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The effects of changes in flow on the ecological conditionof two Arizona streams: analysis of trends in waterchemistry and structure of biological assemblages
Item Type Thesis-Reproduction (electronic); text
Authors Bymers, Leah.
Publisher The University of Arizona.
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Download date 05/05/2018 18:13:48
Link to Item http://hdl.handle.net/10150/191336
THE EFFECTS OF CHANGES IN FLOW ON THE
ECOLOGICAL CONDITION OF TWO ARIZONA STREAMS: ANALYSIS OF
TRENDS IN WATER CHEMISTRY AND STRUCTURE OF
BIOLOGICAL ASSEMBLAGES
by
Leah Bymers
A Thesis Submitted to the Faculty of the
SCHOOL OF NATURAL RESOURCES
In Partial Fulfillment of the Requirements for the Degree of
MASTER OF SCIENCE
WITH A MAJOR IN FISHERIES AND WILDLIFE
In the Graduate College of
THE UNIVERSITY OF ARIZONA
2005
STATEMENT BY AUTHOR
This thesis has been submitted in partial fulfillment of requirements for anadvanced degree at The University of Arizona and is deposited in the University Libraryto be made available to borrowers under rules of the Library.
Brief quotations from this thesis are allowable without special permission,provided that accurate acknowledgment of source is made. Requests for permission forextended quotation from or reproduction of this manuscript in whole or in part may begranted by the head of the major department or the Dean of the Graduate College when inhis or her judgment the proposed use of the material is in the interests of scholarship. Inall other instances, however, permission must be obtained from the author.
2
SIGNED:
APPROVAL BY THESIS DIRECTOR
This thesis has been approved on the date shown below:
Matter DateProfessor of the School of Natural Resources
Dr. 'William/(t, 6" 0 6
2
ACKNOWLEDGEMENTS
I would like to thank the official and unofficial members of my committee, Dr. David
Walker, Dr. Bill Matter, Dr. Kevin Fitzsimmons and Gail Cordy for their feedback and
guidance. Also, I would like to acknowledge David Aiming and Nick Paretti of the USGS
for their help in obtaining and understanding the hydrological data and biological
collection procedures. Chris Goforth was also integral in analysis of macroinvertebrate
data.
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TABLE OF CONTENTS
ABSTRACT 8
INTRODUCTION 9
Seasonal precipitation patterns 11
Streamflow and base flow 12
Effect of precipitation on flow 13
Study sites 13
Ecological assessment 16
METHODS 19
Data 19
Periphyton collection 20
Macroinvertebrate collection 21
Fish collection 22
Metrics 22
RESULTS 23
Historical precipitation and flow 23
Water chemistry 26
Biological assemblages 29
DISCUSSION 31
Fish 31
Macroinvertebrates 33
Periphyton 36
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TABLE OF CONTENTS - Continued
CONCLUSION 39
APPENDIX A 40
REFERENCES 42
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LIST OF FIGURES
FIGURE 1, Historical precipitation patterns 10
FIGURE 2, Study sites on topographical map of Arizona 14
FIGURE 3.1-3.6 Historical precipitation and flow at study sites 23
FIGURE 4, Regression of temperature and DO with flow at the San Pedro River at
Charleston 27
FIGURE 5, Regression of temperature and DO with flow at West Clear Creek near
Camp Verde 28
FIGURE 6, Regression of native:non-native fish with streamflow at the San Pedro
at Charleston 32
FIGURE 7, Regression of native:non-native fish with streamflow at West Clear
Creek near Camp Verde 32
FIGURE 8, Regression of H/QDEQ Biotic Index with flow at the San Pedro River
at Charleston 34
FIGURE 9, Regression of H/ADEQ Biotic Index with flow at West Clear Creek
near Camp Verde 34
FIGURE 10, Regression of FFG with flow at West Clear Creek near Camp Verde.35
FIGURE 11, Regression of periphyton total abundance with flow at West Clear
Creek near Camp Verde 37
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LIST OF TABLES
TABLE 1, Regression table for water chemistry parameters and flow at the San
Pedro River at Charleston 28
TABLE 2, Regression table for water chemistry parameters and flow at West Clear
Creek near Camp Verde 28
TABLE 3, Regression table for all taxa with flow at the San Pedro River at
Charleston 29
TABLE 4, Regression table for FFG with flow at the San Pedro River at
Charleston 30
TABLE 5, Regression table for all taxa with flow at West Clear Creek near Camp
Verde 30
TABLE 6, Regression table for FFG with flow at West Clear Creek near Camp
Verde 30
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ABSTRACT
Analysis of climate observations have led researchers to predict that precipitation
in the Southwest, USA will become more extreme in its variability. This prediction
combined with increasing demand for water for human use support the claim that streams
in the Southwest will experience flows lower than historic minima in the future. In order
to protect stream communities, the effects of decreasing flow need to be studied. I
investigated how changes in streamflow and base flow of the San Pedro River and West
Clear Creek, AZ, affect water chemistry, fish, macroinvertebrates, and periphyton.
Regression analysis showed the macroinvertebrate pollution-tolerance index and
percent Collectors were significantly negatively related to flow. This indicates a
depression of pollution-intolerant species with lower flows. Several other strong
relationships were noted, however, long-term data are needed in order to make
conclusions about the future effects of decreased flow on the biological assemblages of
arid perennial streams.
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INTRODUCTION
The climate of southern and central Arizona is characterized as warm and arid
(Liverman and Merideth 2002, Sheppard et al. 2002 ). Average annual rainfall in Arizona
is only 32.3 cm (Sheppard et al. 2002) and temperatures are consistently among the
highest in the country. Climate data, either actual measurements of temperature and
precipitation, or those derived from other sources (e.g., tree rings and models) show
marked fluctuations through time (Hereford and Webb 1992, Fenbaio et al. 2002,
Sheppard et al. 2002). Both summer and winter precipitation are highly variable across
years. Periods of low or high precipitation have occurred on an annual to multi-decadal
time scale. When examining precipitation patterns back to 1700, it is apparent that in the
recent past (1900 to the present), years of extreme high and low temperature and
precipitation have become even more extreme (Sheppard et al. 2002). In other words, wet
periods are becoming wetter and dry periods are becoming drier. This phenomenon has
been observed in several arid ecosystems around the world (Humphries et al. 2003).
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Figure 1. Long-term precipitation fluctuations. PDSI = Palmer Drought SeverityIndex; higher number =more precipitation. From Sheppard et al. (2002).
The effects of these aspects of climate change on the biological communities of
streams and rivers are largely unknown (Lake 2003). Organisms living in perennial
streams are well-adapted to the characteristic variable stream flow of the Southwest;
however, a change in the extent of variability may alter the structure of the communities.
This is especially true in regions that already experience climate extremes such as the
desert Southwest (Brown et al. 1997). Intense droughts predicted for the future and the
likely increase in anthropogenic water demand could lead to streamflow dropping below
historic minima in coming years. Therefore, research to determine if stream assemblages
have potential to be negatively impacted (e.g., loss of diversity, invasion of non-native
species) by reduced flow, is a necessity. Accurate and in-depth information about how
climate variability affects Arizona's aquatic communities can help in planning for
allocation of water resources (Fenbaio et al. 2002).
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I investigated how precipitation patterns affect annual streamflow and base flow
of two perennial streams of southern and central Arizona and in turn, how those changes
in flow are related to the water chemistry and biological assemblages of the two streams.
Seasonal precipitation patterns
The geographic location of the state of Arizona combined with its diverse
topography results in variable precipitation patterns across the state (Sheppard et al. 2002,
Skirvin et al. 2003). In southeastern and central Arizona, precipitation, although
regionally variable, follows a very pronounced seasonal pattern. Most of the annual
rainfall occurs in winter and summer. Spring and autumn are usually extremely dry.
Winter rains, which provide roughly 30% of the annual total (Sheppard et al.
2002), result from stotins that come from the northwest and pass through California
(Sheppard et al. 2002). Winter rains are greatly affected by El Nino-Southern Oscillation
(ENSO) processes. ENSO is the result of an increase in sea-surface temperature of the
eastern equatorial Pacific Ocean concurrent with movement of the active center of
atmospheric convection from western to central equatorial Pacific. La Nina years occur
when there is a decrease in the sea-surface temperature and no shift in the convection
center in the equatorial Pacific Ocean. El Nino years result in wet winters, whereas La
Nina years are typically more dry (Brown et al. 1997, Sheppard et al. 2002). Another
factor determining the amount of precipitation that falls in the winter is the Pacific
Decadal Oscillation (PDO). The PDO is an index that represents long-term fluctuations in
sea-surface temperature of the northern Pacific Ocean. It can work in conjunction with
ENSO to increase winter precipitation variability throughout the Southwest (Sheppard et
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al. 2002). Winter rain in Arizona is generally more long-lasting and gentle than rain that
falls in summer.
Summer rain in Arizona occurs during what is known as the monsoon season.
Monsoonal storms are a result of a change in wind direction (a subtropical high-pressure
ridge replacing the westerlies) that begins in Mexico and extends northeast through
Arizona and New Mexico (Hereford and Webb 1992, Sheppard et al. 2002). Precipitation
that occurs during the monsoon season is characterized by short, heavy bursts of rain and
provides up to 50% of Arizona's annual precipitation (Sheppard et al. 2002). These
storms usually occur between early July and early September.
Streamflow and base flow
Both summer and winter rains have a large effect on streamflow. Streamflow is
the total volume of water flowing in a stream cross section at any particular time. This
includes groundwater input, rainfall into the stream and runoff from precipitation or
snowmelt. An important parameter to monitor is base flow. Base flow of the stream is the
component of the total flow that comes solely from groundwater. Base flow cannot be
directly measured, but rather is calculated from streamflow records. I chose to use the
BFI model (Base Flow Index) created by the British Institute of Hydrology. This program
uses local minimum streamflow data and a recession slope test to estimate base flow.
Base flow is recognized as a good comparative measure when examining long-term
hydrological trends because it is less affected by rain events than streamflow (Wahl and
Wahl 1988). Extreme events must be taken into consideration when investigating short-
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term implications of changes in flow on the biological assemblages of streams, therefore,
mean streamflow is also used in analyses.
Effect of precipitation on flow
Monsoon rains that occur in Arizona in the summer are heavy and ephemeral and
greatly affect streamflow. A monsoonal rain event often elevates discharge in a stream by
100 or even 1000 fold, but because this water runs off quickly, and because evaporation
rates are high, summer rains do not contribute as much to base flow, as do winter rains
(Bryson and Hare 1974). The more gentle and longer-lasting rains in winter, and snowfall
in higher elevations are more likely to seep into the ground and recharge groundwater
reserves, thus elevating base flow. This makes it imperative to not only monitor annual
precipitation, but also season precipitation when tracking potential drought trends in the
context of its effect on streams.
Study sites
Study sites were the San Pedro River at Charleston and West Clear Creek near
Camp Verde, Arizona. These streams had been selected by the National Water Quality
Assessment program as study sites because they are perennial and considered minimally
affected by anthropogenic disturbance. They also are representative of two different
ecoregions of Arizona; basin and range lowlands and central highlands (Cordy et al.
1998, Gebler 2004). Long-term data from US Geological Survey (USGS) stream gages
are available for both sites.
Figure 2. Southern "X" is the San Pedro River at Charleston and central Arizona
"X" is West Clear Creek near Camp Verde.
The San Pedro River is a tributary of the Gila River and its watershed is roughly
10,000 km2 . The river begins in Sonora, Mexico and flows north into Arizona. It flows
near the towns of and Cananea, Mexico, and Sierra Vista and Fort Huachuca, AZ in its
upper reaches and then progresses northward, to the east of the Huachuca and Santa
Catalina Mountains. The Charleston site is roughly 10 km east of Sierra Vista. The
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substrate of the San Pedro River is characterized as sand and coarse-grained sediment or
basin fill (Cordy et al. 1998). Riparian vegetation in the upper reaches is dominated by
Populus fronontii and Salix goodingii (Gebler 2004). Tamarix spp and Prosopis spp. can
also be found. Chihuahuan desertscrub and semi-arid grassland occur outside of the
riparian corridor (Gebler 2004). Much of the area outside of the riparian corridor is
rangeland used for livestock production. The San Pedro watershed receives an average of
38.2 - 51.0 cm of rain/year with an average of 152.9 - 165.6 cm of free water-surface
evaporation (Cordy et al. 1998, Skirvin et al. 2003). The upper reaches of the river have
been designated a National Riparian Conservation Area since 1988.
West Clear Creek is in a deep valley that cuts through the Mogollon Rim at the
southwest edge of the Colorado Plateau and is a tributary of the Verde River. It remains
minimally impacted primarily because it is remote and difficult to access. A very small
portion of the adjacent area is used as rangeland. The narrow band of riparian vegetation
includes various species of Pinus and Juniperus in the upper elevations and Populus
fremontii, Fraxinus velutina, Juglans major, Platanus wrightii, Acer negundo, Alnus
oblongifolia, and Salix spp. in mid-lower elevations (Galuszka and Kolb 2002). The
substrate of West Clear Creek is a combination of basin fill of gravel, cobbles, and
boulders of sedimentary bedrock, and bedrock of the mountains. The West Clear Creek
region receives an average of 38.2 cm/year of precipitation and has 127.4— 152.9 cm of
evaporation (Cordy et al. 1998).
Ecological assessment
There are many ways to quantitatively estimate the health of a freshwater
biological community (Boulton 1999). There are, in fact, many ways to define the
"health" of a freshwater body, though most authors agree that a healthy system will lack
significant distress, but would be resilient when faced with stress (Karr 1999, Norris and
Thoms1999). In recent years, the health of a river has been determined by assessing the
integrity of its biological assemblages. Karr (1999) defines biological integrity as the
state in which a community is the product of evolutionary and biogeographic processes
with minimal influence from modern human society. Many researchers agree that
sampling the biological assemblages is an essential part of assessing the integrity and
thus the health of a freshwater body (Karr 1999, Norris and Thorns 1999).
Some researchers have simply used abundance and community distribution data
to asses biological integrity (Wood et al. 2000, Alimov 2001, Nelson and Lieberman
2002, Daufresne et al. 2003, Chen et al. 2004, Clavero et al. 2004, Paperno and Brodie
2004, Pegg and McClelland 2004, Wood and Armitage 2004), where as others have
calculated diversity or similarity indices ( Pegg and Pierce 2002, Paperno and Brodie
2004, Pegg and McClelland 2004, Steffy et al. 2004, Wood and Armitage 2004). Species
richness has been recognized as a particularly valuable measure by the UN Convention
on Biological Diversity (Foggo et al. 2003). More recently, the ratio of native fish species
to non-native fish species has become an indication of the integrity of the aquatic
environment (Bernardo et al. 2003, Eby et al. 2003, Feyrer and Healey 2003, Kennard et
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17
al. 2005). Researchers have also considered species turnover rates in fish assemblage
samples when assessing an aquatic environment (Eby et al. 2003, Adams et al. 2004).
Macroinvertebrates can be used as biotic indicators of the integrity of a water
body. Many researchers use the tolerance levels of macroinvertebrates to various
pollutants to formulate a biotic index (Davis et al. 2003). An analysis of the proportion of
the macroinvertebrate community that is represented by each of the several functional
feeding groups (FFG) has also been used to assess the health of an aquatic environment
(Merritt et al. 2002, Stepnuck et al. 2002, Habidija et al. 2003). Macroinvertebrates are
classified as 1.Collectors (those that gather or filter fine particulate matter out of the
water, such as tubificid worms and Ephemeropterans in the Baetidae family); 2.Shredders
(those that break plant material and some animal material into smaller particles through
their feeding and digestive process, such as members of the Plecoptera or stonefly order);
3.Scrapers (those that feed on algae and plant material that is on rocks or plants, such as
Coleopterans in the Elmidae family); 4.0mnivores (those that eat plant and animal
material, such as the Trichopterans in the genus Nectopsyche); and 5.Predators (those that
feed on other macroinvertebrates such as Hemipterans and Odonotans).
When deciding which metrics to use as representatives of the aquatic assemblages
in this study, I considered biological and ecological significance, ability to detect
temporal changes, and frequency of use among recently published studies. Data for fish
were analyzed for abundance, species richness, Simpson's Diversity Index, proportion of
native to non-native species, and species turnover rate. For macroinvertebrates,
abundance and species richness, as well as a modified Hilsenhoff Biotic Index (regional-
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specific input from Arizona Dept. of Environmental Quality), composition of functional
feeding groups, and Simpson's Diversity Index were calculated. Abundance and species
richness, as well as Simpson's Diversity Index were calculated for periphyton data. These
metrics are considered good representations of diversity, and are ecologically relevant
(Washington 1984).
METHODS
Data
I used hydrological and biological data collected by the U.S. Geological Survey
(USGS). Daily flow data were obtained from long-established stream gages. The daily
flow data set for the San Pedro River at Charleston and West Clear Creek near Camp
Verde, AZ is quite complete over the long-term with only a few small gaps. The base
flow data were calculated by entering the daily stream flow into the Baseflow Index
model (BFI). BFI can be downloaded from:
http://www.usbr.gov/pmts/hydraulics lab/twahl/bfilindex.html.
Precipitation data were collected by the Western Regional Climate Center. They
maintain precipitation-monitoring stations throughout Arizona. I chose the two stations
most representative of the stream sites (Tombstone for the San Pedro River, and Happy
Jack for West Clear Creek). Trends in precipitation and stream and base flow were
analyzed using JMP version 4 statistical software.
Biological and water chemistry data were collected as part of the National Water
Quality Assessment (NAWQA) program facilitated by the USGS. This program was
designed to comprehensively analyze the condition of various flowing waters throughout
North America. NAWQA began in 1991 and was primarily concerned with collecting
data from many different locations to assess the occurrence and distribution of various
biological, physical, and chemical features of flowing waters. Recently, the focus of
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NAWQA shifted to collecting the same comprehensive data at a few fixed sites in order
to monitor long-term trends.
Biological and water chemistry data collection at the San Pedro River at
Charleston and West Clear Creek near Camp Verde began in 1995 and continued
somewhat inconsistently until the present. Periphyton and macroinvertebrate data are
available from 1995-1997, and 2001 at the San Pedro and West Clear Creek sites. Data
on fish are available from 1995-1997, 2002, and 2003 from San Pedro River and West
Clear Creek. Fish data were also collected in 2001 from the San Pedro River. Because
water chemistry data were not collected on the same day as the biological samples, the
data used in analysis were taken from the sampling date closest to the biological sampling
day. If a water chemistry sample was not taken within five days of the biological sample,
an average of the measurements from the closest days pre- and post-sample was
averaged.
The collection of fish, macroinvertebrates, and periphyton at West Clear Creek
and the San Pedro River followed the NAWQA protocol. Each community collection was
performed within the same stream reach for each sample date. See Moulton et al. (2002)
for a more detailed description of current sampling protocol. The first two years of
NAWQA (1995/1996) protocol can be found in Cuffney et al. (1993), Meador et al.
(1993), and Porter et al. (1993).
Periphyton collection
Periphyton was sampled from rock surfaces and was collected using the SG-92, a
modified syringe sampling device. Five cobbles from five locations (total of 25 cobbles)
21
representative of each stream reach were collected. The SG-92 device was secured to a
smooth surface of each of the cobbles and a combination of suction, brushing, and
flushing with water, was used to dislodge the periphyton within the barrel of the SG-92.
The periphyton and water solution was then transferred to a sample bottle and preserved
with buffered formalin. The area sampled on each of the cobbles was measured and
recorded. Samples were sent to the Philadelphia Academy of Sciences Laboratory for
processing.
Macroinvertebrate collection
Riffles, from which five distinct sample sites were determined, were selected for
collection. Substrate type, water velocity, depth, and debris accumulation were kept
constant among sample sites within a particular riffle.
To collect macroinvertebrates, a disturbance-removal technique was used. A
sample area was defined and the substrate within that area was disturbed. A 1.25 m2 area,
directly upstream from the slack sampler, was disturbed by digging to a depth of 10 cm
with a hand rake and gently scrubbing the rocks and other debris. The macroinvertebrates
were dislodged washed downstream into a net. The net used, as part of NAWQA
protocol, is called a slack-sampler. It is a rectangular net with a handle and a detachable
collection receptacle.
Everything collected in the net was transferred to a bucket where large debris was
removed and the water volume was reduced. Large invertebrates such as crayfish,
hellgrammites, and mussels were removed and processed separately. The remaining
organisms were transferred to sample bottles and preserved with buffered formalin.
22
Samples were sent to the USGS National Water Quality Laboratory where
macroinvertebrates were counted and identified.
Fish collection
Fish were collected as per NAWQA protocol for "riffle-pool streams." A
combination of electro-fishing and seining were used. In the early years of the NAWQA
program, some fish were sacrificed after collection. In recent years, fish were stunned,
caught, identified and counted, and then released.
Metrics
Before metric calculation, each macroinvertebrate species was assigned a
tolerance value (Hilsenhoff 1988, Arizona Dept. of Enironmental Quality) and functional
feeding group (ADEQ). Abundance, species richness, Simpson's Diversity Indices,
Hilsenhoff Biotic Indices, native to non-native species ratios, and species turnover rates
were calculated using Excel. All index formulas are found in the Appendix.
Regression analyses to detect relationships between flow and each of the biological
metrics were performed using JMP version 4 statistical software. Although the time it
takes for an environmental stressor to impact a biological assemblage may vary among
taxa, for the sake of consistency and because samples were collected roughly yearly, the
average flow of the year prior to each biological sample was used to test for relationships
between biological metrics and flow.
RESULTS
Historical precipitation and flow
Before biological data were analyzed, flow and precipitation were graphed to
detect trends.
---- Base Flow; ---- Strea m ---- Precipitation
CD CO CO 01 r Chi (T) c,c) cor— co min C'") 1,11 C001 CO 1.-(N (7, 511,0 (D CD COCO -
(D CD CD CD CD 1^--CO CO 00 CO CO CO CO CO COCO CO 0101 CO 0101 CO COO) 01=1=CO CO CD CD CD 01 a) CO 07 CO 0101 CO MO, CO CO CO CO (N
year
Figure 3.1. Mean annual precipitation (cm) at Tombstone and mean annualstreamflow and base flow in cubic meters per second in the San Pedro River atCharleston.
23
7.0 7
6.0
5.0
4.0
3.0 -
2.0
1.0
0.0
40 CO N- N CO In CO CO DI (N CD CO al N 14)0) CO 0) 0) N(DID (ON h-- N- COO) CO CO 0=1 OD GO CO CO CO CD 0) a) a) a) a) ar) w a)
0) Cf) 0) a) 0) CO 07 0) 0) 0) 01 07 a) co a) a) a-Jt a) al al a) a) co cr) a) 01 01 a) Cr) 0) 0) a) a)c-w N
year
Figure 3.2. Mean winter precipitation (cm) at Tombstone and mean winterstreamflow and base flow in cubic meters per second in the San Pedro River atCharleston.
U) CD N- OD 0)0) N 1,11 CD DI VI V' 411 CD CO CO C) N V- 1.10 (D 0) 0) (NCO CO CO ID ICD N- C--ID CD (00) OD (0 0) CO CO CO a) 01 a) 07 0) 01 a) 07 01 a) CI iMa) a) a) a) a) ar) a) CI) CI CI) 07 01 C!) 0) a) a) 00 0) CO 01 CO 01 07 01 al 0) a) 0) 0) 0) CI) 01 CD 0 (1)
N (N N
year
Figure 3.3. Mean summer precipitation (cm) at Tombstone and mean summerstreamflow and base flow in cubic meters per second in the San Pedro River atCharleston.
24
C) CN CO •ql- CCI 00 0) D N "4" In CD I,- CO a) SD1"-- I,- OD CO CO 00 CO CO CO CO CO CO 0)
CO 07 al 0) 0) a) al cn 0) 0) a) 0) 01 0) 07 0) 0) 07 a) a) al
N CO ,± CON-COO,C),— N0) CT) 0) Or) Cr) 07 CI Cr) 0, D D0) a) a) 0) 0) a) CD 07 0) D D .. CN CN N
yearFigure 3.4. Mean annual precipitation (cm) at Happy Jack and mean annualstreamflow and base flow in cubic meters per second in West Clear Creek nearCamp Verde.
I's A- F.-- P., F..- I--- CO CO 00 CO CD CO OD CO 00 CO CD 0) 07 0) 0) 01 07 CD 0) CD DCO 0) CO 07 CD 0) DC) DC) CD 01 0) 01 0) CS) 07 0) DC) 07 07 07 07 07 CO 07 07 07 07 Di Di
CN
yearFigure 3.5. Mean winter precipitation (cm) at Happy Jack and mean winterstreamflow and base flow in cubic meters per second in West Clear Creek nearCamp Verde.
25
7.0 —
6.0
5.0
4.0 =
3.0 —-
2.0 --
1.0 —
0.0
li1CONCOM Dv-NM"..1- C) (CINNNNN MMMMM COCOMMCOMMCOM(J) C) MMMMMMMMMMMMMMMMMMM
In CO0) 01 0) 0) 0)01 0) CD 0) 01....
CO y- rgO) CD CD CDCO CD CD CDy- CA C4 CA
year
Figure 3.6. Mean summer precipitation (cm) from Happy Jack and mean summerstreamflow and base flow in cubic meters per second in West Clear Creek nearCamp Verde.
Most likely due to the relatively short time scale of available data, no obvious
precipitation drought trends were detected. It is interesting to note, however, the similar
patterns of precipitation and flow, and the variability in affect of seasonal precipitation on
base flow and streamflow.
Water Chemistry
No statistically significant relationships were found between flow and various
water chemistry parameters, though some trends were detected. At both West Clear
Creek and the San Pedro River at Charleston, dissolved oxygen was strongly positively
associated with flow. Temperature was also somewhat dependent upon flow. At both
sites, temperature increased as flow decreased. The other water chemistry parameters
analyzed showed inconsistent relationships to flow. At the San Pedro River at Charleston,
26
9.25
6 0
Streamflow Base flow
C 9 -CD0)›.5 75-Xo> 5.25-
o
7.25
10
Base flow
R2= - 915
pH was strongly negatively related to flow and at West Clear Creek, total nitrogen and
suspended sediment decreased with flow.
Figure 4. Regression of temperature and DO with flow at the San Pedro River atCharleston.
27
R2= - 547
le 5 17 5
Base flow
ca)r _
-0_0 10_.) > . _.
.
16 5 17 5
Base flow18 5
12 2 = 635
12 2 = - 77R
12
,Streamflow6 15
28
Fig. 5. Regression of temperature and DO with flow at West Clear Creek nearCamp Verde.
Table 1. Regression table for water chemistry parameters and flow (First number =R2 ; second number = p value) at San Pedro River at Charleston.Water quality parameter Stream flow Base flowDO +.657; .190 +.729; .146Temperature -.128; .642 -.235; .515PH +.628; .208 +.726; .148Total Nitrogen +.036; .810 +.002; .958Total Phosphorous +.031; .823 +.001; .968Suspended sediment +.033; .819 +.001; .968
Table 2. Regression table for water chemistry parameters and flow (First number =R2; second number = p value) at West Clear Creek near Camp Verde.Water quality parameter Stream flow Base flowDO +.403; .365 +.635; .203Temperature -.228; .522 -.542; .263PH -.002; .951 -.086; .707Total Nitrogen -.566; .248 -.873; .066Total Phosphorous -.119; .656 -.000; .988Suspended sediment -.629; .207 -.439; .338
29
Biological assemblages
Two statistically significant relationships were found between flow and a
biological metric. At the San Pedro River, the Hilsenhoff Biotic Index and streamflow
were positively related (R 2 = .916 with a p value < .05). Pollution-tolerance level of the
macroinvertebrate community significantly decreased with streamflow, i.e more
macroinvertebrates that cannot tolerate poor water quality were present in the San Pedro
River when streamflow was higher. The percentage of the macroinvertebrates classified
as Collectors was significantly positively related to streamflow (R2 = .912; p < .05) at
West Clear Creek. The proportion of the macroinvertebrate community made up of
Collectors significantly decreased as streamflow increased. Though not statistically
significant, many other strong and therefore potentially ecologically significant,
associations were found.
Table 3. Regression table for all taxa with flow (first number = R2 ; Second number= n value) at San Pedro River at Charleston._Taxa Metric Stream flow Base flowFish Simpson's D +.123; .563 +.323; .318
Number of species -.077; .651 -.133; .547Total abundance +.031; .777 -.001; .965N:N-n ratio +.607; .120 +.009; .881Turnover rate +.275; .476 +.654; .191
Macroinvertebrates Hilsenhoff BioticIndex -.916; .043 -.065; .744Simpson's D -.103; .679 -.036; .810Number of species +.149; .614 -.277; .474Total abundance +.803; .104 +.419; .352
Periphyton Simpson's D -.087; .705 +.646; .196Number of species +.237; .513 -.375; .388Total abundance +.542; .264 +.776; .119
30
Table 4. Regression table for functional feeding group and flow (First number = R2 ;second number = p value) at San Pedro River at Charleston.Group proportion Stream flow Base flow% Collectors -.441; .336 +.159; .601% Shredders +.362; .398 -.307; .446% Scrapers +.281; .469 -.123; .649
Table 5. Regression table for all taxa with flow (first number = R2 ; second number =value) at West Clear Creek near Camo Verde.
Taxa Metric Stream flow Base flowFish Simpson's D -.347; .411 -.338; .419
Number of species -.178; .578 -.184; .571Total abundance +.062; .752 +.034; .851N:N-n ratio +.637; .202 +.864; .070Turnover rate +.913; .191 +.572; .454
Macroinvertebrates Hilsenhoff BioticIndex -.511; .285 -.813; .098Simpson's D +.137; .629 +.265; .486Number of species -.267; .484 -.447; .331Total abundance -.036; .817 +.067; .740
Periphyton Simpson's D +.465; .318 +.231; .591Number of species -.251; .499 -.058; .759Total abundance -.485; .303 -.371; .391
Table 6. Regression table for functional feeding group and flow (First number = R2 ;second number = p value at West Clear Creek near Camp Verde.Group proportion Stream flow Base flow% Collectors -.912; .045 -.544; .263% Shredders -.101; .682 .000; .998% Scrapers +.814; .098 +.394; .373
DISCUSSION
Fish
I found no statistically significant relationships between fish metrics and
streamflow or base flow, though several regressions revealed fairly strong associations.
The ratio of native to non-native fish increased with streamflow at the San Pedro River,
as well as with streamflow and base flow at West Clear Creek. Native fish make up a
larger percentage of the total fish assemblage when average yearly flow is greater.
Though the San Pedro River and West Clear Creek are perennially-flowing streams, they
do experience bursts of increased flow due to rain events. Native fish species such as
roundtail chub (Gila robusta), dace (Rhinichthys osculus, Agosia chtysogaster), and
Sonoran and desert sucker (Catostomus insignis, Catostomus clarki) are morphologically
and behaviorally adapted to survive these events. Perhaps, when insufficient water is
retained in the stream, the floods do not increase streamflow to the levels that usually
wash away non-natives fish. Non-native fish populations that are adapted to more lentic
conditions such as the green sunfish (Lepomis cyanellus), yellow and black bullhead
(Ameiurus natalis, Ameiurus melas), and smallmouth bass (Micropterus dolomieu) are
able to persist and grow when flows are not high enough to remove them.
31
15-
a)> -co 10-
R2= 607
o
a; 5-..>
coZ
610 20 25
Stre am flow
Figure 6. Regression ofNative:non-native fish with streamflow at the San Pedro at Charleston.
25
R 2 = 6'17
40
Stream7low
Figure 7. Regression of native:non-native fish with streamflow at WestClear Creek near Camp Verde.
32
35
The species turnover rate was also strongly connected to flow at both sites. It was
positively related with base flow at the San Pedro River at Charleston, as well as with
stream and base flow at West Clear Creek. The relationship is basically another reflection
of the effect of flow on the native:non-native species ratio. The turnover rate increases
with increased flow as native species are replacing non-native species.
Simpson's Diversity index was moderately sensitive to changes in flow. It was
slightly positively associated with streamflow and base flow at the San Pedro River at
Charleston, and was slightly negatively associated with streamflow and base flow at West
Clear Creek. This may be due to the magnitude of the high flow events that affect the
native and non-native fish composition. At the San Pedro River at Charleston, as flows
increase, native fish replace non-native fish and for a time diversity actually increases as
native and non-native species occupy the same area during the transitional period. Flows
at the San Pedro River during the years I studied were not quite forceful enough to
33
completely change the fish assemblage to all native fish, and thus lower diversity, as was
seen at West Clear Creek near Camp Verde.
Total abundance and species richness did not show a strong relationship to
streamflow or base flow at either site. These results show that although flow does have an
affect on species composition, it does not strongly impact the total number of species or
individuals within the range of conditions I studied.
Macroinvertebrates
The Hilsenhoff Biotic Index (HBI) was negatively related to streamflow at the
San Pedro River indicating that as flow increases, more less-tolerant organisms can
survive. This suggests that water chemistry is better with increased flow. The Hilsenhoff
Biotic Index was also strongly negatively associated with streamflow and base flow at
West Clear Creek. As streamflow changes, vital water chemistry parameters, such as
temperature and dissolved oxygen, will change. My results show that as those
correspondent water chemistry changes occur because of decreased flow, fewer
individuals of less-tolerant species survive. In fact, the entire population of a pollution-
intolerant species may disappear. My results show inconsistent relationships between
flow and species richness.
6,25
R 2 =-016
1cTo
5.75-
OE 5 5-
' 5.25-
15 20 25 30
Streamflow
34
6.25
Figure 8. Regression analysis of Hilsenhoff Biotic Index and flow at the San PedroRiver at Charleston.
66
66 R 2 =-R11
6.2
20
Base flow5
Figure 9. Regression of Hilsenhoff Biotic Index and flow at West Clear Creek nearCamp Verde.
In the context of using analysis of functional feeding groups to assess an aquatic
environment, researchers have suggested that a larger percentage of collector-gatherers
indicates impaired water quality, where as a larger percentage of scrapers and shredders
indicates a healthy stream (Rawer-Jost et al., 2000; Stepnuck et al, 2002). This is in part
due to the generalist nature of the gatherers and the specific food needs of the scrapers
and shredders. At West Clear Creek, the proportion of Collecters decreased significantly
with increased streamflow. This result supports the conclusion drawn from the Hilsenhoff
Biotic Index; water quality deteriorates at lower flows. The proportion of the community
composed of Scrapers was strongly positively associated with flow, thus supporting the
I R 2 =-cm I
210 30 40 50 70
streamflow
12°= 1114
20 30 40 50
streamflow
35
same conclusion. The percent of the community made up of Shredders was not dependent
upon stream or base flow at West Clear Creek.
50
,:33 40
90
80
30
25
520 —
1 E)' , 5 -—
a)2 5 —a)
Figure 10. Regression of functional feeding group and flow at West Clear Creeknear Camp Verde.
At the San Pedro River at Charleston, proportion of Collectors was negatively related to
stream flow, but slightly positively related to base flow. Scraper and Shredder
percentages were minimally related to flow; both positively to stream flow and negatively
to base flow. These results are fairly inconclusive. It is interesting to note, though, that
when looking at stream flow, the changes in Collector, Shredder, and Scraper populations
all reflect the same result as the Hilsenhoff Biotic Index and functional feeding group
analysis at West Clear Creek; increased flow results in better water quality. When
looking at base flow, however, macroinvertebrate populations shifted minimally in the
opposite direction as they did with streamflow. This may be an indication that the
contribution of base flow to the food source of the Collector animals is more significant
than that of streamflow, but this is not true for Scrapers and Shredders.
Total macroinvertebrate abundance was strongly positively related to flow at the
San Pedro River at Charleston, though at West Clear Creek there was minimal
36
relationship. This may be due to the geomorphology of the stream bed. At the San Pedro
River at Charleston, there is a wider, flatter flood plain, so when flow increases there is a
greater, though still quite shallow, area of substrate under water compared to the
constrained channel of West Clear Creek. This would translate into a relatively large area
for macroinvertebrates to live, and thus a greater total abundance with increasing flow at
the San Pedro River at Charleston, but not West Clear Creek.
The Simpson's Diversity Index was not strongly associated with flow at either
site. This could be due to the fact that this diversity index gives a lot of weight to the few
dominant species (Washington 1984).
Periphyton
Analysis of the periphyton community showed the biological metric most strongly
connected to flow was total abundance. At the San Pedro River at Charleston, total
abundance increased with flow, while at West Clear Creek it decreased. As was
suggested with the macroinvertebrate analysis, higher flow leads to wider stream channel
and greater wetted width at San Pedro River at Charleston. This may be the primary
reason total abundance increases with streamflow and base flow. At West Clear Creek, it
appears the total abundance also increases with flow, up to a moderate mean flow. The
sample year when mean streamflow was nearly double the next highest annual mean
flow, corresponds to the period of lower total abundance. My results show that in a low to
moderate flow regime, periphyton total abundance will increase with flow, but when
extremely high flows occur, perhaps because of scouring, less abundance is found.
122=-4R5
10
20 40 50 60
Strearnflow
CD275-
Ci
C, 250-
1-0
76 2 2 5
17:5
200
80
R2=-171
20225
Base flow175
8 275-
CO
-C3 250C
_aCO -
CO_ 225
oH 200
15
Figure 11. Regression of periphyton total abundance and flow at West Clear Creeknear Camp Verde.
Simpson's Diversity index was moderately related to base flow, though not to
streamflow, at the San Pedro River at Charleston. At West Clear Creek, Simpson's D was
slightly positively related to both streamflow and base flow. This may be due to the
greater surface area underwater resulting in availability of more diverse resources and
substrate.
Species richness was slightly positively related to streamflow at San Pedro River
at Charleston. It was slightly negatively related to base flow at the San Pedro River at
Charleston and to streamflow and base flow at West Clear Creek. These results were not
consistent enough to draw conclusions.
Analysis of the effects of streamflow and base flow on water chemistry and
biological assemblage structure has revealed some strong relationships. These results,
though not statistically significant for the most part, suggest that there are trends
occurring in the San Pedro River and West Clear Creek. The trends we have detected,
such as loss of native fish species with lower flows and worsening water quality and
concurrent loss of water quality-sensitive macroinvertebrates with lower flows, are
serious environmental concerns. Not only are they serious concerns, but also potentially
38
widespread, as these negative changes have occurred at sites that represent two different
ecoregions of Arizona. The biological integrity of arid southwest streams has been
affected by lessening of streamflow and base flow as shown by the results of this study.
In order to determine their magnitude of real-life significance, biological samples
need to continue to be collected to enlarge the database and therefore the robustness of
the analysis. Lack of statistically significant results (high R2 values, but also high p
values) supports the claim that this study needs to be long-term. With more data, analyses
such as detection for differences among years and among sensitivity of metrics and taxa
could be done. More data would also help in the fine-tuning of the process of choosing
which biological metrics to use. Not only would one be able to test for their relative
sensitivity to environmental changes, but the shortcomings in their representation of the
community (such as the Simpson's Diversity Index and its insensitivity to rare species)
could be investigated. Long-term data would also enable researchers to differentiate
between natural temporal fluctuations in the aquatic community and more serious
irreversible changes (Bunn and Davies 2000, Wood and Armitage 2004).
Other aspects of this study that could be modified to increase clarity of results
would be time (season) of sampling and number of samples throughout the year. This
would give a better understanding of the physiological and behavioral traits of aquatic
organism that may affect their population numbers at specific times.
CONCLUSION
If preserving native species and diversity of aquatic ecosystem assemblages is a
concern, humans need to be aware of how various environmental factors will impact
organisms that populate freshwater bodies. One of the most apparent environmental
variables in the flowing waters of the arid southwest is the amount of water in the stream
at any one time, i.e. flow. This environmental variable is an important one to research
because, if current predictions are correct, it has potential to change significantly in the
future. An understanding of how stream assemblages change with fluctuations in flow is
important in predicting the future of the arid Southwest's aquatic organisms.
Research to fully understand this situation needs to be long-term and on-going.
This study has shown that lower flows in West Clear Creek and the San Pedro River are
associated with negative changes in the biological assemblages. Most significantly, the
macroinvertebrate community in the San Pedro was composed of more pollution-tolerant
organisms when the year prior to sampling was of relatively lower flow. This paper
documents what would be the initial steps in an investigation into effects of increased
variability in flow on aquatic communities of arid perennial streams.
39
APPENDIX A. Metric formulas
Simpson's D (Washingtion 1986)
Where D = E 1'4E; - 1) N(N-1)
n = number of individuals in a species
N = number of individuals in a sample
Hilsenhoff Biotic Index (Washington 1986)
= E
N = number of individuals in a sample
Ili =number of individuals of species i in a sample
Q = tolerance value
Turnover rate calculated using two consecutive samples (Eby et al. 2003)
T = (C + E)/(S 1 + S2)
T = species turnover rate
C = number of species colonized (species seen in later sample that was not seen in earlier
sample)
E = number of species extirpated (species not seen in later sample that were seen in
earlier sample)
Si, S2 --- number of species present in each sample period
Native/Non-native ratio
40
D ---- Total number of native fish in sample
A ----- Total number of non-native fish in sample
41
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