Landscape influences on brown trout (Salmo trutta) density in the Upper Clark Fork River
Superfund site, Montana
Mariah P. Mayfield
Department of Ecology
Montana State University
310 Lewis Hall
Bozeman, MT 59717
Landscape Ecology Final Project
December 9, 2009
Word Count: 3492
Abstract:
In the upper Clark Fork River Basin, the largest Superfund site in the United States,
heavy metal contamination in the early 1900’s decimated the fish populations in the river and
changed the landscape forever. Now fish populations are returning to the river, although in much
lower numbers than before and there is a wide range of the densities of trout populations
currently found in the river. Brown trout (Salmo trutta), the most prevalent salmonid species in
the system, densities range from 25 to 447 fish greater than 150 mm per mile. The primary
sources of pollution in the system; streamside contaminated sediment deposits and exposed bank
contaminants, were shown to have no relationship with the densities of brown trout. Another
landscape characteristic analyzed in this study, the percent forested land cover type in a 200 m
riparian buffer, was also found to have little effect on the density of brown trout. Further analysis
needs to be completed to determine if and how landscape attributes are driving the trout
populations in the basin.
Keywords: trout density, riparian buffer land cover, mining contamination
Introduction:
Since the beginning of human civilization, societies have exploited the environment to better
serve their goals. Some ecologists estimate that close to 50% of the ice-free land surfaces in the
world have been modified by human activity (Mustard et al 2004). Much of that modification
and exploitation of natural resources has occurred in the form of mining. In the United States
alone, over 240,000 km2 of land has been or is currently being mined, an area similar in size to
the state of Oregon (Luoma et al 2008). In the Clark Fork River basin, located in southwestern
Montana, mining has dramatically changed the environment permanently, with the deposition of
hazardous contaminated sediments, large scale fish kills, and reduced ground water quality.
Mining began in Butte, Montana in 1864, and increased dramatically in the area until
1955 when large open-pit mines replaced most of the small scale mining operations (Luoma et al
2008). As was typical with mining operations in the late 1880's, the “tailings” (contaminated
sediment leftover from the mining process) were allowed to flow directly into local watersheds.
These tailings contained high levels of metals, such as copper, cadmium, mercury, lead, arsenic,
and zinc. Settling ponds were completed in 1959, near the town of Warm Springs, as a way to
collect contaminated tailings and settle them out of Silver Bow Creek, before entering the Clark
Fork River. Although these ponds greatly improved water quality and contaminated sediment
deposition downstream of the ponds, the damage had already been done. Prior to the completion
of the settling ponds, it is estimated that 99.8 billion kilograms of contaminated mining waste
had already been washed into the Clark Fork River basin (Luoma et al 2008).
The deposition of mining waste came at a great cost to the environmental quality of the
Clark Fork River. In 1950, the state of Montana conducted electrofishing surveys on the river
near the town of Garrison and concluded that there were no fish in the river (Phillips and Lipton
1995). Improvements to the Butte waste water treatment facility occurred in the early 1970s,
which helped bring back fish populations, although fish kills were still observed even into the
1990s (Philips and Lipton 1995). These fish kills have been associated with high flow events,
such as summer thunderstorms, where stream banks containing high levels of contaminated soils
wash into the river. Currently, the salmonid species present in the upper Clark Fork River include
brown trout (Salmo trutta), westslope cutthroat trout (Oncoryhnchus clarki lewisi), rainbow trout
(O. mykiss), westslope cutthroat/rainbow trout hybrids (O. spp.), and bull trout (Savelinus
confluentus).
Now that restoration is to take place on a large scale in the upper Clark Fork Basin,
fisheries managers are investigating how fish are reacting to the contaminated environment of
the Clark Fork. Laboratory studies have shown that brown trout will avoid water with
concentrations of copper as low as 2.3 μg/L, which would mean that, in the wild, brown trout
would avoid all areas in the Clark Fork (Woodward et al 1995). One of the biggest factors
contributing to the elevated levels of copper in the water column is the amount of streamside
tailing deposits and, more importantly, the amount of exposed bank tailings, due to the fact that
the exposed bank tailings are continually being washed into the river, especially during high flow
events. Streamside tailings have also been though to have a large impact on the stream water
quality, based on the leeching process that occurs when surface water seeps through the tailing
deposits. Several studies in the Coeur d’Alene River basin, a river with similar mining
contamination issues, have shown that, when given a choice, trout species will avoid more
contaminated reaches of stream (Woodward et al 1997). Another study in the Coeur d’Alene
determined that the distribution and density of coldwater fish species (such as salmonids and
cottids) were negatively correlated with landscape characteristics such as mine location density
and metals concentrations in streambed sediment, and the distribution was found to be
independent of habitat quality (Maret and MacCoy 2002). Based on these laboratory and field
findings, in the upper Clark Fork River it would be reasonable to assume that the presence of
streamside and exposed bank tailings is directly affecting the trout density and distribution in the
river, with exposed bank tailings providing a greater contribution, due to the direct pathway for
contaminates to enter the water column.
Mining is not the only human activity that has changed the Clark Fork River landscape.
Agriculture and ranching is a large part of life in rural Montana, and the Clark Fork basin is no
exception. The upper section of river is characterized by a wide flood valley, used primarily for
ranching and agriculture use. This could also be affecting the trout density in the river, due to the
increased sedimentation, erosion, and homogeneous habitat types present in agricultural streams
(Heitke et al 2006). When looking at the land use in a riparian buffer zone, agricultural land
presence within the riparian buffer has been found to have a significantly negative affect on instream biotic integrity measures (Heitke et al 2006; Lammert and Allen 1999). When forest land
cover types are present in the riparian buffer zone, even if the primary land cover is agriculture,
there is an increase in habitat diversity, fish refuge habitat, large woody debris, and dissolved
oxygen levels. Forested buffer zones also help reduce erosion, water temperatures, turbidity, and
specific conductivity in streams (Heitke et al 2006; Lammert and Allen 1999; Moerke and
Lamberti 2006). Forested buffers have been linked to an increase in the index of biotic integrity
in streams, even when forested zones are as small as 25 m on each side of the stream (Moerke
and Lamberti 2006). While there is some debate about whether looking at landscape
characteristics at the stream buffer scale is as telling as looking at land use through the entire
watershed, several studies have shown that salmonid density can best be described by landscape
characteristics at the buffer scale (Lammert and Allen 1999; Stanfield et al 2006).
Based on the literature and current knowledge of the Clark Fork River, it is reasonable to
infer that landscape level characteristics, such as contaminated soil deposition and presence of
forested land cover in the riparian zone, are affecting fish densities, but the question is what
landscape attributes are contributing the most to the range of brown trout densities throughout
the study area? The presence of hazardous tailing deposits could be driving fish assemblages
based on the poor water quality and difficult living conditions that the tailings contribute to.
Additionally, in this agriculture-heavy environment, forested buffer zones could be creating
diverse habitat that is providing refuge from hazardous metals for trout species and could be
increasing the trout densities. It is these questions that I aim to answer in this study.
Study Area:
The Clark Fork River begins at the outlet of the Warm Springs Settling Ponds, near Warm
Springs, Montana, in the southwestern part of the state. For this study, the upper Clark Fork
River study area is defined as the length of river from the pond outlet to the confluence of Rock
Creek with the Clark Fork, near the town of Clinton, Montana. See Figure 1.
Methods:
Fish Density Sampling: In April 2009, Montana Fish Wildlife and Parks fisheries biologists
conducted electrofishing surveys of the upper Clark Fork River, using a boat mounted
electrofishing unit. The river was dived into 16 reaches, based on accessibility to boat ramps and
fishing access sites. During the first pass, trout were marked using fin clips unique to the reach
that they were captured in. One week later, an additional electrofishing pass was conducted over
the reach and all fish were recorded, taking note of recaptured fish. Using the proportion of
recaptured fish, a total population estimate was calculated. The total trout population was then
divided by the length of the reach (in miles) to get the density of trout per mile (a common unit
of measure for management biologists). Trout smaller than 150 mm were disregarded in the
population estimates based on the low capture rate of fish this size when electrofishing in a large
river. Reach fish densities were divided by trout species when possible, although the low
numbers of other trout species made mark-recapture estimates ineffective for all species other
than brown trout, which I focus on in this study. Since the electrofishing reaches were not even
in length, I also determined the river mile measurement at the start of each reach, as measured
from the Warm Springs Ponds outlet, to aid in analysis of density patterns (Table 1).
Land Cover: GIS layers of the locations of upper Clark Fork streamside contaminated tailings
and exposed streambank tailings, all mapped by the University of Montana Riparian and
Wetlands Research Program, were obtained from the State of Montana’s Natural Resource
Information System website (available from: http://nris.mt.gov/gis/gisdatalib/gisDataList.aspx).
Each of these shapefiles were converted to raster images (10m cells) and imported into ArcInfo
GIS program (version 9.3; ESRI, Redlands, California, USA). To analyze land cover influence
on fish density, the Upper Clark Fork Classified LANDSAT Image (University of North Texas
1992, Montana State Library, Helena, Montana) was also obtained from the Natural Resource
Information System website (website address above). The LANDSAT image has a resolution of
30m, and has ten classifications for land cover variety. For this study, I was only interested in
riparian buffer forestation, so I combined land cover types ‘shrub/mixed’, ‘conifers’, and ‘other
trees’, to create a simple raster of forested areas and non-forested areas. Using a Montana
streams shapefile, obtained from Montana Fish Wildlife and Parks (available from:
http://fwp.mt.gov/insidefwp/fwplibrary/gis/shapefiles/streams.shp.zip), a 200 m (100 m on each
side) buffer was placed around the mainstem of the Clark Fork River. The stream and associated
buffer was then split according to the electrofishing reaches established by the fish sampling
reaches in April 2009, and the area of each stream reach buffer was calculated. Using the buffers
as zones, zonal statistics were run on each raster file and a count for each landscape attribute per
zone was determined. By multiplying the count by the area of the raster cells, the area of each
attribute per reach buffer zone was calculated. Using the total area of the reach buffer and the
areas of each attribute, the percent of each landscape attribute type was calculated. An additional
land use category, ‘Total % Tailings’, was also calculated, and was simply an addition of the
percent of streamside tailings and the percent of exposed bank tailings for each reach. All of the
landscape attributes are displayed in Table 1. Single variable and multi-variable linear regression
models were run using Microsoft Excel and Program R, in an effort to establish the best-fit
model for explaining brown trout density throughout the study area.
Results:
Brown trout density is not even throughout the study area; rather, the highest densities of fish are
found in the middle sections of stream and taper off at the uppermost and lower stream reaches,
as shown in Figure 2. When brown trout densities are compared to the river mile measure, the
relationship is best described with a negative 2nd order polynomial relationship (R2= 0.582). This
relationship implies favorable conditions for fish in the middle sections of stream, with less
favorable conditions surrounding it. To determine what these unfavorable conditions might be, I
examined models based in the landscape attributes of the buffer zones.
Single variable linear regression models were calculated, with brown trout density as the
dependent variable and the various landscape attributes as the independent variables. Results
from the models are shown in Table 2. Figure 3 visually displays brown trout density and the
tailings present in each reach. None of the single variable landscape models were good fits to
explain brown trout density in the study area. The best fit model came from the percent of
exposed bank tailings (R2= 0.180), but, in addition to not being a very good fit, the relationship
between the two variables is a positive relationship, which is not what was expected. The amount
of exposed bank tailings are directly negatively affecting the water quality and it was assumed
that the exposed bank tailings would have a noticeably negative affect on trout densities. Since
the single variable linear models proved to have little connection, I used multi-variable linear
regression as a way to look at potential interactions between all of the landscape attributes and
the density of brown trout. I ran two models; the first took into account percent forested, percent
streamside tailings, percent exposed bank tailings, and the location in the river. Based on the
probability value (‘P Values’) and R-squared value for the model, this was determined to be a
very poor model (see Table 2). Taking the position in the river out of the equation and just
focusing on landscape attributes, the model becomes slightly better, but is still considered a poor
model based on the high probability value. These statistical results show that there is very little
correlation between the landscape attribute types that I examined and the brown trout densities
throughout the upper Clark Fork River.
There are, however, patterns in the landscape attributes along the length of the study area.
Using simple single variable linear regression models, I looked at the percent forested and total
percent tailings as compared to the river mile. While there was little correlation between the
percent forested and the location along the river, there is a moderately strong negative
relationship between the total percent tailings and the position along the river; that is, the further
downstream one travels along the river, less tailings will be present (Table 2). This could have to
do with a variety of landscape features, such as the large flood plain present in the upper reaches
of stream, the narrowing of the river channel as it nears Rock Creek, allowing for less surface
area where contaminated sediment could be deposited by high flows, or the increased river
discharge as major tributaries enter the river. Based on my hypothesis, the reduction of tailings in
the lower reaches of the study area should allow for an increase in brown trout density, although
this is not the case. Other factors that were not examined in this study might be accounting for
the reduction of brown trout density in the lower and upper reaches, such as poor water quality
and lack of suitable habitat.
Discussion:
Although analysis of the various landscape attributes in this study did not offer an explanation of
the variation of brown trout density in the upper Clark Fork River, it did tell us some important
information about the restoration potential of the area. If the presence of tailings is having little
affect on brown trout, then perhaps restoration efforts should be less focused on removal of
tailings and more focused on other factors, such as habitat diversity. Even though the presence of
tailings did not explain fish density, the presence of tailings might be contributing to other factors
not discussed in this paper, such as water quality. Poor water quality is often a cumulative effect:
sources of pollution and other poor water quality events upstream will affect the areas
downstream and, without the inputs of any sources of non-polluted water (example: tributaries
without mining contamination), the pollutants compound and can accumulate in the stream.
Water quality is currently being monitored in the study area, although currently no work has been
done to analyze the data.
Another explanation for the density of brown trout in the study area might be due to
geomorphologic land features. The river follows a gradient of agriculture land with a large
percentage of tailings and wide flood plain near the headwaters, into more ‘natural’ setting with
less agriculture, split channels and more habitat diversity, and followed by the lower section,
which has a very narrow flood plain and many areas of rip-rap with less suitable habitat. The
middle section has some of the highest brown trout densities and it could be due to the habitat
present in that stretch of river. Looking at measures of habitat suitability, stream sinuosity,
geologic composition, and other factors might provide managers with a model of brown trout
density and habitat selection in the study area.
While looking at landscape attributes within a stream buffer has shown to be
demonstrative of fish assemblages in other study areas (Lammert and Allen 1999; Moerke and
Lamberti 2006), other studies have shown that looking at landscape features on the watershed
and/or sub-watershed scale are most appropriate for describing patterns between fish
assemblages and landscape patterns (Wang et al 2003). For future studies, it would be useful to
break the river into reaches based on geomorphology and analyze the sub-watersheds of those
reaches for attributes such as land cover, gradient, and stream sinuosity. At this larger scale, it
might be more likely to observe factors that are driving brown trout densities. With so little work
currently being conducted in mining contaminated aquatic systems, very little is known about
how fish are reacting to this human caused disturbance. Hopefully, with studies like this, we can
begin to understand the factors driving fish density, distribution, and survival, and plan
restoration efforts accordingly.
Acknowledgements:
I would like to thank Jason Lindstrom and Brad Leirmann, fisheries biologists with Montana
Fish Wildlife and Parks, for providing the data on brown trout density. I would also like to
acknowledge Linda Phillips, for assisting with aspects of the GIS process, and Anne Marie
Reinhold for assistance with statistical analysis.
References:
Heitke JD, Pierce CL, Gelwicks GT et al (2006) Habitat, land use, and fish assemblage
relationships in Iowa streams: Preliminary assessment in an agricultural landscape. In: Hughes
RM, Wang W, Seelbach PW (eds) Landscape influences on stream habitats and biological
assemblages, American Fisheries Society Symposium 48. American Fisheries Society, Bethesda,
MD, pp 287-303.
Lammert M, Allen JD (1999) Assessing biotic integrity of streams: Effects of scale in measuring
the influence on land use/cover and habitat structure on fish and macroinvertebrates.
Environmental Management 23(2):257-270.
Luoma SN, JN Moore, AM Farag et al (2008) Mining impacts on fish in the Clark Fork River,
Montana: A field ecotoxicology case study. In: DiGuilio RT and Hinton DE (eds) The
toxicology of fishes, 1st edn. CRC Press, Boca Raton, FL, pp 779-804.
Maret TR, MacCoy DE (2002) Fish assemblages and environmental variables associated with
hard-rock mining in the Coeur d’Alene River Basin, Idaho. Transactions of the American
Fisheries Society 131:865-884.
Moerke AH, Lamberti GA (2006) Relationships between land use and stream ecosystems: A
multistream assessment in Southwestern Michigan. In: Hughes RM, Wang W, Seelbach PW
(eds) Landscape influences on stream habitats and biological assemblages, American Fisheries
Society Symposium 48. American Fisheries Society, Bethesda, MD, pp 323-338.
Mustard JF, Defries RS, Fisher T et al (2004) Land-use and land-cover change pathways and
impacts. In: Gutman G et al (eds) Land Change Science, 1st edn. Kluwer Academic Publishers,
The Netherlands.
Phillips G, Lipton J (1995) Injury to aquatic resources caused by metals in Montana's Clark Fork
River basin: Historic perspective and overview. Canadian Journal of Fisheries and Aquatic
Science 52:1990-1993.
Stanfield LW, Gibson SF, Borwick JA (2006) Using a landscape approach to identify the
distribution and density patterns of salmonids in Lake Ontario tributaties. In: Hughes RM, Wang
W, Seelbach PW (eds) Landscape influences on stream habitats and biological assemblages,
American Fisheries Society Symposium 48. American Fisheries Society, Bethesda, MD, pp 601621.
Wang L, Lyons J, Rasmussen P et al (2003) Watershed, reach, and riparian influences on stream
fish assemblages in the Northern Lakes and Forest ecoregion, U.S.A. Canadian Journal of
Fisheries and Aquatic Sciences 60:491-505.
Woodward DF, Goldstein JN, Farag AM et al (1997) Cutthroat trout avoidance of metals and
conditions characteristic of a mining waste site: Coeur d’Alene River, Idaho. Transactions of the
American Fisheries Society 126:699-706
Table 1: Brown trout density, buffer area, and percent of landscape attributes of interest, by reach
number and location.
Reach
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
BNT
Density
185
68
113
222
235
237
447
291
207
286
323
180
67
63
34
25
Reach
River Mile
Start
0
1.6
3.1
7.5
11.7
15
18.7
28.2
35.2
38.9
49.7
59
72.6
81.4
92.1
100.8
200m Buffer
Area (m2)
%
Forested
%
Streamsi
de
Tailings
323507
399860
968357
1088517
944172
828097
2036792
2208185
1262524
3179653
3012494
3755823
3089094
3277903
2781151
1906516
77.06
49.52
72.68
71.68
69.97
62.28
61.07
69.37
77.84
70.17
76.60
75.36
68.20
63.70
75.11
67.46
0.00
0.63
14.18
7.10
2.90
3.19
6.52
3.71
0.01
0.42
0.00
0.11
0.00
0.00
0.00
0.00
%
Exposed
Bank
Tailings
Total % Tailings
3.12
2.25
5.82
5.44
2.46
3.71
3.93
2.61
2.06
1.49
1.06
1.26
0.08
0.03
0.00
0.07
3.12
2.88
20.00
12.54
5.36
6.90
10.45
6.32
2.07
1.91
1.06
1.36
0.08
0.03
0.00
0.07
Table 2: Statistical models run comparing landscape attributes, brown trout density, and
river mile location, with probability values listed for those models run using Program R, and rsquared values for all models.
Dependent Variable
BNT Density
BNT Density
BNT Density
BNT Density
BNT Density
BNT Density
Total % Tailings
%Forest
BNT Density
Independent Variables
% Forested
% Streamside Tailings
% Exposed Bank Tailings
Total % Tailings
% Forested
% Streamside Tailings
% Exposed Bank Tailings
Reach River Mile Start
% Forested
% Streamside Tailings
% Exposed Bank Tailings
Reach River Mile Start
Reach River Mile Start
Reach River Mile Start
P
values
n/a
n/a
n/a
n/a
R-squared Value
0.007000
0.049000
0.180000
0.088000
0.4372
0.006285
0.3029
0.066480
n/a
n/a
n/a
0.429000
0.055000
0.582000
Relationship
positive
positive
positive
positive
negative
positive
2nd order
polynomial model
Brown Trout Density (fish per mile) by River Mile
600
River Mile vs. BNT Density
500
BNT/mile
400
300
200
100
0
0
20
40
60
80
100
120
River Mile (mile 0 is at headwaters)
Figure 2: Brown trout density (fish greater than 150 mm per mile) as compared to river mile,
measured from the Warm Springs Ponds Outlet. Error bars indicate 95% confidence interval.