1.0 Introduction
A fundamental tenet of ecology is that environmental systems do not operate in isolation.
Instead they are open, interdependent nested systems, whose functioning depends on the
linkages between them. The interconnectedness of ecosystems necessitates strong
understanding of nutrients and biomass flow patterns for landscapes to be managed
effectively.
River networks connect terrestrial environments by transporting water, sediment,
nutrients and biomass from up- to down-stream. Within fluvial systems, the transport of
woody debris is an influential form of nutrient and biomass flux (Bormann & Likens
1979). Woody debris acts a primary link between terrestrial and in-stream ecology
(Richardson et al. 2005). In headwater streams, allochtonous wood can compose up to
90% of available food for aquatic organisms and may provide critical nutrient supplies
when transported downstream (Wipfli et al. 2007). When it is immobile, wood controls
the downstream flow of fine organic matter and sediment (Buffington & Montgomery
1999; Kaufmann et al. 2008; Montgomery et al. 1995). In summary, nutrient retention
and distribution within a watershed is highly dependent on where streams have the
capacity to transport wood.
Little is known about wood transport at the landscape scale (Hassan et al. 2005). Several
potential controlling factors have been identified; they include stream power, wood size
relative to channel size, the position and degree of anchoring of each piece, channel
roughness and drainage area (Hassan et al. 2005, Chang 2006). It is understood that
wood volume decreases from low- to high-order streams (Wohl & Jaeger 2009). The
likelihood of transport has also been observed to be higher in larger streams and when
wood pieces are smaller than bankfull width (Hassan et al. 2005). However, the size
range of small, non-transporting streams is not known. For instance, Young et al. (2006)
unexpectedly found that a large proportion of woody debris was fluvially-transported in
streams of only 4-8 wide. We lack quantitative understanding of where along a river
profile transport initiation begins.
This proposal presents a research project designed to investigate the relationship between
wood transport and location in the stream network. It presents an explanation of the
project’s objectives (p.2), methods of data collection and analysis (p.2), expected results
(p.7) concluding with a brief description of the project’s significance (p.8).
2.0 Objectives and Hypothesis
The research question motivating this study asks “How does woody debris transport
change over the course of a stream, and what are these changes related to?” To address
this question, the following objectives have been developed:
1) To determine where along the stream wood has been reorganized by fluvial processes
2) To identify where in the stream transport begins for wood of different sizes
3) To describe changes in the size of woody debris from up- to downstream
4) To identify the relationship between changes in transport to potential controlling
factors including stream power, slope, width, depth, sinuosity and drainage area
5) To describe differences in wood function between upstream and downstream reaches
Hypothesis:
H0: Wood transport capacity does not change downstream.
H1: Wood transport capacity increases downstream.
3.0 Methods
3.1 Approach
This study will employ a novel method of data collection to identify wood transport.
Most previous studies have examined transport by noting the location of tagged wood
pieces over multiple years (e.g. Martin & Benda, 2001; Lienkaemper & Swanson, 1987).
In this study, transport will be inferred from spatial pattern of wood in streams. Wood
recruited to streams by the continual processes of competition and suppression tends to
be randomly distributed, reflecting the irregular spacing of trees in natural forests. When
woody debris is mobilized and deposited by water, it tends to be develop a clumped, nonrandom spatial distribution. Distribution analysis is an emerging approach which is an
effective, fast and frugal alternative to time-consuming traditional studies.
3.2 Field Site
All research will be conducted within the Foothills Model Forest, which is located in the
upper and lower foothill sub-regions east of the Rocky Mountains in west-central Alberta
(53°0'N 122°53'W). The densely-forested foothills lie in the transitional zone between
the Rocky Mountain Natural Region and the Boreal Forest Natural Region. Riparian
forests are dominated by Lodgepole Pine (Pinus contorta), white spruce (Picea glauca),
black spruce (Picea mariana), and alder (Alnus incana) (Beckingham 1996).
Extensive research has already been undertaken within this forest. Studies by Hassan and
McCleary have identified a 1km2 drainage area threshold at which stream domains shift
from colluvial to fluvial processes. Stream mapping and classification has been
performed in all watersheds of the forest by McCleary (2003a,b). Stream reaches have
been divided into segments with similar drainage area, slope and dominant forest type.
3.3 Study site selection
3.3.1 Stream selection
Six study streams will be selected using the following criteria:
1) Least disturbed riparian forest along its length.
a. Reason: Riparian forests must be present so that enough wood has entered
the stream for LWD to aggregate if transport is occurring.
b. Method: Qualifying streams will be identified by overlaying the FRI’s
logging and fire map layers onto the stream base layer using ArcGIS.
Streams with the shortest length of channel affected by these disturbances
will be chosen.
2) Most longitudinally consistent riparian forest type.
a. Reason: Forests of similar composition and age are assumed to input wood
of similar size class distributions.
b. Method: Measure the total length of channel with similar riparian forest
using McCleary’s (2003) stream classification maps.
3) Reasonably accessible via ATV trail or seismic line.
a. Method: Judge site accessibility using the FRI’s trail maps.
3.3.2 Site Selection
In each selected stream, four new study sites will be established in the four consecutive
reach types, identified by McCleary (2003), downstream of the permanent sample plot.
Each plot will be randomly located in one stream segment (as classified by McCleary
2003). Study sites will be 40 times the bankfull width in length (Scherer 2008). Once
chosen, air photos and trail maps of each study site will be obtained from the FRI, and
access routes planned.
3.4 Data Collection
3.4.1 Stream Measurements
The dimensions and sinuosity of each study reach will be measured to provide a basis for
comparison with wood data. A steel post benchmark will be established at the upstream
end of each study reach, and its UTM co-ordinates recorded. From this point reach
length will be measured with a measuring tape following the thalweg. Every 5m,
bankfull width and depth will be measured using a measuring tape for width, and a
folding meter stick for depth. Depth measurements will be taken from the channel bed
near both stream banks, the stream’s center, and anywhere else a significant shift in depth
is noted. Channel slope will be measured with a clinometer from the midpoint of the
study reach to the benchmark.
Sinuosity will be determined to use as a simple approximation of stream morphology.
Flagged “pigtail” steel pegs will be inserted into the bank at every significant bend in the
channel, to which aluminum labels showing distance from the benchmark will be
attached. Sinuosity will be calculated by dividing the length of the study reach by its
valley length, which will be measured from air photos.
Using bankfull width and depth measurements with a velocity estimation equation, such
as Gauckler–Manning’s, peak discharge will be calculated for each study reach.
Specific stream power will be calculated using the following formula:
Ω = γQS
(1)
Where Ω is total stream power (W), γ is the specific weight of water (9800 N//m2), Q is
the peak discharge (m3/s), and S is stream gradient (m/m). Peak discharge will be
calculated using bankfull width and depth measurements as follows:
Q = vBwBd
(2)
Where v is average velocity, Bw and Bd are bankfull width and depth. Velocity will be
calculated using an equation such as Gauckler–Manning’s:
(3)
Where n is the Manning roughness coefficient and R is, in this case, the mean bankfull
depth (Radecki-Pawlik, 2002).
3.4.2 Wood measurements
Woody debris will be defined as any fallen or suspended trunk or branch of a dead tree
lying completely or partially within the bankfull channel. Logs that are broken and
unattached count as separate wood pieces even if they originate from the same tree
(McCleary et al. 2003). All pieces of wood over 4cm in diameter and over 40cm long
will be inventoried. The distance of each individual piece from the benchmark will be
recorded. If the wood is at an angle to the streambank, the distance from the benchmark
will be recorded using the piece’s centre (Kraft & Warren, 2003). Each piece will be
assigned an identification number and labeled with a tagged pigtail on the nearest stream
bank for use in future studies. Diameter will be measured at the centre and piece ends
using calipers, and the piece’s total length as well as its length within the channel will be
measured. Piece orientation will be recorded as parallel, perpendicular, or diagonal to the
thalweg.
To observe downstream changes in the character of wood debris, the decay and position
class of each log will be recorded (Appendix 1, Tables 1 & 2). The stage of decay of
each piece will be quantified using the categorical classification scheme by employed by
Jones and Daniels’ (2008), which is based on the presence and absence of needles,
branches, and wood softness. See Appendix 2 for examples of data sheet which could be
used to record these measurements.
3.5 Data Analysis
3.5.1 Analysis of spatial distribution
Once this continuous wood size and distance data has been collected, wood will be
classified into size classes. All pieces in the “bridge” position class will be excluded
from the distribution analysis because they could not have been transported by fluvial
processes. The distribution of all pieces, and pieces in separate size classes, will be
analyzed using Ripley’s K function (1977).
(5)
As per Kraft & Warren (2003), where t is a specified distance along the channel; n is the
number of points in the study reach; u ij is the distance between points i and j, and It(uij)
counts whether the distance between i and u is larger (I=0) or smaller (I=1) than t. In
reach segments where K(t)>2t, wood will be classified as clumped, or aggregated. See
Kraft and Warren (2003) for an illustrated application of Ripley’s K.
Among all study reaches in a stream, the farthest-upstream cluster of wood pieces in each
size class will be identified. The location of this cluster will be interpreted as the zone of
transport initiation for that size class. Additionally, the total number of aggregated versus
random pieces will be compared for each study site.
3.5.2 Size class distribution
Inventoried wood pieces will be categorized into size classes. Frequency of wood pieces
per size class will be plotted for each study reach. Changes in presence and frequency of
different size classes will be noted. A test for significance between up and downstream
plots will be performed by comparing against natural variability of equivalent study
reaches between streams. The frequency of position, decay, and orientation classes for
wood in each size classes will also be plotted.
3.5.3 Correlation to stream and basin characteristics
Once the locations of different size classes are identified in each stream, they will be
plotted against the drainage area, stream power, sinuosity, width and depth. A linear
regression will be run to determine their relationships to derive a power law function
relating the transportable size of wood with stream and basin variables. The distribution
of decay class, position class, and orientation will also be plotted against slope, drainage
area and stream dimensions to determine how wood function and characteristics change
over the channel profile.
4.0 Expected Results
Woody debris aggregations are expected to occur more frequently downstream, as
transport becomes more influential. The length and diameter of mobile (aggregated)
wood is expected to increase with increasing drainage area and stream width, as in Figure
1 (Appendix 1). Large woody debris, >10cm in diameter, is predicted to become mobile
shortly downstream from the colluvial-fluvial boundary of 1km2 drainage area, and all
size classes are expected to become mobile suddenly afterwards. Because stream power
correlates with drainage area, the same relationship between power and mobile size
classes is expected.
Correlation between transport capacity and the other stream and basin characteristics are
expected to be weaker because they do not have a linear relationship with drainage area,
which is predicted to be the dominant factor. For instance, a clear trend may not be seen
between mobile size class and stream depth because many wide, powerful streams in this
region are also very shallow. Sinuosity may or may not increase downstream, but the
variance between streams is expected to be greater than any up- and down-stream
changes.
Both size class distributions and functions of woody debris are expected to change
downstream. Wood size is expected to shift from a positively-skewed upstream
distribution to a negatively-skewed distribution downstream. This shift is expected to
occur as transport capacity increases. Where very large wood (>20cm diameter) is
transportable, the smallest sized wood classes are expected to be absent. Parallel wood
positions are expected to be more common in larger streams, and bridged pieces less
frequent. Wood is expected to be more decayed on average in headwaters due to a longer
average in-stream residence time.
5.0 Contributions to science and management
This project is an application of the concept that patterns in the landscape are indicative
of process. It will contribute to the understanding of heterogeneity of nutrient and
biomass fluxes across space. Specifically, it will improve our understanding of
downstream changes in wood transport in small- to medium-sized streams. The results
will help to clarify where stream profiles transition from storing to transporting wood,
and will link this change to stream and basin characteristics. The end product will be a
quantitative, spatially implicit and multi-scale model of wood transport patterns
undisturbed forested watersheds.
This data will contribute specifically to the research Foothills Research Institute’s Fish
and Watershed Program, which actively works to incorporate research findings into new
tools for stream management. For instance, if wood transport is predictable by one
simple basin metric such as drainage area, managers could easily identify areas where
wood transport is likely to be significant using only fine-scale topographic maps.
Different wood transport zones could then be used to identify areas where in-stream
wood regimes are more or less important to conserve. With this data, forest managers at
the FRI and elsewhere could direct their efforts to the most useful parts of a basin. The
insights acquired from this study could be applied to other montane and sub-boreal
ecosystems.