Neighborhood matters: Patterns and controls on wood distribution in old-growth forest streams of the Colorado Front Range, USA

Geomorphology 125 (2011) 132–146

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Geomorphology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / g e o m o r p h

Neighborhood matters: Patterns and controls on wood distribution in old-growth forest streams of the Colorado Front Range, USA Ellen Wohl ⁎, Daniel Cadol 1 Department of Geosciences, Colorado State University, Fort Collins, CO 80523-1482, USA

a r t i c l e

i n f o

Article history: Received 20 February 2010 Received in revised form 10 September 2010 Accepted 14 September 2010 Available online 18 September 2010 Keywords: Instream wood Colorado Front Range Old-growth forest Spatial distribution

a b s t r a c t We surveyed wood characteristics along four headwater channel segments of the Colorado Front Range with drainage areas of 8–82 km2. Stream lengths surveyed range from 3025 to 8980 m and together include a total of 15,706 pieces of wood. Time since last disturbance in the form of a stand-killing fire varied from 31 to N 500 years. Individual pieces of wood were highly aggregated at length scales of 1 to 150 m. Trends among jams were more weakly developed, but jams tended to be more segregated at lengths b10 m, slightly more aggregated at lengths ~ 100–300 m, and to have diverging patterns at lengths N 300 m, with jams along individual channels being aggregated, segregated, or random. Multiple linear regressions failed to produce highly predictive models to explain the response variables of wood load, piece dimensions, or characteristics of jams other than jam volume (which correlated with drainage area and wood load); but examination of downstream patterns suggests that local valley and channel geometry (valley-bottom width, gradient, and sequence of longitudinal channel changes) exert a stronger influence on patterns of wood distribution than either time since last forest disturbance or progressive downstream trends associated with increasing drainage area. The longitudinal sequences of wood recruitment sources, forest stand age, and channel geometry together exert an important control on reach-scale wood load and aggregation. Wood loads in streams draining old-growth (N 250 years) forests of the Colorado Front Range are low compared to oldgrowth sites in other regions of the world, which we interpret to reflect decreased retention of wood recruited to the streams. © 2010 Elsevier B.V. All rights reserved.

1. Introduction An extensive literature now documents the geomorphic and ecological effects of instream wood. These effects include increased boundary roughness and hydraulic resistance (Keller and Tally, 1979; Curran and Wohl, 2003), localized bed and bank scour (Berg et al., 1998), increased storage of fine sediment and organic matter (Bilby and Likens, 1980; Faustini and Jones, 2003), modification of bedforms (Baillie and Davies, 2002; MacFarlane and Wohl, 2003), and greater habitat diversity and abundance (Fausch and Northcote, 1992; Maser and Sedell, 1994), as well as effects on channel planform (Collins and Montgomery, 2002; O'Connor et al., 2003). Less well documented, however, are the relative importance of different processes of wood recruitment and the longitudinal distribution of wood in various settings (Marston, 1982; Wing et al., 1999; May and Gresswell, 2003). Benda and Sias (2003) conceptualize wood recruitment from upstream and from lateral sources via numerous processes, as well

⁎ Corresponding author. Tel.: + 1 970 491 5298; fax: + 1 970 491 6307. E-mail address: [email protected] (E. Wohl). 1 Current address: Department of Geology, College of William and Mary, P.O. Box 8795, Williamsburg, VA 23187-8795. 0169-555X/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.geomorph.2010.09.008

as wood loss through transport downstream or to adjacent floodplains. Except in extreme cases such as massive landslides, however, inferring what proportion of instream wood results from lateral versus upstream sources can be difficult. Similarly, predicting the longitudinal distribution of wood once it enters a stream remains difficult, although the wood is likely to be nonrandomly distributed (Kraft and Warren, 2003; Wohl and Jaeger, 2009). Several studies indicate declines in volume of wood per unit area of channel downstream through a drainage basin (Keller and Swanson, 1979; Keller and Tally, 1979; Hassan et al., 2005; Wohl and Jaeger, 2009), partly in response to increased transport capacity downstream (Marcus et al., 2002; Wohl and Jaeger, 2009), although high spatial variability in wood recruitment and retention appears to be common (Benda et al., 2003; Hassan et al., 2005). More limited work suggests that jams form preferentially in portions of a basin where the combined effects of wood supply and transport capacity are maximized (Wohl and Jaeger, 2009), partly as a result of longitudinal variability in valley morphology (Morris et al., 2009). Despite recent advances in understanding the forces acting on a piece of instream wood and the mechanics of fluvial wood transport (Braudrick and Grant, 2001; Manners et al., 2007; Bocchiola et al., 2008), however, the complex interactions among wood recruitment, channel form, and channel hydraulics make it challenging to quantify wood retention

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and distribution (Swanson, 2003; Hassan et al., 2005), particularly at the reach scale of 101–102 m, as opposed to larger basin scales. Reach-scale channel process and form are commonly of particular interest to resource managers trying to enhance fish habitat or stabilize an eroding channel using engineered logjams (Keim et al., 2000). It is therefore important to refine our understanding of wood transport and retention at this spatial scale by collecting and analyzing field data from diverse settings. An example comes from Kraft and Warren (2003), who pioneered the use of Neighbor K statistics (also known as Ripley's K) to assess aggregation (points closer to one another than random, i.e., clustered) and segregation (points farther from one another than random, i.e., regularly spaced) of wood in streams. Surveying 700- to 1000-m lengths along eight streams in the Adirondack Mountains of New York, USA, they found that individual pieces were aggregated at spatial extents of 0 to 40 m and segregated at spatial extents of 80 to 100 m following extensive wood deposition during an ice storm. Jams were segregated at distances of 100 to 300 m in two of their six study streams. They interpreted the patterns of jam segregation as reflecting unspecified, regularly spaced stream features or processes that facilitated jamming. Given the geomorphic and ecological importance of jams (Lautz et al., 2006; Manners et al., 2007), we need to better understand the spatial patterns of instream wood distribution and the controls on these patterns. With this background, we collected spatially explicit data on wood distribution and forest and channel characteristics along four headwater channel segments ranging from 3025 to 8980 m in length and located in the same drainage basin. These data represent an unusually large spatial inventory of wood loads in second- to thirdorder mountain streams in terms of number of wood pieces surveyed and length of channel measured. These data allowed us to address three primary objectives: (i) to assess the scales of aggregation and segregation present for individual pieces and wood jams at channel lengths of 100–102 m; (ii) to statistically assess the best predictors of six instream wood characteristics (wood volume per unit area of channel, piece length and diameter, proportion of pieces in jams, average jam volume, and downstream distance between jams) and to assess whether basin-scale predictors such as drainage area or elevation correlate more strongly with wood characteristics than local-scale predictors such as forest stand age and channel width; and (iii) to compare wood load in streams draining old-growth forests in the Colorado Front Range to wood load in other, more recently disturbed sites in the region and to old-growth sites from other regions. 2. Study area We collected field data along four channel segments in the headwaters of North St. Vrain Creek in Rocky Mountain National Park, Colorado (Fig. 1). The creek drains 250 km2, flowing from near the Continental Divide at 4050 m elevation down to 1945 m at the base of the mountains, where the creek is tributary to the South Platte River. Mean annual precipitation is 71 cm in the upper basin. Flow is dominated by snowmelt, which produces an annual hydrograph with a sustained May–June peak. The basin is underlain by Precambrianage Silver Plume Granite (Braddock and Cole, 1990). Although bedrock lithology does not vary substantially in the study area, valley geometry is quite variable as a reflection of Pleistocene glacial dynamics (Wohl et al., 2004) and of variations in joint geometry and associated susceptibility to weathering and erosion (Ehlen and Wohl, 2002). Lakes present in the study area occur within glacial cirques. Within the study area, the width and gradient of stream channels vary downstream at lengths of 102–103 m; small bedrock gorges in which both channel and valley-bottom width are b30 m regularly alternate longitudinally with lower gradient (1–2%), wider (several

133

times active channel width) valley segments, and at least one set of waterfalls N10 m tall occurs along each of the four primary channel segments surveyed for this study. Some of the wider valley bottom segments contain a single channel with short (b30 m long) sections of split channels, whereas others contain anastomosing channels. We define anastomosing channels in this context as including more than two (sub)parallel channels that contain flow during the snowmelt runoff peak of early May through mid July. Individual channels extend for distances of 50–300 m between branching from and rejoining the main channel, with forested surfaces between the channels. We observed up to eight (sub)parallel channels, each 2–8 m wide, across valley segments up to 90 m wide. Step-pool channels are most common along the lengths of channel sampled for this study, although cascade, plane-bed, and pool-riffle morphologies (Montgomery and Buffington, 1997) are also present. Substrate is primarily cobble- to boulder-size clasts, although finer gravel is present in zones of flow separation such as upstream from logjams. Sampling began a short distance below timberline (~3200 m elevation) and continued down to ~ 2550 m. This portion of the catchment is above the Pleistocene terminal moraine and is predominantly covered by subalpine forests of Engelmann spruce (Picea engelmannii), subalpine fir (Abies lasiocarpa), lodgepole pine (Pinus contorta), aspen (Populus tremuloides), and limber pine (Pinus flexilis) (Veblen and Donnegan, 2005). Lodgepole pine forests dominate large areas of the subalpine zone, forming the most extensive forest type in the Front Range (Veblen and Donnegan, 2005). More mesic subalpine sites are dominated by Engelmann spruce and subalpine fir, whereas lodgepole pines dominate more xeric sites and are successional to the spruce–fir community. Riparian communities include large numbers of conifers such as Douglas-fir (Pseudotsuga menziesii) and spruce, as well as aspen. Age and size of individual trees varies greatly with site-specific conditions, but typical characteristics are listed in Table 1. In general, trees in the subalpine old-growth forests of the study area are shorter and of smaller diameter than trees from old-growth forests in other environments. Decay rates for standing dead trees or fallen logs of individual species have received relatively little attention in the Front Range, but available estimates are also summarized in Table 1. Decay rates tend to be higher for pines and at lower elevations, likely because the long, cold winters at higher elevations inhibit decomposition (Arthur and Fahey, 1990; Kueppers et al., 2004), but the decay rates summarized in Table 1 are slow relative to most other temperate and tropical environments. Disturbance in Front Range forests takes the form of wildfire, persistent drought, insect outbreak, blowdown, hillslope mass movements (such as debris flows), and floods. Of these, fire and insect outbreaks are the most significant in terms of extent, severity, and frequency in the laterally confined mountain valleys of this study, and time since fire appears to be the single most important control on volume of dead wood in a stand (Mast and Veblen, 1994; Ehle and Baker, 2003; Hall et al., 2006). Infrequent, high severity fires that kill all canopy trees over areas of hundreds to thousands of hectares recur at intervals N100 years in the subalpine zone (Veblen and Donnegan, 2005). We are fortunate in having a detailed map of stand age in the study area developed from tree-ring chronologies (Sibold et al., 2006). Stand-killing disturbances affecting the study area date to A.D. 1654, 1695, 1880, and 1978. Many of the mature lodgepole pines are presently dying because of a widespread outbreak of mountain pine beetle (Dendroctonus ponderosa); such outbreaks recur every few decades throughout the Colorado Rocky Mountains (Romme et al., 2006). Regrowth of woody plants following a disturbance is slow in the semiarid Front Range relative to other temperate forests. Recruitment period following disturbance varies with site conditions, seed sources, and climate, but is typically 30–60 years for the subalpine zone (Veblen and Donnegan, 2005). Old-growth characteristics, however, typically do not emerge for at least 200 years in

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Fig. 1. Location map of upper North St. Vrain Creek drainage and study areas. Portions of the channel network along which wood was surveyed are indicated by heavier lines. Portions of the basin that are not shaded to indicate the year of the most recent burn are above treeline.

subalpine forests (Veblen, 1986). Wood recruitment to streams flowing through the disturbed area can thus increase substantially for a period of decades following a disturbance as dead and dying trees slowly topple, but is then likely to decrease during the period when all dead trees have fallen and new trees are not yet large enough for recruitment; the whole process may require two centuries to reach predisturbance wood dynamics (Bragg, 2000).

channel every 30 m downstream. Elevation, drainage area, and gradient were obtained from 1:24,000-scale topographic maps, with locations along the channel based on coordinates obtained from handheld GPS units, typically with ±3-m horizontal uncertainty. Forest stand age came from maps developed from tree-ring chronologies (Sibold et al., 2006). 3.2. Data analysis

3. Methods 3.1. Field methods Collection of field data focused on spatially continuous instream sampling of wood meeting the minimum size criteria of 1 m long and 10 cm in diameter. For each piece of wood within the bankfull channel, we measured the length, diameter, piece type using five categories, decay class using three categories, longitudinal position of the wood center point, and inclusion in a jam. The bankfull channel represents the portion submerged each year by peak snowmelt flow, as indicated by changes in vegetation and bank slope. Piece type was categorized as bridge (both ends above the bankfull channel), ramp (one end above the bankfull channel), pinned (partially or wholly wedged beneath or upstream from an obstacle such as a boulder or other wood), buried (partially buried in the stream bed), or unattached (wholly within the bankfull channel but not pinned or buried). Decay class was categorized as fresh (retaining needles and bark), partly decayed (retaining some branches and bark), and decayed (all bark and branches gone). A jam was defined as three or more pieces in contact. We measured the top width of the bankfull

3.2.1. Scales of aggregation and segregation Following Kraft and Warren (2003), we used a one-dimensional version of Ripley's K, a second-order statistic that evaluates the spatial pattern of points within a landscape (Ripley, 1977). Both individual wood pieces and jams were characterized as points distributed along a one-dimensional transect consisting of the stream channel. The K statistic is equivalent to the average number of pieces within a given distance of each piece, noted K(t), where t is the length scale being considered. Individual pieces were analyzed at length scales from 1 to 150 m, and jams were analyzed at length scales from 1 to 500 m, both at 1 m intervals. Selection of the upper limits of the scales analyzed was influenced by two main considerations: the computational power of the machine being used (analysis of longer scales was possible for the jams because they were fewer in number), and the length of the study reaches (as the length scale approaches half the length of the study reach, the analysis becomes rapidly less robust). We expect that the length scales analyzed will capture any plausible interactions among wood pieces or jams. Additionally, we divided our longest study reach, the 8980 m surveyed on North St. Vrain Creek, into 18 500-m-long segments and analyzed the K statistics of each segment

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Table 1 Characteristics of dominant tree species in the study area of the Colorado Front Range. Tree species

Height

Diameter

Age

Decay rates

Reference

Lodgepole pine

Rarely N24 m

Typically b 65 cm

Establish over 100–120 y following disturbance such as fire, can live N 200 y, but most trees b 120 y

Knowles and Grant, 1983 Veblen (1986) Kueppers et al. (2004)

Engelmann spruce

Typically b37 m

Typically b 75 cm

Establish over 60–100 y following disturbance, can live N 500 y

Turnover time of 630 ± 400 y in mid-elevation forest (3300–3400 m) and 340 ± 130 y in lower elevation forest (3000–3300 m) Turnover time of 650–920 y; dead trees can stand N 190 y

Subalpine fir

Typically b30 m

Typically b 45 cm

Establish over 90–100 y following disturbance, can live N 350 y but mostly 200–350 y

Fallen trees may require N 150 y to completely disappear; dead trees can stand N 150 y

Limber pine

Typically b18 m

b 65 cm

Can live N300 y

Knowles and Grant, 1983 Veblen (1986) Mast and Veblen (1994) Brown et al., 1998 Kueppers et al. (2004) Knowles and Grant, 1983 Veblen (1986) Roovers and Rebertus, 1993 Mast and Veblen (1994) Brown et al. (1998) Knowles and Grant, 1983 Veblen (1986)

for downstream trends. To enable comparison among the segments, we divided the K(t) values by the average wood frequency (number of pieces in the segment/segment length) of each segment. To evaluate the significance of aggregation and segregation found in our neighbor K analyses, we compared the values of K(t) from each of our study reaches with values of K(t) calculated from 100 simulated wood distributions known to be spatially random. Because K(t) depends on the average spatial density of pieces, the simulations were done using the same reach length and the same number of pieces as the study reach of concern. If the observed values of K(t) were outside the envelope defined by the fifth and ninety-fifth percentiles of the 100 simulations, then there is evidence that the pieces in the study reach are not randomly distributed. Values of K(t) higher than the envelope indicate aggregation at that scale; i.e., pieces are clustered, causing there to be more pieces within distance t than expected in a random distribution. Values of K(t) lower than the envelope indicate segregation at that scale; i.e., pieces are more evenly spaced than they would be in a random distribution. In calculating K(t), an edge effect must be considered. If a piece is closer than distance t to the edge of the study reach, then there is a chance that the number of pieces within distance t of it will be undercounted, artificially lowering K(t). Therefore, in calculating K(t), we did not include associated piece counts for those pieces that were closer than t to the edge, but we did continue to consider those pieces in the associated piece counts for all other pieces. Thus, we had larger sample sizes for smaller t values because, as t increased, more pieces had to be excluded. In some cases, this led to sudden changes in K(t) with increasing t, as significant clusters of pieces became too close to the edge and were excluded from contributing to K(t).

frequent jams, this might not be the case if transport capacity is very high, so that wood is frequently mobilized during the annual snowmelt peak and remains dispersed rather than aggregated, or if transport capacity is very low and pieces never collect into jams.) Regression models were evaluated and selected using best-subset selection targeting models with minimum Mallow's Cp (Kutner et al., 2005). The Cp value is used to find parsimonious models with significant parameter estimates. The lumped data divided each of the four channels into 3 to 12 segments, each of which was 200 to 2300 m in length; divisions were based on physical discontinuities along the channel (substantial change in gradient and valley geometry, the presence of a lake, or abrupt increase in drainage area associated with a tributary junction). In our analysis of the lumped data set, each segment had associated values for gradient and elevation in addition to the independent variables available for the 30-m increments, and we considered distance between jams rather than number of jams. For this data set, we modeled the dependent variables wood volume per hectare, average piece length, average piece diameter, proportion of pieces in jams, average distance between jams, and average jam volume. As described above, we selected the best model for each dependent variable using minimum Mallow's Cp as the criterion.

3.2.2. Predictors of instream wood characteristics We subdivided the data in two different manners. The split data consisted of 30-m-long channel increments. Depending on the total length of the channel survey, this data set contained from 100 to 300 segments per channel. Each 30-m increment was associated with a value for drainage area, forest age, average channel width, wood volume, wood volume per hectare of channel surface area, average piece length, average piece diameter, proportion of pieces in jams, proportion of wood volume in jams, average jam volume, and number of jams. In this analysis, the variable “number of jams” was used instead of distance between jams because increments often contained no jams, making the distance between jams undefined. The independent variables (drainage area, forest age, channel width) were used to model the dependent variables using multiple linear regression analysis. Wood volume was also considered an independent variable for modeling the jamming-related variables. (Although greater wood volume is likely to correlate with larger and/or more

In total, we surveyed 3060 m of channel along Cony Creek, which contained 3103 pieces of wood and 202 jams; 3397 m of channel along Hunters Creek, which contained 1750 pieces and 126 jams; 5010 m along Ouzel Creek, which contained 4969 pieces and 293 jams; and 8980 m along North St. Vrain Creek, which contained 5260 pieces and 360 jams. All piece counts and calculated volumes of wood represent minimum estimates because it was not possible to excavate pieces of wood partially buried in the streambed or to disassemble large and complex logjams. In addition, where multiple channels were present in anastomosing reaches, we measured wood only in the main channel. Substantial volumes of wood are also stored in the secondary channels. In order to evaluate the magnitude of this storage, we measured wood in secondary channels and across the floodplain at two sites with anastomosing channels in the study area along North St. Vrain Creek. Including all wood present in secondary channels would increase the magnitude of wood load by up to five times in anastomosing sections, thus enhancing the patterns discussed below.

3.2.3. Old-growth wood load comparisons These comparisons were qualitative and based on the relatively limited data published for other types of old-growth forest around the world. 4. Results

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Characteristics of the study reaches are summarized in Table 2. The longitudinal distribution of individual pieces and jams are illustrated in Fig. 2. Two of the glacial lakes present in the study area directly influence wood load in the sampled reaches. NSV 1 begins a few hundred meters downstream from Thunder Lake and Ouzel 2 begins within a hundred meters of the outlet of Ouzel Lake. These lakes have little current and lack substantial floating wood (Thunder Lake is close to timberline), so we assume that these lakes contribute little, if any, wood for downstream transport. 4.1. Aggregation and segregation Individual pieces of wood were highly aggregated (closer to one another than random) for all study streams at all length scales analyzed. The results for jams were much less consistent (Table 3). Jams along North St. Vrain Creek were aggregated at length scales N10 m, whereas jams along Cony Creek were random at length scales N10 m. Jams along Hunters Creek are random at length scales b~275 m and segregated at greater length scales. Jams along Ouzel Creek are quite variable between the segments above and below Ouzel Lake (Fig. 3; Table 3). In general, jams are more segregated at shorter lengths (fewer jams within 10 m of one another than would be expected under random spacing) and slightly more aggregated at longer lengths (more jams within 100–500 m of one another than if spacing was random). The strength of segregation and aggregation among jams, however, was consistently weaker than among individual pieces. To summarize, pieces tend to cluster into jams. Jams do not develop too close to one another, probably because there is some minimum accumulation length. But jams do appear to cluster together at scales from 100 to 500 m at some sites, which likely reflects reachscale decrease in wood transport capacity associated with features such

as lower gradient and anastomosing channel planform. We observed no downstream trends in the spatial distribution of wood in the 8980 m of North St. Vrain Creek that we surveyed, but our data do document the range of variability of the K statistic over relatively short (b10 km) distances (Fig. 4). The distance between jams in the Colorado Front Range study streams averages 28 m (range 5–264 m). This equates to an average of 5.4 jams/100 m of channel (range 0.9–10.4). Although no significant relation between interjam spacing and drainage area exists, there is a weakly developed tendency for jams to increase in volume downstream. The proportion of the total wood pieces that are included in a jam within any 30-m channel segment averages 0.46 (range 0–1).

4.2. Predictors of wood characteristics Multiple linear regressions for the split (30-m-increment) data mostly produced low R2 values (typically ≪0.5), although the parameter values of the best-fit models are highly significant. In other words, there are statistically significant trends to the dependent variables in the model space, in part owing to the large size of the data set. However, there is so much variability around these trends that the signal to noise ratio is very low, and thus the predictive value of the models is also low despite the significant trends. Adding total wood volume as an independent variable to any model of jam characteristics greatly improved the predictive performance of the model. Multiple regression analysis was performed on each stream individually and on the data from all four streams together. The only variable for which a model with adjusted R2 N 0.5 was found using the full data set was jam volume, which correlated with drainage area and wood volume (adjusted R2 = 0.54; Table 4). Because wood volume is included as a predictor variable, if wood volume within the 30-m

Table 2 Characteristics of study reachesa. Channel

A (km2)

E (m)

S (m/m)

W (m)

Forest age (yr)

Woodvol (m3/ha)

Jamvol (m3)

Jamdist (m)

Jampropor

WoodL (m)

Woodd (cm)

Hunters 1 Hunters 2 Hunters 3 Cony 1 Cony 2 Cony 3 Cony 4 Ouzel 1 Ouzel 2 Ouzel 3 Ouzel 4 Ouzel 5 Ouzel 6 NSV 1 NSV 2 NSV 3 NSV 4 NSV 5 NSV 6 NSV 7 NSV 8 NSV 9 NSV 10 NSV 11 NSV 12 Average

11.6 12.2 12.5 14.1 19.7 19.8 19.8 7.8 10.9 12.1 13.2 14 14.1 15 16 19.6 21.8 36.4 39.2 59.2 59.4 60.3 60.6 64.2 82.2

3025 2950 2750 3025 2950 2850 2750 3137 3040 3009 2972 2912 2826 3135 3046 2967 2857 2790 2754 2726 2711 2690 2666 2635 2596

0.14 0.04 0.28 0.04 0.07 0.35 0.12 0.09 0.06 0.05 0.15 0.08 0.14 0.14 0.04 0.05 0.11 0.06 0.08 0.11 0.07 0.06 0.07 0.06 0.04 0.10 0.12

5.9 5 6.5 8.3 9 17.0 12.8 7.6 10.4 10.8 8.6 7.1 10.9 6.7 8.6 9.5 9.9 12.3 12.4 15.0 17.4 17.5 19.8 14.7 13.4 11.1 8.6

355 355 242 500 500 333 129 500 324 31 31 31 80 355 355 242 129 129 129 129 129 129 129 129 129

415 64 108 221 90 76 352 75 216 214 378 334 201 256 168 80 109 107 98 74 115 74 94 12 25 158 161

1.0 0.6 0.4 1.2 1.1 3.4 1.3 1.0 1.6 1.6 2.1 1.6 1.5 0.6 1.7 1.0 1.0 1.4 1.7 1.2 2.2 1.5 3.0 0.3 0.6 1.4 1.2

19.2 44.2 23.1 15.6 12.7 14.3 15.7 44.6 16.2 14.2 9.8 12.8 9.8 23.0 21.4 23.6 22.6 20.0 22.5 16.7 13.1 21.7 20.5 91.3 52.1 24.0 23.4

0.59 0.47 0.45 0.61 0.62 0.53 0.77 0.30 0.54 0.56 0.75 0.76 0.70 0.41 0.60 0.47 0.54 0.53 0.58 0.51 0.71 0.65 0.64 0.25 0.32 0.55 0.51

2.5 2.2 2.4 2.6 2.6 2.8 3.3 2.2 3.0 3.1 2.6 2.5 2.6 2.1 2.9 2.2 2.4 2.4 3.2 3.0 3.2 3.4 4.1 2.7 2.8 2.8 2.5

18.1 16.7 15.6 19.7 17.2 17.3 22.6 22.7 21.7 21.1 19.6 20.0 21.9 22.0 22.1 21.1 21.8 21.3 21.1 18.6 23.0 18.2 18.6 16.1 14.7 19.7 19.5

a Segments are based on major changes in gradient and valley geometry, presence of a lake (between Ouzel 1 and 2), or junction of mainstem and large tributaries. Each segment represents a lumping of between 7 and 77 30-m channel reaches. A = drainage area; E = elevation; S = average channel gradient, obtained from 7.5-minute topographic maps; W = average channel width averaged over multiple 30-m-long segments; forest age is time since last stand-killing fire and, where time varies within a channel segment, represents an average; Woodvol = cubic meters of wood per hectare of channel area averaged over multiple 30-m-long segments; Jamvol = average volume of each jam averaged over multiple 30-m-long segments; Jamdist = average longitudinal distance between successive jams averaged over multiple 30-m-long segments; Jampropor = proportion of total wood in channel reach that is included in a jam (calculated based on number of pieces) averaged over multiple 30-m-long segments; WoodL = average wood piece length averaged over multiple 30-m-long segments; Woodd = average piece diameter averaged over multiple 30-m-long segments; gray shading indicates channel segments flowing through old-growth forest (N ~ 250 y). Averages in last row represent total average (above) and old-growth average (below).

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segment is held constant, then jam volume increases with drainage area. Multiple regression analysis of the lumped data set resulted in relatively higher predictive power, but only two response variables, average jam volume and average piece length, had adjusted R2 N 0.5 (Table 5). Drainage area, a basin parameter, generally contributed more information to the model than the local parameters of forest age or channel width. Gradient was rarely selected as a significant parameter, although this may reflect the variability of gradient within each lumped segment and the inability of a single average value to adequately describe gradient throughout the segment. Drainage area is included in all three models of jam characteristics. Holding other controlling variables such as channel width constant, increasing drainage area correlates to a lower proportion of wood in jams, greater distance between jams, and lower average jam volume. Likewise, channel width is included in all three models of jam characteristics, although the correlations have inverse signs relative to those with drainage area. Interpretation of these models could thus be complicated by the correlation between drainage area and channel width, but this correlation tends to be weakly developed in the study area (Wohl et al., 2004). Drainage area and channel width tend to increase downstream, but jamming will be controlled by irregularities in the rates of increase. For example, jamming will be more prominent at locations where the channel is wider than expected for the given drainage area. Nonetheless, although the relationship between drainage area and jamming is significant (highly significant parameter

A

estimates), as is the relationship between channel width and jamming, even together they do not explain a high level of variation in the jam characteristics (moderate R2; Table 5). In addition to the multiple linear regression models, we used simple linear comparisons and t-tests of differences between means to assess the relationships between time since forest disturbance and wood characteristics. These results suggest that time since forest disturbance is not a primary control on wood load along two of the four channel segments (Fig. 5A). Wood load differed significantly between differently aged stands of forest along Ouzel Creek, where wood load increased from the oldest forest stands to those most recently disturbed by a forest fire in 1978. In this case, channel geometry appears to exert a significant control on wood load. The oldest forest stands lie along the upper portion of Ouzel Creek, but much of this upper portion of the channel is in meadows or steep bedrock gorges with minimal lateral wood recruitment (Fig. 2D). Wood load increases significantly, as does the volume of jams, once the creek enters a lower gradient valley and takes on an anastomosing planform. The highest wood loads occur where this channel type coincides with the 1978 burn, which has resulted in increased wood recruitment during the past four decades as standing dead trees from this stand-killing fire gradually fall over and enter the channel. Wood load also differed significantly along North St. Vrain Creek, but not along Hunters Creek, which had a similar bimodal distribution of forest stand ages (Fig. 5A). Along North St. Vrain, Hunters, and Cony Creeks, the oldest forest stands had the highest wood loads, in contrast to Ouzel Creek (Fig. 5A). Again, channel geometry appears to

700 fan avg 405

600

Wood load (m3/ha)

137

anastomosing avg 192

alternating anastomosing & small gorges avg 79

occasional anastomosing avg 85

single channel avg 13

500 North St. Vrain Creek 400 300 gorge avg 49

200 100 0

500

0

1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 9000

16 North St. Vrain jams

Volume of wood in jam (m3)

14

avg 1.4

avg 0.2

avg 0.8

12 avg 1.5 10 avg 1.1

8 6 4

avg 0.3

2 0 0

500

1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000 8500 9000

Cumulative distance downstream (m) Fig. 2. Bar graphs showing longitudinal distribution of wood load by 30-m channel increments and volume for each jam. Important aspects of channel geometry are noted on each graph, as are average values for y-axis variable.

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B

300 some anastomosing avg 76

very steep; gorges and falls avg 53

Wood load (m3/ha)

250

avg 37 Hunters Creek

200

150

100

50

0 1000

1500

2000

2500

3000

3.0

4000

avg 0.3

avg 0.5

Volume of wood in jam (m3)

3500

2.5

avg 0.3 Hunters jams

2.0

1.5

1.0

0.5

0.0 1000

1500

2000

2500

3000

3500

4000

Cumulative distance downstream (m) Fig. 2 (continued).

explain these disparities. The greatest wood loads and largest jams along North St. Vrain Creek occur in the depositional zone immediately downstream from a long gorge in the upper channel and in the substantial portion of the channel where lower gradient anastomosing reaches alternate with short, steep bedrock gorges (Fig. 2A). Wood loads and jam volumes are smaller in the longer bedrock gorge at the upstream end of the study reach and in the steeper single channel at the downstream end of the reach (Fig. 2A). Similarly, on Hunters Creek the greatest wood loads occur in the lower gradient anastomosing reaches, which are primarily in the upper half of the study area, although jam volume does not differ significantly between these upper reaches and the very steep lower reach (Fig. 2B). Along Cony Creek, the highest wood loads occur in the lower gradient, partly anastomosing upper reaches, and in the lower gradient reach below the bedrock gorge and above the junction with North St. Vrain Creek. Enormous logjams in this lower segment dwarf anything found upstream (Fig. 2C). Channel geometry thus appears to exert a stronger control on wood distribution than forest age along most of the study reaches. The patterns for log diameter in relation to forest age are equally mixed. Although older forests have trees of wider maximum diameter, instream wood diameter differed significantly with forest age on Cony and North St. Vrain Creeks, but not on Hunters and Ouzel Creeks (Fig. 5B). The lack of consistent correlation between stand age and piece diameter may reflect the high mobility and long transport distances of smaller unattached pieces that are common in jams and can skew the distribution of piece diameter toward smaller values despite local inputs of large-diameter pieces. Filtering the data to only

include pieces b15 cm in diameter, however, did not create different patterns than those shown in Fig. 5B using all data. Wood load is not significantly correlated with either average channel width or drainage area (Fig. 6). This could have reflected the way data were collected in anastomosing channel reaches. Rather than summing wood load and channel width for the multiple channels in these reaches, we measured these variables only for the largest channel in the reach, as noted earlier. Consequently, narrow channel segments can have low wood loads if they occur in steep bedrock gorges and high wood loads if they occur in lower gradient, anastomosing reaches. To test this possibility, we removed the channel segments classified as anastomosing and repeated the analysis. The scatter shown in Fig. 6 was not substantially reduced and wood load still did not correlate with channel width or drainage area. Despite progressive declines in wood load as either width or drainage area increases, the data show a great deal of scatter in relation to average channel width and to drainage area. The proportion of wood in jams within each 30-m length of channel does not correlate with either drainage area or channel width. 4.3. Old-growth wood load comparisons Limited data exist for comparing old-growth to more recently disturbed sites at similar elevation and drainage area in the Colorado Front Range. However, these data suggest that wood loads are substantially greater, on average, in old-growth sites than in disturbed sites. Richmond and Fausch (1995) found an average of 182 m3/ha

E. Wohl, D. Cadol / Geomorphology 125 (2011) 132–146

C

139

600 Cony Creek

cascades

500

avg 282

avg 57

avg 116

Wood load (m3/ha)

anastomosing anastomosing

partial meadow

400

300

200

100

0 1000

1500

2000

2500

3000

Volume of wood in jam (m3)

40

3500

4000

Cony jams

avg 1.0

avg 6.0

30

avg 0.6

20

10

0 1000

1500

2000

2500

3000

3500

4000

Cumulative distance downstream (m) Fig. 2 (continued).

(range 92–254 m3/ha) in old-growth sites versus an average of 66 m3/ha (range 12–147 m3/ha) in more recently disturbed sites. Our averages for wood load in old-growth versus more recently disturbed sites are much closer in value to each other (Table 2), but this likely reflects the close longitudinal spacing between our old-growth and other sites and the associated potential for downstream transport of wood between channel segments in old-growth and other forest types. When assessed using either wood volume/channel length or wood volume/channel area, average values of wood load in channels of the old-growth subalpine conifer forests of the Colorado Front Range are low compared with channels in old-growth temperate deciduous forest, temperate rainforest, tropical rainforest, or various forest types in the Southern Hemisphere (Table 6). The Front Range sites are also relatively low for streams draining conifer forests in general. Streams draining conifer forests around the world have an average wood load of 240 m3/ha; those in the Pacific Northwest average 812 m3/ha (Gurnell et al., 2002). The Front Range sites average 158 m3/ha, with a high value in old-growth sections of 416 m3/ha (Table 6). 5. Discussion 5.1. Aggregation and segregation As indicated by the results from the K statistics, individual wood pieces are more likely to aggregate into jams than to be randomly distributed in all channel segments surveyed here. This result seems

intuitive for channel segments in the study area: on the one hand, logs in these channel segments are sufficiently mobile for fluvial transport to create aggregation among what presumably originate as randomly spaced individual tree falls. On the other hand, individual tree falls can create ramped pieces that extend across a substantial portion of the channel width. These ramps, along with pronounced irregularities in channel cross-sectional area associated with features such as boulders protruding above the stream bed or lateral bedrock constrictions, can at least temporarily trap logs in transport and facilitate the formation of jams. Kraft and Warren (2003) observed segregation of jams at 100– 300 m scales in one of their study reaches and random distribution in the other study reaches. They were unable to identify the physical mechanisms responsible for differences observed between channels, but noted that changes in channel geometry such as gradient or bends might explain the distribution of jams. The downstream spacing of jams might be regular if they are associated with a repetitive, periodic feature such as meander bends, or irregular if they are associated with less longitudinally repetitive features. In the Front Range study area, jams are more likely to be segregated at shorter lengths and aggregated at scales from 100 to 500 m, although these patterns were not equally observed in all four study streams. Jams in the study area appear to be initiated via ramps or bridges (likely randomly spaced), protruding boulders, and abrupt changes in channel width and gradient. Our subjective assessment of the patterns in Fig. 2 suggests that larger jams occur in segments of wider, lower gradient channel downstream of laterally confined channels. Greater flow

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E. Wohl, D. Cadol / Geomorphology 125 (2011) 132–146

D

800

interspersed anastomosing reaches avg 220 1978 fire avg 243

Ouzel Creek anastomosing avg 123

avg 18

Wood load (m3/ha)

600

interspersed gorges avg 195

Ouzel Lake

avg 59 interspersed falls, gorges, meadows

400

200

0 500

1000

1500

2000

2500

3000

3500

Volume of wood in jams (m3)

8

4000

5000

5500

6000

avg 1.8 avg 1.8

Ouzel jams 6

4500

avg 1.7 avg 0.9

avg 0.1 avg 0.4

4

2

0 1000

2000

3000

4000

5000

6000

Cumulative distance downstream (m) Fig. 2 (continued).

depth (and associated log floating) and greater hydraulic forces (and associated rolling and breaking of logs) in the laterally confined channels likely limit jam formation. In other words, jams appear to be controlled by longitudinal variations in channel and valley geometry at length scales of 102–103 m, rather than by progressive downstream trends associated with increasing drainage area or channel width, across the relatively small range of drainage areas addressed in this study. Morris et al. (2009) also found that jams along rivers in northern Michigan are aggregated at scales up to several kilometers in

association with longitudinal variations in valley geometry; as in the Front Range, jams in the northern Michigan streams cluster in relatively low gradient stream segments with low valley constraint. Limited descriptive statistics have been published for jams along channels draining old-growth forests. Martin and Benda (2001) found that interjam spacing increased with drainage area along streams in southeastern Alaska, reaching up to 200 m at drainage areas of 80 km2, and that jams increased in size downstream. As noted earlier, these downstream trends are weakly developed or absent in the

Table 3 Summary of K statistics results. Cony

Hunters

N. St. Vrain

Ouzel (above Ouzel L.)

Ouzel below Ouzel L.)

Length (m) Pieces Jams Ave. pieces/m

3060 3103 202 1.01

3397 1750 126 0.52

8980 5260 360 0.59

1922 646 42 0.34

3085 4323 251 2.60

Piece K statistics Expected K(1)a Observed K(1)b Summaryc

2 6.7 Very Agg at all scales

1 5.1 Very Agg at all scales

1.2 5.1 Very Agg at all scales

0.7 3.6 Very Agg at all scales

3.2 9.2 Very Agg at all scales

Jam K statistics Summaryc

Seg 1–10 m, Rand 10–500 m

Rand 1–275 m, Seg 275–500

Rand 1–9 m, Agg 10–500 m

Barely Agg at all scales

Seg 1–10 m, Rand 10–80 & 400–500 m, Agg 80–400 m

a b c

Average number of other pieces within 1 m of a piece, assuming random piece distribution. Observed average number of other pieces within 1 m of a piece. Agg = aggregated; Rand = random; Seg = segregated.

E. Wohl, D. Cadol / Geomorphology 125 (2011) 132–146

141

Fig. 3. K statistics for the distribution of jams within the four study streams. Ouzel Creek is divided into the portion above Ouzel Lake and the portion below. The 5th percentile and 95th percentile lines refer to 100 simulations of spatially random jam placement and define the envelope for random jam distribution. Observed values above the 95th percentile indicate evidence for aggregation, while values below the 5th percentile indicate evidence for segregation.

Colorado sites. The proportion of pieces incorporated in jams at oldgrowth sites in Michigan averages 0.62 (range 0.23–0.92), which is higher than the Colorado sites (average 0.46). Streams in Michigan average 3.3 jams/100 m (range 1–5.3) (Morris et al., 2007), and those in southern Argentina average 6.1 jams/100 m (Mao et al., 2008). The Colorado sites are intermediate, with an average of 5.4 jams/100 m. 5.2. Predictors of wood characteristics

Fig. 4. Variation in K statistics among 18 500-m-long segments into which the North St. Vrain Creek study reach was divided. Values of K(t) for each reach have been normalized by piece density to enable direct comparison. Envelopes for the 5th and 95th and percentiles of 100 random simulations are shown for the segment with the most abundant wood load (segment 8) and the least abundant load (segment 16). Pieces are typically aggregated at low to moderate length scales and are rarely segregated. Certainty in the K(t) values decreases to the right as sample size decreases due to the exclusion of pieces in order to accommodate edge effects.

The only comparable study of wood distribution from old-growth forest channels, in terms of number of pieces of wood surveyed, is Fox and Bolton's (2007) comprehensive survey from the U.S. state of Washington (Table 6). They found the strongest correlations between wood load and channel bankfull width, forest type, bedform type, gradient, and confinement; of these, bankfull width was the single best predictor of wood load. As noted above, the way in which we collected data in the anastomosing reaches of the Colorado sites may have limited potential correlations between wood load and channel width, although we interpret the lack of correlations as being real. Fox and Bolton (2007) noted that lower gradient and alluvial channels have greater wood loads than bedrock and higher gradient channels, which corresponds to the patterns observed in the Colorado Front Range sites. They also found that wood load increases with basin size, whereas the inverse relationship is observed at the Colorado sites. The decline in wood load with increasing drainage area along North St. Vrain Creek could reflect the fact that the lower portion of the basin surveyed here was burned in 1880 (Table 2), or it might reflect the

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E. Wohl, D. Cadol / Geomorphology 125 (2011) 132–146

Table 4 Variables selected for best-subset multiple regression models of split (30-m-increment) data set (gray shading indicates models with R2 values N 0.5). Data set

Dependent variables

All Streams Cony Hunters N St Vrain Ouzel All Streams Cony Hunters N St Vrain Ouzel All Streams Cony Hunters N St Vrain Ouzel All Streams Cony Hunters N St Vrain Ouzel All Streams Cony Hunters N St Vrain Ouzel All Streams Cony Hunters N St Vrain Ouzel All Streams Cony Hunters N St Vrain Ouzel

volha volha volha volha volha vol vol vol vol vol propjam propjam propjam propjam propjam voljam voljam voljam voljam voljam jamnum jamnum jamnum jamnum jamnum len len len len len diam diam diam diam diam

a

Parameters in best model (model with lowest Cp)b

R2

Adjusted R2

dr_area, forest dr_area dr_area, forest dr_area dr_area, ch_wid dr_area, ch_wid, forest ch_wid dr_area, ch_wid, forest dr_area, ch_wid, forest dr_area, ch_wid dr_area, wood_vol, forest wood_vol dr_area, wood_vol wood_vol dr_area, wood_vol dr_area, wood_vol wood_vol wood_vol, forest wood_vol wood_vol, forest dr_area, wood_vol, forest wood_vol wood_vol dr_area, wood_vol, forest dr_area, wood_vol dr_area, wood_vol, ch_wid dr_area, wood_vol, forest wood_vol, forest dr_area, wood_vol, ch_wid dr_area, wood_vol, ch_wid dr_area, wood_vol, ch_wid dr_area, wood_vol wood_vol, ch_wid dr_area, wood_vol dr_area, wood_vol, ch_wid, forest

0.14 0.01 0.24 0.09 0.39 0.22 0.11 0.31 0.14 0.41 0.30 0.11 0.31 0.34 0.56 0.54 0.72 0.56 0.47 0.46 0.36 0.07 0.47 0.37 0.56 0.22 0.13 0.13 0.24 0.37 0.17 0.26 0.09 0.34 0.27

0.13 0.01 0.23 0.09 0.38 0.22 0.10 0.30 0.13 0.41 0.30 0.10 0.30 0.34 0.55 0.54 0.72 0.55 0.47 0.45 0.35 0.06 0.47 0.37 0.55 0.21 0.10 0.12 0.23 0.36 0.17 0.24 0.07 0.34 0.25

a Abbreviations: wood load in m3 ha− 1 (volha); wood volume in m3 (vol); proportion of pieces incorporated in jams (propjam); average wood volume per jam (voljam); number of jams in segment (jamnum); average length of pieces (len); average diameter of pieces (diam). b Abbreviations: drainage area in km2 (dr_area); forest age (forest); bankfull channel width (ch_wid); wood volume in m3 (wood_vol).

increasing transport capacity downstream as discharge increases and the presence of anastomosing channel segments becomes uncommon. Several other investigators have attributed downstream declines in wood load to increasing transport capacity (Keller and Swanson, 1979; Piégay et al., 1999; Beechie et al., 2000; Marcus et al., 2002; Wohl and Jaeger, 2009). Although the statistical results are inconclusive, we interpret the patterns illustrated and described in Fig. 2 as indicating that localscale predictors, specifically channel and valley geometry and to a lesser extent forest age, exert a stronger influence on wood characteristics than do basin-scale predictors. In other words,

Table 5 Variables selected for best-subset multiple regression models of lumped data set (gray shading indicates models with R2 values N 0.5). Dependent variablesa

Parameters in best model (model with lowest Cp)b

R2

Adjusted R2

Volha Propjam Jamdist Voljam Len Diam

dr_area− dr_area− dr_area+ dr_area− ch_wid+ dr_area−

0.42 0.35 0.33 0.67 0.60 0.44

0.36 0.26 0.27 0.64 0.59 0.36

forest− forest− ch_wid+ ch_wid− ch_wid+ grad− ch_wid+

Abbreviations: wood load in m3 ha− 1 (volha); proportion of pieces incorporated in jams (propjam); average distance between jams (jamdist); average wood volume per jam (voljam); average length of pieces (len); average diameter of pieces (diam). b Abbreviations: drainage area in km2 (dr_area); forest age (forest); bankfull channel width (ch_wid); stream gradient (grad). Superscript indicates a positive (+) or a negative (−) correlation with the dependent variable. a

progressive downstream trends are not well developed at the spatial scales examined here because of the influence of reach-scale processes on wood dynamics. Wohl and Jaeger (2009) found significant correlations in Front Range stream segments between wood load and several variables, including channel width, total stream power, bed gradient, and elevation. That study included shorter stream reaches (1250 m each) over a greater range of drainage area and elevation and suggests that basin-scale predictors can be useful in assessing patterns at larger spatial scales that range from headwater streams to major drainages. The relations between forest stand age and instream wood loads (Fig. 5A) provide some insight into temporal variations in the study area. The highest average wood loads with respect to stand age are those along channel segments flowing through the area burned in 1978. Almost all of the standing dead trees killed by that fire have now fallen over, and regrowth has thus far produced trees b3 m tall. The relatively rapid addition of wood to the channel in the few decades following the fire is likely to be followed by a period of a few decades with lower recruitment and lower wood loads as the wood already in the channel is transported downstream and lateral recruitment declines dramatically. The period of lower recruitment within the burned area will persist until the adjacent forest once again produces trees N10–15 m tall that can be recruited into the channel. The process of reestablishment is likely to require circa 100 years (Table 1), an estimate that is reinforced by the fact that relatively little difference in wood loads appears consistently between channel segments draining forest stands N100 years in age (Fig. 5). This suggests that, with the exception of recent (b100 y) widespread disturbance that substantially increases wood recruitment, temporal variations exert a less

E. Wohl, D. Cadol / Geomorphology 125 (2011) 132–146

A

143

800 800

Hunters

Cony

All creeks

North St. Vrain

Ouzel

Wood load (m3/ha)

600

Wood vol (m3/ha)

600

400

200

400

200

0 0 65 a

60 a

137 a

70 b

127 a

61 a

157 192 b b

245 c

101 a

67 b

0 355 129 500 333 129 500 314 129

31

5

10

15

20

25

30

35

Channel width (m)

355 129

Time since fire (y)

B

800 50

Hunters

Cony

All creeks

North St. Vrain

Ouzel

600

Wood load (m3/ha)

Log diameter (cm)

40

30

20

10

400

200

0

0 17 a

16 a

19 a

11 b

18 c

19 a

22 b

22 b

355 129 500 333 129 500 314 129

20 a

31

21 a

19 b

355 129

Time since fire (y) Fig. 5. (A) Box plots of wood load versus stand age for four channels. Data are from multiple 30-m channel lengths along each creek. The horizontal line within each box indicates the median value, which is also listed beneath the box. Box ends are the 25th and 75th percentiles, whiskers are the 10th and 90th percentiles, and solid dots are outliers. Significant pairwise differences in means are indicated with contrasting letters below each box. (B) Box plots of piece diameter versus stand age for four channels. Data and plots are as described for (A).

0

20

40

60

80

100

Drainage area (km2) Fig. 6. Scatter plots of wood load versus average channel width and drainage area for all study sites. The segmented nature of data in the drainage area plot reflects the lack of data for some ranges of drainage area. The results for individual channels are similar to the combined data set shown here. Each data point represents a value for a 30-m length of channel.

the site-specific nature of wood recruitment and transport processes will likely limit quantification to order-of-magnitude estimates. 5.3. Old-growth wood load comparisons

important influence on instream wood loads in the study area than spatial variations. The lack of progressive downstream trends in drainage areas up to approximately 100 km2 has important implications for predicting and managing instream wood in the Colorado Front Range. Because of substantial variability in wood loads and jam characteristics at the reach scale of 101–102 m channel lengths, managers cannot precisely quantify expected wood loads as a function of drainage area or channel width. Our results suggest that the longitudinal sequence of recruitment sources and valley geometry exerts an important control on wood loads and aggregation, as well as on channel geometry (Fig. 7). An obvious next step is to identify the specific reach characteristics that facilitate differences in wood retention and spatial distribution. In general, an abrupt downstream increase in valley width and decrease in gradient associated with the formation of anastomosing channels facilitate increased wood load and large jams (Fig. 2), which in turn likely facilitate formation of multiple channels (Fig. 7). More detailed characterization of downstream variation in channel geometry at sites of particularly high wood loads and large jams may improve the ability to quantify a reasonable range of variability for wood loads in relation to channel characteristics, but

The dearth of instream wood in channels flowing through oldgrowth forests in the Colorado Front Range relative to other oldgrowth sites around the world could be influenced by processes of recruitment or retention. Recruitment at the reach scale depends on factors such as forest basal area within the recruitment zone and disturbances that bring trees into the channel. Basal area of forests in this portion of the Rocky Mountains (33 m2/ha; Bragg, 2000) tends to be lower than in other old-growth forests (N40 m2/ha; Meleason et al., 2003; Meleason and Hall, 2005; Warren et al., 2009). Given the existence of periodic stand-killing wildfires, blowdowns, and insect infestations, however, recruitment is unlikely to be consistently lower in the Colorado study sites than in all of the other sites summarized in Table 6. Simulations of wood volume in Rocky Mountain streams suggest a steady-state volume of 6–8 m3/100 m reach after 250 years (Bragg, 2000), which is similar to simulation results for other oldgrowth forests (Meleason and Hall, 2005; Warren et al., 2009) except those in the Pacific Northwest, which can reach volumes 10 times greater (Meleason et al., 2003). We found average loads of 12 m3/ 100 m in Cony Creek, 4 m3/100 m in Hunters Creek, 13 m3/100 m in Ouzel Creek, and 8 m3/100 m in North St. Vrain Creek.

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E. Wohl, D. Cadol / Geomorphology 125 (2011) 132–146

Table 6 Comparison of site characteristics in Front Range old-growth streams with other old-growth sites. Location

Reach L (m); no. reaches

Washington, USA Michigan, USA S. Appalachian Mts., USA New Zealand

100–1000; 150 Temperate rainforest to Douglas-fir–ponderosa pine 300; 12 Hardwood-hemlock (temperate deciduous) 500; 2 Hardwood-hemlock (temperate deciduous) 200; 18 Nothofagus sub-antarctic rainforest & conifer/mixed hardwood subtropical rainforest 150–480; 5 Temperate rainforest

W. Oregon, USA S.E. Alaska, USA Chile

Forest type(s)

Drainage Stream area (km2) gradient

Min. piece L Sample size V/Lb (m), d (cm) (no. pieces)

0.4–325

0.1–47%

2, 10

21,671

(0–305)a

Fox and Bolton (2007)

40

1–5%

1, 10

2402

25 (7–62.3)

Morris et al. (2007)

3.5–11

1%, 4%

1.5, 10

–

Hedman et al. (1996)

0.6–5.8

1.7–9.5%

1, 10

1500

21.7 (21.2–22.3) 18 206 (4.2–49.4) (85–470)

0.1–60.5

3–37%

1.5, 10

624

335–1530; 5

Temperate rainforest

0.7–55

0.8–2.5%

1.5, 20

50–130; 8

Araucaria & Nothofagus

9.1

1, 10

Costa Rica

50; 30

Tropical wet

0.1–8.5

0.03– 0.15 0.2–8%

1, 10

1232

S. Argentina

35–230; 33

Nothofagus

5–12.9

1, 10

2000

Colorado, USA Colorado, USA

220–580; 11

Subalpine conifer

2.4–29

0.02– 0.09 0.4–6.4%

1, 10

1412

1740–3480; 4

Subalpine conifer

7.8–19.8

0.04– 0.35

1, 10

10,177

a b c

V/Ac

Reference

Meleason et al. (2005)

478 (230–750) 24 (7–62)

Lienkaemper and Swanson (1987) Robison and Beschta (1990) 710 (up to N 4000) Andreoli et al. (2007)

12.3 (3–35) 189 (41–612) 105 121 (20–395) (6.6–27.1) 182 (92–254) 7.0 161 (0–708) (0.2–31.7)

Cadol et al. (2009) Mao et al. (2008) Richmond and Fausch (1995) This study

Median varied from 7 to 95, depending on channel bankfull width and forest type. V/L is volume (m3) per length (100 m) of channel; average value, followed by range in parentheses. V/A is volume (m3) per area (ha) of channel; average value, followed by range in parentheses.

Retention reflects both fluvial transport and in situ decay of wood. Decay at the Colorado sites is slower than at the temperate deciduous and tropical sites in Table 6 (Kueppers et al., 2004; Meleason and Hall, 2005). Previous and ongoing work indicates that transport rates of wood at the Colorado sites are likely to be quite high. Some of the sites used in the present study were also part of the 10-year study of wood retention summarized in Wohl and Goode (2008), which found that average wood residence time was only 3.4 years (range 1 to N10 years). Revisiting these sites in summer 2009 as part of the current study revealed further changes. In addition, an unpublished monitoring study of wood retention in jams begun during summer 2008, and which includes jams surveyed as part of the present study, revealed substantial exchange of wood within selected jams during the first year of the study. Wohl and Goode (2008) interpreted the high mobility of wood as reflecting both low wood loads and associated lack of congestion and trapping of wood in transport and smaller values of ratios of wood dimensions to channel dimensions (log length/channel width and log diameter/flow depth) than are present in at least some of the other old-growth environments summarized in Table 6. Further indirect evidence of wood mobility at the study sites comes from the proportion of different piece types if ramps, bridges, and broken bridges are assumed to reflect local lateral recruitment and all other piece types to reflect at least some distance of fluvial transport. The proportion of pieces as ramps and bridges is consistently b0.24 within each of the four study channels, suggesting that the majority of instream wood at any location reflects recruitment via transport downstream. Consequently, we infer that limited retention of instream wood exerts a stronger influence than lower recruitment rates in causing lower wood loads in the Colorado Front Range relative to wood loads in streams draining other types of old-growth forest. 6. Conclusions For drainage basins smaller than ~ 100 km2 in the Colorado Front Range, neighborhood matters, in that progressive downstream trends in instream wood load are obscured by local-scale variability. This implies that reach-scale (101–102 m) characteristics of channel and

valley geometry, wood recruitment sources and forest stand age, along with the characteristics of channel and valley segments immediately upstream, exert an important control on spatial variations in wood load. Temporal variations, as indicated here by differences in stand age, appear to exert less influence on wood load

Fig. 7. Schematic illustration of the influence of local-scale controls on wood load and aggregation. In this hypothetical example, a tree recruited into the channel forms a ramp that initiates a logjam. The logjam is more likely to persist and accumulate wood in wider, lower gradient valley segments than in narrow, steep segments. A larger, more persistent logjam is more likely to force overbank flow that can carry wood in transport onto the floodplain. Overbank flow can also concentrate on the floodplain and create secondary channels and an anastomosing planform. Fine sediment is typically carried in suspension during snowmelt peaks, with very limited deposition in these headwater channels. Overbank flow can also facilitate deposition of fine sediment that promotes infiltration, hyporheic exchange, and the development of spring-head channels across the floodplain, further enhancing anastomosing. We observed that individual logjams affect streambed gradient, velocity, flow depth, and substrate grain size along channel lengths of tens to hundreds of meters in the wider valley segments and a few meters to tens of meters in the narrower valley segments. We hypothesize that the logjams are more persistent in the wider segments and more transient in the narrow segments, where jams tend to be smaller and peak flow depths are greater because the flow does not spread across a floodplain.

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following a recovery period that may be circa 100 years. Although progressive basin-scale trends are likely present at larger spatial scales and coarser spatial resolution (as suggested by the greater importance of drainage area as a predictive variable in our lumped data set relative to our split data set, by the inclusion of drainage area as a significant component of nearly all models that encompassed all four streams, and by previous work by Wohl and Jaeger, 2009), management of instream wood loads at the reach scale cannot rely on target values of wood load developed as a function of drainage area or channel width. Instead, spatial variation and site-specific values of wood load reflect the longitudinal sequence of recruitment sources and channel geometry. The majority of instream wood in most channel segments appears to be transported from upstream sources, and individual pieces are likely to aggregate into jams. Jams may be more widely spaced than random at very short distances (b10 m), but longitudinal jam spacing at greater lengths varies substantially between sites and corresponds to variations in valley geometry. Acknowledgements We thank Rocky Mountain National Park staff for access to the research sites and for helpful discussions of the location of undisturbed, old-growth sites. Jason Sibold provided the fire-history maps that were critical to assessing forest age. EW also thanks the Department of Geography at Durham University, United Kingdom for a Distinguished International Fellowship that facilitated work on this paper during a sabbatical leave. W. Andrew Marcus and an anonymous reviewer provided insightful suggestions for revisions. References Andreoli, A., Comiti, F., Lenzi, M.A., 2007. Characteristics, distribution and geomorphic role of large woody debris in a mountain stream of the Chilean Andes. Earth Surface Processes and Landforms 32, 1675–1692. Arthur, M.A., Fahey, T.J., 1990. Mass and nutrient content of decaying boles in an Engelmann spruce — subalpine fir forest, Rocky Mountain National Park, Colorado. Canadian Journal of Forest Research 20, 730–737. Baillie, B.R., Davies, T.R., 2002. Influence of large woody debris on channel morphology in native forest and pine plantation streams in the Nelson region, New Zealand. New Zealand Journal of Marine and Freshwater Research 36, 763–774. Beechie, T.J., Pess, G., Kennard, P., Bilby, R.E., Bolton, S., 2000. Modeling recovery rates and pathways for woody debris recruitment in northwestern Washington streams. North American Journal of Fisheries Management 20, 436–452. Benda, L.E., Sias, J.C., 2003. A quantitative framework for evaluating the mass balance of in-stream organic debris. Forest Ecology and Management 172, 1–16. Benda, L.E., Miller, D., Sias, J., Martin, D., Bilby, R., Veldhuisen, C., Dunne, T., 2003. Wood recruitment processes and wood budgeting. In: Gregory, S.V., Boyer, K.L., Gurnell, A.M. (Eds.), The Ecology and Management of Wood in World Rivers, 37. American Fisheries Society Symposium, Bethesda, MD, pp. 49–73. Berg, N., Carlson, A., Azuma, D., 1998. Function and dynamics of woody debris in stream reaches in the central Sierra Nevada, California. Canadian Journal of Fisheries and Aquatic Sciences 55, 1807–1820. Bilby, R.E., Likens, G.E., 1980. Importance of organic debris dams in the structure and function of stream ecosystems. Ecology 61, 1107–1113. Bocchiola, D., Rulli, M.C., Rosso, R., 2008. A flume experiment on the formation of wood jams in rivers. Water Resources Research 44, W02408. Braddock, W.A., Cole, J.C., 1990. Geologic Map of Rocky Mountain National Park and Vicinity, Colorado. Miscellaneous Investigation Series Map I-1973, 1:50,000 scale. U.S. Geological Survey, Denver, CO. Bragg, D.C., 2000. Simulating catastrophic and individualistic large woody debris recruitment for a small riparian stream. Ecology 81, 1383–1394. Braudrick, C.A., Grant, G.E., 2001. Transport and deposition of large woody debris in streams: a flume experiment. Geomorphology 41, 263–283. Brown, P.M., Shepperd, W.D., Mata, S.A., McClain, D.L., 1998. Longevity of windthrown logs in a subalpine forest of central Colorado. Canadian Journal of Forest Research 28, 932–936. Cadol, D., Wohl, E., Goode, J.R., Jaeger, K.L., 2009. Wood distribution in neotropical forested headwater streams of La Selva, Costa Rica. Earth Surface Processes and Landforms 34, 1198–1215. Collins, B.D., Montgomery, D.R., 2002. Forest development, wood jams and restoration of floodplain rivers in the Puget Lowland, Washington. Restoration Ecology 10, 237–247. Curran, J.H., Wohl, E.E., 2003. Large woody debris and flow resistance in step-pool channels, Cascade Range, Washington. Geomorphology 51, 141–157. Ehle, D.S., Baker, W.L., 2003. Disturbance and stand dynamics in ponderosa pine forests in Rocky Mountain National Park, USA. Ecological Monographs 73, 543–566.

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