWJ in the
MRT segment (Figure 4a). Segregation in the Big Carp River appeared at scales from about 1000 to 3000 m
(Table IV; Figure 4a), reflecting wide spacing of LWJ over 2.8 km in the downstream portions of the stream
(Figure 3). Most LWJ in the Little Carp River clustered in the MRT segment, leading to statistical aggregation at
almost all scales from 40 to 8.5 km (Figure 4b). LWJ in the Little Carp River were also segregated at the scale of
5 m. LWJ on the Union River were aggregated at scales from approximately 60–1 km and at about 1500 m because
of clustering in the LRP segment (Figure 5a). No segregation of LWJ was apparent in the Union River.
Distribution patterns within segments
No general pattern of aggregation or segregation was apparent for segment classes. LWJ occurred in distributions
not different from random at all scales within 8 of 18 single stream segments (Table IV; Figures 4–5). Aggregation
of LWJ occurred only in the Big Carp River MRT segment at scales of 45–1140 m and at about 1500 m and in the
Upper Carp River LRP segment at scales from 25 to 260 m. LWJ segregation occurred in five segments: the LRP
segment of the Big Carp River (335–410 m), the CLP of the Union River (60–70 m), the LRP segment of the Little
Iron River (10–20 m), Scott Creek (LRP; about 400–450 m) and the MRT segment of the Little Carp River (5 m).
LWJ were too sparse in three study segments to evaluate patterns (Big Carp River CLP, Union River HRT and Little
Iron River CLP).
Copyright # 2009 John Wiley & Sons, Ltd.
River Res. Applic. 26: 835–847 (2010)
DOI: 10.1002/rra
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SPATIAL DISTRIBUTION OF LWJ IN STREAMS
Table IV. Summary of LWJ spatial distributions in surveyed stream areas
Old growth
Combination3
Big Carp
Little Carp
Upper Carp
CLP
Big Carp4
Little Carp
HRT
Big Carp
Little Carp
Upper Carp
LRP
Big Carp
Little Carp
Upper Carp
Scott Creek
MRT
Big Carp
Little Carp
Upper Carp
Second growth
Combination5
Union River
CLP
Little Iron
Union
HRT
Union
LRP
Little iron
Union
MRT
Union
No. LWJ
Scale of aggregation1 (m)
Scale of segregation2 (m)
29
85
43
25–1070
40–8650
–
1145–1195, 2220–2395, 2730–2900
–
–
2
6
na
–
na
–
3
6
12
–
–
–
–
–
–
7
7
12
30
–
–
25–260
–
335–410
–
–
390–425, 455–460
24
73
19
45–1140, 1525–1560
–
–
–
5
–
28
60, 70–955, 1050–1100, 1210–1730, 1775–1960
–
0
10
na
–
na
60–70
0
na
na
20
23
–
–
10–20
–
5
–
–
1
Empirical K-values in the reported ranges exceeded 97.5% of the K-values for 1000 random distributions.
Empirical K-values in the reported ranges exceeded at most 2.5% of the K-values for 1000 random distributions.
The combination section for the Big Carp and Little Carp Rivers included the CLP, HRT and MRT segments and for the Upper Carp River the
LRP, HRT and MRT segments.
4
Not enough LWJ for spatial analysis.
5
Contains the HRT, MRT and LRP segments.
2
3
DISCUSSION
LWJ spatial distribution patterns related predictably with relatively large-scale geomorphic settings. LWJ tended to
cluster in stream segments at scales of a few hundred to a few thousand metres. Aggregation of LWJ was detected
statistically at scales up to several kilometres in three of four study streams where LWJ was surveyed in several
contiguous geomorphically defined stream valley segments. Aggregation of LWJ clearly occurred because some
geomorphically defined areas supported higher wood retention and lower transport than other areas. Relatively lowgradient segments with low valley constraint (LRP and MRT segments) tended to act as wood sinks, where LW
transported from upstream contributed to LWJ. LWJ abundance and retention in LRP and MRT study segments
probably also reflected interactions between the streams and riparia such as more recruitment of large trees in the
areas where the stream meandered more than in the HRT segments. It appeared that the function of geomorphically
defined segment classes as areas of LWJ aggregation also depended on the metastructure of the segment classes
Copyright # 2009 John Wiley & Sons, Ltd.
River Res. Applic. 26: 835–847 (2010)
DOI: 10.1002/rra
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A. E. L. MORRIS ET AL.
Figure 4. Proportional rank (PR) of the K-values of LWJ distributed in the (a) Big Carp River, (b) Little Carp River and (c) Upper Carp River
compared to 1000 random distributions at each scale. In this and subsequent figures T is the scale in metres. Random distribution is indicated by
PR values near 50%, segregation is indicated by low PR values, and aggregation is indicated by high PR values. Top and bottom dotted reference
lines indicate that 97.5 and 2.5% of random K-values were less than the actual (empirical) K values, respectively. Too few LWJ occurred in the
CLP segment of the Big Carp River for K function analysis of that segment alone, so it is not represented in this figure
relative to each other. Segments downstream from better LW-transport segments (e.g. MRT downstream from HRT
in the Little Carp River; Figure 3b) contained relatively more LWJ than the same class of segment downstream from
worse LW-transport segments (e.g. MRT downstream from LRP in the Union River; Figure 3c).
LWJ distributions were random within areas of relatively homogeneous stream valley geomorphology
(study segments) at most scales for almost all study segments and at all scales in 44% of the study segments
(8 of 18 segments). It is probable that random distributions within study segments were evident because of a
multiplicity of LW-retaining structures and processes. In the old-growth forests, stream structure appeared to be
especially complex, with accumulations of wood and with stream channel geomorphic structure remaining from a
long history of stream and riparian interactions. However, random LWJ distributions were also found in study
Copyright # 2009 John Wiley & Sons, Ltd.
River Res. Applic. 26: 835–847 (2010)
DOI: 10.1002/rra
SPATIAL DISTRIBUTION OF LWJ IN STREAMS
845
Figure 5. Proportional rank (PR) of the K-values of LWJ distributed in the (a) Union River and (b) Little Iron River and Scott Creek compared to
1000 random distributions at each scale. No LWJ occurred in the HRT segment of the Union River or the CLP segment of the Little Iron River so
they are not represented in this figure
segments in second-growth forest. We found in a companion study that streams in second-growth forest in the
Porcupine Mountains contained less LW and fewer pieces of LW in LWJ than streams in old-growth forest, and that
second-growth riparian forests contained smaller trees and a lower proportion of conifers (Morris et al., 2007).
Although these factors would have affected the abundance, size and composition of LWJ, apparently wood
transport and retention processes still randomly distributed LWJ at most scales within areas of relatively
homogeneous stream valley geomorphology.
LWJ formed segregated distributions in 27% of the study segments (5 of 18 segments). In comparison, Kraft and
Warren (2003) used neighbour-K analysis to determine that segregated patterns developed in some streams (two out
of eight streams studied) in the Adirondack Mountains in New York. Although determining mechanisms for LWJ
segregation is beyond the scope of this study, it seems likely that stream flows could lead to regularly spaced
distributions of floating or semi-floating materials such as LW, either by directly interacting with the LW or by
causing LW to hang up on other spatially segregated stream structure (Kraft and Warren, 2003). In stream systems,
segregated patterns of scouring and alluvial accumulations reflect mutual adjustments between flow and channel
geomorphology motivated by the energy in the system (Montgomery et al., 1995; Chin, 2002). Other research also
suggests that the distribution of moderate amounts of LWJ might be correlated with pool spacings in some cases:
LW abundance has been shown to be inversely related to pool spacing distance in some stream reaches, but LW does
not necessarily obscure the spacing (Montgomery et al., 1995). In addition, pool spacing may be independent of
LW in some stream reaches, such as those with step pools (Montgomery et al., 1995), so that segregated stream
structures may control locations of LWJ distribution in some reaches.
Identifying spatial distribution patterns of LWJ has value for understanding physical and biological aspects of
ecosystems containing streams and riparian areas. LW (and therefore LWJ) has been shown to affect local channel
hydraulics and sediment transport, influencing erosion and sediment deposition, and to affect larger-scale processes
Copyright # 2009 John Wiley & Sons, Ltd.
River Res. Applic. 26: 835–847 (2010)
DOI: 10.1002/rra
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A. E. L. MORRIS ET AL.
such as stream and riparian interactions (Montgomery et al., 2003). Therefore, understanding LWJ spatial
distribution patterns may be important for understanding watershed geomorphology. It has also been shown that
LW and LWJ form valuable habitat for a variety of stream and riparian organisms (Benke and Wallace, 2003;
Dolloff and Warren, 2003; Zalewski et al., 2003; Steel et al., 2003). The spatial distribution of LWJ patches might
affect the organisms that use LWJ habitat. Although no study that we know of has evaluated the biological role of
LWJ spatial distribution, the spatial arrangement of small leaf or wood habitat patches in streams has been found to
influence invertebrate populations (Palmer et al., 2000; Silver et al., 2004).
Identifying spatial distribution patterns of LWJ has additional practical value. Spatial patterns of LWJ
distribution may be fundamentally important for choosing study reaches including reference reaches for stream
management and restoration, because LWJ spatial distribution patterns can be used to determine the length of
stream to sample to represent natural variability. No generalized sampling distance appeared to be most appropriate
for considering all LWJ variability that would occur along the streams in the Porcupine Mountains, but it is clear
that sampling must occur over quite a long distance. Stream valley segments of relatively homogeneous
geomorphology varied from several hundred to several thousand metres in the Porcupine Mountains. Therefore, in
the Porcupine Mountains, LWJ sampling would need to consider at least hundreds of metres of stream, and
probably thousands of metres. Our observations agree with predictions for and data from other areas. For example,
Swanson (2003) predicted general landscape-scale variability in LW abundance; Benda et al. (2003) used wood
budgets to model LW abundance that varied at scales up to hundreds of m between stream reaches within a
watershed in southwestern Washington; Young et al. (2006) found clumping of LW (variability in counts or volume
between 50-m reaches) high enough that sampling 400–2250 m of stream would be necessary to estimate a mean
value within 25% of the actual mean for LW abundance, and 2750–10 500 m for LW volume, in streams in western
Montana; and Meleason et al. (2007) reported that a randomly selected 200-m reach would underestimate wood
volume in the Waihaha River in New Zealand. As suggested by Young et al. (2006) for LW, reference reaches that
do not take into account relatively large-scale variability will not really represent LWJ abundance. However, simply
sampling some long length of stream will not be the most efficient method to represent LWJ. Our observations
suggest that characterizing LWJ abundance or distribution requires considering the scale at which stream valley
geomorphology varies, then sampling from more than one type of geomorphically defined setting.
Finally, quantifying spatial distribution of LWJ may provide a means for establishing references or goals for
stream management and restoration. For example, evaluation or restoration of a stream to reference conditions
could include a monitoring objective for a range of K-values at some scales. If, during monitoring, K-values were
determined to be outside the target range, additional measures could be taken to restore geomorphic or riparian
structures or interactions that influence LWJ distributions.
ACKNOWLEDGEMENTS
Financial support for this study was provided by a grant from the USDA National Research Initiative Program, the
USDA Forest Service, the Ohio Agricultural Research and Development Center (OARDC), and The Ohio State
University. The authors thank the Bessemer and Ontonagon Ranger Districts of the USDA Forest Service, the
Michigan Division of Natural Resources and the Porcupine Mountains Wilderness State Park. The authors thank
Noel Cressie who provided helpful statistical insight early in the analysis. They also thank the many individuals
who helped with the collection of data, including Mark Morris, Marianne Sonntag, Rebekah Andrus and Heather
McKnight.
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