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Trout Passage of Beaver Dams in Two Northern Utah Tributaries

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Do Beaver Dams Impede the Movement of Trout?
Ryan L. Lokteff
, Brett B. Roper
& Joseph M. Wheaton
U.S. Forest Service, Fish and Aquatic Ecology Unit , 860 North 1200 East, Logan , Utah ,
84321 , USA
Department of Watershed Sciences , Utah State University , 5210 Old Main Hill, Logan ,
Utah , 84322 , USA
Published online: 26 Jun 2013.
To cite this article: Ryan L. Lokteff , Brett B. Roper & Joseph M. Wheaton (2013): Do Beaver Dams Impede the Movement of
Trout?, Transactions of the American Fisheries Society, 142:4, 1114-1125
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Transactions of the American Fisheries Society 142:1114–1125, 2013
American Fisheries Society 2013
ISSN: 0002-8487 print / 1548-8659 online
DOI: 10.1080/00028487.2013.797497
Do Beaver Dams Impede the Movement of Trout?
Ryan L. Lokteff* and Brett B. Roper
U.S. Forest Service, Fish and Aquatic Ecology Unit, 860 North 1200 East, Logan, Utah 84321,
USA; and Department of Watershed Sciences, Utah State University, 5210 Old Main Hill, Logan,
Utah 84322, USA
Joseph M. Wheaton
Department of Watershed Sciences, Utah State University, 5210 Old Main Hill, Logan, Utah 84322, USA
Dams created by North American beavers Castor canadensis (hereafter, “beavers”) have numerous effects on
stream habitat use by trout. Many of these changes to the stream are seen as positive, and many stream restoration
projects seek either to reintroduce beavers or to mimic the habitat that they create. The extent to which beaver
dams act as movement barriers to salmonids and whether successful dam passage differs among species are topics
of frequent speculation and warrant further research. We investigated beaver dam passage by three trout species
in two northern Utah streams. We captured 1,375 trout above and below 21 beaver dams and fitted them with PIT
tags to establish whether fish passed the dams and to identify downstream and upstream passage; 187 individual
trout were observed to make 481 passes of the 21 beaver dams. Native Bonneville Cutthroat Trout Oncorhynchus
clarkii utah passed dams more frequently than nonnative Brown Trout Salmo trutta and nonnative Brook Trout
Salvelinus fontinalis. We determined that spawn timing affected seasonal changes in dam passage for each species.
Physical characteristics of dams, such as height and upstream location, affected the passage of each species. Movement
behaviors of each trout species were also evaluated to help explain the observed patterns of dam passage. Our results
suggest that beaver dams are not acting as movement barriers for Bonneville Cutthroat Trout or Brook Trout but
may be impeding the movements of invasive Brown Trout.
Before settlement by Europeans, North American beavers
Castor canadensis (hereafter, “beavers”) played a significant
role in shaping the habitats of North American fishes (Naiman
et al. 1988). The extensive removal of beavers beginning in the
17th century affected native fish, such as Brook Trout Salvelinus
fontinalis in the east and Cutthroat Trout Oncorhynchus clarkii
in the west. With the recent recovery of beavers in some western
streams (Naiman et al. 1988), the reintroduction of beavers into
other streams, and restoration projects that seek to mimic the
effects of beavers on stream processes (DeVries et al. 2012;
Pollock et al. 2011), a better understanding of the interactions
between beavers and native fish is needed. This understanding
would permit improved choices and prioritization in how and
where these types of restoration activities are used, especially in
*Corresponding author:
Received November 26, 2012; accepted April 15, 2013
the presence of declining native populations of Cutthroat Trout
(Budy et al. 2012) and Brook Trout (Marschall and Crowder
1996; Fausch 2008).
It has been suggested that the increased habitat complexity
found in reaches (especially lower-stream-order reaches) with
beaver dams benefits salmonid species in western North Amer-
ica (Neff 1957; Gard 1961; Collen and Gibson 2001; Kemp et al.
2012) and provides vital habitats for threatened or imperiled
fish species. Deeper water with low velocities and high wood
abundances provides important rearing habitat for Endangered
Species Act (ESA)-listed Coho Salmon O. kisutch (Pollock et al.
2004). White and Rahel (2008) showed that complementation
of different habitat types, including beaver ponds, supported the
needs of multiple life stages of imperiled Bonneville Cutthroat
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Trout O. clarkii utah and increased their recruitment. Beaver
ponds provide vital overwinter habitat in streams that otherwise
may freeze throughout their entire depth (Cunjak 1996;
Lindstrom and Hubert 2004). Collen and Gibson (2001)
identified other benefits to fish dwelling within the beaver pond,
such as cover created by the beaver lodge and food cache; stabi-
lization of streamflows; increased sediment storage in the pond,
thus creating spawning habitats below the dam; and an increase
in lentic invertebrates. These benefits may also be exploited by
numerous pool-dwelling, ESA-listed Pacific salmon (Murphy
et al. 1989). We postulate that native fish are more likely to ben-
efit from the habitat heterogeneity created by beavers if they are
adept at passing beaver dams to access those different habitats.
Even in small streams, beaver dams can be up to 2.5 m tall,
and it is logical that such dams might act as barriers to the
upstream migration of fish (Kemp et al. 2012). However, the di-
versity of flow paths over, through, under, and around (e.g., side
channels that act as fish ladders) such dams provides a num-
ber of plausible pathways for upstream movement (Schlosser
1995). Moreover, these flow paths change regularly with beaver
maintenance and construction activities and with fluctuations in
Whether beaver dams act as barriers to fish and the extent
to which they impede the movement of different species are
questions in need of clarification. Kemp et al. (2012) reviewed
108 studies evaluating the effects of beaver dams on fish and fish
habitat; beaver dams were cited as “barriers to fish movement”
in 43% of the papers, and this was the most common adverse
effect discussed. However, the putative negative effect of beaver
dams as barriers was speculative in that 78% of the studies did
not support this claim with data (Kemp et al. 2012).
The objective of our study was to evaluate whether trout
can pass beaver dams. The Logan River, Utah, serves as an
ideal study area, as it contains native Bonneville Cutthroat Trout
that compete with two nonnative species—the Brook Trout and
Brown Trout Salmo trutta—in beaver-altered habitats. Differ-
ences in passage behaviors among the three trout species may
provide information that is crucial to the future conservation
of Bonneville Cutthroat Trout. Knowledge of dam passage by
trout may also have implications for fisheries and land managers
in streams where beaver dams exist or where beaver dam sur-
rogate structures are being implemented as a means of stream
restoration (Pollock et al. 2011; DeVries et al. 2012).
Temple Fork (watershed area = 41.5 km
) is a third-order
tributary to the Logan River, and Spawn Creek (14.6 km
a second-order tributary to Temple Fork (Figure 1). The Tem-
ple Fork watershed is a good analog for lower-order, montane
trout streams in the intermountain west. The annual hydrograph
consists of peak flows that are dominated by spring snowmelt
and base flow (0.28–1.39 m
/s) that is supported by spring flow
(Seidel 2009). Peak streamflows usually occur in May to June
FIGURE 1. Map of the Temple Fork and Spawn Creek study area in Utah.
Stream line widths and colors indicate different stream reaches. Bar graphs show
the proportion of each trout species among the fish that were initially captured
in each reach (Cutthroat = Bonneville Cutthroat Trout; Brown = Brown Trout;
Brook = Brook Trout). Brook Trout were almost exclusively found in the upper
reach of Spawn Creek. Only Bonneville Cutthroat Trout were found in the
upper reach of Temple Fork. The uppermost beaver dams in each stream were
not evaluated for fish passage in this study. [Figure available online in color.]
and are approximately five times base flow (de la Hoz Franco
and Budy 2005; Seidel 2009). At base flow, wetted widths in
reaches without beaver dams are approximately 5.0 m in Temple
Fork above Spawn Creek and 2.5 m in Spawn Creek.
From 2008 to 2011, beavers maintained 27 dams along
Temple Fork and Spawn Creek (Figures 2, 3). It is worth noting
that just upstream of our study site boundary on Temple Fork,
beavers built 12 new dams within a 200-m reach during 2012.
Of the 21 beaver dams that were evaluated in this study, three
were constructed during the study period and four others were
breached or blown out during the 2011 spring runoff floods
(Table 1). Of the four dams that were impacted by the 2011
floods, all were on Temple Fork: two dams (T3 and T9) were
completely blown out and have not been repaired, while the
other two (T4 and T8) had only minor breaches and have not
been repaired. Many of the ponds in the upper portion of Spawn
Creek are part of a major beaver dam complex that has been
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FIGURE 2. Locations of beaver dams in Temple Fork. Dams are numbered in the upstream direction; side channels are indicated (A, B, and C locations in the
upper right panel correspond to panels A–C). Brown Trout were not observed above dam T4, and Brook Trout were not present in Temple Fork. [Figure available
online in color.]
present for over 30 years (Bernard and Israelsen 1982). By
contrast, the valley bottom road up Temple Fork, which was
not removed until the mid-1990s, minimized development of
beaver ponds in this area until recently. The uppermost dams on
Spawn Creek and Temple Fork were not included in this study
because they were above the area where fish were marked and
they were not consistently scanned for fish presence.
Between 2008 and 2011, we captured 1,375 trout in Spawn
Creek and Temple Fork and fitted them with PIT tags, which
permitted us to track unique fish (Moore 1992). Some fish that
were originally tagged in the Logan River were also detected
in the study streams, and these individuals were included in
our study. Fish were captured during summer months by elec-
trofishing and angling. Upon initial capture of a trout, a Biomark
full-duplex, 12-mm PIT tag was placed subcutaneously behind
the dorsal fin. The capture location was recorded with a hand-
held GPS unit. The numbers of tagged fish varied within and
among streams (Table 2). In addition to their use in evaluating
beaver dam passage by trout, these PIT-tagged fish were part of
a larger study to evaluate trout movement, growth, and habitat
use within these streams.
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FIGURE 3. Locations of beaver dams in Spawn Creek. Dams are numbered in the upstream direction; side channels are indicated (A and B locations in the upper
right panel correspond to panels A, B). Dams S4 and S7 have left and right components. Brown Trout did not pass S7. [Figure available online in color.]
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TABLE 1. Physical characteristics of beaver dams evaluated in Temple Fork and Spawn Creek, Utah. Dam codes correspond to those in Figures 2 and 3 (in the
dam code, T = Temple Fork, S = Spawn Creek; R = right component, L = left component). “Height” is beaver dam height measured from the downstream side
of the dam. “Max depth” is the maximum depth of the pool created by the beaver dam. “Flow” is the dominant flow path at the dam during spring 2011 (over =
flow spilled over the dam; under = flow spilled at the bottom of the dam; through = flow leaked through the entire structure; all = all flow paths). “Side channels”
indicates whether there was a path of water that circumvented the dam structure. “Dam built” indicates the period in which beavers started construction on a dam.
Two dams failed during the spring of 2011.
Height Max Side
Dam code (cm) depth (cm) Flow channels? Dam built
T1 35 50 Over Yes Pre-study
T2 200 125 Under Yes Pre-study
T3 100 60 Under Yes Pre-study; failed in 2011
T4 50 50 Over No Pre-study
T5 75 100 Through Yes Pre-study
T6 125 75 Under Yes Pre-study
T7 90 75 Under Yes Pre-study
T8 125 85 Through Yes Pre-study
T9 200 125 Under Yes Pre-study; failed in 2011
S1 110 70 All No Nov 2010
S2 135 70 All Yes Nov 2010
S3 60 25 Over Yes Pre-study
S4R1 70 40 Under No Pre-study
S4R2 65 60 Under Yes Pre-study
S4L 65 65 All Yes Pre-study
S5 110 50 All Yes Pre-study
S6 75 40 All Yes Pre-study
S7R 85 55 Under Yes Pre-study
S7L 100 15 All Yes Pre-study
S8 75 65 All No June 2010
S9 120 85 All Yes Pre-study
To determine whether the fish passed beaver dams, we used
a variety of spatially explicit data collected for individual fish.
The GPS coordinates of fish locations were taken from capture
locations, stationary antennas, and mobile antennas. Stationary
PIT tag antennas were located (1) in Temple Fork just upstream
from its confluence with the Logan River, (2) in Temple Fork
TABLE 2. Numbers of trout that were captured and tagged during each year
and in each study stream. Tagging numbers were lower in 2011 due to near-
record-high flows. Some trout were tagged in the Logan River and migrated into
the study streams.
Bonneville Brown Brook
Year or stream Cutthroat Trout Trout Trout
2008 39 3 1
2009 491 161 66
2010 478 199 22
2011 150 60 62
Temple Fork 602 190 NA
Spawn Creek 308 124 151
Logan River 248 109 NA
just upstream from its confluence with Spawn Creek, and (3)
in Spawn Creek just upstream from its junction with Temple
Fork (Figure 1). The Temple Fork–Spawn Creek antenna array
began operation in May 2009 and identified fish that moved into
or out of the area with beaver ponds. Active scanning upstream
of the stationary antennas in both creeks by using a mobile
antenna commenced on a monthly basis in May 2009. Mobile
efforts entailed one or two observers moving upstream with
PIT tag receivers attached to a wand that detected fish in the
stream. Detection of individual fish in this small stream system
was aided by the use of two observers with mobile antennas
to actively search all available habitat (Randall 2012). During
mobile scanning, locations of tagged fish were determined by
synchronizing the location of a handheld GPS unit when each
fish was recorded by the PIT tag receiver.
Initial and resight locations of trout were plotted by snapping
the GPS point to the nearest location on a s tream layer that was
digitized from 1-m aerial imagery using ArcGIS version 10.0.
A fish was designated as having passed a beaver dam if we
recorded that fish at locations both above and below a given
dam. Each pass was summarized by pass direction, dam, and
species. Statistical differences in beaver dam passes among the
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trout species were determined by comparing the number of dam
passes made by each species against the expected number of dam
passes based on the proportional representation of each species
among the tagged fish. We used a chi-square test to determine
whether passage differed among the species. The null hypothesis
was that the number of passes for a given species reflected the
proportion of tagged fish of that species. The expected number
of passes for a given species was calculated by multiplying
the total number of passes (i.e., for fish of all species) by that
species’ proportion among the tagged fish. This was done for
both streams (overall) as well as for each stream. Additionally,
we evaluated whether fish passage at a beaver dam was equally
likely to occur in upstream and downstream directions.
Movement direction (upstream or downstream) was deter-
mined based on the locations and dates of the observations. The
date of fish passage at a beaver dam was estimated by assign-
ing the month representing the midpoint between two successive
observations. For situations in which the period between the two
observations exceeded 6 months, we disregarded those data in
our evaluation of fish movement timing. The null hypothesis for
fish movement was that movement was independent of month.
To determine whether dam passage was affected by dif-
ferences in the propensity of each trout species to move, we
summed the absolute values of minimum observed distances
traveled by each individual fish over all observations. These
sums provide information on minimum travel distances be-
cause we only recorded movement between two observations
and not actual fish movement during unobserved periods. The
total movement distances of each fish were used to determine
the median of total movement distance for each species.
To determine the size of fish that passed beaver dams, fish
length on the predicted date of dam passage was estimated.
Growth in length (TL; mm/d) was based on all fish that had
been captured multiple times and was calculated by dividing
the observed growth by the time period between captures. Daily
growth rates were calculated for each species. Average growth
rates were applied to the length of time between the most recent
capture event and the estimated date of beaver dam passage to
determine the length of each fish at the time of passage. To
reduce error with these predictions, we used size-class distri-
butions consisting of 50-mm bins (<150, 151–200, 210–250,
251–300, and >300 mm). The size-classes for the tagged pop-
ulation of each species and the size-classes of fish that passed
dams were compared by using a chi-square test.
The physical characteristics of the beaver dams within both
streams were determined during spring 2011. Attributes that
were recorded included dam height (from the streambed on
the downstream side), maximum pond depth, and whether side
channels were present (Figure 4). In addition, side channels
and dam crests were mapped as polylines, and the upstream
backwater of the pond from the dam was mapped as a point
with a Juniper Archer map-grade GPS unit and ArcPad. Spawn
FIGURE 4. Photo of Temple Fork beaver dam 6 (T6). This dam has been in place since 2004 and contains multiple side channels; it was passed in both directions
(upstream and downstream) by Bonneville Cutthroat Trout. [Figure available online in color.]
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Creek dam 4 (S4) represented a set of three dams built on two
channels (Figure 3). These dams were grouped for the analysis
to avoid ambiguities arising from the fact that fish located above
and below this complex could have passed the dams in either
channel. A similar situation occurred at Spawn Creek dam 7
(S7). For each group of dams, the dam with the shortest height
was used in the analysis of dam height.
We used linear regression to determine which beaver dam
characteristics affected fish passage at the dams. We assumed
that dam height, stream (Spawn Creek or Temple Fork), and
side channels (present or absent) would be the primary dam
characteristics governing fish passage. As such, we considered
six models explaining passage by each trout species: (1) pas-
sage was affected by dam height; (2) passage was affected by
the presence of a side channel around the dam; (3) passage was
affected by dam number, which reflected dam position in the
stream (i.e., higher numbers, such as T7 or S9, represented dams
that were located further upstream); (4) passage was affected by
dam height, with separate intercepts for each stream; (5) passage
was affected by the presence of a side channel, with separate
intercepts for each stream; and (6) passage was affected by dam
number, with different intercepts for each stream. We used these
models to evaluate all passes as well as only upstream passes.
Because five of the dams (T3, T9, S1, S2, and S8) were not in
place for the entire time frame of the study, the number of fish
that would have passed each of those dams was estimated by ex-
panding the number of fish detected as passing a dam to the 3.5-
year study period. The best model for each species was chosen
by using Akaike’s information criterion corrected for small sam-
; package MuMIn in R software; Barton 2012).
The best model was averaged across all models for which AIC
values differed by less than 2.0 (Burnham and Anderson 2002).
An attribute with a model weight of 1.0 meant that it was in-
cluded in all competing models. The closer a model weight was
to 0.0, the less evidence that inclusion of the attribute improved
the understanding of the data. We present adjusted R
values for
the best model to reflect the explained variation in the data.
We recorded 481 individual passage events by trout at beaver
dams. Of those passes, 53 were single passes by unique indi-
FIGURE 5. Number of beaver dams that were passed by tagged trout within
Temple Fork and Spawn Creek (Cutthroat = Bonneville Cutthroat Trout;
Brown = Brown Trout; Brook = Brook Trout). This graph only includes fish
that passed at least one dam in any direction; it excludes the 84.1% of Bonneville
Cutthroat Trout, 95.5% of Brown Trout, and 81.3% of Brook Trout that were
never detected as passing any beaver dam.
viduals, whereas the remaining 428 passes were from fish that
passed multiple dams (Figure 5). Overall, passage at beaver
dams differed significantly among the three species (P < 0.001;
Table 3). Relative to each species’ proportional representation
among the tagged fish, Bonneville Cutthroat Trout were more
likely to pass beaver dams, while Brown Trout were less likely
to pass dams. Brook Trout passed dams as often as expected
given the number of tagged fish.
Among the fish that were tagged in Temple Fork and Spawn
Creek, at least 15.9% of the Bonneville Cutthroat Trout, 4.5% of
the Brown Trout, and 18.7% of the Brook Trout passed at least
one dam. These values represent minimum estimates because
(1) not all tagged fish were relocated and (2) some fish could
have moved over a dam and back to their previous location
between detections and thus would not have been recorded as
passing the dam. Of the fish that passed at least one dam, the
majority were detected as exhibiting passage events at two or
more dams (Figure 5).
TABLE 3. Beaver dam passage by the three trout species in Temple Fork, in Spawn Creek, and overall (both streams). The total number of passes in both
upstream and downstream directions is shown, along with the number expected (Exp; in parentheses) based on the number of tagged fish. The P-values are the
results of chi-square tests.
Bonneville Brown Brook
Location Cutthroat Trout (Exp) Trout (Exp) Trout (Exp) P-value
Overall 394 (312) 29 (107) 58 (55) <0.001
Temple Fork 251 (197) 8 (62) NA <0.001
Spawn Creek 143 (112) 21 (45) 58 (55) <0.001
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FIGURE 6. Direction of movement (downstream or upstream) by tagged trout
at each of the beaver dams studied in Temple Fork (T1–T9) and Spawn Creek
(S1–S9; see Figures 2, 3 and Table 2). For each stream, the x-axis presents dams
in order from downstream to upstream. Note that the scale of the y-axis (number
of passes) differs among species.
Every evaluated dam was passed by trout, and each dam
was associated with both upstream and downstream passage
events (Figure 6). We found that Bonneville Cutthroat Trout
and Brook Trout were significantly (P < 0.001) more likely
to move downstream over dams than to move upstream. The
few Brown Trout that we detected as moving past dams seemed
to have an equal likelihood of moving upstream and moving
Timing of fish movement differed among the trout species
(Figure 7). Bonneville Cutthroat Trout passed beaver dams more
often than expected from May to September and less often than
expected during the remaining months (P < 0.001). Brown Trout
passed dams more often than expected in January, September,
and October and less often than expected during the remaining
months (P = 0.053). Brook Trout passed dams more often than
expected in June and July and less often than expected in other
months (P < 0.001).
Dam passage could be partially explained by the difference
in movement proclivities among species. The median move-
ment distance of fish tagged within the study area was 227 m
for Bonneville Cutthroat Trout, 48 m for Brown Trout, and 8 m
for Brook Trout. The high median movement distance of Bon-
neville Cutthroat Trout and the extended tail of their movement
distribution (Figure 8) corresponded to the more frequent dam
passage of this species. The lower median movement distance
FIGURE 7. Timing of beaver dam passage events (in both upstream and
downstream directions) by each trout species within Temple Fork and Spawn
Creek (Cutthroat = Bonneville Cutthroat Trout; Brown = Brown Trout;
Brook = Brook Trout). Only fish that had repeat observations within 6 months
and that passed a dam are included in this figure. The month of passage was
determined based on the middle date between two successive resight events;
passage events that were separated by a period greater than 6 months were not
used. Percentage of dam passes was calculated separately for each species (i.e.,
the total for each species sums to 100%).
and narrower movement distribution for Brown Trout could par-
tially explain their lower frequency of beaver dam passage. The
very limited distance traveled by Brook Trout did not corre-
spond well with their dam passage counts in Spawn Creek. The
combination of a low movement distance with a relatively high
frequency of dam passage indicates the redistribution of Brook
Trout within a dam complex rather than passage at multiple
dams related to longer migratory movements.
The sizes of fish that passed beaver dams (upstream and
downstream passes combined) differed by species. Our results
indicate that for Bonneville Cutthroat Trout, fewer fish smaller
than 200 mm and more fish greater than 300 mm passed dams
than would be expected based on the size-class distribution
of this population (chi-square test: P 0.001). The size of
Brown Trout that passed dams was different than expected based
on the population’s size-class distribution (P = 0.011). The
number of Brown Trout larger than 300 mm that passed dams
was lower than expected. In contrast, Brown Trout in the 201–
250-mm size-class passed dams nearly 2.5 times more often
than expected. Size was not related to dam passage for Brook
Trout (P = 0.70).
Physical attributes of individual beaver dams differed slightly
between the two evaluated streams (Table 1). Beaver ponds in
Temple Fork were taller on average than those in Spawn Creek
(111 cm versus 89 cm). The depths of pools formed by beaver
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FIGURE 8. Movement distance by trout species within Temple Fork and
Spawn Creek (Brook = Brook Trout; Brown = Brown Trout; Cutthroat =
Bonneville Cutthroat Trout). The line within each box represents the median;
the ends of the box represent the 25th and 75th percentiles; whiskers represent
1.5 times the interquartile range; and circles represent outliers.
ponds were greater in Temple Fork (83 cm) than in Spawn
Creek (53 cm). Almost every beaver pond in both streams could
be circumnavigated by a side channel; in Temple Fork, 89% of
beaver ponds had side channels, whereas in Spawn Creek 75%
of beaver ponds had side channels.
All of the study dams were passed by Bonneville Cutthroat
Trout, regardless of the physical characteristics of the dam.
Even the dams exceeding 2 m in height (T2 and T9) had 5 and 2
upstream passes, respectively, and 18 and 4 downstream passes,
The best model for total dam passage by Bonneville Cut-
throat Trout included a single significant (P = 0.03; adjusted R
= 0.22) slope for dam number (model weight = 1.0; Figure 9).
The further upstream a dam was in each river ( i.e., as reflected
by the dam number), the fewer fish passed that dam. The best
model had the same negative slope for both streams. When only
upstream passes were assessed for Bonneville Cutthroat Trout,
three attributes were present in the best model (R
= 0.21): dam
height (model weight = 0.44), dam number (model weight =
0.39), and side channel presence (model weight = 0.17). All of
these predictors had a negative slope, indicating that dam pas-
sage decreased (1) as dam height increased, (2) as dam number
increased, and (3) if side channels were present. Even though
these predictors were included in the best model using AIC
the slope for each predictor was not significant (P > 0.10).
The best model for Brown Trout included dam number
(model weight = 0.48) as well as dam height (model weight =
0.33) and side channel presence (model weight = 0.19). Passage
at dams decreased as dam number increased and as dam height
increased; passage increased at dams when side channels were
absent. However, the best model did a poor job in explaining the
FIGURE 9. Graph depicting the best model for Bonneville Cutthroat Trout
passage at beaver dams in Temple Fork and Spawn Creek. The x-axis presents
dam number (T1–T9 or S1–S9) from downstream to upstream. The regression
line shows that dams located further upstream were less likely to be passed. The
number of passes at each dam includes both upstream and downstream passes
and was adjusted for dams that were not in place during the entire study period.
data (P > 0.10; adjusted R
= 0.06). When only upstream passes
of Brown Trout were considered, the same three attributes were
present in the best model (R
= 0.15): dam height (model weight
= 0.41), dam number (model weight = 0.33), and side channel
presence (model weight = 0.26). Again, these predictors were
not significant (P > 0.10).
We found that the best model for Brook Trout included stream
(model weight = 1.0), dam number (model weight = 0.37),
side channel presence (model weight = 0.37), and dam height
(model weight = 0.26). The large weight due to stream was
attributable to the absence of Brook Trout in Temple Fork. For
Spawn Creek, the best model indicated that (1) dams higher
in the system (i.e., higher dam number) were more likely to
be passed; (2) the presence of side channels resulted in more
Brook Trout passage events; and (3) as dam height increased,
dam passage by Brook Trout decreased. This model was signifi-
cant (P = 0.02; R
= 0.35), but this was mainly due to the lack of
Brook Trout in Temple Fork. When only upstream passes were
evaluated for Brook Trout, stream was identified as the most
important predictor (model weight = 1.0) and the same three
dam attributes were present in the best model (R
= 0.29): dam
height (model weight = 0.30), dam number (model weight =
0.39), and side channel presence (model weight = 0.31). The
slopes of dam number and side channel presence were both pos-
itive, indicating that dams with side channels and dams located
further upstream were more likely to be passed by Brook Trout.
All three species of trout evaluated in our study passed beaver
dams. Our observations show that Bonneville Cutthroat Trout
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and Brook Trout passed beaver dams more than expected. Brown
Trout passed dams less than expected, and the majority of passes
we observed were at smaller dams where Brown Trout concen-
trations were relatively high. Our results indicate that Bonneville
Cutthroat Trout and Brook Trout are readily capable of nego-
tiating large beaver dams. Brown Trout movements are more
restricted, as shown by a lack of passage at larger dams. It ap-
pears that beaver dams benefit Bonneville Cutthroat Trout in the
presence of Brown Trout by impeding the movements and mi-
grations of Brown Trout and by keeping these nonnative fish out
of upstream reaches. Still, questions remain regarding dam pas-
sage timing, movement behavior, specific mechanisms of dam
passage, and restoration implications. We discuss each of these
Dam Passage Timing
We found that peak passage at beaver dams coincided with
spawning migrations for Bonneville Cutthroat Trout and Brown
Trout but not for Brook Trout. Bonneville Cutthroat Trout passed
beaver dams at high frequencies during their spawning season
in May to July (Seidel 2009), but their passage was also high
in the months after spawning. Movement during these months
spanned the breadth of streamflow conditions, ranging from
peak flows to base flows. Movement in the upstream and down-
stream directions was approximately equal during May–August
(54 upstream passes, 58 downstream passes), but movement was
decidedly greater in the upstream direction during September
(17 upstream passes, 8 downstream passes). Such movement
patterns suggest that Bonneville Cutthroat Trout are seeking
areas in which to recover after spawning. Movement during
late summer indicates that these fish can pass beaver dams in
both directions during times of the year with low base flows.
Elevated dam passage by Brown Trout coincided with spawn-
ing; the highest passage rate occurred just prior to their late-fall
and early winter spawning season (Wood and Budy 2009). The
number of dam passes made by Brown Trout in September and
October was low (4 upstream passes, 5 downstream passes) be-
cause 95.5% of the Brown Trout tagged for this study were never
detected as passing a beaver dam. Observed passage by Brown
Trout during fall low-streamflow periods suggests that they are
able to pass dams at that time. The low passage rate provides
some support to the assertion that passage by Brown Trout at
beaver dams is hindered by low flows (Schlosser and Kallemeyn
2000; Rosell et al. 2005; Taylor et al. 2010). However, our sam-
ple size of Brown Trout passing beaver dams was limited, so
further research is needed to test whether Brown Trout are able
to pass dams at low flows.
Brook Trout passed beaver dams in June, when streamflows
were high and when Bonneville Cutthroat Trout were spawn-
ing. Movement during this time of year would be facilitated by
side channels with sufficient flow and by increased flows passing
over and through dams. Brook Trout movement in June may cor-
respond to Bonneville Cutthroat Trout spawning; Brook Trout
could be relocating to benefit from foraging on Bonneville Cut-
throat Trout eggs and on insects that are displaced during spawn-
ing by the native trout. Although Brook Trout passage was not
related to their fall spawning season, it could reflect an adaptive
history of Brook Trout to redistribute during times of high flow
(Peterson and Fausch 2003). Within the Brook Trout’s native
range, movement in relation to changing flows in small streams
is likely related to rainfall events that are less predictable than
the snowmelt-dominated runoff found in the Spawn Creek wa-
tershed (Scruton et al. 2003). Since this system has predictable
snowmelt-mediated high-flow events, higher passage rates dur-
ing these periods provide evidence that Brook Trout are being
aided in dam passage by flashy, higher-peaked, rain-dominated
flow events.
Dam-Influenced Movement Behaviors
Understanding beaver dam passage as it relates to movement
patterns for each species is complicated. Behaviors inherent to
each of these trout species could affect beaver dam passage, but
the dams could be modifying these behaviors. Based on move-
ment distances, Bonneville Cutthroat Trout encountered dams
more frequently than Brook Trout or Brown Trout, yet approx-
imately the same percentage of tagged Brook Trout passed at
least one dam even though this species demonstrated the most
restricted movement. All three species appeared to be adept at
passing multiple dams (Figure 5). However, only 14 individual
Brown Trout passed any dam at all. The majority of Brown
Trout that passed more than one beaver dam did so in the upper
Spawn Creek dam complex, where dams are closely spaced.
This pattern of passing multiple dams is the same for Brook
Trout in upper Spawn Creek. A higher number of individual
Bonneville Cutthroat Trout (n = 145) passed at least one beaver
dam, and a larger proportion of these fish passed more than two
dams (Figure 5). Future research is needed to determine whether
beaver dams restrict fish movement or whether fish exhibiting
a higher propensity to migrate will pass multiple dams simply
because they encounter more dams.
Beaver dams in downstream locations have the potential to
restrict the movements of Brown Trout. In the Temple Fork
area where Brown Trout are the dominant species, there are
two large dams (T2 and T3) that were passed only seven times.
We have yet to document a Brown Trout that has successfully
passed T2 while moving in an upstream direction. This same
pattern was seen in Spawn Creek at S1 and S2 (which were
only in place during the last year of our study period), where
we have yet to document any passage of Brown Trout. The lack
of upstream passes by Brown Trout over these recently built
beaver dams indicates that the dams have so far impeded the
upstream movements of Brown Trout. Therefore, large dams in
the downstream areas of these streams may slow the movement
of Brown Trout into habitats that are occupied by Bonneville
Cutthroat Trout.
The size-class distributions of fish that passed beaver dams
differed among the three species. Brook Trout appeared to have
the ability to pass dams regardless of fish size. In contrast, we
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observed only one large (>300-mm) Brown Trout passing a
beaver dam in the downstream direction. The high number of
201–250-mm Brown Trout passing dams in both directions (4
upstream passes, 6 downstream passes) may be the result of
an increase in spawning-related movement as the fish reached
sexual maturity. This observation suggests that younger Brown
Trout would be more able to invade stream systems with beaver
ponds. The higher-than-expected passage by large sizes of
Bonneville Cutthroat Trout supports the idea that size does not
affect the passage of this species at beaver dams. The largest
size-class of Bonneville Cutthroat Trout (>350 mm) passed
dams in both directions (4 upstream passes, 2 downstream
passes) over three times more often than expected based on
their frequency in the tagged population.
The high level of passage by Bonneville Cutthroat Trout at
S1 and S2 is remarkable considering the height of these dams
and the relatively short duration for which they were in place.
Bonneville Cutthroat Trout passed S1 14 times and passed S2
17 times, even though the two dams were in place only for the fi-
nal 16 months of the study period. High passage frequency at S1
and S2 is necessary since most of the spawning locations within
Spawn Creek are upstream of these dams. Recently, spawning
activity by Bonneville Cutthroat Trout has increased within the
75 m above the pool of S2 (Brett B. Roper, unpublished data).
Dam Passage Mechanisms
To fully understand the mechanisms of beaver dam passage,
broader samples of streams and beaver ponds are needed. Mea-
suring the geometry of scour pools at the base of dams could
contribute to an understanding of whether it is possible for a fish
to leap over a dam. We need a better understanding of how and
when fish pass beaver dams as well as the characteristics of dam
passage attempts that are unsuccessful. Placement of stationary
antennas along the face of the dam and in side channels could
be configured to provide a more direct measurement of whether
some fish are attempting to pass dams and whether they are
successful. The evaluation of trout passage at beaver dams is
complicated by the dynamic nature of these dams. Beavers fre-
quently reengineer their habitat. Over the course of this study,
beavers constructed two new dams (S1 and S2), and two dams
failed (T3 and T9). Two dams (T2 and S9) increased in height
and length, and their ponds increased in depth. For a number of
dams, flow patterns around (side channels) and through (over the
dam to under the dam) the dams also changed during the study
period. These physical changes can alter whether and how fish
movement is facilitated on a daily to annual basis. The dramatic
and subtle changes we observed in beaver dam configuration
suggest that dam characteristics must be examined more closely
over time rather than measuring them at a single point in time
(i.e., as was done in this study).
Restoration and Conservation Implications
Our findings of the apparent ease with which Bonneville
Cutthroat Trout and Brook Trout passed beaver dams are of
fundamental importance to restoration and conservation efforts
aimed at restoring native trout populations. Our results refute the
largely speculative concerns about beaver dams acting as migra-
tion barriers. This is timely in light of an increasing number of
examples in which dam-building beavers are used to reconnect
floodplains and restore fish habitat (e.g., Pollock et al. 2011)
or in which beaver activity is mimicked to bring about desired
changes in stream habitat (DeVries et al. 2012). Our results also
have positive implications for the management and conserva-
tion of declining native Brook Trout in eastern North America
(Petty et al. 2005). Reintroducing beavers or promoting the
beaver as a conservation species—instead of treating them as a
nuisance—may provide a means to conserve and restore Brook
Trout populations. If nonnative Brown Trout movement is in-
deed constrained by the presence of beaver dams, then beaver
reintroduction may have the added advantage of shifting the
competitive advantage back to native trout species.
We are indebted to Jared Randall, Jared Baker, Todd Wright,
Sara Bangen, Alan Kasprak, Wes Gordon, and Ryan Leary for
their extensive help with fish sampling and mobile antenna sur-
veys. The clarity of the manuscript was improved by a help-
ful review from Florence Consolati. We thank Aggie Air, Nick
Bouwes, Nadine Trahan, and Meagan Polino for providing aerial
imagery with unmanned aerial vehicles. The U.S. Forest Ser-
vice and the Intermountain Center for River Rehabilitation and
Restoration provided funding for this research.
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... Model validation and calibration were also conducted in the Logan River and Blacksmith Fork watersheds in northern Utah, an area where reliable data correlating stream power and beaver dam establishment and persistence exists (Lokteff et al., 2013). The Logan and Blacksmith watersheds are ideal as validation sites because the main stems of each of these rivers have just large enough stream powers that dams are occasionally built but do not persist throughout a given year-(i.e., spring runoff breaches and blows out these dams), whereas the lower order tributaries are home to very high densities of beaver. ...
... Since we did not have comprehensive data in the Escalante to perform such verification, we ran the beaver dam capacity model for the Logan River and Blacksmith Fork watersheds of northern Utah. An example of how well the model compared (with no calibration) to actual dam survey data from Lokteff et al. (2013) in the Temple Fork watershed is shown in Figure 26. The model does a remarkable job of distinguishing between areas with high densities of dams (dam complexes with number of dams are shown as circles in Figure 26) and those areas with no dams or smaller concentrations of dams. ...
Technical Report
Full-text available
Beaver (Castor canadensis) dam-building activities lead to a cascade of hydrologic, geomorphic and ecological effects that increase stream complexity, which benefits a wide-variety of aquatic and terrestrial species. Depending on biophysical and vegetation conditions present, beaver dam-building activities variously trap sediment; raise incised streambeds, often reconnecting them with their floodplains; subirrigate the valley downstream of a dam; create wetlands; slow runoff; mitigate impacts by floods; extend seasonal stream flow; increase stream complexity; extend riparian woody and other vegetation; and create or increase habitat for diverse and sometimes rare species, including amphibians, fish, small mammals, and birds. As a result, beaver are increasingly being used as a critical component of passive stream and riparian restoration strategies.
... To facilitate model validation, actual dam counts were collected using a combination of on-the-ground surveys (e.g. Lokteff et al., 2013), aerial overflights (e.g. , and virtual reconnaissance in Google Earth. For the Fremont, Logan-Little Bear, Strawberry, and Price watersheds, we conducted detailed dam count censuses using Google Earth to navigate up and down every stream in the drainage network at an altitude of roughly 500-600 m above ground. ...
Abstract The construction of beaver dams facilitates a suite of hydrologic, hydraulic, geomorphic, and ecological feedbacks that increase stream complexity and channel–floodplain connectivity that benefit aquatic and terrestrial biota. Depending on where beaver build dams within a drainage network, they impact lateral and longitudinal connectivity by introducing roughness elements that fundamentally change the timing, delivery, and storage of water, sediment, nutrients, and organic matter. While the local effects of beaver dams on streams are well understood, broader coverage network models that predict where beaver dams can be built and highlight their impacts on connectivity across diverse drainage networks are lacking. Here we present a capacity model to assess the limits of riverscapes to support dam-building activities by beaver across physiographically diverse landscapes. We estimated dam capacity with freely and nationally-available inputs to evaluate seven lines of evidence: (1) reliable water source, (2) riparian vegetation conducive to foraging and dam building, (3) vegetation within 100 m of edge of stream to support expansion of dam complexes and maintain large colonies, (4) likelihood that channel-spanning dams could be built during low flows, (5) the likelihood that a beaver dam is likely to withstand typical floods, (6) a suitable stream gradient that is neither too low to limit dam density nor too high to preclude the building or persistence of dams, and (7) a suitable river that is not too large to restrict dam building or persistence. Fuzzy inference systems were used to combine these controlling factors in a framework that explicitly also accounts for model uncertainty. The model was run for 40,561 km of streams in Utah, USA, and portions of surrounding states, predicting an overall network capacity of 356,294 dams at an average capacity of 8.8 dams/km. We validated model performance using 2852 observed dams across 1947 km of streams. The model showed excellent agreement with observed dam densities where beaver dams were present. Model performance was spatially coherent and logical, with electivity indices that effectively segregated capacity categories. That is, beaver dams were not found where the model predicted no dams could be supported, beaver avoided segments that were predicted to support rare or occasional densities, and beaver preferentially occupied and built dams in areas predicted to have pervasive dam densities. The resulting spatially explicit reach-scale (250 m long reaches) data identifies where dam-building activity is sustainable, and at what densities dams can occur across a landscape. As such, model outputs can be used to determine where channel–floodplain and wetland connectivity are likely to persist or expand by promoting increases in beaver dam densities.
... This figure illustrates that the spatial patterns the model produces make sense and resemble what we see on-the-ground. Using surveys from Lokteff et al. (2013), areas predicted as not able to support beaver on the Logan and Temple Fork are areas where we do not see active dams nor historic evidence of dams (either too steep and too much stream power, or devoid of suitable vegetation). Most of Temple Fork and Spawn Creek supports 'occasional' to 'frequent' dams, and we see precisely this -occasional to frequent dam densities. ...
... This figure illustrates that the spatial patterns the model produces make sense and resemble what we see on-the-ground. Using surveys from Lokteff et al. (2013), areas predicted as not able to support beaver on the Logan and Temple Fork are areas where we do not see active dams nor historic evidence of dams (either too steep and too much stream power, or devoid of suitable vegetation). Most of Temple Fork and Spawn Creek supports 'occasional' to 'frequent' dams, and we see precisely this -occasional to frequent dam densities. ...
Full-text available
Stream and floodplain restoration at the reach scale has ranged from expensive, heavy-handed modification of the channel and floodplain to simple, longer-term revegetation efforts. We have developed and implemented a simple approach that emulates the ecosystem engineering effects of beaver. This approach is less expensive and disruptive than typical large-scale engineering efforts and has the potential to restore both fish habitat and floodplain vegetation more rapidly than simply revegetating and waiting for the riparian zone to mature. The approach involves constructing log flow-choke structures that mimic the hydraulic function of a natural beaver dam during flooding. By placing these structures throughout a naturally entrenched stream reach at locations promoting increased frequency of flood connection with floodplain swales and relict channels, we set the stage to restore the riparian corridor and floodplain more quickly than could be achieved through revegetation alone. Monitoring shows that within just one to two years of implementation, beaver are building more persistent dams in close proximity to our structures, and we are seeing increased hydraulic connectivity with the floodplain. Our technique may therefore provide a cost-effective, natural process-based restoration tool with potential large-scale benefits.
Full-text available
This paper reviews the habitat characteristics and the behaviour of selected stream fishes during winter in temperate‐boreal ecosystems. Emphasis is placed on the salmonid,fishes upon which most winter research has been directed. As space is the primary factor regulating stream fish populations in winter, aspects of winter habitat are considered at various spatial scales from microhabitat to stream reach to river basin. Choice of winter habitat is governed,by the need to minimize energy expenditure, with the main criterion being protection from adverse physicochemical conditions (e.g., ice, spates, low oxygen). The distance moved to wintering habitats, and the continued activity by many fishes during winter, need to be considered when,making,management,decisions regarding fish habitat. How habitat is affected by land-use activity in stream catchments is discussed with reference to impacts from water withdrawal, varying discharge regimes, and erosion or sedimentation. Even stream “enhancement” practices can deleteriously affect fish habitat if project managers,are unaware,of winter habitat requirements and stream conditions. Maintenance of habitat complexity, at least at the scale of stream sub-basin, is recommended to ensure the diversity of winter habitats for fish communities. Résumé : Le document,fait l’examen des caractéristiques de l’habitat et du comportement,de certains poissons de cours
Full-text available
Due to significant threats to native species posed by nonnative fishes, it is important to understand how species life history strategies interact with environmental conditions to explain the outcome of nonnative fish invasions. Brown trout Salmo trutta are prolific invaders but often exhibit upstream distributional limits in streams of the intermountain western United States. We used redd counts, embryo survival experiments, and temperature modeling to identify limits to brown trout invasion. Brown trout spawned later than previously reported and established spawning areas in high-elevation stream reaches (1,983-m elevation), where adult recruitment is typically very low. While embryo survival was lower in high-elevation, cooler-water areas, these harsh overwinter conditions did not necessarily preclude hatching success (≥36%). However, model predictions based on winter temperature data indicate that during most years, brown trout fry probably would fail to emerge from the gravel before the onset of peak spring flooding in these high-elevation reaches, suggesting that high spring flows could limit invasion success. A better understanding of mechanistic limits to invasion success across multiple life stages is crucial to predicting the future expansion of exotic fish species.
Full-text available
Multiple age-classes of Bonneville cutthroat trout Oncorhynchus clarkii utah throughout two Rocky Mountain watersheds were influenced by interactions among geomorphology, land use, activity by beavers Castor canadensis, and drought. Age-0 trout were present in a limited portion of the watersheds, and their distribution became increasingly restricted as drought conditions developed over a 3-year period. The Coal Creek watershed (including Huff Creek) produced the most age-0 trout in the first 2 years of the drought, lacked beaver activity, and was affected by land use, suggesting that spawning habitat was determined by geomorphology rather than land use or beaver activity. However, the high abundance of age-0 cutthroat trout in Huff Creek did not result in a high abundance of juvenile and older age-classes of fish in subsequent years, most likely because of the lack of complementary habitats providing refuge for older fish. A nearby watershed and its major stream, Water Canyon, had less spawning habitat and produced fewer age-0 fish during the first 2 years of the study but had more trout in the juvenile and adult age-classes, most likely because of a higher degree of habitat complementation. In Water Canyon, less-intense livestock grazing and the presence of beavers allowed for the development of pools and woody riparian vegetation that provided cover for older trout. Water Canyon was also the only stream to produce age-0 trout during the most severe year of drought, suggesting that streams with more natural habitat may provide a spawning refuge during low-flow conditions that occur periodically in the region. These results demonstrate that habitat complementation is important for the coexistence of multiple age-classes of fish and that the adjacency of spawning habitat and refugia is crucial for the persistence of fish in the face of environmental stress associated with drought.
Spawning migrants entered Spawn Creek when spring freshets from melting snow increased stream flows and turbidity. Adult cutthroat trout traveled to the Logan River after leaving Spawn Creek. Cutthroat trout fry left Spawn Creek in early fall when dwindling stream flows reduced habitat along stream margins. Trout use Spawn Creek for spawning and the Logan River for growing. Spawn Creek contains extensive spawning habitat with little deep-water habitat while the Logan River is its complement.-from Authors
I combined long-term (10 yr) descriptive and short-term experimental studies in a headwater stream in northern Minnesota to assess: (1) the effect of annual variation in stream discharge and spatial proximity of beaver (Castor canadensis) ponds on lotic fish abundance and (2) the subsequent influence of discharge and fish predation on lotic invertebrate colonization. Considerable annual variation in fish density occurred in the stream over the 10-yr period, particularly in pool habitats. Increased fish density was associated with increased stream discharge and creation of beaver ponds downstream from the study site. Wiegert traps used to monitor directional (upstream vs. downstream) fish movement during the last 4 yr of the study indicated annual changes in fish density were associated with the amount of fish dispersal occurring along the stream segment. Downstream fish movement, out of an upstream beaver pond occurred primarily during periods of elevated stream discharge. Upstream movement, out of a downstream beaver pond, occurred over a broader range of discharge conditions. A controlled, @'slpit-stream,@' experiment examining the effect of very low vs. elevated discharge on upstream fish movement indicated, however, that upstream movement of fish out of beaver ponds was also reduced by very low discharge conditions. Movement data for individual fish species revealed considerable variation among the taxa in the tendency for downstream vs. upstream movement, due to variation in the morphology of upstream vs. downstream beaver ponds and its subsequent effects on the composition of fish dispersing from these source areas. Most fish movement occurred over relatively brief time periods, suggesting life history and developmental processes were critical in influencing the timing of dispersal. Size structure of fishes captured in the stream indicated predominantly older age classes (>age I) of fish where dispersing along the stream. However, based on the occurrence of age O individuals only 1 of 12 species, the creek chub Semotilus atromaculatus), routinely reproduced in the stream. Experiments conducted in an artificial stream located below one of the beaver ponds indicated discharge and fish predation have potentially strong and interactive effects on invertebrate colonization in stream ecosystems. Differences in colonization of riffles and pools under low vs. elevated discharge and fish vs. no-fish treatments suggested, however, that the interactive effect of these factors on invertebrate colonization was variable over even small spatial scales. Elevated discharge increased invertebrate colonization in riffles but decreased invertebrate colonization in pools. Contrary to intuitive expectations, fish predation reduced invertebrate colonization more under elevated than low discharge conditions, particularly in pool habitats. Taken together, these results: (1) beaver ponds act as reproductive @'sources@' for fish on the landscape, while adjacent stream environments act as potential reproductive @'sinks,@' (2) large-scale spatial relationships between beaver ponds and streams, along with the influence of discharge on the permeability of the boundaries between these habitats, are critical in controlling fish dispersal between ponds and streams and the subsequent abundance and composition of fish in lotic ecosystems, and (3) fish predation and discharge have potentially cascading effects on invertebrate colonization in lotic ecosystems.
Passive integrated transponder (PIT) tags, manufactured by BioSonics, were implanted into adult channel catfish (Ictalurus punctatus) to determine the suitability of the tag as a method to identify individual fish held in a hatchery. The 10.0 × 2.1-mm tags were injected by syringe into 92 male and 135 female catfish during the 1988 and 1989 spawning seasons. Tags were injected into three areas: (1) base of dorsal fin, (2) anterior base of adipose fin, and (3) posterior base of adipose fin. Examination of tagged fish in 1989 and 1990 revealed 97% tag retention. There was no evidence of tissue rejection, and all tags were easily read with a hand-held wand. The PIT tags appear to be a viable method of marking and identifying channel catfish for hatchery brood stock purposes.
Beavers are increasingly viewed as 'ecological engineers,' having broad effects on physical, chemical, and biological attributes of north-temperate landscapes. We examine the influence of both local successional processes associated with beaver activity and regional geomorphic boundaries on spatial variation in fish assemblages along the Kabetogama Peninsula in Voyageurs National Park, northern Minnesota, USA. Fish abundance and species richness exhibited considerable variation among drainages along the peninsula. Geological barriers to fish dispersal at outlets of some drainages has reduced fish abundance and species richness. Fish abundance and species richness also varied within drainages among local environments associated with beaver pond succession. Fish abundance was higher in upland ponds than in lowland ponds, collapsed ponds, or streams, whereas species richness was highest in collapsed ponds and streams. Cluster analyses based on fish abundance at sites classified according to successional environment indicated that four species (northern redbelly dace, Phoxinus eos; brook stickleback, Culaea inconstans; finescale dace, P. neogaeus; and fathead minnow, Pimephales promelas), were predominant in all successional environments. Several less abundant species were added in collapsed ponds and streams, with smaller size classes of large lake species (e.g., black crappie, Pomoxis nigromaculatus; smallmouth bass, Micropertus dolomieui; yellow perch, Perca flavescens; and burbot, Lota lota) being a component of these less abundant species. The addition of smaller size classes of large lake species indicates that dispersal of early life-history stages from Kabetogama Lake played a role in determining the species richness and composition of less abundant species in successional environments on the peninsula. Furthermore, collapsed-pond and stream environments closer to Kabetogama Lake had higher species richness than similar successional sites located farther from the lake. Cluster analyses based oh fish abundance at sites classified according to drainage indicated that species composition among drainages was influenced both by the presence or absence of geological barriers to fish dispersal and the nonrandom distribution of collapsed ponds and streams. Based on these results, we present a hierarchical conceptual model suggesting how geomorphic boundaries and beaver pond succession interact to influence fish assemblage attributes. The presence of a productive and diverse fish assemblage in headwater streams of north-temperate areas requires the entire spatial and temporal mosaic of successional habitats associated with beaver activity, including those due to the creation and abandonment of beaver ponds. The ultimate impact of the local successional mosaic on fishes, however, will be strongly influenced by the regional geomorphic context in which the mosaic occurs.