Integrating intermittent streams into watershed assessments: applicability of an index of
biotic integrity
Author(s): Raphael D. Mazor, Eric D. Stein, Peter R. Ode, and Ken Schiff
Source: Freshwater Science, Vol. 33, No. 2 (June 2014), pp. 459-474
Published by: The University of Chicago Press on behalf of Society for Freshwater Science
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Integrating intermittent streams into watershed
assessments: applicability of an index of biotic integrity
Raphael D. Mazor1,2,3, Eric D. Stein1,4, Peter R. Ode2,5, and Ken Schiff1,6
1
2
Southern California Coastal Water Research Project, Costa Mesa, California 92626 USA
Aquatic Bioassessment Laboratory, California Department of Fish and Wildlife, Rancho Cordova, California 95670 USA
Abstract: Nonperennial streams are often excluded from biomonitoring programs because of inadequate knowledge about their biological and hydrological characteristics and variability. The ability to apply bioassessment
indices to nonperennial streams would greatly expand the reach of biomonitoring programs. We sampled
12 nonperennial streams (3 of which were minimally stressed) in the San Diego hydrologic region multiple times
to assess whether a benthic macroinvertebrate assessment index (the Southern California Index of Biotic Integrity [IBI]) developed for perennial streams could be used in nonperennial streams. We also sampled 3 minimally
stressed perennial streams. Continuous water-level loggers and repeated site visits revealed that hydrologic regimes varied considerably among streams. Gradual drying was evident at some streams, and multiple drying/
rewetting events were evident at others. Moreover, streams that were nonperennial in one year were perennial in
another. IBI scores from low-stress nonperennial streams were similar to those for low-stress perennial streams,
and false indications of impairment (i.e., low IBI scores) were never observed. Furthermore, IBI scores declined
as stress increased, suggesting that the IBI responded as expected in nonperennial streams. IBI scores were
stable at most sites within and between years, but midsummer declines were observed at high-stress sites. These
declines were associated with declines in discharge, fast-water habitat, and increases in sands and fines and
macroalgae cover. These findings suggest that an assessment tool developed for perennial streams can be used
to assess condition at certain nonperennial streams, and that biomonitoring programs can provide more comprehensive watershed assessments by including nonperennial streams in their surveys.
Key words: biomonitoring, watershed assessment, temporary streams, stress–response
The ecology of nonperennial streams is not well understood (Williams 2008), and they often are excluded from
bioassessment programs (e.g., Hall et al. 1998, Peck et al.
2006). This exclusion is motivated primarily by inadequate knowledge about the applicability to nonperennial
streams of bioassessment tools calibrated for perennial
streams and by questions about whether index scores can
be interpreted correctly (Fritz et al. 2008). As a result, many
surveys of ambient stream condition are incomplete, biomonitoring programs do not provide comprehensive evaluations of stream health or complete assessments of watershed or regional conditions, and watershed-management
and resource-protection programs based on these assessments might be compromised.
Comprehensive assessment and management of watersheds in any climate should include nonperennial streams
(Fritz et al. 2008, Steward et al. 2012). Nonperennial streams
drain large portions of watersheds in arid and wet climates (Tooth 2000, Larned et al. 2010), and they may be
very sensitive to environmental impacts because they have
a disproportionately large interface with terrestrial eco-
systems, where most disturbances occur. Flow intermittence affects leaf-litter breakdown (e.g., Herbst and Reice
1982, Datry et al. 2011), nutrient cycling (e.g., Gómez et al.
2012), and biomass production (e.g., Tronstad et al. 2010).
Therefore, degradation of nonperennial reaches may have
disproportionate impacts on the health of the entire watershed.
Nonperennial streams are stressful environments for
benthic macroinvertebrates because abiotic and biotic conditions change dramatically among seasons and between
years (e.g., Bêche et al. 2006). These changes are driven
principally by the hydrologic regime as the stream passes
from a eurheic state to an arheic, hyporheic, or edaphic
state (following the terminology of Gallart et al. 2012). As
the stream changes state, certain microhabitats (especially
riffles) become scarce or disappear entirely, and the abundance of species that depend on them decreases.
Hydrologic changes can lead to other environmental
changes that affect community composition. When water
levels decline, composition of available substrate, water
chemistry, and concentrations of pollutants (if present)
E-mail addresses: 3raphaelm@sccwrp.org; 4erics@sccwrp.org; 5peter.ode@wildlife.ca.gov; 6kens@sccwrp.org
DOI: 10.1086/675683. Received 14 May 2013; Accepted 06 November 2013; Published online 25 February 2014.
Freshwater Science. 2014. 33(2):459–474. © 2014 by The Society for Freshwater Science.
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may change and increase environmental stress. In particular, temperature, pH, and dissolved O2 concentration can
fluctuate over short time periods (Gasith and Resh 1999).
Biotic pressures intensify as predation and competition become more important because space and resources become limited (Robson et al. 2011). Together, these changes
may lead to differences in community composition during
drying that can confound our ability to distinguish changes
in biotic communities caused by natural variability in environmental conditions from changes caused by anthropogenic stressors (Morais et al. 2004).
Many of the life-history traits that enable survival of
benthic macroinvertebrates in nonperennial streams (e.g.,
tolerance of low O2 or high conductance) are similar to
those that enable survival in degraded streams. Therefore,
indices calibrated with perennial streams (such as the
Southern California Index of Biotic Integrity [IBI]; Ode
et al. 2005) might give false indications of impairment
at nonperennial streams under natural conditions. For example, 2 bioassessment indices (a multimetric and a multivariate index) indicated impairment at 2 minimally disturbed nonperennial streams in northern California (Mazor
et al. 2009). In addition, intra-annual variability in bioassessment was particularly high in nonperennial streams
in Portugal (Morais et al. 2004). Thus, both false positive
and false negative findings of impairment may be common in nonperennial streams. Whether bioassessment indices developed for perennial streams can be applied to
nonperennial streams and whether the indices can distinguish changes caused by anthropogenic stress from those
caused by seasonal fluctuation are unknown.
Our objective was to evaluate the utility in nonperennial
streams of standard assessment tools developed for perennial streams in the San Diego region of southern California, USA. We investigated the questions: 1) How do hydrologic regimes differ between perennial and nonperennial
streams? 2) How does biological community structure vary
over time? 3) Which environmental variables associated
with hydrologic regime are related to changes in biological community structure and environmental indices? We
characterized hydrologic regimes by measuring discharge
and water-level, biological communities by sampling benthic macroinvertebrates and calculating the IBI or multivariate ordination, and environmental variables by measuring physicochemical variables using standard protocols
(Ode 2007).
M E T HO DS
Study area
Southern California has a Mediterranean climate, with
hot, dry summers and cool, wet winters. Intermittent streams
are typical of the region. Much of the coastal, lower-elevation
areas have been converted to agricultural or urban land
uses, and water importation, runoff, and effluent discharges
have perennialized most streams (Roy et al. 2009, Mazor
et al. 2012). However, most of the upper elevations of watersheds remain undeveloped, and have chaparral, grassland, and oak or pine forest land covers. Streams in these
undeveloped portions are mostly nonperennial, although
short perennial lengths can be found near surficial bedrock or spring sources. Much of the region is underlain by
young, erodible sedimentary geology.
Sampling
We worked in 12 nonperennial and 3 perennial streams
in southern California. We defined nonperennial streams
as streams that lack surface flow for at least several days
per year in most years. This definition encompasses a wide
variety of streams, from ephemeral washes and headwaters
that flow for only a few hours after storms, to those with
sustained flows lasting nearly all year or that have perennial flow in a year with heavy rainfall. All nonperennial
streams in our study had flow from groundwater discharge
that persisted ≥1 mo after the last storm. By the definition
of Yavercovski et al. (2004), these streams would be seasonal or near-permanent.
We selected the 12 nonperennial sites to represent a
range of natural conditions (e.g., short and long flow duration, high and low gradient) and anthropogenic stressors
(e.g., urban development, grazing, water diversion). We sampled 3 sites repeatedly over several years, and we deployed
data loggers to measure water level and temperature continuously for 1 y at selected sites (too few loggers were
available for deployment at every site or every year of sampling).
We sampled nonperennial sites during the drying phase,
typically beginning in March or April and ending when
flow was insufficient for sample collection (no surface water in >50% of the sampling reach). Sampling began in the
eurheic phase, continued through the oligorheic (or rarely
the arheic) phases, and ended when the stream was in a
drier hydrologic state (hyporheic or edaphic phases) (terms
follow Gallart et al. 2012). We revisited sites approximately
every 2 to 4 wk. When possible, we increased the frequency
of site visits to weekly near the end of the drying phase.
We sampled perennial sites monthly beginning in April to
overlap the normal index period for bioassessment in perennial streams in southern California (SMC 2007). Sampling of nonperennial streams was funded mostly under
a program that began in 2008, was suspended in 2009 for
budgetary reasons, and resumed in 2010. Sampling of perennial streams was funded mostly under a 2nd program
that began in 2009 and continued in 2010.
Overall, the study period was drier than the long-term
average for the region. Normal annual rainfall for Lindbergh Airport in San Diego is 27.4 cm. In 2008, 2009, and
2010, annual rainfall was 18.3, 23.4, and 26.9 cm, respectively. Drought was even more severe in the 2 y preced-
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Volume 33
ing our study, with only 13.7 and 9.9 cm of rainfall in 2006
and 2007, respectively (http://www.sdcwa.org/annual-rain
fall-lindbergh-field).
Stressor assessment
We used best professional judgment to select nonperennial sites that represented a gradient of stress. We verified or modified a priori selections after evaluating each
site for 50 stressors (hydrologic, physical, land use, biological) associated with the California Rapid Assessment
Method (CRAM) (CWMW 2012), a measure of riverine
wetland condition. A stressor was given a score of 0 if it
was not observed, a score of 0.5 if the stressor was likely
to have a negative effect on the stream, and a score of 1 if
the effect was likely to be large. We examined the distribution of scores to identify 3 groups: low stress (scores ≤ 1),
moderate stress (scores 2–7), and high stress (scores 7.5–9;
higher scores were not observed). A maximum score of
50 is theoretically possible, but scores >6 are uncommon,
except at highly developed sites. The highest score observed in a statewide probabilistic data set of 924 sites
was 21, and only 25% of the sites in this statewide data
set would be classified as high stress (data not shown).
Our approach to establishing a stressor gradient by counting stressors is similar to the approach used by SánchezMontoya et al. (2009a) to identify reference sites. Details
on stressors identified at each site were provided by Mazor
et al. (2012) and are given in Appendix S1.
Assessment of hydrologic regimes
We evaluated hydrologic regimes and stressors with a
combination of data sources: continuous data loggers, direct measurements, and visual observation during site visits. We deployed continuous water-level loggers (HOBO
U20 Water Level Data Logger; Onset, Bourne, Massachusetts) at a subset of sites on the 1st sampling date and retrieved them after the last sampling date. At 4 sites (Bear
Canyon [BC], South Fork Santa Ana River [AN], Cedar
Creek [CD], and Noble Canyon [NC]), we corrected waterlevel measurements for air pressure measured with a 2nd
logger deployed at the site above the water line. At the
other sites, we used air-pressure data from nearby weather
stations. We calculated discharge from water velocity measured with an electromagnetic or propeller-type velocity
meter, but we used flotation time of a neutrally buoyant
object when conditions were too slow or shallow for the
velocity meter. Last, we noted whether streams were flowing on each sampling date. We used these data to identify
periods when the reaches contained flowing water, and
when they were dry (or intermittently dry). We gave visual
observation the highest priority, followed by direct measurements. Long periods (>6 h) during which loggers recorded water-level readings <0 were interpreted as times
when the stream was dry.
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Benthic macroinvertebrate collection
We used standard state bioassessment protocols to
sample benthic macroinvertebrates (Ode 2007). California
protocols are derived from those developed by the US Environmental Protection Agency for national stream surveys (Peck et al. 2006). We divided a 150-m reach into 11
equidistant transects. At each transect, we used a 500-μm
D-frame kick net to sample 929 cm2 of stream bed at 25,
50, or 75% of the transect distance. At 3 low-gradient sites
where stable habitats were restricted to the stream banks
(Aqua Caliente Creek [AC], San Juan mainstem [SJ], and
Pine Valley Creek [PC]), we sampled at 0, 50, and 100% of
the transect distance (Mazor et al. 2010). We combined all
samples into a single composite sample and preserved it in
70% ethanol. For each sample, we removed ∼600 invertebrates from the detritus and identified them to Standard
Taxonomic Effort (STE) Level 2 (mostly to species with
Chironomidae to genus, where possible) established by the
Southwest Association of Freshwater Invertebrate Taxonomists (Richards and Rogers 2011). We sampled macroinvertebrates only when flow was sufficient for sampling, i.e.,
the stream was in a eurheic or oligorheic state for ≥50% of
the reach.
Habitat characterization
We measured physical-habitat variables by standard
state protocols (Ode 2007) with a few modifications. First,
we fixed the locations of the 11 transects so that they did
not vary over the course of the study. Thus, we sampled
well beyond the protocol-mandated time at which a site
would be rejected because of lack of wet habitat (Ode 2007).
Second, we assumed that slope, gradient, bank width, and
bank height were stable, and we measured these variables
only once a year. Third, we added measurements of algal
cover in 2009 and 2010 after standard methods of estimation were published (Fetscher et al. 2009). Where possible,
we analyzed physical-habitat data by calculating metrics published by Kaufmann et al. (1999). We analyzed other data
(e.g., data from field probes) without modification.
Landscape variables
We calculated % impervious surface and road density
for the watershed area upstream of each site. We used National Landcover Data from 2006 (http://www.epa.gov/mrlc
/nlcd-2006.html) to assess imperviousness, and we derived
road density from a custom geographic information system (GIS) road layer (PRO, California Department of Fish
and Wildlife, unpublished data).
Data analysis
Benthic macroinvertebrate community structure We used
the IBI (Ode et al. 2005) and nonmetric multidimensional
scaling (NMDS) to summarize macroinvertebrate community structure. We graphed these measurements over time
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and compared them with environmental variables to examine changes in benthic communities over the course of
the study.
We calculated metrics for the IBI as described by Ode
et al. (2005). To maintain consistent sample sizes, we used
random subsampling to reduce the number of individuals
in each sample to 500, and then aggregated individuals
from STE Level 2 to STE Level 1 (i.e., mostly to genus,
with Chironomidae to family; Richards and Rogers 2011).
We calculated 7 metrics: 1) Coleoptera richness; 2) Ephemeroptera, Plecoptera, and Trichoptera (EPT) richness;
3) predator richness; 4) % collectors; 5) % intolerant individuals; 6) % noninsect taxa; and 7) % tolerant taxa.
These metrics are scored so that higher scores reflect lessdegraded condition. We summed metric scores and rescaled
the sum to a 100-point scale, as described by Ode et al.
(2005). We compared values to a threshold of 39, which is
2 standard deviations (SD) below the reference calibration
mean, as described by Ode et al. (2005).
We ran NMDS on presence/absence data from all
samples to examine differences in assemblage composition
(PC-ORD, version 5.12; McCune and Mefford 2006). We
used Bray–Curtis distance, and tested up to 4 axes via 100
Monte Carlo runs with real and randomized data. We used
the default procedure in PC-ORD to select axes, i.e., the
highest dimension that reduced stress by ≥5 (on a scale of
0–100) and had less stress than that of 95% of runs with
randomized data. We set the maximum number of iterations to 250, the stability criterion to 0.000001, and step
length to 0.2. We rotated final axes with varimax rotation,
and calculated taxon scores with weighted averaging. We
checked scores for association with pollution tolerance
by calculating correlations with tolerance values (from the
California Aquatic Macroinvertebrate Laboratory Network;
CAMLnet 2003).
We used a linear, mixed-effects model to test whether
flow and stress status affected IBI scores. We used the nlme
package in R (version 3.1-109; Pinheiro et al. 2013) to create
a mixed-effects model for each biological response variable
based on 4 classes of flow and stress (perennial low stress,
nonperennial low stress, nonperennial moderate stress, and
nonperennial high stress) with site specified as a random
effect. Differences in variability between low-stress perennial and nonperennial sites were examined with an F-test
using pooled variance estimates.
Relationships between biological and environmental variables We used 2 separate analyses to examine the relationship between environmental variables and assemblage
composition (i.e., IBI scores and NMDS axis scores). First,
we used Spearman rank correlations between within-site
means of environmental and biological variables to evaluate gradients associated with among-site differences. Second, we evaluated the gradients associated with within-
Table 1. Names and characteristics of sites sampled in our study.
Name
Perennial, low stress
South Fork Santa Ana River
Bear Canyon
Cedar Creek
Nonperennial, low stress
Temescal Creek
Agua Caliente Creek
Carney Canyon
Nonperennial, moderate stress
Noble Canyon
Arroyo Seco
San Diego River headwaters
Cañada Verde
Pine Valley Creek
Santa Ysabel Creek
Nonperennial, high stress
Ortega Falls
San Juan mainstem
Trabuco Creek
Watershed area
(km2)
Elevation
(m)
Gradient
Ecoregion
None
None
None
11
65
55
2447
639
522
>2%
>2%
>2%
Mountains
Mountains
Chaparral
0.0
0.5
0.5
None
None
None
22
46
19
333
918
312
>2%
<1%
>2%
Chaparral
Mountains
Chaparral
NC
AS
SR
CV
PC
SY
2.0
2.5
3.0
4.5
5.0
7.0
Altered flow
Altered flow
Grazing
Grazing
Runoff
Grazing
39
34
2
14
74
32
1169
494
1038
954
1132
902
>2%
<1%
>2%
>2%
<1%
1 to 2%
Chaparral
Chaparral
Mountains
Mountains
Chaparral
Mountains
OF
SJ
TC
7.5
8.0
8.5
Altered flow, runoff
Altered flow, runoff
Runoff
16
103
58
575
181
237
>2%
<1%
1–2%
Chaparral
Chaparral
Chaparral
Site
Stress score
AN
BC
CD
0.0
0.0
0.0
TE
AC
CC
Major stressors
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site changes by calculating Spearman rank correlations on
variables after subtracting the within-site mean. We used
only those environmental variables expected to vary with
changing hydrological conditions for the 2nd analysis, and
we excluded variables expected to be constant over the
course of the study (e.g., all landscape variables, bank dimensions). Our goal was to characterize the relationships
between biological and environmental variables, so we did
not test the statistical significance of these correlations
(McCune and Grace 2002).
RESULTS
Stressor assessment
Perennial (AN, BC, and CD) and 3 of the 12 nonperennial sites (Temescal Creek [TE], AC, and Carney Canyon [CC]) were under low stress (Table 1). Six nonperennial sites were under moderate stress (NC, Arroyo Seco
[AS], San Diego River headwaters [SR], Cañada Verde [CV],
PC, and Santa Ysabel Creek [SY]), and 3 were under high
stress (Ortega Falls [OF], SJ, Trabuco Creek [TC]). The
most stressed site (TC) had a score of 8.5. Stressors included grazing (SY, SR, and CV), nutrients (SJ and OF), and
urban runoff (PC and TC) (Table 1, Appendix S1; Mazor
et al. 2012). We found evidence of nonnatural flow regimes
(e.g., floods in the absence of precipitation at AS) at several sites. Communication with nearby land managers indicated that groundwater diversions affect sites SJ and OF.
Patterns in hydrologic regimes
Data-loggers showed that hydrologic patterns differed
among sites, including the 3 perennial sites (Fig. 1). Hydrographs were most stable at perennial sites, particularly
AN, and at certain nonperennial sites (i.e., SY, SR, and
NC), where water levels were stable until they decreased
abruptly. In contrast, SJ and OF showed periods of large
fluctuations. Communication with the manufacturer of the
loggers (Onset) indicated that these fluctuations may indicate periods of extremely low water levels, during which
the logger may be partially exposed to air. During these periods, intermittent drying and rewetting of the logger are
likely. Intermittent drying also was evident at AS, and this
site experienced several short high-flow events, despite the
lack of recent precipitation. We did not deploy loggers at
low-stress nonperennial sites.
Direct observation at 3 sites with multiple years of sampling showed that hydrologic characteristics also varied
among years (Fig. 2A–F). All 3 sites dried in June or July
during the 1st sampling year (2008 for SY and SR, and
2009 for NC). However, in 2010 (the wettest year of the
study), only SR dried in June. SY started to dry in September, and flow never ceased at NC. Data from multiyear
deployment of water-level loggers were not available.
Figure 1. Water surface levels at a subset of sites. Shading
indicates dry or fluctuating periods. Data for SJ, SY, SR, AS,
and OF were obtained in 2008; BC, AN, and NC were obtained
in 2009; and CD were obtained in 2010. Where water levels are
>0 and background color is gray (as at sites SJ and OF), surface
water is inferred to be present during a period of water-level
fluctuation.
Trends and patterns in IBI scores
IBI scores were high at low-stress sites, regardless of
flow status (Fig. 3A, B, Table 2) or discharge (Fig. 4). The
3 low-stress nonperennial sites had IBI scores comparable to scores at low-stress perennial sites, and no sample
from these sites had a score <39 (reference threshold).
Flow status had a small (6.6 ± 5.4 [SE] points) nonsignificant effect on IBI score (linear mixed model, p = 0.65;
Fig. S1), but power was low (only 3 sites in each low-stress
group). IBI scores responded to stress at nonperennial sites
as expected (Fig. 3C, D, Table 2). Mean scores declined as
site quality declined (r = –0.91). Low-stress sites had higher
mean IBI scores (55.6) than moderate-stress sites (40.3),
which had higher scores than high-stress sites (26.8).
Nearly all samples from high-stress sites had scores <<39.
IBI scores were least temporally variable at low-stress
sites (Fig. 3A, B). At low-stress sites, flow status did not
affect variability (pooled SD = 3.3 at perennial sites and
4.0 at low-stress nonperennial sites, F11,11 = 1.47, p =
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Figure 2. Photos of sites SR (A, B), SY (C, D), and NC (E, F) sampled over multiple years. Each photo was taken in the same
month of each year at approximately the same location in each reach.
0.27) and no directional trend was evident at perennial or
nonperennial low-stress sites. In contrast, IBI scores declined sharply near the end of the sampling period (typically in June) at several stressed nonperennial sites (OF,
AS, SJ) and underwent erratic fluctuations at others (CV,
NC). Discharge and IBI score were not strongly related
(Fig. 4). At 2 sites that were resampled over 2 y (SR and
SY), a mid-spring decline in IBI scores was followed by an
early summer increase. This pattern was similar in both
years of sampling, but no individual metrics appeared to
consistently influence this pattern. The 3rd temporally replicated site (NC) did not show similar patterns in each year,
perhaps because it underwent different hydrologic regimes
each year (see below).
Stability of IBI scores arose from patterns that varied
across sites (Fig. 5A–D). In some cases, stability in the IBI
score arose from stability in the underlying metrics (e.g.,
AN; Fig. 5A). In other cases, decreases in some metric
scores were offset by increases in others (e.g., CD; Fig. 5B).
However, large fluctuations in metric scores that yielded
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Figure 3. Trends in Index of Biotic Integrity (IBI) scores at perennial low-stress (A), nonperennial low-stress (B), nonperennial
moderate-stress (C), and nonperennial high-stress (D) sites. Each trajectory represents samples from a single site in a single year of
sampling. See Fig. S1 for trends for individual sites. The dotted line indicates the threshold IBI score for reference condition.
smaller fluctuations in the IBI score were more common
(e.g., TE; Fig. 5C). At sites where the IBI declined (e.g.,
OF; Fig. 5D), metric scores declined in unison near the
end of the sampling period.
Trends and patterns in community structure
Perennial and nonperennial streams did not have distinctly different macroinvertebrate community types. Instead, they represented extremes along a continuous biological gradient (Fig. 6A, B). NMDS resulted in a 3-axis
solution that represented >80% of the total variance with
a final stress of 17.8. Axis 3 represented more variance
(41%) than the other axes (20 and 19% for axes 1 and 2,
respectively). Visual inspection showed weak segregation
of sites by flow status along axes 2 and 3. This segregation was driven substantially by 1 site (BC on axis 2 and
AN on axis 3), but samples from the other 2 perennial
sites were dispersed among the nonperennial sites. Samples also segregated weakly along a stressor gradient, with
low-stress sites clustered at the positive ends of axes 2 and
3, and the negative end of axis 1.
Taxa segregated primarily along axis 3. EPT taxa were
more common at sites with high positive values on axis 3
(Fig. 6D). Coleoptera occupied a slightly lower position
along axis 3, suggesting a shift from EPT taxa to beetles
as sites moved down this axis. In contrast, major taxonomic groups were strongly interspersed along axes 1
and 2 (Fig. 6C). Tolerance values were more strongly correlated with species scores along axis 3 (Spearman’s ρ =
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Table 2. Site stress scores and mean and standard deviation (SD) Index of Biotic Integrity (IBI) scores. n = number of samples, n > 39
= number of samples with scores greater than the threshold for identifying reference condition. See Table 1 for definitions of site
codes.
Flow type
Stress category
Site
Stress score
IBI
SD
n
n > 39
Perennial
Low stress
Nonperennial
Low stress
BC
AN
CD
TE
AC
CC
NC
AS
SR
CV
PC
SY
OF
SJ
TC
0.0
0.0
0.0
0.0
0.5
0.5
2.0
2.5
3.0
4.5
5.5
7.0
7.5
8.0
8.5
73.9
58.6
53.3
57.2
52.0
57.6
43.0
38.7
48.2
30.8
36.1
45.1
27.5
32.8
20.0
4.6
2.7
1.5
5.1
2.6
3.9
12.0
9.8
8.4
8.9
9.4
9.0
14.2
10.5
5.1
5
5
4
5
6
5
10
4
13
5
5
16
5
5
3
5
5
4
5
6
5
6
2
12
1
2
12
1
1
0
Moderate stress
High stress
–0.49) than axis 1 (ρ = 0.36) or axis 2 (ρ = –0.9). Taxa
did not segregate by functional feeding group along any
axis.
We plotted trajectories in ordination scores as change
in score from initial sampling and found that community
composition at most sites was either stable or changed in
unison (Fig. 7A, B). For example, all trajectories were either constant or moved in a positive direction along axes
1 and 2, or in a negative direction along axis 3. Thus, if
Figure 4. Index of Biotic Integrity (IBI) scores vs measured
discharge. For clarity, only a subset of sites are shown. Trajectories connect samples within sites in order of sampling
date, and move in the direction of increasing discharge (i.e.,
toward the left end of the x-axis). The dotted line indicates the
threshold IBI score for reference condition.
sites changed at all, they changed toward communities
more characteristic of nonperennial and high-stress sites,
with fewer EPT taxa and more Coleoptera, Diptera, and
noninsects. In general, trajectories were shortest for perennial and low-stress nonperennial sites, and longest for
high-stress nonperennial sites.
Relationships between biological and
environmental variables
Many environmental variables were associated with biological differences among sites, but few were associated
with differences within sites (Fig. 8A–D, Table 3). For
example, 11 of the 28 environmental variables evaluated
had strong relationships (|ρ| > 0.5) with IBI scores, and
16 variables had strong relationships with ≥1 of the 3 ordination axes. The strongest relationships of environmental variables with IBI scores were observed for stressor
scores (ρ = –0.91), a few habitat variables (e.g., % fastwater habitat: ρ = 0.75; % shading: ρ = 0.68), and specific
conductance (ρ = –0.69). For ordination axes, stressor
scores were less important (i.e., strongest ρ = –0.53, with
axis 2), but many habitat variables, particularly those related to substrate (e.g., % cobble embeddedness: ρ = –0.85
with axis 3), hydrology (e.g., % fast-water habitat: ρ = 0.70
with axis 2), and riparian vegetation (e.g., mean riparian
vegetation cover: ρ = 0.70 with axis 3) showed strong
relationships with site differences. Specific conductance
also was strongly related to ordination axes (e.g., ρ = 0.65
with axis 3).
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Figure 5. Trends in metric scores at AN (A), CD (B), TE (C), and OF (D). Gray lines represent trends in scores for each of the
7 metrics in the Index of Biotic Integrity (IBI). The black line represents the mean metric score.
In contrast, few environmental variables were associated with within-site biological changes, and only 2 were
associated with IBI scores with |ρ| > 0.2 (temperature:
–0.31, wetted width: 0.27). Relationships with ordination
axes were stronger, but only 4 of the 22 variables considered had |ρ| > 0.5. The strongest relationships were for
variables related to water quality (e.g., temperature: ρ =
0.66 with axis 2; dissolved O2: ρ = 0.57 with axis 3) and
hydrology (e.g., discharge: ρ = –0.57 with axis 2, wetted
width: ρ = –0.67 with axis 2). Physical-habitat variables
not directly related to water availability had much weaker
relationships with within-site biological changes. Among
these variables, the strongest relationships were observed
for % sands and fines (ρ = –0.32 with axis 1) and % shading (ρ = 0.43 with axis 2). The strength, and sometimes
the direction, of these relationships varied from site to site.
For example, within-site correlations between IBI scores
and temperature were negative for most sites, but ρ was
positive at TE (ρ = 0.1) and strongly positive at NC (ρ =
1.0).
DISCUSSION
Our results demonstrate that wholesale exclusion of nonperennial streams from bioassessment programs is not justified, despite their biological differences from perennial
streams. The IBI correctly assessed the condition of low-
stress nonperennial streams, scores were consistent over
time, at least at low-stress sites, and seasonal (within-site)
changes affected biological community structure, but not
assessment scores. Therefore, biomonitoring programs may
be able to include nonperennial streams and assess them
with some of the same multimetric indices used in perennial streams. We think that 2 factors contribute to the comparability of bioassessments observed here. First, the regional fauna may be adapted to nonperennial flow regimes.
Second, the relative stability and predictability of nonperennial streams in our study increased the biological similarity
of perennial and nonperennial streams.
Assessment tools work in nonperennial streams
Nonperennial streams are widely described as supporting biological communities that are different from communities in perennial streams (Álvarez and Pardo 2007, Datry
2012, Bogan et al. 2013). However, fauna from the streams
in our study appear to occupy different positions along a
continuous gradient. For example, samples from perennial
and nonperennial streams were interspersed in ordination
space, emphasizing the overall similarity of these stream
types. The relative similarity of perennial and nonperennial
streams observed in our study contrasts with results of several other studies (e.g., Arscott et al. 2010). However, many
of these studies had limited spatial (e.g., Bêche et al. 2006)
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Bioassessment in nonperennial streams
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Figure 6. Results of a nonmetric multidimensional scaling (NMDS) ordination of macroinvertebrate presence–absence data
showing ordination scores for each sample in the study (A, B) and weighted-average taxon scores (C, D) on axes 1 and 2 (A, C) and
1 and 3 (B, D). Centroids for 2 sites referenced in the text (AN and BC) are annotated. Numbers in axis labels show the % total
variance represented in the axis. P-L: perennial, low-stress streams. N-L: nonperennial, low-stress streams. N-M: nonperennial,
moderate-stress streams. N-H: nonperennial, high-stress streams.
or temporal (e.g., Lunde et al. 2013) replication, and these
limits alone could exaggerate the apparent distinctness of
nonperennial streams. Our findings are supported by those
of Gallart et al. (2012), who suggested that ecological status may be assessed in the same way at temporary streams
as at perennial streams if flow permanence and seasonal
predictability are relatively high.
The comparability of bioassessments between perennial and nonperennial streams may be limited to regions
like southern California, where nonperennial streams are
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Figure 7. Trajectories in ordination space for each site and year plotted against time for nonperennial (A) and perennial
(B) streams. In each panel, scores for the initial sampling event were subtracted from scores for all subsequent samples at a site
and year. Abbreviations are the same as in Fig. 6.
the dominant stream type. Benthic macroinvertebrates collected at a site represent a subset of the regional fauna that
can tolerate the local environmental conditions (Southwood
1977, Townsend and Hildrew 1994, Statzner et al. 1997). In
predominantly arid regions, taxa that are dependent on perennial streams may be excluded from the regional fauna
because of the scarcity of habitat (e.g., only 500 km of
streams in the San Diego region are perennial) (NHDSPlus;
www.horizon-systems.com/nhdplus). Thus, communities in
perennial streams in arid regions may be constrained to be
similar to communities in nonperennial streams. In addition, temporal variability in flow status, as observed in our
study and elsewhere (e.g., Gasith and Resh 1999), may further limit the regional fauna to taxa adapted to intermittent flow. The richer regional fauna of wetter regions may
allow greater divergence between perennial and nonperennial streams. The findings of Arscott et al. (2010) support this idea. They observed that communities in intermit-
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Bioassessment in nonperennial streams
R. D. Mazor et al.
Figure 8. Among-site correlations between environmental variables and nonmetric multidimensional scaling (NMDS) ordination
axes calculated from within-site means (A, B) and variables with within-site means subtracted (C, D). The length of each line
represents the strength of correlation (ρ) with the ordination axes. Only selected variables are labeled. Fast = % fast-water habitat,
Slow = % slow-water habitat, Shade = % shading, CPOM = % cover by coarse particulate organic matter, Vel = mean water velocity,
flow = discharge, macro = % macroalgae cover, embed = mean embeddedness, SC = specific conductance, T = water temperature,
stress = stressor score, RipVeg = mean riparian vegetation cover, roads = road density, dry = % dry habitat, big = % particles larger
than cobble, SAFN = % sands and fines, cobble = % cobbles, width = mean wetted width, DO = dissolved O2.
tent and ephemeral streams in New Zealand were nested
subsets of communities found in nearby perennial streams,
and reflected a loss of desiccation-sensitive taxa, rather than
a gain of desiccation-tolerant taxa.
The similarities between communities in perennial and
nonperennial streams in our study may be restricted to the
types of nonperennial streams we studied (long-lasting,
seasonal streams that flow for several months nearly every
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Table 3. Correlations of environmental and biological variables with the Index of Biotic Integrity (IBI) and nonmetric multidimensional scaling (NMDS) ordination axes 1, 2, and 3. Among sites = Spearman rank correlation (ρ) calculated from site means, within
sites = Spearman rank correlation (ρ) calculated from variable values minus within-site means, CPOM = coarse particulate organic
matter, NA = not assessed.
Among sites
Variable
Stressor score
Landscape variables
Latitude
Longitude
Catchment area
% impervious surface
Road density
Physical habitat
Algae cover
% algae cover
% macrophyte cover
Natural habitat cover index
Hydrology
Discharge
Velocity
% dry habitat
% fast-water habitat
% slow-water habitat
Wetted width
Riparian vegetation
% shading
Mean riparian vegetation cover
% reach with 3 vegetation layers present
Substrate
% large substrates
% cobble
% CPOM cover
% sands and fines
Mean embeddedness
Water chemistry
Alkalinity
Dissolved O2
pH
Specific conductance
Temperature
Within sites
IBI
NMDS1
NMDS2
NMDS3
IBI
NMDS1
NMDS2
NMDS3
−0.91
0.36
−0.53
−0.47
NA
NA
NA
NA
0.01
0.03
−0.20
−0.59
−0.59
−0.31
0.09
−0.08
0.26
0.31
0.12
−0.20
0.21
−0.32
−0.42
−0.01
0.19
−0.30
0.02
−0.02
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
−0.31
0.14
0.34
0.24
0.71
0.61
−0.26
0.07
0.41
−0.19
0.57
0.68
−0.06
0.11
0.12
−0.20
0.10
−0.06
−0.20
0.14
−0.01
0.12
−0.11
0.08
0.54
0.20
−0.61
0.75
−0.69
0.07
−0.32
−0.57
0.04
−0.53
0.56
0.20
0.36
−0.14
−0.62
0.70
−0.60
0.04
0.54
−0.05
−0.44
0.38
−0.40
0.34
0.15
0.18
−0.12
0.07
0.03
0.27
−0.32
−0.14
0.56
−0.30
−0.09
−0.41
−0.57
−0.34
0.26
−0.35
0.13
−0.67
0.45
0.19
−0.26
0.24
−0.12
0.64
0.68
0.55
−0.36
−0.20
0.15
−0.60
0.65
0.52
−0.25
0.44
0.70
−0.79
−0.03
0.01
−0.14
0.23
0.06
0.04
0.43
0.08
0.12
−0.23
−0.04
−0.06
0.43
−0.02
0.39
−0.02
−0.48
0.20
0.17
0.55
−0.39
−0.33
0.40
−0.15
0.49
−0.05
−0.59
0.59
0.27
0.68
−0.50
−0.85
0.05
−0.15
−0.02
−0.07
0.11
0.15
0.12
−0.02
−0.32
−0.02
0.00
−0.03
−0.06
−0.10
−0.22
0.13
0.02
0.09
−0.01
0.02
0.04
−0.14
−0.20
−0.69
−0.51
−0.15
0.38
−0.19
0.04
0.32
0.13
0.07
−0.12
−0.50
−0.39
−0.49
0.43
−0.32
−0.65
−0.31
0.16
0.01
−0.16
−0.02
−0.31
−0.07
−0.25
−0.02
0.15
0.29
−0.02
−0.52
−0.12
0.26
0.66
0.04
0.57
0.14
−0.27
−0.50
year). Less-predictable or more-ephemeral streams may support biological communities that differ more from those in
perennial streams than what we observed. Anna et al. (2008)
found large differences in bioassessment indices between
ephemeral and intermittent streams, and Gallart et al. (2012)
found that unpredictable hydrologic regimes decreased the
measured ecological status of temporary streams. Therefore, bioassessment tools developed from perennial streams
may provide comparable interpretations only at nonperennial streams with sufficient relative flow permanence.
Identification of critical thresholds in flow permanence or
predictability should be a focus of further research.
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Bioassessment in nonperennial streams
R. D. Mazor et al.
Assessment scores are consistent over time
Multivariate analyses showed large seasonal changes
in community composition at most sites, but IBI scores
were relatively stable, at least at low-stress sites. This stability was particularly evident at perennial sites, where all
samples ranged ∼5 points at a single site. The consistency
was less pronounced at low-stress nonperennial sites (typical point ranges ≈ 10 points). However, at moderate- and
high-stress nonperennial sites, IBI scores were noticeably
unstable and declined steeply at several sites, particularly
at the end of sampling. Some point ranges at these sites
were >40 on the 100-point IBI scale. In unstressed intermittent streams in Spain, Sánchez-Montoya et al. (2009b)
similarly found low intra-annual variability in multimetric
indices and in certain metric types, such as those derived
from life-history traits, further suggesting that benthic communities in undisturbed nonperennial streams are relatively resilient to seasonal changes.
Stability in IBI scores at nonperennial sites also extended to interannual replicates, at least at 3 of the 4 sites
selected for multiyear sampling, a finding consistent with
the observations of others (Morais et al. 2004, SánchezMontoya et al. 2009b). Despite different weather patterns
over the years of study, these 2 sites had similar patterns
in IBI scores and trajectories in ordination space. The stability in IBI scores at low-stress sites may be a result of
the use of a broad index period (April–October) during
development of the IBI (Ode et al. 2005). Thus, the IBI
appears to integrate the temporal variability evident in
the ordinations and creates a stable measurement of structural and functional attributes of the benthic macroinvertebrate assemblage. Our results suggest that a common
assumption underlying development of many bioassessment indices—the substitution of space for time during
index development—may effectively broaden their applicability (Stoddard et al. 2008, Schoolmaster et al. 2012). That
is, indices calibrated from data covering many sites, but
only a few years, may be resilient to temporal changes.
When a large number of reference sites is used, streams
are likely to be sampled at different stages in their individual phenologies, which may produce biological variability
similar to what would be found in repeated visits and,
thus, a biotic index with resilience to temporal variability.
Few changes in the environment were associated
with trends in biology
Given the overall stability of IBI scores, it is not surprising that few environmental variables were related to withinsite changes in scores. Moreover, only a few variables were
associated with changes in ordination scores. Unsurprisingly, these variables were mostly related to water quantity
or quality (e.g., velocity, % fast-water habitat, temperature,
dissolved O2); relationships with variables related to phys-
ical structure (e.g., substrate composition), primary productivity, and riparian vegetation were weaker. In studies
of temporary streams in other Mediterranean-climate regions, investigators have found similar relationships among
these variables and macroinvertebrate community structure
(e.g., Morais et al. 2004, Álvarez and Pardo 2007). GarcíaRoger et al. (2011) found that most changes in habitat were
site-specific, but those changes related to quantity of aquatic
habitat were strongly associated with changes in community structure. In contrast, among-site differences were stronger and associated with a larger variety of environmental
variables, consistent with many studies of temporary (e.g.,
Álvarez and Pardo 2007) and perennial (e.g., Sandin and
Johnson 2004, Yuan et al. 2008, Mazor et al. 2009) stream
ecosystems.
One of our more surprising findings was the apparently
high sensitivity of nonperennial streams to nonnatural flow
regimes. For example, several sites that had very few disturbances apart from altered hydrology (AS, NC) had relatively low IBI scores, and the lowest scores were observed
at sites with altered hydrology combined with other stressors (TC, OF, and SJ). In contrast, sites with substantially
higher stress but relatively stable hydrographs (SY, SR)
had much higher IBI scores. Skoulikidis et al. (2011) also
found that biological assemblages were particularly sensitive to modified flow regimes and artificial drying, and
recommended distinguishing naturally from artificially nonperennial streams for assessment purposes. Therefore, watershed managers should monitor alterations to hydrologic
regimes in nonperennial streams as much as they do in
perennial streams.
Implications for bioassessment programs
Our study was limited to a small number of sites, but
it illustrates that certain nonperennial streams can be incorporated into routine bioassessment programs. Hydrologically stable and predictable nonperennial streams sampled during eurheic or oligorheic states are biologically
similar to perennial streams, at least in arid regions like
southern California. Expanding water-quality assessment
programs to include nonperennial streams would give resource managers the ability to manage a greater extent of
their streams and to address impacts to some of the most
sensitive portions of their watersheds. These changes may
be most profound in arid regions, like southern California,
but the global ubiquity of nonperennial streams (Tooth
2000) suggests that watershed protection in both wet and
dry climates could be greatly improved by including nonperennial streams in assessment programs.
AC KNOW LE DGEMENTS
We thank Dario Diehl, Betty Fetscher, Chris Solek, Michael
Hang, Aquatic Bioassessment Consulting, Becky Schaffner, Bruce
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Volume 33
Bealer, Shelly Moore, Karen McLaughlin, Lilian Busse, Andy
Rehn, Sean Mundell, Phil Markle, Josh Westfall, Shawn McBride,
Marco Sigala, Cassandra Armstrong, Orange County Parks, San
Diego County Parks, the Cleveland National Forest, the Vista Water Management District, Aquatic Bioassessment Labs, the City
of Los Angeles, the Marine Science Institute, George Clinton,
and the City of San Diego for assistance with planning, sampling, and analyzing data for this study, and the State Water Resources Control Board for funding this study. We thank Associate Editor Lester Yuan for valuable feedback that improved this
manuscript.
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