Transactions of the American Fisheries Society 136:254–271, 2007
Ó Copyright by the American Fisheries Society 2007
DOI: 10.1577/T06-094.1
[Article]
Fish and Amphibian Tolerance Values and an Assemblage
Tolerance Index for Streams and Rivers in the Western USA
THOMAS R. WHITTIER*
AND
ROBERT M. HUGHES
Department of Fisheries and Wildlife, Oregon State University,
200 Southwest 35th Street, Corvallis, Oregon 97333, USA
GREGG A. LOMNICKY
Dynamac Corporation c/o U.S. Environmental Protection Agency,
200 Southwest 35th Street, Corvallis, Oregon 97333, USA
DAVID V. PECK
U.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory,
Western Ecology Division, 200 Southwest 35th Street, Corvallis, Oregon 97333, USA
Abstract.—Aquatic species’ tolerances to overall human disturbance are key components of biological
assessments of aquatic ecosystems. These tolerance classifications enable development of metrics for use in
multimetric indexes, such as the index of biotic integrity (IBI). Usually, species are classified as being
tolerant, moderately tolerant, or intolerant (sensitive) to human disturbance. Traditionally, for fish-based IBIs,
these assignments are based on a combination of professional judgment and information from state fish books.
We used fish and amphibian species data in conjunction with chemical, physical, and landscape indicators of
human disturbance collected at 1,001 stream and river sites in 12 western states (USA) sampled by the
Environmental Monitoring and Assessment Program in 2000–2004. Using principal components analyses, we
created synthetic disturbance variables for water nutrients, site-scale physical habitat, catchment-scale land
use, and overall human disturbance. We calculated species’ tolerance values for the four synthetic disturbance
variables as weighted (by relative abundances) averages plus SDs. For each site, we used the tolerance values
(based on the overall synthetic disturbance variables) and relative abundances of species to calculate an
assemblage tolerance index (ATI) score. We discuss (1) how the tolerance values could be used in
establishing species tolerance classifications appropriate for regional species pools and (2) potential
application of the ATI to the IBI and to bioassessments in general.
Multimetric indices, such as the index of biotic
integrity (IBI; Karr 1981; Karr et al. 1986), are
increasingly used to evaluate the ecological condition
of aquatic ecosystems (Davis and Simon 1995; Hughes
and Oberdorff 1999; Simon 1999). In the USA, many
states use IBIs based on fish or macroinvertebrate
assemblages to evaluate whether water bodies are
meeting their designated aquatic life uses under the
Water Quality Standards Regulation of the U.S.
Environmental Protection Agency (USEPA 1990). In
the European Union, similar indices have been
developed to meet the goals of the Water Framework
Directive (Oberdorff et al. 2002; Pont et al. 2006). In
the last decade, the process of developing these indices
has become more structured and quantitative (e.g.,
Hughes et al. 1998, 2004; McCormick et al. 2001;
Oberdorff et al. 2002; Klemm et al. 2003; Mebane et al.
2003; Bramblett et al. 2005; Ode et al. 2005; Pont et al.
* Corresponding author: [email protected]
Received April 19, 2006; accepted September 24, 2006
Published online January 29, 2007
2006), which increases their acceptability to the
regulatory community.
A key component of many assemblage-based
metrics is the tolerances of individual species to
general human disturbance. For assemblages where
the potential species pool is large (e.g., macroinvertebrates, diatoms), weighted averaging techniques have
been applied to establish numeric tolerance scores for
species based on specific stressors such as organic
pollution or pH (e.g., Hilsenhoff 1977, 1987; Dixit et
al. 1999). However, for vertebrates, the potential
species pool is small enough that species have common
names and professionals have knowledge about a
majority of species in their regions. This has led to a
practice of assigning tolerance classes for fish-based
metrics via information from state fish books, professional judgment, and tolerance assignments from IBIs
in other regions (e.g., Miller et al. 1988; Barbour et al.
1999). Whittier and Hughes (1998) were unable to find
any IBI developers who had applied quantitative
methods to assign tolerance classes to fish species.
To our knowledge, only Meador and Carlisle (in press)
254
WESTERN STREAM FISH AND AMPHIBIAN TOLERANCES
have used quantitative field data to assess fish species’
tolerances. They used U.S. Geological Survey (USGS)
National Water Quality Assessment stream data for
105 common fish species across the USA to develop
separate tolerance values for suspended sediments,
water temperature, and eight water chemistry measures.
They took the average of the individual values as an
overall tolerance value for each species and assigned
tolerance classes based on the overall value.
The judgment-based approach has been fairly
successful for IBIs developed for warmwater streams
similar to those in the midwestern USA, where the IBI
was originally applied. However, for other aquatic
ecosystems (e.g., lakes, coldwater streams) and in other
regions it may not be appropriate to uncritically accept
existing tolerance class assignments. For example,
Whittier and Hughes (1998) found that eight species
usually classified as tolerant or moderately tolerant in
warmwater streams were intolerant or moderately
intolerant of degraded conditions in northeastern lakes
and that five species usually classified as intermediately tolerant were very tolerant. Natural conditions in
some areas (e.g., high turbidity and fine sediments in
the Great Plains) can, in other regions, mimic the
indicators of human disturbances. In addition, there is
relatively little published information on the tolerances
of many aquatic vertebrates, especially small fish and
amphibians.
As used in IBIs, species tolerance is relative to
human disturbance as a whole. Human disturbance is
usually multivariate, affecting chemical, physical, and
biological conditions of aquatic ecosystems and
occurring at multiple scales. People can often integrate
these factors to develop a qualitative mental assessment
of the overall disturbance at a site or set of sites and, by
extension, disturbance to the species that are present or
presumed extirpated at those sites. At the same time,
surveys and monitoring programs generally collect
numerous quantitative measures of individual stressor
indicators. Measures of some human disturbances are
relatively easily combined into a single disturbance
metric (e.g., nutrient concentrations), whereas others
are not (e.g., hydrological regime disruption); still
others are too expensive for routine broad-scale
monitoring (e.g., pesticides). In the context of biological assessment of stream condition, there have been a
number of attempts to combine multiple, disparate
measures into a single quantitative measure of overall
human disturbance (e.g., Bryce et al. 1999; McMahon
and Cuffney 2000; Hughes et al. 2004; Bramblett et al.
2005; Brown and Vivas 2005; Whittier et al. 2006).
However, to our knowledge, only Meador and Carlisle
(in press) for fish and MDEQ (2003) and Blocksom
and Winters (2006) for macroinvertebrates have
255
developed overall disturbance measures to derive
species tolerance values.
Complicating the topic further is that species vary in
the kinds of disturbances to which they are tolerant or
sensitive. For example, many fishes that are native to the
southwestern USA are tolerant of high turbidity,
temperature, and salinity and extreme seasonal flows
but are sensitive to introduced piscivores that flourish
when water quality ‘‘improves’’ in response to flow
regulation (Rinne et al. 2005). Likewise, in the Great
Plains, a number of turbidity-adapted native species
decline when dams reduce turbidity and provide habitat
for nonnative piscivores (e.g., Dieterman and Galat
2004). Finally, although the current practice is to assign
species to tolerance classes, tolerance is actually a
continuum of responses to disturbance, and distinct
classes confound accurate species tolerance assignments.
Despite these challenges, we believe that species
tolerance evaluations should be based on quantitative
measures to the extent possible. In this paper, we
present an approach for using site-scale chemical and
physical habitat data and catchment-scale disturbance
measures to evaluate fish and amphibian tolerances in
western U.S. streams and rivers over a 12-state area. We
use data collected by the Environmental Monitoring and
Assessment Program (EMAP; Stoddard et al. 2005a,
2005b) to develop site disturbance scores for water
quality, site-scale physical habitat, catchment land use,
and overall human disturbance. We then calculate
species tolerance values based on the site disturbance
scores. Finally, we use tolerance values and relative
abundances of species at each site to calculate an
assemblage tolerance index score for each site.
Methods
Study area and sample design.—The EMAP western
survey sampled streams and rivers in 12 states within
USEPA Regions 8–10 (Figure 1). Perennial streams
and rivers were defined as those present on USGS
digital hydrologic maps (1:100,000 scale) that were
incorporated into USEPA’s River Reach File (Version
3), excluding the lower portions of the Columbia,
Missouri, Snake, and Colorado rivers. The survey was
explicitly stratified by the 12 states, and within each
state we used an unequal-probability, spatially balanced survey design (Stevens and Olsen 2004). Sites
were selected by Strahler-order to yield approximately
120 sites each for first-order, second-order, third-order,
and fourth- or higher-order streams (total ¼ 480 sites)
plus 120 sites for large rivers. These 600 sites for the
basic survey were allocated among the 12 states at 50
sites/state. The same unequal-probability, spatially
balanced design was used to select additional sites
for six intensive study areas: northern and southern
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WHITTIER ET AL.
FIGURE 1.—Locations of 1,001 western U.S. stream and river sites (sampled by the U.S. Environmental Protection Agency’s
[USEPA] Environmental Monitoring and Assessment Program during 2000–2004) that were selected to assess species
tolerances. Each site met three criteria: a complete set of disturbance variables, sufficient vertebrate sampling effort, and presence
of vertebrates. Sites were selected using a probability design from the population of perennially flowing waters in 12 states.
Areas with a concentration of sites (e.g., northwestern California and the John Day River basin in Oregon) were given additional
sites (i.e., an increased sampling density within the probability design) to support ongoing USEPA projects. The regions shown
are aggregations of Omernik’s (1987) level III ecoregions.
California coastal streams, Deschutes River and John
Day River basins in Oregon, Wenatchee River basin in
Washington, upper Missouri River basin streams and
rivers, and Colorado plains streams (Figure 1).
Because ecological conditions and vertebrate assemblage characteristics varied widely across the geography of the West, sites were classified into three largescale ecoregions: the western forested mountains, xeric
lands, and the northern Great Plains (hereafter,
Mountains, Xeric, Plains) and 10 intermediate-scale
ecoregions (four each in the Mountains and Xeric; two
in the Plains; Figure 1). At both scales, these were
developed by aggregating Omernik level III ecoregions
(Omernik 1987) into areas that we judged would
encompass the major types of ecological conditions
and assemblages (Stoddard et al. 2005a, 2005b). Least
and most disturbed sites were selected for each largescale ecoregion by EMAP (Stoddard et al. 2005a,
2005b; Whittier et al. 2006) for metric and index
development purposes, based on water quality, physical habitat, and watershed disturbance measures.
Field methods.—Field sampling by state and
contract crews was conducted during base flow periods
(generally late spring through late summer) during
WESTERN STREAM FISH AND AMPHIBIAN TOLERANCES
2000–2004. All crews were trained in the EMAP
sampling protocols and sampled aquatic vertebrates
(fish and amphibians) via backpack or raft electrofishing (Peck et al. 2006, in press). Electrofishing is also
effective for collecting instream amphibians (Connor et
al. 1988; Moyle and Marchetti 1999; Hughes et al.
2004). Backpack electrofishers were used in small
wadeable streams, and rowing rafts were typically used
in nonwadeable, fourth-order to fifth-order and larger
rivers. In wadeable streams, aquatic vertebrates were
sampled within a reach length that was equal to 40
times the average wetted stream width, which is
estimated to collect 90% of potential species present
(Reynolds et al. 2003). Raft electrofishing was
employed near shore for a distance equal to 100 times
the average wetted river width; this distance was
estimated to collect 95% of the fish species present
(Hughes et al. 2002). Collected aquatic vertebrates
were processed continuously; all individuals except
those used as vouchers or in tissue contaminants
analysis were returned alive to the streams. Species
identifications of voucher specimens were confirmed
by the National Museum of Natural History (Smithsonian), where the specimens are now cataloged and
archived.
Field crews also made detailed instream and riparian
physical habitat assessments and collected samples of
water, periphyton, and benthic macroinvertebrates
(Peck et al. 2006, in press). A 5% subset of sites
received repeat sampling visits. We used first visit data
to develop the site disturbance scores and species
tolerance values from species relative abundances.
Disturbance gradients.—We sought to characterize
human disturbance gradients for three components of
lotic ecosystems: water quality, physical habitat at the
sample reach, and human activity in the catchment
above the sample reach. Nutrient enrichment and loss
of water clarity are probably the most widespread water
quality disturbances and are routinely measured.
Nutrient enrichment is often associated with activities
that contribute pesticides and other contaminants to the
water. We selected total phosphorus, total nitrogen, and
turbidity as indicators of nutrient enrichment and water
clarity. For site-scale physical habitat disturbance, we
included both instream and riparian stressor indicators.
For instream disturbance, we selected percent of the
substrate as fines (muck, silt, clay). Although sands are
often included in measures of sedimentation, they were
excluded from our disturbance measure because sand
bottoms are a natural condition in many Plains streams
and some Xeric streams. For riparian zones, we
selected a measure of the maturity of riparian
vegetation: the average areal coverage of woody shrubs
and small trees for the sample reach. In the Mountains
257
and higher-elevation areas of the Xeric, healthy
riparian vegetation usually includes canopy trees, but
these are not expected in undisturbed western Plains
areas. High values for woody shrub–small tree
coverage indicate a relative lack of disturbance. We
also selected a measure of the average number (along
the reach) of human disturbance types (e.g., riprap,
pipes, grazing, and roads) that were visible in the
stream or riparian zone. Details of field sampling and
indicator calculations for the site-scale measures are
presented in Kaufmann et al. (1999). To characterize a
catchment disturbance gradient, we used geographical
information systems (GIS) data on land use and land
cover, and we selected percent urban land, percent
agricultural land, and road density within the catchment
upstream of the sample site. We judged percent
rangeland to be an inconsistent disturbance measure
because it could indicate intensively grazed land;
natural shrub or grassland; or anything in between.
All nine variables were log10 transformed to achieve
a more normal distribution of data values. We ran
separate principal components analyses (PCAs) for
each of the three disturbance types for all sites over the
entire survey area and separate PCAs for Mountains,
Xeric, and Plains. We then ran a single PCA using all
nine variables with each of the same sets of sites. We
retained the first principal component (PC1) from each
PCA as synthetic disturbance variables for water
nutrients and clarity, physical habitat, catchment, and
overall disturbance.
Species tolerance values.—For each synthetic disturbance variable, we used relative abundances (at each
site) to estimate each species’ optimum (weighted
average) and upper tolerance (sum of the weighted
average plus 1 SD; ter Braak and Barendregt 1986;
Birks et al. 1990; Huff et al. 2005). The upper and
lower SDs of the weighted average are used to describe
a species’ realized niche width for that measure (Birks
et al. 1990; Huff et al. 2005). We then plotted realized
niche widths sorted by the optima and by upper
tolerances (two plots per stressor) to visually examine
tolerance patterns. We chose to use each species’ upper
tolerance to each synthetic disturbance variable as the
best characterization of tolerance to those disturbance
types.
To our knowledge, there are no established criteria
for determining the number of sites needed to provide
robust results for the upper tolerance in a weighted
average analysis (J. Van Sickle, USEPA, personal
communication). Requiring a large number of sites will
exclude a large portion of the species, whereas data
from a small number of sites may have large variance
or be subject to random bias. Because our goal was to
assess the tolerance of as many species as possible, we
258
WHITTIER ET AL.
chose to develop most of our tolerance values for those
species collected at five or more sites, an admittedly
low number. To address issues of variance and
potential bias, we subjected these data to a bootstrap
analysis of the overall synthetic disturbance variable
scores, as defined by the PC1 of the nine disturbance
variables combined from all sites. For each species
collected at five or more sites, we ran the bootstrap
analysis with 500 resamples of the upper tolerance
calculations (as described above). We set aside, for
further evaluation, species for which the bootstrap 95%
confidence interval exceeded one-third of the range of
the PC1 scores. We used the bootstrap bias-corrected
estimates of the upper tolerance as the calculated
tolerance values. We rescaled these scores to produce
tolerance values (TV) with a range of 0–10 as
TV ¼ f½ðminimum scoreÞ þ species score=rangeg
3 10:
To develop tolerance values for the 75 species
collected at fewer than five sites and the commonly
collected species that exceeded our variance criterion,
we calculated upper tolerance scores (regardless of
number of sites) and compared them with those of
more commonly collected species in the same genus
and with similar habitat preferences. This was
accomplished by the traditional best professional
judgment approach of evaluating written descriptions
in journal articles and state fish books and drawing
from personal experience. We then combined collection data for sets of similar species (usually within the
same genus), calculated genus or subgenus relative
abundances, and submitted those abundances to the
bootstrap analysis. We rounded the bootstrap estimate
to a whole-number tolerance value for each of these
uncommonly collected species.
We used scatter plots and correlations to evaluate
possible associations between natural gradients (e.g.,
stream size and elevation) or site disturbance scores
and species tolerance values. Finally, we compared our
tolerance values with those developed by Meador and
Carlisle (in press).
Assemblage tolerance index.—Based on the approaches described by Chutter (1972) and Hilsenhoff
(1977, 1987), we developed an assemblage tolerance
index (ATI), a set of site scores that describe the
gradient of disturbance, as shown by the vertebrate
assemblages. For each site, the ATI score was
calculated as the sum of the products of the (rescaled)
tolerance value (TV) and relative abundance (Abund)
of each species i as
X
ATI ¼
ðTVi 3 Abundi Þ:
To evaluate the effect of excluding uncommonly
collected species, we calculated ATIs only for those
species collected at five or more sites that also met our
variance criterion, and again using all tolerance values.
For the restricted ATIs, relative abundances were based
on the number of individuals of species that were not
excluded. We noted the proportion of sites where 50%
or more of the individuals had been excluded. We used
scatter plots and correlation analysis and univariate
analysis of the site score differences to compare the
two sets of ATIs.
We tested whether the ATI was responsive to human
disturbances by plotting ATI scores against individual
disturbance measures for the three large-scale ecoregions. We also produced box plots of ATI scores for
least, intermediately, and most disturbed sites classified
in prior studies (Stoddard et al. 2005a; Whittier et al.
2006, in press[b]); we then conducted one-way analysis
of variance (ANOVA) to test the ability of ATI to
distinguish between least and most disturbed sites.
Tests were conducted separately for the three largescale ecoregions and for the entire survey area. We
calculated a signal to noise ratio (S : N) as the ratio of
variance among sites (signal) to the variance of
repeated visits to the same site (noise, which includes
both natural variance and sampling error). This is a
measure of repeatability or precision (Kaufmann et al.
1999); a low S : N value indicates that a metric or index
has nearly as much variability within a site (over time)
as it does across different sites and thus does not
distinguish well among sites. We also ran a correlation
analysis of the ATI and previously calculated IBI
scores developed for the Mountains, Xeric, and Plains
ecoregions (Stoddard et al. 2005a).
Results
A total of 1,001 stream and river sites sampled
during 2000–2004 met the criteria for inclusion in our
assessment (i.e., a complete set of disturbance
variables, sufficient vertebrate sampling effort, and
vertebrates present at the site; Figure 1). At those sites,
165 fish species and 30 amphibian species were
collected, of which 109 fish species and 11 amphibian
species were collected at five or more sites. The
number of species collected in the intermediate-scale
ecoregions varied from 22 in the southern Rockies to
84 in the northern cultivated Plains (Table 1).
Disturbance Gradients
For all sites in the EMAP West survey, the water
nutrients and clarity, physical habitat, catchment, and
overall disturbance PCAs all produced similar results,
such that for each, the PC1s accounted for a majority of
the variance in the model and the three input variables
259
WESTERN STREAM FISH AND AMPHIBIAN TOLERANCES
TABLE 1.—Summary of the number of stream and river sites
and fish and amphibian species collected in 10 intermediate-scale
aggregated ecoregions in the western USA within three largescale ecoregions: Mountains, Xeric, and Plains. The regions are
depicted in Figure 1 and described by Stoddard (2005b).
Number of
Region
Mountain
Pacific Northwest
Northern Rockies
Southern Rockies
Southwestern
Xeric
Northern Basin
Southern Basin
Eastern Plateau
California Lowlands
Plains
Rangelands
Northern Cultivated
Sites
Species
Species found
at 5 sites
223
151
66
76
75
50
22
42
56
44
22
32
40
59
60
31
36
45
43
43
33
38
40
32
182
113
82
84
66
67
all loaded similarly on PC1 (Table 2). The PCAs
developed for the individual large-scale ecoregions
(and combinations) tended to have lower eigenvalues
for PC1 and lower eigenvector values for the input
variables. The surveywide PC1s for water nutrients and
clarity, physical habitat, and landscape were moderately well correlated with one another (r ¼ 0.52–0.62).
The PC1 of the nine-variable PCA for all sites
accounted for 47% of the variance. The input variables
(0.21–0.43) were not as evenly loaded as they were for
the three-variable disturbance type PCA (Table 2). As a
group, the water nutrients and clarity variables had the
highest loadings but did not appear to dominate PC1.
Based on these results, we judged that the PC1s of the
four PCAs (for the EMAP West survey area) were
reasonable synthetic disturbance variables with which
to assess species tolerances and that the overall
synthetic disturbance variable scores could be used to
calculate species tolerance values.
For all sites, the overall synthetic disturbance
variable scores ranged from 4.1 (least disturbed) to
5.7; the interquartile range was 1.7 to 1.7. Regionally,
the overall synthetic disturbance variable scores were
lowest for the Mountains, highest for the Plains, and
intermediate for the Xeric (Figure 2). For the survey
region, the overall synthetic disturbance variable score
increased with increased stream size (as measured by
log[watershed area]; r ¼ 0.56, P , 0.0001) and
decreased with increasing elevation (r ¼ 0.36, P ,
0.0001). At the scale of the large ecoregions, the
associations with stream size were weaker (Mountains:
r ¼ 0.33, P , 0.0001; Xeric: r ¼ 0.28, P , 0.0001;
Plains: r ¼ 0.17, P ¼ 0.003). The associations between
elevation and the overall synthetic disturbance variable
score varied by large-scale ecoregion and was
significantly correlated for the Xeric (r ¼ 0.32, P ,
TABLE 2.—Principal components (PC) analyses of three indicators each for nutrients and water clarity, physical habitat (site),
and land use (catchment), and all nine indicators combined, as used to examine species tolerances in western U.S. streams.
Eigenvalues and proportions of variance accounted for by PC1 and PC2 are shown for each analysis, as are the PC1 eigenvectors
(loadings) for each indicator. Values for all input variables were log10 transformed. Riparian vegetation describes woody shrub
and small tree areal cover in the riparian zone (this measured a lack of disturbance and loaded negatively on both PC1s), riparian
disturbance describes the average number of kinds of visible human disturbance, weighted by proximity to the stream along the
sample reach.
Individual
Indicators
Nutrients
PC1
PC2
Total P
Total N
Turbidity
Physical Habitat
PC1
PC2
Riparian vegetation
Riparian disturbance
Percent fines
Watershed
PC1
PC2
Percent urban
Percent agriculture
Road density
Combined
PC1
PC1
Eigenvalue Proportion eigenvector Eigenvalue Proportion eigenvector
2.42
0.36
1.65
0.77
1.66
0.71
0.81
0.12
4.25
1.17
0.47
0.13
0.59
0.57
0.57
0.41
0.43
0.36
0.55
0.56
0.62
0.26
0.29
0.32
0.57
0.56
0.60
0.21
0.40
0.25
0.55
0.25
0.55
0.24
260
WHITTIER ET AL.
FIGURE 2.—Distribution of overall synthetic disturbance
variable (SDV) scores for stream and river sites in three
western U.S. ecoregions, as derived from the nine-stressor
principal component 1 (PC1 in Table 2). Boxes show medians
and quartiles, whiskers indicate 10th and 90th percentiles, and
circles indicate outliers. The dotted line marks the median
SDV value for all sites.
0.0001) and for the Plains (r ¼0.48, P , 0.0001) but
not for the Mountains (P ¼ 0.13).
Species Tolerance Values
For the 120 species collected at five or more sites,
the bias-corrected upper tolerance scores (weighted
averages plus SDs of the overall synthetic disturbance
variable scores) from the bootstrap analysis ranged
from 2.9 (Cascades frog Rana cascadae) to 5.1 (white
bass Morone chrysops). The bootstrap 95% confidence
interval exceeded our variance criterion (i.e., one-third
of the range of the overall synthetic disturbance
variable scores, or 3.24) for 11 species collected at
between 5 and 35 sites (Table A.1). Unless otherwise
noted, the following tolerance results refer to the 109
remaining species. The tolerance value for Cascades
frog (seven sites) was an outlier; the next three species
had tolerance raw scores of 1.5 (rough-skinned newt
Taricha granulosa), 1.4 (coastrange sculpin Cottus
aleuticus), and 1.3 (bull trout Salvelinus confluentus).
This gap represented 19% of the range of scores. To
increase discrimination among all the other species, we
arbitrarily changed the Cascades frog raw tolerance
score to 1.7 (0.2 less than the second-lowest tolerance
score) before rescaling these scores into tolerance
values. If we had not done this, the rescaled tolerance
value for the second most sensitive species would have
been 1.9 (out of 10).
There was considerable variation in the concordance
of species’ tolerances to the water nutrients and clarity,
physical habitat, and watershed synthetic disturbance
variables. For example, the Pacific giant salamander
Dicamptodon tenebrosus ranked 10th most sensitive for
the water nutrients synthetic disturbance variable, 9th
for the physical habitat variable, and 14th for the
watershed variable. This high concordance resulted in
an overall tolerance rank of 8 (lower than any of the
individual synthetic disturbance variables). In contrast,
the yellow bullhead Ameiurus natalis ranked 74th, 39th,
and 120th, respectively, for the three synthetic disturbance variables. In other words, the yellow bullhead
was the most tolerant of watershed disturbance but
ranked in the most sensitive third of species for physical
habitat disturbance and near the middle third for water
nutrients and clarity; the overall tolerance rank of the
yellow bullhead was 75, also near the middle. In
addition, the 95% confidence interval for the yellow
bullhead exceeded our criterion despite the fact that this
species was collected at 35 sites, more than twice as
many sites as any other species that failed to meet this
criterion. The yellow bullhead is typically considered to
be very tolerant. Our data indicate that this species may
be found in relatively large numbers over a wide range
of disturbance levels.
Among the 109 species with bootstrap estimates,
those with the lowest tolerance values were characteristic of the Mountains and many of those with high
tolerance values were characteristic of the Plains (Table
A.1). Of the 40 most sensitive species, 39 were
collected in the Mountains, 30 were collected in the
Xeric, and only 9 were collected in the Plains.
Conversely, of the 40 most tolerant species, all were
collected in the Plains, 17 were collected in the Xeric,
and 12 were collected in the Mountains. In the
Mountains and Xeric ecoregions, a large majority of
the species with tolerance values exceeding 6.5 were
nonnatives. Within the Mountains and Xeric ecoregions, there were clear differences among the species
pools within the intermediate-scale ecoregions. For
example, of the 56 species in the Pacific Northwest
Mountains, 43 had tolerance values less than 5.0,
whereas 12 of the 29 species in the southwestern
Mountains had tolerance values less than 5.0 (Figure
3).
The bootstrap-estimated tolerance values were
positively correlated with median watershed size for
sites where the species were collected (r ¼ 0.58, P ,
0.0001), but most species were found over a wide
range of stream sizes. The bootstrap-estimated tolerance values were weakly and negatively correlated
with median elevation (r ¼ 0.18, P ¼ 0.0489).
Fifty-four of these species were also evaluated by
WESTERN STREAM FISH AND AMPHIBIAN TOLERANCES
261
FIGURE 3.—Species tolerance values and percentage of sites where species were collected within two intermediate-scale
ecoregions: (A) tolerance in Pacific Northwest Mountains, (B) tolerance in southwestern Mountains, (C) percentage of sites in
Pacific Northwest Mountains, and (D) percentage of sites in southwestern Mountains. Species are arranged in ascending order of
tolerance values (Table A.1). The species in (B) were placed to match their relative positions in (A). Species number codes match
between (A) and (C) and between (B) and (D) (i.e., within ecoregion only). Solid dots indicate species that are not native to the
ecoregion.
Meador and Carlisle (in press). They developed an
overall tolerance value as the average of the stressorspecific tolerance values. The tolerance values for
overall disturbance were well correlated (r ¼ 0.75, P ,
0.0001); most of the variation occurred among the
more tolerant species.
Assemblage Tolerance Index
We calculated ATI scores for sites in two ways: (1)
with bootstrap-estimated tolerance values for only the
109 species collected at more than five sites and
meeting our variance criterion, and (2) with tolerance
values for all 195 species. Eleven sites had only
uncommon or high-variance species (thus, no ATI by
the first method). The two ATIs for the remaining 990
sites were highly correlated (r ¼ 0.99); 627 sites (63%)
had identical scores and 909 sites had scores that varied
by less than 1%. However, scores for 17 sites varied by
10–46%. At 38 sites, the uncommon and high-variance
species made up more than 50% of the assemblage. We
assume that our tolerance values for the uncommon and
high-variance species are reasonable approximations;
we therefore assume that an ATI that incorporates
those tolerance values more accurately reflects the
assemblage tolerance.
The ATI for all species had an S : N ratio of 39.1,
262
WHITTIER ET AL.
FIGURE 4.—Distribution of assemblage tolerance index
(ATI) scores for stream and river sites in three western U.S.
ecoregions, as derived from the scaled tolerance values for
195 fish and amphibian species (Table A.1). Boxes show
medians and quartiles, whiskers indicate 10th and 90th
percentiles, and circles indicate outliers. The dotted line
marks the median ATI value.
which was quite high; S : N values of 2–3 are
considered acceptable for selecting IBI metrics for
use in a regional-scale assessment (McCormick et al.
2001; Stoddard et al. 2005a). Two sites had an ATI of
0; otherwise, ATI scores had a range of 0.28–9.70 and
an interquartile range of 2.10–7.60. In general, ATI
scores were lowest in the Mountains, highest in the
Plains, and intermediate in the Xeric (Figure 4). The
ATI scores were significantly lower at least disturbed
sites than at most disturbed sites (Mountains: F ¼ 63.9;
Xeric: F ¼ 54.7; Plains: F ¼ 21.4). The ATI scores were
significantly correlated with IBI scores (P , 0.0001);
Pearson’s product-moment correlation coefficients (r)
were 0.77 for all sites, 0.78 for the Mountains,
0.82 for the Xeric, and 0.42 for the Plains (Figure
5).
Discussion
The process we present in this paper and the species
tolerance values and ATI developed by that process
should provide opportunities to strengthen the use of
fish and amphibians as indicators of overall ecological
condition. To our knowledge, our work and the
coincident work of Meador and Carlisle (in press)
mark the first cases in which quantitative tolerance
values for human disturbance have been developed for
fish or amphibians. This paper also represents the first
use of species tolerance values for calculating assemblage-level tolerance scores at sites for fish and
amphibians. Although most of the components we
FIGURE 5.—Association between assemblage tolerance
index scores and index of biotic integrity scores for stream
and river sites within three western U.S. ecoregions, where
tolerances of fish and amphibian species were evaluated.
present here are not new, our application of the
components in developing species tolerance values and
the ATI differs from previous work (Chutter 1972;
Hilsenhoff 1977, 1987; MDEQ 2003; Blocksom and
Winters 2006; Meador and Carlisle, in press).
Disturbance Gradients
Quantifying an overall human disturbance gradient
is probably the most intractable part of the process of
WESTERN STREAM FISH AND AMPHIBIAN TOLERANCES
determining species and assemblage tolerances. In lotic
ecosystems, humans disturb the physical, chemical,
and biological habitat (including the flow regime and
temperature) directly (e.g., from or into pipes,
physically manipulating the stream, introducing species) and indirectly (e.g., urban, agricultural, or forestry
land uses). Although we regularly collect data on many
of these disturbances, it is not clear how to combine
those data into a single measure of overall disturbance.
One of the most widely used sets of species
tolerance values (as opposed to tolerance classifications) was developed by Hilsenhoff (1977, 1987) and is
based on macroinvertebrate species presence or
absence in streams of varying levels of organic
pollution. We know of three other efforts to develop
overall tolerance values that have expanded the scope
of the disturbance gradient assessment beyond water
chemistry to include physical habitat and watershed
disturbances (MDEQ 2003; Blocksom and Winters
2006; Meador and Carlisle, in press). The first two of
these studies also used variables from all three
disturbance types to create, via PCA, a single synthetic
disturbance variable. In their PCA, Blocksom and
Winters (2006) used four water quality measures and
three site-scale physical habitat measures; the latter
were qualitative rapid bioassessment protocol scores
(Barbour et al. 1999). The MDEQ (2003) study used
31 variables in a PCA: three sets of five land use
measures (measured on [1] entire upstream catchment,
[2] 100-m riparian buffer in the entire upstream
network, and [3] 100-m buffer 1 km upstream of the
site); 10 water quality measures; and six site-scale
physical habitat measures.
We arrived at our nine-variable (overall) synthetic
disturbance variable by first developing separate
synthetic disturbance variables for water nutrients and
clarity, physical habitat, and catchments. For each
ecosystem component, our goal was to use a small set
of stressor variables that would produce an easily
interpretable synthetic disturbance variable. We wanted
input variables that (1) had similar loadings, (2) were
not highly correlated, and (3) incorporated distinct
stressor measures. Each of the three-variable synthetic
disturbance variables apparently met the criteria, as did
the overall synthetic disturbance variable. This approach aided us in selecting variables for each synthetic
disturbance variable and in confirming a generally
consistent behavior for the set at the scale of the 12state survey. We also calculated species tolerance
scores for three types of disturbance, which were useful
for understanding the overall tolerance values. Although we did not ultimately use the three-stressor
synthetic disturbance variables in a structured way, the
differences we found in species tolerances to the
263
stressor types may be useful for developing speciesbased diagnostic tools to indicate probable causes of
impaired ecological condition.
Our disturbance gradients, and those developed by
MDEQ (2003) and Blocksom and Winters (2006),
demonstrated the expected patterns based on other
assessments of human disturbance and assemblage
response to disturbance; the gradients thus appear to be
reasonable quantitative approximations of the overall
disturbance gradient. However, there are a number of
important human disturbances to lotic ecosystems that
were not included in any of these assessments. For
example, flow alteration due to water diversion and
dams is recognized as a major disturbance affecting
water temperature, streambed stability and sedimentation, the natural hydrological regime, and sometimes
the availability of water. As important as these factors
are to streams and rivers, the needed data are usually
not available for enough systems to be useful for
broad-scale surveys and, in some cases, neither are the
needed metrics of flow disturbance.
Likewise, the presence of potentially toxic anthropogenic chemicals is an important human disturbance.
However, the expense and complexity of assessing
these stressors puts them beyond the means of most
statewide monitoring programs. The presence of
nonnative species also represents an anthropogenic
disturbance. However, species’ tolerances of this
disturbance vary considerably depending on (among
other factors) whether the nonnative species are
predators, competitors, both, or neither, and how
abundant the nonnative species are. Some nonnative
species appear to be able to eliminate some native
species in certain settings but have little or no
detectable effect in other settings (Moyle and Marchetti
2006). At this time, we know of no satisfactory single
measure of nonnative species disturbance.
Definition of human disturbance gradients and
evaluation of how humans affect ecosystems are also
complicated by the covariance among human activity,
natural gradients (e.g., elevation, slope, precipitation),
and biological assemblages (Whittier et al. 2006). In
general, human presence and activity and natural
ecosystem productivity tend to decrease with increasing elevation and slope but increase with increased
precipitation. The river continuum concept (Vannote et
al. 1980) indicates that larger streams and rivers, which
tend to occur at lower elevations and slopes, will
naturally tend to be warmer, more turbid, and more
productive; some of the very features we associate with
human disturbance. At the same time, humans
extensively and intensively use larger streams and
rivers, such that the problem of defining reference
condition for large rivers is still unresolved. However,
264
WHITTIER ET AL.
Mebane et al. (2003) and Rinne et al. (2005) offer
useful alternatives.
Species Tolerances
Definition of a single measure of species tolerance
faces challenges similar to definition of overall
disturbance. Conceptually it is simple; a species is
sensitive to human disturbance if its range or
abundance decreases are due to human activity, and
tolerant if the opposite happens. Ideally, that is what we
would measure. Unfortunately, for most species, we
lack data collected consistently over sufficient time and
space to quantify range and abundance changes. For
most species, our only recourse is to record their
current occurrences and abundances, and to evaluate
current conditions at those sites relative to our best
estimate of least disturbed conditions. We also note that
even a definition of tolerance based on range and
abundance change can produce contradictory results.
For example, by most standards, the brook trout is
sensitive to anthropogenic disturbance. In the species’
native range, brook trout populations have been
reduced by acidification (from atmospheric deposition
and mine drainage) and by nonnative predators and
competitors. At the same time, the overall geographic
range and numbers of brook trout have been greatly
expanded by stocking in western mountains, where the
species is now well established.
In addition to the difficulties in evaluating certain
types of human disturbance (discussed above), there
are three other issues that can confound assessments of
species tolerance. First, species may be tolerant of, or
sensitive to, specific disturbances yet may have a
different tolerance to overall disturbance. For example,
our data showed the brook stickleback to be very
tolerant of overall disturbance, but it is sensitive to
predation and can be eliminated if cover is removed or
predator species are introduced. Second, species can
have different tolerances in different ecosystems
(Whittier and Hughes 1998). For example, in our
study of perennial flowing streams, plains killifish and
plains topminnow had intermediate tolerances to
overall disturbance, but both species tolerate high
temperatures and salinities and low dissolved oxygen
in pond situations. Third, species at the edges of their
native ranges may appear to be more sensitive to
disturbance than they are elsewhere. For example, the
sauger is highly tolerant of turbid conditions but is a
species of concern at the western edge of its range in
Montana and Wyoming because of declining numbers.
The sauger is naturally limited by cool summer water
temperatures and high-gradient streams and is anthropogenically limited by dams that block fish migration
(Amadio et al. 2004).
Tolerance Values
Species tolerance values have been based on
professional judgment (e.g., Chutter 1972), species
presence or absence (e.g., Hilsenhoff 1977), and
relative abundances at sites of known quality (e.g.,
Huff et al. 2005; Blocksom and Winters 2006). In the
latter case, the tolerance values are usually the species’
optimum values (calculated as the weighted average of
the species’ relative abundances across the range of
concentrations, temperatures, or pollution index
scores). However, we view tolerances of species to a
particular condition as a combination of their optima
(conditions where they are most abundant) and the
range of conditions where they are found. For two
species with identical optima, the one that occupies a
wider range of values (has a larger niche width) is more
tolerant to that condition. For that reason, we used the
weighted average plus 1 SD (i.e., the upper tolerance
value) as the species tolerance.
Our species tolerance values, being based on
synthetic variables, are abstract numbers because they
do not relate directly to single disturbance variables. As
such, we initially understood their meaning and
potential usefulness by examining how species were
ranked. In general, species’ ranks tended to match our
expectations; salmonids, cottids, and other species
typical of mountain streams were ranked at the low end
of the tolerance range, whereas a number of ictalurids,
centrarchids, and some catostomids (usually considered
very tolerant) had high tolerance values (Table A.1).
However, we were initially surprised by some rankings, especially those within the tolerant half of the list.
For example, the green sunfish (tolerance value ¼ 7.3)
and brown bullhead (tolerance value ¼ 7.2) were
ranked 61 and 63 (of 109), whereas the bluegill ranked
85 (tolerance value ¼ 8.2). Our surprise was probably a
function of our own preconceived ideas and our
experiences with these species. Examination of a study
by Whittier and Hughes (1998, their Table 3) showed
disagreements regarding the brown bullhead and
bluegill among six published tolerance classifications
for stream fish; there were disagreements among
classifications for 23 of the 42 species.
It is interesting to note that five of the seven species
with the lowest tolerance values were amphibians
found in the Pacific Northwest and northern Rockies.
These species are often found in streams above natural
barriers to fish passage and, because they are able to
move over land, have evolved to take advantage of
streams in which there is little competition from fish.
Regardless, the prevalence of amphibians in these
streams argues for their inclusion in IBIs in these
regions (Moyle and Marchetti 1999; Hughes et al.
WESTERN STREAM FISH AND AMPHIBIAN TOLERANCES
2004). At the other end of the tolerance spectrum, we
note that some managed sport fish (e.g., sauger,
walleye, white bass) and one federally endangered
species (Topeka shiner) have high tolerance values,
reminders that tolerance values and regulatory classifications are not equal.
We recognize that for many species, our tolerance
values were based on a small number of data points.
Even with 1,001 sites, 100 species were collected at 10
or fewer sites. We hope to be able to incorporate data
from continuing EMAP studies and expect that some of
our tolerances will change with additional data. For the
86 uncommon and highly variable species, we strove to
make our assignment of whole-number tolerance
values as quantitative and as consistent as possible.
In Table A.1, we distinguish between the bootstrap
estimated tolerance values and the tolerance values
based on combining similar species.
Tolerance Classifications
Our initial purpose for this work was to improve the
rigor of our species tolerance classifications by using
quantitative, site-based data. As our assessment
progressed, we became increasingly frustrated with
trying to force distinct tolerance classes onto tolerance
values. Regardless of the ecoregional species pool
being considered, at least one tolerance class delineation (e.g., between intermediate and tolerant in the
Plains) appeared to be completely arbitrary. Thus, we
have presented no tolerance classifications.
We do, however, recognize that classifications can
be useful as a framework to simplify complex problems
and are often required in the regulatory process. For
IBI assessments the goal often is to determine, for
management purposes, whether a site is in a particular
quality class (e.g., good, fair, or poor). We argue that
these evaluations should take advantage of the
continuous nature of the data, by not classifying
(where no clear classes exist) until required at a final
step. A number of IBIs use continuous metric scoring
(e.g., McCormick et al. 2001; Klemm et al. 2003;
Stoddard et al. 2005a; Whittier et al., in press[a]). In
this paper, we have extended that logic to species’
tolerances.
For a number of IBIs, trophic and habitat metrics
have been modified to exclude certain tolerance classes
(e.g., nontolerant piscivores excludes tolerant piscivorous species). Thus, we offer a few comments and
suggestions on the use of tolerance values for species
classifications and metric development.
As stated above, nearly all of the most sensitive
species are characteristic of mountainous ecoregions
and conversely nearly all of the most tolerant species
are characteristic of the Plains. If one develops a single
265
tolerance classification for all sites across the West,
then in the Plains there would be little or no
information in metrics based on intolerant species to
distinguish among sites because nearly all sites would
have no intolerants. Likewise, in the mountains,
tolerant species’ metrics would also contain little
information and would probably be highly correlated
with nonnative species’ metrics. One could classify
species differently depending on the ecoregion. For
example, in the context of the species pools, the
longnose sucker (TV ¼ 4.6) would be intermediately
tolerant in the Mountains and the Xeric but sensitive in
the Plains.
A similar approach could be used for different
stream types and sizes. For example, if the metrics are
to be applied to assemblages in wadeable streams,
consider only species expected in small- to mediumsized streams. It may also be useful to initially remove
very uncommon species to concentrate on those that
provide the greatest assessment power. Finally, for
metrics with tolerance modifiers it may be useful to
select different tolerance values for different metrics
that have the same classification meaning. For
example, ‘‘nontolerant suckers’’ could be based on
catostomids with tolerance values less than 5, while
‘‘nontolerant piscivores’’ could be based on piscivore
species with tolerance values less than 7.
Assemblage Tolerance Index
An ATI for macroinvertebrates, the Hilsenhoff biotic
index (HBI; Hilsenhoff 1977, 1987), has been widely
used to assess lotic ecosystem condition for decades,
but to our knowledge this has not been done previously
for aquatic vertebrate assemblages. A number of
characteristics of our vertebrate ATI indicate that it
will be a useful addition to the set of bioassessment
tools currently in use; it has a high signal-to-noise ratio
(i.e., it is repeatable), and it distinguishes least
disturbed sites from most disturbed sites well (i.e., it
is responsive to human disturbance). The ATI is also
reasonably well correlated with IBI scores. Finally, the
ATI takes advantage of the continuous nature of the
tolerance values.
Although only a few IBI metrics are explicitly based
on species tolerances, we expect vertebrate assemblages at sites with low IBI scores to be predominated by
tolerant species (and vice versa). This pattern is clearly
seen in the Mountains and Xeric ecoregions. In the
Plains, the seven sites with ATI scores less than 5 were
all near the western edge of the Plains and in the upper
20th percentile of Plains elevations, and all had one or
more nonnative trout; the watersheds for five of them
were primarily in the Mountains. The remaining Plains
sites showed the expected, albeit compressed, relation-
266
WHITTIER ET AL.
ship between IBI and ATI. Overall, we take the
patterns shown in Figure 5 as evidence that the ATI is
accurately describing a real characteristic of the
vertebrate assemblages in streams and rivers of the
West.
In some macroinvertebrate IBIs, the HBI is used as a
tolerance metric (Barbour et al. 1999). The ATI may be
a useful replacement for tolerance metrics in fish and
amphibian assemblage IBIs. The HBI also serves as an
independent index in many places. The ATI could be
used as an independent measure of stream condition (in
addition to an IBI), just as the proportion of nonnative
individuals or species has been used (Stoddard et al.
2005b).
Conclusions
The large geographic area and range of stream sizes
(first through eighth order) have allowed us to assess
aquatic vertebrate species’ tolerances over a wide range
of human disturbances and for a large portion of many
species’ ranges. We believe this broad scope provides a
better description of species’ tolerances than was
previously possible using information necessarily
developed from smaller areas. We also believe that
quantifying tolerances with field data produces a more
robust assessment than does professional judgment.
Despite data from 1,001 sites, a fairly large number
of species were collected at only a few sites, either
because they are generally uncommon or because we
sampled near the edges of their ranges. We plan to
incorporate additional sites and data in the future to
strengthen and correct our assessments as warranted by
new data. We also encourage others to examine the
issue of quantifying overall human disturbance to
aquatic ecosystems.
Acknowledgments
The USEPA reports and data described here are
available from the EMAP web site (http://www.epa.
gov/emap/html/pubs/docs/groupdocs/surfwatr/field/
index.html), the Western Ecology Division web site
(http://www.epa.gov/wed/pages/models.htm), and
D.V.P. (e-mail: [email protected]). The field data
were collected by agency or contract crews of the 12
states in the EMAP Western Pilot assessment area and
by Dynamac Corporation crews. Database management
support was provided by the Corvallis, Oregon, staff of
the Computer Sciences Corporation; GIS support was
provided by Indus Corporation. Statistical guidance
was provided by J. Van Sickle and T. Kincaid.
Comments from K. Blocksom, R. Bramblett, L. Yuan,
D. DeVries, and four anonymous reviewers of earlier
versions have improved this paper. This document was
prepared at the USEPA National Health and Environ-
mental Effects Research Laboratory’s Western Ecology
Division in Corvallis through Cooperative Agreement
CR831682-01 to Oregon State University and contracts
68-D01-0005 and EP-D-06-013 to Dynamac Corporation. The paper has been subjected to USEPA peer and
administrative review and approved for publication.
Mention of trade names or commercial products does
not constitute endorsement by the U.S. Government.
References
Amadio, C. J., W. A. Hubert, K. Johnson, D. Oberlie, and D.
Dufer. 2004. Factors affecting the occurrence of saugers
in small high-elevation rivers near the western edge of
the species’ natural distribution. Transactions of the
American Fisheries Society 134:160–171.
Barbour, M. T., J. Gerritsen, B. D. Snyder, and J. B. Stribling.
1999. Rapid bioassessment protocols for use in streams
and wadeable rivers. U.S. Environmental Protection
Agency, EPA/841/B-99/002, Washington, D.C.
Birks, H. J. B., J. M. Line, S. Juggins, A. C. Stevenson, and C.
J. F. ter Braak. 1990. Diatoms and pH reconstruction.
Philosophical Transactions of the Royal Society of
London B 327:263–278.
Blocksom, K., and L. Winters. 2006. The evaluation and
development of a method for creating defendable,
repeatable, objective and accurate tolerance values.
EPA 600/R-06/045, U.S. Environmental Protection
Agency, Cincinnati, Ohio.
Bramblett, R. G., T. R. Johnson, A. V. Zale, and D. G.
Heggem. 2005. Development and evaluation of a fish
assemblage index of biotic integrity for northwestern
Great Plains streams. Transactions of the American
Fisheries Society 134:624–640.
Brown, M. T., and M. B. Vivas. 2005. A landscape
development intensity index. Environmental Monitoring
and Assessment 101:289–309.
Bryce, S. A., D. P. Larsen, R. M. Hughes, and P. R.
Kaufmann. 1999. Assessing relative risks to aquatic
ecosystems: a mid-Appalachian case study. Journal of the
American Water Resources Association 35:23–36.
Chutter, F. M. 1972. An empirical biotic index of the quality
of the water in South African streams and rivers. Water
Resources 6:19–30.
Connor, E. J., W. J. Trush, and A. W. Knight. 1988. Effects of
logging on Pacific giant salamanders: influence of ageclass composition and habitat complexity. Bulletin of the
Ecological Society of America 69(Supplement):104–105.
Davis, W. S., and T. P. Simon. 1995. Biological assessment
and criteria: tools for water resource planning and
decision making. Lewis, Boca Raton, Florida.
Dieterman, D. J., and D. L. Galat. 2004. Large-scale factors
associated with sicklefin chub distribution in the
Missouri and lower Yellowstone rivers. Transactions of
the American Fisheries Society 133:577–587.
Dixit, S. S., J. P. Smol, D. F. Charles, R. M. Hughes, S. G.
Paulsen, and G. B. Collins. 1999. Assessing water quality
changes in the lakes of the northeastern United States
using sediment diatoms. Canadian Journal of Fisheries
and Aquatic Sciences 56:131–152.
Hilsenhoff, W. L. 1977. Use of arthropods to evaluate water
WESTERN STREAM FISH AND AMPHIBIAN TOLERANCES
quality of streams. Wisconsin Department of Natural
Resources, Technical Bulletin 100, Madison.
Hilsenhoff, W. L. 1987. An improved biotic index of organic
stream pollution. Great Lakes Entomologist 20:31–39.
Huff, D. D., S. L. Hubler, and A. N. Borisenko. 2005. Using
field data to estimate the realized thermal niche of aquatic
vertebrates. North American Journal of Fisheries Management 25:346–360.
Hughes, R. M., S. Howlin, and P. R. Kaufmann. 2004. A
biointegrity index for coldwater streams of western
Oregon and Washington. Transactions of the American
Fisheries Society 133:1497–1515.
Hughes, R. M., P. R. Kaufmann, A. T. Herlihy, S. S.
Intelmann, S. C. Corbett, M. C. Arbogast, and R. C.
Hjort. 2002. Electrofishing distance needed to estimate
fish species richness in raftable Oregon rivers. North
American Journal of Fisheries Management 22:529–540.
Hughes, R. M., P. R. Kaufmann, A. T. Herlihy, T. M. Kincaid,
L. Reynolds, and D. P. Larsen. 1998. A process for
developing and evaluating indices of fish assemblage
integrity. Canadian Journal of Fisheries and Aquatic
Sciences 55:1618–1631.
Hughes, R. M., and T. Oberdorff. 1999. Applications of IBI
concepts and metrics to waters outside the United States
and Canada. Pages 79–93 in T. P. Simon, editor.
Assessing the sustainability and biological integrity of
water resources using fish communities. CRC Press,
Boca Raton, Florida.
Karr, J. R. 1981. Assessment of biotic integrity using fish
communities. Fisheries 6(6):21–27.
Karr, J. R., K. D. Fausch, P. L. Angermeier, P. R. Yant, and I.
J. Schlosser. 1986. Assessing biological integrity in
running waters: a method and its rationale. Illinois
Natural History Survey, Special Publication 5, Champaign.
Kaufmann, P. K., P. Levine, E. G. Robison, C. Seeliger, and
D. V. Peck. 1999. Quantifying physical habitat in
wadeable streams. U.S. Environmental Protection Agency, EPA/620/R-99/003, Washington, D.C.
Klemm, D. J., K. A. Blockson, F. A. Fulk, A. T. Herlihy, R.
M. Hughes, P. R. Kaufmann, D. V. Peck, J. L. Stoddard,
W. T. Thoeny, and M. B. Griffith. 2003. Development
and evaluation of a macroinvertebrate biotic integrity
index (MBII) for regionally assessing Mid-Atlantic
Highlands streams. Environmental Management
31:656–669.
McCormick, F. H., R. M. Hughes, P. R. Kaufmann, D. V.
Peck, J. L. Stoddard, and A. T. Herlihy. 2001.
Development of an index of biotic integrity for the
Mid-Atlantic Highlands region. Transactions of the
American Fisheries Society 130:857–877.
McMahon, G., and T. F. Cuffney. 2000. Quantifying urban
intensity in drainage basins for assessing stream
ecological conditions. Journal of the American Water
Resources Association 36:1247–1261.
MDEQ (Mississippi Department of Environmental Quality).
2003. Development and application of the Mississippi
benthic index of stream quality (M-BISQ). Mississippi
Department of Environmental Quality, Jackson. Available: http://deq.state.ms.us/MDEQ.nsf/page/
FS_SurfaceWaterQualityAssessments?OpenDocument.
(January 4, 2007).
267
Meador, M. R., and D. M. Carlisle. In press. Quantifying
tolerance values for common stream fish species of the
United States. Ecological Indicators.
Mebane, C. A., T. R. Maret, and R. M. Hughes. 2003. An
index of biological integrity (IBI) for Pacific Northwest
rivers. Transactions of the American Fisheries Society
132:239–261.
Miller, D. L., P. M. Leonard, R. M. Hughes, J. R. Karr, P. B.
Moyle, L. H. Schrader, B. A. Thompson, R. A. Daniels,
K. D. Fausch, G. A. Fitzhugh, J. R. Gammon, D. B.
Halliwell, P. L. Angermeier, and D. J. Orth. 1988.
Regional applications of an index of biotic integrity for
use in water resource management. Fisheries 13(5):12–
20.
Moyle, P. B., and M. P. Marchetti. 1999. Applications of
indices of biotic integrity to California streams and
watersheds. Pages 367–380 in T. P. Simon, editor.
Assessing the sustainability and biological integrity of
water resource quality using fish communities. CRC
Press, Boca Raton, Florida.
Moyle, P. B., and M. P. Marchetti. 2006. Predicting invasion
success: freshwater fishes in California as a model.
BioScience 56:515–524.
Oberdorff, T., D. Pont, B. Hugueny, and D. Chessel. 2002.
Development and validation of a fish-based index for the
assessment of ‘river health’ in France. Freshwater
Biology 47:1720–1734.
Ode, P. R., A. C. Rehn, and J. T. May. 2005. A quantitative
tool for assessing the integrity of southern California
streams. Environmental Management 35:493–504.
Omernik, J. M. 1987. Ecoregions of the conterminous United
States (map supplement). Annals of the Association of
American Geographers 77:118–125.
Peck, D. V., D. K. Averill, A. T. Herlihy, R. M. Hughes, P. R.
Kaufmann, D. J. Klemm, J. M. Lazorchak, F. H.
McCormick, S. A. Peterson, M. R. Cappaert, T. Magee,
and P. A. Monaco. In press. Environmental Monitoring
and Assessment Program—surface waters: western pilot
study field operations manual for non-wadeable rivers
and streams. U.S. Environmental Protection Agency,
Washington, D.C.
Peck, D. V., A. T. Herlihy, B. H. Hill, R. M. Hughes, P. R.
Kaufmann, D. J. Klemm, J. M. Lazorchak, F. H.
McCormick, S. A. Peterson, P. L. Ringold, T. Magee,
and M. R. Cappaert. 2006. Environmental Monitoring
and Assessment Program—surface waters: western pilot
study field operations manual for wadeable streams. U.S.
Environmental Protection Agency, EPA/620/R-06/003,
Washington, D.C.
Pont, D., B. Hugueny, U. Beier, D. Goffaux, R. Noble, C.
Rogers, N. Roset, and S. Schmutz. 2006. Assessing river
biotic integrity at a continental scale: a European
approach using functional metrics and fish assemblages.
Journal of Applied Ecology 43:70–80.
Reynolds, L., A. T. Herlihy, P. R. Kaufmann, S. V. Gregory,
and R. M. Hughes. 2003. Electrofishing effort requirements for assessing species richness and biotic integrity
in western Oregon streams. North American Journal of
Fisheries Management 23:450–461.
Rinne, J. N., R. M. Hughes, and B. Calamusso, editors. 2005.
Historical changes in large river fish assemblages of the
268
WHITTIER ET AL.
Americas. American Fisheries Society, Symposium 45,
Bethesda, Maryland.
Simon, T. P. 1999. Assessing the sustainability and biological
integrity of water resource quality using fish communities. CRC Press, Boca Raton, Florida.
Stevens, D. L., Jr., and A. R. Olsen. 2004. Spatially-balanced
sampling of natural resources. Journal of American
Statistical Association 99:262–278.
Stoddard, J. L., D. V. Peck, A. R. Olsen, D. P. Larsen, J. Van
Sickle, C. P. Hawkins, R. M. Hughes, T. R. Whittier, G.
Lomnicky, A. T. Herlihy, P. R. Kaufmann, S. A.
Peterson, P. L. Ringold, S. G. Paulsen, and R. Blair.
2005a. Environmental Monitoring and Assessment
Program (EMAP): western streams and rivers statistical
summary. U.S. Environmental Protection Agency, EPA
620/R-05/006, Washington, D.C.
Stoddard, J. L., D. V. Peck, S. G. Paulsen, J. Van Sickle, C. P.
Hawkins, A. T. Herlihy, R. M. Hughes, P. R. Kaufmann,
D. P. Larsen, G. Lomnicky, A. R. Olsen, S. A. Peterson,
P. L. Ringold, and T. R. Whittier. 2005b. An ecological
assessment of western streams and rivers. U.S. Environmental Protection Agency, EPA 620/R-05/005, Washington, D.C.
ter Braak, C. J. F., and L. G. Barendregt. 1986. Weighted
averaging of species indicator values: its efficiency in
environmental calibration. Mathematical Biosciences
78:57–72.
USEPA (U.S. Environmental Protection Agency). 1990.
Biological criteria: national program guidance for surface
waters. U.S. Environmental Protection Agency, EPA
440/5-90/004, Washington, D.C.
Vannote, R. L., G. W. Minshall, K. W. Cummins, J. R. Sedell,
and C. E. Cushing. 1980. The river continuum concept.
Canadian Journal of Fisheries and Aquatic Sciences
37:130–137.
Whittier, T. R., and R. M. Hughes. 1998. Evaluation of fish
species tolerances to environmental stressors in northeast
USA lakes. North American Journal of Fisheries
Management 18:236–252.
Whittier, T. R., R. M. Hughes, J. L. Stoddard, G. A.
Lomnicky, D. V. Peck, and A. T. Herlihy. In press[a]. A
structured approach for developing indices of biotic
integrity: three examples from western USA streams and
rivers. Transactions of the American Fisheries Society.
Whittier, T. R., J. L. Stoddard, R. M. Hughes, and G. A.
Lomnicky. 2006. Associations among catchment- and
site-scale disturbance indicators and biological assemblages at least- and most-disturbed stream and river sites
in the western USA. Pages 641–664 in R. M. Hughes, L.
Wang, and P. W. Seelbach, editors. Landscape influences
on stream habitats and biological assemblages. American
Fisheries Society, Symposium 48, Bethesda, Maryland.
Whittier, T. R., J. L. Stoddard, D. P. Larsen, and A. T.
Herlihy. In press[b]. Selecting reference sites for stream
biological assessments: best professional judgment or
objective criteria. Journal of the North American
Benthological Society.
Appendix: Western Fish and Amphibian Tolerance Values
TABLE A.1.—Tolerance values and distributions of 165 fish and 30 amphibian species collected at 1,001 stream and river sites
in 12 western states. Species are listed alphabetically by family and by scientific name within families. Site disturbance scores
were the first principal component scores (PC1; range ¼4.1 to 5.7) for a principal components analysis of nine water quality,
physical habitat, and watershed disturbance measures. For 120 species collected at five or more sites, the upper tolerance was the
bootstrap estimate (based on 500 resamples) of the weighted average of the site PC1 scores with species relative abundances as
the weights. For 109 species where the 95% confidence interval (CI) was less than the one-third of range of PC1 score (i.e.,
,3.23), tolerance values were the PC1 scores scaled to a range of 0.0–10.0. For the 75 uncommon species (at fewer than five
sites) and the 11 highly variable species (95% CI 3.23), collection data were combined with sets of similar species and
submitted to the bootstrap analysis. Results were rounded to a whole number, to produce tolerance values for those species.
Bootstrap estimates
Common name
Scientific name
Number
of sites
Green sturgeon
White sturgeon
Longtoed salamander
Western toad
Canadian toad
Arizona toad
Red-spotted toad
Woodhouse’s toad
River carpsucker
Quillback
Utah sucker
Longnose sucker
Desert sucker
Bridgelip sucker
White sucker
Bluehead sucker
Sonora sucker
Flannelmouth sucker
Acipenser medirostris
Acipenser transmontanus
Ambystoma macrodactylum
Bufo boreas
Bufo hemiophrys
Bufo microscaphus
Bufo punctatus
Bufo woodhousii
Carpiodes carpio
Carpiodes cyprinus
Catostomus ardens
Catostomus catostomus
Catostomus clarkii
Catostomus columbianus
Catostomus commersonii
Catostomus discobolus
Catostomus insignis
Catostomus latipinnis
1
1
1
11
1
1
2
7
39
16
13
46
24
20
205
20
15
13
Upper
tolerance
0.85
0.24
3.90
3.40
2.71
1.26
0.48
0.09
3.18
1.71
0.32
2.02
95%
CI
2.49
3.23
1.75
0.85
2.58
1.10
1.66
0.74
0.47
2.59
0.92
4.62
Percent of sites
Tolerance
value
Mountains
(N ¼ 516)
2
2
1
4.0
5
5
5
5
8.7
7.9
6.9
4.6
3.4
2.8
7.6
5.3
2.2
6
0.2
Xeric
(N ¼ 190)
Plains
(N ¼ 295)
0.5
0.2
1.0
2.1
0.2
0.2
0.2
0.5
0.5
0.6
2.7
3.5
2.5
1.9
1.2
2.3
0.6
5.3
6.3
3.2
3.7
8.9
7.4
1.6
5.3
0.7
0.3
1.7
13.2
5.4
6.8
60.3
269
WESTERN STREAM FISH AND AMPHIBIAN TOLERANCES
TABLE A.1.—Continued.
Bootstrap estimates
Percent of sites
Common name
Scientific name
Number
of sites
Upper
tolerance
95%
CI
Tolerance
value
Mountains
(N ¼ 516)
Xeric
(N ¼ 190)
Plains
(N ¼ 295)
Largescale sucker
Sacramento sucker
Mountain sucker
Klamath smallscale sucker
Santa Ana sucker
Klamath largescale sucker
Tahoe sucker
Smallmouth buffalo
Bigmouth buffalo
Black buffalo
Silver redhorse
River redhorse
Golden redhorse
Shorthead redhorse
Razorback sucker
Rock bass
Redbreast sunfish
Green sunfish
Pumpkinseed
Orangespotted sunfish
Bluegill
Longear sunfish
Redear sunfish
Smallmouth bass
Spotted bass
Largemouth bass
White crappie
Black crappie
American shad
Gizzard shad
Coastrange sculpin
Prickly sculpin
Mottled sculpin
Paiute sculpin
Slimy sculpin
Shorthead sculpin
Riffle sculpin
Marbled sculpin
Wood River sculpin
Reticulate sculpin
Pit sculpin
Torrent sculpin
Chiselmouth
Longfin dace
Central stoneroller
Goldfish
Lake chub
Grass carp
Satinfin shiner
Red shiner
Spotfin shiner
Common carp
Alvord chub
Utah chub
Tui chub
Humpback chub
Bonytail
Gila chub
Arroyo chub
Roundtail chub
California roach
Western silvery minnow
Brassy minnow
Plains minnow
Hitch
Virgin spinedace
Common shiner
Catostomus macrocheilus
Catostomus occidentalis
Catostomus platyrhynchus
Catostomus rimiculus
Catostomus santaanae
Catostomus snyderi
Catostomus tahoensis
Ictiobus bubalus
Ictiobus cyprinellus
Ictiobus niger
Moxostoma anisurum
Moxostoma carinatum
Moxostoma erythrurum
Moxostoma macrolepidotum
Xyrauchen texanus
Ambloplites rupestris
Lepomis auritus
Lepomis cyanellus
Lepomis gibbosus
Lepomis humilis
Lepomis macrochirus
Lepomis megalotis
Lepomis microlophus
Micropterus dolomieu
Micropterus punctulatus
Micropterus salmoides
Pomoxis annularis
Pomoxis nigromaculatus
Alosa sapidissima
Dorosoma cepedianum
Cottus aleuticus
Cottus asper
Cottus bairdii
Cottus beldingii
Cottus cognatus
Cottus confusus
Cottus gulosus
Cottus klamathensis
Cottus leiopomus
Cottus perplexus
Cottus pitensis
Cottus rhotheus
Acrocheilus alutaceus
Agosia chrysogaster
Campostoma anomalum
Carassius auratus
Couesius plumbeus
Ctenopharyngodon idella
Cyprinella analostana
Cyprinella lutrensis
Cyprinella spiloptera
Cyprinus carpio
Gila alvordensis
Gila atraria
Gila bicolor
Gila cypha
Gila elegans
Gila intermedia
Gila orcuttii
Gila robusta
Hesperoleucus symmetricus
Hybognathus argyritis
Hybognathus hankinsoni
Hybognathus placitus
Lavinia exilicauda
Lepidomeda mollispinis
Luxilus cornutus
40
18
70
13
3
2
9
7
11
4
4
2
6
75
1
14
2
108
29
23
17
1
2
51
5
25
5
15
5
6
7
28
70
28
7
27
13
12
1
21
3
31
13
20
33
2
10
1
1
88
16
159
1
1
2
2
1
3
7
22
15
21
33
19
3
1
63
0.33
0.15
1.11
0.67
1.29
1.58
0.96
1.56
3.2
2.4
4.4
3.7
3
3
4.6
9.1
8.9
9
8
8
7.4
8.0
3
8.0
8
7.2
7.9
9.3
8.2
8
8
3.7
9.1
5.9
8.8
8.3
2.8
7.9
0.6
2.6
1.9
2.0
3
1.5
3.1
2
2
3.5
3
2.8
4.9
5.0
6.3
9
5.5
9
8
7.6
8.2
9.1
4
4
4
4
4
4
4
4.8
5
8.0
9.5
9
6
4
8.2
5.8
1.9
5.4
2.5
0.2
0.4
0.4
5.3
4.2
17.9
2.7
1.26
4.15
4.01
1.97
1.44
0.62
3.06
3.43
1.48
0.82
3.45
1.58
2.96
3.39
4.31
3.61
0.79
1.40
2.49
2.88
0.70
4.14
2.10
3.94
3.65
0.07
3.37
1.35
0.06
0.52
0.46
1.05
0.76
0.29
0.11
1.26
2.83
2.17
1.44
1.23
1.26
0.89
2.27
0.94
0.59
1.27
3.37
0.95
2.11
3.30
0.52
2.00
0.12
1.44
1.50
2.35
1.60
1.66
2.57
0.91
1.81
1.83
3.16
3.58
4.19
0.94
0.44
0.66
1.11
1.38
1.66
3.43
4.41
4.12
3.55
4.15
1.97
5.23
1.95
2.27
2.66
0.81
1.1
3.7
2.4
3.7
1.4
1.4
0.7
2.0
25.4
0.2
0.4
2.7
1.2
7.9
1.6
1.2
3.2
3.9
1.6
0.2
0.8
1.1
6.8
2.1
2.6
4.1
0.7
26.8
6.8
7.8
1.7
0.3
6.1
0.3
4.1
1.4
5.1
0.5
2
1.4
4.7
9.7
4.3
1.4
5.0
2.5
2.3
0.2
4.1
0.6
5.2
1.7
2.5
2.1
7.9
3.2
0.5
2.1
2.1
3.7
0.5
0.5
1.1
2.3
6.8
3.1
16.8
0.5
0.5
0.5
1.1
0.5
0.5
1.6
7.4
1.6
0.2
0.4
0.8
1.6
2.3
1.7
0.5
1.6
0.5
0.5
10.8
0.3
2.7
0.3
0.3
21.4
5.4
37.6
7.1
10.8
6.4
21
270
WHITTIER ET AL.
TABLE A.1.—Continued.
Bootstrap estimates
Common name
Scientific name
Number
of sites
Sturgeon chub
Sicklefin chub
Silver chub
Pearl dace
Peamouth
Hornyhead chub
Golden shiner
Emerald shiner
River shiner
Bigmouth shiner
Blacknose shiner
Spottail shiner
Sand shiner
Topeka shiner
Umpqua chub
Suckermouth minnow
Northern redbelly dace
Bluntnose minnow
Fathead minnow
Flathead chub
Sacramento pikeminnow
Colorado pikeminnow
Northern pikeminnow
Umpqua pikeminnow
Eastern blacknose dace
Longnose dace
Loach minnow
Leopard dace
Speckled dace
Redside shiner
Lahontan redside
Creek chub
Leatherside chub
Idaho giant salamander
California giant salamander
Pacific giant salamander
Tule perch
Northern pike
Muskellunge
Banded killifish
Northern plains killifish
Plains topminnow
Plains killifish
Burbot
Brook stickleback
Threespine stickleback
Goldeye
Mooneye
Canyon tree frog
California tree frog
Boreal chorus frog
Pacific tree frog
White catfish
Black bullhead
Yellow bullhead
Brown bullhead
Channel catfish
Stonecat
Tadpole madtom
Flathead catfish
Tailed frog
Longnose gar
Shortnose gar
White bass
Striped bass
Arkansas darter
Iowa darter
Macrhybopsis gelida
Macrhybopsis meeki
Macrhybopsis storeriana
Margariscus margarita
Mylocheilus caurinus
Nocomis biguttatus
Notemigonus crysoleucas
Notropis atherinoides
Notropis blennius
Notropis dorsalis
Notropis heterolepis
Notropis hudsonius
Notropis stramineus
Notropis topeka
Oregonichthys kalawatseti
Phenacobius mirabilis
Phoxinus eos
Pimephales notatus
Pimephales promelas
Platygobio gracilis
Ptychocheilus grandis
Ptychocheilus lucius
Ptychocheilus oregonensis
Ptychocheilus umpquae
Rhinichthys atratulus
Rhinichthys cataractae
Rhinichthys cobitis
Rhinichthys falcatus
Rhinichthys osculus
Richardsonius balteatus
Richardsonius egregius
Semotilus atromaculatus
Snyderichthys copei
Dicamptodon aterrimus
Dicamptodon ensatus
Dicamptodon tenebrosus
Hysterocarpus traskii
Esox lucius
Esox masquinongy
Fundulus diaphanus
Fundulus kansae
Fundulus sciadicus
Fundulus zebrinus
Lota lota
Culaea inconstans
Gasterosteus aculeatus
Hiodon alosoides
Hiodon tergisus
Hyla arenicolor
Pseudacris cadaverina
Pseudacris maculata
Pseudacris regilla
Ameiurus catus
Ameiurus melas
Ameiurus natalis
Ameiurus nebulosus
Ictalurus punctatus
Noturus flavus
Noturus gyrinus
Pylodictis olivaris
Ascaphus truei
Lepisosteus osseus
Lepisosteus platostomus
Morone chrysops
Morone saxatilis
Etheostoma cragini
Etheostoma exile
4
1
2
10
2
3
8
20
3
36
3
12
141
9
1
4
5
17
197
64
5
5
31
4
27
155
3
2
147
48
10
111
4
2
2
81
1
78
1
1
3
7
19
2
21
29
39
3
1
1
1
16
1
110
35
14
84
51
26
9
59
2
4
11
1
4
19
Upper
tolerance
2.64
95%
CI
0.91
2.42
2.76
2.09
1.39
3.14
0.91
3.25
3.75
4.04
0.63
0.78
1.07
2.78
3.22
3.63
3.42
1.12
0.57
0.56
1.83
1.80
0.64
1.01
3.30
1.88
1.44
2.98
2.28
0.62
1.48
0.87
1.23
0.87
3.19
0.47
1.11
1.88
0.58
0.91
0.90
3.29
0.61
2.24
1.97
1.01
1.22
4.38
1.31
4.27
2.23
3.16
0.65
2.54
3.44
4.52
3.11
2.98
4.02
2.89
3.34
1.73
1.17
1.16
3.36
2.88
1.30
1.19
0.82
4.01
1.14
5.10
3.24
3.13
1.11
Percent of sites
Tolerance
value
9
9
9
6.7
3
6
6.4
6.9
8
7.5
8
7.7
8.5
8.9
5
7
7.0
7.7
8.3
8.0
3
3.5
3.5
3
7.3
6.2
5
5
4.0
4.6
4.0
7.6
4
1
1
1.3
8
7.8
8
6
6
6.1
5.7
2
9.4
4.7
9.3
9
6
6
6
6
7
9.7
7
7.3
8.9
7.1
7.8
9
0.9
8
8
10.0
10
7
7.5
Mountains
(N ¼ 516)
Xeric
(N ¼ 190)
Plains
(N ¼ 295)
1.4
0.3
0.7
3.4
0.4
2.6
1
2.7
6.8
1.0
12.2
1.0
4.1
46.1
3.1
0.2
3.3
0.6
0.2
5.2
0.8
0.2
8.1
0.4
0.2
15.7
6.6
0.6
0.4
0.2
15.5
0.2
0.5
14.2
0.5
1.1
2.1
2.1
12.1
0.5
0.5
34.7
7.4
5.3
2.6
2.1
0.5
0.5
0.5
1.1
1.1
4.3
1.4
1.7
5.4
51.9
21.4
8.8
30.5
34.9
25.4
0.3
0.3
1.0
2.4
5.8
0.7
7.1
3.7
13.2
1.0
0.5
0.2
0.3
2.5
1.2
2.9
1.6
0.6
0.2
1.0
11.4
1.6
0.5
5.3
4.7
0.5
4.2
0.5
0.5
0.5
31.9
3.7
1.7
24.7
16.9
8.8
1.0
0.7
1.4
3.4
1.4
6.4
271
WESTERN STREAM FISH AND AMPHIBIAN TOLERANCES
TABLE A.1.—Continued.
Bootstrap estimates
Common name
Scientific name
Number
of sites
Johnny darter
Orangethroat darter
Yellow perch
Blackside darter
Sauger
Walleye
Trout-perch
Sand roller
Western brook lamprey
Klamath river lamprey
Pacific lamprey
African clawed frog
Western mosquitofish
Red-legged frog
Plains leopard frog
Foothill yellow-legged frog
Cascades frog
American bullfrog
Green frog
Columbia spotted frog
Mountain yellow-legged frog
Northern leopard frog
Spotted frog
Wood frog
Lowland leopard frog
Columbia torrent salamander
Rough-skinned newt
California newt
Golden trout
Cutthroat trout
Gila trout
Chum salmon
Coho salmon
Rainbow trout
Sockeye salmon
Chinook salmon
Mountain whitefish
Brown trout
Bull trout
Brook trout
Lake trout
Freshwater drum
Central mudminnow
Etheostoma nigrum
Etheostoma spectabile
Perca flavescens
Percina maculata
Sander canadensis
Sander vitreus
Percopsis omiscomaycus
Percopsis transmontana
Lampetra richardsoni
Lampetra similis
Lampetra tridentata
Xenopus laevis
Gambusia affinis
Rana aurora
Rana blairi
Rana boylii
Rana cascadae
Rana catesbeiana
Rana clamitans
Rana luteiventris
Rana muscosa
Rana pipiens
Rana pretiosa
Rana sylvatica
Rana yavapaiensis
Rhyacotriton kezeri
Taricha granulosa
Taricha torosa
Oncorhynchus aguabonita
Oncorhynchus clarkii
Oncorhynchus gilae
Oncorhynchus keta
Oncorhynchus kisutch
Oncorhynchus mykiss
Oncorhynchus nerka
Oncorhynchus tshawytscha
Prosopium williamsoni
Salmo trutta
Salvelinus confluentus
Salvelinus fontinalis
Salvelinus namaycush
Aplodinotus grunniens
Umbra limi
56
5
38
33
15
44
20
3
8
1
38
1
29
6
2
37
7
32
4
3
1
50
2
2
1
2
12
3
4
136
4
1
31
365
2
34
59
130
19
113
1
22
3
Upper
tolerance
95%
CI
3.30
2.27
3.03
3.48
4.26
3.87
3.68
0.68
1.12
0.96
0.41
1.47
0.89
0.50
0.88
1.92
0.18
1.08
3.95
0.36
2.40
2.01
1.11
2.95
2.55
0.72
1.58
3.09
3.14
1.52
0.75
1.42
0.75
0.65
0.35
0.34
1.51
0.43
0.08
0.09
0.02
1.30
0.61
0.94
0.89
0.48
1.30
0.62
4.74
1.74
Percent of sites
Tolerance Mountains
Xeric
Plains
value
(N ¼ 516) (N ¼ 190) (N ¼ 295)
7.8
6.2
7.4
8.0
9.3
8.7
8.4
8
1.3
2
2.4
10
8.8
2.1
5
0.9
0.0
6.6
5
1
1
7.5
1
5
5
1
0.3
0
0
1.5
2
2
2.1
2.1
2
2.5
2.5
2.7
0.7
1.7
2
10.0
8
0.5
0.8
0.6
1.6
0.2
6.8
1.2
1.0
1.6
1.6
0.5
9.5
0.5
18.6
1.7
10.5
11.2
5.1
14.9
6.8
1.7
0.7
6.8
1.4
2.3
0.6
0.2
1.0
0.4
1.1
5.3
3.4
1.4
3.2
13.2
0.7
0.2
0.4
2.3
0.4
0.6
23.8
0.8
0.2
6.0
59.1
0.4
5.8
8.1
15.1
3.7
16.3
0.2
0.5
0.5
6.8
27.4
2.7
2.1
6.8
22.1
1.4
3.4
12.1
2.0
7.5
1.0